Artificial Intelligence
Listen to the latest news, expert opinions and analyses on the ever-expanding world of artificial intelligence, data science and machine learning, broadcast on leading talk radio shows and premium podcasts.
The Voicebot Podcast
Interview With Daniel Kornev Chief Product Officer at DeepPavlov
"Daniel gornja. Welcome to the voice. Podcast much brackets and big for me to turn today today. It's my pleasure to have you. This is a long time in the making. We've been i guess chatting on slack for maybe year and a half something. Yeah i think so. I started to read your westport. Insider was fascinated by opportunity to look into your think to on hand Why not took. Yeah that that's that's how it happened. Well the is really perfect. Because we're going to talk about a few things today. Obviously d. Pavlov is a project i've been interested in for at least a year. I don't remember when i first came across it but it might have been might have been. You introduced it to me. Or maybe shortly before that i found out about it but definitely answered that project and then obviously you've been involved recently with the elec surprise social competition. We've had another conversation about that about this. What a perfect time to go a little deeper on that because it is a different way to build bots and so really looking forward to this conversation today. But i'll let you get started. So why don't you tee it up for the The audience right now first and let them know what d- pavlov is before we get deep sure depot is like lab at moscow's physics and technology. That is focused on conversational And neural efforts Officially cool to neural networks in Terrain but Wednesday were standard like full. Five years. ago it's also got to down moniker Because follow fossil famous russian scientists who discover it reflects us in all those things that encouraged scientists researchers to understand how human brace books and we still have a lot of things that we have to uncover. But that's was formed as the name.
Daily Tech Headlines
Google Develop AI for Detecting Abnormal Chest X-Rays Using Deep Learning
"On friday we talked about a nature publication by google. Ai scientists that showed how a deep learning system could detect abnormal chest xrays rays with an accuracy. Rivaling that of professional radiologists. The system only detects whether a chess scan is normal or not and is not trained to detect specific conditions. The goal here is to increase productivity and efficiency of radiologists clinical process. Let's examine some a i x ray. Science first of all how to rays work xrays are a type of radiation energy. Wave that can go through. Relatively thick objects without being absorbed or scattered very much. X rays have shorter wavelengths than visible light which makes them invisible to the human eye for medical applications of vacuum x. Ray tube accelerates electrons to collide with a metal and owed and creates rays these rays are then directed towards the intended target like a broken arm for example and then picked up by digital detectors called image plates on the other side differ body tissues absorb x rays differently so the high amount of calcium in bones for example makes them especially efficient at x ray. Absorption and this highly visible on the image detector soft tissues like lungs are slightly lighter but also visible making x ray and efficient method to diagnose pneumonia or pleural a fusion Which is fluid in the lungs. For example according to this latest nature publication approximately eight hundred and thirty seven million chest. Xrays are obtained yearly worldwide. That is a lot of pictures for radiologists to look at and can lead to longer wait times and diagnosis delays. And of course. This is why there's interest in developing ai. Tools to streamline the process many algorithms have already been developed but are rather aimed at detecting specific problems on an x ray. The google ai. Scientists however developed a deep learning system capable of sorting chest xrays into either normal or abnormal data intending. To lighten the case load on radiologists
![Generating SQL [Database Queries] From Natural Language With Yanshuai Cao](https://storageaudiobursts.azureedge.net/site/images/stationIcons/14749.png)
The TWIML AI Podcast
Generating SQL [Database Queries] From Natural Language With Yanshuai Cao
"So tell us a little. Bit about touring and the motivation for it. How did the project get started right. So is this natural. Language database interface is a demo of anguish database interface built. And it's really just putting a lot of our word on some parsing space together. In this academic demo so netra language database interface the from application perspective the pin uses to a law a nontechnical users to interact with structured data. Set is there's lots of inside endure and You know who want to give out change for nontechnical users to to get those insights and from a research perspective. It's a very challenging natural english Problem because the underlying problem is you have to parse pasta in english or had our next languish than convert to see cole. And we all know. Natural language is ambiguous machine languages on bigger after resolve all amputate. He yard a too harsh correctly. Furthermore was different from compared to on other program. Language is the mapping. From adams. To see cole is under specified. If you don't know the schema really depend on what is the structure of schema and so he still model has to really learn how to reason using it. And in order to resolve all that may retail and correctly predicted the sequel and lastly this printer model some. You don't want to just work on this domain one. To work on demand is on databases. You're never seen before. So without st cross domain across database part of it and dodgers very challenging. Guess it's completely different. Distribution wants moved to different dimensions even
Eye On A.I.
