Humor can be quite useful.
Let’s see how.
Suppose you are having a bad day (I realize this seems a bit dour and gloomy, but the venerated gallantry of well-placed humor will turn this around, wait and see).
Deep Learning: Alchemy or Science?
Topic: The Epistemology of Deep Learning.
Speaker: Yann LeCun.
Affiliation: Facebook AI Research/New York University.
Date: February 22, 2019
Continue reading “The Epistemology of Deep Learning — Yann LeCun” »
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Everything we do as living organisms is dependent, in some capacity, on time. The concept is so complex that scientists still argue whether it exists or if it is an illusion. In this video, astrophysicist Michelle Thaller, science educator Bill Nye, author James Gleick, and neuroscientist Dean Buonomano discuss how the human brain perceives of the passage of time, the idea in theoretical physics of time as a fourth dimension, and the theory that space and time are interwoven. Thaller illustrates Einstein’s theory of relativity, Buonomano outlines eternalism, and all the experts touch on issues of perception, definition, and experience. Check Dean Buonomano’s latest book Your Brain Is a Time Machine: The Neuroscience and Physics of Time at https://amzn.to/2GY1n1z.
Continue reading “Time: Do the past, present, and future exist all at once? | Big Think” »
Recent projects used machine learning to resurrect paintings by Klimt and Rembrandt. They raise questions about what computers can understand about art.
Full Story:
IN 1945, FIRE claimed three of Gustav Klimt’s most controversial paintings. Commissioned in 1,894 for the University of Vienna, “the Faculty Paintings”—as they became known—were unlike any of the Austrian symbolist’s previous work. As soon as he presented them, critics were in an uproar over their dramatic departure from the aesthetics of the time. Professors at the university rejected them immediately, and Klimt withdrew from the project. Soon thereafter, the works found their way into other collections. During World War II, they were placed in a castle north of Vienna for safekeeping, but the castle burned down, and the paintings presumably went with it. All that remains today are some black-and-white photographs and writings from the time. Yet I am staring right at them.
Continue reading “Can AI Truly Give Us a Glimpse of Lost Masterpieces?” »
Solid-state nuclear magnetic resonance (NMR) spectroscopy—a technique that measures the frequencies emitted by the nuclei of some atoms exposed to radio waves in a strong magnetic field—can be used to determine chemical and 3D structures as well as the dynamics of molecules and materials.
A necessary initial step in the analysis is the so-called chemical shift assignment. This involves assigning each peak in the NMR spectrum to a given atom in the molecule or material under investigation. This can be a particularly complicated task. Assigning chemical shifts experimentally can be challenging and generally requires time-consuming multi-dimensional correlation experiments. Assignment by comparison to statistical analysis of experimental chemical shift databases would be an alternative solution, but there is no such database for molecular solids.
A team of researchers including EPFL professors Lyndon Emsley, head of the Laboratory of Magnetic Resonance, Michele Ceriotti, head of the Laboratory of Computational Science and Modeling and Ph.D. student Manuel Cordova decided to tackle this problem by developing a method of assigning NMR spectra of organic crystals probabilistically, directly from their 2D chemical structures.
NVIDIA recently rolled out a demo of GAUGAN 2, an artificial intelligence-based text to image creation tool. GAUGAN 2 takes keywords and phrases you type in as input, and then generates unique images based on them.
In NVIDIA’s demo video, a user inputs “mountains by a lake” and GAUGAN 2 spits out a beautiful alpine landscape with a small lake in the foreground. We tried using GAUGAN 2 and, in practice, things aren’t as smooth as the demo implies. Certain keywords resulted in bizarre, terrifying results. GAUGAN 2 used this author’s name, for instance, to output an image of what looked like fungi on legs, walking down a street.
GAUGAN 2 is early in development at this point, and likely been trained only on a rather limited data set. Regardless, when it works, it offers a breathtaking snapshot of how AI technology could transform asset creation in movies in games in the years to come, with unique photorealistic landscapes and objects generated from just a few words of user input.
A new Artificial Intelligence model manages to do complex physics simulations in real time with only using a fraction of the power that a traditionally computed simulation would use. These simulations could soon be used for things like biotechnology, gaming, weather predictions and more. Two Minute Papers has done several videos on it before, but this is a more complex AI with a wider range of applications.
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TIMESTAMPS:
00:00 The Future of Advanced Physics Simulations.
01:57 How this new approach to AI works.
04:03 Are medical simulations a possibility?
06:02 Last Words.
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#ai #physics #simulation
A new and revolutionary approach to building Artificial Intelligence models has shown promise of enabling almost any device, regardless of how powerful it is, to run enormous and intelligent Artificial Intelligence’s in a similar way to how our Human Brain operate. This is partially done with new and improved Neuromorphic Computing Hardware which is modelled after our real brains. We may soon see AI beating humans at many different general tasks like an Artificial General Intelligence.
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TIMESTAMPS:
00:00 The Impossibility of Human AI
01:54 A new Approach is in town.
04:33 Other approaches to AI
06:44 Is this the Future of Artificial Intelligence?
09:43 Last Words.
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#ai #agi #neuralcomputing
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