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?

The past years have witnessed strong emergence for different datasets and algorithms repositories. Some inquiries accompanied this emergence. An increasing amount of market research started to investigate which is more important for the development of Artificial Intelligence (AI) sciences, which segments are of highest demand and can have greater market share in the future. By reviewing the artificial intelligence (AI) breakthroughs timeline over 30 years, Wissner-Gross found that the availability of high-quality datasets was the key limiting factor for AI advances and not algorithms. He also found that high-quality dataset availability can cause a breakthrough in the field of AI six times faster than Algorithms.

The dream of one day having an entity with artificial intelligence diagnose your condition and recommend the best treatment may still be years away, but at IBM Watson Health and elsewhere, the technology and capability is evolving at such a rapid pace that such a function could well be part of regular healthcare practices. About a month ago I interviewed Deborah DiSanzo, who is IBM's General Manager for Watson Health. She was previously the CEO of Phillips Healthcare but now spearheads the development of Watson Health into a multi-billion-dollar business unit for IBM. "I was at one of our larger partners who is actually using our application from IBM called Clinical Trial Matching, which enables oncologists to, from the hundreds of thousands of clinical trials that are going on, match the appropriate clinical trial to the patient. And the breast oncologist that I was speaking to said it is fantastic because it "enables me to speak to my patients better, I turn the screen around and I show her what the particular type of breast cancer she has, how that matches with the top three clinical trials that she could go on.""

After conventional methods of detection failed, a team of Japanese researchers from the University of Tokyo's Institute of Medical Science used IBM Watson to successfully diagnose a 60 year-old woman where physicians were unable to, according to NDTV. The patient was initially diagnosed with acute myeloid leukemia, but treatments for that condition proved ineffective. Watson was able to identify the more rare form of leukemia she suffered from and ultimately provide a different, more successful form of treatment, according to the report. Artificial intelligence systems like IBM Watson may still be a ways off from being regularly used in hospitals, as they require large amounts of comparative data, according to Engadget. However, when given access to that type of information, AI systems can work quickly -- Watson produced the accurate diagnosis for the Japanese patient after comparing her genetic data against a database of 20 million researcher papers in just ten minutes.

Intel is bigger than all but 50 other U.S. companies, and that's because of something called the CPU. If you were around in the '90s or the early aughts, you saw the TV ads. For decades, Intel has supplied a majority of the chips that sit at the heart of our personal computers, including desktops as well as laptops. These chips are called central processing units, CPUs for short. They handle most all of the digital calculations that drive our PCs.

In machine learning, data are king. The algorithms and models used to make predictions with the data are important, and very interesting, but ML is still subject to the idea of garbage-in-garbage-out. With that in mind, let's look at a little subset of those input data: categorical variables. Categorical variables (wiki) are those that represent a fixed number of possible values, rather than a continuous number. Each value assigns the measurement to one of those finite groups, or categories. They differ from ordinal variables in that the distance from one category to another ought to be equal regardless of the number of categories, as opposed to ordinal variables which have some intrinsic ordering.