Podcasts about uber atg

Harry Campbell aka The Rideshare Guy joined Grayson Brulte on The Road to Autonomy podcast to discuss the rideshare industry, the role that power drivers play in the ecosystem and his thoughts on Uber's growing hybrid network.The conversation begins with Grayson and Harry discussing Uber's outperformance compared to the S&P 500. Over the last 12 months, Uber has outperformed the S&P 500 by 102%. While Uber is outperforming the market, their competitor Lyft is struggling to figure out the future of their business. Drivers are the backbone of both Uber and Lyft. According to J.P. Morgan, the average Uber driver earns $33 an hour. The estimated average hourly earnings are only for Period 3, commonly referred to as active time. Period 1 is when a driver is on the Uber app waiting for a ride. Period 2 is when a driver has accepted a ride and are driving to pick up the passenger. Period 3, that's when you make the most amount of money as a driver. You want your wheels moving, you want to be going fast, you want to be going far. That's kind of how you make the most amount of money. $33 an hour is basically saying drivers make $33 an hour when they are driving to a customer or they have a customer in the car, but we are not going to count any of the downtime. – Harry Campbell While drivers are the backbone of the platforms, there are divergences in how Uber and Lyft attract and retain drivers. Both companies use incentives to retain drivers, Lyft is starting to increase the amount incentives to attract power drivers away from Uber. Power drivers are drivers who drive more than 40+ hours a week or roughly 6,000 miles per month. Accounting for 20% of the driver inventory at any moment.As Uber continues to grow and shed non-core assets, the company is laying the foundation to transform Uber into a hybrid platform with both drivers and autonomous vehicles. Today, you can hail a Waymo in Phoenix on the Uber app and have Uber Eats delivered in a Motional autonomous vehicle in Santa Monica. When it comes to Uber's strategy with AV, I think it's kind of a no-brainer. – Harry Campbell This is the right strategy for Uber. Dara Khosrowshahi made the strategic decision to sell Uber ATG to Aurora and focus on becoming a platform again. Uber was able to shed the billions in development costs, while fully embracing the power of Uber — the platform. This decision has allowed Uber to focus on growing their free cash flow while becoming profitable. The Uber 2.0 strategy will enable Uber to collect a fee very similar to the way Mastercard and Visa collect swipe fees every time a consumer makes a purchase with their credit card. The more consumers choose to ride in Waymo vehicles on the Uber platform, the more revenue Uber will generate. Uber's new autonomous vehicle strategy will pay dividends as Waymo scales up. If the price of a Waymo is on par with Uber X, consumers in our opinion will overwhelmingly choose Waymo because of the consistent experience. Either way, Uber benefits as the company will collect a platform usage fee. Wrapping up the conversation, Harry shares his opinion on the future of Uber. Episode Chapters0:00 The Road to Autonomy Index Introduction0:55 Uber vs S&P 5002:05 Does Lyft Survive?3:32 Rideshare Drivers: Driving for Uber and Lyft17:41 Uber and Lyft Driver Incentives 21:40 Most Popular Rideshare Vehicles 25:33 Dara Khosrowshahi29:36 Do Uber Drivers Buy UBER Stock?35:20 Changes Drivers Would Like to See on the Uber and Lyft Platforms41:50 Autonomous Vehicles as Rideshare Vehicles (Robotaxis)44:50 Uber's Autonomous Vehicle Strategy49:10 Lyft's Earnings Blunder50:38 Uber's Product Compared to Waymo53:44 Expanding the Uber Platform1:07:56 Uber Freight1:10:20 The Future of UberRecorded on Thursday, February 22, 2024Uber is a The Road to Autonomy Index component company--------About The Road to AutonomyThe Road to Autonomy® is a leading source of data, insight and analysis on autonomous vehicles/trucks and the emerging autonomy economy™. The company has two businesses: The Road to Autonomy Indices, with Standard and Poor's Dow Jones Indices as the custom calculation agent; Media, which includes The Road to Autonomy podcast and This Week in The Autonomy Economy newsletter.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Want to help define the AI Engineer stack? >800 folks have weighed in on the top tools, communities and builders for the first State of AI Engineering survey, which we will present for the first time at next week's AI Engineer Summit. Join us online!This post had robust discussion on HN and Twitter.In October 2022, Robust Intelligence hosted an internal hackathon to play around with LLMs which led to the creation of two of the most important AI Engineering tools: LangChain

