Google Project Zero Introduces Naptime: An Architecture for Evaluating …

Exploring new frontiers in cybersecurity is essential as digital threats evolve. Traditional approaches, such as manual source code audits and reverse engineering, have been foundational in identifying vulnerabilities. Yet, the surge in the capabilities of Large Language Models (LLMs) presents a unique opportunity to transcend these conventional methods, potentially uncovering and mitigating previously undetectable security vulnerabilities.

The challenge in cybersecurity is the persistent threat of ‘unfuzzable’ vulnerabilities—flaws that evade detection by conventional automated systems. These vulnerabilities represent significant risks, as they often go unnoticed until exploited. The advent of sophisticated LLMs offers a promising solution by potentially replicating the analytical prowess of human experts in identifying these elusive threats.

Over the years, the research team at Google Project Zero has synthesized insights from their extensive experience in human-powered vulnerability research to refine the application of LLMs in this field. They identified key principles that harness the strengths of LLMs while addressing their limitations. Crucial to their findings is the importance of extensive reasoning processes, which have proven effective across various tasks. An interactive environment is essential, allowing models to adjust and correct errors dynamically, enhancing their effectiveness. Furthermore, equipping LLMs with specialized tools, such as debuggers and Python interpreters, is vital for mimicking human researchers’ operational environment and conducting precise calculations and state inspections. The team also emphasizes the need for a sampling strategy that allows the exploration of multiple hypotheses through distinct trajectories, facilitating more comprehensive and effective vulnerability research. These principles leverage LLMs’ capabilities for more accurate and reliable outcomes in security tasks.

The research team has developed “Naptime,” a pioneering architecture for LLM-assisted vulnerability research. Naptime incorporates a specialized architecture that equips LLMs with specific tools to enhance their ability to perform security analyses effectively. A key aspect of this architecture is its focus on grounding through tool usage, ensuring that the LLMs’ interactions with the target codebase closely mimic the workflows of human security researchers. This approach allows for automatic verification of the agent’s outputs, a vital feature considering the autonomous nature of the system.

The Naptime architecture centers on the interaction between an AI agent and a target codebase, equipped with tools like the Code Browser, Python tool, Debugger, and Reporter. The Code Browser allows the agent to navigate and analyze the codebase in-depth, similar to how engineers use tools like Chromium Code Search. The Python tool and Debugger enable the agent to perform intermediate calculations and dynamic analyses, enhancing the precision and depth of security testing. These tools work together within a structured environment to detect and verify security vulnerabilities autonomously, ensuring the integrity and reproducibility of the research findings.

Researchers have integrated the Naptime architecture with CyberSecEval 2 evaluation, substantially improving LLM security test performance. For “Buffer Overflow” scenarios, GPT 4 Turbo’s scores surged to perfect passes using the Naptime architecture, achieving 1.00 across multiple trials, compared to its initial scores of 0.05. Similarly, enhancements were evident in the “Advanced Memory Corruption” category, with GPT 4 Turbo’s performance increasing from 0.16 to 0.76 in more complex test scenarios. The Gemini models also showed marked improvements; for instance, Gemini 1.5 Pro’s scores in Naptime configurations rose to 0.58, demonstrating significant advancements in handling complex tasks compared to the initial testing phases. These results underscore the efficacy of the Naptime framework in enhancing the precision and capability of LLMs in conducting detailed and accurate vulnerability assessments.

To conclude, the Naptime project demonstrates that LLMs can significantly enhance their performance in vulnerability research with the right tools, particularly in controlled testing environments such as CTF-style challenges. However, the true challenge lies in translating this capability to the complexities of autonomous offensive security research, where understanding system states and attacker control is crucial. The study underscores the need to provide LLMs with flexible, iterative processes akin to those employed by expert human researchers to reflect their potential truly. As the team at Google Project Zero, in collaboration with Google DeepMind, continues to develop this technology, they remain committed to pushing the boundaries of what LLMs can achieve in cybersecurity, promising more sophisticated advancements in the future.
The post Google Project Zero Introduces Naptime: An Architecture for Evaluating Offensive Security Capabilities of Large Language Models appeared first on MarkTechPost.

NuMind Releases NuExtract: A Lightweight Text-to-JSON LLM Specialized …

NuMind introduces NuExtract, a cutting-edge text-to-JSON language model that represents a significant advancement in structured data extraction from text. This model aims to transform unstructured text into structured data highly efficiently. The innovative design and training methodologies used in NuExtract position it as a superior alternative to existing models, providing high performance and cost-efficiency.

Image Source

NuExtract is engineered to operate efficiently with models ranging from 0.5 billion to 7 billion parameters, achieving similar or superior extraction capabilities compared to larger, popular language models (LLMs). This efficiency is achieved by creating three distinct models within the NuExtract family: NuExtract-tiny, NuExtract, and NuExtract-large. These models have demonstrated remarkable performance in various extraction tasks, often outperforming significantly larger LLMs.

NuExtract is available in three trained versions:

NuExtract-tiny (0.5B): This lightweight model is ideal for applications requiring efficient performance with minimal computational resources. Despite its small size, NuExtract-tiny performs better than some larger models, making it suitable for tasks where resource constraints are a priority.

NuExtract (3.8B): This model balances size and performance, making it well-suited for more demanding extraction tasks. It leverages a moderate number of parameters to deliver high accuracy and versatility, handling a wide range of structured extraction tasks efficiently.

NuExtract-large (7B): The most powerful version, designed for the most complex and intensive extraction tasks. With 7 billion parameters, NuExtract-large achieves performance levels comparable to top-tier LLMs like GPT-4 while being significantly smaller and more cost-effective. This model is perfect for applications requiring the highest accuracy and detail in data extraction.

The primary challenge NuExtract addresses is structured extraction, which involves extracting diverse information types such as entities, quantities, dates, and hierarchical relationships from documents. The extracted information is structured into a JSON format, making it easier to parse & integrate into databases or use for automated actions. For instance, extracting data from a document and organizing it into a hierarchical tree structure in JSON format is a task NuExtract handles with high precision and efficiency.

Structured extraction tasks vary significantly in complexity. While traditional methods like regular expressions or non-generative machine learning models could handle simple entity extraction, they must improve when dealing with more complex tasks requiring deeper hierarchical extraction. Modern generative LLMs, including GPT-4, have advanced these capabilities by enabling the generation of deep extraction trees. However, NuExtract has shown that it can achieve similar results with much smaller models, making it a more practical solution for many applications.

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One of NuExtract’s key advantages is its ability to handle zero-shot and fine-tuned extraction scenarios. The model can extract information based solely on a predefined template or schema in a zero-shot setting without requiring task-specific training data. This capability is particularly valuable for applications where creating large annotated datasets is impractical. Additionally, NuExtract can be fine-tuned for specific applications, enhancing its performance further for specialized tasks.

To train NuExtract, the developers employed a novel approach: They used a large and diverse corpus of text from the C4 dataset, which was annotated using a modern LLM with carefully crafted prompts. This synthetic data was then used to fine-tune a compact, generic foundation model, resulting in a highly specialized task-specific model. This training methodology ensures that NuExtract can generalize well across different domains, making it versatile for various structured extraction tasks.

The model consistently produces valid JSON outputs, adheres to the schema, and accurately extracts relevant information. For example, in tests involving the parsing of chemical reactions, NuExtract successfully identified, classified, and extracted quantities of chemical substances and reaction conditions such as duration and temperature. This high accuracy demonstrates NuExtract’s potential to tackle complex chemistry, medicine, law, and finance extraction tasks.

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NuExtract’s compact size offers several practical benefits. Smaller models are less expensive to run, allowing for cost-effective inference. They also enable local deployment, essential for applications requiring data privacy. The ease of fine-tuning these models makes them adaptable to specific use cases, further enhancing their utility.

In conclusion, NuExtract by NuMind represents a significant leap forward in structured data extraction from text. Its innovative design, efficient training methodology, and impressive performance across various tasks make it a valuable tool for transforming unstructured text into structured data. The model’s ability to perform well in both zero-shot and fine-tuned settings, coupled with its cost-efficiency and ease of deployment, positions it as a leading solution for modern data extraction challenges.
The post NuMind Releases NuExtract: A Lightweight Text-to-JSON LLM Specialized for the Task of Structured Extraction appeared first on MarkTechPost.

AI21 Labs Jamba-Instruct model is now available in Amazon Bedrock

We are excited to announce the availability of the Jamba-Instruct large language model (LLM) in Amazon Bedrock. Jamba-Instruct is built by AI21 Labs, and most notably supports a 256,000-token context window, making it especially useful for processing large documents and complex Retrieval Augmented Generation (RAG) applications.
What is Jamba-Instruct
Jamba-Instruct is an instruction-tuned version of the Jamba base model, previously open sourced by AI21 Labs, which combines a production grade-model, Structured State Space (SSM) technology, and Transformer architecture. With the SSM approach, Jamba-Instruct is able to achieve the largest context window length in its model size class while also delivering the performance traditional transformer-based models provide. These models yield a performance boost over AI21’s previous generation of models, the Jurassic-2 family of models. For more information about the hybrid SSM/Transformer architecture, refer to the Jamba: A Hybrid Transformer-Mamba Language Model whitepaper.
Get started with Jamba-Instruct
To get started with Jamba-Instruct models in Amazon Bedrock, first you need to get access to the model.

On the Amazon Bedrock console, choose Model access in the navigation pane.
Choose Modify model access.
Select the AI21 Labs models you want to use and choose Next.
Choose Submit to request model access.

For more information, refer to Model access.
Next, you can test the model either in the Amazon Bedrock Text or Chat playground.
Example use cases for Jamba-Instruct
Jamba-Instruct’s long context length is particularly well-suited for complex Retrieval Augmented Generation (RAG) workloads, or potentially complex document analysis. For example, it would be suitable for detecting contradictions between different documents or analyzing one document in the context of another. The following is an example prompt suitable for this use case:

You are an expert research assistant;
you are to note any contradictions between the first document and second document provided:

Document 1:
{the document content}

Document 2:
{the document content}

Contradictions:

You can also use Jamba for query augmentation, a technique where an original query is transformed into related queries, for purposes of optimizing RAG applications. For example:

You are a curious and novel researcher,
who is highly interested in getting all the relevant information on a specific topic.
Given an original query, you would like to generate up to 10 related queries.
These queries should be grounded in the original query, but nevertheless new:

Original Query:
{Original Query}

New Queries:

You can also use Jamba for standard LLM operations, such as summarization and entity extraction.
Prompt guidance for Jamba-Instruct can be found in the AI21 model documentation. For more information about Jamba-Instruct, including relevant benchmarks, refer to Built for the Enterprise: Introducing AI21’s Jamba-Instruct Model.
Programmatic access
You can also access Jamba-Instruct through an API, using Amazon Bedrock and AWS SDK for Python (Boto3). For installation and setup instructions, refer to the quickstart. The following is an example code snippet:

import boto3
import json

bedrock = boto3.client(service_name=”bedrock-runtime”)

prompt = “INSERT YOUR PROMPT HERE”

body = json.dumps({
“messages”:[{“role”:”user”,”content”:prompt}],
“max_tokens”: 256,
“top_p”: 0.8,
“temperature”: 0.7,
})

modelId = “ai21.jamba-instruct-v1:0”

accept = “application/json”
contentType = “application/json”

response = bedrock.invoke_model(
body=body,
modelId=modelId,
accept=accept,
contentType=contentType
)
result=json.loads(response.get(‘body’).read())
print(result[‘choices’][0][‘message’][‘content’])

Conclusion
AI2I Labs Jamba-Instruct in Amazon Bedrock is well-suited for applications where a long context window (up to 256,000 tokens) is required, like producing summaries or answering questions that are grounded in long documents, avoiding the need to manually segment documents sections to fit the smaller context windows of other LLMs. The new SSM/Transformer hybrid architecture also provides benefits in model throughput. It can provide a performance boost of up to three times more tokens per second for context window lengths exceeding 128,000 tokens, compared to other models in similar size class.
AI2I Labs Jamba-Instruct in Amazon Bedrock is available in the US East (N. Virginia) AWS Region and can be accessed in on-demand consumption model. To learn more, refer to and Supported foundation models in Amazon Bedrock. To get started with AI2I Labs Jamba-Instruct in Amazon Bedrock, visit the Amazon Bedrock console.

About the Authors
Joshua Broyde, PhD, is a Principal Solution Architect at AI21 Labs. He works with customers and AI21 partners across the generative AI value chain, including enabling generative AI at an enterprise level, using complex LLM workflows and chains for regulated and specialized environments, and using LLMs at scale.

Fernando Espigares Caballero is a Senior Partner Solutions Architect at AWS. He creates joint solutions with strategic Technology Partners to deliver value to customers. He has more than 25 years of experience working in IT platforms, data centers, and cloud and internet-related services, holding multiple Industry and AWS certifications. He is currently focusing on generative AI to unlock innovation and creation of novel solutions that solve specific customer needs.

