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Breakthrough suggests technology behind ChatGPT and Bard can generate information that goes beyond human knowledge

Artificial intelligence researchers claim to have made the worlds first scientific discovery using a large language model, a breakthrough that suggests the technology behind ChatGPT and similar programs can generate information that goes beyond human knowledge.

The finding emerged from Google DeepMind, where scientists are investigating whether large language models, which underpin modern chatbots such as OpenAIs ChatGPT and Googles Bard, can do more than repackage information learned in training and come up with new insights.

When we started the project there was no indication that it would produce something thats genuinely new, said Pushmeet Kohli, the head of AI for science at DeepMind. As far as we know, this is the first time that a genuine, new scientific discovery has been made by a large language model.

Large language models, or LLMs, are powerful neural networks that learn the patterns of language, including computer code, from vast amounts of text and other data. Since the whirlwind arrival of ChatGPT last year, the technology has debugged faulty software and churned out everything from college essays and travel itineraries to poems about climate change in the style of Shakespeare.

But while the chatbots have proved extremely popular, they do not generate new knowledge and are prone to confabulation, leading to answers that, in keeping with the best pub bores, are fluent and plausible but badly flawed.

To build FunSearch, short for searching in the function space, DeepMind harnessed an LLM to write solutions to problems in the form of computer programs. The LLM is paired with an evaluator that automatically ranks the programs by how well they perform. The best programs are then combined and fed back to the LLM to improve on. This drives the system to steadily evolve poor programs into more powerful ones that can discover new knowledge.

The researchers set FunSearch loose on two puzzles. The first was a longstanding and somewhat arcane challenge in pure mathematics known as the cap set problem. It deals with finding the largest set of points in space where no three points form a straight line. FunSearch churned out programs that generate new large cap sets that go beyond the best that mathematicians have come up with.

The second puzzle was the bin packing problem, which looks for the best ways to pack items of different sizes into containers. While it applies to physical objects, such as the most efficient way to arrange boxes in a shipping container, the same maths applies in other areas, such as scheduling computing jobs in datacentres. The problem is typically solved by either packing items into the first bin that has room, or into the bin with the least available space where the item will still fit. FunSearch found a better approach that avoided leaving small gaps that were unlikely ever to be filled, according to results published in Nature.

In the last two or three years there have been some exciting examples of human mathematicians collaborating with AI to obtain advances on unsolved problems, said Sir Tim Gowers, professor of mathematics at Cambridge University, who was not involved in the research. This work potentially gives us another very interesting tool for such collaborations, enabling mathematicians to search efficiently for clever and unexpected constructions. Better still, these constructions are humanly interpretable.

Researchers are now exploring the range of scientific problems FunSearch can handle. A major limiting factor is that the problems need to have solutions that can be verified automatically, which rules out many questions in biology, where hypotheses often need to be tested with lab experiments.

The more immediate impact may be for computer programmers. For the past 50 years, coding has largely improved through humans creating ever more specialised algorithms. This is actually going to be transformational in how people approach computer science and algorithmic discovery, said Kohli. For the first time, were seeing LLMs not taking over, but definitely assisting in pushing the boundaries of what is possible in algorithms.

Jordan Ellenberg, professor of mathematics at the University of Wisconsin-Madison, and co-author on the paper, said: What I find really exciting, even more so than the specific results we found, is the prospects it suggests for the future of human-machine interaction in math.

Instead of generating a solution, FunSearch generates a program that finds the solution. A solution to a specific problem might give me no insight into how to solve other related problems. But a program that finds the solution, thats something a human being can read and interpret and hopefully thereby generate ideas for the next problem and the next and the next.

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Original post:
AI scientists make exciting discovery using chatbots to solve maths problems - The Guardian

DeepMinds FunSearch AI can tackle mathematical problems

alengo/Getty Images

Google DeepMind claims to have made the first ever scientific discovery with an AI chatbot by building a fact-checker to filter out useless outputs, leaving only reliable solutions to mathematical or computing problems.

Previous DeepMind achievements, such as using AI to predict the weather or protein shapes, have relied on models created specifically for the task at hand, trained on accurate and specific data. Large language models (LLMs), such as GPT-4 and Googles Gemini, are instead trained on vast amounts of varied data to create a breadth of abilities. But that approach also makes them susceptible to hallucination, a term researchers use for producing false outputs.

Gemini which was released earlier this month has already demonstrated a propensity for hallucination, getting even simple facts such as the winners of this years Oscars wrong. Googles previous AI-powered search engine even made errors in the advertising material for its own launch.

One common fix for this phenomenon is to add a layer above the AI that verifies the accuracy of its outputs before passing them to the user. But creating a comprehensive safety net is an enormously difficult task given the broad range of topics that chatbots can be asked about.

Alhussein Fawzi at Google DeepMind and his colleagues have created a generalised LLM called FunSearch based on Googles PaLM2 model with a fact-checking layer, which they call an evaluator. The model is constrained to providing computer code that solves problems in mathematics and computer science, which DeepMind says is a much more manageable task because these new ideas and solutions are inherently and quickly verifiable.

