Imagine if our computers could think more like usâlearning from experience, adapting on the go, and doing all this while using just a fraction of the energy. Thatâs not science fiction anymore. Welcome to the world of Neuromorphic Computing đ§ âa field thatâs redefining how machines process information by taking inspiration from the most powerful processor we know: the human brain.
In this second episode of the AI Bros podcast Bruce and John go completely unscripted and just letting the conversation flow where it goes.
They talk about everything from Sam Altmanâs controversial TED interview to Johnâs Animated Action Figure that came to life and broke out of its packaging. Itâs interesting, fun and informative.
Subscribe, Like, Follow, and Share the AI Bros to your favorite social media platforms. The AI Bros Podcast is where artificial intelligence meets real talk.
Both Bruce and John share their hot takes, perspective and journey with AI as they learn it and use it themselves in their own day to day workflow.
The AI Bros podcast is a weekly series, and a part of the Neural News Network, and the (A)bsolutely (I)ncredible podcast channel. John Lawson III is the CEO of ColderICE Media and is highly recognized in e-commerce, and AI.
John is an Amazon #1 best-selling author, IBM Futurist, and an eBay Influencer.
Heâs celebrated as one of the Top 100 Small Business Influencers in America, and crowned âSavviest in Social Medialâ by StartUp Nation. You can connect with John directly on his website at www.johnlawson. comJohn is an internationally-recognized keynote speaker, a âCommerce Evangelistâ and an absolute wealth of knowledge on all things e-retail, and online marketing strategy.
John is a pioneer in the online retail vertical space, and founder of The E-commerce Group, a global community of e-commerce vendors, and online marketers.
A newly discovered electrically conductive organism that transports electrical current like a wire could usher in an era of bioelectronics.
External inputs reshape the Hopfield energy landscape and drive memory retrieval, even under noise and ambiguity.
Classical physics theories suggest that when two or more electromagnetic waves interfere destructively (i.e., with their electric fields canceling each other out), they cannot interact with matter. In contrast, quantum mechanics theory suggests that light particles continue interacting with other matter even when their average electric field is equal to zero.
Researchers from Federal University of SĂŁo Carlos, ETH Zurich and the Max Planck Institute of Quantum Optics recently carried out a study exploring this contrast between classical and quantum mechanics theories through the lens of quantum optics, the field of study exploring interactions between light and matter at a quantum level. Their paper, published in Physical Review Letters, proposes that classical interference arises from specific two-mode binomial states, which are collective bright and dark entangled states of light.
âAfter a long-standing and fruitful collaboration on cavity QED topics with the first author, Celso J. Villas-Boas, he and I exchanged many insightful ideas concerning the reported topic over a period of several years or so,â Gerhard Rempe, senior author of the paper, told Phys.org.
Indiana University School of Medicine scientists have developed a powerful new imaging technique to study bone marrow in mouse models. By overcoming key challenges unique to imaging this complex tissue, this advancement could support future drug development and therapies for conditions involving bone marrow, including cancers, autoimmune diseases and musculoskeletal disorders.
The new method was made possible by the multiplex imaging tool Phenocycler 2.0, which enabled researchers to visualize a record number of cellular markers within intact bone marrow tissue from mice. The findings are published in Leukemia.
âBone marrow is difficult to study because it is gelatinous and encased in hard bone,â said Sonali Karnik, Ph.D., assistant research professor of orthopedic surgery at the IU School of Medicine and co-lead author of the study. âSince bone marrow plays an important role in blood and immune cell formation and houses valuable stem cells, our unique imaging approach offers a useful tool for a variety of research applications.â
A study led by Pompeu Fabra University reveals which brain mechanisms allow psychosis to remit. The results of this pioneering research could have important clinical implications for exploring new intervention strategies in patients with psychosis. The study was carried out in collaboration with one of the main psychiatry groups at Lausanne University Hospital (Switzerland).
The study examines differences in the neural connectivity patterns of patients who have recovered from psychosis and subjects who have not. Identifying these differences using computational models has enabled determining which patterns of neural connectivity facilitate the remission of the disease.
The results of the research have recently been published in an article in the journal Nature Mental Health. Its principal author is Ludovica Mana, a doctor and neuroscientist of the Computational Neuroscience group at the UPF Center for Brain and Cognition (CBC). The main co-investigators are Gustavo Deco and Manel-Vila Vidal, director and researcher with the same research group, respectively.
Using the Australian Square Kilometer Array Pathfinder (ASKAP), astronomers have discovered 15 new giant radio galaxies with physical sizes exceeding 3 million light years. The finding was reported in a research paper published April 9 on the arXiv preprint server.
The so-called giant radio galaxies (GRGs) have an overall projected linear length exceeding at least 2.3 million light years. They are rare objects grown usually in low-density environments and display jets and lobes of synchrotron-emitting plasma. GRGs are important for astronomers studying the formation and the evolution of radio sources.
ASKAP is a 36-dish radio-interferometer operating at 700 to 1,800 MHz. It uses novel technology to achieve extremely high survey speed, making it one of the best instruments in the world for mapping the sky at radio wavelengths. Due to its large field of view, high resolution, dynamic range and good sensitivity to low-surface brightness structures, ASKAP has been essential in the search for new GRGs.
The detection of dark matter, an elusive form of matter believed to account for most of the universeâs mass, remains a long-standing goal within the physics research community. As this type of matter can only emit, reflect or absorb light very weakly, it cannot be observed using conventional telescopes and experimental methods.
Physicists have thus been trying to predict what it may consist of and proposing alternative approaches that could enable its detection. Dark compact objects are a class of dense and invisible structures that could be made up of dark matter, but that have never been directly observed so far.
Researchers at Queenâs University and the Arthur B. McDonald Canadian Astroparticle Physics Research Institute recently introduced a new possible method for detecting dark compact objects by probing their interactions with photons (i.e., light particles). Their newly proposed approach, outlined in a paper published in Physical Review Letters, is based on the idea that as dark compact objects pass between the Earth and a distant star, they will dim the light emitted by this star.
Coordinating complicated interactive systems, whether itâs the different modes of transportation in a city or the various components that must work together to make an effective and efficient robot, is an increasingly important subject for software designers to tackle. Now, researchers at MIT have developed an entirely new way of approaching these complex problems, using simple diagrams as a tool to reveal better approaches to software optimization in deep-learning models.
They say the new method makes addressing these complex tasks so simple that it can be reduced to a drawing that would fit on the back of a napkin.
The new approach is described in the journal Transactions of Machine Learning Research, in a paper by incoming doctoral student Vincent Abbott and Professor Gioele Zardini of MITâs Laboratory for Information and Decision Systems (LIDS).