Month: May 2024

We’ve talked a lot about Facebook and Meta ads over the past few months here at Customers.ai.

From performance issues to rising costs to attribution challenges and more, there’s been no shortage of Meta-related problems.

The thing is, I still believe in Meta ads.

It is an unbelievably cost-effective marketing channel and with the right targeting, can be a huge driver of sales and revenue for ecommerce companies.

Why do I believe in this so strongly?

Because as we speak, our customers are seeing unreal results when using Customers.ai with Meta ads. Here are just two recent examples:

Restore Custom Audience: 40,000 Visitors, $9k in Revenue

An ecommerce site using our website visitor ID X-ray pixel captured 40,000 visitors to their site.

Those visitors were put into a custom audience, pushed to their Facebook ads campaign, and tested against the existing Facebook pixel audience.

Same creative. Same budget. Same everything…except the audience.

In just ONE WEEK, the Restore custom audience brought $9K in additional revenue!

This is revenue they would not have seen otherwise.

It gets better:

Only 8% of the CAI audience overlapped with their existing FB pixel audience. Meaning 92% of the audience was net new to Facebook

ROAS improved from 2.1 to 2.58

CPA dropped from $40 to $34

Super CAPI: 100% Increase in Sales, 50% reduction in CPA

Another customer set up Restore along with Super CAPI. 

In just three weeks they saw a jump in their Event Match Quality Score from an average of 4.1 to 6.2, including a jump from 7 to 8 on purchases:

More importantly, they saw a huge reduction in CPA.

Using the same parameters as the other customer (same creative, same budget, same audience), sales doubled and CPA went from $46 to $23.

That is HUGE!

These are the types of results that allow advertisers to scale. 

These are the types of results that allow advertisers to make better decisions. 

And here’s the thing.

As we’ve started seeing these types of results roll in, we realized that while yes, we help you reach more customers, we also help you identify who is actually clicking on your ads and better understand which ads work.

How to Identify Who Is Clicking Your Facebook Ads

It’s no secret we’ve lost a ton of click data over the past few years.

Attribution has kind of become a nightmare and it’s getting harder to know which ads are converting.

We can fix this AND help you lower costs!

Here’s how…

1. Add the Website Visitor ID pixel to your site 

To install the Website Visitor ID X-Ray Pixel, sign up (for FREE!), go to your dashboard, and navigate to My Automations. 

Select + New Automation and get your pixel. We have easy install options for Google Tag Manager, WordPress, and Shopify, or you can install the pixel manually.

2. Ensure you are using unique tracking parameters for your ads

When setting up Meta ads, make sure you are adding tracking parameters to each ad set. This will allow you to know which ads are being clicked on.

3. See your ad clickers in Customers.ai

With our customer journey filter, you can see exactly who landed on any particular page, including those with parameters (see where I am going with this?). 

Now add in our direct CRM integrations and you can know if and when those people turned into sales. 

Cool, right?

Lowering Costs & Improving Performance

What makes this so valuable is that it goes above and beyond website visitor identification.

Yes, we can see who is clicking our ads and track their journey but we can also scale our campaigns with this knowledge!

By understanding which ads are truly performing, we can adjust and grow our strategies.

We can also spend less money on ads.

Think about it – website visit campaigns are expensive. 

But if you’re using a visitor identification tool and the process outlined above, you can run lower-cost awareness campaigns and still see who’s clicking and visiting your site.

The world is your oyster after that!

Put lower-intent visitors into retargeting ad campaigns. 

Send high-intent visitors directly into Klaviyo campaigns.

Retarget shoppers on other ad platforms.

This isn’t about simply capturing clicks, it’s about creating a holistic marketing strategy that reaches the right people in the right places.

This is game-changing stuff and we are excited to see more and more advertisers using the Customers.ai platform to help improve their Meta performance.

Making Meta Ads Work for You

As I said earlier, Meta Ads are a cost-effective way to reach shoppers and with Customers.ai there is so much you can do to help improve your ad performance.

Want to get started? Try Customers.ai free for 7 days or talk to one of our Meta Ads experts and see how we can help you get results in just 30 days. 

Unlock High-Intent Leads Hiding on Your Site

Book a demo of Customers.ai’s U.S. website visitor identification, customer journey insights and remarketing platform to skyrocket conversions and sales.

Book a Demo

Important Next Steps

See what targeted outbound marketing is all about. Capture and engage your first 500 website visitor leads with Customers.ai X-Ray website visitor identification for free.

Talk and learn about sales outreach automation with other growth enthusiasts. Join Customers.ai Island, our Facebook group of 40K marketers and entrepreneurs who are ready to support you.

Advance your marketing performance with Sales Outreach School, a free tutorial and training area for sales pros and marketers.

The post Want to See Who Clicked Your Meta Ads? Now You Can appeared first on Customers.ai.

LLMs have emerged as powerful tools for a wide range of applications. However, their open-ended nature poses unique challenges when it comes to security, safety, reliability, and ethical use….topics essential when building for a production level AI solutions. 

Example of Risks :

Rogue chatbot: The Air Canada chatbot promised a discount, and now the airline has to honor such a discount.

Rogue chatbot: Chevy car dealership accepted a $1 offer for a 2024 Chevy Tahoe worth $76,000

Leaking confidential information: Employees might accidentally input sensitive data into AI software, leading to confidentiality breaches, legal issues, and leakage of competitive information. For example, Samsung employees leaked sensitive information by using ChatGPT.

Guardrails, as a concept, provide a crucial solution to mitigate risks and ensure production-ready AI development.

What are AI Guardrails?

Guardrails are protective mechanisms designed to guide and constrain the behavior of LLMs. They act as a safety net, preventing unintended consequences such as biased responses, harmful instructions, generation of toxic language or security attacks.

How Guardrails Work

Guardrails operate on various levels to safeguard AI systems:

Topical Guardrails: These steer conversations towards appropriate topics and prevent LLMs from venturing into sensitive or irrelevant areas. For example, a customer service chatbot can be restricted to discussing product-related queries and avoiding political discussions.

Safety Guardrails: These filter out harmful or inappropriate content, including hate speech, profanity, or personal attacks. This is essential for creating a safe and inclusive user experience.