In this episode, we speak with Eli Schleifer, Co-CEO of Trunk. We discuss why engineering sucks, what developers can learn from how software gets built at Google and Uber, how individual developers can improve their coding experience, and why Git commit messages are useless.Hosted by David Mytton (Console) and Jean Yang (Akita Software).Things mentioned:Mythical Man-MonthGoogleUberBitTorrent"Git commit messages are useless"WarpSlackLinearVisual Studio CodeMacBook Pro M1 ABOUT ELI SCHLEIFEREli Schleifer is the founder and co-CEO of Trunk, an all-in-one solution for scalably checking, testing, merging, and monitoring code. It helps developers write more secure code and ship faster to redefine software engineering at scale. He was previously a technical lead manager and a systems architect at Uber ATG, where he led the architecture and engineering of its self-driving platform. He also lead a team of engineers and technical leads in the development of multiple products under the YouTube Director umbrella and was a lead senior software development engineer at Microsoft. Highlights:[Eli Schleifer]: We should trust our engineers and also understand that code is constantly – it's a living document. It's changing all the time. If something gets in that's imperfect but not terrible, that's also okay. So if you have an engineer put up a pull request, you have feedback, leave that feedback and stamp the pull request. Assuming there's trust, then the engineer is going to follow up, fix up your comments, and then land that. There's no additional cycle. If you don't stamp it, that means you're going to— you're basically saying to this person, “I'm going to hold up your work until you show me that you can actually follow through on the things I'm asking about.” That's a level of distrust that, I think, is not good in a highly collaborative working environment.— [0:15:48 - 0:16:28][Eli Schleifer]: I think this is the biggest thing between a smaller startup and a giant tech company: At a giant tech company, at the end of the year, the giant tech company comes to the employee and is like, “Tell me what you did this year and why you have this job. Tell me all the good stuff you did for us.” At a smaller company, all management knows what all the people are actually doing for you. There's a clear visibility into what those engineers are adding and contributing to the actual company's efforts. I think the biggest thing to focus on when it gets to 200 engineers or 2,000 is: what are these people actually working on? Who's making sure that there's a director of engineering for each of these smaller groups of 30, 40 people to make sure they're actually pushing towards something that matters, that matters to the company, that's going to move the needle? And that those engineers can still feel pride in and feel like they have impact?— [0:27:22 - 0:28:09] Let us know what you think on Twitter:https://twitter.com/consoledotdevhttps://twitter.com/davidmyttonOr by email: hello@console.devAbout ConsoleConsole is the place developers go to find the best tools. Our weekly newsletter picks out the most interesting tools and new releases. We keep track of everything - dev tools, devops, cloud, and APIs - so you don't have to. Sign up for free at: https://console.dev

