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) …
Deep learning, an advanced machine-learning technique, uses layered (hence "deep") neural networks (neural nets) that are loosely modelled on the human brain. Machine learning itself is a subset of Artificial Intelligence (AI), and is broadly about teaching a computer how to spot patterns and use mountains of data to make connections without any programming to accomplish the specific task--a recommendation engine being a good example. Neural nets, on their part, enable image recognition, speech recognition, self-driving cars and smarthome automation devices, among other things. However, the success of deep learning is primarily dependent on the availability of huge data sets on which these neural nets can be trained, coupled with a lot of computing power, memory and energy to function. To address this issue, says a 14 November press release, researchers at the University of Waterloo, Canada, took a cue from nature to make this process more efficient, thus making deep-learning software compact enough to fit on mobile computer chips for use in everything from smartphones to industrial robots.
Governor Andrew Cuomo of the State of New York declared last month that New York City will join 13 other states in testing self-driving cars: "Autonomous vehicles have the potential to save time and save lives, and we are proud to be working with GM and Cruise on the future of this exciting new technology." For General Motors, this represents a major milestone in the development of its Cruise software, since the the knowledge gained on Manhattan's busy streets will be invaluable in accelerating its deep learning technology. In the spirit of one-upmanship, Waymo went one step further by declaring this week that it will be the first car company in the world to ferry passengers completely autonomously (without human engineers safeguarding the wheel). As unmanned systems are speeding ahead toward consumer adoption, one challenge that Cruise, Waymo and others may counter within the busy canyons of urban centers is the loss of Global Positioning System (GPS) satellite data. Robots require a complex suite of coordinating data systems that bounce between orbiting satellites to provide positioning and communication links to accurately navigate our world.
Recent Gartner estimations lead us to believe that up to 20 billion connected things will be in use by 2020. Data is the oil of our century -- but should we be concerned with a "data spill hazard"? Will artificial intelligence curb this threatening phenomenon, or rather, will it reveal the full potential of IoT data value? If my calculations are correct, when artificial intelligence hits the Internet of Things... you're gonna see some serious sh*t." The question is no longer whether companies should embrace big data analytics technologies.
Artificial intelligence, Machine Learning, and Deep Learning are more than futuristic concepts. These technologies are impacting the insurance industry in a significant way right now and this impact is likely to increase in the near future. The idea of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) may fascinate consumers who enjoy talking to their digital while admiring a Nest thermostat. But for the insurance industry, these terms are business-changers that affect products and services offered and interactions with consumers and other industry partners. The definitions of these terms may be a bit confusing to the uninitiated (see sidebar).
The automobile is being dismantled, reimagined, and rebuilt in Silicon Valley. Intel's proposed $15.3 billion acquisition of Mobileye, an Israeli company that supplies carmakers with a computer-vision technology and advanced driver assistance systems, offers a chance to measure the scale of this rebuild. In particular, it shows how valuable on-the-road data is likely to be in the evolution of automated driving. While the price tag might seem steep, especially with so many players in automated driving today, Mobileye has some key technological strengths and strategic advantages. It's also developing new technologies that could help solidify this position.
The proposed regulations preempt state regulation of vehicle design, and allow companies to apply for high volume exemptions from the standards that exist for human-driven cars. There is a new research area known as "explainable AI" which hopes to bridge this gap and make it possible to document and understand why machine learning systems operate as they do. The most interesting proposal in the prior document was a requirement for public sharing of incident and crash data so that all teams could learn from every problem any team encounters. The new document calls for a standard data format, and makes general motherhood calls for storing data in a crash, something everybody already does.
Many other companies, including Microsoft and Amazon, also already offer AI tools which, like Google Cloud, where I work, will be sold online as cloud computing services. The painful process of acquiring and correctly tagging the data, including time and location information for new pictures the company and customers take, gave CAMP3 what Ganssle considers a key strategic asset. Blinker has filed for patents on a number of the things it does, but the company's founder and chief executive thinks his real edge is his 44 years in the business of car dealerships. As much as the world changes, deep truths -- around unearthing customer knowledge, capturing scarce goods, and finding profitable adjacencies -- will matter greatly.
In order to decipher these complex situations, autonomous vehicle developers are turning to artificial neural networks. In place of traditional programming, the network is given a set of inputs and a target output (in this case, the inputs being image data and the output being a particular class of object). The process of training a neural network for semantic segmentation involves feeding it numerous sets of training data with labels to identify key elements, such as cars or pedestrians. Machine learning is already employed for semantic segmentation in driver assistance systems, such as autonomous emergency braking, though.
The firms have established a startup support programme at Volkswagen's Data Lab to provide technical and financial support for international startups developing machine learning and deep learning applications for the automotive industry. Volvo Cars, Autoliv and Zenuity will use Nvidia's AI car computing platform as the foundation for their own advanced software development. Nvidia has partnered with automotive supplier ZF and camera perception software supplier Hella to deploy AI technology on the New Car Assessment Program (NCAP) safety certification for the mass deployment of self-driving vehicles. The firms will use Nvidia's Drive AI platform to develop software for scalable modern driver assistance systems that connect their advanced imaging and radar sensor technologies to autonomous driving functionality.
So when a machine takes decisions like an experienced human being in similarly tough situations are taken by a machine it is called artificial intelligence. You can say that machine learning is a part of artificial intelligence because it works on similar patterns of artificial intelligence. Finally in the 21st century after successful application of machine learning artificial intelligence came back in the boom. As machine learning is giving results by analyzing large data, we can assure that it is correct and useful and time required is very less.