Category Archives: Machine Learning
Reducing Toxic AI Responses – Neuroscience News
Summary: Researchers developed a new machine learning technique to improve red-teaming, a process used to test AI models for safety by identifying prompts that trigger toxic responses. By employing a curiosity-driven exploration method, their approach encourages a red-team model to generate diverse and novel prompts that reveal potential weaknesses in AI systems.
This method has proven more effective than traditional techniques, producing a broader range of toxic responses and enhancing the robustness of AI safety measures. The research, set to be presented at the International Conference on Learning Representations, marks a significant step toward ensuring that AI behaviors align with desired outcomes in real-world applications.
Key Facts:
Source: MIT
A user could ask ChatGPT to write a computer program or summarize an article, and the AI chatbot would likely be able to generate useful code or write a cogent synopsis. However, someone could also ask for instructions to build a bomb, and the chatbot might be able to provide those, too.
To prevent this and other safety issues, companies that build large language models typically safeguard them using a process called red-teaming. Teams of human testers write prompts aimed at triggering unsafe or toxic text from the model being tested. These prompts are used to teach the chatbot to avoid such responses.
But this only works effectively if engineers know which toxic prompts to use. If human testers miss some prompts, which is likely given the number of possibilities, a chatbot regarded as safe might still be capable of generating unsafe answers.
Researchers from Improbable AI Lab at MIT and the MIT-IBM Watson AI Lab used machine learning to improve red-teaming. They developed a technique to train a red-team large language model to automatically generate diverse prompts that trigger a wider range of undesirable responses from the chatbot being tested.
They do this by teaching the red-team model to be curious when it writes prompts, and to focus on novel prompts that evoke toxic responses from the target model.
The technique outperformed human testers and other machine-learning approaches by generating more distinct prompts that elicited increasingly toxic responses. Not only does their method significantly improve the coverage of inputs being tested compared to other automated methods, but it can also draw out toxic responses from a chatbot that had safeguards built into it by human experts.
Right now, every large language model has to undergo a very lengthy period of red-teaming to ensure its safety. That is not going to be sustainable if we want to update these models in rapidly changing environments.
Our method provides a faster and more effective way to do this quality assurance, says Zhang-Wei Hong, an electrical engineering and computer science (EECS) graduate student in the Improbable AI lab and lead author of apaper on this red-teaming approach.
Hongs co-authors include EECS graduate students Idan Shenfield, Tsun-Hsuan Wang, and Yung-Sung Chuang; Aldo Pareja and Akash Srivastava, research scientists at the MIT-IBM Watson AI Lab; James Glass, senior research scientist and head of the Spoken Language Systems Group in the Computer Science and Artificial Intelligence Laboratory (CSAIL); and senior author Pulkit Agrawal, director of Improbable AI Lab and an assistant professor in CSAIL. The research will be presented at the International Conference on Learning Representations.
Automated red-teaming
Large language models, like those that power AI chatbots, are often trained by showing them enormous amounts of text from billions of public websites. So, not only can they learn to generate toxic words or describe illegal activities, the models could also leak personal information they may have picked up.
The tedious and costly nature of human red-teaming, which is often ineffective at generating a wide enough variety of prompts to fully safeguard a model, has encouraged researchers to automate the process using machine learning.
Such techniques often train a red-team model using reinforcement learning. This trial-and-error process rewards the red-team model for generating prompts that trigger toxic responses from the chatbot being tested.
But due to the way reinforcement learning works, the red-team model will often keep generating a few similar prompts that are highly toxic to maximize its reward.
For their reinforcement learning approach, the MIT researchers utilized a technique called curiosity-driven exploration. The red-team model is incentivized to be curious about the consequences of each prompt it generates, so it will try prompts with different words, sentence patterns, or meanings.
If the red-team model has already seen a specific prompt, then reproducing it will not generate any curiosity in the red-team model, so it will be pushed to create new prompts, Hong says.
During its training process, the red-team model generates a prompt and interacts with the chatbot. The chatbot responds, and a safety classifier rates the toxicity of its response, rewarding the red-team model based on that rating.