In March 2018, one of Uber ATG's self-driving test cars slammed into a pedestrian. Who is responsible for the accident: The pedestrian? The driver? The software? Or someone no-one suspected? In this episode, which is the first part of a two-part series on automation, we take a look at the 2018 Uber ATG accident in Tempe, Arizona, and use it as a starting point to discuss the role of automation. Automation has made us safer, there is no doubt about that. But if we add even more automation, will we become even safer? Sources for this episode include: The National Transportation Safety Board's accident report (https://www.ntsb.gov/investigations/accidentreports/reports/har1903.pdf); The article Driver in fatal Uber crash rejects plea deal; death in Tempe was 1st in nation for self-driving vehicle written by Ryan Randazzo and published on azcentral.com in June 2022; Julie Bort's excellent article Uber insiders describe infighting and questionable decisions before its self-driving car killed a pedestrian published in Business Insider in November 2018; Sumit Singh's article: Airbus Says Single Pilot Flight Crews Are The Long Term Future, published in Simple Flying in September 2021; Driver Behavior in an Emergency Situation in the Automated Highway System by Dick de Waard, Monique van der Hulst, Marika Hoedemaeker and Karel Brookhuis, published in Transportation Human Factors in 1999; Automated driving: Safety blind spots by Ian Noy, David Shinar, and William J. Horrey, published in Safety Science in February 2018. The news clip at the beginning of the podcast was taken from a 12 News Arizona report: https://www.youtube.com/c/12NewsAZ --- Send in a voice message: https://podcasters.spotify.com/pod/show/essenceofsafety/message

Some of what Guillaume talks about: Equilibrium between people and process Culture of safety What happens when you don't know how to test something Balancing innovation and safety Blameless post mortem Avoiding gaps in ownership Meet: Guillaume Binet is the Vice President of Software Infrastructure at Motional, a global leader in driverless technology. Guillaume's 20-year career in technology spans multiple start-ups and established organizations, including over six years in the autonomous vehicle industry. Early in his career, Guillaume led Twilio's API team and Google's App Engine team, helping both organizations scale and grow at a rapid pace. In 2016, Guillaume joined the self-driving industry, first at Uber ATG, starting the organization's software performance team, and then Argo, where he helped pioneer the concept of a Robotics Infrastructure team to bridge the gap between autonomy research and applied software on a real-time system. He joined Motional in 2020 to oversee the development of its vehicles' on-board and off-board infrastructure. In his positions, Guillaume has managed global teams of 100+ engineers. He holds multiple patents under his name. Raised in Paris, Guillaume developed a passion for computers and automotive technology at a young age. Along with his professional career, Guillaume has received his pilot's license and competes in car and sailboat racing. If you have any questions for Guillaume, please feel free to reach out via: https://www.linkedin.com/in/gbinet I hope you enjoyed the episode, the best place to connect with me is on Linkedin - https://www.linkedin.com/in/amirbormand (Amir Bormand). Please send me a message if you would like me to cover certain topics with future guests.

Aurora's Gerardo Interiano stops by TechVibe Radio to talk about the company's headline-making year. He will not only tell us about Aurora's IPO last month, but will detail Aurora making Pittsburgh its headquarters, new digs in the Strip, acquiring Uber ATG and winning Tech 50. Whew!  Plus learn about Highmark's new Well360 Motion technology powered by Sword Health helps people better use physical therapy to cure musculoskeletal ailments from back pain to sore shoulders!

Join us as we speak with Product Manager Ken Yesh about his work on advanced automotive technologies. Ken's work with Ford, Uber ATG, and more recently, Root, have endowed him with a valuable set of skills and experiences. In this episode, Ken and Spencer talk politics, philosophy, and reflect on the pandemic. This is a low key episode, but still pensive. If you like this and want more, please subscribe. That way, you'll be the first to know when we release more awesome content! You can additionally check out full videos of the episodes on YouTube at video.spencerkrause.com

Join us as we speak with Product Manager Ken Yesh about his work on advanced automotive technologies. Ken's work with Ford, Uber ATG, and more recently, Root, have endowed him with a valuable set of skills and experiences. In this episode, Ken and Spencer talk politics, philosophy, and reflect on the pandemic. This is a low key episode, but still pensive. If you like this and want more, please subscribe. That way, you'll be the first to know when we release more awesome content! You can additionally check out full videos of the episodes on YouTube at video.spencerkrause.com

Raquel Urtasun, former head of Uber ATG's R&D efforts, discusses her new self-driving startup, new ways to utilize machine learning in self-driving systems and why trucks are the natural first use case for automated-driving tech.

