Looking ahead, we face some difficult – and yet exciting – research challenges in AI design. AI systems feed off of both positive and negative interactions with people. In that sense, the challenges are just as much social as they are technical. We will do everything possible to limit technical exploits but also know we cannot fully predict all possible human interactive misuses without learning from mistakes. To do AI right, one needs to iterate with many people and often in public forums.

I actually started to look for old hard drive where I'd written my own code. But cmon, I know newer widely used library will be better. Googling led to too many options, and a slight feeling of being overwhelmed. I'm pretty much just looking to make a few classification networks. Cross Entropy is my cost function, not MSE.

IBM researchers, Tayfun Gokmen and Yurii Vlasov, unveiled a paper in which they invented the concept for a new chip called a Resistive Processing Unit (RPU) that can accelerate Deep Neural Networks training by up to 30,000x compared to conventional CPUs. A Deep Neural Network (DNN) is an artificial neural network with multiple hidden layers that can be trained in an unsupervised or supervised way, resulting in machine learning (or artificial intelligence) that can "learn" on its own. This is similar to what Google's AlphaGo AI has been using to learn playing Go. AlphaGo used a combination of a search-tree algorithm and two deep neural networks with multiple layers of millions of neuron-like connections. One, called the "policy network," would calculate which move has the highest chance of helping the AI win the game, and another one, called the "value network," would estimate how far it needs to predict the outcome of a move before it has a high enough chance to win in a localized battle. Many machine learning researchers have begun focusing on deep neural networks because of their promising potential.

IBM researchers, Tayfun Gokmen and Yurii Vlasov, unveiled a paper in which they invented the concept for a new chip called a Resistive Processing Unit (RPU) that can accelerate Deep Neural Networks training by up to 30,000x compared to conventional CPUs. A Deep Neural Network (DNN) is an artificial neural network with multiple hidden layers that can be trained in an unsupervised or supervised way, resulting in machine learning (or artificial intelligence) that can "learn" on its own. This is similar to what Google's AlphaGo AI has been using to learn playing Go. AlphaGo used a combination of a search-tree algorithm and two deep neural networks with multiple layers of millions of neuron-like connections. One, called the "policy network," would calculate which move has the highest chance of helping the AI win the game, and another one, called the "value network," would estimate how far it needs to predict the outcome of a move before it has a high enough chance to win in a localized battle. Many machine learning researchers have begun focusing on deep neural networks because of their promising potential.

News of Google's self-driving car getting in a minor collision with a bus has been all over the internet recently. At the same time, I also came across this article in Forbes magazine, "Machine Learning Needs a Human-In-The-Loop." Both topics raise questions about the boundaries of autonomous operations.I started to consider this in relation to my own experience in the world of IT automation. The fact is that while automation is now deeply embedded in most manufacturing processes, IT has been comparatively slow in routinely applying automation technology to large areas of IT and security operations. Why is that the case?

Over the past couple of years I have had the good fortune to discuss the need for improved understand of profitability and capital utilization in broker dealers. Discussions on the need to find the right clients to meet the balance sheet utilization mix meander with no clear end in sight. Investment managers are moving their books of business, not based upon desire, but based upon the BD needing to achieve the correct balance sheet mix. BDs are looking at return on capital as much as profitability of clients. Machine learning is a field of artificial intelligence that involves developing self-learning algorithms.

AI as management assistant: The artificial intelligence program AlphaGo got a lot of attention for beating 18-time Go world champion Lee Sedol four out of five games last week. The significance of this achievement is rooted in the extraordinary number of possible moves in Go: 2.08168199382 … 10170, reportedly more than the number of atoms in the universe. That's too many possibilities for brute computing force to handle (which is how IBM's Deep Blue beat chess master Garry Kasparov 20 years ago). Yet AlphaGo, created by Google DeepMind, formerly British AI company DeepMind Technologies, mastered the 2,500-year-old board game on its own in a matter of months. "It started by studying a database of about 100,000 human matches, and then continued by playing against itself millions of times," reported science correspondent Geoff Brumfiel at NPR. Go bragging rights are nice for Google, but what does AlphaGo's victory mean for management?

Every week, CIO Journal offers a glimpse into the mind of the CEO, whose view of technology is shaped by stories in management journals, General interest magazines and, of course, in-flight publications. AI may undermine big-company advantages. Machine learning – software that can improve itself without human intervention – may mean trouble for big companies that depend on their heft to outmaneuver smaller upstarts, writes Howard Yu for the Harvard Business Review. And for a sneak preview of where the world is headed, one need not look further than the success story of AlphaGo, an artificial intelligence that beat a champion of the ancient game of Go, something that was previously thought to be impossible. "It is easy to imagine a world where self-taught algorithms will play a much bigger role in coordinating economic transactions; AlphaGo simply shows us what is possible in the near future. With instantaneous adjustment, automatic optimization, and continuous improvement all quietly managed by unsupervised algorithms, the redundancy of production facilities and wastage in the supply chain should become headaches of the past."

The Terminator series has long warned us of the perils of artificial intelligence. Fortunately, before the machines decimate humanity, they'll at least provide some levity. Just see the catastrophic failure of Microsoft's recent "Tay" experiment, which was spectacularly summarized with this headline from The Telegraph: "Microsoft deletes'teen girl' AI after it became a Hitler-loving sex robot within 24 hours." Our civilization had a pretty good run. Here's what else happened this week: Hit the links to get the full story.

Reports in the press today that just 24 hours after release, Microsoft closed down Tay, it's new artificially intelligent software chatbot. In its first day, Tay was seen tweeting anti-Semitic rants. See Microsoft Muzzles Artificially Intelligent Chatbot. I guess the real question here is this: Is the design of Tay at fault, or is this more a criticism of the design of us and our current society? I think that might be outside the scope of this blog.