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) …
Scientists have used deep learning algorithms with multiple processing layers (hence "deep") to make better models from large quantities of unlabeled data (such as photos with no description, voice recordings or videos on YouTube). Google's voice recognition algorithms operate with a massive training set -- yet it's not nearly big enough to predict every possible word or phrase or question you could put to it. And Google's deep learning algorithm discovers cats. Algorithms perform superior face recognition tasks using deep network that take into account 120 million parameters.
Enter Trainerbot, the smart ping pong robot with a wicked serve. Harrison started working on a ping pong robot made from a household garbage can. Puma has developed a racing robot to push runners, with the idea that competing against an opponent helps improve athletes' performance. For a totally customizable game, users can control the motors via a mobile app.
Extending potential AI applications beyond the personal level to the'social network' level, we are faced with another graph searching opportunity: the social graph. Many highly active groups showed no social cohesion, while several lower activity groups showed very high social cohesion. Clearly the "intelligence" provided by traditional top down activity measures provide only "artificial value" in trying to predict collaboration performance. What it will take however, is careful management of the solution search space, matched with appropriate relationship centered analytics and search, if real value is to be now achieved from AI.
More importantly, however, Google and its competitors are moving towards keying their search algorithms to understand natural speech as well, in anticipation of more and more voice search. But new machine learning algorithms are making more accurate, real-time translations possible. You might also be interested in my new big data case study collection, which you can download for free from here: Big Data Case Study Collection: 7 Amazing Companies That Really Get Big Data. My current book is Big Data: Using Smart Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance' and my new books (available to pre-order now) are Key Business Analytics: The 60 Business Analysis Tools Every Manager Needs To Know and Big Data in Practice.
That's the backlog of pre-orders that Tesla Motors tallied up in the days after announcing its latest car, the Tesla Model 3. Aside from a handful of parts that need routine replacement--think tires and wiper blades--the bulk of the vehicle's components and functions were designed to be upgraded, not by mechanics wielding wrenches, but by software engineers working in Tesla's Silicon Valley research and development labs. A fix, the message informed him, was automatically downloaded to Robert's car (and every other Tesla) overnight while it charged in his garage. And this is happening not just in transportation but virtually every industry, as I write in my latest book "The Digital Revolution: How Connected Digital Innovations Are Transforming Your Industry, Company and Career."
Needless to say people are talking positive things about Flipkart on world book day, while Snapdeal is missing out on the opportunity presented by this occasion. Tweets, comments and other conversations on social media about a product or a brand helps marketer gauge the performance of their products/brands. Timely monitoring of these online conversations can help marketers in many ways like when to finalize the launch of a product, gauge the performance of the competitor's product, spot trends and discover dip points, identify the voice of what others are saying, discover the spaces where your customers and competitors are hanging out, key influencers in your niche market, identify the areas in your business which needs developments, keep yourself abreast with the recent happenings in the clientele, identity the future content, find sales opportunities etc. It helps analyze the sentiments of the conversation on social media, categorizes the social media conversation into standard taxonomy for easy access to data and filtering, it suggests related keywords for your searches based on trending tweets, generate automated social media report, news alert and timely escalation of issues.
However, preprocessing data does not occur in a vacuum. This is just to say that preprocessing is a means to an end and there are no hard and fast rules: there are standard practices, as we shall see, and you can develop an intuition for what will work but, in the end, preprocessing is generally part of a results-oriented pipeline and its performance needs to be judged in context. In this article, I'll use the example of scaling numerical data (numerical data: data consisting of numbers, as opposed to categories/strings; scaling: using basic arithmetic to change the range of the data; more details to follow) to demonstrate the importance of considering preprocessing as part of a greater structure, the machine learning (ML) pipeline. To appreciate the importance of scaling numerical data in such a setting, I'll need to introduce measures of model performance and the concepts of training and test sets.
And one must not underestimate China's Google equivalent, Baidu, which has launched its intelligent virtual assistant Duer. Quill Engage aims to provide intelligent narratives that efficiently communicate the insights buried in big data that people can comprehend, act on and trust. Automated Insights also offers specific services aimed at the marketing industry: it can automatically generate campaign summaries, ad performance overviews and brand management reports. The Grid offers AI websites that design themselves by algorithmically generating website designs and improving them based on user behaviour.
And one must not underestimate China's Google equivalent, Baidu, which has launched its intelligent virtual assistant Duer. Quill aims to provide intelligent narratives that efficiently communicate the insights buried in big data that people can comprehend, act on and trust. Automated Insights also offers specific services aimed at the marketing industry: it can automatically generate campaign summaries, ad performance overviews and brand management reports. The Grid offers AI websites that design themselves by algorithmically generating website designs and improving them based on user behaviour.
These intelligent RPA systems, which use the latest cognitive computing technology, have huge potential to step up management effectiveness. Our goal was to assess the potential impact of cognitive computing on their jobs and to understand their perceptions of the how their work would change as a result of this new technology. The vast majority of managers, 84 percent, believe intelligent machines will make them more effective and make their work more interesting. In my next blog post I'll highlight two further obstacles that might hamper business leaders' efforts to boost management performance with intelligent machines.