Claude 3.5 Sonnet, beats out OpenAI and NVIDIA and Synthetic Data

Claude 3,5 The New 'Best' Model

Anthropic announced yesterday the launch of Claude 3.5 Sonnet, it's latest AI model. Claude 3.5 Sonnet boasts superior benchmarks, outperforming competitors and previous versions in reasoning, knowledge, coding, and content creation. Its enhanced speed and cost-effectiveness makes it a real alternative to OpenAI models. Key improvements include advanced vision capabilities, enabling tasks like chart interpretation and image transcription. A new "Artifacts" feature transforms Claude into a collaborative workspace, allowing real-time interaction with AI-generated content. Anthropic emphasises its commitment to safety and privacy, highlighting rigorous testing, external evaluations, and a policy that prioritises user privacy. Anthropic concludes by teasing upcoming releases and features, including new models and a "Memory" function, demonstrating Anthropic's commitment to continuous improvement based on user feedback.

NVIDIA's New Trainer

Alongside this was the recent announcement by NVIDIA. NVIDIA's Nemotron-4 340B family of models, is designed for Synthetic Data Generation (SDG). They emphasise the importance of high-quality data in developing accurate AI systems, particularly LLMs (Large Language Models) and SLMs (Small Language Models). Here (https://developer.nvidia.com/blog/leverage-our-latest-open-models-for-synthetic-data-generation-with-nvidia-nemotron-4-340b/) they explain how SDG can augment existing data stores by leveraging LLMs to create customised, high-quality data in large volumes. The source then delves into the specifics of the Nemotron-4 340B models, including the Reward Model and its use in ranking synthetic responses based on attributes like helpfulness and coherence. It concludes by illustrating a typical SDG pipeline, highlighting its effectiveness with a case study, and emphasising the transformative potential of SDG in enhancing various Gen AI applications.

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What is happening inside of the black box?

Neel Nanda is involved in Mechanistic Interpretability research at DeepMind, formerly of AnthropicAI, what's fascinating about the research conducted by Nanda is he gets to peer into the Black Box to figure out how different types of AI models work. Anyone concerned with AI should understand how important this is. In this video Nanda discusses some of his findings, including 'induction heads', which turn out to have some vital properties. Induction heads are a type of attention head that allows a language model to learn long-range dependencies in text. They do this by using a simple algorithm to complete token sequences like [A][B] ... [A] -> [B]. For example, if a model is given the sequence "The cat sat on the mat," it can use induction heads to predict that the word "mat" will be followed by the word "the". Induction heads were first discovered in 2022 by a team of researchers at OpenAI. They found that induction heads were present in

The AI Dilemma and "Gollem-Class" AIs

From the Center for Humane Technology Tristan Harris and Aza Raskin discuss how existing A.I. capabilities already pose catastrophic risks to a functional society, how A.I. companies are caught in a race to deploy as quickly as possible without adequate safety measures, and what it would mean to upgrade our institutions to a post-A.I. world. This presentation is from a private gathering in San Francisco on March 9th with leading technologists and decision-makers with the ability to influence the future of large-language model A.I.s. This presentation was given before the launch of GPT-4. One of the more astute critics of the tech industry, Tristan Harris, who has recently given stark evidence to Congress. It is worth watching both of these videos, as the Congress address gives a context of PR industry and it's regular abuses. "If we understand the mechanisms and motives of the group mind, it is now possible to control and regiment the masses according to our will without their

Gemini LLM, an increase in benefits, and risks.

Gemini LLM is being developed by Google Brain and Deepmind that was introduced at Google I/O 2023, and is expected to have a trillion parameters, like GPT-4. The project is using tens of thousands of Google's TPU AI chips for training, and could take months to complete. It may be introduced early next year. Gemini is being trained on a massive dataset of text, audio, video, images, and other media. This will allow it to 'understand' and respond to a wider range of input than previous LLMs. It will also be able to use other tools and APIs, which will make it more versatile and powerful. It's clearly looking to compete with a future GPT-5, this time Google are looking to get ahead of the curve. Training Gemini in a multimodal manner is significant because it allows the model to learn from a wider range of data. This should improve the model's accuracy and performance on a variety of tasks. For example, if Gemini is trained on both text and images, it can learn to as

Charting the emergence of AGI?

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