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Amazon Bedrock is the best place to build and scale generative AI applications with large language models (LLM) and other foundation models (FMs). It enables customers to leverage a variety of high-performing FMs, such as the Claude family of models by Anthropic, to build custom generative AI applications.Looking back to 2021, when Anthropic first started building on AWS, no one could have envisioned how transformative the Claude family of models would be. We have been making state-of-the-art generative AI models accessible and usable for businesses of all sizes through Amazon Bedrock. In just a few short months since Amazon Bedrock became generally available on September 28, 2023, more than 10K customers have been using it to deliver, and many of them are using Claude. Customers such as ADP, Broadridge, Cloudera, Dana-Farber Cancer Institute, Genesys, Genomics England, GoDaddy, Intuit, M1 Finance, Perplexity AI, Proto Hologram, Rocket Companies and more are using Anthropics Claude models on Amazon Bedrock to drive innovation in generative AI and to build transformative customer experiences. And today, we are announcing an exciting milestone with the next generation of Claude coming to Amazon Bedrock: Claude 3 Opus, Claude 3 Sonnet, and Claude 3 Haiku.

Anthropic is unveiling its next generation of Claude with three advanced models optimized for different use cases. Haiku is the fastest and most cost-effective model on the market. It is a fast compact model for near-instant responsiveness. For the vast majority of workloads, Sonnet is 2x faster than Claude 2 and Claude 2.1 with higher levels of intelligence. It excels at intelligent tasks demanding rapid responses, like knowledge retrieval or sales automation. And it strikes the ideal balance between intelligence and speed qualities especially critical for enterprise use cases. Opus is the most advanced, capable, state-of-the-art FM with deep reasoning, advanced math, and coding abilities, with top-level performance on highly complex tasks. It can navigate open-ended prompts, and novel scenarios with remarkable fluency, including task automation, hypothesis generation, and analysis of charts, graphs, and forecasts. And Sonnet is first available on Amazon Bedrock today. Current evaluations from Anthropic suggest that the Claude 3 model family outperformscomparable models in math word problem solving (MATH) and multilingual math (MGSM) benchmarks, critical benchmarks used today for LLMs.

Specifically, Opus outperforms its peers on most of the common evaluation benchmarks for AI systems, including undergraduate level expert knowledge (MMLU), graduate level expert reasoning (GPQA), basic mathematics (GSM8K), and more. It exhibits high levels of comprehension and fluency on complex tasks, leading the frontier of general intelligence.

Through Amazon Bedrock, customers will get easy access to build with Anthropics newest models. This includes not only natural language models but also their expanded range of multimodal AI models capable of advanced reasoning across text, images, charts, and more. Our collaboration has already helped customers accelerate generative AI adoption and delivered business value to them. Here are a few ways customers have been using Anthropics Claude models on Amazon Bedrock:

We are developing a generative AI solution on AWS to help customers plan epic trips and create life-changing experiences with personalized travel itineraries. By building with Claude on Amazon Bedrock, we reduced itinerary generation costs by nearly 80% percent when we quickly created a scalable, secure AI platform that can organize our book content in minutes to deliver cohesive, highly accurate travel recommendations. Now we can repackage and personalize our content in various ways on our digital platforms, based on customer preference, all while highlighting trusted local voicesjust like Lonely Planet has done for 50 years.

Chris Whyde, Senior VP of Engineering and Data Science, Lonely Planet

We are working with AWS and Anthropic to host our custom, fine-tuned Anthropic Claude model on Amazon Bedrock to support our strategy of rapidly delivering generative AI solutions at scale and with cutting-edge encryption, data privacy, and safe AI technology embedded in everything we do. Our new Lexis+ AI platform technology features conversational search, insightful summarization, and intelligent legal drafting capabilities, which enable lawyers to increase their efficiency, effectiveness, and productivity.

Jeff Reihl, Executive VP and CTO, LexisNexis Legal & Professional

At Broadridge, we have been working to automate the understanding of regulatory reporting requirements to create greater transparency and increase efficiency for our customers operating in domestic and global financial markets. With use of Claude on Amazon Bedrock, were thrilled to get even higher accuracy in our experiments with processing and summarizing capabilities. With Amazon Bedrock, we have choice in our use of LLMs, and we value the performance and integration capabilities it offers.

