Techno-optimism is defined as the belief that technology can improve the lives of people. It was famously satired in the U.S. television comedy series "Silicon Valley," with a startup-company's founders pledging to "make the world a better place through Paxos algorithms for consensus protocols." But some people take techno-optimism very seriously. Ray Kurzweil, an accomplished tech innovator, described his techno-optimistic vision in his books: The Age of Spiritual Machines, How to Create a Mind: The Secret of Human Thought Revealed, and The Singularity Is Near. In a keynote address (see https://goo.gl/RwkwK1) at the 2016 meeting of the Computing Research Association, Kentaro Toyama argued that "In spite of the do-gooder rhetoric of Silicon Valley, it is no secret that computing technology in and of itself cannot solve systemic social problems."

Artificial intelligence (AI) got a lot of press in 2016, not least because of the victory of Google's AI program over Lee Sedol, the world's best Go player. That triumph of machine over human elicited numerous responses, some enthusiastic and some anxious, all sharing the assumption that the goal of artificial intelligence is to achieve "human-level intelligence" or, as some predict, "superintelligence." "I don't care so much whether what we are building is real intelligence," says Peter Norvig, Director of Research at Google. "We know how to build real intelligence--my wife and I did it twice, although she did a lot more of the work. We don't need to duplicate humans. That's why I focus on having tools to help us rather than duplicate what we already know how to do. We want humans and machines to partner and do something that they cannot do on their own."

Acing analytics is not optional anymore. Managing by the numbers, using data to learn about your customers and how they are using your products is the only way to survive whether you are a series A start up or a Fortune 100 blue chip company. Yet, the landscape of analytics is getting murkier by the day. You are wooed daily by companies touting artificially intelligent bots that can shift through petabytes of data and tell you where the problems are in your business. Every day you see news of your competitors successfully increasing their revenue by x% using some fancy machine-learning algorithm.

There are lots of economic opportunities coming thanks to gains in artificial intelligence, the White House said in a report today, but that same report warns that millions of jobs could be displaced while the technology improves. Artificial intelligence, the report notes, accelerates trends seen since the industrial revolution, as people lose jobs to automation and are forced to learn new skills to find new career paths. How fast we'll see those impacts is the question. The report notes that researchers' estimates about jobs threatened ranges widely from 9 to 47 percent, and notes that because "AI is not a single technology, but rather a collection of technologies that are applied to specific tasks, the effects of AI will be unevenly felt throughout the economy." That said, the general assessment is that the jobs hardest hit are those that are more easily automated, which disproportionately impacts people with less educational attainment.

Bayesian Reasoning and Machine Learning by David Barber has a chapter on Approximate Sampling Christophe Andrieu et al. have written an introductory tutorial (pdf) on MCMC methods that covers most of the MCMC algorithms Dr. Daphne Koller offers an online course on Coursera, Probabilistic Graphical Models, which also covers the Gibbs Sampler and the Metropolis-Hastings Algorithm Dr. A. Taylan Cemgil has prepared very useful lecture notes (pdf) for his Monte Carlo methods course

Artificial intelligence (or AI) has permeated most facets of our lives. Algorithms suggest our social media mates. But could the arrival of the robots be applied to education? Jozef Misik, managing director of Knowble, a language tech start-up whose products are built on AI, believes so: "Most educational technology products will have an AI or deep learning component in future," he says. Already, AI is able to address common learning challenges.

Artificial Intelligence Artificial intelligence and neuroscience researchers have taken inspiration from the human brain in creating a new deep learning system that enables computers to learn about the visual world largely on their own, just like human babies do. Artificial intelligence and neuroscience experts from Rice University and Baylor College of Medicine using inspiration from the human brain have developed a new deep learning method that lets computers learn about the visual world largely on their own, much the same way human babies do. In tests, the group's "deep rendering mixture model" (DRMM) largely taught itself how to distinguish handwritten digits using a standard dataset of 10,000 digits written by federal employees and high school students. The results which were presented this month at the Neural Information Processing Systems (NIPS) conference in Barcelona,the researchers described how they trained their algorithm by giving it just 10 correct examples of each handwritten digit between zero and nine and then presenting it with several thousand more examples that it used to further teach itself. The algorithm was more accurate at correctly distinguishing handwritten digits than almost all previous algorithms that were trained with thousands of correct examples of each digit.

Microsoft is trying to help create machines that can have conversations by releasing a new set of data for free. The data, called the Microsoft Machine Reading Comprehension dataset (MS MARCO) is a bundle of 100,000 English queries along with corresponding answers. It's supposed to help people build artificial intelligence systems that can understand human written language. The company is opening up its dataset in the hope that Microsoft can work with other organizations on making machines better at reading comprehension, said Rangan Majumder, program manager for the Microsoft Partner Group, in a blog post on Friday. The queries in MS MARCO are based on anonymized questions that were submitted to Microsoft's Bing search engine and Cortana virtual assistant.

I'm a software engineer by training and I've had little interaction with AI. I had always wanted to delve deeper into machine learning, but never really found my "in". That's why when Google open sourced TensorFlow in November 2015, I got super excited and knew it was time to jump in and start the learning journey. Not to sound dramatic, but to me, it actually felt kind of like Prometheus handing down fire to mankind from the Mount Olympus of machine learning. In the back of my head was the idea that the entire field of Big Data and technologies like Hadoop were vastly accelerated when Google researchers released their Map Reduce paper. This time it's not a paper – it's the actual software they use internally after years and years of evolution.

In the startup world, companies seem to live and die by buzzwords. If your company isn't using the latest "artificially-intelligent blockchain-based crypto-kazoo", then you're not startup sexy and investors' attention will be lost. Machine learning is a collection of statistical methods that used to only be sexy in the data and computer science academic world. Over the last three years, Google searches for the term "deep learning" has grown by 770%. And more and more startups are being created with machine learning at their core.