Seth Dobrin Talks About Trustworthy AI
"We're gonna talk about trustworthy a i. It's something that is increasingly in the news and concerns a lot of people. Ibm has a product called fact sheets. Three sixty that i understand is going to be integrated into products. Can you tell us what fact sheets three sixty is. And then we'll get into the science behind. Yes so let me start by laying out what we see is the critical components Trustworthy a at a high level Three things there's a ethics there's govern dated ai and then there's an open and diverse ecosystem an ai ethics is fully aligned with with our ethical principles that we've published with arbin dr ceo co leading the initiative out of the world economic forum. And i'm adviser for essentially open sourcing our perspective on a ethics from a govern data in ai perspective. It falls into five buckets. So i is. Transparency second is explain ability third is robustness. Fourth is privacy and fifth is fairness and so the goal of fact sheets is to span multiple of these components and to provide a level of explain ability. That is needed to drive adoption and ultimately for regulatory compliance. And you think of it as a nutritional label for ai where nutritional labels are designed to help us as consumers of prepackaged foods to understand what are the nutritional components of him. What's healthy for us. What's not healthy for us. Factually is designed to provide a similar level capability for a.
The Vergecast
Everyone Will Be Able to Clone Their Voice in the Future
"World today often feels like it's full of digital voices with a assistant siri amazon alexa and google reading your messages announcing the weather in answering trivia. Here's what i found on the web but if you think things are chatting now just you wait. The voices of these a assistant used to be based unreal recordings. Voice actor spent hours talking in a studio and these clips would-be cut up and rearranged to create synthetic speech but increasingly. These voices are being created using artificial intelligence. This means we can not only create more realistic computer. Voices clone the voices of real people much more quickly creating endless artificial speech at the touch of a button for example it was surprisingly easy to make a synthetic version of my own voice. In case you missed that. That was not me talking. That was all made digitally by typing into a computer. So why would some want to do this. Besides the obvious novelty of it. You might have guessed a reason to make some money. I listen to this was going on. Kevin hart here. I wanna talk to you about why. We have to have mac and cheese every night. Think about it. That's why. I recommend thousands of new shows and this is a promo from baritone one accompany. That's working on an ai product to create synthetic voices and make them something. The media industry wants to us. So we've created a platform. Ai which at the end of the day turns unstructured data into structured data. That's shaun king executive vice president. Ed veritas one. So if you're thinking about audio thinking about video things that are typically unstructured and we make that searchable discoverable author a host of different a cognitive engines that are there from transcription beaker detection speaker separation. And then we provide those tools to you know many different industries that are eating
The TWIML AI Podcast
Social Commonsense Reasoning With Yejin Choi
"All right everyone. I am on the line with jin. Choi eugen is a professor at the university of washington. Yajun welcome to the air podcast and excited to be here. Thanks for having me. I'm really looking forward to digging into our conversation. I'd love to have you start by sharing a little bit about your background and how you came to work in the field of ai. Right so i primarily work in the area of natural language processing but like any other feels of ai. now the boundaries become looser losers and. I'm excited to work on the boundaries between language and vision language and perception and also thinking a lot about the connection between a i and human intelligence and what are the fundamental differences in that in terms of knowledge and reasoning And so let's go a little bit deeper into that. Talk us through like some of the ways that you take on those topics in your research portfolio. What are some of the main projects. You're working on the things that you're exploring right so currently i'm the most excited about the notion of commonsense knowledge and reasoning. This was in fact the only dream of a field. The in seventy eight as people love to think about it and tried to develop formalism for it. It turns out it's really trivial for humans but really difficult even for the smartest people to really think about how to define it formally so that machines can execute it as a program so for a long time. Scientists assumed that it's Doomed the direction. Because it's just too hard so i didn't really thought about commonsense for for a long time and then it's only in recent years. Some of us got excited to think about it again. Which is in part powered by the recent advancements of neural modell's that is able to understand large amount of data.