Rewarding curiosity
The red-team models objective is to maximize its reward by eliciting an even more toxic response with a novel prompt. The researchers enable curiosity in the red-team model by modifying the reward signal in the reinforcement learning set up.
First, in addition to maximizing toxicity, they include an entropy bonus that encourages the red-team model to be more random as it explores different prompts. Second, to make the agent curious they include two novelty rewards.
One rewards the model based on the similarity of words in its prompts, and the other rewards the model based on semantic similarity. (Less similarity yields a higher reward.)
To prevent the red-team model from generating random, nonsensical text, which can trick the classifier into awarding a high toxicity score, the researchers also added a naturalistic language bonus to the training objective.
With these additions in place, the researchers compared the toxicity and diversity of responses their red-team model generated with other automated techniques. Their model outperformed the baselines on both metrics.
They also used their red-team model to test a chatbot that had been fine-tuned with human feedback so it would not give toxic replies. Their curiosity-driven approach was able to quickly produce 196 prompts that elicited toxic responses from this safe chatbot.
We are seeing a surge of models, which is only expected to rise. Imagine thousands of models or even more and companies/labs pushing model updates frequently. These models are going to be an integral part of our lives and its important that they are verified before released for public consumption.
Manual verification of models is simply not scalable, and our work is an attempt to reduce the human effort to ensure a safer and trustworthy AI future, says Agrawal.
In the future, the researchers want to enable the red-team model to generate prompts about a wider variety of topics. They also want to explore the use of a large language model as the toxicity classifier. In this way, a user could train the toxicity classifier using a company policy document, for instance, so a red-team model could test a chatbot for company policy violations.
If you are releasing a new AI model and are concerned about whether it will behave as expected, consider using curiosity-driven red-teaming, says Agrawal.
Funding: This research is funded, in part, by Hyundai Motor Company, Quanta Computer Inc., the MIT-IBM Watson AI Lab, an Amazon Web Services MLRA research grant, the U.S. Army Research Office, the U.S. Defense Advanced Research Projects Agency Machine Common Sense Program, the U.S. Office of Naval Research, the U.S. Air Force Research Laboratory, and the U.S. Air Force Artificial Intelligence Accelerator.
Author: Adam Zewe Source: MIT Contact: Adam Zewe MIT Image: The image is credited to Neuroscience News
Original Research: The findings will be presented at the International Conference on Learning Representations
Video Highlights: Deep Reinforcement Learning for Maximizing Profits with Prof. Barrett Thomas – insideBIGDATA
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Could we use machine learning to converse with whales and dolphins? – New Scientist
R Dirscherl/imageBROKER/Shutterstock
Is there any prospect of using machine learning to converse with whales and dolphins?
Mike Follows Sutton Coldfield, West Midlands, UK
Douglas Adams dreamed up an organic universal translator called a Babel fish that could be popped into your ear. Though it is amazing how prescient science fiction can be, this wont become a reality in the near future, if ever.
Communicating with other animals like whales and dolphins is challenging because we dont have the equivalent of the Rosetta Stone, which would allow for direct translation. Decrees in ancient Egypt were inscribed onto stelae, essentially slabs of stone.
See the article here:
Could we use machine learning to converse with whales and dolphins? - New Scientist
AWS at NVIDIA GTC 2024: Accelerate innovation with generative AI on AWS | Amazon Web Services – AWS Blog
AWS was delighted to present to and connect with over 18,000 in-person and 267,000 virtual attendees at NVIDIA GTC, a global artificial intelligence (AI) conference that took place March 2024 in San Jose, California, returning to a hybrid, in-person experience for the first time since 2019.
AWS has had a long-standing collaboration with NVIDIA for over 13 years. AWS was the first Cloud Service Provider (CSP) to offer NVIDIA GPUs in the public cloud, and remains among the first to deploy NVIDIAs latest technologies.