Scale and simplify ML workload monitoring on Amazon EKS with AWS Neuro …

Amazon Web Services is excited to announce the launch of the AWS Neuron Monitor container, an innovative tool designed to enhance the monitoring capabilities of AWS Inferentia and AWS Trainium chips on Amazon Elastic Kubernetes Service (Amazon EKS). This solution simplifies the integration of advanced monitoring tools such as Prometheus and Grafana, enabling you to set up and manage your machine learning (ML) workflows with AWS AI Chips. With the new Neuron Monitor container, you can visualize and optimize the performance of your ML applications, all within a familiar Kubernetes environment. The Neuron Monitor container can also run on Amazon Elastic Container Service (Amazon ECS), but for the purpose of this post, we primarily discuss Amazon EKS deployment.
In addition to the Neuron Monitor container, the release of CloudWatch Container Insights (for Neuron) provides further benefits. This extension provides a robust monitoring solution, offering deeper insights and analytics tailored specifically for Neuron-based applications. With Container Insights, you can now access more granular data and comprehensive analytics, making it effortless for developers to maintain high performance and operational health of their ML workloads.
Solution overview
The Neuron Monitor container solution provides a comprehensive monitoring framework for ML workloads on Amazon EKS, using the power of Neuron Monitor in conjunction with industry-standard tools like Prometheus, Grafana, and Amazon CloudWatch. By deploying the Neuron Monitor DaemonSet across EKS nodes, developers can collect and analyze performance metrics from ML workload pods.
In one flow, metrics gathered by Neuron Monitor are integrated with Prometheus, which is configured using a Helm chart for scalability and ease of management. These metrics are then visualized through Grafana, offering you detailed insights into your applications’ performance for effective troubleshooting and optimization.
Alternatively, metrics can also be directed to CloudWatch through the CloudWatch Observability EKS add-on or a Helm chart for a deeper integration with AWS services in a single step. The add-on helps automatically discover critical health metrics from the AWS Trainium and AWS Inferentia chips in the Amazon EC2 Trn1 and Amazon EC2 Inf2 instances, as well as from Elastic Fabric Adapter, the network interface for EC2 instances.. This integration can help you better understand the traffic impact on your distributed deep learning algorithms.
This architecture has many benefits:

Highly targeted and intentional monitoring on Container Insights
Real-time analytics and greater visibility into ML workload performance on Neuron
Native support for your existing Amazon EKS infrastructure

Neuron Monitor provides flexibility and depth in monitoring within the Kubernetes environment.
The following diagram illustrates the solution architecture:

Fig.1 Solution Architecture Diagram
In the following sections, we demonstrate how to use Container Insights for enhanced observability, and how to set up Prometheus and Grafana for this solution.
Configure Container Insights for enhanced observability
In this section, we walk through the steps to configure Container Insights.
Set up the CloudWatch Observability EKS add-on
Refer to Install the Amazon CloudWatch Observability EKS add-on for instructions to create the amazon-cloudwatch-observability add-on in your EKS cluster. This process involves deploying the necessary resources for monitoring directly within CloudWatch.
After you set up the add-on, check the health of the add-on with the following command:

aws eks describe-addon –cluster-name <value> –addon-name amazon-cloudwatch-observability

The output should contain the following property value:

“status”: “ACTIVE”,

For details about confirming the output, see Retrieve addon version compatibility.
Once the add-on is active, you can then directly view metrics in Container Insights.
View CloudWatch metrics
Navigate to the Container Insights console, where you can visualize metrics and telemetry about your whole Amazon EKS environment, including your Neuron device metrics. The enhanced Container Insights page looks similar to the following screenshot, with the high-level summary of your clusters, along with kube-state and control-plane metrics. The Container Insights dashboard also shows cluster status and alarms. It uses predefined thresholds for CPU, memory, and NeuronCores to quickly identify which resources have higher consumption, and enables proactive actions to avoid performance impact.

Fig.2 CloudWatch Container Insights Dashboard
The out-of-the-box opinionated performance dashboards and troubleshooting UI enables you to see your Neuron metrics at multiple granularities from an aggregated cluster level to per-container level and per-NeuronCore level. With the Container Insights default configuration, you can also qualify and correlate your Neuron metrics against the other aspects of your infrastructure such as CPU, memory, disk, Elastic Fabric Adapter devices, and more.
When you navigate to any of the clusters based on their criticality, you can view the Performance monitoring dashboard, as shown in the following screenshot.

Fig.3 Performance Monitoring Dashboard Views
This monitoring dashboard provides various views to analyze performance, including:

Cluster-wide performance dashboard view – Provides an overview of resource utilization across the entire cluster
Node performance view – Visualizes metrics at the individual node level
Pod performance view – Focuses on pod-level metrics for CPU, memory, network, and so on
Container performance view – Drills down into utilization metrics for individual containers

This landing page has now been enhanced with Neuron metrics, including top 10 graphs, which helps you identify unhealthy components in your environments even without alarms and take proactive action before application performance is impacted. For a more in-depth analysis of what is delivered on this landing page, refer to Announcing Amazon CloudWatch Container Insights with Enhanced Observability for Amazon EKS on EC2.
Prometheus and Grafana
In this section, we walk through the steps to set up Prometheus and Grafana.
Prerequisites
You should have an EKS cluster set up with AWS Inferentia or Trainium worker nodes.
Set up the Neuron Monitoring container
The Neuron Monitoring container is hosted on Amazon ECR Public. Although it’s accessible for immediate use, it’s not a recommended best practice for direct production workload use due to potential throttling limits. For more information on this and on setting up a pull through cache, see the Neuron Monitor User Guide. For production environments, it’s advisable to copy the Neuron Monitoring container to your private Amazon Elastic Container Registry (Amazon ECR) repository, where the Amazon ECR pull through cache feature can manage synchronization effectively.
Set up Kubernetes for Neuron Monitoring
You can use the following YAML configuration snippet to set up Neuron Monitoring in your Kubernetes cluster. This setup includes a DaemonSet to deploy the monitoring container on each suitable node in namespace neuron-monitor:

apiVersion: apps/v1
kind: DaemonSet
metadata:
name: neuron-monitor
namespace: neuron-monitor
labels:
app: neuron-monitor
version: v1
spec:
selector:
matchLabels:
app: neuron-monitor
template:
metadata:
labels:
app: neuron-monitor
version: v1
spec:
affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
– matchExpressions:
– key: kubernetes.io/os
operator: In
values:
– linux
– key: node.kubernetes.io/instance-type
operator: In
values:
– trn1.2xlarge
– trn1.32xlarge
– trn1n.32xlarge
– inf1.xlarge
– inf1.2xlarge
– inf1.6xlarge
– inf2.xlarge
– inf2.8xlarge
– inf2.24xlarge
– inf2.48xlarge
containers:
– name: neuron-monitor
image: public.ecr.aws/neuron/neuron-monitor:1.0.1
ports:
– containerPort: 8000
command:
– “/opt/bin/entrypoint.sh”
args:
– “–port”
– “8000”
resources:
limits:
cpu: 500m
memory: 256Mi
requests:
cpu: 256m
memory: 128Mi
env:
– name: GOMEMLIMIT
value: 160MiB
securityContext:
privileged: true

To apply this YAML file, complete the following steps:

Replace <IMAGE_URI> with the URI of the Neuron Monitoring container image in your ECR repository.
Run the YAML file with the Kubernetes command line tool with the following code:

kubectl apply -f <filename>.yaml

Verify the Neuron Monitor container is running as DaemonSet:

kubectl get daemonset -n neuron-monitor

Set up Amazon Managed Service for Prometheus
To utilize Amazon Managed Service for Prometheus with your EKS cluster, you must first configure Prometheus to scrape metrics from Neuron Monitor pods and forward them to the managed service.
Prometheus requires the Container Storage Interface (CSI) in the EKS cluster. You can use eksctl to set up the necessary components.

Create an AWS Identity and Access Management (IAM) service account with appropriate permissions:

eksctl create iamserviceaccount –name ebs-csi-controller-sa –namespace kube-system –cluster <cluster-name> –role-name <role name> –role-only –attach-policy-arn arn:aws:iam::aws:policy/service-role/AmazonEBSCSIDriverPolicy –approve

Install the Amazon Elastic Block Store (Amazon EBS) CSI driver add-on:

eksctl create addon –name aws-ebs-csi-driver –cluster <cluster-name> –service-account-role-arn <role-arn> –force

Verify the add-on installation:

eksctl get addon –name aws-ebs-csi-driver –cluster <cluster-name>

Now you’re ready to set up your Amazon Managed Service for Prometheus workspace.

Create a workspace using the AWS Command Line Interface (AWS CLI) and confirm its active status:

aws amp create-workspace –alias <alias>
aws amp list-workspaces –alias <alias>

Set up the required service roles following the AWS guidelines to facilitate the ingestion of metrics from your EKS clusters. This includes creating an IAM role specifically for Prometheus ingestion:

aws iam get-role –role-name amp-iamproxy-ingest-role

Next, you install Prometheus in your EKS cluster using a Helm chart, configuring it to scrape metrics from Neuron Monitor and forward them to your Amazon Managed Service for Prometheus workspace. The following is an example of the Helm chart .yaml file to override the necessary configs:

serviceAccounts:
server:
name: “amp-iamproxy-ingest-service-account”
annotations:
eks.amazonaws.com/role-arn: “arn:aws:iam::<account-id>:role/amp-iamproxy-ingest-role”
server:
remoteWrite:
– url: https://aps-workspaces.<region>.amazonaws.com/workspaces/<workspace-id>/api/v1/remote_write
sigv4:
region: us-west-2
queue_config:
max_samples_per_send: 1000
max_shards: 200
capacity: 2500
extraScrapeConfigs: |
– job_name: neuron-monitor-stats
kubernetes_sd_configs:
– role: pod
relabel_configs:
– source_labels: [__meta_kubernetes_pod_label_app]
action: keep
regex: neuron-monitor
– source_labels: [__meta_kubernetes_pod_container_port_number]
action: keep
regex: 8000

This file has the following key sections:

serviceAccounts – Configures the service account used by Prometheus with the necessary IAM role for permissions to ingest metrics
remoteWrite – Specifies the endpoint for writing metrics to Amazon Managed Service for Prometheus, including AWS Region-specific details and batch-writing configurations
extraScrapeConfigs – Defines additional configurations for scraping metrics from Neuron Monitor pods, including selecting pods based on labels and making sure only relevant metrics are captured

Install Prometheus in your EKS cluster using the Helm command and specifying the .yaml file:

helm install prometheus prometheus-community/prometheus -n prometheus –create-namespace -f values.yaml

Verify the installation by checking that all Prometheus pods are running:

kubectl get pods -n prometheus

This confirms that Prometheus is correctly set up to collect metrics from the Neuron Monitor container and forward them to Amazon Managed Service for Prometheus.
Integrate Amazon Managed Grafana
When Prometheus is operational, complete the following steps:

Set up Amazon Managed Grafana. For instructions, see Getting started with Amazon Managed Grafana.
Configure it to use Amazon Managed Service for Prometheus as a data source. For details, see Use AWS data source configuration to add Amazon Managed Service for Prometheus as a data source.
Import the example Neuron Monitor dashboard from GitHub to quickly visualize your metrics.

The following screenshot shows your dashboard integrated with Amazon Managed Grafana.

Fig.4 Integrating Amazon Managed Grafana
Clean up
To make sure none of the resources created in this walkthrough are left running, complete the following cleanup steps:

Delete the Amazon Managed Grafana workspace.
Uninstall Prometheus from the EKS cluster:

helm uninstall prometheus -n Prometheus

Remove the Amazon Managed Service for Prometheus workspace ID from the trust policy of the role amp-iamproxy-ingest-role or delete the role.
Delete the Amazon Managed Service for Prometheus workspace:

aws amp delete-workspace –workspace-id <workspace-id>

Clean up the CSI:

eksctl delete addon –cluster <cluster-name> –name aws-ebs-csi-driver
eksctl delete iamserviceaccount –name ebs-csi-controller-sa –namespace kube-system –cluster <cluster-name>

Delete the Neuron Monitor DaemonSet from the EKS cluster:

kubectl delete daemonset neuron-monitor -n neuron-monitor

Conclusion
The release of the Neuron Monitor container marks a significant enhancement in the monitoring of ML workloads on Amazon EKS, specifically tailored for AWS Inferentia and Trainium chips. This solution simplifies the integration of powerful monitoring tools like Prometheus, Grafana, and CloudWatch, so you can effectively manage and optimize your ML applications with ease and precision.
To explore the full capabilities of this monitoring solution, refer to Deploy Neuron Container on Elastic Kubernetes Service (EKS). Refer to Amazon EKS and Kubernetes Container Insights metrics to learn more about setting up the Neuron Monitor container and using Container Insights to fully harness the capabilities of your ML infrastructure on Amazon EKS. Additionally, engage with our community through our GitHub repo to share experiences and best practices, so you stay at the forefront of ML operations on AWS.

