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When self-driving cars are coming, for real

#artificialintelligence

Self-driving features have been creeping into automobiles for years, and Tesla (TSLA) even calls its autonomous system "full self-driving." That's hype, not reality: There's still no car on the market that can drive itself under all conditions with no human input. But researchers are getting close, and automotive supplier Mobileye just announced it's deploying a fleet of self-driving prototypes in New York City, to test its technology against hostile drivers, unrepentant jaywalkers, double parkers, omnipresent construction and horse-drawn carriages. The company, a division of Intel (INTC), describes NYC as "one of the world's most challenging driving environments" and says the data from the trial will push full self-driving capability closer to prime time. In an interview, Mobileye CEO Amnon Shashua said fully autonomous cars could be in showrooms by the end of President Biden's first term.


Self-driving cars confront a daunting new challenge: New York City streets

Washington Post - Technology News

Mobileye received a special permit from New York state, allowing manufacturers of "autonomous vehicle technology" to test on public streets. The permit requires that drivers be present in the vehicle but allows them to keep their hands off the steering wheel yet "be prepared to take control when required to … operate the vehicle safely and lawfully." It's unclear whether others may have applied. The state hasn't responded to a request for comment.


Intel's Mobileye takes its autonomous vehicle testing program to New York City – TechCrunch

#artificialintelligence

Mobileye, a subsidiary of Intel, has expanded its autonomous vehicle testing program to New York City as part of its strategy to develop and deploy the technology. New York City joins a number of other cities, including Detroit, Paris, Shanghai and Tokyo, where Mobileye has either launched testing or plans to this year. Mobileye launched its first test fleet in Jerusalem in 2018 and added one in Munich in 2020. "If we want to build something that will scale, we need to be able to drive in challenging places and almost everywhere," Mobileye president and CEO Amnon Shashua said during a presentation Tuesday that was streamed live. As part of the announcement, Mobileye also released a 40-minute unedited video of one of its test vehicles equipped with a self-driving system navigating New York's city streets.


Watch Intel's Mobileye test autonomous cars in NYC for the first time

Mashable

Mobileye, an Israeli autonomous vehicle startup acquired by Intel, is putting its self-driving system to the ultimate test: the mean streets of New York City. At an event this week, Intel announced that its Mobileye self-driving cars were approved to drive through NYC streets. It's also the first company to have a dedicated testing program in the country's most populous municipality. San Francisco and the Bay Area, along with Arizona, have been the usual testing sites for other companies like Waymo, Cruise, and Aurora. As the video shows, NYC is full of pedestrians (and jaywalkers!), buses, construction zones, aggressive drivers, double-parked cars, bridges, tunnels, delivery vans, and so much more.


