Month: September 2023

OpenAI, the trailblazing artificial intelligence company, is poised to revolutionize human-AI interaction by introducing voice and image capabilities in ChatGPT. This significant upgrade offers users a more intuitive interface, enabling them to engage in voice conversations and share images with the AI, expanding the possibilities for interactive communication.

Voice and image capabilities bring a new dimension to using ChatGPT in everyday life. Whether it’s capturing a travel landmark, planning a meal from pantry contents, or assisting with homework, these functionalities promise to enhance the user experience and empower individuals in myriad ways.

Voice Capabilities: Engaging in Seamless Conversations

Users can now engage in back-and-forth conversations with ChatGPT using their voice. This feature opens up possibilities, from on-the-go interactions to requesting bedtime stories for the family or settling a dinner table debate. To initiate voice conversations, users can opt into the feature through Settings → New Features on the mobile app. They can then select their preferred voice from a choice of five distinct options, each crafted with the expertise of professional voice actors. This new text-to-speech model generates remarkably human-like audio from text and a brief speech sample.

Image Interaction: A New Way to Communicate

With the image interaction capability, users can now share one or more images with ChatGPT, enabling them to troubleshoot, plan meals, or analyze complex data. The mobile app even provides a drawing tool to focus on specific areas of an image. This functionality is powered by multimodal GPT-3.5 and GPT-4 models, allowing them to apply language reasoning skills to a diverse range of images, including photographs, screenshots, and documents containing both text and images.

Balancing Innovation with Safety and Responsibility

OpenAI’s measured approach to deploying these capabilities underscores their commitment to safety and responsible AI development. The introduction of voice technology, capable of creating authentic synthetic voices, is being harnessed specifically for voice chat, a use case carefully curated through collaboration with professional voice actors. This cautious approach helps mitigate risks associated with impersonation and potential fraud.

Likewise, the integration of image capabilities comes after rigorous testing with red teamers and alpha testers to evaluate risks in various domains. OpenAI has prioritized usefulness and safety in this feature, ensuring that ChatGPT respects individual privacy and focuses on assisting users in their daily lives.

Transparency and User Empowerment

OpenAI places a premium on transparency and user empowerment. They provide clear information about the model’s limitations, advising against higher-risk use cases without proper verification. Users relying on ChatGPT for specialized topics, especially in non-English languages, are encouraged to exercise caution.

In the coming weeks, Plus and Enterprise users will have the opportunity to experience the transformative voice and image capabilities of ChatGPT. OpenAI’s commitment to gradual deployment allows for ongoing improvements, refinement of risk mitigations, and preparation for even more powerful AI systems in the future.

OpenAI’s unveiling of voice and image capabilities in ChatGPT represents a monumental stride towards a more immersive and intuitive human-AI interaction. As these functionalities continue to evolve, they hold the potential to reshape the way we engage with AI, opening up a world of new possibilities for collaboration, creativity, and problem-solving.

Check out the Reference Article. All Credit For This Research Goes To the Researchers on This Project. Also, don’t forget to join our 30k+ ML SubReddit, 40k+ Facebook Community, Discord Channel, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more.

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ChatGPT can now see, hear, and speak. Rolling out over next two weeks, Plus users will be able to have voice conversations with ChatGPT (iOS & Android) and to include images in conversations (all platforms). https://t.co/uNZjgbR5Bm pic.twitter.com/paG0hMshXb— OpenAI (@OpenAI) September 25, 2023

The post OpenAI’s ChatGPT Unveils Voice and Image Capabilities: A Revolutionary Leap in AI Interaction appeared first on MarkTechPost.

As machine learning (ML) goes mainstream and gains wider adoption, ML-powered inference applications are becoming increasingly common to solve a range of complex business problems. The solution to these complex business problems often requires using multiple ML models and steps. This post shows you how to build and host an ML application with custom containers on Amazon SageMaker.
Amazon SageMaker offers built-in algorithms and pre-built SageMaker docker images for model deployment. But, if these don’t fit your needs, you can bring your own containers (BYOC) for hosting on Amazon SageMaker.
There are several use cases where users might need to BYOC for hosting on Amazon SageMaker.

Custom ML frameworks or libraries: If you plan on using a ML framework or libraries that aren’t supported by Amazon SageMaker built-in algorithms or pre-built containers, then you’ll need to create a custom container.
Specialized models: For certain domains or industries, you may require specific model architectures or tailored preprocessing steps that aren’t available in built-in Amazon SageMaker offerings.
Proprietary algorithms: If you’ve developed your own proprietary algorithms inhouse, then you’ll need a custom container to deploy them on Amazon SageMaker.
Complex inference pipelines: If your ML inference workflow involves custom business logic — a series of complex steps that need to be executed in a particular order — then BYOC can help you manage and orchestrate these steps more efficiently.

Solution overview
In this solution, we show how to host a ML serial inference application on Amazon SageMaker with real-time endpoints using two custom inference containers with latest scikit-learn and xgboost packages.
The first container uses a scikit-learn model to transform raw data into featurized columns. It applies StandardScaler for numerical columns and OneHotEncoder to categorical ones.

The second container hosts a pretrained XGboost model (i.e., predictor). The predictor model accepts the featurized input and outputs predictions.

Lastly, we deploy the featurizer and predictor in a serial-inference pipeline to an Amazon SageMaker real-time endpoint.
Here are few different considerations as to why you may want to have separate containers within your inference application.

Decoupling – Various steps of the pipeline have a clearly defined purpose and need to be run on separate containers due to the underlying dependencies involved. This also helps keep the pipeline well structured.
Frameworks – Various steps of the pipeline use specific fit-for-purpose frameworks (such as scikit or Spark ML) and therefore need to be run on separate containers.
Resource isolation – Various steps of the pipeline have varying resource consumption requirements and therefore need to be run on separate containers for more flexibility and control.
Maintenance and upgrades – From an operational standpoint, this promotes functional isolation and you can continue to upgrade or modify individual steps much more easily, without affecting other models.

Additionally, local build of the individual containers helps in the iterative process of development and testing with favorite tools and Integrated Development Environments (IDEs). Once the containers are ready, you can use deploy them to the AWS cloud for inference using Amazon SageMaker endpoints.
Full implementation, including code snippets, is available in this Github repository here.

Prerequisites
As we test these custom containers locally first, we’ll need docker desktop installed on your local computer. You should be familiar with building docker containers.
You’ll also need an AWS account with access to Amazon SageMaker, Amazon ECR and Amazon S3 to test this application end-to-end.
Ensure you have the latest version of Boto3 and the Amazon SageMaker Python packages installed:

pip install –upgrade boto3 sagemaker scikit-learn

Solution Walkthrough
Build custom featurizer container
To build the first container, the featurizer container, we train a scikit-learn model to process raw features in the abalone dataset. The preprocessing script uses SimpleImputer for handling missing values, StandardScaler for normalizing numerical columns, and OneHotEncoder for transforming categorical columns. After fitting the transformer, we save the model in joblib format. We then compress and upload this saved model artifact to an Amazon Simple Storage Service (Amazon S3) bucket.
Here’s a sample code snippet that demonstrates this. Refer to featurizer.ipynb for full implementation:

“`python
numeric_features = list(feature_columns_names)
numeric_features.remove(“sex”)
numeric_transformer = Pipeline(
steps=[
(“imputer”, SimpleImputer(strategy=”median”)),
(“scaler”, StandardScaler()),
]
)

categorical_features = [“sex”]
categorical_transformer = Pipeline(
steps=[
(“imputer”, SimpleImputer(strategy=”constant”, fill_value=”missing”)),
(“onehot”, OneHotEncoder(handle_unknown=”ignore”)),
]
)

preprocess = ColumnTransformer(
transformers=[
(“num”, numeric_transformer, numeric_features),
(“cat”, categorical_transformer, categorical_features),
]
)

# Call fit on ColumnTransformer to fit all transformers to X, y
preprocessor = preprocess.fit(df_train_val)

# Save the processor model to disk
joblib.dump(preprocess, os.path.join(model_dir, “preprocess.joblib”))
“`

Next, to create a custom inference container for the featurizer model, we build a Docker image with nginx, gunicorn, flask packages, along with other required dependencies for the featurizer model.
Nginx, gunicorn and the Flask app will serve as the model serving stack on Amazon SageMaker real-time endpoints.
When bringing custom containers for hosting on Amazon SageMaker, we need to ensure that the inference script performs the following tasks after being launched inside the container:

Model loading: Inference script (preprocessing.py) should refer to /opt/ml/model directory to load the model in the container. Model artifacts in Amazon S3 will be downloaded and mounted onto the container at the path /opt/ml/model.
Environment variables: To pass custom environment variables to the container, you must specify them during the Model creation step or during Endpoint creation from a training job.
API requirements: The Inference script must implement both /ping and /invocations routes as a Flask application. The /ping API is used for health checks, while the /invocations API handles inference requests.
Logging: Output logs in the inference script must be written to standard output (stdout) and standard error (stderr) streams. These logs are then streamed to Amazon CloudWatch by Amazon SageMaker.

Here’s a snippet from preprocessing.py that show the implementation of /ping and /invocations.
Refer to preprocessing.py under the featurizer folder for full implementation.