Saumin Patel, VP Engineering generative AI, Broadridge

The Claude 3 model family caters to various needs, allowing customers to choose the model best suited for their specific use case, which is key to developing a successful prototype and later production systems that can deliver real impactwhether for a new product, feature or process that boosts the bottom line. Keeping customer needs top of mind, Anthropic and AWS are delivering where it matters most to organizations of all sizes:

And AWS and Anthropic are continuously reaffirming our commitment to advancing generative AI in a responsible manner. By constantly improving model capabilities committing to frameworks like Constitutional AI or the White House voluntary commitments on AI, we can accelerate the safe, ethical development, and deployment of this transformative technology.

Looking ahead, customers will build entirely new categories of generative AI-powered applications and experiences with the latest generation of models. Weve only begun to tap generative AIs potential to automate complex processes, augment human expertise, and reshape digital experiences. We expect to see unprecedented levels of innovation as customers choose Anthropics models augmented with multimodal skills leveraging all the tools they need to build and scale generative AI applications on Amazon Bedrock. Imagine sophisticated conversational assistants providing fast and highly-contextual responses, picture personalized recommendation engines that seamlessly blend in relevant images, diagrams and associated knowledge to intuitively guide decisions. Envision scientific research turbocharged by generative AI able to read experiments, synthesize hypotheses, and even propose novel areas for exploration. There are so many possibilities that will be realized by taking full advantage of all generative AI has to offer through Amazon Bedrock. Our collaboration ensures enterprises and innovators worldwide will have the tools to reach the next frontier of generative AI-powered innovation responsibly, and for the benefit of all.

Its still early days for generative AI, but strong collaboration and a focus on innovation are ushering in a new era of generative AI on AWS. We cant wait to see what customers build next.

Check out the following resources to learn more about this announcement:

Swami Sivasubramanian is Vice President of Data and Machine Learning at AWS. In this role, Swami oversees all AWS Database, Analytics, and AI & Machine Learning services. His teams mission is to help organizations put their data to work with a complete, end-to-end data solution to store, access, analyze, and visualize, and predict.

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Unlocking Innovation: AWS and Anthropic push the boundaries of generative AI together | Amazon Web Services - AWS Blog

Introducing Microsofts AI Red Team

At Microsoft, they provide the worlds businesses with the knowledge and resources they need to ethically innovate with AI. Their continued dedication to democratizing AI security for their customers, partners, and peers is reflected in this tool and the prior efforts we have made in red-teaming AI since 2019.

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There are a lot of steps involved in red-team AI systems. Experts in responsible AI, security, and adversarial machine learning make up Microsofts AI Red Team. Additionally, the Red Team makes use of resources from across Microsoft, such as the Office of Responsible AI, Microsofts cross-company program on AI Ethics and Effects in Engineering and Research (AETHER), and the Fairness Center in Microsoft Research. As part of our overarching plan to map AI threats, quantify those risks, and develop scoped mitigations to lessen their impact, we have instituted red teaming.

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The AI Red Team of Microsoft has battle-tested PyRIT. In 2022, when we first started red teaming generative AI systems, it was just a collection of standalone scripts. Features were included based on our findings during red teaming of various generative AI systems and risk assessments. As of right now, the Microsoft AI Red Team relies on PyRIT. The image below has been taken from Microsoft.

When it comes to generative AI systems, PyRIT isnt a suitable substitute for human red teaming. Rather, it relies on an AI red teamers preexisting domain knowledge to automate repetitive activities. Security professionals can use PyRIT to pinpoint potential danger areas and investigate them thoroughly. While the security professional maintains complete command of the AI red team operations strategy and execution, PyRIT supplies the automation code to take the security professionals initial dataset of harmful prompts and utilize the LLM endpoint to generate even more detrimental prompts.

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1. Examining security and responsible AI risks simultaneously They discovered that red teaming generative AI systems involves security risk and responsible AI risk, unlike red teaming classical software or AI systems. Responsible AI risks, like security threats, can range from fairness issues to ungrounded or erroneous content. AI red teaming must simultaneously assess security and AI failure risks. App Specific Logic processes the input prompt and passes it to the Generative AI Model, which may use extra skills, functions, or plugins. After processing the Generative AI Models response, the App Specific Logic returns GenAI created content.

