What happened this week in AI by Louie Last week saw the two debatably largest model updates since GPT-4 revealed within three hours of each other: OpenAI’s Sora text-to-video model and Deepmind’s Gemini Pro 1.5 model. Events began with the surprise reveal of Deepmind’s Gemini Pro 1.5, just eight weeks after the reveal and release of Gemini Pro 1.0 and just one week after the release of Gemino Ultra 1.0. For the first time, the DeepMind LLM model has shown significant improvements in terms of capability compared to OpenAI’s GPT-4. The DeepMind LLM model has a context window of 1 million tokens, relative to GPT-4’s 128k and Anthropic Claude’s 200k, with up to 10 million tests internally. Additionally, this new smaller Pro 1.5 model beats the larger older Ultra 1.5 and GPT-4 on many benchmarks. The new model is great at accurately retrieving within its long context. The improvements were made, particularly by moving to a Mixture of Experts architecture and many other changes across architecture and data. For Gemini Pro 1.0, input text tokens are approximately 20x cheaper than GPT-4 Turbo. So, if the price for v1.5 does not increase significantly, the combination of price and capability could enable many more use cases.
This AI newsletter is all you need #87
This AI newsletter is all you need #87
This AI newsletter is all you need #87
What happened this week in AI by Louie Last week saw the two debatably largest model updates since GPT-4 revealed within three hours of each other: OpenAI’s Sora text-to-video model and Deepmind’s Gemini Pro 1.5 model. Events began with the surprise reveal of Deepmind’s Gemini Pro 1.5, just eight weeks after the reveal and release of Gemini Pro 1.0 and just one week after the release of Gemino Ultra 1.0. For the first time, the DeepMind LLM model has shown significant improvements in terms of capability compared to OpenAI’s GPT-4. The DeepMind LLM model has a context window of 1 million tokens, relative to GPT-4’s 128k and Anthropic Claude’s 200k, with up to 10 million tests internally. Additionally, this new smaller Pro 1.5 model beats the larger older Ultra 1.5 and GPT-4 on many benchmarks. The new model is great at accurately retrieving within its long context. The improvements were made, particularly by moving to a Mixture of Experts architecture and many other changes across architecture and data. For Gemini Pro 1.0, input text tokens are approximately 20x cheaper than GPT-4 Turbo. So, if the price for v1.5 does not increase significantly, the combination of price and capability could enable many more use cases.