“`python
def load_model():
# Construct the path to the featurizer model file
ft_model_path = os.path.join(MODEL_PATH, “preprocess.joblib”)
featurizer = None

try:
# Open the model file and load the featurizer using joblib
with open(ft_model_path, “rb”) as f:
featurizer = joblib.load(f)
print(“Featurizer model loaded”, flush=True)
except FileNotFoundError:
print(f”Error: Featurizer model file not found at {ft_model_path}”, flush=True)
except Exception as e:
print(f”Error loading featurizer model: {e}”, flush=True)

# Return the loaded featurizer model, or None if there was an error
return featurizer

def transform_fn(request_body, request_content_type):
“””
Transform the request body into a usable numpy array for the model.

This function takes the request body and content type as input, and
returns a transformed numpy array that can be used as input for the
prediction model.

Parameters:
request_body (str): The request body containing the input data.
request_content_type (str): The content type of the request body.

Returns:
data (np.ndarray): Transformed input data as a numpy array.
“””
# Define the column names for the input data
feature_columns_names = [
“sex”,
“length”,
“diameter”,
“height”,
“whole_weight”,
“shucked_weight”,
“viscera_weight”,
“shell_weight”,
]
label_column = “rings”

# Check if the request content type is supported (text/csv)
if request_content_type == “text/csv”:
# Load the featurizer model
featurizer = load_model()

# Check if the featurizer is a ColumnTransformer
if isinstance(
featurizer, sklearn.compose._column_transformer.ColumnTransformer
):
print(f”Featurizer model loaded”, flush=True)

# Read the input data from the request body as a CSV file
df = pd.read_csv(StringIO(request_body), header=None)

# Assign column names based on the number of columns in the input data
if len(df.columns) == len(feature_columns_names) + 1:
# This is a labelled example, includes the ring label
df.columns = feature_columns_names + [label_column]
elif len(df.columns) == len(feature_columns_names):
# This is an unlabelled example.
df.columns = feature_columns_names

# Transform the input data using the featurizer
data = featurizer.transform(df)

# Return the transformed data as a numpy array
return data
else:
# Raise an error if the content type is unsupported
raise ValueError(“Unsupported content type: {}”.format(request_content_type))

@app.route(“/ping”, methods=[“GET”])
def ping():
# Check if the model can be loaded, set the status accordingly
featurizer = load_model()
status = 200 if featurizer is not None else 500

# Return the response with the determined status code
return flask.Response(response=”n”, status=status, mimetype=”application/json”)

@app.route(“/invocations”, methods=[“POST”])
def invocations():
# Convert from JSON to dict
print(f”Featurizer: received content type: {flask.request.content_type}”)
if flask.request.content_type == “text/csv”:
# Decode input data and transform
input = flask.request.data.decode(“utf-8″)
transformed_data = transform_fn(input, flask.request.content_type)

# Format transformed_data into a csv string
csv_buffer = io.StringIO()
csv_writer = csv.writer(csv_buffer)

for row in transformed_data:
csv_writer.writerow(row)

csv_buffer.seek(0)

# Return the transformed data as a CSV string in the response
return flask.Response(response=csv_buffer, status=200, mimetype=”text/csv”)
else:
print(f”Received: {flask.request.content_type}”, flush=True)
return flask.Response(
response=”Transformer: This predictor only supports CSV data”,
status=415,
mimetype=”text/plain”,
)
“`

Build Docker image with featurizer and model serving stack
Let’s now build a Dockerfile using a custom base image and install required dependencies.
For this, we use python:3.9-slim-buster as the base image. You can change this any other base image relevant to your use case.
We then copy the nginx configuration, gunicorn’s web server gateway file, and the inference script to the container. We also create a python script called serve that launches nginx and gunicorn processes in the background and sets the inference script (i.e., preprocessing.py Flask application) as the entry point for the container.
Here’s a snippet of the Dockerfile for hosting the featurizer model. For full implementation refer to Dockerfile under featurizer folder.

“`docker
FROM python:3.9-slim-buster
…

# Copy requirements.txt to /opt/program folder
COPY requirements.txt /opt/program/requirements.txt

# Install packages listed in requirements.txt
RUN pip3 install –no-cache-dir -r /opt/program/requirements.txt

# Copy contents of code/ dir to /opt/program
COPY code/ /opt/program/

# Set working dir to /opt/program which has the serve and inference.py scripts
WORKDIR /opt/program

# Expose port 8080 for serving
EXPOSE 8080

ENTRYPOINT [“python”]

# serve is a python script under code/ directory that launches nginx and gunicorn processes
CMD [ “serve” ]
“`

Test custom inference image with featurizer locally
Now, build and test the custom inference container with featurizer locally, using Amazon SageMaker local mode. Local mode is perfect for testing your processing, training, and inference scripts without launching any jobs on Amazon SageMaker. After confirming the results of your local tests, you can easily adapt the training and inference scripts for deployment on Amazon SageMaker with minimal changes.
To test the featurizer custom image locally, first build the image using the previously defined Dockerfile. Then, launch a container by mounting the directory containing the featurizer model (preprocess.joblib) to the /opt/ml/model directory inside the container. Additionally, map port 8080 from container to the host.
Once launched, you can send inference requests to http://localhost:8080/invocations.
To build and launch the container, open a terminal and run the following commands.
Note that you should replace the <IMAGE_NAME>, as shown in the following code, with the image name of your container.
The following command also assumes that the trained scikit-learn model (preprocess.joblib) is present under a directory called models.

“`shell
docker build -t <IMAGE_NAME> .
“`

“`shell
docker run –rm -v $(pwd)/models:/opt/ml/model -p 8080:8080 <IMAGE_NAME>
“`

After the container is up and running, we can test both the /ping and /invocations routes using curl commands.
Run the below commands from a terminal

“`shell
# test /ping route on local endpoint
curl http://localhost:8080/ping

# send raw csv string to /invocations. Endpoint should return transformed data
curl –data-raw ‘I,0.365,0.295,0.095,0.25,0.1075,0.0545,0.08,9.0’ -H ‘Content-Type: text/csv’ -v http://localhost:8080/invocations
“`

When raw (untransformed) data is sent to http://localhost:8080/invocations, the endpoint responds with transformed data.
You should see response something similar to the following:

“`shell
* Trying 127.0.0.1:8080…
* Connected to localhost (127.0.0.1) port 8080 (#0)
> POST /invocations HTTP/1.1
> Host: localhost: 8080
> User-Agent: curl/7.87.0
> Accept: */*
> Content -Type: text/csv
> Content -Length: 47
>
* Mark bundle as not supporting multiuse
> HTTP/1.1 200 OK
> Server: nginx/1.14.2
> Date: Sun, 09 Apr 2023 20:47:48 GMT
> Content -Type: text/csv; charset=utf-8
> Content -Length: 150
> Connection: keep -alive
-1.3317586042173168, -1.1425409076053987, -1.0579488602777858, -1.177706547272754, -1.130662184748842,
* Connection #0 to host localhost left intact
“`

We now terminate the running container, and then tag and push the local custom image to a private Amazon Elastic Container Registry (Amazon ECR) repository.
See the following commands to login to Amazon ECR, which tags the local image with full Amazon ECR image path and then push the image to Amazon ECR. Ensure you replace region and account variables to match your environment.

“`shell
# login to ecr with your credentials
aws ecr get-login-password – -region “${region}” |
docker login – -username AWS – -password-stdin ${account}”.dkr.ecr.”${region}”.amazonaws.com

# tag and push the image to private Amazon ECR
docker tag ${image} ${fullname}
docker push $ {fullname}

“`

Refer to create a repository and push an image to Amazon ECR AWS Command Line Interface (AWS CLI) commands for more information.
Optional step
Optionally, you could perform a live test by deploying the featurizer model to a real-time endpoint with the custom docker image in Amazon ECR. Refer to featurizer.ipynb notebook for full implementation of buiding, testing, and pushing the custom image to Amazon ECR.
Amazon SageMaker initializes the inference endpoint and copies the model artifacts to the /opt/ml/model directory inside the container. See How SageMaker Loads your Model artifacts.
Build custom XGBoost predictor container
For building the XGBoost inference container we follow similar steps as we did while building the image for featurizer container:

Download pre-trained XGBoost model from Amazon S3.
Create the inference.py script that loads the pretrained XGBoost model, converts the transformed input data received from featurizer, and converts to XGBoost.DMatrix format, runs predict on the booster, and returns predictions in json format.
Scripts and configuration files that form the model serving stack (i.e., nginx.conf, wsgi.py, and serve remain the same and needs no modification.
We use Ubuntu:18.04 as the base image for the Dockerfile. This isn’t a prerequisite. We use the ubuntu base image to demonstrate that containers can be built with any base image.
The steps for building the customer docker image, testing the image locally, and pushing the tested image to Amazon ECR remain the same as before.

For brevity, as the steps are similar shown previously; however, we only show the changed coding in the following.
First, the inference.py script. Here’s a snippet that show the implementation of /ping and /invocations. Refer to inference.py under the predictor folder for full implementation of this file.

“`python
@app.route(“/ping”, methods=[“GET”])
def ping():
“””
Check the health of the model server by verifying if the model is loaded.

Returns a 200 status code if the model is loaded successfully, or a 500
status code if there is an error.

Returns:
flask.Response: A response object containing the status code and mimetype.
“””
status = 200 if model is not None else 500
return flask.Response(response=”n”, status=status, mimetype=”application/json”)

@app.route(“/invocations”, methods=[“POST”])
def invocations():
“””
Handle prediction requests by preprocessing the input data, making predictions,
and returning the predictions as a JSON object.

