Nobel laureate and Google DeepMind CEO Demis Hassabis said Tuesday (Jan. 21) that he expects to see pharmaceutical drugs designed by artificial intelligence (AI) to be in clinical trials by the end of the year.
During a fireside chat at the World Economic Forum in Davos, Switzerland, Hassabis said these drugs are being developed at Isomorphic Labs, a for-profit venture created by Google parent firm Alphabet in 2021 that was tasked to reinvent the entire drug discovery process based on first principles and led by AI.
“That’s the plan,” Hassabis said. — Read More
Monthly Archives: January 2025
AI Mistakes Are Very Different from Human Mistakes
Humans make mistakes all the time. All of us do, every day, in tasks both new and routine. Some of our mistakes are minor and some are catastrophic. Mistakes can break trust with our friends, lose the confidence of our bosses, and sometimes be the difference between life and death.
Over the millennia, we have created security systems to deal with the sorts of mistakes humans commonly make. … Humanity is now rapidly integrating a wholly different kind of mistake-maker into society: AI. Technologies like large language models (LLMs) can perform many cognitive tasks traditionally fulfilled by humans, but they make plenty of mistakes. It seems ridiculous when chatbots tell you to eat rocks or add glue to pizza. But it’s not the frequency or severity of AI systems’ mistakes that differentiates them from human mistakes. It’s their weirdness. AI systems do not make mistakes in the same ways that humans do.
Much of the friction—and risk—associated with our use of AI arise from that difference. We need to invent new security systems that adapt to these differences and prevent harm from AI mistakes. — Read More
Evolving Deeper LLM Thinking
We explore an evolutionary search strategy for scaling inference time compute in Large Language Models. The proposed approach, Mind Evolution, uses a language model to generate, recombine and refine candidate responses. The proposed approach avoids the need to formalize the underlying inference problem whenever a solution evaluator is available. Controlling for inference cost, we find that Mind Evolution significantly outperforms other inference strategies such as Best-of-N and Sequential Revision in natural language planning tasks. In the TravelPlanner and Natural Plan benchmarks, Mind Evolution solves more than 98% of the problem instances using Gemini 1.5 Pro without the use of a formal solver. — Read More
#performanceChina to host world’s first human-robot marathon as robotics drives national goals
For the first time, dozens of humanoid robots are expected to join a half-marathon to be held in the capital’s Daxing district in April, according to local authorities.
This comes as China ramps up efforts to develop artificial intelligence and robotics, to gain an edge in the tech rivalry with the US as well as combat the challenges of an ageing society and a falling birth rate.
Some 12,000 humans will take part in the coming race – and running alongside them on the 21km (13-mile) route will be robots from more than 20 companies, according to the administrative body of Beijing Economic-Technological Development Area, or E-Town.
Prizes will be offered for the top three runners. — Read More
Artificial Super Intelligence (ASI) is imminent – Cognitive Hyper Abundance is coming
Avataar releases new tool to create AI-generated videos for products
Generative AI models have reached a baseline capability of producing at least a passable video from a single image or short sentence. Companies building products around these models are claiming that anyone can make a snazzy promo video if they have some images or recordings — and videos usually perform better than static images or documents.
Peak XV and Tiger Global-backed Avataar released a new tool on Monday called Velocity. It creates product videos directly based on a product link. The company would be going against the likes of Amazon and Google, which are also experimenting with AI-powered video tools for ads. — Read More
AI researcher François Chollet founds a new AI lab focused on AGI
François Chollet, an influential AI researcher, is launching a new startup that aims to build frontier AI systems with novel designs.
The startup, Ndea, will consist of an AI research and science lab. It’s looking to “develop and operationalize” AGI. AGI, which stands for “artificial general intelligence,” typically refers to AI that can perform any task a human can. It’s a goalpost for many AI companies, including OpenAI.
… Ndea plans to use a technique called program synthesis, in tandem with other technical approaches, to unlock AGI. — Read More
The Inherent Limits of Pretrained LLMs
Large Language Models (LLMs), trained on extensive web-scale corpora, have demonstrated remarkable abilities across diverse tasks, especially as they are scaled up. Nevertheless, even state-of-the-art models struggle in certain cases, sometimes failing at problems solvable by young children, indicating that traditional notions of task complexity are insufficient for explaining LLM capabilities. However, exploring LLM capabilities is complicated by the fact that most widely-used models are also `instruction-tuned’ to respond appropriately to prompts. With the goal of disentangling the factors influencing LLM performance, we investigate whether instruction-tuned models possess fundamentally different capabilities from base models that are prompted using in-context examples. Through extensive experiments across various model families, scales and task types, which included instruction tuning 90 different LLMs, we demonstrate that the performance of instruction-tuned models is significantly correlated with the in-context performance of their base counterparts. By clarifying what instruction-tuning contributes, we extend prior research into in-context learning, which suggests that base models use priors from pretraining data to solve tasks. Specifically, we extend this understanding to instruction-tuned models, suggesting that their pretraining data similarly sets a limiting boundary on the tasks they can solve, with the added influence of the instruction-tuning dataset. — Read More
How is Google using AI for internal code migrations?
In recent years, there has been a tremendous interest in using generative AI, and particularly large language models (LLMs) in software engineering; indeed there are now several commercially available tools, and many large companies also have created proprietary ML-based tools for their own software engineers. While the use of ML for common tasks such as code completion is available in commodity tools, there is a growing interest in application of LLMs for more bespoke purposes. One such purpose is code migration.
This article is an experience report on using LLMs for code migrations at Google. It is not a research study, in the sense that we do not carry out comparisons against other approaches or evaluate research questions/hypotheses. Rather, we share our experiences in applying LLM-based code migration in an enterprise context across a range of migration cases, in the hope that other industry practitioners will find our insights useful. Many of these learnings apply to any application of ML in software engineering. We see evidence that the use of LLMs can reduce the time needed for migrations significantly, and can reduce barriers to get started and complete migration programs. — Read More
What to expect from Neuralink in 2025
In November, a young man named Noland Arbaugh announced he’d be livestreaming from his home for three days straight. His broadcast was in some ways typical fare: a backyard tour, video games, meet mom.
The difference is that Arbaugh, who is paralyzed, has thin electrode-studded wires installed in his brain, which he used to move a computer mouse on a screen, click menus, and play chess. The implant, called N1, was installed last year by neurosurgeons working with Neuralink, Elon Musk’s brain-interface company.
The possibility of listening to neurons and using their signals to move a computer cursor was first demonstrated more than 20 years ago in a lab setting. Now, Arbaugh’s livestream is an indicator that Neuralink is a whole lot closer to creating a plug-and-play experience that can restore people’s daily ability to roam the web and play games, giving them what the company has called “digital freedom.”
But this is not yet a commercial product. — Read More
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