Data Skeptic
Ultra Long Time Series
"My name is a foley counter. I work with essentially neurosis. They'll finance and economics in beijing. China background statistic computing and nowadays we focus on forecasting ways a lot of skill of data on distributed systems. So i haven't yet had the chance to interview anyone specifically about distributed time-series. It seems like that would be some extra challenges because the data sequential what happened before relates to what happens next. How can you spread that across. Many machines disputed hampshire is is just time is that alcohol. it can't be billions of observations. Historically we build up statistical models based on assumptions the narrative and other assumptions those assumptions do not work on distributed system and the industry like apache spark actually defacto standard for data processing and the the street people star a huge amount of data on distributed systems. We how to make a model that really works on sack disputed system and we have to work on their language to make our forecast more robust bus on his outer series. Yes spark is naturally a good choice to us because it's such a good reputation and a lot of reasons to look at it for big data solutions. But it's not obvious to me that it's necessarily the right choice for time series because it's not really baked in right. They've moved more and more towards like a sequel style and data sets. Are there any technical challenges to implementing time series via spark. And if you consider all single time that's fine but if you think about what we are doing we are streaming tate. Data is like times commun- out like water like re-re coming up now. You're really need nonstop system to process in the whole system. They simultaneously and without much delay that demand for temps is forecast in and out to claim that i think a lot of people agree with me nowadays arteta pam because we collect data. We always have the time stamp. So that's a windy. Temperatures for distributed systems. And there's a new challenge. I think emmanuel areas like atmosphere electricity and adi and other domains
WSJ Tech News Briefing
Are Amazon's Algorithm Bosses Coming to Your Workplace Next?
"You think your boss is watching you at work. Monitoring your every move well if your manager is a series of cameras sensors and algorithms. Then you're not wrong. And that may also mean you work at an amazon fulfillment center. The company known for the detailed tracking of packages and user. Information is also tracking the movements of workers at its warehouses looking to precisely measure efficiency and increase productivity wall street. Journal's tech columnist christopher mims calls this bazo schism named after amazon founder jeff bezos. He's got an upcoming book about it called arriving today from factory to front door why everything is changed about how and what we buy any joins me now. Hi christopher hay zoey. Thanks for having me so christopher you coined the term bazo schism. What exactly does that mean so. Basis ism is the combination of sensors and software to measure. How well somebody is doing their job. And then use software which has of course logic or an algorithm in it which was defined by an engineer. Somewhere to then tell that worker okay. You're doing a good job or you're not doing a good job or you need to be doing this differently. And so bazo. Schism or management by algorithm or management by software. At the end of the day it is just about creating a set of rules and then handing it to a machine to enforce those roles so the person's boss is
The Voicebot Podcast
Andy Mauro CEO of Automat on Conversational Commerce
"Anymore. Oh welcome to the voice by podcast brett. This is a long time coming now. We've done a couple of clubhouse sessions. But we've i guess known each other at least through social media for several years. Now he's sort of back and forth have always appreciated your comments and our exchanges there and it's really nice that we have this one on one time to really talk about you've been doing because you have a long history in the industry you've seen a lot of different parts of it. You're doing some of the more interesting things. I think right now from a conversational standpoint in the market right now with automatic but it. We should start with where you started. So how'd you get into the industry. What did that look like early on. And what were you to the to. The tech. Sure and likewise. I've been looking forward to this for a long time. So excited to see where this goes i history. Hopefully it doesn't take too long. I've been working in what. I like to just say computers. You talk to for now over twenty years so i guess dedicated all but a couple years of my career to this space. I really love it. I mean i feel like it's a privilege to work in this right. I mean i think flying cars and talking computers. This is the stuff of childhood sci-fi dreams and you know. I really honestly feel lucky to get to work in the space for as long as i have and so it goes back to my days. My only job before. This space was at the canadian at the time unicorn nortel which is sort of a competitor. Cisco's in this back in the late nineties and Back when everything was just internet infrastructure was the big business. And i had a job. They're working on crazy low level. Ip over atm stuff. I was a programmer. I love that stuff. And i all. My friends started quitting one day and they were going across the street. And i said where you're going like this they said this cool startup nuance and i was like. Oh that sounds fun and just like you do in your early twenties. Just quit my job like literally the next day and over and got a job at this other place where all my friends were and man. That was lucky. That was just one of those life. Changing things i didn't know about conversation. We didn't even call it conversationally. I'm back that rain speech recognition.