The underlying AI can still hallucinate and provide inaccurate or misleading results, but the evaluator filters out erroneous outputs and leaves only reliable, potentially useful concepts.

We think that perhaps 90 per cent of what the LLM outputs is not going to be useful, says Fawzi. Given a candidate solution, its very easy for me to tell you whether this is actually a correct solution and to evaluate the solution, but actually coming up with a solution is really hard. And so mathematics and computer science fit particularly well.

DeepMind claims the model can generate new scientific knowledge and ideas something LLMs havent done before.

To start with, FunSearch is given a problem and a very basic solution in source code as an input, then it generates a database of new solutions that are checked by the evaluator for accuracy. The best of the reliable solutions are given back to the LLM as inputs with a prompt asking it to improve on the ideas. DeepMind says the system produces millions of potential solutions, which eventually converge on an efficient result sometimes surpassing the best known solution.

For mathematical problems, the model writes computer programs that can find solutions rather than trying to solve the problem directly.

Fawzi and his colleagues challenged FunSearch to find solutions to the cap set problem, which involves determining patterns of points where no three points make a straight line. The problem gets rapidly more computationally intensive as the number of points grows. The AI found a solution consisting of 512 points in eight dimensions, larger than any previously known.

When tasked with the bin-packing problem, where the aim is to efficiently place objects of various sizes into containers, FunSearch found solutions that outperform commonly used algorithms a result that has immediate applications for transport and logistics companies. DeepMind says FunSearch could lead to improvements in many more mathematical and computing problems.

Mark Lee at the University of Birmingham, UK, says the next breakthroughs in AI wont come from scaling-up LLMs to ever-larger sizes, but from adding layers that ensure accuracy, as DeepMind has done with FunSearch.

The strength of a language model is its ability to imagine things, but the problem is hallucinations, says Lee. And this research is breaking that problem: its reining it in, or fact-checking. Its a neat idea.

Lee says AIs shouldnt be criticised for producing large amounts of inaccurate or useless outputs, as this is not dissimilar to the way that human mathematicians and scientists operate: brainstorming ideas, testing them and following up on the best ones while discarding the worst.

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DeepMind AI with built-in fact-checker makes mathematical discoveries - New Scientist

Google DeepMind has introduced a new method called FunSearch. This method uses Large Language Models (LLMs) to search for new solutions in mathematics and computer science. The method is described in a paper published in Nature. FunSearch is an evolutionary method that promotes and develops the highest-scoring ideas which reflects as computer programs. The running and evaluation of these programs are automatic. The system selects some programs from the current pool of programs, which are fed to an LLM. The LLM creatively builds upon these and generates new programs, which are automatically evaluated. The best ones are added back to the pool of existing programs, creating a self-improving loop. FunSearch uses Googles PaLM 2, but it is compatible with other LLMs trained on code.

According to Google,FunSearch can calculate upper-limit problems and a series of complex problems involving mathematics and computer science. FunSearch model training method mainly introduces an Evaluator system for the AI model. The AI model outputs a series of creative problem-solving methods and evaluation methods. The processor is responsible for judging the problem-solving method output by the model. After multiple iterations, an AI model with stronger mathematical capabilities can join the training.

Google DeepMind used the PaLM 2 model for testing. The researchers established a dedicated code pool, used code form to input a series of questions for the model, and set up an evaluator process. After that, the model would automatically be drawn from the code pool in each iteration. Select problems, generate creative new solutions and submit them to the evaluator for evaluation. The best solution will be re-added to the code pool and start another iteration.

FunSearch uses an iterative procedure. First, the user writes a description of the problem in the form of code. This description comprises a procedure to evaluate programs, and a seed program used to initialize a pool of programs. At each iteration, the system selects some programs from the current pool of programs, which are fed to an LLM. The LLM creatively builds upon these and generates new programs which get automatic evaluation. The best ones are added back to the pool of existing programs, creating a self-improving loop. FunSearch uses Googles PaLM 2, but it is compatible with other LLMs trained on code.

Discovering new mathematical knowledge and algorithms in different domains is notoriously and largely beyond the power of the most advanced AI systems. To tackle such challenging problems with FunSearch, there is a need to use multiple key components. FunSearch generates programs that describe how those solutions were arrived at. This show-your-working approach is how scientists generally operate, with discoveries or phenomena explained through the process used to produce them. FunSearch favours finding solutions represented by highly compact programs solutions with a low Short program can describe very large objects, allowing FunSearch to scale.

Google said the FunSearch training method is particularly good at Discrete Mathematics (Combinatorics). The model trained by the training method can easily solve extreme value combinatorial mathematics problems. The researchers introduced in a press release a process method for model calculation of upper-level problems (a central problem in mathematics involving counting and permutations).

To test its versatility, the researchers used FunSearch to approach another hard problem in math: the bin packing problem, which involves trying. The researchers left out the lines in the program that would specify how to solve it. That is where FunSearch comes in. It gets Codey to fill in the blanksin effect, to suggest code that will solve the problem. A second algorithm then checks and scores what Codey comes up with. The best suggestionseven if not yet correctare saved and given back to Codey, which tries to complete the program again. Many will be nonsensical, some will be sensible, and a few will be decent, says Kohli. You take those truly inspired ones and you say, Okay, take these ones and repeat..

