Lifeboat Foundation

Marcus says he feels “sick to his stomach” as the OpenAI board—ostensibly in control with an eye on the nonprofit mission—appears to get overpowered.

A fundamental question in neuroscience is what are the constraints that shape the structural and functional organization of the brain. By bringing biological cost constraints into the optimization process of artificial neural networks, Achterberg, Akarca and colleagues uncover the joint principle underlying a large set of neuroscientific findings.

Artificial Intelligence.

Thought Is the New Currency.

Unearthing extraordinary riches from our “cognitive cache” through AI.

Investors are always looking for the next great breakthrough in technology. As computers are indispensable tools for managing everything from finance to healthcare and smart cities, it only makes sense to look at the next stage of development and A-rated quantum computing stocks.

Quantum computing is still in its early stages, but companies are already making inroads. Zapata surveyed executives at 300 companies with revenues of $250 million and computing budgets over $1 million. Of those, over two-thirds spent more than $1 million annually to develop quantum computing applications.

Quantum computer stocks represent companies trying to revolutionize cryptography, optimization, drug discovery and artificial intelligence. It holds promise for solving complex problems currently infeasible for classical computers due to their exponential time requirements.

In Pennsylvania and elsewhere, A.I. is being applied to the nation’s aging infrastructure. Is that wise?

Senior staff are among those who have signed a letter calling on board members to go.

Large Language Models (LLMs) have shown great capabilities in various natural language tasks such as text summarization, question answering, generating code, etc., emerging as a powerful solution to many real-world problems. One area where these models struggle, though, is goal-directed conversations where they have to accomplish a goal through conversing, for example, acting as an effective travel agent to provide tailored travel plans. In practice, they generally provide verbose and non-personalized responses.

Models trained with supervised fine-tuning or single-step reinforcement learning (RL) commonly struggle with such tasks as they are not optimized for overall conversational outcomes after multiple interactions. Moreover, another area where they lack is dealing with uncertainty in such conversations. In this paper, the researchers from UC Berkeley have explored a new method to adapt LLMs with RL for goal-directed dialogues. Their contributions include an optimized zero-shot algorithm and a novel system called imagination engine (IE) that generates task-relevant and diverse questions to train downstream agents.

Since the IE cannot produce effective agents by itself, the researchers utilize an LLM to generate possible scenarios. To enhance the effectiveness of an agent in achieving desired outcomes, multi-step reinforcement learning is necessary to determine the optimal strategy. The researchers have made one modification to this approach. Instead of using any on-policy samples, they used offline value-based RL to learn a policy from the synthetic data itself.

In another twist on the OpenAI saga that raged over the weekend, Microsoft has swooped in and hired Sam Altman and Greg Brockman.

With 3D inkjet printing systems, engineers can fabricate hybrid structures that have soft and rigid components, like robotic grippers that are strong enough to grasp heavy objects but soft enough to interact safely with humans.

These multimaterial 3D printing systems utilize thousands of nozzles to deposit tiny droplets of resin, which are smoothed with a scraper or roller and cured with UV light. But the smoothing process could squish or smear resins that cure slowly, limiting the types of materials that can be used.

Researchers from MIT, the MIT spinout Inkbit, and ETH Zurich have developed a new 3D inkjet printing system that works with a much wider range of materials. Their printer utilizes computer vision to automatically scan the 3D printing surface and adjust the amount of resin each nozzle deposits in real time to ensure no areas have too much or too little material.