Lifeboat Foundation

Using artificial intelligence, physicists have compressed a daunting quantum problem that until now required 100,000 equations into a bite-size task of as few as four equations—all without sacrificing accuracy. The work, published in the September 23 issue of Physical Review Letters, could revolutionize how scientists investigate systems containing many interacting electrons. Moreover, if scalable to other problems, the approach could potentially aid in the design of materials with sought-after properties such as superconductivity or utility for clean energy generation.

“We start with this huge object of all these coupled-together differential equations; then we’re using machine learning to turn it into something so small you can count it on your fingers,” says study lead author Domenico Di Sante, a visiting research fellow at the Flatiron Institute’s Center for Computational Quantum Physics (CCQ) in New York City and an assistant professor at the University of Bologna in Italy.

The formidable problem concerns how electrons behave as they move on a gridlike lattice. When two electrons occupy the same lattice site, they interact. This setup, known as the Hubbard model, is an idealization of several important classes of materials and enables scientists to learn how electron behavior gives rise to sought-after phases of matter, such as superconductivity, in which electrons flow through a material without resistance. The model also serves as a testing ground for new methods before they’re unleashed on more complex quantum systems.

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The circuits are found in several forms and in different locations. Some offer faster creation of new AI models. They use multiple processing circuits in parallel to churn through millions, billions or even more data elements, searching for patterns and signals. These are used in the lab at the beginning of the process by AI scientists looking for the best algorithms to understand the data.

After 45 years of voicing one of the most iconic characters in cinema history, James Earl Jones has said goodbye to Darth Vader. At 91, the legendary actor recently told Disney he was “looking into winding down this particular character.” That forced the company to ask itself how do you even replace Jones? The answer Disney eventually settled on, with the actor’s consent, involved an AI program.

If you’ve seen any of the recent Star Wars shows, you’ve heard the work of Respeecher. It’s a Ukrainian startup that uses archival recordings and a “proprietary AI algorithm” to create new dialogue featuring the voices of “performers from long ago.” In the case of Jones, the company worked with Lucasfilm to recreate his voice as it had sounded when film audiences first heard Darth Vader in 1977.

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Marketers are standing at a precipice when it comes to strategy and automation. Advanced systems are seen as the next step in marketing, but for many businesses, the concept is still uncharted territory. However, those who don’t adopt the rapidly advancing technology into their marketing plans will quickly be at a huge disadvantage.

According to International Data Corporation (IDC), the U.S. market for AI-powered software, hardware and services is expected to break $120 billion by 2025. The marketing intelligence firm also found that banks and retailers were the biggest spenders on AI, with retail having already invested upwards of $5.9 billion in these systems for marketing alone in 2019. And spending on these advanced systems for this specific purpose has only increased since then. It is evident that algorithmic systems are the future of marketing, and those who don’t invest in them will be left behind.

Skoltech scientists modeled the behavior of nanobubbles appearing in van der Waals heterostructures and the behavior of substances trapped inside the bubbles. In the future, the new model will help obtain equations of state for substances in nano-volumes, opening up new opportunities for the extraction of hydrocarbons from rock with large amounts of micro-and nanopores. The results of the study were published in the Journal of Chemical Physics.

The van der Waals nanostructures hold much promise for the study of tiniest samples with volumes from 1 cubic micron down to several cubic nanometers. These atomically thin layers of two-dimensional materials, such as graphene, hexagonal boron nitride (hBN) and dichalcogenides of transition metals, are held together by weak van der Waals interaction only. Inserting a sample between the layers separates the upper and bottom layers, making the upper layer lift to form a nanobubble. The resulting structure will then become available for transmission electron and atomic force microscopy, providing an insight into the structure of the substance inside the bubble.

The properties exhibited by substances inside the van der Waals nanobubbles are quite unusual. For example, water trapped inside a nanobubble displays a tenfold decrease in its dielectric constant and etches the diamond surface, something it would never do under normal conditions. Argon which typically exists in liquid form when in large quantities can become solid at the same pressure if trapped inside very small nanobubbles with a radius of less than 50 nanometers.

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According to recent research from Sweden’s Lund University, the most commonly used analytical method in population genetics is deeply flawed. This could have caused incorrect results and misconceptions regarding ethnicity and genetic relationships. The method has been used in hundreds of thousands of studies, influencing findings in medical genetics and even commercial ancestry tests. The findings were recently published in the journal Scientific Reports.

The pace at which scientific data can be gathered is increasing rapidly, resulting in huge and very complex databases, which has been nicknamed the “Big Data revolution.” Researchers employ statistical techniques to condense and simplify the data while maintaining the majority of the important information in order to make the data more manageable. PCA (principal component analysis) is perhaps the most widely used approach. Imagine PCA as an oven with flour, sugar, and eggs serving as the input data. The oven may always perform the same thing, but the ultimate result, a cake, is highly dependent on the ratios of the ingredients and how they are mixed.

