Google is updating critical features for the millions of drivers who depend on its technology to help them get around. The tech giant announced the upcoming changes Thursday to Google Assistant and Android Auto driving modes and a new automaker, Honda, will have Google technology installed in its vehicles. Google said that drivers using Google Assistant on Android phones will soon see a new dashboard they say will reduce "the need to fiddle with your phone while also making sure you stay focused on the road." Instead of scrolling while driving, Google said drivers could tap to see who just called or sent a text and have access to several apps to listen to music with the new dashboard. The dashboard will also include a new messaging update where drivers can say, "Hey Google, turn on auto-read," to hear their new messages read aloud when they come in and respond by voice.
Table-based fact verification task aims to verify whether the given statement is supported by the given semi-structured table. Symbolic reasoning with logical operations plays a crucial role in this task. Existing methods leverage programs that contain rich logical information to enhance the verification process. However, due to the lack of fully supervised signals in the program generation process, spurious programs can be derived and employed, which leads to the inability of the model to catch helpful logical operations. To address the aforementioned problems, in this work, we formulate the table-based fact verification task as an evidence retrieval and reasoning framework, proposing the Logic-level Evidence Retrieval and Graph-based Verification network (LERGV). Specifically, we first retrieve logic-level program-like evidence from the given table and statement as supplementary evidence for the table. After that, we construct a logic-level graph to capture the logical relations between entities and functions in the retrieved evidence, and design a graph-based verification network to perform logic-level graph-based reasoning based on the constructed graph to classify the final entailment relation. Experimental results on the large-scale benchmark TABFACT show the effectiveness of the proposed approach.
In this decade, companies across the globe have embraced the potential of artificial intelligence for digital transformation and enhanced customer experience. One important application of AI is enabling companies to use the pools of data available with them for smart business use. BMW is one of the world's leading manufacturers of premium automobiles and mobility services. BMW uses artificial intelligence in critical areas like production, research and development, and customer service. BMW also runs a project dedicated to this technology called Project AI, for efficient use of artificial intelligence.
Even if your 2004 Toyota Camry runs like a champ, you're still stuck using Google Maps with your iPhone in a mount. But there's a workaround to get Apple CarPlay running in older cars that weren't sold with the ability to mirror your phone display onto the car's dash screen (if it even has one). You can finally have all your music, podcast, navigation, and messaging apps on one convenient screen. And you'll even be able to use Siri for voice control. First, you need to buy the screen.
The detailed review of Automotive Artificial Intelligence was conducted in the Global Automotive Artificial Intelligence Market 2020 Survey to collect important and substantive data on Automotive Artificial Intelligence market size, growth rate, potential demand, and Automotive Artificial Intelligence sales forecasts from 2021 to 2026. It gives an analysis of the industry chain situation, key market players, market volume, upstream raw material, production cost, and marketing channels, volume, region-wise import/export analysis, and forecast market from 2021-2026. The Automotive Artificial Intelligence market has been changing everywhere throughout the world and we have been seeing an extraordinary development in the Automotive Artificial Intelligence and this growth is expected to be huge by 2026. The report covers Automotive Artificial Intelligence applications, market elements, and the analysis of rising and existing market segments. It shows the market outline, product classification, application, and market volume forecast from 2021-2026. The report includes insightful information about the primary part of the Automotive Artificial Intelligence market.
Alteryx, the public company best known in the self-service data preparation and pipeline realm, has always had interesting and significant AI/machine learning (ML) capabilities as part of its Designer platform. But today, at its Virtual Global Inspire event, the company is announcing some significant new AI/ML capabilities that should resonate with business users and power users alike. Also read: Alteryx says let's get visual ZDNet was briefed on the new products by Alteryx's Chief Data and Analytics Officer (CDAO), Alan Jacobson, who joined the company two years ago from his post as director of global analytics at Ford Motor Company. Alteryx's Intelligence Suite brings Machine Learning and Text Mining tabs into Designer, adding Natural Language Processing (NLP) and text mining; computer vision capabilities for image-based data and optical character recognition (OCR); as well as topic modeling and sentiment analysis. Jacobson described this set of features as the "Pythonic" equivalent of Alteryx's longstanding predictive capabilities based on the R programming language.
Multinational automaker Nissan manufactures vehicles in 20 countries worldwide, with production volume exceeding 5.6 million vehicles. And while its production assets were generating an abundance of operational and production data, the company lacked sufficient skilled resources to perform analysis on all of that data adequately. However, through an engagement with its vendor, Senseye, and using artificial intelligence (AI), Nissan was able to analyze that data and generate predictions on when its production machines would need maintenance. Before, the automaker used static metrics (such as the number of process cycles or the number of hours in service) to determine when a machine needs to be taken out of service for maintenance. Nissan used machine learning (ML) algorithms to monitor and spot patterns in the operational data of more than 9,000 connected assets and more than 30 different machine types, including robots, conveyors, drop lifters, pumps, motors, and press/stamping machines.
That's something you'll soon be able to ask in Ford's newest cars, including the upcoming electric F-150 Lightning pickup and first electric Mustang Mach-E. On Thursday, the Michigan-based company announced that Amazon's Alexa digital assistant would be built into future Ford cars. It'll be available in all cars, not just those with premium packages. The first three years of the voice system will be free. Ford expects to have Alexa available in at least 700,000 cars in the U.S. and Canada by the end of this year.
The importance of semiconductors in society has reached such a point that supply chain constraints in the sector are having drastic impacts on other parts of society. Last week, American auto giants General Motors and Ford said they would idle some of their factories due to a shortage of semiconductors, sending tens of thousands of workers onto approximately 75% pay, the Washington Post reported. It is expected that worldwide production drops will be measured in the millions. In the wake of such developments, Nvidia CEO Jensen Huang has said the automotive supply chain needs to be re-engineered. "The automotive industry supply chain has to be reinvented -- that's very clear," he told journalists this week after delivering the GTC 2021 keynote.
As part of its annual Women Leaders in AI program, today IBM (NYSE: IBM) recognized 40 innovative female business leaders from 18 countries who are using IBM Watson to help drive transformation, growth and innovation across a wide variety of industries. This year honorees include women leaders from The Clorox Company, City of Austin, The Depository Trust and Clearing Corporation, EY, Ford Motor Company, ServiceNow and many more. Celebrating diverse talent in the field of AI and creating a culture of inclusion are important as the adverse impact of the COVID-19 pandemic on women in the workplace becomes more apparent. A recent IBM Institute for Business Value study revealed that despite heightened awareness of the challenges facing women in the workplace driven by the pandemic, gender equality is still not a top priority for 70 percent of global businesses, according to business professionals surveyed. Moreover, fewer women surveyed hold senior vice president, vice president, director and manager roles in 2021 than they did in 2019.