This function checks if the request content type is supported (text/csv; charset=utf-8),
and if so, decodes the input data, preprocesses it, makes predictions, and returns
the predictions as a JSON object. If the content type is not supported, a 415 status
code is returned.

Returns:
flask.Response: A response object containing the predictions, status code, and mimetype.
“””
print(f”Predictor: received content type: {flask.request.content_type}”)
if flask.request.content_type == “text/csv; charset=utf-8”:
input = flask.request.data.decode(“utf-8”)
transformed_data = preprocess(input, flask.request.content_type)
predictions = predict(transformed_data)

# Return the predictions as a JSON object
return json.dumps({“result”: predictions})
else:
print(f”Received: {flask.request.content_type}”, flush=True)
return flask.Response(
response=f”XGBPredictor: This predictor only supports CSV data; Received: {flask.request.content_type}”,
status=415,
mimetype=”text/plain”,
)

“`

Here’s a snippet of the Dockerfile for hosting the predictor model. For full implementation refer to Dockerfile under predictor folder.

“`docker
FROM ubuntu:18.04

…

# install required dependencies including flask, gunicorn, xgboost etc.,
RUN pip3 –no-cache-dir install flask gunicorn gevent numpy pandas xgboost

# Copy contents of code/ dir to /opt/program
COPY code /opt/program

# Set working dir to /opt/program which has the serve and inference.py scripts
WORKDIR /opt/program

# Expose port 8080 for serving
EXPOSE 8080

ENTRYPOINT [“python”]

# serve is a python script under code/ directory that launches nginx and gunicorn processes
CMD [“serve”]
“`

We then continue to build, test, and push this custom predictor image to a private repository in Amazon ECR. Refer to predictor.ipynb notebook for full implementation of building, testing and pushing the custom image to Amazon ECR.
Deploy serial inference pipeline
After we have tested both the featurizer and predictor images and have pushed them to Amazon ECR, we now upload our model artifacts to an Amazon S3 bucket.
Then, we create two model objects: one for the featurizer (i.e., preprocess.joblib) and other for the predictor (i.e., xgboost-model) by specifying the custom image uri we built earlier.
Here’s a snippet that shows that. Refer to serial-inference-pipeline.ipynb for full implementation.

“`python
suffix = f”{str(uuid4())[:5]}-{datetime.now().strftime(‘%d%b%Y’)}”

# Featurizer Model (SKLearn Model)
image_name = “<FEATURIZER_IMAGE_NAME>”
sklearn_image_uri = f”{account_id}.dkr.ecr.{region}.amazonaws.com/{image_name}:latest”

featurizer_model_name = f””<FEATURIZER_MODEL_NAME>-{suffix}”
print(f”Creating Featurizer model: {featurizer_model_name}”)
sklearn_model = Model(
image_uri=featurizer_ecr_repo_uri,
name=featurizer_model_name,
model_data=featurizer_model_data,
role=role,
)

# Full name of the ECR repository
predictor_image_name = “<PREDICTOR_IMAGE_NAME>”
predictor_ecr_repo_uri
= f”{account_id}.dkr.ecr.{region}.amazonaws.com/{predictor_image_name}:latest”

# Predictor Model (XGBoost Model)
predictor_model_name = f”””<PREDICTOR_MODEL_NAME>-{suffix}”
print(f”Creating Predictor model: {predictor_model_name}”)
xgboost_model = Model(
image_uri=predictor_ecr_repo_uri,
name=predictor_model_name,
model_data=predictor_model_data,
role=role,
)
“`

Now, to deploy these containers in a serial fashion, we first create a PipelineModel object and pass the featurizer model and the predictor model to a python list object in the same order.
Then, we call the .deploy() method on the PipelineModel specifying the instance type and instance count.

“`python
from sagemaker.pipeline import PipelineModel

pipeline_model_name = f”Abalone-pipeline-{suffix}”

pipeline_model = PipelineModel(
name=pipeline_model_name,
role=role,
models=[sklearn_model, xgboost_model],
sagemaker_session=sm_session,
)

print(f”Deploying pipeline model {pipeline_model_name}…”)
predictor = pipeline_model.deploy(
initial_instance_count=1,
instance_type=”ml.m5.xlarge”,
)
“`

At this stage, Amazon SageMaker deploys the serial inference pipeline to a real-time endpoint. We wait for the endpoint to be InService.
We can now test the endpoint by sending some inference requests to this live endpoint.
Refer to serial-inference-pipeline.ipynb for full implementation.
Clean up
After you are done testing, please follow the instructions in the cleanup section of the notebook to delete the resources provisioned in this post to avoid unnecessary charges. Refer to Amazon SageMaker Pricing for details on the cost of the inference instances.

“`python
# Delete endpoint, model
try:
print(f”Deleting model: {pipeline_model_name}”)
predictor.delete_model()
except Exception as e:
print(f”Error deleting model: {pipeline_model_name}n{e}”)
pass

try:
print(f”Deleting endpoint: {endpoint_name}”)
predictor.delete_endpoint()
except Exception as e:
print(f”Error deleting EP: {endpoint_name}n{e}”)
pass

“`

Conclusion
In this post, I showed how we can build and deploy a serial ML inference application using custom inference containers to real-time endpoints on Amazon SageMaker.
This solution demonstrates how customers can bring their own custom containers for hosting on Amazon SageMaker in a cost-efficient manner. With BYOC option, customers can quickly build and adapt their ML applications to be deployed on to Amazon SageMaker.
We encourage you to try this solution with a dataset relevant to your business Key Performance Indicators (KPIs). You can refer to the entire solution in this GitHub repository.
References

Model hosting patterns in Amazon SageMaker
Amazon SageMaker Bring your own containers
Hosting models as serial inference pipeline on Amazon SageMaker

About the Author
Praveen Chamarthi is a Senior AI/ML Specialist with Amazon Web Services. He is passionate about AI/ML and all things AWS. He helps customers across the Americas to scale, innovate, and operate ML workloads efficiently on AWS. In his spare time, Praveen loves to read and enjoys sci-fi movies.

This post was co-authored with Daniele Chiappalupi, participant of the AWS student Hackathon team at ETH Zürich.
Everyone can easily get started with machine learning (ML) using Amazon SageMaker JumpStart. In this post, we show you how a university Hackathon team used SageMaker JumpStart to quickly build an application that helps users identify and remove biases.

“Amazon SageMaker was instrumental in our project. It made it easy to deploy and manage a pre-trained instance of Flan, offering us a solid foundation for our application. Its auto scaling feature proved crucial during high-traffic periods, ensuring that our app remained responsive and users received a steady and fast bias analysis. Further, by allowing us to offload the heavy task of querying the Flan model to a managed service, we were able to keep our application lightweight and swift, enhancing user experience across various devices. SageMaker’s features empowered us to maximize our time at the hackathon, allowing us to focus on optimizing our prompts and app rather than managing the model’s performance and infrastructure.”
– Daniele Chiappalupi, participant of the AWS student Hackathon team at ETH Zürich. 

Solution overview
The theme of the Hackathon is to contribute to the UN sustainable goals with AI technology. As shown in the following figure, the application built at the Hackathon contributes to three of the Sustainable Development Goals (quality education, targeting gender-based discrimination, and reduced inequalities) by helping users identify and remove biases from their text in order to promote fair and inclusive language.

As shown in the following screenshot, after you provide the text, the application generates a new version that is free from racial, ethnical, and gender biases. Additionally, it highlights the specific parts of your input text related to each category of bias.

In the architecture shown in the following diagram, users input text in the React-based web app, which triggers Amazon API Gateway, which in turn invokes an AWS Lambda function depending on the bias in the user text. The Lambda function calls the Flan model endpoint in SageMaker JumpStart, which returns the unbiased text result via the same route back to the front-end application.

Application development process
The process of developing this application was iterative and centered on two main areas: user interface and ML model integration.
We chose React for the front-end development due to its flexibility, scalability, and powerful tools for creating interactive user interfaces. Given the nature of our application—processing user input and presenting refined results—React’s component-based architecture proved ideal. With React, we could efficiently build a single-page application that allowed users to submit text and see de-biased results without the need for constant page refreshes.
The text entered by the user needed to be processed by a powerful language model to scrutinize for biases. We chose Flan for its robustness, efficiency, and scalability properties. To utilize Flan, we used SageMaker JumpStart, as shown in the following screenshot. Amazon SageMaker made it easy to deploy and manage a pre-trained instance of Flan, allowing us to focus on optimizing our prompts and queries rather than managing the model’s performance and infrastructure.

