uber


Artificial Intelligence for Automotive Service - ShiftMobility Inc.

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"The future is already being automated, and it's enabled by AI" Uber, whose AI is so central to its business model that employees "…don't even think about it anymore," is betting big on self-driving cars driving down costs. As their core driver of competitiveness, it stands to reason that if Artificial Intelligence is smart enough to drive a car it can surely help the shop owner who doubles as its sole mechanic. Our previous entry explored how AI will impact the manufacturing and distribution of auto parts, but what about the businesses that purchase and use them on a daily basis? For service centers doing everything they can to move jobs out of the bays and customers through their doors, activities that add value or increase average ticket prices can fall by the wayside. "Advances in computing power will give machines abilities once reserved for humans--the ability to understand and organize unstructured data such as photos and speech, to recognize patterns, and to learn from past experiences how to improve future performance."


Last Week in AI

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Every week, Invector Labs publishes a newsletter that covers the most recent developments in AI research and technology. You can find this week's issue below. You can sign up for it below. Training is one of the frequently overlooked elements of building machine learning solutions at scale. While training machine learning models seems relatively simple conceptually, it gets really complicated when applied to large models or to a large number of models.


Turing Award For Pixar, EfficientNet Lite Release And More:Top AI News

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Regardless of what is happening around the world, the AI community are one productive bunch, and they have something interesting to share almost every day. Here's what is new this week: The short history of deep learning indicates the incredible effectiveness of infinitely wide networks. Insights from these infinitely wide networks can be used as a lens to study deep learning. However, implementing infinite-width models in an efficient and scalable way requires significant engineering proficiency. To address these challenges and accelerate theoretical progress in deep learning, Google's AI team released Neural Tangents, a new open-source software library written in JAX.


Turing Award For Pixar, EfficientNet Lite Release And More:Top AI News

#artificialintelligence

Regardless of what is happening around the world, the AI community are one productive bunch, and they have something interesting to share almost every day. Here's what is new this week: The short history of deep learning indicates the incredible effectiveness of infinitely wide networks. Insights from these infinitely wide networks can be used as a lens to study deep learning. However, implementing infinite-width models in an efficient and scalable way requires significant engineering proficiency. To address these challenges and accelerate theoretical progress in deep learning, Google's AI team released Neural Tangents, a new open-source software library written in JAX.


Why Haven't Companies Delivered On Their Autonomous Driving Promise

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Elon Musk promised us that automated vehicles would be on the streets in 2019, he made this claim back in 2017. And it wasn't only him, other companies also promised autonomous cars and failed to deliver. The vision of self-driving cars has encountered a lot of pitfalls, and with them not being on the roads yet, it seems that autonomous vehicles have posed problems that weren't expected by the manufacturers. These autonomous cars/vehicles hold a lot of potential like reducing the traffic-related accidents by 90%, reducing 60% of harmful emissions, 40% reduction in travel time etc. With so many possibilities, one has to wonder what happened?


Former Google engineer Anthony Levandowski guilty of stealing secrets

The Japan Times

SAN FRANCISCO – A former Google star engineer charged with stealing trade secrets from its self-driving car program has agreed to plead guilty in a deal with prosecutors, according to court documents filed Thursday. Anthony Levandowski, 39, was a founding member of an autonomous vehicle project in 2009 called "Chauffeur," one of Google's more ambitious undertakings. Several years later Levandowski began thinking of leaving Google for another self-driving endeavor that was eventually named "Otto," the plea deal said. He began negotiating with ride-sharing giant Uber to invest in or buy Otto while he was still working at Google, and admits having downloaded a whole series of documents a few months before his resignation in January 2016. "Prior to my departure, I downloaded thousands of files related to Project Chauffeur," Levandowski said in court documents.


Can Tesla Beat Google and Uber to Self-Driving Car Dominance? The Motley Fool

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Follow me on LinkedIn for writing tips, professional guidance, and occasional entertainment. If you've got ideas for coverage, feedback on my work, or would like to be interviewed for a future article, please reach out to me on LinkedIn or Twitter. That's all an engineer from one major Japanese automaker could say after seeing the central control unit for Tesla's (NASDAQ:TSLA) Autopilot technology, extracted during Nikkei Business Publications' recent teardown (an investigative disassembling of tech hardware) of a Model 3. Japanese automakers might not be able to match Tesla's self-driving technological achievements, but that doesn't make Tesla the leader by default. Other companies, most notably Alphabet's (NASDAQ:GOOG) (NASDAQ:GOOGL) Waymo unit, have been hard at work on self-driving vehicle technology for years. And while Elon Musk may have the world's attention, Tesla simply doesn't possess as many resources to throw at the self-driving problem as the techies in Mountain View, California.


The Tech Behind Uber's Bet On Self-Driving Cars -

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For the first time, ride-hailing company Uber has opened up about what is going on under the hood of their ATG's machine learning infrastructure and versioning control platform for autonomous driving vehicles. ATG is the Advanced Technologies Group, which concentrates and researches on self-driving vehicles by deploying machine learning models into the cars. The self-driving division at Uber has more than 450 employees who have been working on autonomous vehicle technology for several years now. Recently, the self-driving team at Uber developed a set of tools and microservices to support the ML workflow known as VerCD. The team also discussed their self-driving vehicle components, which use machine learning models as well as the machine learning model life cycle.


Uber details VerCD, the AI tech powering its self-driving cars

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Uber, which hasn't publicly discussed the architecture of its autonomous car platform in great detail, today published a post laying out the technologies that enable engineers within its Advanced Technologies Group (ATG) to test, validate, and deploy AI models to cars. It gives a glimpse into the complexities of self-driving car development generally, and perhaps more importantly, it serves as a yardstick for Uber's driverless efforts, which suffered a setback following an accident in Tempe, Arizona in May 2018. According to Uber, the most important component of the ATG's workflow is VerCD, a set of tools and microservices developed specifically for prototyping self-driving vehicles. It tracks the dependencies among the various codebases, data sets, and AI models under development, ensuring that workflows start with a data set extraction stage followed by data validation, model training, model evaluation, and model serving stages. "VerCD … has become a reliable source of truth for self-driving sensor training data for Uber ATG," wrote Uber.


Autonomous vehicles get in the fast lane for next decade

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By 2030, a tenth of vehicles worldwide will be self-driving, and the market volume of fully automated cars getting into gear by this time is expected to be worth $13.7bn, according to the latest DossierPlus report from Statista. The analyst's study said that after billions of miles of tests in simulations or on public roads, self-driving cars are beginning to leave the test tracks. Autonomous driving has come a long way since Waymo (previously named the Google Self-Driving Car Project) started testing self-driving cars. The report noted that digital taxi firm Uber has invested more than $1bn over three years on self-driving cars. Statista also observed that when General Motors subsidiary Cruise received US$3.4bn in funding in 2018, the overall automotive startup funding had increased ten-fold over the past five years, reaching a record-breaking $US 27.5bn in 2018.