“It is expected that this method will give correct results because it is so frequently used. But it is neither a guarantee of reliability nor produces statistically robust conclusions,” says Dr. Eran Elhaik, Associate Professor in molecular cell biology at Lund University.

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Started out as an interview ended up being a discussion between Hugo de Garis and (off camera) Adam Ford + Michel de Haan.
00:11 The concept of understanding under-recognised as an important aspect of developing AI
00:44 Re-framing perspectives on AI — the Chinese Room argument — and how can consciousness or understanding arise from billions of seemingly discreet neurons firing? (Should there be a binding problem of understanding similar to the binding problem of consciousness?)
04:23 Is there a difference between generality in intelligence and understanding? (and extentionally between AGI and artificial understanding?)
05:08 Ah Ha! moments — where the penny drops — what’s going on when this happens?
07:48 Is there an ideal form of understanding? Coherence & debugging — ah ha moments.
10:18 Webs of knowledge — contextual understanding.
12:16 Early childhood development — concept formation and navigation.
13:11 The intuitive ability for concept navigation isn’t complete.
Is the concept of understanding a catch all?
14:29 Is it possible to develop AGI that doesn’t understand? Is generality and understanding the same thing?
17:32 Why is understanding (the nature of) understanding important?
Is understanding reductive? Can it be broken down?
19:52 What would be the most basic primitive understanding be?
22:11 If (strong) AI is important, and understanding is required to build (strong) AI, what sorts of things should we be doing to make sense of understanding?
Approaches — engineering, and copy the brain.
24:34 Is common sense the same thing as understanding? How are they different?
26:24 What concepts do we take for granted around the world — which when strong AI comes about will dissolve into illusions, and then tell us how they actually work under the hood?
27:40 Compression and understanding.
29:51 Knowledge, Gettier problems and justified true belief. Is knowledge different from understanding and if so how?
31:07 A hierarchy of intel — data, information, knowledge, understanding, wisdom.
33:37 What is wisdom? Experience can help situate knowledge in a web of understanding — is this wisdom? Is the ostensible appearance of wisdom necessarily wisdom? Think pulp remashings of existing wisdom in the form of trashy self-help literature.
35:38 Is understanding mapping knowledge into a useful framework? Or is it making accurate / novel predictions?
36:00 Is understanding like high resolution carbon copy like models that accurately reflect true nature or a mechanical process?
37:04 Does understanding come in gradients of topologies? Is there degrees or is it just on or off?
38:37 What comes first — understanding or generality?
40:47 Minsky’s ‘Society of Mind’
42:46 Is vitalism alive in well in the AI field? Do people actually think there are ghosts in the machines?
48:15 Anthropomorphism in AI literature.
50:48 Deism — James Gates and error correction in super-symmetry.
52:16 Why are the laws of nature so mathematical? Why is there so much symmetry in physics? Is this confusing the map with the territory?
52:35 The Drake equation, and the concept of the Artilect — does this make Deism plausible? What about the Fermi Paradox?
55:06 Hyperintelligence is tiny — the transcention hypothesis — therefore civs go tiny — an explanation for the fermi paradox.
56:36 Why would *all* civs go tiny? Why not go tall, wide and tiny? What about selection pressures that seem to necessitate cosmic land grabs?
01:01:52 The Great Filter and the The Fermi Paradox.
01:02:14 Is it possible for an AGI to have a deep command of knowledge across a wide variety of topics/categories without understanding being an internal dynamic? Is the turing test good enough to test for understanding? What kinds of behavioral tests could reliably test for understanding? (Of course without the luxury of peering under the hood)
01:03:09 Does AlphaGo understand Go, or DeepBlue understand chess? Revisiting the Chinese Room argument.
01:04:23 More on behavioral tests for AI understanding.
01:06:00 Zombie machines — David Chalmers Zombie argument.
01:07:26 Complex enough algorithms — is there a critical point of complexity beyond which general intelligence likely emerges? Or understanding emerges?
01:08:11 Revisiting behavioral ‘turing’ tests for understanding.
01:13:05 Shape sorters and reverse shape sorters.
01:14:03 Would slightly changing the rules of Go confuse AlphaGo (after it had been trained)? Need for adaptivity — understanding concept boundaries, predicting where they occur, and the ability to mine outwards from these boundaries…
01:15:11 Neural nets and adaptivity.
01:16:41 AlphaGo documentary — worth a watch. Progresses in AI challenges human dignity which is a concern, but the DeepMind and the AlphaGo documentary seemed to be respectful. Can we manage a transition from human labor to full on automation while preserving human dignity?

Filmed in the dandenong ranges in victoria, australia.

Many thanks for watching!