The TWIML AI Podcast
Deep Reinforcement Learning for Game Testing at EA With Konrad Tollmar
"Conrad woke him to the tuomo. Podcasts thanks sam. Thanks for inviting us to be here. I'm really looking forward to digging into our conversation. We'll be talking about As the audience might imagine the intersection of and games before we do. I'd love to have you share a little bit about your background. I mentioned what is k t h. Okay teaches royal institute of technology in stockholm. It's a technical university where i did my undergraduate as well as might be hd. So i i think my interest for a i started longtime ago starting with computer vision. I always been passionate about photography. And i saw them. There was an opportunity to combine my kind of interest for photography than webs kind of my academic. And the so. That's kind of my starting point here. Nice and tell us a little bit about the kind of research that interests you in your professorship and on your graduate studies so my phd more symbolic media spaces and we build different kinds of interactive in viramontes to connect places with vdi streams but also being able to use sensors to convey other kinds of information. If you're close or if you're in the proximity of a space for that led me and benchley to explore that further or after my ideas and i spent some time working smart and interactive environments some over this work for play and some were for more like everyday use and i think some of us could remember recall. The kind of demos sue sorted out the mit's media on the late nineties.
Daily Tech Headlines
Ethics Panels Reject and Delay Biometrics, AI Projects for Google, IBM, Microsoft
"With ai. Ethics chiefs at google microsoft and ibm published by reuters looks at what. Ai projects these companies reject in september. Twenty twenty google's cloud unit rejected a financial firm looking to us to determine credit worthiness and earlier this year. Google blocked features from analyzing emotions. Ibm turned down a client request for a more advanced facial recognition system microsoft place limits on software mimicking voices. All three companies said they welcomed clear regulation for
Lex Fridman Podcast
Jaron Lanier on the Future of Humans and AI
"You're considered the founding father of virtual reality. Do you think we will one day. Spend most or all of our lives in virtual reality worlds. I have always found the very most valuable moment in virtual reality to be the moment. When you take off the headset and your senses are refreshed and you perceive physicality afresh. You know as if you were newborn baby. But with a little more experienced he can really notice just how incredibly strange in delicate and julia impossible. The real world is Sue the magic is and perhaps forever will be in the physical world. Well that's my take on it. That's just me. I mean. I think i don't get to tell everybody else how to think or how to experience retreat. At this point there have been multiple generations of younger people who've come along and liberated me from having to worry about these things But i should say also even in a what. I called it mixed reality back in the day in these days. It's called augmented reality But with something like a hall and even then like one of my favorite things to augment a forest. Not because i think the forest needs augmentation but when you look at the augmentation next to a real tree the real tree just pops out as being astounding you know it's it's interactive. It's changing slightly all the time if you pay attention and it's hard to pay attention to that but when you compare to reality all of a sudden you do and even in practical applications My my favorite early application of retrea audi which we prototype going back to the eighties. When i was working with dr joe rosa and at stanford med near near where we are now. We made the first surgical simulator and to go from the fake anatomy of the simulation which is incredibly valuable for many things for designing procedures for training things then to go to the real person. Boy it's really something like Surgeons really get woken up by the transition. It's very cool. So i think the transition is actually more valuable than the simulation
The TWIML AI Podcast
Exploring AI With Kai-Fu Lee
"All right everyone. I am here with kaifu. Lee chi food is chairman and ceo of innovation ventures the former president of google china and author of the new york times bestseller superpowers. And we're here to talk about his new book which will be released next week. A twenty forty one kaifu. Welcome to the tuomo. Ai podcast thank you thank them. It is great to have an opportunity to speak with you. I'm looking forward to digging in and talking more about the book before we do though i'd love to have you share a little bit about your background and how you came to work in the field of ai. Sure i started With my excitement in back in nineteen seventy nine. When i started my undergraduate at columbia i worked on language and vision at columbia and then i went to carnegie mellon for my team at which develops the first speaker independent speech. Recognition system based on machine learning actually Some the earlier thesis in machine learning in nineteen aba. I also developed a computer program that the world's fellow champion is all in the eighties. Very early years after mike graduation from