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) …
Trust me, I have no intention of trusting autonomous vehicle braking. One of the terms we see pop up in almost every technical vector is autonomous vehicles. As with 5G, the autonomous vehicle landscape is fraught with hype. That has even spilled over to the consumer marketing arena with tons of ads for automobiles showing hands-off braking, lane navigation, self-parking, and more. Depending upon with whom one speaks, autonomous vehicles are anywhere from level 3 to level 5. Of course, the only one who believes we are at level 5 is Elon Musk, with his claims for Teslas.
Managing supply chain is one of the biggest technological opportunities in the world. The potential to disrupt inefficiencies using innovations has led several startups, especially in India, to rise to the challenge. GoBOLT, a Gurugram-based tech startup, was founded in late 2015, to take on the mammoth and unorganised logistics industry in India. Founders Sumit Sharma, Parag Aggarwal, Naitik Baghlaall come from corporate backgrounds, having worked in companies like Ernst & Young, J M Financial, GSK, and Tata Motors. The idea for GoBOLT was born during Sumit's travels, while travelling to developed economies like the US and Canada, where asset utilisation in the trucking industry is very high.
Global Artificial Intelligence (AI) in Automotive Market has valued 566.80 Mn in 2016 and is estimated to reach US$ 10,600.3 Global Artificial Intelligence (AI) in Automotive Market is segmented by technology, offering, process, application, and geography. By technology, Global Artificial Intelligence (AI) in the automotive market is divided into Computer Vision, Machine Learning, Context Awareness, natural language processing. Based on the offering, Artificial Intelligence (AI) in Automotive Market is categorized hardware and software. By process, the market is fragmented into Data Mining, Signal Recognition, and Image Recognition.
Are you looking to incorporate AI tech in your existing business model or are you generally curious about this technology? In either case, there are some mind-boggling essential facts that you must know about AI. Starting with the basics, we are quickly briefing you about this technology. In the current industry scenario, some industry sectors are at the start of their AI journey, while others are veterans. Artificial Intelligence and Machine Learning are now considered one of the significant innovations since the microchip.
Deep learning also uses deduction, but in a linear, basic, and one-dimensional way. Training the artificial neural networks to classify lions as dangerous might make them sensitive only to lions. A bear can't get classified as dangerous automatically. Training them to identify a cat will only make them recognize a cat, but not deduce that a leopard belongs to the cat family. Similarly, through facial recognition, deep learning can tag faces on photos but might stumble when there are faces of siamese twins.
The automobile is a transformative invention that changed the technological, social, and economic fabric of our society. Now, we are witnessing a new era of accelerated automotive disruption, which according to McKinsey is likely to be far more impactful than the previous 50 years. While industry analysts are not entirely clear what the future of automotive technology holds, we know that SAP customers in the industry are focused on three central trends: driving the business with artificial intelligence, applying automation everywhere, and moving to autonomous vehicles. These trends are putting pressure on automotive businesses to make their operations fully digital, organize and manage their data more effectively, and integrate and connect their critical business systems.
The development of driverless car technology is on the rise, and automakers are investing millions and billions to be the first to market with their lineup of autonomous vehicles. But which company has made the largest investment in self-driving cars? Here's a look at what some of the top companies have invested in their driverless vehicle programs so far. The investment into the autonomous vehicle industry has reached over $100 billion, with the leader in spending investing more than half of this number, according to a report by Leasing Options. The report indicated that Volkswagen is driving the charge when it comes to driverless technology with an investment of $54.2 billion and 57 percent share in total industry investment of self-driving cars.
If you have seen one of the many schematic charts full of logos illustrating the autonomous vehicle ecosystem, you would be forgiven for being confused. Most, like the one linked to in the above paragraph, dive deep into the layers of technology involved in enabling cars to drive themselves. It provides a nice summary for people in the industry (with good eyesight). To the layperson, however, this can add to the confusion about how autonomous vehicles work. Also, it is important to note that the majority of the companies and the technologies represented only have to do with the vehicles.
AI is poised to benefit a multitude of industries in a variety of different ways. What does artificial intelligence in the near-term look like? How is it impacting industries and what should companies know about AI to remain competitive over the next few years? What are the early adopters of AI doing right now? Early adopters of AI include everything from automotive to marketing.
Machines' ability to learn by processing data gleaned from sensors underlies automated vehicles, medical devices and a host of other emerging technologies. But that learning ability leaves systems vulnerable to hackers in unexpected ways, researchers at Princeton University have found. In a series of recent papers, a research team has explored how adversarial tactics applied to artificial intelligence (AI) could, for instance, trick a traffic-efficiency system into causing gridlock or manipulate a health-related AI application to reveal patients' private medical history. As an example of one such attack, the team altered a driving robot's perception of a road sign from a speed limit to a "Stop" sign, which could cause the vehicle to dangerously slam the brakes at highway speeds; in other examples, they altered Stop signs to be perceived as a variety of other traffic instructions. "If machine learning is the software of the future, we're at a very basic starting point for securing it," said Prateek Mittal, the lead researcher and an associate professor in the Department of Electrical Engineering at Princeton.