Security Guardrails: These protect against malicious use of LLMs, such as attempts to generate phishing emails, exploit vulnerabilities in other systems, or exploit the LLMs themselves.

Retrieval Guardrails: Protects against accessing unauthorized data

Specific Examples of Guardrails in Action

Healthcare: Guardrails can ensure that medical chatbots provide accurate and safe information, avoiding any misleading or potentially harmful advice.

Education: In educational settings, guardrails can prevent LLMs from generating biased or discriminatory content, promoting a fair and inclusive learning environment.

Finance: For financial applications, guardrails can help prevent fraud by detecting and blocking suspicious requests or transactions.

Customer Service: Guardrails can ensure that chatbots remain helpful and professional, avoiding offensive language and staying on topic.

Recruiting: guardrails can prevent LLMs from generating biased or discriminatory decision or analysis.

Why Developers Should Prioritize Guardrails

Risk Mitigation: Guardrails reduce the likelihood of unintended negative consequences, protecting both users and the reputation of the AI system.

Improved User Experience: By ensuring appropriate and safe interactions, guardrails enhance user trust and satisfaction.

Ethical Considerations: Guardrails help address ethical concerns surrounding AI, promoting fairness, transparency, and accountability.

Regulatory Compliance: As AI regulations evolve, guardrails can assist in meeting legal requirements and industry standards.

Basic Guardrails in an AI Architecture

This schema was provided by Nvidia and is a simple architectural representation of where the guardrails sit in the data flow.

The Future of Guardrails in AI

The development and implementation of guardrails is an ongoing process. As LLM technology advances, so too will the sophistication and effectiveness of these protective mechanisms. Guardrails have already quickly evolved in the last 12 months and are evolving from rule based solutions to programmatic solutions to LLM powered solutions themselves.

Key Takeaways for Developers:

Guardrails are essential for production AI development.

They can be implemented at various levels to mitigate risks and ensure safety.

Prioritizing guardrails enhances user experience, builds trust and protects resources

By embracing guardrails as part of your architecture design, we can unlock the full potential of AI while minimizing its risks.
The post Building Production-Ready AI Solutions: The Essential Role of Guardrails appeared first on MarkTechPost.

In a recent study, a team of researchers from MIT introduced the linear representation hypothesis, which suggests that language models perform calculations by adjusting one-dimensional representations of features in their activation space. According to this theory, these linear characteristics can be used to understand the inner workings of language models. The study has looked into the idea that some language model representations could be multi-dimensional by nature. 

In order to tackle this, the team has precisely defined irreducible multi-dimensional features. The incapacity of these features to split down into separate or non-co-occurring lower-dimensional aspects is what distinguishes them. A feature that is truly multi-dimensional cannot be reduced to a smaller one-dimensional component without losing useful information.

The team has created a scalable technique to identify multi-dimensional features in language models using this theoretical framework. Sparse autoencoders, which are neural networks built to develop effective, compressed data representations, have been used in this technique. Sparse autoencoders are used to automatically recognise multi-dimensional features in models such as Mistral 7B and GPT-2. 

The team has identified several multidimensional features that are remarkably interpretable. For example, circular representations of the days of the week and the months of the year have been found. These circular properties are especially interesting since they naturally express cyclic patterns, which makes them useful for calendar-related tasks involving modular arithmetic, such as figuring out the day of the week for a given date.

Studies on the Mistral 7B and Llama 3 8B models have been performed to further validate the results. For tasks involving days of the week and months of the year, these trials have shown that the circular features found were crucial to the computational processes of the models. The changes in the models’ performance on pertinent tasks could be seen by adjusting these variables, indicating their crucial relevance. 

The team has summarized their primary contributions as follows. 

Multi-dimensional language model characteristics have been defined in addition to one-dimensional ones. An updated superposition theory has been proposed to explain these multi-dimensional characteristics. 

The team has analysed how employing multi-dimensional features reduces the representation space of the model. A test has been created to identify irreducible features that are both empirically feasible and theoretically supported.  

An automated method has been introduced to discover multi-dimensional features using sparse autoencoders. Multi-dimensional representations in GPT-2 and Mistral 7B, such as circular representations for the days of the week and months of the year, can be found using this method. It is the first time that emergent circular representations have been discovered in a big language model. 

Two challenges have been suggested that involve modular addition in terms of months of the year and days of the week, assuming that these circular representations will be used by the models for these tasks. Mistral 7B and Llama 3 8B intervention tests have demonstrated that models employ circular representations. 

In conclusion, this research shows that certain language model representations are multi-dimensional by nature, which calls into question the linear representation theory. This study contributes to a better understanding of the intricate internal structures that allow language models to accomplish a wide range of tasks by creating a technique to identify these features and verify their significance through experiments.

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The post This AI Study from MIT Proposes a Significant Refinement to the simple one-dimensional linear representation hypothesis appeared first on MarkTechPost.

Large Language Models (LLMs) have advanced natural language processing tasks significantly. Recently, using LLMs for physical world planning tasks has shown promise. However, LLMs, primarily autoregressive models, often fail to understand the real world, leading to hallucinatory actions and trial-and-error behavior. Unlike LLMs, humans utilize global task knowledge and local state knowledge to mentally rehearse and execute tasks efficiently, avoiding blind trial-and-error and confusion during the planning and execution stages.

Existing work in LLM-based agent systems focuses on agent planning, external tool utilization, and code generation, often fine-tuning open-source LLMs. These approaches may lead to trial-and-error actions due to a lack of environmental cognition. Knowledge-augmented agent planning, using pre-trained knowledge or structured prompts, faces challenges in transferring across tasks. 

Inspired by the human approach to planning, researchers from Zhejiang University – Ant Group Joint Laboratory of Knowledge Graph, National University of Singapore, and Alibaba Group developed a parametric World Knowledge Model (WKM) for agent planning. WKM is built on knowledge from both expert and explored trajectories. The agent model synthesizes task knowledge by comparing these trajectories and summarizes state knowledge for each planning step. This knowledge is integrated into expert trajectories to train the WKM. During planning, WKM provides global task knowledge and maintains dynamic state knowledge, guiding the agent and preventing hallucinatory actions through kNN retrieval and weighted predictions.