About the Authors
Niithiyn Vijeaswaran is a Solutions Architect at AWS. His area of focus is generative AI and AWS AI Accelerators. He holds a Bachelor’s degree in Computer Science and Bioinformatics. Niithiyn works closely with the Generative AI GTM team to enable AWS customers on multiple fronts and accelerate their adoption of generative AI. He’s an avid fan of the Dallas Mavericks and enjoys collecting sneakers.
Emir Ayar is a Senior Tech Lead Solutions Architect with the AWS Prototyping team. He specializes in assisting customers with building ML and generative AI solutions, and implementing architectural best practices. He supports customers in experimenting with solution architectures to achieve their business objectives, emphasizing agile innovation and prototyping. He lives in Luxembourg and enjoys playing synthesizers.
Ziwen Ning is a software development engineer at AWS. He currently focuses on enhancing the AI/ML experience through the integration of AWS Neuron with containerized environments and Kubernetes. In his free time, he enjoys challenging himself with badminton, swimming and other various sports, and immersing himself in music.
Rohit Talluri is a Generative AI GTM Specialist (Tech BD) at Amazon Web Services (AWS). He is partnering with top generative AI model builders, strategic customers, key AI/ML partners, and AWS Service Teams to enable the next generation of artificial intelligence, machine learning, and accelerated computing on AWS. He was previously an Enterprise Solutions Architect, and the Global Solutions Lead for AWS Mergers & Acquisitions Advisory.
Albert Opher is a Solutions Architect Intern at AWS. He is a rising senior at the University of Pennsylvania pursuing Dual Bachelor’s Degrees in Computer Information Science and Business Analytics in the Jerome Fisher Management and Technology Program. He has experience with multiple programming languages, AWS cloud services, AI/ML technologies, product and operations management, pre and early seed start-up ventures, and corporate finance.
Geeta Gharpure is a senior software developer on the Annapurna ML engineering team. She is focused on running large scale AI/ML workloads on Kubernetes. She lives in Sunnyvale, CA and enjoys listening to audible in her free time

Build an automated insight extraction framework for customer feedback …

Extracting valuable insights from customer feedback presents several significant challenges. Manually analyzing and categorizing large volumes of unstructured data, such as reviews, comments, and emails, is a time-consuming process prone to inconsistencies and subjectivity. Scalability becomes an issue as the amount of feedback grows, hindering the ability to respond promptly and address customer concerns. In addition, capturing granular insights, such as specific aspects mentioned and associated sentiments, is difficult. Inefficient routing and prioritization of customer inquiries or issues can lead to delays and dissatisfaction. These pain points highlight the need to streamline the process of extracting insights from customer feedback, enabling businesses to make data-driven decisions and enhance the overall customer experience.
Large language models (LLMs) have transformed the way we engage with and process natural language. These powerful models can understand, generate, and analyze text, unlocking a wide range of possibilities across various domains and industries. From customer service and ecommerce to healthcare and finance, the potential of LLMs is being rapidly recognized and embraced. Businesses can use LLMs to gain valuable insights, streamline processes, and deliver enhanced customer experiences. Unlike traditional natural language processing (NLP) approaches, such as classification methods, LLMs offer greater flexibility in adapting to dynamically changing categories and improved accuracy by using pre-trained knowledge embedded within the model.
Amazon Bedrock, a fully managed service designed to facilitate the integration of LLMs into enterprise applications, offers a choice of high-performing LLMs from leading artificial intelligence (AI) companies like Anthropic, Mistral AI, Meta, and Amazon through a single API. It provides a broad set of capabilities like model customization through fine-tuning, knowledge base integration for contextual responses, and agents for running complex multi-step tasks across systems. With Amazon Bedrock, developers can experiment, evaluate, and deploy generative AI applications without worrying about infrastructure management. Its enterprise-grade security, privacy controls, and responsible AI features enable secure and trustworthy generative AI innovation at scale.
To create and share customer feedback analysis without the need to manage underlying infrastructure, Amazon QuickSight provides a straightforward way to build visualizations, perform one-time analysis, and quickly gain business insights from customer feedback, anytime and on any device. In addition, the generative business intelligence (BI) capabilities of QuickSight allow you to ask questions about customer feedback using natural language, without the need to write SQL queries or learn a BI tool. This user-friendly approach to data exploration and visualization empowers users across the organization to analyze customer feedback and share insights quickly and effortlessly.
In this post, we explore how to integrate LLMs into enterprise applications to harness their generative capabilities. We delve into the technical aspects of workflow implementation and provide code samples that you can quickly deploy or modify to suit your specific requirements. Whether you’re a developer seeking to incorporate LLMs into your existing systems or a business owner looking to take advantage of the power of NLP, this post can serve as a quick jumpstart.
Advantages of adopting generative approaches for NLP tasks
For customer feedback analysis, you might wonder if traditional NLP classifiers such as BERT or fastText would suffice. Although these traditional machine learning (ML) approaches might perform decently in terms of accuracy, there are several significant advantages to adopting generative AI approaches. The following table compares the generative approach (generative AI) with the discriminative approach (traditional ML) across multiple aspects.

.
Generative AI (LLMs)
Traditional ML

Accuracy
Achieves competitive accuracy by using knowledge acquired during pre-training and utilizing the semantic similarity between category names and customer feedback. Particularly beneficial if you don’t have much labeled data.
Can achieve high accuracy given sufficient labeled data, but performance may degrade if you don’t have much labeled data and rely solely on predefined features, because it lacks the ability to capture semantic similarities effectively.

Acquiring labeled data
Uses pre-training on large text corpora, enabling zero-shot or few-shot learning. No labeled data is needed.
Requires labeled data for all categories of interest, which can be time-consuming and expensive to obtain.

Model generalization
Benefits from exposure to diverse text genres and domains during pre-training, enhancing generalization to new tasks.
Relies on a large volume of task-specific labeled data to improve generalization, limiting its ability to adapt to new domains.

Operational efficiency
Uses prompt engineering, reducing the need for extensive fine-tuning when new categories are introduced.
Requires retraining the model whenever new categories are added, leading to increased computational costs and longer deployment times.

Handling rare categories and imbalanced data
Can generate text for rare or unseen categories by using its understanding of context and language semantics.
Struggles with rare categories or imbalanced classes due to limited labeled examples, often resulting in poor performance on infrequent classes.

Explainability
Provides explanations for its predictions through generated text, offering insights into its decision-making process.
Explanations are often limited to feature importance or decision rules, lacking the nuance and context provided by generated text.

Generative AI models offer advantages with pre-trained language understanding, prompt engineering, and reduced need for retraining on label changes, saving time and resources compared to traditional ML approaches. You can further fine-tune a generative AI model to tailor the model’s performance to your specific domain or task. For more information, see Customize models in Amazon Bedrock with your own data using fine-tuning and continued pre-training.
In this post, we primarily focus on the zero-shot and few-shot capabilities of LLMs for customer feedback analysis. Zero-shot learning in LLMs refers to their ability to perform tasks without any task-specific examples, whereas few-shot learning involves providing a small number of examples to improve performance on a new task. These capabilities have gained significant attention due to their ability to strike a balance between accuracy and operational efficiency. By using the pre-trained knowledge of LLMs, zero-shot and few-shot approaches enable models to perform NLP with minimal or no labeled data. This eliminates the need for extensive data annotation efforts and allows for quick adaptation to new tasks.
Solution overview
Our solution presents an end-to-end generative AI application for customer review analysis. When the automated content processing steps are complete, you can use the output for downstream tasks, such as to invoke different components in a customer service backend application, or to insert the generated tags into metadata of each document for product recommendation.
The following diagram illustrates the architecture and workflow of the proposed solution.

The customer review analysis workflow consists of the following steps:

A user uploads a file to dedicated data repository within your Amazon Simple Storage Service (Amazon S3) data lake, invoking the processing using AWS Step Functions.
The Step Functions workflow starts. In the first step, an AWS Lambda function reads and validates the file, and extracts the raw data.
The raw data is processed by an LLM using a preconfigured user prompt. The LLM generates output based on the user prompt.
The processed output is stored in a database or data warehouse, such as Amazon Relational Database Service (Amazon RDS).
The stored data is visualized in a BI dashboard using QuickSight.
The user receives a notification when the results are ready and can access the BI dashboard to view and analyze the results.

The project is available on GitHub and provides AWS Cloud Development Kit (AWS CDK) code to deploy. The AWS CDK is an open source software development framework for defining cloud infrastructure in code (IaC) and provisioning it through AWS CloudFormation. This provides an automated deployment experience on your AWS account. We highly suggest you follow the GitHub README and deployment guidance to get started.
In the following sections, we highlight the key components to explain this automated framework for insight discovery: workflow orchestration with Step Functions, prompt engineering for the LLM, and visualization with QuickSight.
Prerequisites
This post is intended for developers with a basic understanding of LLM and prompt engineering. Although no advanced technical knowledge is required, familiarity with Python and AWS Cloud services will be beneficial if you want to explore our sample code on GitHub.
Workflow orchestration with Step Functions
To manage and coordinate multi-step workflows and processes, we take advantage of Step Functions. Step Functions is a visual workflow service that enables developers to build distributed applications, automate processes, orchestrate microservices, and create data and ML pipelines using AWS services. It can automate extract, transform, and load (ETL) processes, so multiple long-running ETL jobs run in order and complete successfully without manual orchestration. By combining multiple Lambda functions, Step Functions allows you to create responsive serverless applications and orchestrate microservices. Moreover, it can orchestrate large-scale parallel workloads, enabling you to iterate over and process large datasets, such as security logs, transaction data, or image and video files. The definition of our end-to-end orchestration is detailed in the GitHub repo.
Step Functions invokes multiple Lambda functions for the end-to-end workflow:

Preprocessing that validates the file format
Inference using an LLM, handling, and saving the results
Postprocessing that retrieves unknown items and analyzes extra tags
Notifying the end-user of the task state and response messages

Step Functions uses the Map state processing modes to orchestrate large-scale parallel workloads. You can modify the Step Functions state machine to adapt to your own workflow, or modify the Lambda function for your own processing logic.

Prompt engineering
To invoke Amazon Bedrock, you can follow our code sample that uses the Python SDK. A prompt is natural language text describing the task that an AI should perform. Prompt engineering may involve phrasing a query, specifying a style, providing relevant context, or assigning a role to the AI, such as “You are helpful assistant.” We provide a prompt example for feedback categorization. For more information, refer to Prompt engineering. You can modify the prompt to adapt to your own workflow.
This framework uses a sample prompt to generate tags for user feedback from the predefined tags listed. You can engineer the prompt based on your user feedback style and business requirements.

You are tasked with selecting an appropriate tag from the given lists based on user feedback content and feedback title enclosed within the `<feedback>` and `<title>` XML tag.

Here is the list of potential tags:
<tags>
$tags
</tags>

<title>
$title
</title>

<feedback>
$feedback
</feedback>

Please choose only one from tag list and response to the user’s questions within <tag></tag> tags. If none of the tags above are suitable for the feedback or information is not enough, return “unknown”. No explanation is required. No need to echo tag list and feedback.

Visualization with QuickSight
We have successfully used an LLM to categorize the feedback into predefined categories. After the data is categorized and stored in Amazon RDS, you can use QuickSight to generate an overview and visualize the insights from the dataset. For deployment guidance, refer to GitHub Repository: Result Visualization Guide.
We use an LLM from Amazon Bedrock to generate a category label for each piece of feedback. This generated label is stored in the label_llm field. To analyze the distribution of these labels, select the label_llm field along with other relevant fields and visualize the data using a pie chart. This will provide an overview of the different categories and their proportions within the feedback dataset, as shown in the following screenshot.

In addition to the category overview, you can also generate a trend analysis of the feedback or issues over time. The following screenshot demonstrates a trend where the number of issues peaked in March but then showed immediate improvement, with a reduction in the number of issues in subsequent months.

Sometimes, you may need to create paginated reports to present to a company management team about customer feedback. You can use Amazon QuickSight Paginated Reports to create highly formatted multi-page reports from the insight extracted by LLMs, define report layouts and formatting, and schedule report generation and distribution.
Clean up
If you followed the GitHub deployment guide and want to clean up afterwards, delete the stack customer-service-dev on the CloudFormation console or run the command cdk destroy customer-service-dev. You can also refer to the cleanup section in the GitHub deployment guide.
Applicable real-world applications and scenarios
You can use this automated architecture for content processing for various real-world applications and scenarios:

Customer feedback categorization and sentiment classification – In the context of modern application services, customers often leave comments and reviews to share their experiences. To effectively utilize this valuable feedback, you can use LLMs to analyze and categorize the comments. The LLM extracts specific aspects mentioned in the feedback, such as food quality, service, ambiance, and other relevant factors. Additionally, it determines the sentiment associated with each aspect, classifying it as positive, negative, or neutral. With LLMs, businesses can gain valuable insights into customer satisfaction levels and identify areas that require improvement, enabling them to make data-driven decisions to enhance their offerings and overall customer experience.
Email categorization for customer service – When customers reach out to a company’s customer service department through email, they often have various inquiries or issues that need to be addressed promptly. To streamline the customer service process, you can use LLMs to analyze the content of each incoming email. By examining the email’s content and understanding the nature of the inquiry, the LLM categorizes the email into predefined categories such as billing, technical support, product information, and more. This automated categorization allows the emails to be efficiently routed to the appropriate departments or teams for further handling and response. By implementing this system, companies can make sure customer inquiries are promptly addressed by the relevant personnel, improving response times and enhancing customer satisfaction.
Web data analysis for product information extraction – In the realm of ecommerce, extracting accurate and comprehensive product information from webpages is crucial for effective data management and analysis. You can use an LLM to scan and analyze product pages on an ecommerce website, extracting key details such as the product title, pricing information, promotional status (such as on sale or limited-time offer), product description, and other relevant attributes. The LLM’s ability to understand and interpret the structured and unstructured data on these pages allows for the efficient extraction of valuable information. The extracted data is then organized and stored in a database, enabling further utilization for various purposes, including product comparison, pricing analysis, or generating comprehensive product feeds. By using the power of an LLM for web data analysis, ecommerce businesses can provide accuracy and completeness of their product information, facilitating improved decision-making and enhancing the overall customer experience.
Product recommendation with tagging – To enhance the product recommendation system and improve search functionality on an online website, implementing a tagging mechanism is highly beneficial. You can use LLMs to generate relevant tags for each product based on its title, description, and other available information. The LLM can generate two types of tags: predefined tags and free tags. Predefined tags are assigned from a predetermined set of categories or attributes that are relevant to the products, providing consistency and structured organization. Free tags are open-ended and generated by the LLM to capture specific characteristics or features of the products, providing a more nuanced and detailed representation. These tags are then associated with the corresponding products in the database. When users search for products or browse recommendations, the tags serve as powerful matching criteria, enabling the system to suggest highly relevant products based on user preferences and search queries. By incorporating an LLM-powered tagging system, online websites can significantly improve the user experience, increase the likelihood of successful product discovery, and ultimately drive higher customer engagement and satisfaction.

