Hanson: Terry, thanks so much for joining this videocast or podvideo, I don't really know what to call it. When I started trying to conceptualize what I was getting at, I wanted to talk to people who had a clear and obvious perspective on what they thought AI is. And you're particularly unique, and special in this context, because you have been consistent since… Well, there's a great book that you have a chapter in and I think Jim Anderson edited in 1981, called "Parallel Models of Associative Memory". Sejnowski: It's interesting you brought that up because I met Geoff Hinton in San Diego in 1979 at a workshop he and Jim organized that resulted in that book. It was my first neural network workshop. We were all interested the same things. There was no neural network organization or community at that time – We were a bunch of isolated researchers working on our own. Hanson: And probably not well appreciated, by talking about neural networks, or neural modelling. Sejnowski: We were the outliers. But we had a great time talking with each other. Hanson: Going back to the book, you had a chapter called skeleton filters in the brain. I think that was the name of it. Perhaps not the best title in the world, but still… "Skeleton filters" is a little scary, I gotta say. But, it was a really incredibly easy read – I just read it the other day again. And, in it, you're really going in a subtle way from biophysics, modelling a neuron and referencing everybody, you know Cowen, and everybody who'd developed a differential equation, or anything up to semantics and cognition. But biophysical modeling, this kind of category you might associate with biophysics of neural modelling, in that neurons and circuits matter and that's what we're modelling, for that purpose – that's the purpose of it. For example, I think you mentioned Hartline and Ratliff, and Limulus crab retina. And this provided an enormous amount of data well into the 60s where people were actually modelling and there were predictions and it was very tightly tied to the crab. Sejnowski: By the way, although it's called a Horseshoe Crab, and looks like one, Limulus has eight legs, so it's an arachnid.

OrCam, a company that makes products to aid accessibility for the visually impaired, has won a CES innovation award for its glasses-mounted MyEye Pro device. It aids the blind and visually impaired by reading out printed and digital text, recognizing people, identifying products, and more. OrCam took the prize in both the CES innovation accessibility and health and wellness categories. "We are living in uncertain times, yet... our users' challenges related to access have not stopped during the pandemic. If anything, they have intensified," said OrCam co-founder and co-chairman Prof. Amnon Shashua in OrCam's blog post.

In 1970, Marvin Minsky, recipient of the Turing Award ("the Nobel Prize of Computing"), predicted that within "three to eight years we will have a machine with the general intelligence of an average human being." The fundamental roadblock is that, although computer algorithms are really, really good at identifying statistical patterns, they have no way of knowing what these patterns mean because they are confined to MathWorld and never experience the real world. It's a brown-throated thrush, but in Germany it's called a halsenflugel, and in Chinese they call it a chung ling and even if you know all those names for it, you still know nothing about the bird–you only know something about people; what they call that bird. Now that thrush sings, and teaches its young to fly, and flies so many miles away during the summer across the country, and nobody knows how it finds its way," and so forth. There is a difference between the name of the thing and what goes on.

AI Human video synthesis platform'AI STUDIOS' by DeepBrain AI won CES Innovation Award 2022 in Streaming category. DeepBrain AI is coming in January to demonstrate its innovative technology in person at CES Las Vegas and NRF New York. Seoul, South Korea, November 11th, 2021 – DeepBrain AI is proud to announce that it has been named a CES 2022 Innovation Awards Honoree for its AI Studios script to video solution in'Streaming' category. This year's CES Innovation Awards program received a record high number of over 1800 submissions. The announcement was made ahead of CES 2022, the world's most influential technology event, happening Jan. 5-8 in Las Vegas, NV and digitally.

OrCam MyEye PRO is a wearable assistive technology device for people who are blind, visually impaired or have reading challenges. It's lightweight, finger-size and magnetically mounts on eyeglass frames. The device instantly reads aloud any printed text (books, menus, signs) and digital screens (computer, smartphone), recognizes faces, and identifies products/bar codes, money notes and colors – all in real time and offline. The interactive Smart Reading feature enables users to tailor their assistive reading experience, and Orientation assists with guidance and identification of objects. Newly released "Hey OrCam" enables control of all device features and settings hands-free, using voice commands.

The Consumer Electronics Show (CES) 2022 is underway, kicking off with its'Unveiled' vent on Monday evening that provided a sneak peak into what we can expect to be showcased at the tech conference. Attendees saw several new technologies including a Star Wars-like face mask that protects against pollution and the coronavirus, a wearable that stops premature ejaculation and a brain scanning device that can detect early signs of mental conditions. Monday's event also hosted several innovations that were awarded the CES 2022 Innovation Award, which was given to Kura for its AR glasses and solar shingles that can be nailed directly to the roof. CES, which is held in Las Vegas, is expected to host more than 50,000 people and 2,200 exhibitors, but is also closing its doors a day early due to coronavirus cases spiking around the US. Among those showcasing their latest and greatest innovations was Airxom Mask - a mask with a white plastic shell that covers the mouth and nose.