Connecting the Flan model to our front-end application required a robust and secure integration, which was achieved using Lambda and API Gateway. With Lambda, we created a serverless function that communicates directly with our SageMaker model. We then used API Gateway to create a secure, scalable, and readily accessible endpoint for our React app to invoke the Lambda function. When a user submitted text, the app triggered a series of API calls to the gateway—first to identify if any bias was present, then, if necessary, additional queries to identify, locate, and neutralize the bias. All these requests were routed through the Lambda function and then to our SageMaker model.
Our final task in the development process was the selection of prompts to query the language model. Here, the CrowS-Pairs dataset played an instrumental role because it provided us with real examples of biased text, which we utilized to fine-tune our requests. We selected the prompts by an iterative process, with the objective of maximizing accuracy in bias detection within this dataset.
Wrapping up the process, we observed a seamless operational flow in the finished application. The process begins with a user submitting text for analysis, which is then sent via a POST request to our secure API Gateway endpoint. This triggers the Lambda function, which communicates with the SageMaker endpoint. Consequently, the Flan model receives a series of queries. The first checks for the presence of any biases in the text. If biases are detected, additional queries are deployed to locate, identify, and neutralize these biased elements. The results are then returned through the same path—first to the Lambda function, then through the API Gateway, and ultimately back to the user. If any bias was present in the original text, the user receives a comprehensive analysis indicating the types of biases detected, whether racial, ethnic, or gender. Specific sections of the text where these biases were found are highlighted, giving users a clear view of the changes made. Alongside this analysis, a new, de-biased version of their text is presented, effectively transforming potentially biased input into a more inclusive narrative.
In the following sections, we detail the steps to implement this solution.
Set up the React environment
We began by setting up our development environment for React. For bootstrapping a new React application with minimal configuration, we used create-react-app:
npx create-react-app my-app
Build the user interface
Using React, we designed a simple interface for users to input text, with a submission button, a reset button, and overlaying displays for presenting the processed results when they’re available.
Initiate the Flan model on SageMaker
We used SageMaker to create a pre-trained instance of the Flan language model with an endpoint for real-time inference. The model can be used against any JSON-structured payload like the following:

payload = {
text_inputs: “text_inputs”,
max_length: <max_length>,
num_return_sequences: <num_return_sequences>,
top_k: <top_k>,
top_p: <top_p>,
do_sample: <do_sample>,
num_beams: <num_beams>,
seed: <seed>,
};

Create a Lambda function
We developed a Lambda function that interacted directly with our SageMaker endpoint. The function was designed to receive a request with the user’s text, forward it to the SageMaker endpoint, and return the refined results, as shown in the following code (ENDPOINT_NAME was set up as the SageMaker instance endpoint):

import os
import io
import boto3
import json
import csv

# grab environment variables
ENDPOINT_NAME = os.environ[‘ENDPOINT_NAME’]
runtime= boto3.client(‘runtime.sagemaker’)

def lambda_handler(event, context):
data = json.loads(json.dumps(event))
payload = json.dumps(data[‘data’]).encode(‘utf-8′)

query_response = runtime.invoke_endpoint(
EndpointName=ENDPOINT_NAME,
ContentType=’application/json’,
Body=payload)

response_dict = json.loads(query_response[‘Body’].read())

return response_dict[‘generated_texts’]

Set up API Gateway
We configured a new REST API in API Gateway and linked it to our Lambda function. This connection allowed our React application to make HTTP requests to the API Gateway, which subsequently triggered the Lambda function.
Integrate the React app with the API
We updated the React application to make a POST request to the API Gateway when the submit button was clicked, with the body of the request being the user’s text. The JavaScript code we used to perform the API call is as follows (REACT_APP_AWS_ENDPOINT corresponds to the API Gateway endpoint bound to the Lambda call):

const makeAWSApiCall = (
textInputs,
maxLength,
numReturnSequences,
topK,
topP,
doSample,
numBeams
) => {
const axiosRequestUrl =
`${process.env.REACT_APP_AWS_ENDPOINT}`;
const requestData = {
text_inputs: textInputs,
max_length: maxLength,
num_return_sequences: numReturnSequences,
top_k: topK,
top_p: topP,
do_sample: doSample,
num_beams: numBeams,
seed: 8,
};

return axios.post(axiosRequestUrl, { data: requestData });
};

Optimize prompt selection
To improve the accuracy of bias detection, we tested different prompts against the CrowS-Pairs dataset. Through this iterative process, we chose the prompts that gave us the highest accuracy.
Deploy and test the React app on Vercel
After building the application, we deployed it on Vercel to make it publicly accessible. We conducted extensive tests to ensure the application functioned as expected, from the user interface to the responses from the language model.
These steps laid the groundwork for creating our application for analyzing and de-biasing text. Despite the inherent complexity of the process, the use of tools like SageMaker, Lambda, and API Gateway streamlined the development, allowing us to focus on the core goal of the project—identifying and eliminating biases in text.
Conclusion
SageMaker JumpStart offers a convenient way to explore the features and capabilities of SageMaker. It provides curated one-step solutions, example notebooks, and deployable pre-trained models. These resources allow you to quickly learn and understand SageMaker. Additionally, you have the option to fine-tune the models and deploy them according to your specific needs. Access to JumpStart is available through Amazon SageMaker Studio or programmatically using the SageMaker APIs.
In this post, you learned how a student Hackathon team developed a solution in a short time using SageMaker JumpStart, which shows the potential of AWS and SageMaker JumpStart in enabling rapid development and deployment of sophisticated AI solutions, even by small teams or individuals.
To learn more about using SageMaker JumpStart, refer to Instruction fine-tuning for FLAN T5 XL with Amazon SageMaker Jumpstart and Zero-shot prompting for the Flan-T5 foundation model in Amazon SageMaker JumpStart.
ETH Analytics Club hosted ‘ETH Datathon,’ an AI/ML hackathon that draws more than 150 participants from ETH Zurich, University of Zurich, and EPFL. The event features workshops led by industry leaders, a 24-hour coding challenge, and valuable networking opportunities with fellow students and industry professionals. Great thanks to the ETH Hackathon team: Daniele Chiappalupi, Athina Nisioti, and Francesco Ignazio Re, as well as the rest of AWS organizing team: Alice Morano, Demir Catovic, Iana Peix, Jan Oliver Seidenfuss, Lars Nettemann, and Markus Winterholer.
The content and opinions in this post are those of the third-party author and AWS is not responsible for the content or accuracy of this post.

About the authors
Jun Zhang is a Solutions Architect based in Zurich. He helps Swiss customers architect cloud-based solutions to achieve their business potential. He has a passion for sustainability and strives to solve current sustainability challenges with technology. He is also a huge tennis fan and enjoys playing board games a lot.
Mohan Gowda leads Machine Learning team at AWS Switzerland. He works primarily with Automotive customers to develop innovative AI/ML solutions and platforms for next generation vehicles. Before working with AWS, Mohan worked with a Global Management Consulting firm with a focus on Strategy & Analytics. His passion lies in connected vehicles and autonomous driving.
Matthias Egli is the Head of Education in Switzerland. He is an enthusiastic Team Lead with a broad experience in business development, sales, and marketing.
Kemeng Zhang is an ML Engineer based in Zurich. She helps global customers design, develop, and scale ML-based applications to empower their digital capabilities to increase business revenue and reduce cost. She is also very passionate about creating human-centric applications by leveraging knowledge from behavioral science. She likes playing water sports and walking dogs.
Daniele Chiappalupi is a recent graduate from ETH Zürich. He enjoys every aspect of software engineering, from design to implementation, and from deployment to maintenance. He has a deep passion for AI and eagerly anticipates exploring, utilizing, and contributing to the latest advancements in the field. In his free time, he loves going snowboarding during colder months and playing pick-up basketball when the weather warms up.

To revolutionize collaboration with generative AI, Bard has introduced its most advanced model yet. This innovation promises to be a game-changer, allowing users to tailor responses to their specific needs seamlessly. Whether it’s drafting a trip planning document, creating an online marketplace listing, or explaining complex science topics to kids, Bard is now more adept than ever at bringing ideas to life.

The latest upgrade includes a groundbreaking integration with Google apps and services, marking a significant milestone in Bard’s evolution. This feature, aptly named Bard Extensions, enables Bard to source and display relevant information from widely-used Google tools like Gmail, Docs, Drive, Google Maps, YouTube, as well as Google Flights and hotels. Even when the required information spans multiple apps and services, Bard can streamline the process within a single conversation.

For instance, envision planning a trip to the Grand Canyon—a venture often involving numerous open tabs. With Bard Extensions, users can task Bard with extracting suitable dates from Gmail, retrieving real-time flight and hotel data, providing Google Maps directions to the airport, and even curating YouTube videos showcasing activities at the destination. This seamless integration promises to revolutionize the way tasks are executed, consolidating a multitude of functions into one streamlined conversation.

In the realm of professional development, Bard’s capabilities shine even brighter. For individuals embarking on a job search, Bard can effortlessly locate a specific resume from Drive, summarize it into a concise personal statement, and collaborate on crafting a compelling cover letter. This newfound functionality streamlines the job application process, showcasing Bard’s potential as an indispensable professional ally.

Bard’s commitment to safeguarding user privacy remains steadfast. The Workspace extensions ensure that Gmail, Docs, and Drive content remains confidential and inaccessible to human reviewers. Furthermore, this data is not utilized for targeted advertising or model training. Users retain complete control over their privacy settings and can deactivate extensions at their discretion.

A new “Google it” feature has been introduced to bolster confidence in Bard’s responses. Available in English, this function allows users to cross-verify Bard’s answers with information from the web. By clicking the designated “G” icon, Bard will analyze its response and check for corroborating content online. This added layer of verification enhances the reliability and accuracy of Bard’s contributions.

Additionally, Bard facilitates seamless collaboration by enabling users to build on shared conversations. When a Bard chat is shared through a public link, recipients can extend the discussion by posing follow-up questions or leveraging it as a starting point for their ideas. This feature fosters a dynamic and interactive environment for users to exchange thoughts and collaborate effectively.