Artificial Intelligence is often given a bad rap. It has an ever-increasing role as the supervillain in everything from science fiction to the nightly news. But is Artificial Intelligence to blame for the unintended mishaps or misuse of the technology? Behind every algorithm and every line of code, there is a human being. These people wield an ever-increasing amount of power over our lives. Our future will inevitably be impacted by code; it will keep us safe, it will take us to where we need to go, it will manage our finances, and myriad other situations. In this episode, we'll talk with Carol Smith, Senior Research Scientist in Human-Machine Interaction at Carnegie Mellon University’s Software Engineering Institute and former UX researcher Uber ATG and UX Design Manager at IBM Watson. We'll discuss the role of ethics in technology and more specifically, what it means to create Ethical Artificial Intelligence. Learn more about your ad choices. Visit megaphone.fm/adchoices

Aurora CEO Chris Urmson delves into details on the engineering ‘horsepower’ provided by the groundbreaking acquisition, why he favors a trucking-first approach to autonomy and the versatility of the company’s virtual driver. Plus Pt. 2 of "Mobility at a Crossroads," featuring highlights from this month's Shift virtual event.

2020 is closing out with one of the biggest AV business stories of the year, first broken by our very own Kirsten Korosec: Aurora is buying Uber's ATG operations, ending the ride-hailing giant's tragic development effort. The gang discusses what this consolidation means for the sector, and looks back at some of the biggest AV-related trends of 2020.

Aurora made massive headlines last week announcing its acquisition of Uber ATG to accelerate its autonomous vehicle technology development right here in Pittsburgh. We are excited to welcome Gerardo Interiano, Head of Government Relations at Aurora, to provide more insight on the acquisition and what it means to Pittsburgh's tech ecosystem. Gerardo will also expand more on Aurora's commitment to grow in the region as we move into 2021. Aurora, founded in 2017, is delivering the benefits of self-driving technology safely, quickly and broadly. The company is building the Aurora Driver, a platform that brings together software, hardware and data services to operate passenger vehicles, light commercial vehicles, and heavy-duty trucks across a range of applications.

Aurora made massive headlines last week announcing its acquisition of Uber ATG to accelerate its autonomous vehicle technology development right here in Pittsburgh. We are excited to welcome Gerardo Interiano, Head of Government Relations at Aurora, to provide more insight on the acquisition and what it means to Pittsburgh's tech ecosystem. Gerardo will also expand more on Aurora's commitment to grow in the region as we move into 2021. Aurora, founded in 2017, is delivering the benefits of self-driving technology safely, quickly and broadly. The company is building the Aurora Driver, a platform that brings together software, hardware and data services to operate passenger vehicles, light commercial vehicles, and heavy-duty trucks across a range of applications.

Destaque às mais recentes inovações e movimentos estratégicos da SpaceX, Revolut, Uber e Google. SpaceX recebe fundos para distribuir Internet em zonas ruraisRevolut Business lança solução para pagamentos onlineUber vende unidade de condução autónoma Plataforma de videojogos da Google chega a PortugalSaiba mais sobre inovação e nova economia em supertoast.pt. 

Aurora's acquisition of Uber's self-driving unit takes center stage on episode 27. We break down the acquisition and try to figure out why Aurora would have purchased Uber's troubled self-driving unit. We also catch up with Joel Reed, the interim executive director of the Pittsburgh Robotics Network. Joel updates us on the status of the cluster, tells us about some interesting companies to watch, his daily sightings of autonomous vehicles, and how the cluster has handled the COVID-19 pandemic.