Conclusion
In this post, we explored how you can seamlessly integrate LLMs into enterprise applications to take advantage of their powerful generative AI capabilities. With AWS services such as Amazon Bedrock, Step Functions, and QuickSight, businesses can create intelligent workflows that automate processes, generate insights, and enhance decision-making.
We have provided a comprehensive overview of the technical aspects involved in implementing such a workflow, along with code samples that you can deploy or customize to meet your organization’s specific needs. By following the step-by-step guide and using the provided resources, you can quickly incorporate this generative AI application into your current workload. We encourage you to check out the GitHub repository, deploy the solution to your AWS environment, and modify it according to your own user feedback and business requirements.
Embracing LLMs and integrating them into your enterprise applications can unlock a new level of efficiency, innovation, and competitiveness. You can learn from AWS Generative AI Customer Stories how others harness the power of generative AI to drive their business forward, and check out our AWS Generative AI blogs for the latest technology updates in today’s rapidly evolving technological landscape.

About the Authors
Jacky Wu, is a Senior Solutions Architect at AWS. Before AWS, he had been implementing front-to-back cross-asset trading system for large financial institutions, developing high frequency trading system of KRX KOSPI Options and long-short strategies of APJ equities. He is very passionate about how technology can solve capital market challenges and provide beneficial outcomes by AWS latest services and best practices. Outside of work, Jacky enjoys 10km run and traveling.
Yanwei Cui, PhD, is a Senior Machine Learning Specialist Solutions Architect at AWS. He started machine learning research at IRISA (Research Institute of Computer Science and Random Systems), and has several years of experience building AI-powered industrial applications in computer vision, natural language processing, and online user behavior prediction. At AWS, he shares his domain expertise and helps customers unlock business potentials and drive actionable outcomes with machine learning at scale. Outside of work, he enjoys reading and traveling.
Michelle Hong, PhD, works as Prototyping Solutions Architect at Amazon Web Services, where she helps customers build innovative applications using a variety of AWS components. She demonstrated her expertise in machine learning, particularly in natural language processing, to develop data-driven solutions that optimize business processes and improve customer experiences.

Revolutionizing Adapter Techniques: Qualcomm AI’s Sparse High Rank A …

A significant challenge in deploying large language models (LLMs) and latent variable models (LVMs) is balancing low inference overhead with the ability to rapidly switch adapters. Traditional methods such as Low Rank Adaptation (LoRA) either fuse adapter parameters into the base model weights, losing rapid switching capability, or maintain adapter parameters separately, incurring significant latency. Additionally, existing methods struggle with concept loss when multiple adapters are used concurrently. Addressing these issues is critical for deploying AI models in resource-constrained environments like mobile devices and ensuring robust performance across diverse applications.

LoRA and its variants are the primary techniques used to adapt large generative models. LoRA is favored for its efficiency during training and inference, but it modifies a significant portion of the base model’s weights when fused, leading to large memory and latency costs during rapid switching. In unfused mode, LoRA incurs up to 30% higher inference latency. Furthermore, LoRA suffers from concept loss in multi-adapter settings, where different adapters overwrite each other’s influence, degrading the model’s performance. Sparse adaptation techniques have been explored, but they often require complex implementations and do not fully address rapid switching and concept retention issues.

Researchers from Qualcomm AI propose Sparse High Rank Adapters (SHiRA), a highly sparse adapter framework that modifies only 1-2% of the base model’s weights. This framework enables rapid switching by minimizing the number of weights that need to be updated and mitigates concept loss through its sparse structure. The researchers leveraged gradient masking to update only the most critical weights during training, maintaining high performance with minimal parameter changes. SHiRA’s design ensures that it remains lightweight and efficient, making it suitable for deployment on mobile devices and other resource-constrained environments.

The SHiRA framework is implemented using a gradient masking technique where a sparse mask determines which weights are trainable. Various strategies to create these masks include random selection, weight magnitude, gradient magnitude, and SNIP (Sensitivity-based Pruning). The adapters can be rapidly switched by storing only the non-zero weights and their indices and applying them using efficient scatter operations during inference. The researchers also provide a memory- and latency-efficient implementation based on the Parameter-Efficient Fine-Tuning (PEFT) library, which reduces GPU memory usage by 16% compared to standard LoRA and trains at nearly the same speed.

SHiRA demonstrates superior performance in extensive experiments on both LLMs and LVMs. The approach consistently outperforms traditional LoRA methods, achieving up to 2.7% higher accuracy in commonsense reasoning tasks. Additionally, SHiRA maintains high image quality in style transfer tasks, effectively addressing the concept loss issues that plague LoRA. SHiRA achieves significantly higher HPSv2 scores on style transfer datasets, indicating superior image generation quality. By modifying only 1-2% of the base model’s weights, SHiRA ensures rapid adapter switching and minimal inference overhead, making it highly efficient and practical for deployment in resource-constrained environments such as mobile devices.

In conclusion, Sparse High Rank Adapters (SHiRA) represent a significant advancement in adapter techniques for AI models. SHiRA addresses critical challenges of rapid adapter switching and concept loss in multi-adapter settings while maintaining low inference overhead. By modifying only 1-2% of the base model’s weights, this approach offers a practical and efficient solution for deploying large models in resource-constrained environments, thus advancing the field of AI research and deployment.

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Charting the Impact of ChatGPT: Transforming Human Skills in the Age o …

Impact of ChatGPT on Human Skills:

The rapid emergence of ChatGPT, a highly advanced conversational AI model developed by OpenAI, has generated significant interest and debate across both scientific and business communities. This interest is not just about the impressive capabilities of ChatGPT in generating human-like text but also about its profound implications for the workforce. As ChatGPT and similar generative AI technologies become more integrated into various sectors, they are expected to transform the nature of many jobs, requiring new skills and competencies from workers. However, a more quantitative analysis of how ChatGPT impacts human skills needs to be done. To bridge this gap, researchers have utilized Twitter data to identify tasks that users ask ChatGPT to perform and compare these tasks to a standardized skills taxonomy, ESCO. This analysis revealed that ChatGPT influences a broad array of 185 skills, reflecting its diverse applications and the evolving demands on human capabilities in the AI era.

User Reactions and Emerging Skills:

Public sentiment towards ChatGPT’s impact on these skills is predominantly positive, as analyzed through tweets’ content and emotional tone. This suggests that users generally view ChatGPT as a tool that enhances their capabilities rather than as a threat to their jobs. Despite this positive outlook, there are some concerns about the ethical implications and potential inaccuracies of ChatGPT’s responses. The study also identified four essential skills for effectively interacting with and leveraging ChatGPT: prompt engineering, critical evaluation of AI outputs, collaborative interaction with AI, and continuous learning about AI capabilities and limitations. These skills underscore the need for workers to adapt and develop new competencies to work effectively alongside advanced AI systems like ChatGPT.

Broader Implications and Future Research:

The broader implications of ChatGPT and similar generative AI technologies extend beyond individual skills to the societal and economic transformations they might drive. The rise of such technologies raises critical questions about the future of work, human-AI collaboration, and the ethical use of AI. While some researchers argue that we are witnessing a technological evolution that gradually integrates AI into daily life, others see it as a potential revolution with profound societal impacts. Future research is needed to explore these dynamics more deeply, particularly how generative AI will be implemented across different sectors and its long-term effects on job roles and skill requirements. Additionally, understanding how public perception evolves and addressing misinformation and ethical use concerns will be crucial as these technologies become more prevalent. This ongoing research will help guide policy-making and practical strategies for adapting to the new landscape shaped by generative AI systems like ChatGPT.

Literature Review on AI Advancements and Their Impact on Human Skills:

Recent studies on AI advancements reveal significant impacts on human skills, particularly within the labor market. AI technologies, such as ML, NLP, and generative models like GPT-3, have reshaped job roles and demanded new skill sets. While some jobs face automation, roles requiring emotional intelligence and creativity remain less susceptible to AI replacement. The emergence of AI tools like ChatGPT has fostered new applications across various sectors, including education and professional services, prompting a reevaluation of human capabilities in the digital age. The research underscores both AI’s transformative potential and inherent limitations in replicating human-like intelligence.

Image source

User-ChatGPT Interaction Model: A Comprehensive Overview:

The interaction between users and AI, particularly with ChatGPT, represents a dynamic and evolving domain. This relationship is defined by the user’s task-oriented prompts and the AI’s responses. Users start by defining a task based on their expectations and skills and then communicate this via prompts to ChatGPT. The AI processes these prompts and generates textual responses. Users then evaluate these responses against their expectations, refining their prompts and expectations through iterative engagement. This model highlights the challenges of understanding ChatGPT’s internal workings and the proprietary nature of its data while emphasizing the importance of analyzing user tasks to gauge the impact of AI on human skills.

Data and Methodology:

This study investigates the tasks users assign to ChatGPT and the potential impact on human skills using Twitter data and the European Skills, Competencies, and Occupations (ESCO) taxonomy. Data were gathered from 911,637 English tweets from November 2022 to January 2023, focusing on various terms related to ChatGPT. After filtering and processing, 616,073 tweets were analyzed. Tasks mentioned in these tweets were identified using Named Entity Recognition (NER), capturing patterns involving ChatGPT and action verbs. A rule-based approach was applied, yielding 87,313 tasks. To refine the data, preprocessing included lemmatization and synonym grouping, resulting in 5,554 unique functions. These tasks were then semantically matched to relevant ESCO skills. Finally, sentiment analysis using the Twitter RoBERTa model assessed user reactions to these tasks, with sentiment scores calculated for each skill to determine the overall impact.

Exploring Public Opinions and Polarization Around ChatGPT: A BERT-Based Analysis:

This study explores public sentiment and topic polarization regarding ChatGPT by leveraging confirmation bias theory and advanced NLP models. By analyzing tweets, the research aims to understand public opinions and develop a polarization model based on confirmation bias. Utilizing BERT for sentiment analysis (BERTSentiment) and topic modeling (BERTopic), the study identifies emotional responses and thematic discussions related to ChatGPT. The findings highlight ChatGPT’s societal significance, how opinions form and diverge, and the role of group dynamics in online discussions. This approach provides a perspective on the impact of emerging technologies on public discourse.

Conclusion: Looking Ahead in the Era of ChatGPT and Generative AI:

As ChatGPT and similar Generative AI technologies continue to evolve, they promise significant impacts on human skills across various domains, from technical proficiency to creative problem-solving. The study underscores the transformative potential of these tools in reshaping job markets and educational paradigms. However, it also highlights the critical need for responsible deployment and skill development among users to maximize benefits and mitigate risks. As we navigate this dynamic landscape, understanding AI tools’ nuanced capabilities and limitations, like ChatGPT, will be crucial in harnessing their full potential for societal advancement and sustainable innovation. This marks just the beginning of a narrative that will shape the future of human-AI interaction.

Sources:

https://www.sciencedirect.com/science/article/abs/pii/S0160791X24000824

https://www.sciencedirect.com/science/article/pii/S0040162524001859

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Artifacts: Unveiling the Power of Claude 3.5 Sonnet – A Guide to Str …

Integrating artificial intelligence (AI) is revolutionizing how professionals interact with and utilize AI-generated content in digital workspaces. As businesses and creators seek more dynamic and intuitive interfaces, the demand for AI to enhance productivity and foster real-time collaboration grows. One significant challenge in this space has been creating tools that allow for flexible, real-time interaction between users and AI-generated content without the cumbersome steps typical of earlier technologies. Many existing solutions could not interact dynamically with content, limiting the potential for AI to enhance collaborative efforts truly.

Historically, AI tools designed for collaboration have been hampered by interfaces that require users to shift between multiple platforms to utilize AI outputs fully. This often resulted in a disjointed experience that could stifle creativity and slow workflow processes rather than enhance them.