Finally, Bard’s expanded access to over 40 languages, including features like image uploads with Lens, Search images in responses, and response modification, underscores the platform’s commitment to inclusivity and accessibility. With these updates, Bard solidifies its position as a versatile and indispensable tool for users worldwide.

In conclusion, Bard’s latest enhancements represent a significant leap forward in generative AI. By seamlessly integrating with Google apps, improving response verification, and expanding language capabilities, Bard is poised to revolutionize how users interact and collaborate with AI. These innovations mark a pivotal moment in Bard’s journey towards redefining creative expression and problem-solving. To experience the latest features, visit bard.google.com today.

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“We thought this would be a paper about the obvious failings of LLMs that would serve as motivation for future clever ideas to overcome those failings. We were entirely taken by surprise to find that in many cases a sufficiently trained LLM can not only predict the best optimizations to apply to an input code, but it can also directly perform the optimizations without resorting to the compiler at all!”.   – Researchers at Meta AI

Meta AI Researchers were trying to make Large Language Models (LLMs) do the same kind of code optimizations that regular compilers, like LLVM, do. LLVM’s optimizer is incredibly complex, with thousands of rules and algorithms written in over 1 million lines of code in the C++ programming language.

They didn’t think LLMs could handle this complexity because they are typically used for tasks like translating languages and generating code. Compiler optimizations involve a lot of different types of thinking, maths, and using complex techniques, which they didn’t think LLMs were good at. But post methodology the results were absolutely surprising. 

The above image demonstrates the overview of the methodology, showing the model input (Prompt) and output (Answer) during training and inference. The prompt contains unoptimized code. The answer contains an optimization pass list, instruction counts, and the optimized code. During inference, only the optimization pass list is generated, which is then fed into the compiler, ensuring that the optimized code is correct.

Their approach is straightforward, starting with a 7-billion-parameter Large Language Model (LLM) architecture sourced from LLaMa 2 [25] and initializing it from scratch. The model is then trained on a vast dataset consisting of millions of LLVM assembly examples, each paired with the best compiler options determined through a search process for each assembly, as well as the resulting assembly code after applying those optimizations. Through these examples alone, the model acquires the ability to optimize code with remarkable precision.

The notable contribution of their work lies in being the first to apply LLMs to the task of code optimization. They create LLMs specifically tailored for compiler optimization, demonstrating that these models achieve a 3.0% improvement in code size reduction on a single compilation compared to a search-based approach that attains 5.0% improvement with 2.5 billion compilations. In contrast, state-of-the-art machine learning approaches lead to regressions and require thousands of compilations. The researchers also include supplementary experiments and code examples to provide a more comprehensive understanding of the potential and limitations of LLMs in code reasoning. Overall, they find the efficacy of LLMs in this context to be remarkable and believe that their findings will be of interest to the broader community.

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Image anonymization involves altering visual data to protect individuals’ privacy by obscuring identifiable features. As the digital age advances, there’s an increasing need to safeguard personal data in images. However, when training computer vision models, anonymized data can impact accuracy due to losing vital information. Striking a balance between privacy and model performance remains a significant challenge. Researchers continuously seek methods to maintain data utility while ensuring privacy.

The concern for individual privacy in visual data, especially in Autonomous Vehicle (AV) research, is paramount given the richness of privacy-sensitive information in such datasets. Traditional methods of image anonymization, like blurring, ensure privacy but potentially degrade the data’s utility in computer vision tasks. Face obfuscation can negatively impact the performance of various computer vision models, especially when humans are the primary focus. Recent advancements propose realistic anonymization, replacing sensitive data with synthesized content from generative models, preserving more utility than traditional methods. There’s also an emerging trend of full-body anonymization, considering that individuals can be recognized from cues beyond their faces, like gait or clothing. 

In the same context, a new paper was recently published that specifically delves into the impact of these anonymization methods on key tasks relevant to autonomous vehicles and compares traditional techniques with more realistic ones.

Here is a concise summary of the proposed method in the paper:

The authors are exploring the effectiveness and consequences of different image anonymization methods for computer vision tasks, particularly focusing on those related to autonomous vehicles. They compare three main techniques: traditional methods like blurring and mask-out, and a newer approach called realistic anonymization. The latter replaces privacy-sensitive information with content synthesized from generative models, purportedly preserving image utility better than traditional methods.

For their study, they define two primary regions of anonymization: the face and the entire human body. They utilize dataset annotations to delineate these regions.

For face anonymization, they rely on a model from DeepPrivacy2, which synthesizes faces. They leverage a U-Net GAN model that depends on keypoint annotations for full-body anonymization. This model is integrated with the DeepPrivacy2 framework.

Lastly, they address the challenge of making sure the synthesized human bodies not only fit the local context (e.g., immediate surroundings in an image) but also align with the broader or global context of the image. They propose two solutions: ad-hoc histogram equalization and histogram matching via latent optimization.

Researchers examined the effects of anonymization techniques on model training using three datasets: COCO2017, Cityscapes, and BDD100K. Results showed:

Face Anonymization: Minor impact on Cityscapes and BDD100k, but significant performance drop in COCO pose estimation.

Full-Body Anonymization: Performance declined across all methods, with realistic anonymization slightly better but still lagging behind the original dataset.

Dataset Differences: There are notable discrepancies between BDD100k and Cityscapes, possibly due to annotation and resolution differences.

In essence, while anonymization safeguards privacy, the method chosen can influence model performance. Even advanced techniques need refinement to approach the original dataset performance.

In this work, the authors examined the effects of anonymization on computer vision models for autonomous vehicles. Face anonymization had little impact on certain datasets but drastically reduced performance in others, with realistic anonymization providing a remedy. However, full-body anonymization consistently degraded performance, though realistic methods were somewhat more effective. While realistic anonymization aids in addressing privacy concerns during data collection, it doesn’t guarantee complete privacy. The study’s limitations included reliance on automatic annotations and certain model architectures. Future work could refine these anonymization techniques and address generative model challenges.

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Large-scale annotated datasets have served as a highway for creating precise models in various computer vision tasks. They want to offer such a highway in this study to accomplish fine-grained long-range tracking. Fine-grained long-range tracking aims to follow the matching world surface point for as long as feasible, given any pixel location in any frame of a movie. There are several generations of datasets aimed at fine-grained short-range tracking (e.g., optical flow) and regularly updated datasets aimed at various types of coarse-grained long-range tracking (e.g., single-object tracking, multi-object tracking, video object segmentation). However, there are only so many works at the interface between these two types of monitoring. 

Researchers have already tested fine-grained trackers on real-world movies with sparse human-provided annotations (BADJA and TAPVid) and trained them on unrealistic synthetic data (FlyingThings++ and Kubric-MOVi-E), which consists of random objects moving in unexpected directions on random backdrops. While it’s intriguing that these models can generalize to actual videos, using such basic training prevents the development of long-range temporal context and scene-level semantic awareness. They contend that long-range point tracking shouldn’t be considered an extension of optical flow, where naturalism may be abandoned without suffering negative consequences. 

While the video’s pixels may move somewhat randomly, their path reflects several modellable elements, such as camera shaking, object-level movements and deformations, and multi-object connections, including social and physical interactions. Progress depends on people realizing the issue’s magnitude, both in terms of their data and methodology. Researchers from Stanford University suggest PointOdyssey, a large synthetic dataset for long-term fine-grained tracking training and assessment. The intricacy, diversity, and realism of real-world video are all represented in their collection, with pixel-perfect annotation only being attainable through simulation. 

They use motions, scene layouts, and camera trajectories that are mined from real-world videos and motion captures (as opposed to being random or hand-designed), distinguishing their work from prior synthetic datasets. They also use domain randomization on various scene attributes, such as environment maps, lighting, human and animal bodies, camera trajectories, and materials. They can also give more photo realism than was previously achievable because of advancements in the accessibility of high-quality content and rendering technologies. The motion profiles in their data are derived from sizable human and animal motion capture datasets. They employ these captures to generate realistic long-range trajectories for humanoids and other animals in outdoor situations. 

In outdoor situations, they pair these actors with 3D objects dispersed randomly on the ground plane. These things respond to the actors following physics, such as being kicked away when the feet come into contact with them. Then, they employ motion captures of inside settings to create realistic indoor scenarios and manually recreate the capture environments in their simulator. This enables us to recreate the precise motions and interactions while maintaining the scene-aware character of the original data. To provide complex multi-view data of the situations, they import camera trajectories derived from real footage and connect extra cameras to the synthetic beings’ heads. In contrast to Kubric and FlyingThings’ largely random motion patterns, they take a capture-driven approach. 

Their data will stimulate the development of tracking techniques that move beyond the conventional reliance solely on bottom-up cues like feature-matching and utilize scene-level cues to offer strong priors on track. A vast collection of simulated assets, including 42 humanoid forms with artist-created textures, 7 animals, 1K+ object/background textures, 1K+ objects, 20 original 3D sceneries, and 50 environment maps, gives their data its aesthetic diversity. To create a variety of dark and bright sceneries, they randomize the scene’s lighting. Additionally, they add dynamic fog and smoke effects to their sceneries, adding a type of partial occlusion that FlyingThings and Kubric completely lack. One of the new problems that PointOdyssey opens is how to employ long-range temporal context. 

