In Part 1, I discussed some compelling new uses of mainframe machine data that go far beyond today's common use cases. I looked at how Google is using interaction between machine learning teams and the rest of its employees to drive transformative thinking in all product development, and began to explore how ML is part of predictive analytics on z/OS. Now, let's look at what IBM, Elite Analytics, Syncsort and Splunk are doing to leverage ML for next gen analytics, and how bots can become the development teachers of the very near future. IBM is already using Predictive Failure Analysis inside z/OS to anticipate certain types of failure -- based on ML using data on its own behavior. Predictive Failure Analysis (PFA) pulls data from IBM Health Checker for z/OS and uses ML to recognize opportunities to alert operators to potential problems in advance of a serious problem.

Could the future of online shopping be as simple as sketching out what you're looking for and letting a computer figure out the rest? They've taught a deep learning neural network -- an incredibly powerful tool that mimics the way that the human brain works -- to recognize hand-drawn sketches and use them to search for real-life products. The network was "trained" to match sketches to photos based on a data set consisting of around 30,000 sketch-photo comparisons. From these it learned to interpret the way that people depict real objects in hand drawing. Most impressive of all is the fact that the sketches drawn by users don't even have to be all that detailed -- but the more detail users do add, the more accurate the search results become.

The UK and the rest of Europe may be parting ways, but at least the Brits can drown their sorrows in a rather unique way. A new company based in London is now selling beer designed by a machine learning algorithm. The company, IntelligentX, is promoting their new offering as "the world's first beer brewed by AI." According to the company's website, they've developed a "complex machine learning algorithm" that uses customer feedback to match flavor preferences to brewing techniques. There are four different styles of beers to sample and evaluate.

A few months ago, I wrote a post about optimization using gradient descent, which involves searching for a model that best meets certain criteria by repeatedly making adjustments that improve things a little bit at a time. In many situations, this works quite well and will always or almost always finds the best solution. But in other cases, it's possible for this approach to fall into a locally optimal solution that isn't the overall best, but is better than any nearby solution. A common way to deal with this sort of situation is to add some randomness into the algorithm, making it possible to jump out of one of these locally optimal solutions into a slightly worse solution that is adjacent to a much better one. In this post, I want to explore one such approach, called a genetic algorithm (or an evolutionary algorithm), which I'll illustrate with a specific type of genetic algorithm called symbolic regression.

In an effort to apply data-driven insights to one of the most fundamental aspects of running a health care system, UPMC announced today that it has formed Pensiamo, an independent company that aims to help hospitals improve supply chain performance through a comprehensive source-to-pay offering, including cognitive analytics with IBM Watson Health technologies. IBM (NYSE: IBM) is a minority owner of Pensiamo. Supply chain costs are the second-largest and fastest-growing expense behind labor costs for health care providers [1]. The Institute of Medicine estimates that nearly one-third of health care spending is waste [2]. In today's dynamic environment, providers face mounting pressure to improve the effectiveness of patient care while controlling costs.

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Powerful new technologies have a way of making the old ones irrelevant. Everyone in tech is well aware of this fact. This was the case with the emergence of the web in the 90s and social-mobile in the 2000s. The current wave is driven by Artificial Intelligence (AI) as the technology that will transform industries as we know them and create new ones we have yet to imagine. The widespread adoption of smartphones and the exponential growth of devices with sensors and internet connectivity create a new paradigm, which we at Glasswing Ventures call Pervasive Connectivity.

Recent AI advances in speech recognition, game-playing, image understanding, and language translation have all been based on a simple concept: multiply some numbers together, set some of them to zero, and then repeat. Since "multiplying and zeroing" doesn't inspire investors to start throwing money at you, these models are instead presented under the much loftier banner of "deep neural networks." Ever since the first versions of these networks were invented by Frank Rosenblatt in 1957, there has been controversy over how "neural" these models are. The New York Times proclaimed these first programs (which could accomplish tasks as astounding as distinguishing shapes on the left side versus shapes on the right side of a paper) to be "the first device to think as the human brain." Deep neural networks remained mostly a fringe idea for decades, since they typically didn't perform very well, due (in retrospect) to the limited computational power and small dataset sizes of the era.

Scientists have designed a robotic stingray that could help our understanding of the human heart. The miniature robot, one-tenth the scale of the actual fish, moves using heart cells taken from a rat. Researchers hope the robotic ray will give new insight into the heart's ability to pump blood and its potential implications in heart disease. The research is published in the journal, Science. "It turns out the musculature in the stingray has to do the same thing as the heart does: it has to move fluids," said lead researcher, Prof Kevin Kit Parker of Harvard University, US.

In a disappointing defeat against France, the human German soccer team was knocked out of the European Championship July 7. But at least the nation can take heart that its robots proved victorious in the annual RoboCup robot soccer tournament that took place this past weekend in Leipzig, Germany. The winning team, from the University of Bremen, called "B-Human," beat out the University of Texas, Austin's team--the wonderfully named "Austin Villa"--on penalties after a goalless draw in the final of the tournament, according to The Telegraph. Robot teams play two 10-minute halves on a 9-by-6-meter pitch, autonomously roving about the field trying to pass the ball to teammates and score goals. But unlike the cool efficiency that the German human soccer team is known for, the robots are a bit more ramshackle in their approach to the sport.