How AI is shaping the future of education.
Category: robotics/AI – Page 177
Researchers used machine learning to optimize the process by which a tiny cage is opened to release a molecule.
Researchers have designed a tiny structure that could help deliver drugs inside the body [1]. The theoretical and computational work required machine learning to optimize the parameters for the structure, which could stick to a closed shell containing a small molecule and cause the shell to open. The results demonstrate the potential for machine learning to assist in the development of artificial systems that can perform complex biomolecular processes.
Researchers are developing artificial molecular-scale structures that could perform functions such as drug delivery or gene editing. Creating such artificial systems, however, usually entails a frustrating tradeoff. If the components are simple enough to be computationally tractable, they are unlikely to yield complex interactions. But if the components are too complex, they become harder to combine and coordinate. Machine learning can reduce the computational cost of designing useful artificial systems, according to graduate student Ryan Krueger of Harvard University.
There are over 30,000 weather stations in the world, measuring temperature, precipitation and other indicators often on a daily basis. That’s a massive amount of data for climate researchers to compile and analyze to produce the monthly and annual global and regional temperatures (especially) that make the news.
Now researchers have unleashed artificial intelligence (AI) on these datasets to analyze temperature extremes in Europe, finding excellent agreement compared to existing results that used traditional methods, and as well have uncovered climate extremes not previously known. Their work has been published in Nature Communications.
With the world’s climate changing rapidly, it is important to know how temperature and precipitation extremes are changing, so planners can adapt to the extremes here now and to what’s coming.
Kuhn’s taxonomy of consciousness connects various theories to deep questions about human existence and AI, based on his extensive dialogue with over 200 experts.
“Out of meat, how do you get thought? That’s the grandest question,” said philosopher Patricia Churchland to Robert Lawrence Kuhn, the producer and host of the acclaimed PBS program Closer to Truth and member of FQxI’s scientific advisory council.
Kuhn has now published a comprehensive taxonomy of proposed solutions and theories regarding the hard problem of consciousness. His organizing framework aims to assess their impact on meaning, purpose, and value, as well as on AI consciousness, virtual immortality, survival beyond death, and free will. His work, titled ‘Landscape of Consciousness,’ appeared in the August 2024 issue of the journal Progress in Biophysics and Molecular Biology.
Traditional large language model (LLM) agent systems face significant challenges when deployed in real-world scenarios due to their limited flexibility and adaptability. Existing LLM agents typically select actions from a predefined set of possibilities at each decision point, a strategy that works well in closed environments with narrowly scoped tasks but falls short in more complex and dynamic settings. This static approach not only restricts the agent’s capabilities but also requires considerable human effort to anticipate and implement every potential action beforehand, which becomes impractical for complex or evolving environments. Consequently, these agents are unable to adapt effectively to new, unforeseen tasks or solve long-horizon problems, highlighting the need for more robust, self-evolving capabilities in LLM agents.
Researchers from the University of Maryland and Adobe introduce DynaSaur: an LLM agent framework that enables the dynamic creation and composition of actions online. Unlike traditional systems that rely on a fixed set of predefined actions, DynaSaur allows agents to generate, execute, and refine new Python functions in real-time whenever existing functions prove insufficient. The agent maintains a growing library of reusable functions, enhancing its ability to respond to diverse scenarios. This dynamic ability to create, execute, and store new tools makes AI agents more adaptable to real-world challenges.
The technical backbone of DynaSaur revolves around the use of Python functions as representations of actions. Each action is modeled as a Python snippet, which the agent generates, executes, and assesses in its environment. If existing functions do not suffice, the agent dynamically creates new ones and adds them to its library for future reuse. This system leverages Python’s generality and composability, allowing for a flexible approach to action representation. Furthermore, a retrieval mechanism allows the agent to fetch relevant actions from its accumulated library using embedding-based similarity search, addressing context length limitations and improving efficiency.
Combining quantum science with machine learning has led to a model that can accurately measure how surfaces feel to the touch.
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#physics #AI #NobelPrize
Physics, AI, and the nobel prize.
Speakers: Cecile Tamura, Robert Pray DVMedia, Ivan Maksymov, Riju Pahwa
Researchers at University of California San Diego analyzed the genomes of hundreds of malaria parasites to determine which genetic variants are most likely to confer drug resistance.
The findings, published in Science, could help scientists use machine learning to predict antimalarial drug resistance and more effectively prioritize the most promising experimental treatments for further development. The approach could also help predict treatment resistance in other infectious diseases, and even cancer.
“A lot of drug resistance research can only look at one chemical agent at a time, but what we’ve been able to do here is create a roadmap for understanding antimalaria drug resistance across more than a hundred different compounds,” said Elizabeth Winzeler, Ph.D., a professor at UC San Diego Skaggs School of Pharmacy and Pharmaceutical Sciences and the Department of Pediatrics at UC San Diego School of Medicine.
Australian researchers have created building blocks out of DNA to construct a series of nano-scale objects and shapes, from a rod and a square to an infinitesimally small dinosaur.
The approach turns DNA into a modular material for building nanostructures – thousands of times narrower than a human hair. Developed by researchers from the University of Sydney Nano Institute and published in the journal Science Robotics, it suggests exciting possibilities for future use of nanobot technology.
A quiet revolution is brewing in labs around the world, where scientists’ use of AI is growing exponentially. One in three postdocs now use large language models to help carry out literature reviews, coding, and editing. In October, the creators of our AlphaFold 2 system, Demis Hassabis and John Jumper became Nobel Laureates in Chemistry for using AI to predict the structure of proteins, alongside the scientist David Baker, for his work to design new proteins. Society will soon start to feel these benefits more direct ly, with drugs and materials designed with the help of AI currently making their way through development.
In this essay, we take a tour of how AI is transforming scientific disciplines from genomics to computer science to weather forecasting. Some scientists are training their own AI models, while others are fine-tuning existing AI models, or using these models’ predictions to accelerate their research. Scientists are using AI as a scientific instrument to help tackle important problems, such as designing proteins that bind more tightly to disease targets, but are also gradually transforming how science itself is practised.
There is a growing imperative behind scientists’ embrace of AI. In recent decades, scientists have continued to deliver consequential advances, from Covid-19 vaccines to renewable energy. But it takes an ever larger number of researchers to make these breakthroughs, and to transform them into downstream applications. As a result, even though the scientific workforce has grown significantly over the past half-century, rising more than seven fold in the US alone, the societal progress that we would expect to follow, has slowed. For instance, much of the world has witnessed a sustained slowdown in productivity growth that is undermining the quality of public services. Progress towards the 2030 Sustainable Development Goals, which capture the biggest challenges in health, the environment, and beyond, is stalling.