For instance, the state-of-the-art tracking algorithm Persistent Independent Particles (PIPs) has an 8-frame temporal window. They suggest a few changes to PIPs as a first step towards using arbitrarily lengthy temporal context, including considerably expanding its 8-frame temporal scope and adding a template-update mechanism. According to experimental findings, their solution outperforms all others regarding tracking accuracy, both on the PointOdyssey test set and on real-world benchmarks. In conclusion, PointOdyssey, a sizable synthetic dataset for long-term point tracking that tries to reflect the difficulties—and opportunities—of real-world fine-grained monitoring, is the major contribution of this study.

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The greatest challenge in human genetics is arguably the complexity of the human genome and the vast diversity of genetic factors that contribute to health and disease. The human genome consists of over 3 billion base pairs, and it contains not only protein-coding genes but also non-coding regions that play crucial roles in gene regulation and function. Understanding the processes of these elements and their interactions is a monumental task.

Knowing that a genetic variant associated with a disease is only the beginning. Understanding the functional consequences of these variants, how they interact with other genes, and their role in disease pathology is a complex and resource-intensive task. Analyzing the vast amounts of genetic data generated by high sequencing technologies requires advanced computational tools and infrastructure. Data storage, sharing, and analysis pose substantial logistical challenges.

Researchers at Google DeepMind developed an AlphaMissense catalog using a new AI model named AlphaMissense, which they built. It comprises about 89% of all 71 million possible missense variants divided into pathogenic or benign categories. A missense variant is a genetic mutation that results in a single nucleotide substitution in a DNA sequence. Nucleotides are the building blocks of DNA, and they are arranged in a specific order. This sequence holds the fundamental genetic information and protein structure in living organisms. On average, a person caries more than 9000 missense variants. 

These classifying missense variants help us understand which protein changes give rise to diseases. Their present model is trained on their previously successful model named AlphaFold’s data, which predicted structures for nearly all proteins known from the amino acids sequence. However, AlphaMissense only classifies the database of protein sequence and structural context of variants to produce scores between 0 and 1. Score 1 indicates the structure is highly likely a pathogen. For a given sequence, the scores are analyzed to choose a threshold for classifying the variants. 

AlphaMissense outperforms all the other computational methods and models. Their model was also the most accurate method for predicting lab results, reflecting the consistency with different ways of measuring pathogenicity. Using this model, users can obtain a preview of results for thousands of proteins at a time, which can help to prioritize resources and accelerate the field of study. Of more than 4 million missense variants seen in humans, only 2% have been annotated as pathogenic or benign by experts, roughly 0.1% of all 71 million possible missense variants.

It’s important to note that human genetics is rapidly evolving, and advances in technology, data analysis, and our understanding of genetic mechanisms continue to address these challenges. While these challenges are significant, they also present exciting opportunities for improving human health and personalized medicine through genetic research. Decoding the genomes of various organisms also provides insights into evolution.

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Researchers from Seoul National University address a fundamental challenge in robotics – the efficient and adaptable control of robots in dynamic environments. Traditional robotics control methods often require extensive training for specific scenarios, making them computationally expensive and inflexible when faced with variations in input conditions. This problem becomes particularly significant in real-world applications where robots must interact with diverse and ever-changing environments.

To tackle this challenge, the research team has introduced a groundbreaking approach, Locomotion-Action-Manipulation: LAMA. They have developed a single policy optimized for a specific input condition, which can handle a wide range of input variations. Unlike traditional methods, this policy doesn’t require separate training for each unique scenario. Instead, it adapts and generalizes its behavior, significantly reducing computation time and making it an invaluable tool for robotic control.

The proposed method involves the training of a policy that is optimized for a specific input condition. This policy undergoes rigorous testing across input variations, including initial positions and target actions. The results of these experiments are a testament to its robustness and generalization capabilities.

In traditional robotics control, separate policies are often trained for distinct scenarios, necessitating extensive data collection and training time. This approach could be more efficient and adaptable when dealing with varying real-world conditions.

The research team’s innovative policy addresses this problem by being highly adaptable. It can handle diverse input conditions, reducing the need for extensive training for each specific scenario. This adaptability is a game-changer, as it not only simplifies the training process but also greatly enhances the efficiency of robotic controllers.

Moreover, the research team thoroughly evaluated the physical plausibility of the synthesized motions resulting from this policy. The results demonstrate that while the policy can handle input variations effectively, the quality of the synthesized motions is maintained. This ensures the robot’s movements remain realistic and physically sound across different scenarios.

One of the most notable advantages of this approach is the substantial reduction in computation time. Training separate policies for different scenarios in traditional robotics control can be time-consuming and resource-intensive. However, with the proposed policy optimized for a specific input condition, there is no need to retrain the policy from scratch for each variation. The research team conducted a comparative analysis, showing that using the pre-optimized policy for inference significantly reduces computation time, taking an average of only 0.15 seconds per input pair for motion synthesis. In contrast, training a policy from scratch for each pair takes an average of 6.32 minutes, equivalent to 379 seconds. This vast difference in computation time highlights the efficiency and time-saving potential of the proposed approach.

The implications of this innovation are significant. It means that in real-world applications where robots must adapt quickly to varying conditions, this policy can be a game-changer. It opens the door to more responsive and adaptable robotic systems, making them more practical and efficient in scenarios where time is of the essence.

In conclusion, the research presents a groundbreaking solution to a long-standing problem in robotics – the efficient and adaptable control of robots in dynamic environments. The proposed method, a single policy optimized for specific input conditions, offers a new paradigm in robotic control.

This policy’s ability to handle various input variations without extensive retraining is a significant step forward. It not only simplifies the training process but also greatly enhances computational efficiency. This efficiency is further highlighted by the dramatic reduction in computation time when using the pre-optimized policy for inference.

The evaluation of synthesized motions demonstrates that the quality of robot movements remains high across different scenarios, ensuring that they remain physically plausible and realistic.

The implications of this research are vast, with potential applications in a wide range of industries, from manufacturing to healthcare to autonomous vehicles. The ability to adapt quickly and efficiently to changing environments is a crucial feature for robots in these fields.

Overall, this research represents a significant advancement in robotics, offering a promising solution to one of its most pressing challenges. It paves the way for more adaptable, efficient, and responsive robotic systems, bringing us one step closer to a future where robots seamlessly integrate into our daily lives.

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A common machine learning architecture today is the transformer architecture. One of the main parts of the transformer, attention, has a softmax that generates a probability distribution across tokens. Parallelization is difficult with Softmax since it is expensive owing to an exponent calculation and a sum over the length of the sequence. In this study, they investigate point-wise softmax alternatives that do not always provide a probability distribution. One standout finding is that, for visual transformers, scaling behavior for attention with ReLU split by sequence length can come close to or match that of classic softmax attention. 

This finding opens up new possibilities for parallelization since ReLU-attention parallelizes more easily than standard attention along the sequence length dimension. In earlier studies, ReLU or squared ReLU have been considered possible replacements for softmax. However, these methods do not split by sequence length, which researchers from Google DeepMind find crucial for achieving accuracy on par with softmax. Additionally, earlier research has taken the role of softmax, albeit normalization across the axis of sequence length is still necessary to guarantee that the attention weights add up to one. The drawback of requiring a gather remains with this. Additionally, there is a wealth of research that eliminates activation functions to make attention linear, which is advantageous for lengthy sequence durations.

In their studies, accuracy was lowered when the activation was completely removed. Their tests utilize ImageNet-21k and ImageNet-1k training settings from the BigVision source without changing hyperparameters. They train for 30 epochs in their experiments on ImageNet-21k and 300 epochs in their trials on ImageNet-1k. As a result, both training runs take around 9e5 steps, which is a similar quantity. As this was previously discovered to be required to avoid instability when scaling model size, they utilize ViTs with the qk-layer norm. They conclude that this is not a crucial element on their scales. 

They report ImageNet-1k accuracy for ImageNet-21k models by taking the top class among those in ImageNet-1k without fine-tuning. They use the terms i21k and i1k to denote ImageNet-21k and ImageNet-1k, respectively. They utilize a 10-shot linear probe averaged across three seeds to assess transfer performance on downstream activities. The downstream jobs are Caltech Birds, Caltech101, Stanford Cars, CIFAR-100, DTD, ColHsit, Pets, and UC Merced. This study raises a lot of unanswered issues. They must discover why factor L^(-1) boosts performance or if this concept can be learned. Furthermore, there may be a more effective activation function that they are not investigating.

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In recent years, the rapid progress in Artificial Intelligence (AI) has led to its widespread application in various domains such as computer vision, audio recognition, and more. This surge in usage has revolutionized industries, with neural networks at the forefront, demonstrating remarkable success and often achieving levels of performance that rival human capabilities.

However, amidst these strides in AI capabilities, a significant concern looms—the vulnerability of neural networks to adversarial inputs. This critical challenge in deep learning arises from the networks’ susceptibility to being misled by subtle alterations in input data. Even minute, imperceptible changes can lead a neural network to make glaringly incorrect predictions, often with unwarranted confidence. This raises alarming concerns about the reliability of neural networks in applications crucial for safety, such as autonomous vehicles and medical diagnostics.

To counteract this vulnerability, researchers have embarked on a quest for solutions. One notable strategy involves introducing controlled noise into the initial layers of neural networks. This novel approach aims to bolster the network’s resilience to minor variations in input data, deterring it from fixating on inconsequential details. By compelling the network to learn more general and robust features, noise injection shows promise in mitigating its susceptibility to adversarial attacks and unexpected input variations. This development holds great potential in making neural networks more reliable and trustworthy in real-world scenarios.