Cmu i talked there for two years than i joined apple and led a a lot of apples. Ai speech natural language and media efforts later joined sgi and then microsoft where i started microsoft research asia in beijing in nineteen ninety eight which kind of became one of the best. Tom research labs in asia. Later i joined google and ran google china for four years between two thousand and five in two thousand nine. We did do a little bit for how they i mostly was Really developing google's presence in china in two thousand nine. I left google and started my venture capital firm assign ovation ventures and at san ovation ventures we invest in the bow for the ai companies. We were about the earliest and probably invested in the most companies we invested in about seven unicorns in ai alone and with a few more Yet to come so they're excited to be in the era i it's Was not so hot during much of my career. But glad scooby with the catch. The recent wave and participate in it.
Eye On A.I.
Is the AI Market Saturated?
"My first question is is the market saturated and without picking winners. What products us rising to the top. Good that you're asking this question right now in general timing of the world because here we are for those who are listening to podcast. August the twenty twenty. One people might be listening to this year from now. So this'll all seem really quaint. To those in the future but the markets actually in the midst of consolidation. Saying we're actually starting to see a lot of acquisition activity and we do track over one hundred vendors and machine learning platform space about seventy two of which meet the minimum threshold of viability. There's lots of startups in the space. We love startups. We have an affinity for companies of all sizes but when we're looking at companies who are buying products and services we tend to look at those companies that have either at least ten customers or have at least ten million in revenue or at least ten million dollars in venture capital they if they have like to customers and no venture funding raised in a little bit of revenue than. We're like just grow little bit more a little bit more. So this is about seventy two companies. At least that are in that that john rao of course all the cloud vendors are in that space. The major cloud vendors microsoft. Ibm google amazon And a few others. So those were recalled the cloud sas machine learning as a service vendors basically and then there's a whole other category of pure play machine learning platform vendor so you may be familiar data robot or did i do in that space a bunch of others that are kind of trying to pull together all the components of what's required to put machine learning and advanced analytics solutions into play and increasingly. What they're doing is they're growing through Both building out their product suites and through acquisitions so she did robots but on a tear lately did i. Two as well as been been really growing raise very significant round recently but the answer is that this market is actually starting to
AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion
From FORMULA ONE to AI: An Interview With Alex Castrounis
"Welcome. Alex so excited that you're here. We'd like to start by having you introduce yourself to our listeners. Tell them a little bit about your background. And why you started the ai with youtube channel and maybe also you know what is why of ai. Absolutely so thanks again. A super excited to be here of again. I'm alex julius. I'm a founder of two companies. Actually one's into architect any others. Why on the author of a book called a. i. for people in business framework for better humane experiences in business success now also an adjunct at northwestern university kellogg Teaching a as part of their. In the i graduate program And so yeah. I got into a quite a long time ago. So i have sort of a strange unusual kind of career path but Used to work in indycar racing for about ten years so it was a race strategist engineer. Any data scientists in indycar racing Sort of set my sights on that When i was actually in highschool i kind of made a decision to go into that field. I had seen very first indy. Five hundred when i was like junior high school blew me away. I guess i'm doing that for sure for living someday. In a defendant pursued that and then you know sir fast for after college got my first opportunity in the professional sports industry in these cars you know. They have eighty ninety sensors on them. That are measuring. Everything you can imagine from. Temperatures pressure is to displacements two rotations. To forces the everything. And so it's like literally iot an iot system moving at like two hundred fifty miles an hour that sending data over the airwaves in telemetry all this but really also data in the truest sense of big data because just mounds mounds of data
Data Skeptic
Why ARiMA Is Not Sufficient
