The young sun may have captured several Mars-or Mercury-size exoplanets that now orbit in the outer reaches of the solar system, but identifying them will be extremely challenging.
Backpropagation is the key algorithm that makes training deep models computationally tractable. For modern neural networks, it can make training with gradient descent as much as ten million times faster, relative to a naive implementation. That’s the difference between a model taking a week to train and taking 200,000 years.
Beyond its use in deep learning, backpropagation is a powerful computational tool in many other areas, ranging from weather forecasting to analyzing numerical stability – it just goes by different names. In fact, the algorithm has been reinvented at least dozens of times in different fields (see Griewank (2010)). The general, application independent, name is “reverse-mode differentiation.”
Fundamentally, it’s a technique for calculating derivatives quickly. And it’s an essential trick to have in your bag, not only in deep learning, but in a wide variety of numerical computing situations.
Can the intrinsic physics of multicomponent systems show neural network like #Computation? A new study shows how molecules draw on the rules of #physics to perform computations similar to neural networks:
Examination of nucleation during self-assembly of multicomponent structures illustrates how ubiquitous molecular phenomena inherently classify high-dimensional patterns of concentrations in a manner similar to neural network computation.
North Korea claimed to have launched a new solid-fuel, intermediate-range missile with a hypersonic warhead, aiming to test its reliability and maneuverability. The missile, designed to strike U.S. military bases in Guam and Japan, flew approximately 620 miles before landing between the Korean Peninsula and Japan. The test follows a previous claim of successfully testing […] The post North Korea Unveils New Missile Designed for US Mainland…
They’re two great tastes that taste great together. Or rather, they’re two technologies that, put together in collaborative ways, are becoming much more powerful!
Marvin Minsky famously said that the brain is not one computer, but several hundred computers working in tandem. If that’s true, ChatGPT’s cognitive power just got a boost with the creation of a Wolfram Alpha plug-in that allows for the two systems to send and receive natural language input, so that ChatGPT systems can utilize a different system of symbolic representation that had already been pioneered before the days when we could just ask a computer to write an essay.
We heard early this year that teams were working on this merge, and it’s been interesting to the AI community. Now it’s come to fruition.
Recent advances in generative AI help to explain how memories enable us to learn about the world, relive old experiences and construct totally new experiences for imagination and planning, according to a new study by UCL researchers.
The study, published in Nature Human Behaviour, uses an AI computational model —known as a generative neural network—to simulate how neural networks in the brain learn from and remember a series of events (each one represented by a simple scene).
The model featured networks representing the hippocampus and neocortex, to investigate how they interact. Both parts of the brain are known to work together during memory, imagination and planning.
When neurons are activated in the hippocampus, not all are going to be firing at once.
Think of a time when you had two different but similar experiences in a short period. Maybe you attended two holiday parties in the same week or gave two presentations at work. Shortly afterward, you may find yourself confusing the two, but as time goes on that confusion recedes and you are better able to differentiate between these different experiences.
New research published in Nature Neuroscience reveals that this process occurs on a cellular level, findings that are critical to the understanding and treatment of memory disorders, such as Alzheimer’s disease.
In recent years, research has begun to reveal that the lines of communication between the body’s organs are key regulators of aging. When these lines are open, the body’s organs and systems work well together. But with age, communication lines deteriorate, and organs don’t get the molecular and electrical messages they need to function properly.
A new study from Washington University School of Medicine in St. Louis identifies, in mice, a critical communication pathway connecting the brain and the body’s fat tissue in a feedback loop that appears central to energy production throughout the body. The research suggests that the gradual deterioration of this feedback loop contributes to the increasing health problems that are typical of natural aging.
The study—published in the journal Cell Metabolism—has implications for developing future interventions that could maintain the feedback loop longer and slow the effects of advancing age.