The second edition of Deep Learning Interviews is home to hundreds of fully-solved problems, from a wide range of key topics in AI. It is designed to both rehearse interview or exam specific topics and provide machine learning MSc / PhD. students, and those awaiting an interview a well-organized overview of the field. The problems it poses are tough enough to cut your teeth on and to dramatically improve your skills-but they're framed within thought-provoking questions and engaging stories. That is what makes the volume so specifically valuable to students and job seekers: it provides them with the ability to speak confidently and quickly on any relevant topic, to answer technical questions clearly and correctly, and to fully understand the purpose and meaning of interview questions and answers. Those are powerful, indispensable advantages to have when walking into the interview room. The book's contents is a large inventory of numerous topics relevant to DL job interviews and graduate level exams. That places this work at the forefront of the growing trend in science to teach a core set of practical mathematical and computational skills. It is widely accepted that the training of every computer scientist must include the fundamental theorems of ML, and AI appears in the curriculum of nearly every university. This volume is designed as an excellent reference for graduates of such programs.

Deep reinforcement learning has gathered much attention recently. Impressive results were achieved in activities as diverse as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to solve difficult problems. They have learned to fly model helicopters and perform aerobatic manoeuvers such as loops and rolls. In some applications they have even become better than the best humans, such as in Atari, Go, poker and StarCraft. The way in which deep reinforcement learning explores complex environments reminds us of how children learn, by playfully trying out things, getting feedback, and trying again. The computer seems to truly possess aspects of human learning; this goes to the heart of the dream of artificial intelligence. The successes in research have not gone unnoticed by educators, and universities have started to offer courses on the subject. The aim of this book is to provide a comprehensive overview of the field of deep reinforcement learning. The book is written for graduate students of artificial intelligence, and for researchers and practitioners who wish to better understand deep reinforcement learning methods and their challenges. We assume an undergraduate-level of understanding of computer science and artificial intelligence; the programming language of this book is Python. We describe the foundations, the algorithms and the applications of deep reinforcement learning. We cover the established model-free and model-based methods that form the basis of the field. Developments go quickly, and we also cover advanced topics: deep multi-agent reinforcement learning, deep hierarchical reinforcement learning, and deep meta learning.

Torrey Smith, Co-Founder of Endiatx, is changing the reputation endoscopies have for being uncomfortable. At Endiatx, they are developing a pill-sized robot that you swallow, which will then livestream your digestive system for a doctor to view. Our interviewer Abate dives in. Torrey Smith Torrey Smith is the Co-Founder & CEO of Endiatx, a medical robotics company that manufactures tiny robotic pills capable of active movement inside the human stomach with control over internet protocol. Prior to launching Endiatx, he developed medical devices in the areas of endometrial ablation, atherectomy, therapeutic hypothermia, sleep apnea, and vascular closure. An aerospace engineer by training, he takes a keen interest in the deep tech sector and is a proud mentor of up-and-coming founders at the Founder Institute. He is also the principal founder of the international arts collective known as Sextant, and he has had his art featured in the Smithsonian. Abate De Mey: Welcome to the robo hub podcast. Super excited to have you on here. So Torrey, could you introduce yourself a little bit? Well, you know, I originally originally studied aerospace engineering because my goal was to build the future of science fiction that I had read about as a kid. I had some relatives you know, come down with some gnarly health conditions. I lost an aunt to a brain cancer. I became very passionate about the world of medical devices and maybe more importantly, just health and technology and how we can merge those. Because I, I think if you asked a 14 year old kid who reads science fiction, what they think the future of healthcare looks like, they would probably say, oh, it's going to be like nano robots. That would go in like an army of tiny machines and kill any tumor. And then if you ask a doctor, Hey, I've got a glioblastoma. The doctor's going to say, well, we're going to cut an incision over here. I'm going to cut out a piece of your skull and put it in a steel dish. Then I'm going to go in and do my best to remove some of this brain tumor. And we're going to put you back together. We're going to put you on drugs. You know, we'll put you on chemo and you know, in six to nine months, you're going to be dead. And my question is simple.

Artificial intelligence can enhance efficiency and productivity in financial services and is hence emerging as an important tool in the industry. It can reduce human errors and biases, along with improving the quality by spotting anomalies that cannot be picked up from current reporting methods. We caught up with Jayashree Mitra, the head of technology (end-user services), Asia Pacific, to understand more about AI and automation in this industry. She has 23 years of cross-industry experience, of which she has spent 20 years working in Financial Services Technology for Standard Chartered Bank and UBS. Jayashree Mitra: Throughout my childhood, I was always taught the virtues of self-reliance.