Lifeboat Foundation

Artificial intelligence researchers claim to have made the world’s first genuine scientific discovery using a large language model (LLM), which is behind ChatGPT and similar programs. This signals a major breakthrough.

The discovery was made by Google DeepMind, an AI research laboratory where scientists are investigating whether LLMs can do more than just repackage information learned in training and actually generate new insights.

Continue reading “Google researchers make AI tech solve math puzzles ‘beyond human knowledge’” »

No one knows who might get there first. The United States and China are considered the leaders in the field; many experts believe America still holds an edge.

As the race to master quantum computing continues, a scramble is on to protect critical data. Washington and its allies are working on new encryption standards known as post-quantum cryptography – essentially codes that are much harder to crack, even for a quantum computer. Beijing is trying to pioneer quantum communications networks, a technology theoretically impossible to hack, according to researchers. The scientist spearheading Beijing’s efforts has become a minor celebrity in China.

Quantum computing is radically different. Conventional computers process information as bits – either 1 or 0, and just one number at a time. Quantum computers process in quantum bits, or “qubits,” which can be 1, 0 or any number in between, all at the same time, which physicists say is an approximate way of describing a complex mathematical concept.

The teacher shortage crisis is a major concern, casting a shadow on educational quality across the globe. In this academic climate, the rise of AI in the classroom sparks both hope and skepticism. Alpha school is leading the way, devoid of traditional teachers and reliant on its AI-powered curriculum and “guide” system. This innovative approach offers a glimpse of a promising future where technology and human ingenuity merge to redefine education.

AI has become a game-changer in education by customizing learning experiences according to students’ individual learning styles and paces. Alpha’s app-based tutoring system is a prime example of this. It is personalized for each student’s strengths and weaknesses, a significant departure from the traditional “one-size-fits-all” classroom approach. For instance, consider a child who struggles with math concepts. AI can modify the exercises and explanations to suit their learning style, enabling them to understand the material better.

Moreover, this AI-driven education system offers instant and detailed feedback, which may be lacking in some schools. Such immediate response fosters a deeper understanding and encourages a more engaged learning process. This level of individualized attention is a powerful tool for enhancing knowledge and engagement.

DeepMind’s FunSearch discovers new mathematical knowledge and algorithms.

Google DeepMind has triumphantly cracked an age-old mathematical mystery using a method called FunSearch.

The math problem that FunSearch has solved is the famous cap set problem in pure mathematics, which has stumped even the brightest human mathematicians.

Continue reading “Google’s DeepMind AI decodes age-old math equation, stumping humans” »

They had to throw away most of what it produced but there was gold among the garbage.

For the first time ever, researchers show how a large language model can help discover novel solutions to long-standing problems in math and computer science.

The card game Set has long inspired mathematicians to create interesting problems.

Now, a technique based on large language models (LLMs) is showing that artificial intelligence (AI) can help mathematicians to generate new solutions.

Continue reading “DeepMind AI outdoes human mathematicians on unsolved problem” »

The AI company DeepMind claims it has developed a way to harness the creativity of chatbots to solve mathematical problems while filtering out mistakes.

By Matthew Sparkes

There is no computer even remotely as powerful and complex as the human brain. The lumps of tissue ensconced in our skulls can process information at quantities and speeds that computing technology can barely touch.

Key to the brain’s success is the neuron’s efficiency in serving as both a processor and memory device, in contrast to the physically separated units in most modern computing devices.

There have been many attempts to make computing more brain-like, but a new effort takes it all a step further – by integrating real, actual, human brain tissue with electronics.

The mini-brain functioned like both the central processing unit and memory storage of a supercomputer. It received input in the form of electrical zaps and outputted its calculations through neural activity, which was subsequently decoded by an AI tool.

When trained on soundbites from a pool of people—transformed into electrical zaps—Brainoware eventually learned to pick out the “sounds” of specific people. In another test, the system successfully tackled a complex math problem that’s challenging for AI.

The system’s ability to learn stemmed from changes to neural network connections in the mini-brain—which is similar to how our brains learn every day. Although just a first step, Brainoware paves the way for increasingly sophisticated hybrid biocomputers that could lower energy costs and speed up computation.