OpenAI CEO Altman calls for energy breakthrough to develop AGI, according to The Register

AI In summary Sam Altman, CEO of OpenAI, believes that a breakthrough in energy production is necessary to advance increasingly capable and power-hungry AI models.

During a panel discussion with Bloomberg at Davos last week, he stated “There’s no way to get there without a breakthrough.” Altman favors renewable energy sources like nuclear fusion and is committed to continuing to invest in the technology. He personally invested $375 million into Helion Energy – a nuclear fusion startup that has signed a deal to supply energy to Microsoft in the next few years.

AI models consisting of billions of parameters require massive amounts of energy to train. According to AI company Numenta, OpenAI’s old GPT-3 system reportedly consumed 936 megawatt hours (MWh). The US Energy Information Administration estimates that the average household consumes about 10.5 MWh per year. This means training GPT-3 consumed as much energy as about 90 households consume in a year.

Larger models will necessitate even more energy. “We’re not done with scaling [LLMs] – we still need to push up,” declared Aiden Gomez, CEO of Cohere, during another discussion at Davos.

AlphaGeometry represents a breakthrough in AI reasoning

Researchers at Google DeepMind have trained an AI system to demonstrate geometric theorems at almost the same level achieved by human mathematics Olympiad gold medalists.

In a paper published in Nature last week, the team DeepMind unveiled AlphaGeometry – a system consisting of a language model and a symbolic deduction engine. The former generates potential mathematical strategies to solve a specific problem, while the latter attempts to deduce a final solution.

“With AlphaGeometry, we demonstrate AI’s growing ability to reason logically, and to discover and verify new knowledge,” wrote co-authors Trieu Trinh and Thang Luong. “Solving Olympiad-level geometry problems is an important milestone in developing deep mathematical reasoning on the path towards more advanced and general AI systems.”

Interestingly, the system was trained on 100 million samples of synthetic data, depicting random geometric diagrams. AlphaGeometry was tasked with learning all the relationships between the points and lines in shapes to figure out all the geometric proofs.

In a test benchmarking the system’s performance, it managed to solve 25 out of 30 geometry questions from Olympiad competitions – given a few hours. For comparison, the average human gold medalist can solve about 25.9 of these in the same time.

Google DeepMind has released the code for its model here.

AI Medical chatbots may not democratize healthcare

The World Health Organization (WHO) isn’t hopeful that medical AI systems will be beneficial for poorer countries if they are developed by organizations in wealthier nations that neglect to train them on more diverse data.

Developers like Google believe that AI can assist those with limited access to healthcare in the future. But officials at the WHO believe that the technology may not adequately serve them – particularly if they aren’t representative of the patients used to source clinical data to train these systems.

“The very last thing that we want to see happen as part of this leap forward with technology is the propagation or amplification of inequities and biases in the social fabric of countries around the world,” stated Alain Labrique, the WHO’s director for digital health and innovation, according to Nature.

Labrique and his colleagues argued that the development of medical AI shouldn’t be dominated by large tech businesses, and their technologies should be audited by independent third parties before they are released. Developers are currently constructing models capable of automatically generating clinical notes from meetings, assisting doctors to diagnose diseases, and more.

Potential issues – such as different accents, languages, or medical histories that aren’t in its training data – could potentially throw these systems off, leading to lower performance and poor patient outcomes.

Amazon introduces experimental AI shopping assistant

Consumers can now question an AI chatbot about a specific item sold on Amazon with the online marketplace’s mobile app.

The “Looking for specific info” tab, which previously displayed product reviews and answers to common questions, has been replaced with a large language model, Marketplace Pulse first reported. The system appears to function by absorbing and summarizing information from the product’s listing page.

Users can inquire about the item they’re interested in. The chatbot doesn’t compare products or suggest alternatives. Nor can it carry out actions like adding items to shoppers’ virtual carts or disclose pricing history. An Amazon spokesperson confirmed to CNBC that it was testing the chatbot.

“We’re constantly innovating to help make customers’ lives better and easier, and are currently testing a new feature powered by generative AI to improve shopping on Amazon by helping customers get answers to commonly asked product questions,” explained Maria Boschetti. Like all chatbots, Amazon’s latest system is liable to hallucination – so take what it says with a pinch of salt.

Remarkably, the virtual shopping assistant’s capabilities are quite open ended. It can reportedly write jokes, poems, or even generate code based on information on a product across multiple languages. ®

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