As with other technologies introduced in the past decade, artificial intelligence is the subject of many market predictions. But what exactly is current commercial adoption of AI beyond academic labs? In this ten-page report, Spiderbook cofounder Aman Naimat provides the results of a data-driven analysis into the U.S. industries and companies using or building AI products right now. Although some so-called AI applications aren't actually cognitive, there are technologies capable of achieving human- or superhuman-level intelligence on given tasks. Naimat and his team canvassed nearly 500,000 companies and used Spiderbook's graph-based machine-learning model to read the business Internet and classify businesses into different levels of AI maturity.

Today, The Washington Post posted a new in-depth interview with Tim Cook, where he discusses his first five years as Apple CEO, the company's future, artificial intelligence, virtual reality, augmented reality, and much more. The interview, which includes not only text but also video of Cook talking over a variety of topics, is lengthy and covers a lot of content. Cook talked about services and how important they are to Apple's present and future: "Its services business, which includes things like iTunes, iCloud and a mobile payments service, is projected to be the size of a Fortune 100 business next year -- all on its own." Cook also discussed some of the mistakes that Apple has made in the past, including the hire of John Browett, who ran Apple Retail stores for a short period of time. Today we have a product we're proud of.

The role of technology within our personal and professional lives continues evolving at an exceptionally fast pace. From utility-based mobile apps and wearable devices, to the emergence of augmented and virtual reality, the digital revolution is expanding to cover every aspect of the human experience. In an era of entrepreneurship, founders rely heavily on advancements in technology to develop cutting edge products, platforms and experiences that meet the growing demands of a global consumer base. As content remains essential to building a brand or launching a business, it's also critical that companies have the capability to swiftly adapt in changing markets. Being able to successfully scale a business, amidst the inevitable pivots and unexpected turns, requires having access to the tools and solution-based software needed to create, modify and fix things on-demand.

If you've watch TV shows like House, you probably know that there are some conditions that people suffer from that are rare and aren't as easy to diagnose. However it seems that in the future, such conditions could be diagnosed much quicker thanks to the help of AI, which is what doctors in Japan did. In what could be described as a world's first, doctors in Japan relied on artificial intelligence to help diagnose a woman who was suffering from a rare form of leukemia. The patient was initially treated for acute myeloid leukemia, but her recovery from post-remission therapy was slow, which is when doctors decided that the initial diagnosis could have been wrong. This is when they turned to IBM's Watson to help them with their case.

Consider the following expenses claims: registration fees for a cancelled seminar, two separate claims for mileage when the employees travelled together, and a sandwich-and-coffee dinner claimed as the full per diem. While it's easy to believe that a few dishonest claims won't hurt, for individual victims, expenses fraud can be costly. Research conducted by the National Fraud Authority suggests that exaggerated expenses claims cost the British economy around 100 million annually; the private sector alone lost 80 million in 2013. Imagine if 20 per cent of your staff added 10 per cent to each mileage claim; the cumulative loss for the company would quickly become significant. Existing fraud detection systems flag dubious-looking expenses according to a set of rules, such as challenging claims in excess of a fixed cash amount or those that are 5 percent higher than claims submitted by peers in similar positions.

Do machine-learning algorithms processing search engine queries bring on prejudice, discrimination and stereotyping in query results? The paper submitted to the International Conference on Social Informatics scheduled for publication analyzes how Google and Bing represent female beauty in their image search results, particularly when it comes to different age and racial groups. For nearly every country analyzed, white women appear more in the "beautiful" results, and black and Asian women appear in the "ugly" ones, per The Washington Post, which initially pointed to the study. Searches for "ugly" women return images of those about 60% white and 20% black between the ages of 30 to 50.

Do machine-learning algorithms processing search engine queries bring on prejudice, discrimination and stereotyping in query results? Search results have been known to highlight these negative attributes in the past. Now researchers at Brazil's Universidade Federal de Minas Gerais suggest it could be true when it comes to female physical attractiveness in images available across the Web. The paper submitted to the International Conference on Social Informatics scheduled for publication analyzes how Google and Bing represent female beauty in their image search results, particularly when it comes to different age and racial groups. They then passed the more than 2,000 images through a program, which estimates subject age, race and gender with an estimated 90% accuracy.

While Deep Learning has shown itself to be very powerful in applications, the underlying theory and mathematics behind it remains obscure and vague. Deep Learning works, but theoretically we do not understand much why it works. Some leading machine learning theorists like Vladimir Vapnik criticise Deep Learning for its ad-hoc approach that gives a strong flavour of brute force rather than technical sophistication. Deep Learning is not theory intensive; it is empirical based more (hence causing battle of viewpoints between empiricism and realism) and relies on clever tweakings [1].[1] This is why'Deep Learning' is viewed as a black box and why we preferred to use Theano instead of other packages as it allowed us better view inside the workings of the model (which is still not enough to fully overcome the black box criticism).

Lots of people want to judge machine intelligence based on human intelligence. It dates back to Turing who proposed his eponymous Turing test: can machines "pass" as human beings? If the man were to try and pretend to be the machine he would clearly make a very poor showing. He would be given away at once by slowness and inaccuracy in arithmetic. May not machines carry out some-thing which ought to be described as thinking but which is very different from what a man does?