Elon Musk invested millions in an effort to make sure that artificial intelligence is used for good instead of evil, but for much of the general public AI still seems like science fiction -- something far out in the distant future. However, if you talk to people who work closely with this kind of technology, which has been called deep neural networks, deep learning, smart machines, or machine intelligence, you'll find out that it has advanced significantly in the past few years, and even bigger progress is coming very soon. There are several signposts that indicate this progress, including big enterprises running their own experiments with AI systems, as well as a sudden wave of tech giants taking certain technologies open source. "The vast preponderance [of projects in enterprises] is still experimentation," Gartner Fellow and vice president Tom Austin told InformationWeek in an interview. He estimates that about half of large enterprises are experimenting with "smart computing" projects.

Traditional neural networks rely on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layer, or so-called more depth. These kind of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. TensorFlow is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs.

On the basis of the amount of start-ups working on bot related activities at Web Summit 2016, it would seem as if bots may be about to become mainstream after all. I counted 23 bot related companies exhibiting at the early stage (Alpha) area at Web Summit last week. These included bots for different kinds of services as well as bot building platforms. A year ago, this area might have been taken up by start-ups working on mobile apps. Is this a clear sign that bots are about to move beyond the nascent stage?

A country's dexterity with artificial intelligence technology might be the next strong source of national pride and international power. Knowing it would lay the foundation for the future of medicine, IBM captured the world's imagination in 2011 with Watson, a supercomputer that not only won Jeopardy!, but beat trivia superstar Ken Jennings in the process. The novel cognitive computing technology was quickly adapted to "read" the thousands of medical research papers published weekly in order to diagnose cancer patients more accurately than human doctors seemingly could. It's a banner technology for IBM, a company that remains no slouch in its 105 years of operation Now five years after Watson's debut, Japanese researchers at Kyoto University and Fujitsu are collaborating to build their own computing technology that's fairly characterized as a response to Watson. Skipping the game shows and going straight to medical applications, the Japanese system aims to close the gap in understanding how our genes determine our health by accounting for a patient's genetic code in its computer-generated diagnoses.

The company recently switched its Translate feature to the deep-learning Google Neural Machine Translation (GNMT) system. That's an "end-to-end learning framework that learns from millions of examples," the company says, and has drastically improved translation quality. The problem is, Google Translate works with 103 languages, meaning there are 5,253 language "pairs" to be translated. If you multiply that by the millions of examples needed for training, it's insanely CPU intensive. After training the system with several language pairs like English-to-Japanese and English-to-Korean, researchers wondered if they could translate a pair that the system hadn't learned yet.

University researchers in Swansea have trained computers to detect cancer cells using artificial intelligence algorithms. Using similar technology to face and fingerprint recognition software, the computers have been taught to recognise cells and pinpoint them. It means cancer cells can be identified quicker, speeding up diagnosis times. The project is in collaboration with specialists in the US, Germany, London and Newcastle. Prof Paul Rees, from the university's college of engineering, said in the past, finding cancer cells had been like "looking for a needle in a haystack" and the new method was a "world-leading development".

The comparison is performed on a data set where linear regression works well: salary offered to a candidate, based on programming language requirements in the job ad: Python, R or SQL. This is a follow-up to the article highest paying programming skills. The increased accuracy of linear regression estimates is negligible, and well below the noise level present in the data set. The Jackknife method has the advantage to be more stable, easy to code, easy to understand (no need to know matrix algebra), and easy to interpret (meaningful coefficients). Jackknife is not the first regression approximation developed by the author: check my book pages 172-176 for other examples.

Artificial intelligence and its machine learning subset are all the rage these days. That was evident when I spoke this week at the AI World event, which was packed with vendors and users seeking to understand what the hell AI and machine learning are--and wanting to know how they could use this old but revitalized technology effectively. Amazon Web Services, Google, IBM, Microsoft, and the other major cloud providers all have machine learning services in their clouds now. But most enterprises have no clue on what the heck to do with machine learning systems, whether cloud or on-premises. It is critical to find the right uses for machine learning.

Armin Prommersberger is senior vice president, Technology -- Lifestyle Audio Division, HARMAN International. How to join the network Today's competitive business world demands innovation. Corporations need to innovate to inspire, compete and survive. However, the burden of innovation has largely rested on startups. Large corporations and established businesses are expected to out-think their rivals, but more often we see that they rely on minor product updates or acquisitions in place of home-grown innovation. Startups are moving too fast these days for a complacent strategy to be enough. No longer can companies and business leaders rely on slow-moving corporate or product strategies to withstand the attack from disruptive upstarts.

The way a message is communicated is almost as important as the message itself. Our world is moving towards a more data-oriented approach to decision making in every walk of life. Packaging the analysis in a way that's easy to digest can increase its reach and effectiveness. Humans have evolved to develop a very acute sense of pattern recognition. Using storytelling by representing your data through various graphical and pictorial tools gives the audience an intuitive grasp of the matter, enabling them to easily process and digest the information.