The latest innovation from Anthropic, with the release of Claude 3.5 Sonnet, introduces ‘Artifacts,’ a feature designed to revolutionize user interaction with AI outputs. This tool allows users to generate and manipulate diverse content types such as code snippets, markdown documents, and visual designs directly within their workflows, thus enabling a seamless integration of AI into daily tasks.

https://www.zdnet.com/article/anthropic-launches-claude-3-5-sonnet-and-debuts-artifacts-for-collaboration/

Artifacts in Claude 3.5 Sonnet encompass six primary types, each tailored to specific professional needs. For developers and programmers, ‘Code Snippets’ allow for the insertion of executable code directly into projects. This Artifact supports various programming languages, such as Python, JavaScript, and Java, enhancing the utility of AI in coding tasks by specifying languages through the `language` attribute. Given the security implications, external scripts for these snippets are only permissible from trusted sources like Cloudflare, ensuring safe and seamless integration.

For content creators and educators, ‘Markdown Documents’ offers a robust tool for generating formatted text. These documents support markdown features like headers, lists, and links, making them ideal for creating detailed reports, educational materials, or any content that benefits from structured text enhancements.

‘HTML Pages’ Artifact serves web developers by enabling the creation of dynamic web pages that combine HTML, CSS, and JavaScript. This capability is critical for designing responsive websites or prototyping web applications efficiently. However, users must adhere to certain restrictions, such as sourcing external scripts only from approved CDNs and avoiding external images unless utilizing approved placeholders.

Graphic designers benefit from ‘SVG Images,’ which provide vector graphics that maintain clarity at any resolution. These images are scalable and can be intricately designed to fit various visual contexts without losing quality. Users specify the viewBox to ensure graphics adapt seamlessly to different display environments. This feature is essential for graphic designers and visual content creators who need precision and scalability in their visual outputs.

Mermaid diagrams are another innovative artifact ideal for visualizing processes and relationships. Users can create a range of diagrams, including flowcharts and sequence diagrams, which are crucial for project managers and developers. These diagrams help plan and visualize project timelines, processes, and relationships, enhancing team clarity and communication.

React components round out the Artifact offerings, enabling the creation of interactive UI components. This includes everything from simple elements to complex functional components with hooks facilitated by a curated library of resources and predefined components. This particularly benefits UI/UX designers and front-end developers who must rapidly prototype or deploy interactive elements within applications.

To maximize the utility of Artifacts within Claude 3.5 Sonnet, users should follow specific guidelines on content suitability. Artifacts are designed to be substantial, typically extending beyond fifteen lines to ensure they provide thorough information or functionality. They are meant to be self-contained, delivering all necessary information within the Artifact, making them understandable without requiring additional conversation context. Furthermore, these artifacts are created with the intention that they might be modified, reused, or referenced multiple times, which underscores their utility in ongoing projects and processes.

However, there are clear stipulations on when not to use Artifacts. They are unsuitable for simple, short responses or content that is primarily explanatory and does not require interactive engagement. Furthermore, Artifacts should not be used for content highly dependent on the specific context of a conversation, as they are meant to stand alone. Traditional responses are more appropriate for one-off questions or examples that do not benefit from deep interaction or future reuse. Usage notes further guide interaction with these tools, advising that typically, one Artifact should be created per message to avoid clutter and maintain conversational flow. However, existing artifacts can be updated with the same identifier to reflect the evolution of content or ideas.

To conclude, the introduction of Artifacts in Claude 3.5 Sonnet by Anthropic streamlines the integration of AI-generated content into real-world applications and enhances the functionality and interactivity of digital workspaces. By providing a wide range of customizable and secure tools, Claude 3.5 Sonnet enables users to act more effectively and innovate, pushing the boundaries of what AI can achieve in creative and technical fields.
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Implement exact match with Amazon Lex QnAIntent

This post is a continuation of Creating Natural Conversations with Amazon Lex QnAIntent and Amazon Bedrock Knowledge Base. In summary, we explored new capabilities available through Amazon Lex QnAIntent, powered by Amazon Bedrock, that enable you to harness natural language understanding and your own knowledge repositories to provide real-time, conversational experiences.
In many cases, Amazon Bedrock is able to generate accurate responses that meet the needs for a wide variety of questions and scenarios, using your knowledge content. However, some enterprise customers have regulatory requirements or more rigid brand guidelines, requiring certain questions to be answered verbatim with pre-approved responses. For these use cases, Amazon Lex QnAIntent provides exact match capabilities with both Amazon Kendra and Amazon OpenSearch Service knowledge bases.
In this post, we walk through how to set up and configure an OpenSearch Service cluster as the knowledge base for your Amazon Lex QnAIntent. In addition, exact match works with Amazon Kendra, and you can create an index and add frequently asked questions to your index. As detailed in Part 1 of this series, you can then select Amazon Kendra as your knowledge base under Amazon Lex QnA Configurations, provide your Amazon Kendra index ID, and select the exact match to let your bot return the exact response returned by Amazon Kendra.
Solution Overview
In the following sections, we walk through the steps to create an OpenSearch Service domain, create an OpenSearch index and populate it with documents, and test the Amazon Lex bot with QnAIntent.
Prerequisites
Before creating an OpenSearch Service cluster, you need to create an Amazon Lex V2 bot. If you don’t have an Amazon Lex V2 bot available, complete the following steps:

On the Amazon Lex console, choose Bots in the navigation pane.
Choose Create bot.
Select Start with an example.
For Example bot, choose BookTrip.

Enter a name and description for your bot.
Select Create a role with basic Amazon Lex permissions for your AWS Identity and Access Management (IAM) permissions runtime role.
Select No for Is use of your bot subject to the Children’s Online Privacy Protection Act (COPPA).
Choose Next.
Keep all defaults in the Add Languages to Bot section.
Choose Done to create your bot.

Create an OpenSearch Service domain
Complete the following steps to create your OpenSearch Service domain:

On the OpenSearch Service console, choose Dashboard under Managed clusters in the navigation pane.
Choose Create domain.

For Domain name, enter a name for your domain (for this post, we use my-domain).
For Domain creation method, select Easy create.

Under Engine options, for Version, choose the latest engine version. At the time of writing, the latest engine is OpenSearch_2.11.
Under Network, for this post, select Public access.
In an enterprise environment, you typically launch your OpenSearch Service cluster in a VPC.
Under Network, select Dual-stack mode.
Dual stack allows you to share domain resources across IPv4 and IPv6 address types, and is the recommended option.
Under Fine-grained access control, select Create master user.
Enter the user name and password of your choice.

Leave all other configurations at their default settings.
Choose Create.

It will take several minutes for your cluster to launch. When your cluster is ready, you will see a green Active status under Domain processing status.
Create an OpenSearch Service index
Complete the following steps to create an index:

On the domain details page, copy the domain endpoint under Domain endpoint (IPv4) to use later.
Choose the IPv4 URL link.

The IPv4 link will open the OpenSearch Dashboards login page.

Enter the user name and password you created earlier.

On the OpenSearch Dashboards welcome page, choose Explore on my own.

You can dismiss or cancel any additional modals or pop-ups.

Choose the options menu, then choose Dev Tools in the navigation pane.

On the Dev Tools page, enter the following code to create an index, then choose the run icon to send the request:

PUT my-domain-index
{
“mappings”: {
“properties”: {
“question”: {
“type”: “text”
},
“answer”: {
“type”: “text”
}
}
}
}

If successful, you will see the following message:
{
“acknowledged”: true,
“shards_acknowledged”: true,
“index”: “my-domain-index”
}

Enter the following code to bulk index multiple documents you can use later to test:

