Go is the most complex strategy game. There are more possibilities in a game of Go than there are atoms in the universe. Thus, it offers huge challenges for artificial intelligence (AI). Recent successes though give valuable decision making tips for humans. As of this writing, AlphaGo, Google's AI system has beaten a top-50 Go professional.

Jeff J Mitchell / Getty ImagesThe robots are coming. But it's not just low-paying positions that will get replaced. AI also could cause high earning (like top 5% of American salaries) jobs to disappear. That's the theme of New York Times reporter Nathaniel Popper's new feature, The Robots Are Coming for Wall Street. The piece is framed around Daniel Nadler, the founder of Kensho, an analytics company that's transforming finance.

There is no question that machine learning is at the top of the hype curve. And, of course, the backlash is already in full force: I've heard that old joke "Machine learning is like teenage sex; everyone is talking about it, no one is actually doing it" about 20 times in the past week alone. But from where I sit, running a company that enables a huge number of real-world machine-learning projects, it's clear that machine learning is already forcing massive changes in the way companies operate. And it's not just being done by companies that we normally think of as having huge R&D budgets like Google and Microsoft. In reality, I would bet that nearly every Fortune 500 company is already running more efficiently -- and making more money -- because of machine learning.

When Willie Kate Friar wakes in the middle of the night, the octogenarian doesn't have to turn on the lights or crane her neck to find out the time. She simply asks her digital assistant, who responds in a life-like voice. "I've found Alexa is like a companion," Friar said of Amazon Echo's new voice-controlled assistant, a black cylinder called Alexa. A Panama-based retiree who writes and lectures on cruise boats, Friar is recuperating from a recent fall and asks Alexa to play music during her physical therapy sessions. "The music lifts my spirits," she said.

During Chevron Corporation's (NYSE:CVX) Security Analyst meeting on March 8, several big pieces of news came out. A day before the meeting, Chevron issued a press release stating that its 54 billion Gorgon LNG facility in Australia had just started producing LNG (liquefied natural gas) and condensate. After originally estimated to be operational by the end of 2014 for under 30 billion USD, the project was delayed as costs skyrocketed. As the operator with a 47.3% stake, Chevron lost a lot of credibility due to the massive cost of its mishaps, as did its partners ExxonMobil (NYSE:XOM) and Royal Dutch Shell (NYSE:RDS.A) (NYSE:RDS.B), who each own 25% of the venture. The first cargo of LNG is expected to be shipped out very soon, potentially marking the beginning of a strong source of growth after all the headaches it took to get here.

Episode #506 of Six Pixels of Separation - The Mirum Podcast is now live and ready for you to listen to. What will near-to-future technologies do to change business as we know it? Better question: how are these technologies already being used in business? These are the questions that journalist, strategist, consultant and author, John C. Havens has been investigating. His latest business book is called, Heartificial Intelligence, and it looks at what our world could look like, if we don't begin figuring out what robots and machine learning-based business landscapes could look like.

Ishiguro does not expect the average household to buy a Geminoid -- in part because of the 100,000 price tag -- but he already has some orders from researchers. He does expect his smaller CommU communicative robots to make their way inside many households within the next couple of years. Like Amazon's Echo -- but much cuter -- these chatty robots use voice recognition technology and artificial intelligence to simulate conversation. An example of where they can be useful is in tutoring, said Ishiguro. Many Japanese learners struggle with speaking English because they do not get enough practice.

We consider integrated process planning and scheduling for remanufacturing. Two potentially conflicting objective functions are considered simultaneously. A simulation-based genetic algorithm approach is developed. Key parameters of the algorithm have been fine-tuned. Extensive computational experiments and evaluations have been performed. Remanufacturing has attracted growing attention in recent years because of its energy-saving and emission-reduction potential.

A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and tractability. The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data. This approach allows us to rapidly learn, sample from, and evaluate probabilities in deep generative models with thousands of layers or time steps, as well as to compute conditional and posterior probabilities under the learned model. We additionally release an open source reference implementation of the algorithm.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.It is available as a free download under a Creative Commons license. You are free to share the book, translate it, or remix it. Before you is a tool for learning basic data mining techniques. Most data mining textbooks focus on providing a theoretical foundation for data mining, and as result, may seem notoriously difficult to understand. Don't get me wrong, the information in those books is extremely important.