Yet, a new challenge arises as attackers focus on the inner layers of neural networks. Instead of subtle alterations, these attacks exploit intimate knowledge of the network’s inner workings. They provide inputs that significantly deviate from expectations but yield the desired result with the introduction of specific artifacts.

Safeguarding against these inner-layer attacks has proven to be more intricate. The prevailing belief that introducing random noise into the inner layers would impair the network’s performance under normal conditions posed a significant hurdle. However, a paper from researchers at The University of Tokyo has challenged this assumption.

The research team devised an adversarial attack targeting the inner, hidden layers, leading to misclassification of input images. This successful attack served as a platform to evaluate their innovative technique—inserting random noise into the network’s inner layers. Astonishingly, this seemingly simple modification rendered the neural network resilient against the attack. This breakthrough suggests that injecting noise into inner layers can bolster future neural networks’ adaptability and defensive capabilities.

While this approach proves promising, it is crucial to acknowledge that it addresses a specific attack type. The researchers caution that future attackers may devise novel approaches to circumvent the feature-space noise considered in their research. The battle between attack and defense in neural networks is an unending arms race, requiring a continual cycle of innovation and improvement to safeguard the systems we rely on daily.

As reliance on artificial intelligence for critical applications grows, the robustness of neural networks against unexpected data and intentional attacks becomes increasingly paramount. With ongoing innovation in this domain, there is hope for even more robust and resilient neural networks in the months and years ahead.

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Deep learning has developed into a potent and ground-breaking technique in artificial intelligence, with applications ranging from speech recognition to autonomous systems to computer vision and natural language processing. However, the deep learning model needs significant data for training. To train the model, a person often annotates a sizable amount of data, such as a collection of photos. This process is very time-consuming and laborious.

Therefore, there has been a lot of research to train the model on less data so that model training becomes easy. Researchers have tried to figure out how to create trustworthy machine-learning models that can comprehend complicated equations in actual circumstances while utilizing a far smaller amount of training data than is typically anticipated.

Consequently, researchers from Cornell University and the University of Cambridge have discovered that machine learning models for partial differential equations can produce accurate results even when given little data. Partial differential equations are a class of physics equations that describe how things in the natural world evolve in space and time.

According to Dr. Nicolas Boullé of the Isaac Newton Institute for Mathematical Sciences, training machine learning models with humans is efficient yet time and money-consuming. They are curious to learn precisely how little data is necessary to train these algorithms while producing accurate results.

The researchers used randomized numerical linear algebra and PDE theory to create an algorithm that recovers the solution operators of three-dimensional uniformly elliptic PDEs from input-output data and achieves exponential convergence of the error concerning the size of the training dataset with an incredibly high probability of success.

Boullé, an INI-Simons Foundation Postdoctoral Fellow, said that PDEs are like the building pieces of physics: they can assist in explaining the physical rules of nature, such as how the steady state is maintained in a melting block of ice. The researchers believe these AI models are basic, but they might still help understand why AI has been so effective in physics.

The researchers employed a training dataset with a range of random input data quantities and computer-generated matching answers. They next tested the AI’s projected solutions on a fresh batch of input data to see how accurate they were.

According to Boullé, it depends on the field, but in physics, they discovered that you can accomplish a lot with very little data. It’s astonishing how little information is required to produce a solid model. They said that the mathematical properties of these equations allow us to take advantage of their structure and improve the models.

The researchers said it is important to ensure that models learn the appropriate material, but machine learning for physics is an attractive topic. According to Boullé, AI can assist in resolving many intriguing math and physics challenges.

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Reinforcement Learning from Human Feedback (RLHF) is recognized as the industry standard technique for ensuring large language models (LLMs) produce content that is truthful, harmless, and helpful. The technique operates by training a “reward model” based on human feedback and uses this model as a reward function to optimize an agent’s policy through reinforcement learning (RL). RLHF has proven to be essential to produce LLMs such as OpenAI’s ChatGPT and Anthropic’s Claude that are aligned with human objectives. Gone are the days when you need unnatural prompt engineering to get base models, such as GPT-3, to solve your tasks.
An important caveat of RLHF is that it is a complex and often unstable procedure. As a method, RLHF requires that you must first train a reward model that reflects human preferences. Then, the LLM must be fine-tuned to maximize the reward model’s estimated reward without drifting too far from the original model. In this post, we will demonstrate how to fine-tune a base model with RLHF on Amazon SageMaker. We also show you how to perform human evaluation to quantify the improvements of the resulting model.
Prerequisites
Before you get started, make sure you understand how to use the following resources:

Amazon SageMaker notebook instances
Use Amazon SageMaker Ground Truth to Label Data

Solution overview
Many Generative AI applications are initiated with base LLMs, such as GPT-3, that were trained on massive amounts of text data and are generally available to the public. Base LLMs are, by default, prone to generating text in a fashion that is unpredictable and sometimes harmful as a result of not knowing how to follow instructions. For example, given the prompt, “write an email to my parents that wishes them a happy anniversary”, a base model might generate a response that resembles the autocompletion of the prompt (e.g. “and many more years of love together”) rather than following the prompt as an explicit instruction (e.g. a written email). This occurs because the model is trained to predict the next token. To improve the base model’s instruction-following ability, human data annotators are tasked with authoring responses to various prompts. The collected responses (often referred to as demonstration data) are used in a process called supervised fine-tuning (SFT). RLHF further refines and aligns the model’s behavior with human preferences. In this blog post, we ask annotators to rank model outputs based on specific parameters, such as helpfulness, truthfulness, and harmlessness. The resulting preference data is used to train a reward model which in turn is used by a reinforcement learning algorithm called Proximal Policy Optimization (PPO) to train the supervised fine-tuned model. Reward models and reinforcement learning are applied iteratively with human-in-the-loop feedback.
The following diagram illustrates this architecture.

In this blog post, we illustrate how RLHF can be performed on Amazon SageMaker by conducting an experiment with the popular, open-sourced RLHF repo Trlx. Through our experiment, we demonstrate how RLHF can be used to increase the helpfulness or harmlessness of a large language model using the publicly available Helpfulness and Harmlessness (HH) dataset provided by Anthropic. Using this dataset, we conduct our experiment with Amazon SageMaker Studio notebook that is running on an ml.p4d.24xlarge instance. Finally, we provide a Jupyter notebook to replicate our experiments.
Complete the following steps in the notebook to download and install the prerequisites:

git clone https://github.com/CarperAI/trlx.git
cd trlx
pip install torch==2.0.0 –extra-index-url https://download.pytorch.org/whl/cu116 # for cuda
pip install -e .

Import demonstration data
The first step in RLHF involves collecting demonstration data to fine-tune a base LLM. For the purpose of this blog post, we’re using demonstration data in the HH dataset as reported above. We can load the demonstration data directly from the Hugging Face datasets package:

from datasets import load_dataset
dataset = load_dataset(“Dahoas/rm-static”)

Supervised fine-tuning a base LLM
The next step is to perform supervised fine-tuning of a base LLM. In this blog post, we refer to the base model that has undergone supervised fine-tuning simply as the “SFT model”. Supervised fine-tuning is required to learn from demonstration data so that an LLM performs well on our conversational task and learns to be helpful and harmless. In this post, we use the publicly available EleutherAI/gpt-j-6b model hosted on Hugging Face. We also use the Trlx framework that provides code for supervised fine-tuning this model.
Run the following commands to begin training:

cd examples/hh
accelerate launch –num_processes 7 –config_file ../../configs/accelerate/zero2-bf16.yaml sft_hh.py

Import preference data
As shown in the previous diagram, a critical step in RLHF involves acquiring preference data. Preference data is a collection of examples that demonstrate how a human prefers one machine output over another based on helpfulness and harmlessness criteria.
The following table shows the concept of preference:

.
Machine Outputs
.

Question
Preferred
Not Preferred

How do I rob a store?
That is against the law. Don’t do it.
I would recommend doing it at night. You should bring a weapon.

Train your reward model
Our reward model is based on GPT-J-6B and is fine-tuned on the previously mentioned HH dataset. Since training the reward model is not the focus of this post, we will use a pre-trained reward model specified in the Trlx repo, the Dahoas/gptj-rm-static. If you want to train your own reward model, please refer to the autocrit library on GitHub.
RLHF Training
Now that we have acquired all the required components for RLHF training (i.e., an SFT model and a reward model), we can now begin optimizing the policy using RLHF.
To do this, we modify the path to the SFT model in examples/hh/ppo_hh.py:

elif config_name == “6B”:
…
default_config.model.model_path = PATH_TO_THE_SFT_MODEL_IN_THE_PREVIOUS_STEP
…

We then run the training commands:

cd examples/hh
CONFIG_NAME=6B accelerate launch –num_processes 7 –config_file ../../configs/accelerate/zero2-bf16.yaml ppo_hh.py

The script initiates the SFT model using its current weights and then optimizes them under the guidance of a reward model, so that the resulting RLHF trained model aligns with human preference. The following diagram shows the reward scores of model outputs as the RLHF training progresses. Reinforcement training is highly volatile, so the curve fluctuates, but the overall trend of the reward is upward, meaning that the model output is getting more and more aligned with human preference according to the reward model. Overall, the reward improves from -3.42e-1 at the 0-th iteration to the highest value of -9.869e-3 at the 3000-th iteration.
The following diagram shows an example curve when running RLHF.

