Lifeboat Foundation

Recent projects used machine learning to resurrect paintings by Klimt and Rembrandt. They raise questions about what computers can understand about art.

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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?” »

Advanced hydrogel used in SmartFarm was also tested for space-based agriculture.

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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Continue reading “Yann LeCun” »

Deep Learning systems can achieve remarkable, even super-human performance through supervised learning on large, labeled datasets. However, there are two problems: First, collecting ever more labeled data is expensive in both time and money. Second, these deep neural networks will be high performers on their task, but cannot easily generalize to other, related tasks, or they need large amounts of data to do so. In this blog post, Yann LeCun and Ishan Misra of Facebook AI Research (FAIR) describe the current state of Self-Supervised Learning (SSL) and argue that it is the next step in the development of AI that uses fewer labels and can transfer knowledge faster than current systems. They suggest as a promising direction to build non-contrastive latent-variable predictive models, like VAEs, but ones that also provide high-quality latent representations for downstream tasks.

OUTLINE:
0:00 — Intro & Overview.
1:15 — Supervised Learning, Self-Supervised Learning, and Common Sense.
7:35 — Predicting Hidden Parts from Observed Parts.
17:50 — Self-Supervised Learning for Language vs Vision.
26:50 — Energy-Based Models.
30:15 — Joint-Embedding Models.
35:45 — Contrastive Methods.
43:45 — Latent-Variable Predictive Models and GANs.
55:00 — Summary & Conclusion.

Continue reading “Yann LeCun — Self-Supervised Learning: The Dark Matter of Intelligence (FAIR Blog Post Explained)” »

Women constitute a mere 22 per cent or less than a quarter of professionals in the field of AI and Data Science.

There is a troubling and persistent absence of women when it comes to the field of artificial intelligence and data science. Women constitute a mere 22 per cent or less than a quarter of professionals in this field, as says the report “Where are the women? Mapping the gender job gap in AI,” from The Turing Institute. Yet, despite low participation and obstacles, women are breaking the silos and setting an example for players out in the field of AI.

To honour their commitment and work done, we have listed some of the women innovators and researchers who have worked tirelessly and contributed significantly to the field of AI and data science. The list below is provided in no particular order.

Continue reading “Women Innovators And Researchers Who Made A Difference In AI In 2021” »