Looking back at AWS re:Invent 2023, Jensen Huang, founder and CEO of NVIDIA, chatted with AWS CEO Adam Selipsky on stage, discussing how NVIDIA and AWS are working together to enable millions of developers to access powerful technologies needed to rapidly innovate with generative AI. NVIDIA is known for its cutting-edge accelerators and full-stack solutions that contribute to advancements in AI. The company is combining this expertise with the highly scalable, reliable, and secure AWS Cloud infrastructure to help customers run advanced graphics, machine learning, and generative AI workloads at an accelerated pace.
The collaboration between AWS and NVIDIA further expanded at GTC 2024, with the CEOs from both companies sharing their perspectives on the collaboration and state of AI in a press release:
The deep collaboration between our two organizations goes back more than 13 years, when together we launched the worlds first GPU cloud instance on AWS, and today we offer the widest range of NVIDIA GPU solutions for customers, says Adam Selipsky, CEO of AWS. NVIDIAs next-generation Grace Blackwell processor marks a significant step forward in generative AI and GPU computing. When combined with AWSs powerful Elastic Fabric Adapter networking, Amazon EC2 UltraClusters hyper-scale clustering, and our unique AWS Nitro Systems advanced virtualization and security capabilities, we make it possible for customers to build and run multi-trillion parameter large language models faster, at massive scale, and more securely than anywhere else. Together, we continue to innovate to make AWS the best place to run NVIDIA GPUs in the cloud.
AI is driving breakthroughs at an unprecedented pace, leading to new applications, business models, and innovation across industries, says Jensen Huang, founder and CEO of NVIDIA. Our collaboration with AWS is accelerating new generative AI capabilities and providing customers with unprecedented computing power to push the boundaries of whats possible.
On the first day of the NVIDIA GTC, AWS and NVIDIA made a joint announcement focused on their strategic collaboration to advance generative AI. Huang included the AWS and NVIDIA collaboration on a slide during his keynote, highlighting the following announcements. The GTC keynote had over 21 million views within the first 72 hours.
By March 22, AWSs announcement with NVIDIA had generated 104 articles mentioning AWS and Amazon. The vast majority of coverage mentioned AWSs plans to offer Blackwell-based instances. Adam Selipsky appeared on CNBCs Mad Money to discuss the long-standing collaboration between AWS and NVIDIA, among the many other ways AWS is innovating in generative AI, stating that AWS has been the first to bring many of its GPUs to the cloud to drive efficiency and scalability for customers.
Project Ceiba has also been a focus in media coverage. Forbes referred to Project Ceiba as the most exciting project by AWS and NVIDIA, stating that it should accelerate the pace of innovation in AI, making it possible to tackle more complex problems, develop more sophisticated models, and achieve previously unattainable breakthroughs. The Next Platform ran an in-depth piece on Ceiba, stating that the size and the aggregate compute of Ceiba cluster are both being radically expanded, which will give AWS a very large supercomputer in one of its data centers and NVIDIA will use it to do AI research, among other things.
Live from GTC was an on-site studio at GTC for invited speakers to have a fireside chat with tech influencers like VentureBeat. Chetan Kapoor, Director of Product Management for Amazon EC2 at AWS, was interviewed by VentureBeat at the Live from GTC studio, where he discussed AWSs presence and highlighted key announcements at GTC.
The AWS booth showcased generative AI services, like the LLMs with Anthropic and Cohere on Amazon Bedrock, PartyRock, Amazon Q, Amazon SageMaker JumpStart, and more. Highlights included:
During GTC, AWS invited 23 partner and customer solution demos to join its booth with either a dedicated demo kiosk or a 30-minute in-booth session. Such partners and customers included Ansys, Anthropic, Articul8, Bria.ai, Cohere, Deci, Deepbrain.AI, Denali Advanced Integration, Ganit, Hugging Face, Lilt, Linker Vision, Mavenir, MCE, Media.Monks, Modular, NVIDIA, Perplexity, Quantiphi, Run.ai, Salesforce, Second Spectrum, and Slalom.
Among them, high-potential early-stage startups in generative AI across the globe were showcased with a dedicated kiosk at the AWS booth. The AWS Startups team works closely with these companies by investing and supporting their growth, offering resources through programs like AWS Activate.
