Confidence interval is abbreviated as CI. In this new article (part of our series on robust techniques for automated data science) we describe an implementation both in Excel and Perl, and discuss our popular model-free confidence interval technique introduced in our original Analyticbridge article, as part of our (open source) intellectual property sharing. This is part of our series on data science techniques suitable for automation, usable by non-experts. The next one to be detailed (with source code) will be our Hidden Decision Trees. Figure 1 is based on simulated data that does not follow a normal distribution: see section 2 and Figure 2 in this article. Classical CI's are just based on 2 parameters: mean and variance.

Demand for data by today's business users is growing exponentially in two ways. First, business users have exhausted the opportunities in the data they hold. They want more sources of data to find new value, and they want the data to be accurate to deliver analytic outcomes. Second, the number of data-savvy business analysts is larger than ever and growing fast. To satisfy the increasing demand, IT departments must field a continuous stream of data requests--big and small.

If manufacturers want to sustain and grow their customer bases in a competitive environment, their products need to fulfill increasingly high quality and reliability standards. Automakers, for example, now have a target defect rate for the integrated systems of less than 1 percent. That's putting pressure on the original equipment makers (OEMs) and their suppliers who have to meet these targets at the same time that products and manufacturing processes are becoming increasingly complex and featuring numerous activities that impact quality, performance, and yield. To prevent failures of components, systems, and ultimately the product, these manufacturers need reliable methods to find defects. But quality control today is, in many cases, still performed by human inspectors, which limits its reliability and efficiency.

The era of Big Data is upon us and small businesses are, at least inadvertently, collecting lots of it. Data is collected in just about every small business function. Small business functions are delivering data within the company. Company employees are creating and processing data, too. Now, what do small businesses do with that data?

Machine Learning (ML) algorithms can learn from data and improve themselves. In a way, that learning process is akin to the way the humans learn from daily experience and improve their own skill sets. "So, together AI and ML are capable of delivering smart robots who can learn from daily experience and keep refining their abilities. Businesses typically have about 65 to 70 percent of their programming tasks that the staff workers conduct by themselves. When all these tasks become automated through Machine Learning powered Artificial Intelligence, about three quarters of the employed manpower may potentially lose their jobs, or their jobs will have to be restructured in different ways.

Elucidating the behavior of quantum interacting systems of many particles remains one of the biggest challenges in physics. Traditional numerical methods often work well, but some of the most interesting problems leave them stumped. Carleo and Troyer harnessed the power of machine learning to develop a variational approach to the quantum many-body problem (see the Perspective by Hush). The method performed at least as well as state-of-the-art approaches, setting a benchmark for a prototypical two-dimensional problem. With further development, it may well prove a valuable piece in the quantum toolbox.

Machine learning has been used to beat a human competitor in a game of Go (1), a game that has long been viewed as the most challenging of board games for artificial intelligence. Research is now under way to investigate whether machine learning can be used to solve long outstanding problems in quantum science. Carleo and Troyer used an artificial neural network to represent the wave function of a quantum many-body system and to make the neural network'learn' what the ground state (or dynamics) of the system is. Their approach is found to perform better than the current state-of-the-art numerical simulation methods.

The same type of artificial intelligence that mastered the ancient game of Go could help wrestle with the amazing complexity of quantum systems containing billions of particles. Google's AlphaGo artificial neural network made headlines last year when it bested a world champion at Go. After marvelling at this feat, Giuseppe Carleo of ETH Zurich in Switzerland thought it might be possible to build a similar machine-learning tool to crack one of the knottiest problems in quantum physics. Now, he has built just such a neural network – which could turn out to be a game changer in understanding quantum systems. Go is far more complex than chess, in that the number of possible positions on a Go board could exceed the number of atoms in the universe.

What is a plausible path (if any) towards AI becoming a threat to humanity? I don't think at AI will become an existential threat to humanity. I'm not saying that it's impossible, but we would have to be very stupid to let that happen. Others have claimed that we would have to be very smart to prevent that from happening, but I don't think it's true. If we are smart enough to build machine with super-human intelligence, chances are we will not be stupid enough to give them infinite power to destroy humanity.

Experiences, not price, will be the battleground of the future. Customers value experiences, and those experiences often come in the form of stories. Selfies, social feeds, chats and influence stats grab consumers' attention, who in turn share their personal encounters. We have Snapchat stories, Instagram stories. We even have interactive personalized video stories where every audience member charts their own unique rich media experience.