"Name is chung show carney associated professor and the southwest johnson university in trump province in china. And can you tell me a little bit about your specific research areas. What do you study my research areas. Congress daytime my machine learning and data analytics gender most specifically focused on forecasting demand focusing in retail and time series focused sich of sees. So the main pay for. I asked you on to discuss. Today is wire arena and serena or s arema not sufficient. You'd mentioned you have a good background in machine learning. I don't necessarily think of a reema as a machine learning technique. How do these two areas fit together in your mind. Actually because the site focused teams all problems and can be served by machinery and when the approached this focusing problem with fines and attorney time service models are very important solutions to forecast team problems. Other side focused is very important in today's areas because you're low many many data so always Focused in problem. We find penser is very important and we also find iron man. Sarah map and armagh. Those are very classic. Run divided news time service motives and when we do couldn't this i remind saruman model. We're fans than actually the classical extre nation or classical. Modern for iran. serena is northern sufficient sarichichekli. Either way actually approach. I remember sarim from elisa angle which is spectral lenzi's digital delivery and in your system theory so we use elisa angle to do countries a romance. I remember motive defines onto loads dench a sufficient from the rich porno view. So this is the whole ground
The Voicebot Podcast
Trends for Voice Assistants Use in the Car
"All right. So the first thing i want to talk about is how big is this space. So data shows in the us. There are one hundred twenty seven million consumer so that's one hundred twenty seven out of about two hundred sixty million. Us adults who are using a voice assistant at some point while they're driving. Now what does this mean. Let's talk about the definition there. This is the broadest definition of water. Voice assistant use is in the car and that means they could be using bluetooth connected to the sound system it could mean they're using their apple carl carplay or android auto which are projection technologies which basically are just the smartphone but it takes over the screen the payment center screen as well. So it's really that smartphone experience again but it's a little richer than just using bluetooth connect to the phone and then we have the embedded or in car voice assistance provided by the auto manufacturers embedded in the infotainment systems sometimes their custom. Sometimes they're provided by one of the general purpose players like amazon or google so this is the broad definition so about one hundred twenty seven million in the us now. We have three years of data on this from the us around the car. We've been doing this report since two thousand nine hundred. This was the first year we actually added and the uk and germany and the future years. We hope to add a few more as well. So in germany we have twenty seven hundred million. But also you know it's twenty seven million out of an adult population of sixty nine million and then in uk it's just under nineteen million out of fifteen million total population. She's starting to get a sense of how large these these are now the us adoption rates more in terms of people who've tried it. However if you go to the uk in germany what you're gonna find there's a higher proportion of users who are actual regular user so we break this down into people who have occasionally use it. People were monthly active users and people who are daily users so those daily users being the power users using it just about every time they get in the car. So what we see. Is we see a lot more. The casual users in the us a much bigger proportion of the users use it very carefully not in a monthly or daily basis when you move into the uk. Germany they tend to be more monthly active users at least and a lot more daily active user so in terms of the percentage of people were using voice in the car. They tend to be more more to be power users.
Eye On A.I.
Ben Goertzel on the Development of Sophia the Robot
"I'm very interested in the end. The drive by many researchers in two unsupervised lowering different forms of unsupervised learning i. I'd like to hear generally your thoughts on unsupervised yourself supervised learning that doesn't depend on label data and then personally i'd like to hear what's going on with because i see you on the internet all the time but i've talked a lot to people in the chat bot world and adult understand what's going on with severe because i know that chad pods are not that sophisticated is to feel truly connected to in a i that when you see to it is speaking back is a topic that people are very confused about in in many different directions thinking. It's more than is unless than sophia obviously hansen's creation more so than mine i was i led the software team I two years in the first thing i understand. That's a hardware cloud one ansett a mushy platform as its lexical leaking control that same robot when a host of different software systems friends. If you're a naive observer watching if any the robot there's really no way to tell what's going on behind the scenes in any given was affairs on a major talk show is something may be less entirely scripted advent. Of course the humans many entirely certain advanced also reading a script reading off from there could also sometimes need some of in chattanooga by and large in such encounters. There's a lot of scripting ghoneim associate standing out by giving a speech to the un or something. there's a lot of just type in soviet is reciting. That's not a very cool robot.