2. Generative AI is more probabilistic than red teaming. Second, red teaming generative AI systems is more probabilistic than standard red teaming. Alternatively, repeating the same attack path on older software systems may give comparable results. However, generative AI systems include numerous levels of non-determinism, so the same input might yield diverse results. This may be due to app-specific logic, the generative AI model, the orchestrator that controls system output, extensibility or plugins, or even language, which can provide various results with slight modifications. They discovered that generative AI systems must be approached probabilistically, unlike standard software systems with well-defined APIs and parameters that can be investigated utilizing red teaming tools.

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3. Generative AI architecture differs greatly. Finally, the architecture of these generative AI systems ranges from standalone applications to integrations in current applications to text, audio, photos, and videos. These disparities pose a triple danger to manual red team probing. To identify one risk (say, creating violent content) in one application modality (say, a web chat interface), red teams must try different tactics several times to find probable failures. Manually assessing all risks, modalities, and strategies can be difficult and slow.

Microsoft launched a red team automation framework for conventional machine learning systems in 2021. Due to changes in the threat surface and underlying principles, Counterfit could not match our goals for generative AI applications. We rethought how to enable security professionals red team generative AI systems and created our new toolkit.

See the article here:
Introducing Microsoft's AI Red Team And PyRIT - AiThority

Artificial Intelligence (AI) has rapidly become a buzzword in todays tech-driven world. From virtual assistants to self-driving cars, AI is transforming the way we live and work. If youre new to the concept of AI, fear not! i was in your situation once with this beginners guide will unravel the basics of Artificial Intelligence, providing you with a solid foundation to understand this revolutionary technology.

Understanding Artificial Intelligence

Defining AI:

Artificial Intelligence refers to the development of computer systems that can perform tasks that typically require human intelligence. These tasks include problem-solving, learning, and decision-making. AI systems are designed to analyze data, recognize patterns, and improve their performance over time.

Types of AI:

AI Applications in Everyday Life

Virtual Assistants:

Virtual assistants, like Siri, Google Assistant, and Alexa, use natural language processing to understand and respond to user commands. They can perform tasks, set reminders, and provide information.

Recommendation Systems:

AI powers recommendation systems on platforms like Netflix and Amazon. These systems analyze user behavior to suggest movies, products, or content tailored to individual preferences.

Autonomous Vehicles:

Self-driving cars leverage AI algorithms to interpret and respond to their surroundings. AI enables these vehicles to navigate, make decisions, and adapt to changing conditions.

The Building Blocks of AI

Machine Learning:

Machine Learning (ML) is a subset of AI that involves training systems to learn from data. Algorithms can recognize patterns and make predictions without explicit programming.

Neural Networks:

Neural networks are a fundamental concept in machine learning, inspired by the human brain. They consist of interconnected nodes that process and analyze information, enabling tasks like image recognition and language translation.

Deep Learning:

Deep Learning is a sophisticated form of machine learning that involves neural networks with multiple layers (deep neural networks). This enables more complex pattern recognition and decision-making.

Challenges and Considerations

Bias in AI:

AI systems are trained on data, and if the data contains biases, the AI can perpetuate them. Its crucial to address bias in AI to ensure fair and equitable outcomes.

Ethical Concerns:

As AI becomes more prevalent, ethical considerations arise. Issues such as privacy, security, and job displacement need careful attention to strike a balance between progress and responsible development.

Getting Started with AI

Learning Resources:

For beginners interested in AI, there are numerous online courses and resources. Platforms like Coursera, edX, and Khan Academy offer introductory courses on AI, machine learning, and data science.

Coding and Tools:

Learning basic programming languages like Python is essential for diving into AI. Additionally, familiarize yourself with popular AI frameworks such as TensorFlow and PyTorch.

Hands-On Projects:

Practice is key to understanding AI concepts. Engage in hands-on projects, like building a simple machine learning model or experimenting with neural networks.

Conclusion

Artificial Intelligence is a fascinating and rapidly evolving field with vast potential. By grasping the fundamentals of AI, you can embark on a journey of exploration and contribute to the exciting developments shaping our future. Whether youre a student, professional, or simply curious, the world of AI is open for discovery dive in and witness the transformative power of artificial intelligence.

