#92 - OpenAI’s DALL-E 2, Google’s PaLM, AI Intel in Ukraine, Hugging Robots

<p>Our 92nd episode with a summary and discussion of last week's big AI news!</p> <p>Outline:</p> <ul><li>(0:00) Intro</li> <li style="font-weight:400;">Research & Advancements <ul><li style="font-weight:400;">(01:15) <a href='https://www.techradar.com/news/openais-new-dalle-model-turns-your-words-into-realistic-images'>OpenAI's new DALL.E model turns your words into pieces of art</a></li> <li style="font-weight:400;">(5:45) <a href='https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html'>Pathways Language Model (PaLM): Scaling to 540 Billion Parameters for Breakthrough Performance</a></li> <li style="font-weight:400;">(10:00) Lighting Round <ul><li style="font-weight:400;"><a href='https://www.marktechpost.com/2022/03/31/stanford-researchers-have-developed-a-machine-learning-based-algorithm-to-detect-autism-in-brain-fingerprints/'>Stanford Researchers Have Developed a Machine Learning-Based Algorithm To Detect Autism in Brain “Fingerprints”</a> - "Patients with autism who are diagnosed early and definitively may benefit from earlier therapies and better results. Stanford researchers have created an algorithm that can tell if someone has autism by analyzing brain images."</li> <li style="font-weight:400;"><a href='https://www.fiercebiotech.com/medtech/johns-hopkins-heart-scanning-ai-predicts-cardiac-arrests-10-years-ahead'>Johns Hopkins’ heart-scanning AI predicts cardiac arrests up to 10 years ahead</a> - "Researchers at Johns Hopkins University have developed an artificial intelligence approach they say can help predict if and when a person could die of cardiac arrest based on imaging scans of the heart."</li> <li style="font-weight:400;"><a href='https://www.bio-itworld.com/news/2022/04/05/ai-betting-agents-weigh-in-on-reproducibility-of-research-findings'>AI Betting Agents Weigh In On Reproducibility Of Research Findings</a> - "Penn State researchers have created synthetic “prediction markets” for speculating on the replicability of published studies with unparalleled accuracy."</li> <