3 Artificial Intelligence Stocks Leading the New Wave

#artificialintelligence

Sometimes, a new technology will change the world forever. 5,000 years ago, a nameless Sumerian started marking clay tablets with a stylus, and invented writing; a little over three centuries ago, the steam engine took its place in our lives; early in the last century, Henry Ford came up with the assembly line. There’s no telling what innovation will prove to be game-changing; but it is possible to narrow the field down. And that brings us to AI. Artificial Intelligence, AI, may just be the next big idea. It’s not quite new – computer scientists and programmers have been working on ‘intelligent machines’ since the 1950s, at least – but the tech is finally maturing, and autonomous computers, capable of collating data and making decisions in real time, are no longer a pipe dream. The implications are staggering. Practical AI makes it possible for machines to learn, and to apply that learning. AI programs underly advanced voice and facial recognition systems and fraud detection programs, applications that depend on pattern recognition. More advanced AI is being applied to the automotive industry, where it is used to monitor automobile systems in real time – and to permit driverless vehicles. And this has not been ignored by Wall Street. Analysts say that plenty of compelling investments can be found within this space. With this in mind, We’ve opened up TipRanks’ database, and pulled three AI stocks that are on the leading edge of the technology. Importantly, all three earn Moderate or Strong Buy consensus ratings from the analyst community, and boast considerable upside potential. TuSimple Holdings (TSP) The first AI stock we’re looking at here, TuSimple Holdings, is deeply involved in the autonomous vehicle industry. The company is working on AI systems that will power self-driving trucks, allowing for greater efficiency and safety in the long-haul trucking industry. TuSimple has developed an advanced autonomous driving system specifically for the needs of the trucking industry; the company’s AI backs a long-range perception system that can spot, recognize, and identify objects as far away as 1,000 meters. In another achievement, TuSimple last summer launched an Autonomous Freight Network, through which the company will address the trucking industry’s challenges. TuSimple’s AI tech will allow the company’s trucks to conduct long-haul freight runs. The AI will monitor sensor systems to keep the truck on the road, and navigate to the destination – in all weather, and even in traffic conditions. To raise capital, TuSimple held its IPO last month, offering 33.75 million shares to the public at $40 per share. Of those shares, 27 million were offered by the company, with an existing shareholder putting 6.75 million shares on the market. TuSimple received the proceeds from the shares it sold directly, totaling over $1.08 billion before expenses. Writing from Canadian investment bank RBC, analyst Joseph Spak notes that TuSimple is highly speculative – but that if it succeeds, the rewards will be enormous. “We understand concerns about vetting the technology, adoption and the path towards revenue and profitability. But if TuSimple succeeds, the equity value is significantly higher. As such, we view TuSimple very much like a venture investment in the public markets or perhaps, a biotech stock. The upside opportunity is massive. Proof points (milestones, orders) along the way should increase the market’s confidence in TuSimple’s mid-term targets and long-term opportunity, thereby increasing its stock price,” Spak explained. In line with his comments, Spak rates TSP an Outperform (i.e. Buy), and sets a $52 price target that suggests an upside of 44% in the next 12 months. (To watch Spak’s track record, click here) Overall, TuSimple personifies everything that risk-loving investors want in the stock market. It uses cutting edge tech; it has staked out a position in a field that is not quite here, but is coming; and it’s an early adopter. While still in early stages of building out its products and AI systems, the stock has attracted 7 analyst reviews – 6 to Buy, and 1 to Hold – giving it a Strong Buy consensus rating. The shares are selling for $36.08, and their $54.70 average price target imply a one-year upside of ~52%. (See TSP stock analysis on TipRanks) Nvidia Corporation (NVDA) Next up, Nvidia, is one of the giants of the silicon microprocessor industry. These are the computer chips that make all of the high tech systems possible. Nvidia was the eighth largest chip maker last year, with more than $16 billion in total sales, up 53% from the year before. Nvidia’s chief connection to AI is through the automotive industry. The company has long sold chips to car makers – automotive business makes up between 5% and 10% of Nvidia’s sales – but the car makers over the past year have been ordering more AI capable systems. Nvidia delivers chips and associated packages that allow an autonomous vehicle’s AI system to build perception, mapping, planning, and monitoring capabilities. Nvidia is working on transferring its automotive AI systems into the data center segment; the monitoring needs of large server stacks are comparable to those of autonomous vehicles, and will benefit from the application of machine learning. Covering NVDA for Baird, 5-star analyst Tristan Gerra rates the stock an Outperform (i.e. Buy) along with an $800 price target, which implies ~45% upside. The bull thesis is based on "Nvidia’s strong near-term positioning in AI data center markets and longer-term opportunities across many accelerated computing applications." (To watch Gerra’s track record, click here) "As Nvidia increasingly moves to platform solutions targeting and enabling all AI markets, while diversifying its architecture offering, the company is poised to over time dominate data center. Omniverse gives us an early glimpse of a virtual 3D world which Nvidia is at the forefront and ultimately yielding to a matrix computing world. More near term, GTC-announced foray into CPUs will expand Nvidia's computing TAM," Gerra opined. Overall, no fewer than 27 analysts have put reviews on NVDA on record, and of those, 24 are to Buy against just 3 to Hold. NVDA shares are selling for $550.34; the average price target of $682.20 implies an upside of 24% from that level. (See Nvidia stock analysis on TipRanks) Upstart Holdings (UPST) We’ll finish in financial tech, where Upstart Holdings has applied AI technology to power a lending platform. Using AI, the company aims to evaluate borrowers to determine actual risk levels and creditworthiness. A clearer understanding of the natural risks of lending money will allow lenders to approve more transactions, give otherwise marginal borrowers greater access to capital, and provide cost savings on both ends. Upstart boasts that its AI analysis platform has helped more than 698,000 customers to acquire loans, and that its model provides for 27% more loan approvals than traditional credit-scoring methods. Upstart’s AI evaluates 1,600 data points, and results in borrowers accessing funds at 16% lower rates than would otherwise be possible. The company has been in business since 2012, and went public on the NASDAQ in December of 2020. The IPO saw the company make 9 million shares made available to the public at $20 each, raising $180 million. In March of this year, Upstart released its first quarterly report as a publicly traded entity. The company reported $86.7 million in total revenues, up 39% from one year earlier. Of that total, $84.4 million was derived from usage fees. For the full year 2020, Upstart saw a 42% yoy increase in revenue, to $233.4 million. Among the bulls is Piper Sandler analyst Arvind Ramnani, who is impressed by both the company’s model, and its forward prospects. "We expect Upstart to expand its market share well beyond its primary product focus of unsecured personal loans, and its recently announced auto loans... Key to Upstart’s AI offering is its a) inherent training data advantage backed by the >1,620 variables aggregated to inform their models; b) AI algorithms that have been extensively tested and refined; c) Over 10.5M discrete repayment events that further validate the data and algorithms. Upstart’s SaaS-based revenue model (only ~1% balance sheet loan exposure) has the ability to deliver upside to our 58% CAGR (2020-2023E), in a massive market ($700B NT; $3.4T LT opportunity),” Ramnani opined. To this end, the analyst rates UPST shares an Overweight (i.e. Buy), and his $143 price target implies an upside of 65%. (To watch Ramnani’s track record, click here) Let’s take a look at how the rest of the Street sees 2021 panning out for UPST. Based on 4 Buys and 2 Holds, the stock has a Moderate Buy consensus rating. The average price target is $123.50 suggesting a 34.5% upside potential from the trading price of $91.82. (See UPST stock analysis on TipRanks) To find good ideas for AI stocks trading at attractive valuations, visit TipRanks’ Best Stocks to Buy, a newly launched tool that unites all of TipRanks’ equity insights. Disclaimer: The opinions expressed in this article are solely those of the featured analysts. The content is intended to be used for informational purposes only. It is very important to do your own analysis before making any investment.