The agent model self-synthesizes task knowledge by comparing expert and sampled trajectories. An experienced agent generates high-quality rejected trajectories, enhancing task knowledge beyond supervised fine-tuning. Task knowledge guides global planning, avoiding blind trial-and-error. State knowledge, summarized at each planning step from expert trajectories, constrains local planning to prevent hallucinatory actions. A state knowledge base, formed by combining state knowledge with preceding and subsequent actions, facilitates retrieval without overloading the context, ensuring effective and accurate agent planning.

The method is evaluated on ALFWorld, WebShop, and ScienceWorld datasets, with unseen tasks testing generalization. ALFWorld uses binary rewards, while WebShop and ScienceWorld use dense rewards. The models tested include Mistral-7B, Gemma-7B, and Llama-3-8B, compared against prompt-based baselines (REACT, Reflexion), fine-tuning baselines (NAT, ETO), KNOWAGENT, and ChatGPT/GPT-4. The approach, through LoRA training alone, surpasses GPT-4 on ALFWorld (44.29→73.57 on seen, 38.05→76.87 on unseen) and WebShop (62.76→66.64), and fine-tuning baselines, demonstrating that integrating world knowledge is more effective than further fine-tuning on negative examples. WKM shows superior performance and generalization compared to human-designed knowledge methods like KNOWAGENT.

This research develops a parametric WKM to enhance language agent model planning. The WKM provides task knowledge for global planning and state knowledge for local planning. Results show WKM’s superior performance on GPT-4 and state-of-the-art models, outperforming strong baselines. Analytical experiments demonstrate WKM’s ability to reduce trial-and-error, improve generalization to unseen tasks, achieve weak-guide-strong, and extend to unified world knowledge training. 

Check out the Paper. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter. Join our Telegram Channel, Discord Channel, and LinkedIn Group.

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The post Optimizing Agent Planning: A Parametric AI Approach to World Knowledge appeared first on MarkTechPost.

LLMs demonstrate emergent intelligence with increased parameters, computes, and data, hinting at artificial general intelligence. Despite advancements, deployed LLMs still exhibit errors like hallucinations, bias, and factual inaccuracies. Also, the constant evolution of knowledge challenges their pretraining. Addressing errors promptly during deployment is crucial, as retraining or finetuning is often prohibitively costly, posing sustainability issues for accommodating lifelong knowledge growth.

While long-term memory can be updated through (re)pretraining, finetuning, and model editing, working memory aids inference, enhanced by methods like GRACE. However, debates persist on the efficacy of fine-tuning versus retrieval. Current knowledge injection methods face challenges like computational overhead and overfitting. Model editing techniques, including constrained finetuning and meta-learning, aim to efficiently edit LLMs. Recent advancements focus on lifelong editing but require extensive domain-specific training, posing challenges in predicting upcoming edits and accessing relevant data.

After studying the above issues and approaches thoroughly, researchers from Zhejiang University and Alibaba Group propose their method, WISE, a dual parametric memory scheme, comprising a main memory for pretrained knowledge and a side memory for edited knowledge. Only the side memory undergoes edits, with a router determining which memory to access for queries. For continual editing, WISE employs a knowledge-sharing mechanism, segregating edits into distinct parameter subspaces to prevent conflicts before merging them into a shared memory.

WISE comprises two main components: Side Memory Design and Knowledge Sharding and Merging. The former involves a side memory, initialized as a copy of a certain FFN layer of the LLM, storing edits, and a routing mechanism for memory selection during inference. The latter employs knowledge sharding to divide edits into random subspaces for editing and knowledge merging techniques to combine these subspaces into a unified side memory. Also, WISE introduces WISE-Retrieve, allowing retrieval among multiple side memories based on activation scores, enhancing lifelong editing scenarios.

WISE demonstrates superior performance compared to existing methods in both QA and Hallucination settings. It outperforms competitors, particularly in long editing sequences, achieving significant improvements in stability and managing sequential edits effectively. While methods like MEND and ROME are competitive initially, they falter as edit sequences lengthen. Directly editing long-term memory leads to significant declines in locality, impairing generalization. GRACE excels in locality but sacrifices generalization in continual editing. WISE achieves a balance between reliability, generalization, and locality, outperforming baselines across various tasks. In out-of-distribution evaluation, WISE exhibits excellent generalization performance, surpassing other methods.

This research identifies the challenge of achieving reliability, generalization, and locality simultaneously in current lifelong modeling editing approaches, attributing it to the gap between working and long-term memory. To overcome this issue, WISE is proposed, comprising side memory and model merging techniques. Results indicate that WISE shows promise in simultaneously achieving high metrics across various datasets and LLM models.

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The post Achieving Balance in Lifelong Learning: The WISE Memory Approach appeared first on MarkTechPost.

Proof assistants like Lean ensure high accuracy in mathematical proofs, addressing the growing complexity of modern mathematics that often leads to errors. Formal languages like Lean, Isabelle, and Coq create computer-verifiable proofs but require significant effort and expertise. Automated theorem proving is increasingly important, with new methods focusing on search algorithms to explore potential solutions. Despite LLM improvements, these methods need more training data. Advances in autoformalization offer some relief, but the datasets remain too small to leverage LLM capabilities fully.

Researchers from DeepSeek, Sun Yat-sen University, the University of Edinburgh, and MBZUAI have developed a method to generate extensive LFourfour proof data from high-school and undergraduate math competition problems. By translating these problems into formal statements, filtering low-quality ones, and generating proofs, they created an 8 million statement dataset. Fine-tuning the DeepSeekMath 7B model on this data, they achieved 46.3% accuracy in whole-proof generation on the Lean 4 miniF2F test, surpassing GPT-4’s 23.0%. Their model also solved 5 out of 148 FIMO benchmark problems, outperforming GPT-4. This work advances theorem proving by leveraging large-scale synthetic data.

