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) …
That's the opinion of Stephen Hawking along with other leaders in the AI field, noted in a 2014 article in The Independent. Much of the media portrays a negative perception of AI, publishing articles and airing news segments about the technology with images of The Terminator. However, not many members of the media are asking what can be done to reap the benefits of the technology and avoid the risks. Those questions require a deeper look, which is why the IEEE Standards Association formed the IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems, for which I serve as vice chair. In the past year, the initiative brought together more than 100 experts to collaborate on the report "Ethically Aligned Design."
BENGALURU/CHENNAI:Cruising his way to the quarterly financial briefing in a self-driven car, Infosys CEO Vishal Sikka set the tone for the IT gaint's future on Friday. Artificial Intelligence and automation -- two crucial technologies behind the driverless car technology -- were the points Sikka stressed on during the briefing as the'future of the company', internally and in the market. On the one hand, increasing automation inside the company is already freeing up immense effort in terms of manpower. In just one quarter (April - June, 2017) automation freed up the effort of more than 3,600 people. Just a few weeks earlier, Infosys had announced that automation had freed up effort worth 11,000 employees in FY17.
Chess was originally considered an exercise that captures the essential tactical and strategic elements of human intelligence, and so it became the standard by which new AI algorithms were tested. For decades, programmers made little progress in defeating human players. But in 1997, Deep Blue, a computer developed by IBM, won the match against the world champion. Still, many people were disappointed when they realized that solving chess was not the same as solving artificial general intelligence. They did not like that Deep Blue relied heavily on brute force and memory.
Today, we can store and process so much data that we have nearly captured reality; no more sampling biases/ errors or related issues - this is my definition of Big Data; not tera or peta bytes! If you have measured the entire population (or close to it) and not sample just a small fraction, resulting data is BIG Data! There ARE subtle technical differences but let us just call it "Analytics", at least in business applications! When you hear "dynamics", time always comes to mind first but it is only one of the many possibilities. Dynamics could be over any independent variable!
This article was written by Koustubh. Unless you have been living under the rock, you must have heard of the revolution that deep learning and convolutional neural networks have brought in computer vision. Computers have achieved near-human level accuracy for most of the tasks. This problem gets worse for an application like object detection where multiple windows at different locations and scale need to be processed. Models that achieve state of the art accuracy are too large to fit into mobile devices or small devices like Raspberry Pi.
Advances in deep learning and other machine learning algorithms are currently causing a tectonic shift in the technology landscape. Technology behemoths like Google, Microsoft, Amazon, Facebook and Salesforce are engaged in an artificial intelligence (AI) arms race, gobbling up machine learning talent and startups at an alarming pace. They are building AI technology war chests in an effort to develop an insurmountable competitive advantage. Today, you can watch a 30-minute deep learning tutorial online, spin up a 10-node cluster over the weekend to experiment, and shut it down on Monday when you're done – all for the cost of a few hundred bucks. Betting big on an AI future, cloud providers are investing resources to simplify and promote machine learning to win new cloud customers.
IDC sees widespread adoption of cognitive systems and artificial intelligence (AI) across a broad range of industries will drive worldwide revenues from nearly $8.0 billion in 2016 to more than $47 billion in 2020. According to a new Worldwide Semiannual Cognitive/Artificial Intelligence Systems Spending Guide from International Data Corporation (IDC), the market for cognitive/AI solutions will experience a compound annual growth rate (CAGR) of 55.1% over the 2016-2020 forecast period. "Near-term opportunities for cognitive systems are in industries such as banking, securities and investments, and manufacturing," said Jessica Goepfert, program director, Customer Insights and Analysis at IDC. "In these segments, we find a wealth of unstructured data, a desire to harness insights from this information, and an openness to innovative technologies. For instance, cognitive technologies are being used in the banking industry to detect and combat fraud – consistently a top industry pain point. Meanwhile, in manufacturing, executives cite improving product quality as a top initiative.
The General Services Administration is looking to speed up acquisition by harnessing innovative machine learning and blockchain technologies. The administration released a request for quotes June 19 to improve its Multiple Award Schedules FASt Lane program. FASt Lane was implemented in 2016 to give government agencies timely access to new technology innovation by shortening processing times. Now, GSA says it has up to $149,999 to offer to contractors for a proof of concept that can further improve FASt Lane processing and proposal review times with distributed ledger technology -- the foundation of blockchain technology, which also forms the basis of cryptocurrencies like bitcoin -- automated machine learning, artificial intelligence based technologies and electronic interchange technology. "The mission is to reduce the amount of human interaction required to review new proposal documents, improve offeror experience during the new offer proposal process, and reduce the review time for new proposal reviews to award," the RFQ reads.
I have been around the block for a while and I have seen this happening already in a form or another but this time is big. This time is crazy big. I am talking about the machine learning madness that took over everything. Long time ago in the field of computer science a brand new area of research was born: we were in the 60s and this new field of investigation aimed at understanding how the human mind worked was -- without false modesty -- called artificial intelligence. The general idea was that if you are able to create something that looks intelligent or indistinguishable from something intelligent, then, it must be intelligent.