AI's carbon footprint problem

#artificialintelligence

For all the advances enabled by artificial intelligence, from speech recognition to self-driving cars, AI systems consume a lot of power and can generate high volumes of climate-changing carbon emissions. A study last year found that training an off-the-shelf AI language-processing system produced 1,400 pounds of emissions -- about the amount produced by flying one person roundtrip between New York and San Francisco. The full suite of experiments needed to build and train that AI language system from scratch can generate even more: up to 78,000 pounds, depending on the source of power. But there are ways to make machine learning cleaner and greener, a movement that has been called "Green AI." Some algorithms are less power-hungry than others, for example, and many training sessions can be moved to remote locations that get most of their power from renewable sources.


Podcast: How Russia's everything company works with the Kremlin

MIT Technology Review

Russia's biggest technology company enjoys a level of dominance that is unparalleled by any one of its Western counterparts. Think Google mixed with equal parts Amazon, Spotify and Uber and you're getting close to the sprawling empire that is Yandex--a single, mega-corporation with its hands in everything from search to ecommerce to driverless cars. But being the crown jewel of Russia's silicon valley has its drawbacks. The country's government sees the internet as contested territory amid ever-present tensions with US and other Western interests. As such, it wants influence over how Yandex uses its massive trove of data on Russian citizens. Foreign investors, meanwhile, are more interested in how that data can be turned into growth and profit. For the September/October issue of MIT Technology Review, Moscow-based journalist Evan Gershkovich explains how Yandex's ability to walk a highwire between the Kremlin and Wall Street could potentially serve as a kind of template for Big Tech.


How an Automated Data Labeling Platform Fuels Self-driving Industry?

#artificialintelligence

NEW YORK, NY / ACCESSWIRE / August 26, 2020 / "I'm extremely confident that self-driving cars or essentially complete autonomy will happen, and I think it will happen very quickly," Tesla CEO Elon Musk said in a virtual speech to the World Artificial Intelligence Conference in July, 2020. Musk mentioned Tesla will have basic functionality for level-five complete autonomy this year. The self-driving vehicles is not just hot in Silicon Valley. In China, the largest automobile market worldwide, companies are also getting on board to develop autonomous driving technology, including China's internet search tycoon Baidu, also referred to as the "Google of China." Baidu has been developing the autonomous driving technology through its "Apollo" project (also known as open-source Apollo platform) launched in April 2017.


Open Compound Domain Adaptation

#artificialintelligence

Imagine we want to train a self-driving car in New York so that we can take it all the way to Seattle without tediously driving it for over 48 hours. We hope our car can handle all kinds of environments on the trip and send us safely to the destination. We know that road conditions and views can be very different. It is intuitive to simply collect road data of this trip, let the car learn from every possible condition, and hope it becomes the perfect self-driving car for our New York to Seattle trip. It needs to understand the traffic and skyscrapers in big cities like New York and Chicago, more unpredictable weather in Seattle, mountains and forests in Montana, and all kinds of country views, farmlands, animals, etc. However, how much data is enough?


Open compound domain adaptation

AIHub

Imagine we want to train a self-driving car in New York so that we can take it all the way to Seattle without tediously driving it for over 48 hours. We hope our car can handle all kinds of environments on the trip and send us safely to the destination. We know that road conditions and views can be very different. It is intuitive to simply collect road data of this trip, let the car learn from every possible condition, and hope it becomes the perfect self-driving car for our New York to Seattle trip. It needs to understand the traffic and skyscrapers in big cities like New York and Chicago, more unpredictable weather in Seattle, mountains and forests in Montana, and all kinds of country views, farmlands, animals, etc.