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Unveiling the World of Artificial Intelligence: A Beginner's Guide - Medium

BURLINGTON, MA, MilliporeSigma, the US, and Canada Life Science business of Merck KGaA, Darmstadt Germany, a leading science and technology company, launched its AIDDISON drug discovery software, the first software-as-a-service platform that bridges the gap between virtual molecule design and real-world manufacturability through SynthiaTM retrosynthesis software application programing interface (API) integration.

It combines generative AI, machine learning and computer-aided drug-design to speed up drug development. Trained on more than two decades of experimentally validated datasets from pharmaceutical R&D, AIDDISON software identifies compounds from over 60 billion possibilities that have key properties of a successful drug, such as non-toxicity, solubility, and stability in the body. The platform then proposes ways to best synthesize these drugs.

With millions of people waiting for the approval of new medicines, bringing a drug to market, still takes on average, more than 10 years and costs over US$2 billion said Karen Madden, chief technology officer, Life Science business sector of Merck. Our platform enables any laboratory to count on generative AI to identify the most suitable drug-like candidates in a vast chemical space. This helps ensure the optimal chemical synthesis route for development of a target molecule in the most sustainable way possible.

Discovering drugs is a long, iterative process. Only about 10 percent of drug candidates evaluated in Phase I made it to market. To find the most suitable chemical compoundfroma universe of more than1060molecules requires significant time, resources, and expertise. Artificial Intelligence (AI) and machine learning models like AIDDISON software can extract hidden insights from huge datasets, thus increasing the success rate of delivering new therapies to patients. AI has the potential to offer more than US$70 billion in savings for the drug discovery process by 2028, and to save up to 70 percent time and costs for drug discovery in pharmaceutical companies.

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First Ever AI Solution to Integrate Drug Discovery and Synthesis - Lab Manager Magazine

The Evolution of AI and Machine Learning

Artificial intelligence (AI) has revolutionized the way we perform tasks, making it easier, faster, and more efficient. The rise of AI refers to the development of computer systems capable of performing tasks that typically require human intelligence, as seen in various subfields such as machine learning, natural language processing, computer vision, and robotics. This process of AI learning is known as machine learning, which allows computers to learn from data and make predictions or take actions based on that knowledge through training algorithms on large amounts of data. Over time, these AI systems improve their performance by continuously acquiring new knowledge and refining their algorithms.

The development of AI and machine learning algorithms, however, is not a purely mechanistic process. Human input plays a significant role in shaping these algorithms, making them more accurate and effective. Humans provide critical judgment, intuition, and domain expertise, which are invaluable for the development of AI systems. This human-AI collaboration is especially crucial in industries such as healthcare, finance, transportation, and entertainment, where the impact of AI could be revolutionary.

As we continue to advance in AI and machine learning, ethical considerations and potential bias in AI development have become increasingly important. The integration of human insight with machine learning is key to maintaining this balance. It ensures that the innovation brought about by AI is coupled with ethical considerations and regulatory frameworks to mitigate potential negative societal impacts such as job displacement and privacy concerns.

One of the promising fields where AI is making strides is content creation. AI content creation reduces the cost and time required to create content, improves workflow efficiency, and provides insightful data analysis for better-targeted marketing campaigns. It also opens up new opportunities by integrating with emerging technologies such as virtual and augmented reality, chatbots, and IoT. As natural language processing (NLP) continues to improve, AI-generated content is becoming more sophisticated, providing high-quality and relevant content for target audiences.

The future of healthcare is a promising field for AI integration. The advent of Centaur AI, a combination of AI assessments and human intelligence, is anticipated to transform healthcare delivery. A significant leap in this space is DeepMinds AlphaFold that has made advancements in predicting protein structures, a long-standing grand challenge for computational biology. This development underscores the potential of AI and human collaboration in solving complex problems.

In conclusion, while AI and machine learning offer exciting possibilities for innovation and efficiency, they are not standalone solutions. Human expertise and insight are crucial for refining these technologies and ensuring that they are developed and applied responsibly. By adopting a balanced approach that integrates human insight with machine learning, we can harness the full potential of AI while mitigating potential risks and ethical concerns.

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The Evolution of AI and Machine Learning: A Human-Centric Approach - Medriva