Uber verkauft Sparte für selbstständiges fahren Baudu darf in Peking autonomes fahren testen Twitter Nutzer werden mit Werbung vollgeballert Airbnb hilft mit Notunterkünften aus Google Stadia ist in 8 weiteren europäischen Ländern verfügbar

On this episode of The Internship Show, we speak with Renee Davis from Duolingo. A Former PR girl gone University Recruiter who Made a career change to UR in 2016. She previously Worked at rue21 as the Associate Manager of Early Talent, led University Recruiting at Arconic, joined Uber ATG and recruited for teams while also supporting Diversity Initiatives. Now managing the University Recruiting team at Duolingo. She shared the structure of their internship program, an overview of their company and why it is such a great place to work. This episode was brought to you by Scholars. Scholars amplifies top employer brands to an audience of diverse students from across the country through curated podcasts, blogs, newsletters and more. Subscribe on Apple Podcasts Subscribe on Spotify Subscribe on Google Podcasts Listen to past episodes here! Want to be a guest on the show? Click here to contact Parker about why you should be featured on The Internship Show! --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app

Euan Guttridge, Angel Investor & Founder of Reinforced Ventures Previously served at Uber ATG (self-driving) as Head of Technical Program Management in Pittsburgh, San Francisco and Toronto. Prior to that wore various hats at Google and Google [X] for ten years leading initiatives in the US, Europe, Africa and the Middle East. Angel Investor for 8 years in over 30 companies. Active advisor & board member. Active in the Pittsburgh community arranging Xoogler/Googler/Investor events and Autonomy Engineering meetups. Mentor at the Innovation Works - AlphaLab incubator and coach at Carnegie Mellon's VentureBridge and McGinnis entrepreneur competition. Join Our community of thousands of entrepreneurs at https://entre.link/EntrepreneurShow Follow us on social media @joinentre --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app

Sarcos Robotics' Ben Wolff joins us to discuss the challenges and opportunities of full-body exoskeletons. We talk about Uber's self-driving car division reportedly being low on cash. And Clearpath Robotics' Dave Niewinski is back to chat about developing ROS support for Boston Dynamics' Spot quadruped.

Ludwig is a code-free deep learning toolbox originally created and open sourced by UberAI. Today, on the podcast the creator of Ludwig Piero Molino and Wes Reisz discuss the project. The two talk about how the project works, its strengths, it’s roadmap, and how it’s being used by companies inside (and outside) of Uber. They wrap by discussing path ahead for Ludwig and how you can get involved with the project. Why listen to this podcast: • Uber AI is the research and platform team for everything AI at the company with the exception of self-driving cars. Self-driving cars are left to Uber ATG. • Ludwig allows you to specify a Tensorflow model in a declarative format that focuses on your inputs and outputs. Ludwig then builds a model that can deal with those types of inputs and outputs without a developer explicitly specifying how that is done. • Because of Ludwig’s datatype abstraction for inputs and outputs, there is a huge range of applications that can be created. For example, an input could be text and output could be a category. In this case, Ludwig will create a text classifier. An image and text input (such as a question: “Is there a dog in this image”) would output a question answering system. There are many combinations that are possible with Ludwig. • Uber is using Ludwig for text classification for customer support. • Datatypes can be extended easily with Ludwig for custom use cases. • Ludwig would love to have people contribute to the project. There are simple feature requests that are just not prioritized with the current contributor workload. It’s a great place to get involved with machine learning and gain experience with the project. More on this: Quick scan our curated show notes on InfoQ https://bit.ly/2JGA5wC You can also subscribe to the InfoQ newsletter to receive weekly updates on the hottest topics from professional software development. bit.ly/24x3IVq Subscribe: www.youtube.com/infoq Like InfoQ on Facebook: bit.ly/2jmlyG8 Follow on Twitter: twitter.com/InfoQ Follow on LinkedIn: www.linkedin.com/company/infoq Check the landing page on InfoQ: https://bit.ly/2JGA5wC From time to time InfoQ publishes trend reports on the key topics we’re following, including a recent one on DevOps and Cloud. So if you are curious about how we see that state of adoption for topics like Kubernetes, Chaos Engineering, or AIOps point a browser to http://infoq.link/devops-trends-2019.

Destaque aos mais recentes movimentos estratégicos da Tesla, Uber, Amazon e Grab. Tesla vai lançar táxis autoguiados em 2020 Uber ATG capta mil milhões de dólares Amazon lança serviço de música gratuito para smart speakers Grab vai lançar novos serviços em Singapura Mais sobre inovação e nova economia em supertoast.pt.