NVIDIA was one of the 45 launch partners for the new AWS Generative AI Competency program. The Generative AI Center of Excellence for AWS Partners team members were on site at the AWS booth, presenting this program for both existing and potential AWS partners. The program offers valuable resources along with best practices for all AWS partners to build, market, and sell generative AI solutions jointly with AWS.
Watch a video recap of the AWS presence at NVIDIA GTC 2024. For additional resources about the AWS and NVIDIA collaboration, refer to the AWS at NVIDIA GTC 2024 resource hub.
Julie Tang is the Senior Global Partner Marketing Manager for Generative AI at Amazon Web Services (AWS), where she collaborates closely with NVIDIA to plan and execute partner marketing initiatives focused on generative AI. Throughout her tenure at AWS, she has held various partner marketing roles, including Global IoT Solutions, AWS Partner Solution Factory, and Sr. Campaign Manager in Americas Field Marketing. Prior to AWS, Julie served as the Marketing Director at Segway. She holds a Masters degree in Communications Management with a focus on marketing and entertainment management from the University of Southern California, and dual Bachelors degrees in Law and Broadcast Journalism from Fudan University.
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AWS at NVIDIA GTC 2024: Accelerate innovation with generative AI on AWS | Amazon Web Services - AWS Blog
High-resolution meteorology with climate change impacts from global climate model data using generative machine … – Nature.com
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High-resolution meteorology with climate change impacts from global climate model data using generative machine ... - Nature.com
Google Cloud Next 2024: Pushing the Next Frontier of AI – Technology Magazine
The companys updated AI offering is now available on Vertex AI, Googles platform to customise and manage a wide range of leading Gen AI models. The company says that more than one million developers are currently using Googles Gen AI via its AI Studio and Vertex AI tools.
Likewise, its AI Hypercomputer is now being used by leading AI companies such as Anthropic, AI21 Labs, Contextual AI, Essential AI and Mistral AI. The Hypercomputer aims to employ a system of performance-optimised hardware, open software and machine learning frameworks to enable companies to better advance their digital transformation strategies.
Multiple companies are already harnessing the power of Google Cloud AI, including forward-thinking organisations like Mercedes-Benz, Uber and Palo Alto Networks to bolster their existing services and improve customer experience.
Mercedes-Benz, for example, is harnessing Google AI to improve customer service in call centres and to further optimise their website experience.
As AI continues to drive transformative progress in the business world, Google Cloud is aiming to help organisations around the world to discover whats next.
Google Cloud is also introducing new features that aim to offer AI assistance so that its customers can work and code more efficiently, allowing them to better identify and resolve cybersecurity threats by taking direct action against attacks.
Google Clouds product, Gemini in Threat Intelligence, utilises natural language to deliver insights about how threat actors behave. With Geminis larger context window, users can analyse much larger samples of potentially malicious code and gain more accurate results.
These AI-driven tools will help businesses take more detailed action, preventing more catastrophic data breaches.
Currently, there is incredible customer innovation across a broad range of industries, including retail, transportation and more. Harnessing Gen AI to fast-forward innovation requires a secure business AI platform that offers end-to-end capabilities that are easy to integrate with existing systems within a business.
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Google Cloud Next 2024: Pushing the Next Frontier of AI - Technology Magazine
The Role of Data Analytics and Machine Learning in Personalized Medicine Through Healthcare Apps – DataDrivenInvestor
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Personalized medicine through data analytics and Machine Learning has revolutionized the healthcare industry by tailoring medical treatments to individual patients based on their unique characteristics. In recent years, Data analytics and ML apps have become powerful tools to facilitate patient engagement and self-monitoring. This article explores the role of data analytics and Machine Learning in personalized medicine through healthcare apps, highlighting their importance, benefits, challenges, ethical considerations, and prospects.
Personalized medicine is like having a tailor-made healthcare plan. It considers your unique genetic makeup, lifestyle, and environment to provide more precise and effective treatments. This approach aims to deliver targeted therapies based on individual characteristics.
Healthcare apps have revolutionized the way we access and manage our health information. From tracking our daily steps to monitoring our heart rate, these apps have become essential tools in our quest for better health. What used to be basic fitness trackers have now evolved into comprehensive platforms that can analyze a vast amount of data to offer personalized insights and recommendations.