Automated theorem proving (ATP) has been a key AI research area since its inception. It has evolved from efficient first-order provers like E and Vampire to handling complex theorems in modern proof assistants such as Lean, Isabelle, and Coq. Recent advances in deep learning and model-guided search have revitalized ATP, combining neural models with tree search algorithms and reinforcement learning. These methods, though powerful, are resource-intensive. Autoformalization, converting natural language into formal statements, addresses limited training data. Recent efforts synthesize larger formal proof datasets using LLMs to improve neural provers’ performance on complex mathematical problems significantly.

The approach comprises four main stages. Formal mathematical statements are initially generated from a large collection of informal math problems. These auto-formalized statements undergo filtering through model scoring and hypothesis rejection to select high-quality ones. The DeepSeek-Prover model then attempts to prove these statements with correctness verified by the Lean 4 formal verifier, resulting in validated formal statements and proofs. This data is used to fine-tune the DeepSeek-Prover, and the process repeats until improvements become marginal. To enhance proof efficiency, both original statements and their negations are proved concurrently, swiftly discarding invalid statements. 

DeepSeek-Prover, based on the DeepSeekMath-Base 7B model, was fine-tuned with a global batch size of 512 and a constant learning rate of 1 × 10^−4, including 6,000 warmup steps using synthetic data. Its performance was compared against GPT-3.5, GPT-4, and several advanced methods like GPT-f, Proof Artifact Co-Training, ReProver, Llemma, and COPRA. Evaluations on the miniF2F and FIMO benchmarks revealed that DeepSeek-Prover outperformed others, achieving 60.2% on miniF2F-valid and 52.0% on miniF2F-test, significantly higher than GPT-4’s 25.41% and 22.95%. The FIMO benchmark successfully proved five theorems with varying attempts, surpassing GPT-4, which failed to establish any.

In conclusion, the study devised a method for generating extensive synthetic proof data from high-school and undergraduate-level math competition problems. By translating natural language problems into formal statements, filtering out low-quality data, and using iterative proof generation, 8 million proof data points were created, significantly enhancing the DeepSeekMath 7B model’s performance in ATP. The model surpasses GPT-4 and other benchmark methods like miniF2F and FIMO. The open-sourced dataset and model aim to advance ATP research and improve large language models’ capabilities in formal mathematical reasoning, with plans to broaden the range of addressed mathematical problems in future work.

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The post Revolutionizing Theorem Proving: How Synthetic Proof Data Transforms LLM Capabilities appeared first on MarkTechPost.

Anthropic AI’s Claude family of models represents a great challenging feat for GPT models in AI technology. With the release of the Claude 3 series, Anthropic has expanded its models’ capabilities and performance, catering to various applications from text generation to advanced vision processing. Let’s have an overview of these developments, highlighting the advancements and comparative features of the models within the Claude family.

Claude 3: The New Generation

The Claude 3 series includes three models: Claude 3 Opus, Claude 3 Sonnet, and Claude 3 Haiku. Each model addresses specific needs & offers a balance of performance, speed, and cost.

Claude 3 Opus: This is the most powerful model in the Claude 3 series. It can handle highly complex tasks with remarkable fluency and human-like understanding. It is ideal for scenarios requiring top-tier performance and intelligence, making it suitable for sophisticated AI applications.

Claude 3 Sonnet: Positioned as the balanced model, Claude 3 Sonnet offers a blend of intelligence and speed. It is particularly well-suited for enterprise workloads and scaled AI deployments, providing reliable performance at a lower cost than Opus.

Claude 3 Haiku: This model is designed for speed and responsiveness. It is the fastest and most compact in the series, making it perfect for applications needing near-instantaneous responses and seamless AI interactions.

Key Features of Claude 3 Models

The Claude 3 models come equipped with several advanced features:

Multilingual Capabilities: Improved fluency in non-English languages, including Spanish and Japanese, enabling robust translation services and global content creation.

Vision and Image Processing: All Claude 3 models can process and analyze visual inputs, making them suitable for tasks like document analysis, web UI processing, and image catalog metadata generation.

Steerability and Ease of Use: Enhanced ability to follow directions, giving users more control over the model’s behavior and output quality.

Model Upgrades: Periodic updates to enhance performance and capabilities, with each update pinned to a new model version to ensure backward compatibility.

Comparative Overview of Claude Models

The table provides a comparative overview of the key features and capabilities of each model in the Claude family:

Conclusion

The Claude 3 series by Anthropic AI provides versatile and high-performing models tailored to various applications. Whether users want the raw power of Claude 3 Opus, the balanced efficiency of Claude 3 Sonnet, or the swift responsiveness of Claude 3 Haiku, they can choose a model that best fits their specific needs. 

Sources

https://docs.anthropic.com/en/docs/models-overview

The post Developments in Family of Claude Models by Anthropic AI: A Comprehensive Review appeared first on MarkTechPost.

Unlocking the potential of large multimodal language models (MLLMs) to handle diverse modalities like speech, text, image, and video is a crucial step in AI development. This capability is essential for applications such as natural language understanding, content recommendation, and multimodal information retrieval, enhancing the accuracy and robustness of AI systems.

Traditional methods for handling multimodal challenges often rely on dense models or single-expert modality approaches. Dense models involve all parameters in every computation, leading to increased computational overhead and reduced scalability as the model size grows. On the other hand, single-expert approaches lack the flexibility and adaptability required to effectively integrate and comprehend diverse multimodal data. These methods often struggle with complex tasks that involve multiple modalities simultaneously, such as understanding long speech segments or processing intricate image-text combinations.

The researchers from Harbin Institute of Technology have proposed the innovative Uni-MoE approach, which leverages a Mixture of Experts (MoE) architecture along with a strategic three-phase training strategy. Uni-MoE optimizes expert selection and collaboration, allowing modality-specific experts to work synergistically to enhance model performance. The three-phase training strategy includes specialized training phases for cross-modality data, which improves model stability, robustness, and adaptability. This new approach not only overcomes the drawbacks of dense models and single-expert approaches but also demonstrates significant advancements in the capabilities of multimodal AI systems, particularly in handling complex tasks that involve diverse modalities.