The Bin Packing Problem is a problem of putting items of different sizes into a minimum number of containers. FunSearch provides a solution for the Bin Packing Problem. This is a just-in-time solution that generates a program that automatically adjusts based on the existing volume of the item.Researchers mentioned that comparedwith other AI training methods that use neural networks to learn, the output code of the model trained by the FunSearch training method is easier to check and deploy. This means that it is easier to be integrated into the actual industrial environment.

The AI system, called FunSearch, made progress on Set-inspired problems in combinatorics. This is a field of mathematics that studies how to count the possible arrangements of sets. FunSearch automatically creates requests for a specially trained LLM, asking it to write short computer programs that can generate solutions to a particular scenario. The system then checks quickly to see whether those solutions are better than known ones. If not, it provides feedback to the LLM so that it can improve at the next round. The way we use the LLM is as a creativity engine, says DeepMind computer scientist Bernardino. Not all programs that the LLM generates are useful, and some are so incorrect that they wouldnt even be able to run, he says.

What I find really exciting, even more so than the specific results we found, is the prospects it suggests for the future of human-machine interaction in math. Instead of generating a solution, FunSearch generates a program that finds the solution. A solution to a specific problem might give me no insight into how to solve other related problems. But a program that finds the solution Artificial intelligence researchers claim to have made the worlds first scientific discovery using a breakthrough that suggests the technology behind ChatGPT and similar programs can generate.

FunSearch is a new method that uses Large Language Models (LLMs) to search for new solutions in mathematics and computer science. The details of the description of this method are in an academic paper in Nature, a top academic journal. FunSearch is an evolutionary method that promotes and develops the highest-scoring ideas in computer programs. The process of running and evaluating these programs is automatic. The system selects some programs from the current pool of programs, which are fed to an LLM. FunSearch uses Googles PaLM 2, but it is compatible with other LLMs that use the same code for training. FunSearch can improve manufacturing algorithms thereby optimizing logistics, and reducing energy consumption.

Efe Udin is a seasoned tech writer with over seven years of experience. He covers a wide range of topics in the tech industry from industry politics to mobile phone performance. From mobile phones to tablets, Efe has also kept a keen eye on the latest advancements and trends. He provides insightful analysis and reviews to inform and educate readers. Efe is very passionate about tech and covers interesting stories as well as offers solutions where possible.

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Google DeepMind announces the FunSearch training method - Gizchina.com

Researchers at Google DeepMind have used artificial intelligence to discover new crystals and inorganic materials that could power future technologies as part of a landmark study.

Using the Graph Networks for Materials Exploration (GNoME) deep learning tool, researchers found 2.2 million new crystals, including 380,000 stable materials.

The discovery could represent a landmark moment in the discovery of materials used to power modern technologies, such as computer chips, batteries, and solar panels - all of which rely on inorganic crystals.

Availability and stability of these materials is a common hurdle in the development of such technologies. However, researchers said that in using the GNoME AI tool, they were able to dramatically increase the speed and efficiency of discovery by predicting the stability of new materials.

To enable new technologies, crystals must be stable otherwise they can decompose, and behind each new, stable crystal can be months of painstaking experimentation, the study noted.

With GNoME, weve multiplied the number of technologically viable materials known to humanity. Of its 2.2 million predictions, 380,000 are the most stable, making them promising candidates for experimental synthesis.

Among these candidates are materials that have the potential to develop future transformative technologies ranging from superconductors, powering supercomputers, and next-generation batteries to boost the efficiency of electric vehicles.

Google DeepMinds findings were published in the Nature journal, with the firm noting that, over the last decade, more than 28,000 new materials have been discovered following extensive research.

However, traditional AI-based approaches to searching for novel crystal structures has typically been an expensive, trial-and-error process that could take months to deliver minimal results.

AI-guided approaches hit a fundamental limit in their ability to accurately predict materials that could be experimentally viable, the study said.

GNoMEs recent discovery of 2.2 million materials would be equivalent to about 800 years worth of knowledge, researchers said, which highlights the transformative power and accuracy now afforded to scientists operating in the field.

Around 52,000 new compounds similar to graphene have been discovered as part of the project, which the study said has the potential to revolutionize electronics and superconductor development.

In previous years, just 1,000 materials of this kind had been identified via previous techniques.

We also found 528 potential lithium ion conductors, 25 times more than a previous study, which could be used to improve the performance of rechargeable batteries.

Long-term, Google DeepMind researchers said the GNoME project aims to drive down the cost of discovering new materials.

So far, external researchers have created 736 of materials discovered through GNoME in a lab environment. The company also plans to release its database of newly discovered crystals and share its findings with the research community.

By giving scientists the full catalog of the promising recipes for new candidate materials, we hope this helps them to test and potentially make the best ones.

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Google's AI powerhouse finds millions of new crystals that could change the fate of humanity forever and, for better ... - TechRadar