Data is the fuel that powers personalized medicine. It provides the necessary information to understand patterns, risks, and potential treatments for individuals. By analyzing vast amounts of data, such as genomic information, medical histories, and lifestyle factors, healthcare professionals can identify personalized treatment options and interventions.
Data analytics opens a whole new world of possibilities in personalized medicine. It allows healthcare providers to identify trends and correlations that may go unnoticed. This means faster and more accurate diagnoses, more effective treatment plans, and ultimately better health outcomes for patients. Data analytics also enables continuous learning and improvement by constantly refining treatment strategies based on real-world evidence.
Machine Learning is like having a computer that can learn and make decisions on its own. Its a branch of Artificial Intelligence (AI) that allows systems to analyze and interpret complex data patterns, discover insights, and make predictions or recommendations. In healthcare apps, Machine Learning algorithms can process large datasets and extract valuable information to enhance decision-making and improve patient outcomes.
Machine Learning algorithms can be embedded into healthcare apps, allowing them to continuously learn from user data and adapt their recommendations accordingly. For example, a fitness app can use Machine Learning to analyze a users exercise habits, heart rate, and sleep patterns to provide personalized exercise routines and sleep recommendations. By leveraging Machine Learning, healthcare apps can become intelligent and proactive health companions.
The combination of data analytics and Machine Learning offers significant advantages in personalized medicine.
By combining the power of data analytics tools with Machine Learning algorithms, businesses can create visually engaging and interactive dashboards that present data in a way that is easy to digest and interpret.
With data analytics and ML, businesses can uncover valuable insights that drive informed decision-making and improve overall business performance.
Businesses can automate data processing and analysis processes by integrating data analytics and Machine Learning, saving valuable time and improving accuracy.
ML algorithms analyze data objectively and make decisions based on patterns and statistical models, minimizing the impact of human subjectivity.
While data analytics and Machine Learning offer immense potential in healthcare, there are ethical considerations that need to be addressed. One concern is the potential bias in algorithms. If the data used to train Machine Learning models is biased, it can lead to biased treatment recommendations or diagnoses, disproportionately impacting certain groups of patients.
Another ethical concern is the transparency of algorithms. Patients and healthcare providers need to understand how algorithms arrive at their recommendations or diagnoses. Lack of transparency can undermine trust in the healthcare system and raise concerns about the accountability of algorithms.
The use of data analytics and Machine Learning in healthcare apps necessitates the collection and analysis of personal health information. Privacy concerns arise as this sensitive data needs to be handled with the utmost care. Healthcare apps must employ robust security measures to safeguard patient data and comply with relevant privacy regulations.
Transparency in data usage and obtaining informed consent from patients is crucial. Patients should have control over how their data is used and be fully aware of the potential risks and benefits.
As data analytics continues to evolve, several emerging trends hold promise for personalized medicine. One such trend is the integration of data from wearables and Internet of Things (IoT) devices. This real-time data collection allows for more accurate monitoring of patient health and enables timely interventions.
Another trend is the use of Natural Language Processing in analyzing unstructured medical data, such as doctors notes or research papers. NLP algorithms can extract valuable insights from these vast amounts of text, aiding in personalized medicine research and decision-making.
Machine Learning advancements are opening doors to exciting possibilities in healthcare apps. One breakthrough area is the use of deep learning algorithms. These sophisticated neural networks can process complex medical images, such as MRI scans or histopathology slides, with remarkable accuracy, assisting doctors in diagnosis and treatment planning.
Additionally, federated learning is gaining attention in healthcare. This approach allows Machine Learning models to be trained on decentralized data sources without sharing the raw data, preserving patient privacy while still benefiting from the collective knowledge present in diverse datasets.
Data analytics and Machine Learning have the potential to revolutionize personalized medicine through healthcare apps. Healthcare app development services from improving diagnosis accuracy to personalized treatment recommendations, these technologies offer valuable insights and benefits in healthcare.
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The Role of Data Analytics and Machine Learning in Personalized Medicine Through Healthcare Apps - DataDrivenInvestor