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.

Those gazing into the night sky have speculated about life beyond Earth since Zeus was a boy. Deep learning now holds the promise of zeroing in on an answer. A deep learning system devised by astronomers at University College London sifts through data from telescopes trained on faraway solar systems to detect planets with the potential to sustain life. "We want to know which planets are worth further study and which aren't, and we want to automate that," said Ingo Waldmann, the University College London post-doctoral researcher who leads the development team. He calls the GPU-accelerated deep learning program RobERt, short for Robotic Exoplanet Recognition.

This is the third article in a series taken from The insideHPC Guide to The Industrialization of Deep Learning. The recent introduction of new high end processors from Intel combined with accelerator technologies such as NVIDIA Tesla GPUs and Intel Xeon Phi provide the raw'industry standard' materials to cobble together a test platform suitable for small research projects and development. When combined with open source toolkits some meaningful results can be achieved, but wide scale enterprise deployment in production environments raises the infrastructure, software and support requirements to a completely different level. If we begin by considering a deep learning focused rack mount system that is designed for production use such as the HPE Apollo 6500 system, density and performance are extremely impressive. However, such capability brings other considerations to the forefront from the infrastructure perspective.

The way in which people engage with brands has changed. Gone are the days when it was a one-way interaction; younger demographics especially love a value-add exchange with brands they can identify with. Chatbots have been heralded as one way of opening up the channels, but as with every new marketing tech innovation, you need to get it right. Brands need access to tech that can do the job well and not make the experience clunky and robotic. One company that recognises this issue is Israel/US based imperson.

In March this year, Microsoft CEO Satya Nadella talked about the industry trend of using human language more pervasively for interaction with computing devices, a trend he called "conversation as a platform." He also announced several bot initiatives, including the company's bot framework. In April, Facebook launched its Messenger platform with bots. Then, in May, Google announced its attempt to develop AI-powered bots, called Google Assistant. Since then, bots have been widely regarded as a new user interface (UI) to fundamentally change how computing will be experienced by people.

As Samantha's psychological and intellectual capacities grow, so does Theodore and Samantha's love for each other. The line between the consciousness experienced by intelligent machines and human beings will be blurrier than we would like to admit. Programming AI to have the capacity to feel love can allow us to create more compassionate AI and may be the very key to avoiding the AI apocalypse many fear. The person, place, event or service making the request or statement haven't realized that they are already serving or love to begin with.