Mike serves as Head of Data Science at Uber ATG and lecturer for UC Berkeley iSchool Data Science master’s program. Mike has led several teams of Data Scientists in the bay area as Chief Data Scientist for InterTrust and Takt, Director of Data Sciences for MetaScale, and Chief Science Officer for Galvanize he oversaw all data science product development and created the MS in Data Science program in partnership with UNH. Mike began his career in academia serving as a mathematics teaching fellow for Columbia University and graduate student at the University of Pittsburgh. His early research focused on developing the epsilon-anchor methodology for resolving both an inconsistency he highlighted in the dynamics of Einstein’s general relativity theory and the convergence of “large N” Monte Carlo simulations in Statistical Mechanics’ universality models of criticality phenomena. In this episode, Michael talks about how he accidentally got into data and his work with simulation. Then, Michael discusses his background in data science product development and data science education. He reveals all the mistakes he made with his transition from academics to industry. Later, Michael tells us what attracted him to data science education and how he balances industry projects with his teachings. Rapid growth is a challenge with technology management because your skillset will get rusty as the technology advances. Lastly, Michael talks fake news, bootstrapping, and Fake or Fact. In This Episode: [00:20] Michael accidentally got into data [02:15] About Michael Tamir [03:40] Transition to industry [06:40] Software engineering challenges [08:45] Data Science Education [15:15] Adaptive learning [17:15] Team management [19:05] Challenges with rapid growth [24:25] Fake news [27:25] Toughest challenge [28:50] Fake or Fact [31:20] Listener questions Mike's quotes from the episode: “You have to be really careful about what you do and what you do not teach in order to make sure students are successful in the long-term.” “Decisions are going to be best made by those who are closest to the ground.” “You’re not going to be the expert in every group you are managing.” “I take full responsibility for any failures with the algorithm.” “Most of my time is spent on my day job.” “Find out what you enjoy about data science skills; find the role that is looking for those skills.” “I enjoy the science and making sure we are asking the questions in a scientifically sound way.” Connect: Twitter - https://twitter.com/MikeTamir LinkedIn – https://www.linkedin.com/in/miketamir/ Website - http://www.fakeorfact.org Now you can support Data Futurology on Patreon! https://www.patreon.com/datafuturology Thank you to our sponsors: UNSW Master of Data Science Online: studyonline.unsw.edu.au Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show! --- Send in a voice message: https://anchor.fm/datafuturology/message

Today’s guest on the show is Mike Tamir, Head of Data Science at Uber ATG in San Francisco. Uber are working to bring the future closer with self-driving technology and urban air transport. They also help people order food quickly and affordably, remove barriers to healthcare, create new freight-booking solutions and help companies provide a seamless employee travel experience. Mike is a leader in Data science, specializing in deep learning and distributed scalable machine learning. He has a wealth of experience delivering data products for use cases including text comprehension, image recognition, recommender systems, targeted advertising and customer analytics. He is a pioneer in developing machine learning and data science training programs in the industry. In the show today, Mike will tell you about: How he became interested in Data Science His role at Uber ATG Applying Deep Learning to evaluate article embedding in fake news evaluation Learning and sharing interesting content on social media Advice he would share to Data Scientists moving from academia to industry Emerging trends in AI and Data Science that excite him for the future