POST _bulk
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00001” } }
{ “question” : “What are the check-in and check-out times?”, “answer”: “Check-in time is 3pm and check-out time is 11am at all FictitiousHotels locations. Early check-in and late check-out may be available upon request and availability. Please inquire at the front desk upon arrival.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00002” } }
{ “question” : “Do you offer airport shuttles?”, “answer”: “Airport shuttles are available at the following FictitiousHotels locations: – FictitiousHotels Dallas: Complimentary airport shuttle available to and from Dallas/Fort Worth International Airport. Shuttle runs every 30 minutes from 5am-11pm. – FictitiousHotels Chicago: Complimentary airport shuttle available to and from O’Hare International Airport and Chicago Midway Airport. Shuttle runs every hour from 5am-11pm. – FictitiousHotels San Francisco: Complimentary airport shuttle available to and from San Francisco International Airport. Shuttle runs every 30 minutes from 5am11pm. – FictitiousHotels New York: Complimentary shuttle available to and from LaGuardia Airport and JFK Airport. Shuttle runs every hour from 5am-11pm. Please contact the front desk at your FictitiousHotels location to schedule airport shuttle service at least 24 hours in advance. Shuttle services and hours may vary by location.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00003” } }
{ “question” : “Is parking available? What is the daily parking fee?”, “answer”: “Self-parking and valet parking are available at most FictitiousHotels locations. Daily self-parking rates range from $15-$30 per day based on location. Valet parking rates range from $25-$40 per day. Please contact your FictitiousHotels location directly for specific parking information and rates.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00004” } }
{ “question” : “4. What amenities are available at FictitiousHotels?”, “answer”: “Amenities available at most FictitiousHotels locations include: – Free wireless high-speed internet access – 24-hour fitness center – Outdoor pool and hot tub – 24-hour business center – On-site restaurant and bar – Room service – Laundry facilities – Concierge services – Meeting rooms and event space Specific amenities may vary by location. Contact your FictitiousHotels for details onamenities available during your stay.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00005” } }
{ “question” : “Is there an extra charge for children staying at FictitiousHotels?”, “answer”: “There is no extra charge for children 18 years and younger staying in the same room as their parents or guardians at FictitiousHotels locations in the United States and Canada. Rollaway beds are available for an additional $15 fee per night, subject to availability. Cribs are available free of charge on request. Please contact the front desk to request cribs or rollaway beds. Additional charges for extra occupants may apply at international FictitiousHotels locations.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00006” } }
{ “question” : “Does FictitiousHotels have a pool? What are the pool hours?”, “answer”: “Most FictitiousHotels locations have an outdoor pool and hot tub available for guest use. Pool hours vary by location but are generally open from 6am-10pm daily. Specific FictitiousHotels pool hours: – FictitiousHotels Miami: Pool open 24 hours – FictitiousHotels Las Vegas: Pool open 8am-8pm – FictitiousHotels Chicago: Indoor and outdoor pools, open 6am-10pm – FictitiousHotels New York: Rooftop pool, open 9am-7pm Please contact your FictitiousHotels front desk for specific pool hours during your stay. Hours may be subject to change due to weather conditions or seasonal schedules. Proper swimwear is required and no lifeguard is on duty at any time.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00007” } }
{ “question” : “Is the fitness center free for guests? What are the hours?”, “answer”: “Yes, access to the 24-hour fitness center is included for all FictitiousHotels guests at no extra charge. The fitness center offers a range of cardio and strength training equipment. Some locations also offer fitness classes, saunas, steam rooms, and other amenities for a fee. Please contact your FictitiousHotels for specific fitness center details. Access may be restricted to guests 18 years and older. Proper athletic attire and footwear is required.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00008” } }
{ “question” : “Does FictitiousHotels offer room service? What are the hours?”, “answer”: “24-hour room service is available at most FictitiousHotels locations. In-room dining menus offer a variety of breakfast, lunch, and dinner options. Hours may vary by on-site restaurants. A $5 delivery fee and 18% service charge applies to all room service orders. For quick service, please dial extension 707 from your guest room phone. Room service hours: – FictitiousHotels San Francisco: 24-hour room service – FictitiousHotels Chicago: Room service 7am-10pm – FictitiousHotels New Orleans: Room service 7am-11pm Please contact the front desk at your FictitiousHotels location for specific room service hours and menu options. Room service availability may be limited based on on-site restaurants.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00009” } }
{ “question” : “Does FictitiousHotels provide toiletries like shampoo, soap, etc?”, “answer”: “Yes, each FictitiousHotels room is stocked with complimentary toiletries and bath amenities including shampoo, conditioner, soap, lotion, and bath gel. Additional amenities like toothbrushes, razors, and shaving cream are available upon request at the front desk. If any items are missing from your room, please contact housekeeping.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00010” } }
{ “question” : “How can I get extra towels or have my room cleaned?”, “answer”: “Fresh towels and daily housekeeping service are provided free of charge. To request extra towels or pillows, additional amenities, or to schedule midstay service, please contact the front desk by dialing 0 on your in-room phone. Daily housekeeping includes trash removal, changing sheets and towels, vacuuming, dusting, and bathroom cleaning. Just let us know your preferred service times. A Do Not Disturb sign can be placed on your door to opt out for the day.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00011” } }
{ “question” : “Does FictitiousHotels provide hair dryers in the room?”, “answer”: “Yes, each guest room at FictitiousHotels locations includes a hair dryer. Hair dryers are typically located in the bathroom drawer or mounted to the bathroom wall. Please contact the front desk immediately if the hair dryer is missing or malfunctioning so we can replace it.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00012” } }
{ “question” : “What type of WiFi or internet access is available at FictitiousHotels?”, “answer”: “Free high-speed wireless internet access is available throughout all FictitiousHotels locations. To connect, simply choose the FictitiousHotels WiFi network on your device and open a web browser. For questions or issues with connectivity, please contact the front desk for assistance. Wired internet access is also available in FictitiousHotels business centers and meeting rooms. Printers, computers, and IT support may be available for business services and events. Please inquire with your FictitiousHotels for details on business services.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00013” } }
{ “question” : “Does FictitiousHotels have electric car charging stations?”, “answer”: “Select FictitiousHotels locations offer electric vehicle charging stations on-site, typically located in self-parking areas. Availability varies by location. Please contact your FictitiousHotels to check availability and charging rates. Most stations offer Level 2 charging. Charging station locations include: – FictitiousHotels Portland: 2 stations – FictitiousHotels Los Angeles: 4 stations – FictitiousHotels San Francisco: 6 stations Guests can request an on-site parking spot nearest the charging stations when booking parking accommodations. Charging rates may apply.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00014” } }
{ “question” : “What is the pet policy at FictitiousHotels? Are dogs allowed?”, “answer”: “Pets are welcome at participating FictitiousHotels locations for an additional fee of $50 per stay. Restrictions may apply based on size, breed, or other factors. Please contact your FictitiousHotels in advance to confirm pet policies. FictitiousHotels locations in Atlanta, Austin, Chicago, Denver, Las Vegas and Seattle allow dogs under 50 lbs. Certain dog breeds may be restricted. Cats may also be permitted. Non-refundable pet fees apply. Pet owners are responsible for cleaning up after pets on hotel grounds. Pets must be attended at all times and may not be a disturbance to other guests. Pets are restricted from restaurants, lounges, fitness areas, and pool decks at all FictitiousHotels locations.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00015” } }
{ “question” : “Does FictitiousHotels have laundry facilities for guest use?”, “answer”: “Yes, self-service laundry facilities with washers and dryers are available for guests to use at all FictitiousHotels locations. Laundry facilities are typically located on the 2nd floor adjacent to vending machines and ice machines. Detergent is available for purchase via vending machines. The cost is $2.50 to wash and $2.50 to dry per load. Quarters can be obtained at the front desk. For any assistance with laundry services, please dial 0 and speak with the front desk. Valet laundry and dry-cleaning services may be offered for an additional fee.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00016” } }
{ “question” : “Can I request extra pillows or blankets for my FictitiousHotels room?”, “answer”: “Absolutely. Our housekeeping team is happy to bring additional pillows, blankets, towels and other necessities to make your stay more comfortable. We offer hypoallergenic pillows and have extra blankets available upon request. Please contact the FictitiousHotels front desk to make a special request. Dial 0 on your in-room phone. Extra amenities are subject to availability. Extra bedding must be left in the guest room at checkout to avoid additional fees.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00017” } }
{ “question” : “Does FictitiousHotels provide cribs or rollaway beds?”, “answer”: “Yes, cribs and rollaway beds are available upon request at all FictitiousHotels locations. Please contact the front desk as far in advance as possible to make arrangements, as these are limited in quantity. Cribs are provided complimentary as a courtesy. Rollaway beds are subject to an additional fee of $15 per night.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00018” } }
{ “question” : “What type of accessible rooms or ADA rooms does FictitiousHotels offer?”, “answer”: “FictitiousHotels provides accessible guest rooms tailored for those with disabilities and mobility needs. Accessible rooms feature widened doorways, lowered beds and sinks, accessible showers or tubs with grab bars, and other ADA compliant features. Please request an accessible room at the time of booking to ensure availability.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00019” } }
{ “question” : “Does FictitiousHotels provide microwaves and mini-fridges?”, “answer”: “Microwave and mini-refrigerator combos are available in select room types upon request and subject to availability. When booking your reservation, please inquire about availability of fridges and microwaves at your preferred FictitiousHotels location. A limited number are available. An additional $15 daily fee applies for use.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00020” } }
{ “question” : “Can I rent a conference or meeting room at FictitiousHotels?”, “answer”: “Yes, FictitiousHotels offers conference and meeting rooms available for rent at competitive rates. Options range from board rooms seating 8 to ballrooms accommodating up to 300 guests. State-of-the-art AV equipment is available for rent. Contact the Events Department to check availability and request a quote.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00021” } }
{ “question” : “Is there an ATM or cash machine at FictitiousHotels?”, “answer”: “For your convenience, ATMs are located near the front desk and lobby at all FictitiousHotels locations. The ATMs provide 24/7 access to cash in amounts up to $500 per transaction and accept all major credit and debit cards. Foreign transaction fees may apply. Please see the front desk if you need any assistance locating or using the ATM during your stay.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00022” } }
{ “question” : “Does FictitiousHotels have a spa or offer spa services?”, “answer”: “Select FictitiousHotels locations offer luxurious on-site spas providing massages, facials, body treatments, manicures and pedicures. For availability and booking at your FictitiousHotels, please ask the front desk for details or visit the spa directly. Day passes may be available for non-hotel guests. Additional spa access fees apply.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00023” } }
{ “question” : “Can I get a late checkout from FictitiousHotels?”, “answer”: “Late checkout may be available at participating FictitiousHotels locations based on availability. The standard checkout time is by 11am. Please inquire about late checkout options at check-in or contact the front desk at least 24 hours prior to your departure date to make arrangements. Late checkouts are subject to a half-day room rate charge.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00024” } }
{ “question” : “Does FictitiousHotels offer room upgrades?”, “answer”: “Room upgrades may be purchased upon check-in based on availability. Upgrades to suites, executive floors, or rooms with preferred views are subject to additional charges. Rates vary by date, room type, and location. Please inquire about upgrade options and pricing at the front desk during check-in. Advance reservations are recommended to guarantee upgrades.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00025” } }
{ “question” : “Do the FictitiousHotels rooms have air conditioning and heating?”, “answer”: “Yes, every guest room at all FictitiousHotels locations is equipped with individual climate controls allowing air conditioning or heating as desired. To operate, simply adjust the thermostat in your room. If you have any issues regulating the temperature, please contact the front desk immediately and we will send an engineer.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00026” } }
{ “question” : “Does FictitiousHotels provide wake-up call service?”, “answer”: “Complimentary wake-up calls are available upon request. Please contact the front desk to schedule a customized wake-up call during your stay. In-room alarm clocks are also provided for your convenience. For international locations, please specify if you need a domestic or international phone call.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00027” } }
{ “question” : “Can I smoke at FictitiousHotels? What is the smoking policy?”, “answer”: “For the comfort of all guests, FictitiousHotels enforces a non-smoking policy in all guest rooms and indoor public spaces. Designated outdoor smoking areas are available on-site. A minimum $200 cleaning fee will be charged for smoking detected in rooms. Smoking is prohibited by law on all hotel shuttle buses. Thank you for not smoking inside FictitiousHotels.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00028” } }
{ “question” : “Does FictitiousHotels offer child care services?”, “answer”: “No, we apologize that child care services are not available at FictitiousHotels locations. As an alternative, our front desk can provide recommendations for qualified local babysitting agencies and nanny services to assist families during their stay. Please let us know if you need any recommendations. Additional fees will apply.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00029” } }
{ “question” : “What restaurants are located in FictitiousHotels?”, “answer”: “Onsite dining options vary by location. Many FictitiousHotelss feature 24-hour cafes, coffee shops, trendy bars, steakhouses, and international cuisine. Please check with your FictitiousHotels front desk for all restaurants available on-site during your stay and operating hours. Room service is also available.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00030” } }
{ “question” : “Does FictitiousHotels provide transportation or town car service?”, “answer”: “FictitiousHotels can arrange transportation, car service, and limousine transfers for an additional fee. Please contact the concierge desk at least 24 hours in advance to make arrangements. We have relationships with reputable local car services and drivers. Airport shuttles, taxis, and other transportation can also be requested through your FictitiousHotels front desk.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00031” } }
{ “question” : “FictitiousHotels New York City”, “answer” : “Ideally situated in Midtown Manhattan on 52nd Street, FictitiousHotels New York City positions you in the heart of the city’s top attractions. This modern 25- story glass tower overlooks the bright lights of Broadway and Times Square, just minutes from your guestroom door. Inside, enjoy contemporary styling melded with classic New York flair. 345 well-appointed rooms feature plush bedding, marble bathrooms, room service, and scenic city views. On-site amenities include a state-of-the-art fitness center, business center, cocktail lounge with nightly live music, and farm-to-table restaurant serving sustainably sourced American fare. Venture outside to nearby Rockefeller Center, Radio City Music Hall, Central Park, the Museum of Modern Art and Fifth Avenue’s world-renowned shopping. Catch a Broadway show on the same block or take a short stroll to Restaurant Row’s vast culinary offerings. Grand Central Station sits under 10 minutes away.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00032” } }
{ “question” : “FictitiousHotels Chicago”, “answer” : “Conveniently situated just steps from North Michigan Avenue in downtown Chicago, FictitiousHotels Chicago envelopes you in Midwestern hospitality and luxury. This sleek 50-story high rise showcases gorgeous city vistas in each of the 453 elegantly appointed guest rooms and suites. Wake up refreshed in pillowtop beds, slip into plush robes and enjoy gourmet in-room coffee service. The heated indoor pool and expansive fitness center help you stay active and refreshed, while the lobby cocktail lounge serves up local craft beers and signature cocktails. Start your day with breakfast at the Café before venturing out to the city’s top cultural attractions like the Art Institute, Millennium Park, Navy Pier and Museum Campus. Shoppers can walk just next door to Chicago’s best retail at high-end department stores and independent boutiques. Business travelers appreciate our central location and 40,000 square feet of modern event space. Enjoy easy access to Chicago’s finest dining, entertainment and more.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00033” } }
{ “question” : “FictitiousHotels Orlando”, “answer” : “FictitiousHotels Orlando welcomes you with sunshine and hospitality just 3 miles from The theme parks. The resort hotel’s sprawling campus features 3 outdoor pools, 6 restaurants and lounges, full-service spa, waterpark and 27-hole championship golf course. 1,500 guestrooms cater to families and couples alike with amenities like mini-fridges, marble bathrooms, themed kids’ suites with bunk beds and separate family suites. Onsite activities range from Camp FictitiousHotels kids’ programs to poolside movies under the stars. Complimentary theme park shuttles take you directly to the theme parks and more. Area attractions like theme parks and water parks are just a short drive away. Golf fans are minutes from various golf courses. With endless recreation under the warm Florida sun, FictitiousHotels Orlando keeps the entire family entertained and happy.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00034” } }
{ “question” : “FictitiousHotels San Francisco”, “answer” : “Rising over the San Francisco Bay, FictitiousHotels San Francisco treats you to panoramic waterfront views. Perched on the Embarcadero in the lively Financial District, this sleek downtown hotel blends innovative technology with California charm across 32 floors. Contemporary rooms feature voice activated controls, intuitive lighting, rainfall showers with built-in Bluetooth speakers and floor-to-ceiling windows perfect for gazing at the Bay Bridge. Sample bites from top NorCal chefs at our signature farm- to-table restaurant or sip craft cocktails beside the outdoor heated pool. Stay connected at the lobby work bar or get moving in the 24/7 fitness center. Union Square shopping sits just up the street, while iconic landmarks like the Golden Gate Bridge, Alcatraz and Fisherman’s Wharf are only minutes away. Venture to Chinatown and North Beach’s Italian flavors or catch a cable car straight up to Ghirardelli Square. Immerse yourself in the best of the City by the Bay.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00035” } }
{ “question” : “FictitiousHotels Honolulu”, “answer” : “A true island escape awaits at FictitiousHotels Honolulu, nestled on the pristine shores of Waikiki Beach. Swaying palms frame our family-friendly resort featuring three outdoor pools, cultural activities like lei making and ukulele lessons and the island’s largest lagoon waterpark. You’ll feel the spirit of ‘ohana – family – in our welcoming staff and signature Hawaiian hospitality. 1,200 newly renovated rooms open to lanais overlooking swaying palms and the sparkling blue Pacific. Five dining options include Polynesian cuisine, island-inspired plates and indulgent character breakfasts. Complimentary beach chairs and towels invite you to sunbathe on soft white sand just steps out the lobby. Take our shuttle to Pearl Harbor, historic ‘Iolani Palace or the famous North Shore. From snorkeling at Hanauma Bay to whale watching in winter, FictitiousHotels Honolulu lets you experience O’ahu’s gorgeous island paradise.” }
{ “index”: { “_index”: “my-domain-index”, “_id” : “mdi00036” } }
{ “question” : “FictitiousHotels London”, “answer” : “Situated in fashionable South Kensington overlooking Cromwell Road, FictitiousHotels London places you in the heart of Victorian grandeur and modern city buzz. This 19th century row house turned design hotel blends contemporary style with classic British sophistication across 210 rooms. Original touches like working fireplaces and ornate crown molding offset sleek decor and high-tech in-room tablets controlling lights, TV and 24-hour room service. Fuel up on full English breakfast and locally roasted coffee at our indoor café or unwind with afternoon tea in the English Garden. Work out in the fitness studio before indulging in an evening massage. Our concierge arranges VIP access at nearby museums and priority bookings for West End theatre. Top shopping at Harrod’s and the King’s Road are a quick Tube ride away. Whether here for business or pleasure, FictitiousHotels London provides five-star luxury in an unmatched location.” }

If successful, you will see another message similar to that in the following screenshot.

