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Technology Go – How Artificial Intelligence Influences Automotive Logistics

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Two weeks ago, the e-commerce retailer Amazon opened its first offline convenience store, Amazon Go – without a cashier. On January 22, the first visitors of the Seattle store were tracked in the shop using image recognition and machine learning algorithms. The technology finds out what the visitors...


AI And Automotive Retail In 2018: The Future Is Here

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CarStory uses big data to help match car buyers with potential vehicles in their area. Data is the lifeblood of AI. In the last two years alone, approximately 90% of the world's data was created. In combination with processing power, data has allowed for companies to make greater advancements in th...


There's a big problem with AI: even its creators can't explain how it works

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Last year, a strange self-driving car was released onto the quiet roads of Monmouth County, New Jersey. The experimental vehicle, developed by researchers at the chip maker Nvidia, didn't look different from other autonomous cars, but it was unlike anything demonstrated by Google, Tesla, or General ...


Artificial Intelligence in Transportation Industry Is Moving Fast Here Is What You Need To Know

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The artificial intelligence in transportation market is projected to grow at a CAGR of 17.87% from 2017 to 2030, and the market size is expected to grow from USD 1.21 Billion in 2017 to USD 10.30 Billion by 2030. The increasing government regulations for vehicle safety, growing adoption of advanced ...


Deep Learning for Recommender Systems – eBay Tech Berlin

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Finding a car that fits your preferences can be a very time-consuming task and may drive you crazy. On the other hand, with approximately 1.5 million cars on our platform, vehicle descriptions that are constantly changing and users that are still exploring may also drive us as the solution provider ...


Instilling AI safety into robotics through reinforcement learning - Enterprise IT Watch Blog

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Artificial intelligence (AI) is the perfect laughingstock. Any phenomenon that takes itself as seriously as AI is just asking to be ridiculed. What's even funnier is when AI comes in humanoid form, as is the case with the smart robotics that are penetrating every aspect of our lives. As Bill Vorhies discussed in his recent column, robot fails can be comedic gold. As the brains behind autonomous devices, AI can dampen the laughter only by helping devices master their assigned tasks so well and performing them so inconspicuously that we never give them a second thought. Where robotics are concerned, this involves the trial-and-error statistics-driven approach known as reinforcement learning (RL). Under this approach, the robot explores the full range of available actions--moving, grappling, voicing, etc.–that may or may not contribute to its achieving a desired outcome. Depending on your point of view, humor is baked into RL's intrinsically trial-and-error process. As a robot searches for the optimal sequence of actions to achieve its intended outcome, it will of necessity take more counterproductive actions than optimal paths. If you're the developer who's doing the training, this might be a long, frustrating, and tedious process. You may need to revise RL procedures and the robot's algorithmic cognition countless times till you get it to work in a way that can be generalized to future scenarios of the type for which the mechanism is being trained. This trial-and-error RL process may be humorous to observe in a laboratory setting. But when your AI-driven robot hasn't been trained effectively and commits these errors in production environments, it may not be funny in the least. This is amply clear from the incidents that Vorhies cites. No one will tolerate robots that routinely smash into people, endanger passengers riding in autonomous vehicles, or order products online without their owners' authorization. If we can draw any lesson from these incidents, it's that robotics developers will need to incorporate the following scenarios into their RL procedures before they release their AI-powered creations to the wider world: Controlled trial-and-error is how most robotics, edge computing, and self-driving vehicle solutions will acquire and evolve their AI smarts. To the extent that you're capturing an AI-driven device's RL training on video, it could prove to be the perfect "blooper reel" to show later on when your creation is a smashing success. For regulatory compliance and legal discovery purposes, this video may also help you prove that you've RL-trained your device in every relevant scenario, be it actual or simulated. In the near future, a video audit log of your RL process may become required for passing muster with stakeholders who require certifications that your creations meet all reasonable "AI safety" criteria. Considering the life-or-death scenarios in which the robots of the future will serve us, this is no laughing matter.


5 ways artificial intelligence is driving the automobile industry

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Artificial intelligence is taking the automobile industry by storm while all the major automobile players are utilizing their resources and technology to come up with the best. The beauty of devices with artificial intelligence is that it tries to learn from sensory inputs like real sounds and imag...


How to control a machine using your mind

BBC News

Imagine being able to make a machine do your bidding with your thoughts alone, no button pressing, typing, screen tapping or fumbling with remote controls, just brain power. Well, this sci-fi scenario could be closer to reality than you think. Bill Kochevar's life was changed, seemingly irrevocably, when he was paralysed from the shoulders down following a cycling accident nearly a decade ago. But last year he was fitted with a brain-computer interface, or BCI, that enabled him to move his arm and hand for the first time in eight years. Sensors were implanted in his brain, then over a four-month period Mr Kochevar trained the system by thinking about specific movements, such as turning his wrist or gripping something.


5 Exciting Machine Learning Use Cases in Business IoT For All

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The release of two machine learning (ML) model builders have made it easier for software engineers to create and run ML models, even without specialized training. Microsoft and Amazon Web Services' (AWS) Gluon is an open source project that eliminates some of the difficult work required to develop artificial intelligence (AI) systems. It provides training algorithms and neural network models, two important components of a deep learning system, that developers can use to develop their own ML systems. Google's ML engine is part of its cloud platform and is offered as a managed service for developers to build ML models that work on any type of data, of any size. Similar to Gluon, Google's service provides pre-trained models for developers to generate their own tailored ML models.


Trends In Analytics - 2020

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Data has evolved to become the lifeblood of every organization and analytics has grown and expanded enough that almost every organization today, recognizes the business value that analytics offers.Significantly improvedcomputational power, combined with low-cost storage and increasingly sophisticated algorithms mean that the next two-three years could possibly usher in the most exciting phase for analytics. Let's take a look at some of the trends that could dominate the near future. For the last couple of years, the trendwas to label everything that does something remotely clever or unexpected as Artificial Intelligence. While AI is certainly worthy of attention,2018 promises to be the year that separates the reality from the hype.Analytically mature organizations have already embarked on small scale experiments to embed greater smartness in their systems in areas of Chat Bots, Fraud detection, and so on. Those who have applied AI in a practical and clearly defined manner will see success.