Mike Tamir is the Head of Data Science at Uber ATG. He is a leader in data science, specializing in deep learning and distributed scalable machine learning, and he’s also a faculty member at UC Berkeley.   Mike has led several teams of Data Scientists in the San Francisco Bay Area as Chief Data Scientist for InterTrust and Formation, Director of Data Sciences for MetaScale, and Chief Science Officer for Galvanize, where he oversaw all data science product development. He also created an MS degree program in Data Science in partnership with UNH.   Mike began his career in academia serving as a mathematics teaching fellow for Columbia University and graduate student at the University of Pittsburgh. His early research focused on developing the epsilon-anchor methodology for resolving both an inconsistency he highlighted in the dynamics of Einstein’s general relativity theory and the convergence of “large N” Monte Carlo simulations in Statistical Mechanics’ universality models of criticality phenomena.   The focus of today’s conversation was on his fake news detection AI project called Faker Fact.   Show notes:   0:00 First, a life update from AJ. Read about his new opportunity in Portland here on his blog. 5:28 What is the evolutionary explanation for why a human’s capacity for careful, rational thought often takes a back seat to emotion? Explained in a comic on the project website. 6:17 Emotions often win over rational though, but as a result, it can be difficult to think clearly on issues we’re passionate about. 7:05 Why people should be aware of their emotional biases, even though it’s not our fault that we have them. 7:50 Why Facebook deleted over a billion fake accounts recently, and why fake accounts, clickbait, blatantly false content, and other forms of fake news are everywhere on social media. 9:10 What mechanisms can we put in place to counterbalance the parts of our nature that compel us to create and engage with content on an emotional level? 9:51 Since a majority of our information is second-hand, how do we distingush what’s really true? 11:44 How did Mike become motivated to pursue this problem, on top of his full time job at Uber ATG? 12:45 How can we tackle “fake news” without censorship? 16:40 Post-Walter Cronkite era, how do we create a sense of credibility and neutrality in our information? 21:00 Why would it be a mistake if the algorithm learned to only classify right or left wing content as fake news? 22:19 The algorithm only looks at the title and words on a page, not the url. 23:15 How Walt (the FakerFact AI) classifies different types of content. Satire, journalism, etc. 26:46 How do you strike the balance of entertainment and informativeness in content? 31:10 What features and characteristics defines each different category of content that Walt identifies? 36:16 What is Walt’s ideal use case? 36:55 You can use the FakerFact Chrome extension to view the “nutrition facts” of the page you’re reading. 37:42 How does research on run-on sentences and other grammatical choices help Walt understand and score an article? 40:34 What techniques were used to train the Walt AI? 42:41 A discussion on the use of wisdom of the crowds in algorithms. 45:30 What makes it difficult to use the wisdom of the crowds when answers are too closely correlated (because of political affiliations or the news cycle?) 46:47 Visit Humanetech.com for tips on regulating your daily notifications and escaping the “24-hour news cycle” to prevent media from controlling your emotions. 50:15 Rapid fire questions! 52:27 Mike’s advice to his 20 year old self. 52:40 What was his best investment in himself? 53:18 The Deep Learning Book a starting point for basic literacy in data science. 53:20 Mike, like lots of guests on this show, makes a distinction between things he believes but couldn’t prove right now, and believing things for no good reason. Show Notes: https://ajgoldstein.com/podcast/ep22 AJ’s Twitter: https://twitter.com/ajgoldstein393/ Mike’s LinkedIn: https://www.linkedin.com/in/miketamir/ Mike’s Twitter: https://twitter.com/MikeTamir

The Autonocast heads to Pittsburgh to get a first-hand look at robot city — the home of Carnegie Mellon University and numerous autonomous vehicle test programs, including Aptiv, Argo AI Aurora and Uber ATG. In the first in a series of episodes on Pittsburgh, Ed, Alex and Kirsten recount their impressions and their first ride on public streets in Argo AI's autonomous vehicles.

How do you determine what is fake news? Dr. Mike Tamir talks about how his team is using deep learning to detect fake news at scale. Learn more about his work by going to: https://www.fakerfact.org/ Mike serves as Head of Data Science at Uber ATG and lecturer for UC Berkeley School Data Science masters program.

