New techniques are speeding up how AI models are trained
New techniques are speeding up how AI models are trained
Nvidia unveils successor to its all-conquering AI processor
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New techniques are speeding up how AI models are trained
Nvidia unveils successor to its all-conquering AI processor
Japan's AI matchmaking efforts
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New techniques are speeding up how AI models are trained
It is no secret that building a large language model (LLM) requires vast amounts of data. In conventional training, an LLM is fed mountains of text, and encouraged to guess each word before it appears. With each prediction, the LLM makes small adjustments to improve its chances of guessing right. The end result is something that has a certain statistical “understanding” of what is proper language and what isn’t.
But an LLM that has only undergone this so-called “pretraining” is not yet particularly useful. When asked for a joke to cheer your correspondent up, for instance, the pretrained model GPT-2 just repeated the question back three times. When asked who the American president was, it responded: “The answer is no. The president is not the president.” Clearly, teaching an LLM to do what humans want requires something more.
One way to align such models with users’ expectations is through reinforcement learning from human feedback (RLHF). OpenAI, an American startup, introduced this technique in a preprint published in March 2022. It was a major ingredient in its recipe for ChatGPT, which was released eight months later.
RLHF normally involves three steps. First, human volunteers are asked to choose which of two potential LLM responses might better fit a given prompt. This is then repeated many thousands of times over. This data set is then used to train a second LLM to, in effect, stand in for the human being. This so-called reward model, designed to assign higher scores to responses a human would like, and lower scores to everything else, is then used to train the original LLM. As a final touch, a machine-learning technique called reinforcement learning tweaks the knobs and levers of the original LLM to help reinforce the behaviors that earn it a reward.
This way of doing RLHF is quite involved—using two separate LLMs takes time and money, and the algorithm used for reinforcement learning is, to quote Rafael Rafailov at Stanford University, “quite painful”. This has meant that, outside of OpenAI, Google and their rivals, nobody has really exploited its full potential.
It now turns out that the same results can be achieved for a fraction of the effort. Dr Rafailov and his colleagues, including Archit Sharma and Eric Mitchell, presented this alternative in December 2023 at NeurIPS, an AI conference. Their method, Direct Preference Optimization (DPO), relies on a satisfying mathematical trick….
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Nvidia Unveils Successor to Its All-Conquering AI Processor
Nvidia Corp. Chief Executive Officer Jensen Huang showed off new chips aimed at extending his company’s dominance of artificial intelligence computing, a position that’s already made it the world’s third-most-valuable business.
A new processor design called Blackwell is multiple times faster at handling the models that underpin AI, the company said at its GTC conference in San Jose, California. That includes the process of developing the technology — a stage known as training – and the running of it, which is called inference.
The Blackwell chips, which are made up of 208 billion transistors, will be the basis of new computers and other products being deployed by the world’s largest data center operators — a roster that includes Amazon.com Inc., Microsoft Corp., Alphabet Inc.’s Google and Oracle Corp. Blackwell-based products will be available later this year, Nvidia said.
Huang, Nvidia’s co-founder, said AI is the driving force in a fundamental change in the economy and that Blackwell chips are “the engine to power this new industrial revolution.”
Nvidia is “working with the most dynamic companies in the world, we will realize the promise of AI for every industry,” he said at Monday’s conference, the company’s first in-person event since the pandemic. The GB200 Grace Blackwell Superchip, which consists of multiple chips in a single package, promises up to 30 times performance increase for LLM inference workloads compared to previous iterations….
AI shows: Who your customers are & where to find them.
M1-project - an AI tool that crafts your Ideal Customer Profile.
Just feed it your product description, and it'll tell you everything about your best audience (Goals, Problems, Pains and much more).
Visit and turbocharge your targeting 🚀
Japan's AI matchmaking efforts
Japan's efforts to address its declining birth rate and aging population through AI-driven matchmaking services have become a multifaceted approach involving both local and national government initiatives. These efforts aim to reverse the trend of dwindling marriage numbers and, by extension, encourage population growth.
The Japanese government, recognizing the critical issue of the country's declining birth rate, has started to subsidize AI matchmaking services. This initiative is part of a broader strategy to encourage more marriages and, consequently, increase the birth rate. As of early 2024, 31 out of Japan's 47 prefectures were offering AI matchmaking services, with Tokyo Metropolitan Government joining the initiative in December of the previous year. The central government's support for these initiatives has been expanding since fiscal 2021, indicating a significant investment in reversing the population decline.
The AI matchmaking services involve a rigorous process where participants provide personal information, which AI algorithms then analyze to match potential partners based on compatibility. Some regions, like Ehime Prefecture, go beyond traditional matchmaking criteria by incorporating data such as internet browsing history to find deeper compatibility between potential partners….
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