- 4 AI Things
- Posts
- Anthropic's AI Safety Quest
As we dive back in, let's highlight a remarkable advancement in AI from Gradient, in partnership with Crusoe. They've developed an open large language model (LLM) with a staggering one-million-token context window, significantly enhancing its ability to manage extensive codebases and complex programming tasks. This development not only boosts the model's accuracy and efficiency in generating code but also makes sophisticated computational processes more affordable and accessible for broader enterprise applications. With AI funding for startups continuing to surge, illustrating robust confidence and growth in the sector, there’s truly never been a better time to engage with AI, whether you're looking to start a new career or innovate within your current one.
Here are the 4 interesting AI things that I learned and enjoyed this week.
4 AI Things
Gradient, in partnership with Crusoe, developed an open large language model (LLM) with a massive one-million-token context window, enhancing its ability to handle extensive codebases and complex programming tasks. This advancement allows the LLM to analyze and integrate vast amounts of data at once, improving accuracy and efficiency in generating code and other outputs. Their innovative approach leverages distributed attention techniques and collaborative research, making high-end computational processes more affordable and accessible for enterprise applications. This development marks a significant step forward in the functionality and commercial viability of LLMs in tech industries.
Dario Amodei, formerly of OpenAI, now leads Anthropic, an AI lab known for its focus on safety. Despite being smaller and less funded compared to giants in the field, Anthropic competes by prioritizing safe AI development. They've just released Claude 3.5 Sonnet, an AI model excelling in reasoning and math, claiming to surpass OpenAI's latest. Anthropic aims to balance rapid AI advancement with safety, reflecting a cautious approach amid the tech industry's competitive push
This week we are going to look at Hallucinations that transformers experience. Hallucinations are words that are generated by the model that are often non-sensical and grammatically incorrect. They can be caused by factors such as model being trained on noisy data, not enough data, not given context or not enough constraints. The output text is difficult to understand. The model might generate incorrect or misleading information. This leads us to formulating a better prompt design to avoid hallucinations.
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Alter3 is a GPT-4-powered humanoid robot developed by researchers at the University of Tokyo and Alternative Machine. It can interpret natural language commands and execute complex tasks, such as taking a selfie or mimicking a ghost. Utilizing GPT-4, Alter3 can map spoken instructions into robot actions using an advanced AI framework. This robot represents significant progress in blending robotics with powerful language models to create more interactive and responsive robots. The technology allows Alter3 to mimic human poses and behaviors, showing the potential for more advanced interactions between humans and robots.
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⏭️ Stay curious, keep questioning.