We introduce FrontierMath, a benchmark of hundreds of original, exceptionally challenging mathematics problems crafted and vetted by expert mathematicians. The questions cover most major branches of modern mathematics — from computationally intensive problems in number theory and real analysis to abstract questions in algebraic geometry and category theory. Solving a typical problem requires multiple hours of effort from a researcher in the relevant branch of mathematics, and for the upper end questions, multiple days. FrontierMath uses new, unpublished problems and automated verification to reliably evaluate models while minimizing risk of data contamination. Current state-of-the-art AI models solve under 2% of problems, revealing a vast gap between AI capabilities and the prowess of the mathematical community. As AI systems advance toward expert-level mathematical abilities, FrontierMath offers a rigorous testbed that quantifies their progress. — Read More

#machine-learning

The development of cancer is an evolutionary process involving the sequential acquisition of genetic alterations that disrupt normal biological processes, enabling tumor cells to rapidly proliferate and eventually invade and metastasize to other tissues. We investigated the genomic evolution of prostate cancer through the application of three separate classification methods, each designed to investigate a different aspect of tumor evolution. Integrating the results revealed the existence of two distinct types of prostate cancer that arise from divergent evolutionary trajectories, designated as the Canonical and Aalternative evolutionary disease types. We therefore propose the evotype model for prostate cancer evolution wherein Alternative-evotype tumors diverge from those of the Canonical-evotype through the stochastic accumulation of genetic alterations associated with disruptions to androgen receptor DNA binding. Our model unifies many previous molecular observations, providing a powerful new framework to investigate prostate cancer disease progression. — Read More

#machine-learning

It may sound like fantasy or fiction, but people predict the future all the time. Real-world fortune tellers—we call them actuaries and meteorologists—have successfully used computer models for years. And today’s accelerating advances in machine learning are quickly upgrading their digital crystal balls. Now a new artificial intelligence system that treats human lives like language may be able to competently guess whether you’ll die within a certain period, among other life details, according to a recent study in Nature Computational Science.

The study team developed a machine-learning model called life2vec that can make general predictions about the details and course of people’s life, such as forecasts related to death, international moves and personality traits. The model draws from data on millions of residents of Denmark, including details about birth dates, sex, employment, location and use of the country’s universal health care system. The study metrics found the new model to be more than 78 percent accurate at predicting mortality in the research population over a four-year period, and it significantly outperformed other predictive methods such as an actuarial table and various machine-learning tools.  – Read More

Read the Paper

#machine-learning

An Illustrated Machine Learning series that takes a (boring sounding) machine learning concept and makes it fun by illustrating it! — Read More

#machine-learning

We analyze the growth of dataset sizes used in machine learning for natural language processing and computer vision, and extrapolate these using two methods: using the historical growth rate and estimating the compute-optimal dataset size for future predicted compute budgets. We investigate the growth in data usage by estimating the total stock of unlabeled data available on the internet over the coming decades. Our analysis indicates that the stock of high-quality language data will be exhausted soon; likely before 2026. By contrast, the stock of low-quality language data and image data will be exhausted only much later; between 2030 and 2050 (for low-quality language) and between 2030 and 2060 (for images). Our work suggests that the current trend of ever-growing ML models that rely on enormous datasets might slow down if data efficiency is not drastically improved or new sources of data become available. Read More

#machine-learning

Algorithms have helped mathematicians perform fundamental operations for thousands of years. The ancient Egyptians created an algorithm to multiply two numbers without requiring a multiplication table, and Greek mathematician Euclid described an algorithm to compute the greatest common divisor, which is still in use today. 

During the Islamic Golden Age, Persian mathematician Muhammad ibn Musa al-Khwarizmi designed new algorithms to solve linear and quadratic equations. In fact, al-Khwarizmi’s name, translated into Latin as Algoritmi, led to the term algorithm. But, despite the familiarity with algorithms today – used throughout society from classroom algebra to cutting edge scientific research – the process of discovering new algorithms is incredibly difficult, and an example of the amazing reasoning abilities of the human mind. 

In our paper, published today in Nature, we introduce AlphaTensor, the first artificial intelligence (AI) system for discovering novel, efficient, and provably correct algorithms for fundamental tasks such as matrix multiplication. This sheds light on a 50-year-old open question in mathematics about finding the fastest way to multiply two matrices. Read More

#machine-learning

All intelligent life forms instinctively model their surrounding environment in order to actively navigate through it with their actions. In Artificial Intelligence (AI) research, we then try to understand and automate this interesting ability of living systems with machine learning (ML) at the core.

• Generally speaking, deriving mathematical models of complex systems is at the core of any scientific discipline. Researchers have always tried to come up with equations governing the behavior of their systems of interest, ranging from physics and biology to economics.

Machine learning then instantiates the scientific method of searching for a mathematical hypothesis (model) that best fits the observed data. However, thanks to the advances in computing, it allows to further automate this process into searching through large prefabricated hypothesis spaces in a heavily data-driven fashion. This is particularly useful in the modeling of complex systems for which the structure of the underlying hypothesis space is too complex, or even unknown, but large amounts of data are available. Read More #machine-learning

A monthly selection of ML papers by Zeta Alpha: Reinforcement Learning, Multimodality, Language Models as a service, Computer Vision, Information Retrieval and more. Read More

#machine-learning