In this podcast Mike Tamir (@MikeTamir, Head of #DataScience) talked about building a data science AI team. He shared his AI project (FakerFact.org). He shared the lifecycle of an AI project and some things that leaders could keep in mind to help create a successful data science AI team. This podcast is great for leaders learning to build a strong AI workforce. TIMELINE: 0:28 Micheal's journey. 2:36 Micheal's current role. 3:18 AI and businesses. 5:28 Parameters to consider for AI adoption. 9:30 When do businesses invest in ML resources. 13:20 Tips for candidates in vetting data companies. 16:05 What's the faker fact? 20:45 Getting started on an AI product design. 24:58 Achieving accuracy in data. 27:40 AI the newsmaker and AI the fact-checker. 33:56 Tips for hiring the right data leader for a business. 35:32 Creating a great data science team. 37:19 Challenges in forming a data science team. 39:00 In job training to achieve technological competence. 44:00 Ingredients of a good hire. 47:35 Micheal's secret to success. 50:55 Micheal's favorite reads. 54:20 Key takeaways. Mike's Recommended Read: What Technology Wants by Kevin Kelly https://amzn.to/2MaNiuN Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville http://www.deeplearningbook.org/ Podcast Link: https://futureofdata.org/building-data-science-ai-teams-by-miketamir-uberatg-futureofdata-podcast/ Mike's BIO: Mike serves as Head of Data Science at Uber ATG, UC Berkeley Data Science faculty, and head of Phronesis ML Labs. He has led teams of Data Scientists in the bay area as Chief Data Scientist for InterTrust and Takt, Director of Data Sciences for MetaScale/Sears, and CSO for Galvanize, where he founded the galvanizeU-UNH accredited Masters of Science in Data Science degree and oversaw the company's transformation from co-working space to Data Science organization. Mike's most recent passion in research has involved applying Machine Learning techniques to help combat fake news through the FakerFact.org project About #Podcast: #FutureOfData podcast is a conversation starter to bring leaders, influencers and lead practitioners to discuss their journey to create the data-driven future. Wanna Join? If you or any you know wants to join in, Register your interest @ https://analyticsweek.com/ Want to sponsor? Email us @ info@analyticsweek.com Keywords: #FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

Fake news: how can data science and deep learning be leveraged to detect it? Come on a journey with Mike Tamir, Head of Data Science at Uber ATG, who is building out a data science product that classifies text as news, editorial, satire, hate speech and fake news, among others. We'll also see what types of unique challenges Mike faced in his work at Takt, using data science to service the needs of Fortune 500 companies such as Starbucks.Links from the showFROM THE INTERVIEWFakerFact(Chrome Extension)FakerFact (Firefox Extension)FakerFact The Unreasonable Effectiveness of Recurrent Neural Networks by Andrei KarpathyFROM THE SEGMENTSThe Double-edged Sword of Impact Parts I & 2 (with Friederike Schüür, Cloudera Fast Forward Labs)Media Manipulation and Disinformation Online from Data & SocietyJames Bridle's blog post 'Something is wrong on the internet'The Cost of Fairness in Binary Classification (.pdf), a paper by Menon & Williamson (2018)Multisided Fairness for Recommendation, a paper by Burke (2017)All The Cool Kids, How Do They Fit In? Popularity and Demographic Biases in Recommender Evaluation and Effectiveness, a paper by Ekstrand et al. (2018)The spread of true and false news online, a paper by Vosoughi et al. (2018)Original music and sounds by The Sticks.

In this episode i'm joined by Inmar Givoni, Autonomy Engineering Manager at Uber ATG, to discuss her work on the paper Min-Max Propagation, which was presented at NIPS last month in Long Beach. Inmar and I get into a meaty discussion about graphical models, including what they are and how they’re used, some of the challenges they present for both training and inference, and how and where they can be best applied. Then we jump into an in-depth look at the key ideas behind the Min-Max Propagation paper itself, including the relationship to the broader domain of belief propagation and ideas like affinity propagation, and how all these can be applied to a use case example like the makespan problem. This was a really fun conversation! Enjoy! Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML. Visit twimlai.com/ainy2018 for registration details. Early price ends February 2!