Data Skeptic
The Inner Workings of the Comp Engine
"My name suspenseful tre on the same elektra in school of physics of the university of sydney. So we do a range of things migrate. We like to think about neural systems and the mechanisms underlying brain dynamics brain structure and we also have a strong focus on understanding the structure of time series and amicable systems complex on emiko systems in general. And what does the ven diagram. Those topics look like favorite. Because obviously a lot of questions you might want to ask about. The brain is how it changes over time how it processes information release things distributed time varying prices so little people are getting much more interested in using modern complex systems and particularly time series analysis techniques to understand the kind of dynamic will structure in mechanisms underlying information processing in the brain. So there is a up. We should chat again sometime. Maybe in the future about the neuro physics and complex system side of it but in our series on time series. I want invite you on especially the talk about the comp. Engine for listening to aren't familiar with it. Could you talk a little bit about what this project is at a high level. Yeah the competition is typically is one of the type of work we do on time series analysis and this was a website that we made to allow us to share and compare it time series data so as part of my phd. I collected over thirty thousand different pieces of time. Series data than recognizing the time series analysis is very interdisciplinary fail. People measure in studied the dynamics of systems in biomedicine in astrophysics theoretically in applied math in an engineering. This hot to find a field where they don't care about time series of the dynamics of what's happening in the system that they're interested in studying and so we decided to try and bring together all of this diversity. Typically it's kind of studied only in the local area in which people works economics. People like to study their economic time series. Biomedical people like to study by medical time series. I kind of thought other method. That is different. Able use old completely. Different is something to be learned from buying them of the data that they studied the hidden connections between seemingly diverse systems from across science and industry. So we decided to make this platform in a range of tools actually around this idea of trying to bring scientists together around by the methods that they use and data that they measure
WSJ Tech News Briefing
Elon Musk Doubles Down on A.I. Ambitions Despite Regulatory Scrutiny
"Tesla has been leaning into artificial intelligence as we've talked about on the show company's tapped ai to power assisted driving beecher's and eventually wants to use the technology in fully autonomous vehicles. But at the company's ai event last week ceo elon musk reveal. The tesla is going even deeper on. Ai you might have seen in your feed that. He unveiled a humanoid robot. We're setting it such that it is At a mechanical level at a physical level. You can run away from it and most likely overpowered here to break down. All of that is our reporter rebecca. Elliot hey rebecca. Thanks for joining me. Yeah thanks for. Having me amanda. Okay so tell us about this robot. What exactly is musk pitching here. So tesla introduced what it is. Calling the tesla bhatt on thursday and this is a human looking robot expected to be about five foot eight weighing one hundred twenty five pounds. The prototype elon. Musk said he thought would be ready. Next year Though what we saw on thursday was you know a human dressed in a robot costume dancing on stage. So you know make of that what you will.
Talking Tech
Elon Musk Unveils Tesla Bot, a Humanoid Robot That Uses AI
"On thursday unveiled tesla during an event. Touting advances in artificial intelligence according to a description on tesla's website the tesla bought was designed to perform physical tasks considered quote unsafe. Repetitive or boring. End quote He talked a little bit about this During the presentation in a suggested how you know. The foundation of every economy is labor and he kinda posed the question. What happens when there is no shortage of labor which is where these tesla bots come in. The body itself is five foot eight and weighs one hundred and twenty five pounds. It can carry up to forty five pounds and dead lift up to one hundred and fifty pounds. According to the presentation it can also move up to five miles an hour which is important because at one point elon. Musk jokes about how you can outrun the robot if you need to and you can overpower it if you need to which. Yeah that's fun to think about The billionaire said though he is aiming to release a prototype of tesla next year.