Uni-MoE’s technical advancements include a MoE framework specializing in different modalities and a three-phase training strategy for optimized collaboration. Advanced routing mechanisms allocate input data to relevant experts, optimizing computational resources, while auxiliary balancing loss techniques ensure equal expert importance during training. These intricacies make Uni-MoE a robust solution for complex multimodal tasks.

Results showcase Uni-MoE’s superiority with accuracy scores ranging from 62.76% to 66.46% across evaluation benchmarks like ActivityNet-QA, RACE-Audio, and A-OKVQA. It outperforms dense models, exhibits better generalization, and handles long speech understanding tasks effectively. Uni-MoE’s success marks a significant leap forward in multimodal learning, promising enhanced performance, efficiency, and generalization for future AI systems. 

In conclusion, Uni-MoE represents a significant leap forward in the realm of multimodal learning and AI systems. Its innovative approach, leveraging a Mixture of Experts (MoE) architecture and a strategic three-phase training strategy, addresses the limitations of traditional methods and unlocks enhanced performance, efficiency, and generalization across diverse modalities. The impressive accuracy scores achieved on various evaluation benchmarks, including ActivityNet-QA, RACE-Audio, and A-OKVQA, underscore Uni-MoE’s superiority in handling complex tasks such as long speech understanding. This groundbreaking technology not only overcomes existing challenges but also paves the way for future advancements in multimodal AI systems, reaffirming its pivotal role in shaping the future of AI technology. 

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The post Uni-MoE: A Unified Multimodal LLM based on Sparse MoE Architecture appeared first on MarkTechPost.

GPT-4 and other Large Language Models (LLMs) have proven to be highly proficient in text analysis, interpretation, and generation. Their exceptional effectiveness extends to a wide range of financial sector tasks, including sophisticated disclosure summarization, sentiment analysis, information extraction, report production, and compliance verification. 

However, studies have been still going on about their function in making well-informed financial decisions, especially when it comes to numerical analysis and judgment-based tasks. Because LLMs are good at processing and producing language-based material, they perform well in textual domains. Their skill set enables them to help with tasks like compiling compliance reports, extracting important information from massive datasets, conducting sentiment analysis on market news, and summarising intricate financial paperwork. 

The fundamental question, though, is whether LLMs can be applied to financial statement analysis (FSA), a field that has historically placed a strong emphasis on numerical data and human judgment. Financial statement analysis (FSA) is assessing a company’s financial standing and forecasting its future results using its financial statements, including income and balance sheets. In addition to being purely mathematical, this calls for a thorough comprehension of financial ratios, trends, and related company information.

In recent research, a team of researchers from the University of Chicago studied the possibility that a Large Language Model like GPT-4 could carry out financial statement analysis in a way that was similar to that of skilled human analysts. The team gave GPT-4 anonymized, standardized financial statements to analyze in order to forecast the future direction of earnings. Crucially, the model was only provided with the numerical data seen in the financial records; it was not provided with any narrative or industry-specific information.

GPT-4 proved better at anticipating changes in earnings than human financial professionals. This dominance was especially noticeable in situations where human analysts usually have difficulties. This implies that even in the lack of contextual narratives, the LLM has a distinct advantage in managing complex financial facts.

Moreover, the predictive power of GPT-4 was shown to be on par with popular  Machine Learning models that are specially trained for these kinds of tasks. With performance comparable to specialized predictive models, GPT-4 can analyze and interpret financial data with high accuracy.

The results included the critical finding that the predicted accuracy of GPT-4 is independent of its training memory. Rather, the model uses the data it analyses to produce insightful narratives about how a company will perform going forward. Apart from surpassing human analysts and corresponding specialized models, the team also examined the usefulness of GPT-4’s forecasts in trading tactics. Compared to strategies based on other models, these strategies based on the model’s forecasts produced greater alphas and Sharpe ratios. This indicates that trading strategies based on the predictions made by GPT-4 were not only more successful but also provided superior returns when adjusted for risk.

In conclusion, these findings imply that LLMs such as GPT-4 may be crucial in financial decision-making. Together with their strong performance in real-world trading applications, LLMs’ capacity to accurately analyze financial statements and produce insightful predictions suggests that in the future, they may even completely replace certain tasks currently carried out by human analysts.

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The post This AI Research from the University of Chicago Explores the Financial Analytical Capabilities of Large Langauge Models (LLMs) appeared first on MarkTechPost.

Advancements in AI have led to proficient systems that make unclear decisions, raising concerns about deploying untrustworthy AI in daily life and the economy. Understanding neural networks is vital for trust, ethical concerns like algorithmic bias, and scientific applications requiring model validation. Multilayer perceptrons (MLPs) are widely used but lack interpretability compared to attention layers. Model renovation aims to enhance interpretability with specially designed components. Based on the Kolmogorov-Arnold Networks (KANs) offer improved interpretability and accuracy based on the Kolmogorov-Arnold theorem. Recent work extends KANs to arbitrary widths and depths using B-splines, known as Spl-KAN.

Researchers from Boise State University have developed Wav-KAN, a neural network architecture that enhances interpretability and performance by using wavelet functions within the KAN framework. Unlike traditional MLPs and Spl-KAN, Wav-KAN efficiently captures high- and low-frequency data components, improving training speed, accuracy, robustness, and computational efficiency. By adapting to the data structure, Wav-KAN avoids overfitting and enhances performance. This work demonstrates Wav-KAN’s potential as a powerful, interpretable neural network tool with applications across various fields and implementations in frameworks like PyTorch and TensorFlow.

Wavelets and B-splines are key methods for function approximation, each with unique benefits and drawbacks in neural networks. B-splines offer smooth, locally controlled approximations but struggle with high-dimensional data. Wavelets, excelling in multi-resolution analysis, handle both high and low-frequency data, making them ideal for feature extraction and efficient neural network architectures. Wav-KAN outperforms Spl-KAN and MLPs in training speed, accuracy, and robustness by using wavelets to capture data structure without overfitting. Wav-KAN’s parameter efficiency and lack of reliance on grid spaces make it superior for complex tasks, supported by batch normalization for improved performance.

