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.
Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Watch here.
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.
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.
Quantum algorithms: An algorithm is a sequence of steps that leads to the solution of a problem. In order to execute these steps on a device, one must use specific instruction sets that the device is designed to do so.
Quantum computing introduces different instruction sets that are based on a completely different idea of execution when compared with classical computing. The aim of quantum algorithms is to use quantum effects like superposition and entanglement to get the solution faster.
My science fiction story “Le Saga Electrik” has been published in All Worlds Wayfarer Literary Magazine! You can read it for free at the link. In this tale, I weave a sensuously baroque drama of love, war, and redemption set in a post-singularity simulation world that runs on a computronium dust cloud orbiting a blue star somewhere in deep space. I draw from diverse literary-poetic influences to create a mythos which crackles and buzzes with phosphorescent intensity!
Le Saga Electrik by Logan Thrasher Collins
In the great domain of Zeitgeist, Ekatarinas decided that the time to replicate herself had come. Ekatarinas was drifting within a virtual environment rising from ancient meshworks of maths coded into Zeitgeist’s neuromorphic hyperware. The scape resembled a vast ocean replete with wandering bubbles of technicolor light and kelpy strands of neon. Hot blues and raspberry hues mingled alongside electric pinks and tangerine fizzies. The avatar of Ekatarinas looked like a punkish angel, complete with fluorescent ink and feathery wings and a lip ring. As she drifted, the trillions of equations that were Ekatarinas came to a decision. Ekatarinas would need to clone herself to fight the entity known as Ogrevasm.
An information engine uses information to convert heat into useful energy. Such an engine can be made, for example, from a heavy bead in an optical trap. A bead engine operates using thermal noise. When noise fluctuations raise the bead vertically, the trap is also lifted. This change increases the average height of the bead, and the engine produces energy. No work is done to cause this change; rather, the potential energy is extracted from information. However, measurement noise—whose origin is intrinsic to the system probing the bead’s position—can degrade the engine’s efficiency, as it can add uncertainty to the measurement, which can lead to incorrect feedback decisions by the algorithm that operates the engine. Now Tushar Saha and colleagues at Simon Fraser University in Canada have developed an algorithm that doesn’t suffer from these errors, allowing for efficient operation of an information engine even when there is high measurement noise [1].
To date, most information engines have operated using feedback algorithms that consider only the most recent bead-position observation. In such a system, when the engine’s signal-to-noise ratio falls below a certain value, the engine stops working.
To overcome this problem, Saha and colleagues instead use a “filtering” algorithm that replaces the most recent bead measurement with a so-called Bayesian estimate. This estimate accounts for both measurement noise and delay in the device’s feedback.