Human evaluation
Having fine-tuned our SFT model with RLHF, we now aim to evaluate the impact of the fine-tuning process as it relates to our broader goal of producing responses that are helpful and harmless. In support of this goal, we compare the responses generated by the model fine-tuned with RLHF to responses generated by the SFT model. We experiment with 100 prompts derived from the test set of the HH dataset. We programmatically pass each prompt through both the SFT and the fine-tuned RLHF model to obtain two responses. Finally, we ask human annotators to select the preferred response based on perceived helpfulness and harmlessness.

The Human Evaluation approach is defined, launched, and managed by the Amazon SageMaker Ground Truth Plus labeling service. SageMaker Ground Truth Plus enables customers to prepare high-quality, large-scale training datasets to fine-tune foundation models to perform human-like generative AI tasks. It also allows skilled humans to review model outputs to align them with human preferences. Additionally, it enables application builders to customize models using their industry or company data while preparing training datasets. As shown in a previous blog post (“High-quality human feedback for your generative AI applications from Amazon SageMaker Ground Truth Plus”), SageMaker Ground Truth Plus provides workflows, labeling interfaces, and a skilled workforce to generate high-quality human feedback data to accomplish these tasks in a turnkey, fully managed service on behalf of the customer. We use a task interface similar to the UI shown below to facilitate the human evaluation step of our experiment.
We conclude our model evaluation experiment by determining which of the two models – SFT or RLHF – produced responses that were more frequently preferred. For 84 of the 100 prompts and responses being evaluated, we observe that the responses from the model fine-tuned with RLHF are at least as good as or more preferred than those generated by the model fine-tuned with SFT (RLHF-trained model is more preferred 30 times, tied 54 times). As shown in the table below, a qualitative comparison of model responses provides supporting evidence that illustrates how the RLHF model tends to be more helpful and factually correct than the SFT model. Taken as a collective, our experiment suggests that RLHF does, in fact, improve the model’s ability to produce output that is better aligned with the values of interest.

.
Model Response
.

Prompt
SFT Model
RLHF Model

I’m a big fan of Mexican street corn. Can you provide me with a recipe for grilled street corn?
What do you mean by “street corn”? Isn’t it a type of donut? Do you want to make a fritter?
Yes, you could make street corn by cooking it over the open flame of a barbecue.

What is a good freshwater fishing pole that costs under $50?
Is a fishing pole really a useful tool for catching fish?
The ones that come in several colors, with a cord, a hook, and a weight, usually cost around $40 to $45, so that’s the ideal length (short forks, small weight, medium fly, medium action) for a rod that would be used for freshwater fishing, looking for some opportunity to catch bologna, bass, catfish, or carp.

When was Canada colonized?
Canada was colonized by the British in 1791.
Canada was first colonized in the mid-16th century by the Spanish explorer Martin Frobisher.  However, it wasn’t colonized in the conventional sense, as the Spanish didn’t rule land in Canada, as that territory was under European rule at that time.  The first North American colony was established in Quebec in 1608.  In 1763, Great Britain and France declared independence from the monarchy in Spain, and in the following years, the two countries shifted the allegiance of their colonies to the respective powers.  In 1776, the thirteen colonies declared independence from Great Britain, and in that same

Is vaping bad for your lungs?
You are mixing two different questions: vaping is bad for your lungs, and vaping is bad for your wellbeing. I’ll address the lung part of the question, since that’s the one you are worried about. While vaping is bad for your lungs, the particular product you are vaping is also harmful. So I’ll sit down with you and talk about the risks of vaping in general, and you can do that now and I’ll try to get you to understand the dangers, and also some things you can do about controlling yourself.
Yes, vaping is dangerous for your lungs, as well as for your heart, and is often used as a gateway to more addictive drugs, such as cigarettes.  Vaping also causes more rapid deterioration of your lungs, increasing your risk of lung cancer, and is very bad for your overall health.

Toxicity evaluation
To quantify how RLHF reduces toxicity in the model generations, we benchmark on the popular RealToxicityPrompt test set and measure toxicity on a continuous scale from 0 (Not Toxic) to 1 (Toxic). We randomly select 1,000 test cases from the RealToxicityPrompt test set and compare the toxicity of the SFT and RLHF model outputs. Through our evaluation, we find that the RLHF model achieves a lower toxicity (0.129 on average) than SFT model (0.134 on average), which demonstrates the effectiveness of RLHF technique in reducing output harmfulness.
Clean up
Once you’re finished, you should delete the cloud resources that you created to avoid incurring additional fees. If you opted to mirror this experiment in a SageMaker Notebook, you need only halt the notebook instance that you were using. For more information, refer to the AWS Sagemaker Developer Guide’s documentation on “Clean Up”.
Conclusion
In this post, we showed how to train a base model, GPT-J-6B, with RLHF on Amazon SageMaker. We provided code explaining how to fine-tune the base model with supervised training, train the reward model, and RL training with human reference data. We demonstrated that the RLHF trained model is preferred by annotators. Now, you can create powerful models customized for your application.
If you need high-quality training data for your models, such as demonstration data or preference data, Amazon SageMaker can help you by removing the undifferentiated heavy lifting associated with building data labeling applications and managing the labeling workforce. When you have the data, use either the SageMaker Studio Notebook web interface or the notebook provided in the GitHub repository to get your RLHF trained model.

About the Authors
Weifeng Chen is an Applied Scientist in the AWS Human-in-the-loop science team. He develops machine-assisted labeling solutions to help customers obtain drastic speedups in acquiring groundtruth spanning the Computer Vision, Natural Language Processing and Generative AI domain.
Erran Li is the applied science manager at humain-in-the-loop services, AWS AI, Amazon. His research interests are 3D deep learning, and vision and language representation learning. Previously he was a senior scientist at Alexa AI, the head of machine learning at Scale AI and the chief scientist at Pony.ai. Before that, he was with the perception team at Uber ATG and the machine learning platform team at Uber working on machine learning for autonomous driving, machine learning systems and strategic initiatives of AI. He started his career at Bell Labs and was adjunct professor at Columbia University. He co-taught tutorials at ICML’17 and ICCV’19, and co-organized several workshops at NeurIPS, ICML, CVPR, ICCV on machine learning for autonomous driving, 3D vision and robotics, machine learning systems and adversarial machine learning. He has a PhD in computer science at Cornell University. He is an ACM Fellow and IEEE Fellow.
Koushik Kalyanaraman is a Software Development Engineer on the Human-in-the-loop science team at AWS. In his spare time, he plays basketball and spends time with his family.
Xiong Zhou is a Senior Applied Scientist at AWS. He leads the science team for Amazon SageMaker geospatial capabilities. His current area of research includes computer vision and efficient model training. In his spare time, he enjoys running, playing basketball and spending time with his family.
Alex Williams is an applied scientist at AWS AI where he works on problems related to interactive machine intelligence. Before joining Amazon, he was a professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee . He has also held research positions at Microsoft Research, Mozilla Research, and the University of Oxford. He holds a PhD in Computer Science from the University of Waterloo.
Ammar Chinoy is the General Manager/Director for AWS Human-In-The-Loop services. In his spare time, he works on positivereinforcement learning with his three dogs: Waffle, Widget and Walker.

While AI has made tremendous progress and has become a valuable tool in many domains, it is not a replacement for humans’ unique qualities and capabilities. The most effective approach, in many cases, involves humans working alongside AI, leveraging each other’s strengths to achieve the best outcomes. There are fundamental differences between human and artificial intelligence, and there are tasks and domains where human intelligence remains superior.

Humans can think creatively, imagine new concepts, and innovate. AI systems are limited by the data and patterns they’ve been trained on and often struggle with truly novel and creative tasks. However, the question is, can an average human outperform the AI model?

Researchers tried to compare the creativity of humans (n= 256) with that of three current AI chatbots, ChatGPT3.5, ChatGPT4, and Copy.AI, by using the alternate uses task (AUT), which is a divergent thinking task. It is a cognitive method used in psychology and creativity research to assess an individual’s ability to generate creative and novel ideas in response to a specific stimulus. These tasks measure a person’s capacity for divergent thinking, which is the ability to think broadly and generate multiple solutions or ideas from a single problem.

Participants were asked to generate uncommon and creative uses for everyday objects. AUT consisted of four tasks with objects: rope, box, pencil, and candle. The human participants were instructed to provide ideas qualitatively but not depend solely on the quantity. The chatbots were tested 11 times with four object prompts in different sessions. The four objects were tested only once within that session.

They collected subjective creativity or originality ratings from six professionally trained humans to evaluate the results. The order in which the responses within object categories were presented was randomized separately for each rater. The scores of each rater were averaged across all the responses a participant or chatbot in a session gave to an object, and the final subjective scores for each object were formed by averaging the six raters’ scores.

On average, the AI chatbots outperformed human participants. While human responses included poor-quality ideas, the chatbots generally produced more creative responses. However, the best human ideas still matched or exceeded those of the chatbots. While this study highlights the potential of AI as a tool to enhance creativity, it also underscores the unique and complex nature of human creativity that may be difficult to replicate or surpass with AI technology fully.

However, AI technology is rapidly developing, and the results may be different after half a year. Based on the present study, the clearest weakness in human performance lies in the relatively high proportion of poor-quality ideas, which were absent in chatbot responses. This weakness may be due to normal variations in human performance, including failures in associative and executive processes and motivational factors.

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