KANs are inspired by the Kolmogorov-Arnold Representation Theorem, which states that any multivariate function can be decomposed into the sum of univariate functions of sums. In KANs, instead of traditional weights and fixed activation functions, each “weight” is a learnable function. This allows KANs to transform inputs through adaptable functions, leading to more precise function approximation with fewer parameters. During training, these functions are optimized to minimize the loss function, enhancing the model’s accuracy and interpretability by directly learning the data relationships. KANs thus offer a flexible and efficient alternative to traditional neural networks.

Experiments with the KAN model on the MNIST dataset using various wavelet transformations showed promising results. The study utilized 60,000 training and 10,000 test images, with wavelet types including Mexican hat, Morlet, Derivative of Gaussian (DOG), and Shannon. Wav-KAN and Spl-KAN employed batch normalization and had a structure of [28*28,32,10] nodes. The models were trained for 50 epochs over five trials. Using the AdamW optimizer and cross-entropy loss, results indicated that wavelets like DOG and Mexican hat outperformed Spl-KAN by effectively capturing essential features and maintaining robustness against noise, emphasizing the critical role of wavelet selection.

In conclusion, Wav-KAN, a new neural network architecture, integrates wavelet functions into KAN to improve interpretability and performance. Wav-KAN captures complex data patterns using wavelets’ multiresolution analysis more effectively than traditional MLPs and Spl-KANs. Experiments show that Wav-KAN achieves higher accuracy and faster training speeds due to its unique combination of wavelet transforms and the Kolmogorov-Arnold representation theorem. This structure enhances parameter efficiency and model interpretability, making Wav-KAN a valuable tool for diverse applications. Future work will optimize the architecture further and expand its implementation in machine learning frameworks like PyTorch and TensorFlow.

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The post Enhancing Neural Network Interpretability and Performance with Wavelet-Integrated Kolmogorov-Arnold Networks (Wav-KAN) appeared first on MarkTechPost.

Regarding robotic learning, the standard practice is to use datasets tailored to the particular robot and job at hand to train policies. Starting from scratch in this manner necessitates a substantial amount of data collection for every activity, and the policies that are produced typically display little generalizability. Theoretically, data gathered from previous robots and jobs could be a solution; training models on various control issues could enhance their ability to generalize and perform better on subsequent tasks. In contrast to the pervasiveness of general-purpose models in computer vision and natural language processing, creating a “general-purpose robot model” capable of controlling various robots has proven to be a formidable challenge. Dealing with robot embodiments, sensor configurations, action spaces, task specifications, surroundings, and compute budgets are unique issues when training a unified control strategy in robotics.

Several publications have put forward robotic foundation models that accomplish just that—directly translate robot observations into actions—and offer generalizability to new domains and robots with zero or few shots. Because of their versatility in low-level visuomotor control across activities, settings, and robotic systems, these models are generally called “generalist robot policies” (GRPs). While there has been progress toward a “general-purpose robot model,” these models still have a ways to go. For example, they don’t allow for effective finetuning to new domains; the biggest ones aren’t even available to the public. Another issue is that they limit downstream users to a pre-defined and often restrictive set of input observations, like a single camera stream.

To better accommodate the variety of user interfaces found in robotic applications further down the line, researchers from UC Berkeley, Stanford, Carnegie Mellon University, and Google Deepmind provide a method for pretraining generalist robot policies. 

Octo is a transformer-based strategy pre-trained using 800k robot demonstrations from the Open X-Embodiment dataset, the largest dataset on robot manipulation. Octo is the first generalist robot manipulation policy to be completely open-source, including the data, model checkpoints, and training pipeline. It is also the first GRP to be effectively fine tuned to new observations and action spaces. 

When trained on a varied dataset of robots and tasks, the model is a transformer architecture that can convert any number of input tokens—generated from observations and tasks—into actions. This policy may be trained once and used for several robots, different camera setups (e.g., wrist or workspace cameras), and other input methods (e.g., language commands, goal images) by simply switching the tokens provided into the model. The model can be easily adjusted to accommodate other robot configurations, sensory inputs, action spaces, or morphologies by incorporating the necessary adapters and refining it using a small dataset from the target domain and a reasonable computing budget.

Previous research has delved into the individual components of Octo, such as a transformer backbone, goal image specification support, and a diffusion head to model expressive action distributions. However, the true power of this combination as a generalist robot policy is a new and innovative concept. The researchers conducted extensive experiments on nine robots from four different universities, demonstrating that their integrated system achieves state-of-the-art results in out-of-the-box multi-robot control for single and dual-arm manipulation tasks. They also showed that Octo can be effectively used as an initialization for fine-tuning to new observation and action spaces in unseen setups. Throughout these experiments, they analyzed the impact of several design choices on the pretrained GRP’s quality, including data distribution, model architecture, and policy formulation. The evaluation underscored the importance of scale and flexibility in achieving optimal performance. 

In addition to this publication, the team is making all the necessary resources available for training, using, reproducing, and refining an Octo model. With 27M and 93M parameters, respectively, their pretrained Octo model checkpoints allow language and goal image task specification out of the box and multiple RGB camera inputs. In addition to their whole pre-training pipeline, which includes optimal data loaders, transformer implementations for multimodal inputs, and tools to monitor training progress, they also offer scripts for fine-tuning these models on new domains.

While the team acknowledges that there is still room for improvement in the model, such as language conditioning, support for wrist cameras, and the incorporation of data beyond ideal demonstrations, Octo represents a significant step towards creating generalist robot policies that are compatible with a variety of robot settings. Octo aims to provide a practical platform where researchers and practitioners can access larger datasets related to robotics. They envision that their work will enable the use of pretrained models for rapid task learning and generalization, thereby advancing the field of robotics and machine learning. 

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The post Octo: An Open-Sourced Large Transformer-based Generalist Robot Policy Trained on 800k Trajectories from the Open X-Embodiment Dataset appeared first on MarkTechPost.

Reinforcement learning (RL) is predicated on agents learning to make decisions by interacting with an environment. RL has achieved remarkable feats in various applications, including games, robotics, and autonomous systems. The goal is to develop algorithms that enable agents to perform tasks efficiently by maximizing cumulative rewards through trial-and-error interactions. By continuously adapting to new data, these algorithms help improve performance over time, making RL a vital component in developing intelligent systems.

