If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Autonomous vehicle technology is just starting to go mainstream, which means, for the most part, it's still only available to those who can afford a Tesla with Autopilot or a Cadillac with SuperCruise. Both of those cars start at around 60 to 70 grand by the way. Famed hacker George Hotz has been developing a driver-assistance system, which can retrofit existing vehicles (with Level 2 autonomy), for a number of years now. However, we haven't been afforded a demo of the technology since 2015, when the company ran into regulatory issues with the US government. But with Friday's update release of Openpilot 0.5, the company's open source autonomy software, Comma.ai was back on the roads, taking Engadget for a spin to show off the system's new bells and whistles.
The hardware in charge of processing data is just as important as the hardware used to collect all that data in the first place. Silicon Valley will soon have even more autonomous vehicles littering its landscape. Bosch and Daimler announced this week that they will work to bring self-driving taxis to the Silicon Valley area, Automotive News reports. It was part of a discussion of a growing partnership that will see the supplier and automaker team up with chipmaker Nvidia for its future self-driving vehicles. The autonomous taxis will reportedly arrive next year and will be capable of proper autonomy.
An ex-Apple engineer has been charged with stealing secret blueprints for the tech giant's automated car project before trying to flee the US for China. Xiaolang Zhang was arrested by FBI agents at San Jose airport in California on Saturday when he passed through a security checkpoint. He is accused of downloading the plan for a circuit board for the automated car just days before he quit to go to a Chinese self-driving car startup. The charge is punishable by 10 years in prison and a $250,000 fine. A criminal complaint filed on Monday said Zhang was hired by Apple in December of 2015 to develop software and hardware for the company's autonomous vehicle project, where he designed and tested circuit boards to analyze sensor data.
Artificial intelligence (AI) is positively impacting our world in previously unimaginable ways across many different industries. The use of AI is particularly interesting in the cybersecurity industry because of its unique ability to scale and prevent previously unseen, aka zero-day, attacks. But remember, similar to how drug cartels built their own submarines and cellphone towers to evade law enforcement, and the Joker arose to fight Batman, so too will cyber-criminals build their own AI systems to carry out malicious counter-attacks. An August 2017 survey commissioned by Cylance discovered that 62% of cybersecurity experts believe weaponized AI attacks will start occurring in 2018. AI has been heavily discussed in the industry over the past few years, but most people do not realize that AI is not just one thing, but that it is made up of many different subfields.
Grab your beverage of choice and enjoy the read! Do hit reply if you're up for a brainstorming session on use cases, new research or ways to future proof your SaaS or enterprise product by implementing ML where it makes sense. On the current "AI revolution": In a lovely piece, Prof. Michael Jordan of Berkeley explores many of the central tenets driving the excitement around AI today. He makes the case for a new engineering discipline, defines the differences between human-imitative AI (i.e. "The current focus on doing AI research via the gathering of data, the deployment of "deep learning" infrastructure, and the demonstration of systems that mimic certain narrowly-defined human skills -- with little in the way of emerging explanatory principles -- tends to deflect attention from major open problems in classical AI. These problems include the need to bring meaning and reasoning into systems that perform natural language processing, the need to infer and represent causality, the need to develop computationally-tractable representations of uncertainty and the need to develop systems that formulate and pursue long-term goals. These are classical goals in human-imitative AI, but in the current hubbub over the "AI revolution," it is easy to forget that they are not yet solved."
Your next car probably won't be autonomous. But, it will still have artificial intelligence (AI). While most of the attention has been on advanced driver assistance systems (ADAS) and autonomous driving, AI will penetrate far deeper into the car. These overlooked areas offer fertile ground for incumbents and startups alike. Where is the fertile ground for these features?
We have spoken about machine learning and the internet of things as tools to optimize location analytics in logistics and supply chain management. It's an accepted fact that technology, especially cloud-based, can benefit companies by optimizing routes and predicting the accurate estimated time of arrivals (ETAs). The direct business value of this optimization lies in the streamlining of various fixed and variable costs associated with logistics. The Internet of Things (IoT) world may be exciting, but there are serious technical challenges that need to be addressed, especially by developers. In this handbook, learn how to meet the security, analytics, and testing requirements for IoT applications.
Artificial intelligence is quickly growing in importance in the'smart building' sector. Paul Skelton looks at the road ahead for a complex technology. When Mark Chung received an unexpectedly high $500 monthly electricity bill, he turned to his utility for help and answers. However, despite'smart' meters being installed in his home, they were no help. So Mark – an electrical engineer trained at Stanford University – took matters into his own hands.
Tesla Inc. TSLA 3.24% late Friday acknowledged its semiautonomous Autopilot system was engaged by the driver in the seconds before a fatal crash last week, raising more questions about the safety of self-driving technology on public roads. Federal investigators this week began examining the March 23 crash of a Model X sport-utility vehicle that was traveling south on Highway 101, near Mountain View, Calif., before it struck a barrier, then was hit by two other vehicles and caught fire. The driver of the Model X was killed. Tesla said its vehicle logs show the driver's hands weren't detected on the wheel for six seconds before the collision, and he took no action despite having five seconds and about 500 feet of unobstructed view of a concrete highway divider. On Tuesday, the National Transportation Safety Board dispatched investigators to the scene, the second Tesla crash to attract the agency's attention this year.
Nvidia has on Tuesday announced Drive Constellation, a cloud-based system for testing autonomous vehicles using photorealistic simulation via virtual reality, aiming to speed up the delivery of autonomous cars in a safer and more scalable way. According to Nvidia senior director of Automotive Danny Shapiro, every two minutes, four to five people die in vehicle-related accidents -- totalling 3,000 people per day globally. "This is a big problem, so we're really focused on bringing the hardware and software to solve the challenge," he said. Drive Constellation is a computing platform based on two different servers. The first runs the Nvidia Drive Sim software to simulate an autonomous vehicle's sensors, such as cameras, lidar, and radar; while the second boasts Nvidia Drive Pegasus, which is an artificial intelligence car computer that runs the autonomous vehicle software stack and processes the simulated data as if it was being fed in from sensors on a real car.