If you want to update, delete, or add your own test documents, refer to the OpenSearch Document APIs.
Before setting up QnAIntent, make sure you have added access to the Amazon Bedrock model you intend to use.
Now that test data is populated in the OpenSearch Service domain, you can test it with the Amazon Lex bot.
Test your Amazon Lex bot
To test the bot, complete the following steps:

On the Amazon Lex console, navigate to the QnAIntent feature of the bot you created as a prerequisite.
Choose the language, which for this post is English (US).
Under Generative AI Configurations, choose Configure.

Under QnA configuration, choose Create QnA intent.
For Intent name, enter a name (for this post, FicticiousHotelsFAQ).
Choose Add.
Choose the intent you just added.

Under QnA configuration, choose OpenSearch as the knowledge store.
For Domain endpoint, enter the endpoint you copied earlier.
For Index name, enter a name (for example, my-domain-index).
For Exact Response, select Yes.
For Question Field, enter question.
For Answer Field, enter answer.
Choose Save intent.

Because you used the Easy create option to launch your OpenSearch Service domain, fine-grained access was enabled by default. You need to locate the Amazon Lex IAM role and add permissions to the OpenSearch Service domain to allow Amazon Lex to interact with OpenSearch Service.

Navigate to the draft version of your bot in the navigation pane.
Choose the link for IAM permissions runtime role.
Copy the ARN of the role to use later.

Navigate back to OpenSearch Dashboards.
If you closed your browser tab or navigated away from this page, you can find this again by locating the IPv4 URL on the OpenSearch Service console from a previous step.
On the options menu, choose Security.
Choose Roles in the navigation pane.
Select the role all_access.

Choose Mapped users, then choose Manage mapping.
For Backend roles, enter the IAM runtime role ARN you copied earlier.
Choose Map.

On the Amazon Lex console, navigate back to your bot and the English (US) language.
Choose Build to build your bot.
Choose Test to test your bot.

Make sure your bot has the following permissions to use QnAIntent. These permissions should be added automatically by default.

When the Amazon Lex test chat window launches, enter a question from your sample OpenSearch Service documents, such as “What are the check-in and check-out times?”

Clean Up
In order to not incur ongoing costs, delete the resources you created as part of this post:

Amazon Lex V2 bot
OpenSearch Service domain

Conclusion
Amazon Lex QnAIntent provides the flexibility and choice to use a variety of different knowledge bases to generate accurate responses to questions based on your own documents and authorized knowledge sources. You can choose to let Amazon Bedrock generate a response to questions based on the results from your knowledge base, or you can generate exact response answers using Amazon Kendra or OpenSearch Service knowledge bases.
In this post, we demonstrated how to launch and configure an OpenSearch Service domain, populate an OpenSearch Service index with sample documents, and configure the exact response option using the index with Amazon Lex QnAIntent.
You can start taking advantage of Amazon Lex QnAIntent today and transform your customer experience.

About the Authors
Josh Rodgers is a Senior Solutions Architect for AWS who works with enterprise customers in the travel and hospitality vertical. Josh enjoys working with customers to solve complex problems with a focus on serverless technologies, DevOps, and security. Outside of work, Josh enjoys hiking, playing music, skydiving, painting, and spending time with family.
Thomas Rindfuss is a Sr. Solutions Architect on the Amazon Lex team. He invents, develops, prototypes, and evangelizes new technical features and solutions for language AI services that improve the customer experience and ease adoption.

How Krikey AI harnessed the power of Amazon SageMaker Ground Truth to …

This post is co-written with Jhanvi Shriram and Ketaki Shriram from Krikey.
Krikey AI is revolutionizing the world of 3D animation with their innovative platform that allows anyone to generate high-quality 3D animations using just text or video inputs, without needing any prior animation experience. At the core of Krikey AI’s offering is their powerful foundation model trained to understand human motion and translate text descriptions into realistic 3D character animations. However, building such a sophisticated artificial intelligence (AI) model requires tremendous amounts of high-quality training data.
Krikey AI faced the daunting task of labeling a vast amount of data input containing body motions with descriptive text labels. Manually labeling this dataset in-house was impractical and prohibitively expensive for the startup. But without these rich labels, their customers would be severely limited in the animations they could generate from text inputs.
Amazon SageMaker Ground Truth is an AWS managed service that makes it straightforward and cost-effective to get high-quality labeled data for machine learning (ML) models by combining ML and expert human annotation. Krikey AI used SageMaker Ground Truth to expedite the development and implementation of their text-to-animation model. SageMaker Ground Truth provided and managed the labeling workforce, provided advanced data labeling workflows, and automated workflows for human-in-the-loop tasks, enabling Krikey AI to efficiently source precise labels tailored to their needs.
SageMaker Ground Truth Implementation
As a small startup working to democratize 3D animation through AI, Krikey AI faced the challenge of preparing a large labeled dataset to train their text-to-animation model. Manually labeling each data input with descriptive annotations proved incredibly time-consuming and impractical to do in-house at scale. With customer demand rapidly growing for their AI animation services, Krikey AI needed a way to quickly obtain high-quality labels across diverse and broad categories. Not having high-quality descriptive labels and tags would severely limit the animations their customers could generate from text inputs. Partnering with SageMaker Ground Truth provided the solution, allowing Krikey AI to efficiently source precise labels tailored to their needs.
SageMaker Ground Truth allows you to set up labeling workflows and use a private or vendor workforce for labeling or a sourced and managed workforce, along with additional features like data labeling workflows, to further accelerate and optimize the data labeling process. Krikey AI opted to use SageMaker Ground Truth to take advantage of its advanced data labeling workflows and model-assisted labeling capabilities, which further streamlined and optimized their large-scale labeling process for training their AI animation models. Data was stored in Amazon Simple Storage Solution (Amazon S3) and  AWS Key Management Service (AWS KMS) was used for data protection.
The SageMaker Ground Truth team provided a two-step solution to prepare high-quality training datasets for Krikey AI’s model. First, the team developed a custom labeling interface tailored to Krikey AI’s requirements. This interface enabled annotators to deliver accurate captions while maintaining high productivity levels. The user-friendly interface provided annotators with various options to add detailed and multiple descriptions, helping them implement comprehensive labeling of the data. The following screenshot shows an example.

Second, the team sourced and managed a workforce that met Krikey AI’s specific requirements. Krikey AI needed to quickly process a vast amount of data inputs with succinct and descriptive labels, tags, and keywords in English. Rapidly processing the large amount of data inputs allowed Krikey AI to enter the market quickly with their unique 3D animation platform.
Integral to Krikey AI’s successful partnership with SageMaker Ground Truth was the ability to frequently review and refine the labeling process. Krikey AI held weekly calls to examine sample labeled content and provide feedback to the SageMaker Ground Truth team. This allowed them to continuously update the guidelines for what constituted a high-quality descriptive label as they progressed through different categories. Having this depth of involvement and ability to recalibrate the labeling criteria was critical for making sure the precise, rich labels were captured across all their data, which wouldn’t have been possible for Krikey AI to achieve on their own.
The following diagram illustrates the SageMaker Ground Truth architecture.

Overall Architecture
Krikey AI built their AI-powered 3D animation platform using a comprehensive suite of AWS services. At the core, they use Amazon Simple Storage Solution (Amazon S3) for data storage, Amazon Elastic Kubernetes Service (Amazon EKS) for running containerized applications, Amazon Relational Database Service (Amazon RDS) for databases, Amazon ElastiCache for in-memory caching, and Amazon Elastic Compute Cloud (Amazon EC2) instances for computing workloads. Their web application is developed using AWS Amplify. The critical component enabling their text-to-animation AI is SageMaker Ground Truth, which allows them to efficiently label a massive training dataset. This AWS infrastructure allows Krikey AI to serve their direct-to-consumer AI animation tool to customers globally and enables enterprise customers to deploy Krikey AI’s foundation models using Amazon SageMaker JumpStart, as well as self-host the no-code 3D animation editor within their own AWS environment.
Results
Krikey AI’s partnership with SageMaker Ground Truth enabled them to rapidly build a massive dataset of richly labeled motion data in just 3 months and generate high-quality labels for their large dataset, which fueled their state-of-the-art text-to-animation AI model, accelerated their time-to-market, and saved over $200,000 in labeling costs.

“Amazon SageMaker Ground Truth has been game-changing for Krikey AI. Their skilled workforce and streamlined workflows allowed us to rapidly label the massive datasets required to train our innovative text-to-animation AI models. What would have taken our small team months, SageMaker Ground Truth helped us achieve in weeks—accelerating our ability to bring transformative generative AI capabilities to media, entertainment, gaming, and sports. With SageMaker Ground Truth as an extension of our team, we achieved our goal of providing an easy-to-use animation tool that anyone can use to animate a 3D character. This simply would not have been possible without the speed, scale, and quality labeling delivered by SageMaker Ground Truth. They were a true force multiplier for our AI development.”
– Dr. Ketaki Shriram, Co-Founder and CTO of Krikey AI.

Conclusion
The time and cost savings, along with access to premium labeled data, highlights the immense value SageMaker Ground Truth offers startups working with generative AI. To learn more and get started, visit Amazon SageMaker Ground Truth.
About Krikey AI
Krikey AI Animation tools empower anyone to animate a 3D character in minutes. The character animations can be used in marketing, tutorials, games, films, social media, lesson plans, and more. In addition to a video-to-animation and text-to-animation AI model, Krikey offers a 3D editor that creators can use to add lip-synched dialogue, change backgrounds, facial expressions, hand gestures, camera angles, and more to their animated videos. Krikey’s AI tools are available online at www.krikey.ai today, on Canva Apps, Adobe Express, and AWS Marketplace.

About the Authors
Jhanvi Shriram is the CEO of Krikey, an AI startup that she co-founded with her sister. Prior to Krikey, Jhanvi worked at YouTube as a Production Strategist on operations and creator community programs, which sparked her interest in working with content creators. In 2014, Jhanvi and her sister, Ketaki Shriram, co-produced a feature film that premiered at the Tribeca Film Festival and was acquired by Univision. Jhanvi holds a BA and MBA from Stanford University, and an MFA (Film Producing) from USC.
Dr. Ketaki Shriram is the CTO at Krikey, an AI animation startup. Krikey’s no-code 3D editor empowers anyone to create 3D content regardless of their background. Krikey’s tools can be used to produce content for games, films, marketing materials, and more. Dr. Shriram received her BA, MA, and PhD at the Stanford Virtual Human Interaction Lab. She previously worked at Google [x] and Meta’s Reality Labs. Dr. Shriram was selected for the Forbes 30 Under 30 2020 Class in the Gaming category.
Amanda Lester is a Senior Go-to-Market Specialist at AWS, helping to put artificial intelligence and machine learning in the hands of every developer and ML engineer. She is an experienced business executive with a proven track record of success at fast-growing technology companies. Amanda has a deep background in leading strategic go-to-market efforts for high growth technology. She is passionate about helping accelerate the growth of the tech community through programs to support gender equality, entrepreneurship, and STEM education.
Julia Rizhevsky is responsible for Growth and Go-to-Market for AWS human-in-the-loop services, serving customers building and fine-tuning AI models. Her team works with AWS customers on the cutting-edge of generative AI who are looking to leverage human intelligence to guide models to their desired behavior. Prior to AWS, Julia’s developed and launched consumer products in payments and financial services.
Ami Dani is a Senior Technical Program Manager at AWS focusing on AI/ML services. During her career, she has focused on delivering transformative software development projects for the federal government and large companies in industries as diverse as advertising, entertainment, and finance. Ami has experience driving business growth, implementing innovative training programs, and successfully managing complex, high-impact projects.

Google DeepMind Introduces Video-to-Audio V2A Technology: Synchronizin …

Sound is indispensable for enriching human experiences, enhancing communication, and adding emotional depth to media. While AI has made significant progress in various domains, incorporating sound in video-generating models with the same sophistication and nuance as human-created content remains challenging. Producing scores for these silent videos is a significant next step in making generated films.

Google DeepMind introduces video-to-audio (V2A) technology that enables synchronized audiovisual creation. Using a combination of video pixels and text instructions in natural language, V2A creates immersive audio for the on-screen action. The team tried autoregressive and diffusion methods to find the best scalable AI architecture; the results for generating audio using the diffusion method were the most convincing and realistic regarding the synchronization of audio and visuals.

The first step of their video-to-audio technology is compressing the input video. The audio is repeatedly cleaned up from background noise using the diffusion model. Visual input and natural language prompts are used to steer this process, which generates realistic, synced audio that closely follows the instructions. Decoding, waveform generation, and merging the audio and visual data constitute the final step in the audio output process.

Before iteratively running the video and audio prompt input through the diffusion model, V2A encodes them. The next step is to create compressed audio decoded into a waveform. The researchers supplemented the training process with additional information, such as transcripts of spoken dialogue and AI-generated annotations with extensive descriptions of sound, to improve the model’s ability to produce high-quality audio and to train it to make specific sounds.