A significant challenge in RL is sample inefficiency, where agents require extensive interactions with the environment to learn effective policies. This limitation hinders the practical application of RL in real-world scenarios, especially in environments where obtaining samples is costly or time-consuming. Addressing this problem is crucial for deploying RL in practical applications, such as autonomous driving and robotic automation, where real-world testing can be expensive and time-consuming.

Existing research includes world models like SimPLe and Dreamer, which train RL agents in simulated environments. SimPLe applies world models to Atari, focusing on sample efficiency, while Dreamer introduces learning from latent space. DreamerV2 and DreamerV3 further improve this with discrete latents and fixed hyperparameters. Other models like TWM and STORM adapt Dreamer’s architecture using transformers. IRIS uses a discrete autoencoder and autoregressive transformer to model environment dynamics over time.

Researchers from the University of Geneva, the University of Edinburgh, and Microsoft Research have introduced DIAMOND (DIffusion As a Model Of eNvironment Dreams), a novel RL agent trained using a diffusion-based world model. DIAMOND leverages the strengths of diffusion models, which are prominent in high-resolution image generation. By integrating these models into world modeling, DIAMOND aims to preserve visual details often lost in traditional methods, thereby improving the fidelity of simulated environments and the overall training process.

DIAMOND’s methodology involves training the agent in a diffusion-based world model, where the environment’s visual details are preserved more effectively compared to traditional discrete latent variable models. The diffusion process reverses a noising procedure, creating detailed and accurate environment simulations that aid the agent’s training and performance. This approach requires careful design choices to ensure the diffusion model remains stable over long time horizons and maintains computational efficiency. The research team implemented several key design choices, including enhanced visual representation techniques and adaptive noise schedules, to optimize the diffusion process for world modeling.

The performance of DIAMOND is evaluated on the Atari 100k benchmark, where it achieves a mean human-normalized score of 1.46, setting a new benchmark for agents trained entirely within a world model. The benchmark involves 26 games, each testing different aspects of the agent’s capabilities. DIAMOND’s performance significantly surpasses other world model-based agents. For example, it achieves scores of 4031.2 on Breakout and 12250 on UpNDown, highlighting its superior ability to learn and adapt in complex environments. This improved performance is attributed to the enhanced visual detail and stability the diffusion model provides, leading to better decision-making and learning efficiency. The researchers demonstrated that DIAMOND not only performs well in scores but also shows consistency in its decision-making process across different games.

In conclusion, DIAMOND represents a significant advancement in RL by addressing the challenge of sample inefficiency through improved world modeling. The researchers’ diffusion model approach enhances visual detail and stability, leading to superior performance in training RL agents. This innovative method has the potential to revolutionize how RL agents are trained, making them more efficient and capable of operating in complex, real-world environments. Integrating diffusion models into world modeling marks a step forward in developing more robust and effective RL systems, paving the way for broader applications and improved AI performance.

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The post DIAMOND (DIffusion as a Model of Environment Dreams): A Reinforcement Learning Agent Trained in a Diffusion World Model appeared first on MarkTechPost.

The proliferation of machine learning (ML) models in high-stakes societal applications has sparked concerns regarding fairness and transparency. Instances of biased decision-making have led to a growing distrust among consumers who are subject to ML-based decisions. 

To address this challenge and increase consumer trust, technology that enables public verification of the fairness properties of these models is urgently needed. However, legal and privacy constraints often prevent organizations from disclosing their models, hindering verification and potentially leading to unfair behavior such as model swapping.

In response to these challenges, a system called FairProof has been proposed by researchers from Stanford and UCSD. It consists of a fairness certification algorithm and a cryptographic protocol. The algorithm evaluates the model’s fairness at a specific data point using a metric known as local Individual Fairness (IF). 

Their approach allows for personalized certificates to be issued to individual customers, making it suitable for customer-facing organizations. Importantly, the algorithm is designed to be agnostic to the training pipeline, ensuring its applicability across various models and datasets.

Certifying local IF is achieved by leveraging techniques from the robustness literature while ensuring compatibility with Zero-Knowledge Proofs (ZKPs) to maintain model confidentiality. ZKPs enable the verification of statements about private data, such as fairness certificates, without revealing the underlying model weights. 

To make the process computationally efficient, a specialized ZKP protocol is implemented, strategically reducing the computational overhead through offline computations and optimization of sub-functionalities.

Furthermore, model uniformity is ensured through cryptographic commitments, where organizations publicly commit to their model weights while keeping them confidential. Their approach, widely studied in ML security literature, provides a means to maintain transparency and accountability while safeguarding sensitive model information.

By combining fairness certification with cryptographic protocols, FairProof offers a comprehensive solution to address fairness and transparency concerns in ML-based decision-making, fostering greater trust among consumers and stakeholders alike.

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Delighted to receive a Best Paper Award for my latest work — FairProof : Confidential and Certifiable Fairness for Neural Networks (https://t.co/Q9RvmWQhJ1)— at the Privacy-ILR Workshop @iclr_conf ! (my 1st Will also be presented @icmlconfSlides : https://t.co/YBDq6FbAhQ pic.twitter.com/3l3bVUSacr— Chhavi Yadav (@chhaviyadav_) May 11, 2024

The post FairProof: An AI System that Uses Zero-Knowledge Proofs to Publicly Verify the Fairness of a Model while Maintaining Confidentiality appeared first on MarkTechPost.

It’s exciting to note that LLMWare.ai has been selected as one of the 11 outstanding open-source AI projects shaping the future of open source AI, and invited to join the 2024 GitHub Accelerator.   

LLMWare has been unique in its focus on small, specialized language models, recognizing early that as model technology improved, small models offered many advantages in ease of integration into enterprise processes, enormous benefits in terms of privacy and security, and tremendous cost and speed benefits to be adapted and integrated into almost any enterprise back-end process.   To use smaller models, however, requires a lot of expertise, and innovating a different set of underlying technologies and capabilities.  To support and enable this vision of privately-deployed, decentralized AI, LLMWare has launched in breakneck pace over the last 8 months, both a comprehensive enterprise-grade RAG platform (llmware) and a growing collection of its own specialized models finetuned for key enterprise automation tasks under the brands BLING, DRAGON, SLIM and Industry-Bert.

