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) …
No longer was it an esoteric discipline commanded by the few, the proud, the data scientists. Now it was, in theory, everyone's business. Machine learning's power and promise, and all that surrounded and supported it, moved more firmly into the enterprise development mainstream. GET A 15% DISCOUNT through Jan.15, 2017: Use code 8TIISZ4Z. Cut to the key news in technology trends and IT breakthroughs with the InfoWorld Daily newsletter, our summary of the top tech happenings.
More of our online interaction with customer services is probably not going be with a human, but more likely with an artificial intelligence-powered chatbot. Investors looking to benefit from this in 2017 need to know which businesses will make the best use of AI and the vast amounts of data needed to drive the technology, according to Beijia Ma, equity strategist at BofA Merrill Lynch. "Big data is the input for things like artificial intelligence, which Amazon tends to dominate in, and it's really AI that is making us smarter about what to do with the big data (and) how do we actually improve our goods and services." "We're still looking at the providers of AI software. Primarily the Googles, Amazons, Microsofts and IBMs of the world who are very strong at that, but I think increasingly so we're going to be focusing on the holders of data which essentially is the feed stock that goes into things like artificial intelligence."
Pundits are quick to hype AI and machine learning as the future of everything. But, anyone who has been caught screaming at Siri for its lack of understanding of the most basic of queries knows that we have a long, ponderous way to go before "we have arrived." That's why I find Gil Press' summary of the recent O'Reilly AI Conference so helpful and important. Some of the observations are banal ("AI is not going to exterminate us, AI is going to empower us"), but others capture the essence of what makes AI so promising...and beguiling. The first observation ("AI is difficult") seems obvious, yet for all the wrong reasons.
Uber envisions a future in which a fleet of vehicles can make the most complex maneuvers while carting passengers around without the help of a driver. To achieve that, cars will need to get a whole lot smarter. Enter Gary Marcus and Zoubin Ghahramani. The two men are being appointed as co-directors of Uber's new in-house research arm on artificial intelligence, which the ride-hailing company unveiled on Monday. The research arm's aim is to apply A.I. in areas like self-driving vehicles, along with solving other technological challenges through machine learning.
When creating a new AI-based app, there are many generic problems that are already being solved by other companies, for example face and gesture detection. Unless this is the main business and focus of the company, they will prefer to look for an out-of-the-box AI-as-a-service solution which will save them time, expertise and money. This type of solutions are called AI platforms and give their users many out-of-the-box services, such as computer vision (feature/face and gesture detection), natural language processing (NLP), speech to text, and translations between different language. Many companies including Google and Amazon sell this kind of AI services. During 2017, we will continue to see many improvements in those platforms mainly in the ease of use, accuracy and performance.
The business world is in the midst of a digital transformation that is quickly separating the wheat from the chaff. The following article unveils the technology trends that will allow you to successfully and confidently navigate the digital era in the coming year, and considers how conversational systems, humanised big data, and augmented information will benefit everyday business. Recommender systems or so-called chatbots that carry on a dialogue with the user in order to guide him through a business process are no longer a rarity. However, the recommendations are usually so vague and the intelligence of the chatbots so limited that the effectiveness of the process suffers. This is about to change.
We feature speakers at Global Artificial Intelligence Conference Jan 19 - 21, 2017 to catch up and find out what he or she is working on now and what's coming next. This week we're talking to Debajyoti Ray, Chief Data Officer, VideoAmp. I'm the Chief Data Officer at VideoAmp, a startup focused on cross-screen advertising. I led the team to build a data platform on Apache Spark that handles over 300,000 requests per second, and machine learning pipelines that process close to a petabyte of data. Previously, I was the Chief Data Scientist for Pasadena Labs, a machine learning startup for online marketing.
For many people, the word "digital" is synonymous with modern, technologically advanced programs or devices capable of performing complex processes in a fraction of the time that it would take a manual or analog system to do the same thing. Analog, on the other hand, is generally thought of as old-fashioned and something that needs to be converted to digital in order to be in line with the modern technology, even though much of what we take for granted in terms of technology actually runs on analog components. In fact, one of the most transformative trends in technology today relies heavily on analog technology. When you think of artificial intelligence, you probably think of robots or at least high-powered computers like IBM's Watson-- the epitome of modern technology -- you probably don't think analog, with its reliance on capturing real-time data and measuring the changes in the signals put out by physical phenomena. The Executive Office of the President recently said that advances in AI will make it easier than ever to search public records and streamline healthcare.
The deep learning process could be about to change dramatically thanks to work being carried out Cray, Microsoft and the Swiss National Supercomputing Centre. In existing architectures and conventional systems, deep learning requires a slow training process that can take months, something that can lead to significantly higher costs and delays in making scientific discoveries. Cray believes that its work with Microsoft and CSSC could have solved this problem by applying supercomputing architectures to accelerate the training process. The three worked together to scale the Microsoft Cognitive Toolkit on a Cray XC50 supercomputer at CSCS nicknamed "Piz Daint". According to the supercomputer manufacturer, deep learning problems share algorithmic similarities with applications that are traditionally run on a massively parallel supercomputer.
We tend to think of machines, in particular smart machines, as somehow cold, calculating and unbiased. We believe that self-driving cars will have no preference during life or death decisions between the driver and a random pedestrian. We trust that smart systems performing credit assessments will ignore everything except the genuinely impactful metrics, such as income and FICO scores. And we understand that learning systems will always converge on ground truth because unbiased algorithms drive them. For some of us, this is a bug: Machines should not be empathetic outside of their rigid point of view.