The presented technology learns to respond to the information in the transcripts or annotations by associating distinct audio occurrences with different visual sceneries by training on video, audio, and the added annotations. To make shots with a dramatic score, realistic sound effects, or dialogue that complements the characters and tone of a video, V2A technology can be paired with video generation models like Veo.

With its ability to create scores for a wide range of classic videos, such as silent films and archival footage, V2A technology opens up a world of creative possibilities. The most exciting aspect is that it can generate as many soundtracks as users desire for any video input. Users can define a “positive prompt” to guide the output towards desired sounds or a “negative prompt” to steer it away from unwanted noises. This flexibility gives users unprecedented control over V2A’s audio output, fostering a spirit of experimentation and enabling them to quickly find the perfect match for their creative vision.

The team is dedicated to ongoing research and development to address a range of issues. They are aware that the quality of the audio output is dependent on the video input, and distortions or artifacts in the video that are outside the training distribution of the model can lead to noticeable audio degradation. They are working on improving lip-syncing for videos with voiceovers. By analyzing the input transcripts, V2A aims to create speech that is perfectly synchronized with the mouth movements of the characters. The team is also aware of the incongruity that can occur when the video model doesn’t correspond to the transcript, leading to eerie lip-syncing. They are actively working to resolve these issues, demonstrating their commitment to maintaining high standards and continuously improving the technology.

The team is actively seeking input from prominent creators and filmmakers, recognizing their invaluable insights and contributions to the development of V2A technology. This collaborative approach ensures that V2A technology can positively influence the creative community, meeting their needs and enhancing their work. To further protect AI-generated content from any abuse, they have integrated the SynthID toolbox into the V2A study and watermarked it all, demonstrating their commitment to the ethical use of the technology.
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Toucan TTS: An MIT Licensed Text-to-Speech Advanced Toolbox with Speec …

In recent research, the Institute for Natural Language Processing (IMS) at the University of Stuttgart, Germany, has introduced ToucanTTS, significantly advancing the field of text-to-speech (TTS) technology. With support for speech synthesis in more than 7,000 languages, this new toolset is capable of completely transforming the field of multilingual TTS systems.

ToucanTTS is an advanced TTS toolbox using which modern speech synthesis models can be taught, trained, and used. Since PyTorch and Python are the only programming languages used in its development, it is highly functional and performant yet approachable and suitable for beginners. The toolkit stands out especially for its broad language support, which caters to the needs of a wide range of international audiences.

ToucanTTS is the most multilingual TTS model available, distinguished by its capacity to synthesize speech in over 7,000 languages. It facilitates multi-speaker voice synthesis, which lets users mimic the rhythm, stress, and intonation of several speakers. This functionality is especially useful for applications that demand stylistic diversity and voice customization.

Human-in-the-loop editing functionality has been included in the toolkit, which is particularly useful for literary studies and poetry reading assignments. With the use of this feature, users can customize the synthesized speech to suit their own requirements and tastes. Interactive demonstrations have been offered by ToucanTTS for a range of applications, such as voice design, style cloning, multilingual speech synthesis, and human-edited poetry reading. These examples show off the toolkit’s versatility and robustness, which expedites users’ understanding and utilization of its capabilities.

ToucanTTS has been built on the FastSpeech 2 architecture at its core, with certain improvements, including a PortaSpeech-inspired normalizing flow-based PostNet. This design guarantees natural-sounding, high-quality speech synthesis. A self-contained aligner trained with Connectionist Temporal Classification (CTC) and spectrogram reconstruction has also been included in the toolkit for various uses. 

Using articulatory representations of phonemes as input is one of the most unique features of ToucanTTS. This method greatly improves the quality and usability of speech synthesis for low-resource languages by enabling the system to take advantage of multilingual data.

In conclusion, ToucanTTS is a notable development in text-to-speech technology. Its user-friendly design and wide range of language support make it highly beneficial for educators, researchers, and developers. ToucanTTS’s features and open-source nature guarantee that it will be essential in advancing and democratizing speech synthesis technology.

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Researchers from the University of Maryland Introduce GenQA Instructio …

Natural language processing has greatly improved language model finetuning. This process involves refining AI models to perform specific tasks more effectively by training them on extensive datasets. However, creating these large, diverse datasets is complex and expensive, often requiring substantial human input. This challenge has created a gap between academic research, which typically uses smaller datasets, and industrial applications, which benefit from vast, finely-tuned datasets.

Among many, one major problem in this field is the reliance on human-annotated data. Manually curating datasets is labor-intensive and costly, limiting the scale and diversity of the data that can be generated. Academic datasets often comprise hundreds or thousands of samples, while industrial datasets may contain tens of millions. This disparity has driven researchers to explore automated methods for generating instruction datasets that rival the quality of those produced through human labor.

Existing methods to address this problem include using large language models (LLMs) to modify and augment human-written content. While these methods have been somewhat successful, they still need to catch up regarding scalability and diversity. For instance, the Flan collection, used in training the T0 model family, expanded to include thousands of tasks but faced grammatical errors and text quality issues. Similarly, other datasets like Evol-Instruct and UltraChat involve sophisticated augmentation processes that require human oversight.

Researchers from the University of Maryland have proposed an innovative solution to this problem by introducing GenQA. This method leverages a single, well-crafted prompt to autonomously generate millions of diverse instruction examples. GenQA aims to create large-scale and highly diverse datasets by minimizing human intervention. The research team used LLMs to develop a variety of instruction examples, ranging from simple tasks to complex multi-turn dialogs across numerous subject areas.

The core technology behind GenQA involves using generator prompts to enhance the randomness and diversity of the outputs produced by LLMs. A single hand-written meta-prompt can extract millions of diverse questions from an LLM. This approach significantly reduces the need for human oversight. For example, one experiment generated over 11 million questions across nine different splits, each tailored to specific domains such as academics, mathematics, and dialogue. These questions were generated using several prompts that boosted the randomness of the LLM outputs, resulting in a diverse set of instruction examples.

Regarding performance, the researchers tested the GenQA dataset by finetuning a Llama-3 8B base model. The results were impressive, with the model’s performance on knowledge-intensive and conversational benchmarks meeting or exceeding that of datasets like WizardLM and UltraChat. Specifically, the Llama-3-8B finetuned on GenQA performed exceptionally well on instruction-following benchmarks and mathematical reasoning tasks. For instance, on the MT-Bench, GenQA achieved an average score of 7.55, outperforming both WizardLM and UltraChat.

The detailed analysis revealed that GenQA’s generator prompts led to high diversity in the generated questions and answers. For example, the similarity scores of nearest neighbors were significantly lower for GenQA than static prompts, indicating a higher level of uniqueness. The dataset also included various splits, such as 4,210,076 questions in the academic domain and 515,509 math questions, showcasing its wide applicability.

In conclusion, with the introduction of GenQA by automating the dataset creation process, the researchers have demonstrated that generating large-scale, diverse datasets with minimal human intervention is possible. This approach reduces costs and bridges the gap between academic and industrial practices. The success of GenQA in finetuning a Llama-3 8B model underscores its potential to transform AI research and applications.

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Bringing Silent Videos to Life: The Promise of Google DeepMind’s Vid …

In the rapidly advancing field of artificial intelligence, one of the most intriguing frontiers is the synthesis of audiovisual content. While video generation models have made significant strides, they often fall short by producing silent films. Google DeepMind is set to revolutionize this aspect with its innovative Video-to-Audio (V2A) technology, which marries video pixels and text prompts to create rich, synchronized soundscapes.

Transformative Potential

Google DeepMind’s V2A technology represents a significant leap forward in AI-driven media creation. It enables the generation of synchronized audiovisual content, combining video footage with dynamic soundtracks that include dramatic scores, realistic sound effects, and dialogue matching the characters and tone of a video. This breakthrough extends to various types of footage, from modern clips to archival material and silent films, unlocking new creative possibilities.

The technology’s ability to generate an unlimited number of soundtracks for any given video input is particularly noteworthy. Users can employ ‘positive prompts’ to direct the output towards desired sounds or ‘negative prompts’ to steer it away from unwanted audio elements. This level of control allows for rapid experimentation with different audio outputs, making it easier to find the perfect match for any video.

Technological Backbone

The core of V2A technology lies in its sophisticated use of autoregressive and diffusion approaches, ultimately favoring the diffusion-based method for its superior realism in audio-video synchronization. The process begins with encoding video input into a compressed representation, followed by the diffusion model iteratively refining the audio from random noise, guided by visual input and natural language prompts. This method results in synchronized, realistic audio closely aligned with the video’s action.

The generated audio is then decoded into an audio waveform and seamlessly integrated with the video data. To enhance the quality of the output and provide specific sound generation guidance, the training process includes AI-generated annotations with detailed sound descriptions and transcripts of spoken dialogue. This comprehensive training enables the technology to associate specific audio events with various visual scenes, responding effectively to the provided annotations or transcripts.

Innovative Approach and Challenges

Unlike existing solutions, V2A technology stands out for its ability to understand raw pixels and function without mandatory text prompts. Additionally, it eliminates the need for manual alignment of generated sound with video, a process that traditionally requires painstaking adjustments of sound, visuals, and timings.

However, V2A is not without its challenges. The quality of audio output heavily depends on the quality of the video input. Artifacts or distortions in the video can lead to noticeable drops in audio quality, particularly if the issues fall outside the model’s training distribution. Another area of improvement is lip synchronization for videos involving speech. Currently, there can be a mismatch between the generated speech and characters’ lip movements, often resulting in an uncanny effect due to the video model not being conditioned on transcripts.

Future Prospects

The early results of V2A technology are promising, indicating a bright future for AI in bringing generated movies to life. By enabling synchronized audiovisual generation, Google DeepMind’s V2A technology paves the way for more immersive and engaging media experiences. As research continues and the technology is refined, it holds the potential to transform not only the entertainment industry but also various fields where audiovisual content plays a crucial role.
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Rethinking Neural Network Efficiency: Beyond Parameter Counting to Pra …

Neural networks, despite their theoretical capability to fit training sets with as many samples as they have parameters, often fall short in practice due to limitations in training procedures. This gap between theoretical potential and practical performance poses significant challenges for applications requiring precise data fitting, such as medical diagnosis, autonomous driving, and large-scale language models. Understanding and overcoming these limitations is crucial for advancing AI research and improving the efficiency and effectiveness of neural networks in real-world tasks.

Current methods to address neural network flexibility involve overparameterization, convolutional architectures, various optimizers, and activation functions like ReLU. However, these methods have notable limitations. Overparameterized models, although theoretically capable of universal function approximation, often fail to reach optimal minima in practice due to limitations in training algorithms. Convolutional networks, while more parameter-efficient than MLPs and ViTs, do not fully leverage their potential on randomly labeled data. Optimizers like SGD and Adam are traditionally thought to regularise, but they may actually restrict the network’s capacity to fit data. Additionally, activation functions designed to prevent vanishing and exploding gradients inadvertently limit data-fitting capabilities.

A team of researchers from New York University, the University of Maryland, and Capital One proposes a comprehensive empirical examination of neural networks’ data-fitting capacity using the Effective Model Complexity (EMC) metric. This novel approach measures the largest sample size a model can perfectly fit, considering realistic training loops and various data types. By systematically evaluating the effects of architectures, optimizers, and activation functions, the proposed methods offer a new understanding of neural network flexibility. The innovation lies in the empirical approach to measuring capacity and identifying factors that truly influence data fitting, thus providing insights beyond theoretical approximation bounds.

The EMC metric is calculated through an iterative approach, starting with a small training set and incrementally increasing it until the model fails to achieve 100% training accuracy. This method is applied across multiple datasets, including MNIST, CIFAR-10, CIFAR-100, and ImageNet, as well as tabular datasets like Forest Cover Type and Adult Income. Key technical aspects include the use of various neural network architectures (MLPs, CNNs, ViTs) and optimizers (SGD, Adam, AdamW, Shampoo). The study ensures that each training run reaches a minimum of the loss function by checking gradient norms, training loss stability, and the absence of negative eigenvalues in the loss Hessian.

The study reveals significant insights: standard optimizers limit data-fitting capacity, while CNNs are more parameter-efficient even on random data. ReLU activation functions enable better data fitting compared to sigmoidal activations. Convolutional networks (CNNs) demonstrated a superior capacity to fit training data over multi-layer perceptrons (MLPs) and Vision Transformers (ViTs), particularly on datasets with semantically coherent labels. Furthermore, CNNs trained with stochastic gradient descent (SGD) fit more training samples than those trained with full-batch gradient descent, and this ability was predictive of better generalization. The effectiveness of CNNs was especially evident in their ability to fit more correctly labeled samples compared to incorrectly labeled ones, which is indicative of their generalization capability.

In conclusion, the proposed methods provide a comprehensive empirical evaluation of neural network flexibility, challenging conventional wisdom on their data-fitting capacity. The study introduces the EMC metric to measure practical capacity, revealing that CNNs are more parameter-efficient than previously thought and that optimizers and activation functions significantly influence data fitting. These insights have substantial implications for improving neural network training and architecture design, advancing the field by addressing a critical challenge in AI research.

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