The end-to-end unified framework provided by LLMWare.ai make it the perfect candidate for developers and enterprises looking to build high-quality, fact-based LLM-based automation workflows privately, cost-effectively, and fine-tuned for the needs of their process – and to “break-through” the bottlenecks of POCs that fail to scale into production. 

LLMWare.ai has two main offerings today:

RAG Pipeline – integrated components for the full lifecycle of connecting knowledge sources to generative AI models; and

50+ small, specialized models fine-tuned for key tasks in enterprise process automation, including fact-based question-answering, classification, summarization, and extraction.

By bringing together both of these components, along with integrating leading open source models and underlying technologies, llmware offers a comprehensive set of tools to rapidly build knowledge-based enterprise LLM applications, along with over 100 out-of-the-box examples, recipes and best practice scripts.

According to founder Namee Oberst, “We are thrilled to be selected for the Github Accelerator Program, and honored to be recognized for our contributions to the open source AI community.  When we started llmware, our vision was bringing together our expertise in models, data pipeline tools, and business domain expertise to create compelling gen AI solutions for the financial services and legal industries.   Being part of the Github Accelerator Program is a great milestone, and an opportunity to learn from Github and the smartest people across open source – and bringing those benefits back to our community.”  

In conclusion, the innovative advancements and comprehensive offerings of LLMWare.ai have undoubtedly secured its position as one of the eleven distinguished projects selected for the 2024 GitHub Accelerator Program. By addressing the critical needs of enterprises—such as integrating LLMs into workflows, orchestrating complex multi-step processes, and providing structured outputs—LLMWare.ai stands out in the open-source AI community. The LLMWare framework, the SLIMs models, and the DRAGON series of RAG-specialized LLMs exemplify their commitment to creating scalable, secure, and efficient solutions tailored for financial and legal institutions. Also, with over 50 specialized models and a versatile data pipeline, LLMWare.ai empowers developers of all levels to build sophisticated, knowledge-based enterprise applications easily.

Thanks to AI Bloks for the thought leadership/ Educational article. AI Bloks has supported us in this content/article.
The post LLMWare.ai Selected for 2024 GitHub Accelerator: Enabling the Next Wave of Innovation in Enterprise RAG with Small Specialized Language Models appeared first on MarkTechPost.

Machine learning interpretability is a critical area of research for understanding complex models’ decision-making processes. These models are often seen as “black boxes,” making it difficult to discern how specific features influence their predictions. Techniques such as feature attribution and interaction indices have been developed to shed light on these contributions, thereby enhancing the transparency and trustworthiness of AI systems. The ability to interpret these models accurately is essential for debugging and improving models and ensuring they operate fairly and without unintended biases.

A significant challenge in this field is effectively allocating credit to various features within a model. Traditional methods like the Shapley value provide a robust framework for feature attribution but must catch up when capturing higher-order interactions among features. Higher-order interactions refer to the combined effect of multiple features on a model’s output, which is crucial for a comprehensive understanding of complex systems. Without accounting for these interactions, interpretability methods can miss important synergies or redundancies between features, leading to incomplete or misleading explanations.

Current tools such as SHAP (SHapley Additive exPlanations) leverage the Shapley value to quantify the contribution of individual features. These tools have made significant strides in improving model interpretability. However, they primarily focus on first-order interactions and often fail to capture the nuanced interplay between multiple features. While extensions like KernelSHAP have improved computational efficiency and applicability, they still need to fully address the complexity of higher-order interactions in machine learning models. These limitations necessitate the development of more advanced methods capable of capturing these complex interactions.

Researchers from Bielefeld University, LMU Munich, and Paderborn University have introduced a novel method called KernelSHAP-IQ to address these challenges. This method extends the capabilities of KernelSHAP to include higher-order Shapley Interaction Indices (SII). KernelSHAP-IQ utilizes a weighted least square (WLS) optimization approach to capture and quantify interactions beyond the first order accurately. Doing so provides a more detailed and precise framework for model interpretability. This advancement is significant as it allows for the inclusion of complex feature interactions often present in sophisticated models but should be noticed by traditional methods.

KernelSHAP-IQ constructs an optimal approximation of the Shapley Interaction Index using iterative k-additive approximations. It starts with first-order interactions and incrementally includes higher-order interactions. The method leverages weighted least square (WLS) optimization to capture feature interactions accurately. The approach was tested on various datasets, including the California Housing regression dataset, a sentiment analysis model using IMDB reviews, and image classifiers like ResNet18 and Vision Transformer. By sampling subsets and solving WLS problems, KernelSHAP-IQ provides a detailed representation of feature interactions, ensuring computational efficiency and precise interpretability.

The performance of KernelSHAP-IQ has been evaluated across various datasets and model classes, demonstrating state-of-the-art results. For instance, in experiments with the California Housing regression dataset, KernelSHAP-IQ significantly improved the mean squared error (MSE) in estimating interaction values, outperforming baseline methods substantially. The process achieved a mean squared error of 0.20 compared to 0.39 and 0.59 for existing techniques. Furthermore, KernelSHAP-IQ’s ability to identify the ten highest interaction scores with high precision was evident in tasks involving sentiment analysis models and image classifiers. The empirical evaluations highlighted the method’s capability to capture and accurately represent higher-order interactions, which are crucial for understanding complex model behaviors. The research showed that KernelSHAP-IQ consistently provided more accurate and interpretable results, enhancing the overall understanding of model dynamics.

In conclusion, the research introduced KernelSHAP-IQ, a method for capturing higher-order feature interactions in machine learning models using iterative k-additive approximations and weighted least square optimization. Tested on various datasets, KernelSHAP-IQ demonstrated enhanced interpretability and accuracy. This work addresses a critical gap in model interpretability by effectively quantifying complex feature interactions, providing a more comprehensive understanding of model behavior. The advancements made by KernelSHAP-IQ contribute significantly to the field of explainable AI, enabling better transparency and trust in machine learning systems.

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The post This AI Paper Introduces KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions appeared first on MarkTechPost.