From gene mapping to space exploration, humanity continues to generate ever-larger sets of data -- far more information than people can actually process, manage, or understand. Machine learning systems can help researchers deal with this ever-growing flood of information. Some of the most powerful of these analytical tools are based on a strange branch of geometry called topology, which deals with properties that stay the same even when something is bent and stretched every which way. Such topological systems are especially useful for analyzing the connections in complex networks, such as the internal wiring of the brain, the U.S. power grid, or the global interconnections of the Internet. But even with the most powerful modern supercomputers, such problems remain daunting and impractical to solve.

Machine learning has crossed from the lab to the business world. Machine learning provides insights that help to create more intelligent data-driven applications that improve business processes, operation, and easier decision making. In a conversation at Structure Data 2016 conference in San Francisco, Dr. Peter Lee, Corporate Vice President, Microsoft Research and Jack Clark, Bloomberg News – San Francisco, talked about the advances we made in Artificial Intelligence (AI) and machine learning in recent years. Dr. Lee is responsible for Microsoft Research New Experiences and Technologies. He said that AI is essentially used to really understand what customers want.

Google's artificial intelligence program AlphaGo's overwhelming win over South Korean go grandmaster Lee Sedol in a five-game tournament this month has shown that machine intelligence is rapidly evolving and underlined the possibility that it will catch up with and eventually surpass human intelligence. The time has come for us to think how best to use AI in ways that will contribute to -- and not detract from -- our well-being. In the tournament held in Seoul, the program built by a Google subsidiary DeepMind defeated Lee, a 33-year-old 9-dan professional go player with 18 world titles, in a 4-1 victory. Google had chosen Lee as an opponent in view of his impressive records, considering him as the world's strongest player of the board game. The outcome has stunned go players, professional programmers and the public alike -- given that experts had previously expected it would take more than 10 years for an AI program to beat a world-class professional go player.

At a debate Wednesday night co-hosted by Intelligence Squared US and the 92 Street Y in New York City, a largely white, middle-aged audience was easily convinced that the supposed bright future of artificial intelligence is perhaps not all that it's cracked up to be. The nonprofit debate series, syndicated as a podcast on NPR, works something like this: Before the debate begins, the audience votes in favor of the motion, against the motion, or as undecided. Last night's motion, "Don't Trust The Promises of Artificial Intelligence," was affirmed by 30 percent of the audience and negated by 41 percent at the start of the debate. A whopping 29 percent were undecided, indicating they knew little about the topic beforehand. The elderly white woman next to me, who would be asleep by the end of the debate, chose to abstain.

The folks over at x.ai – creators of Amy, the artificial intelligence answer to scheduling meetings – have had a shot at showing exactly what it looks like inside their bot's brain, using AI, of course. The team used a powerful deep-learning model, a Recurrent Neural Network (RNN), to trawl 500,000 words in its database, looking at their sequence in a sentence to understand what they mean, then predicting how to categorize them. Get your company on stage at TNW Europe. Without a human ever telling the RNN the definitions of different word groups, it has managed to understand that Stanford is different from Instagram, and that Jesse, Luke and Jason are names. This data was cut to down to the 3,500 most frequently used words and has then been projected into a 2D shape in order to show the relationships the AI has made between different words.

This week in big data we've got an acquisition by Hadoop distributor Cloudera, what Nvidia's CEO thinks of the state of AI, news out of the Adobe Summit 2016, Google's machine learning pitch, and more. Plus, we've got a quick look at a new book about applying statistics and analytics to college basketball. Let's start with the news from Cloudera. This week the company quietly acquired Sense, a big data cloud platform that lets data scientists collaborate with each other. "We launched Sense with the mission of helping data scientists and data engineers focus on what's important -- extracting value rather than managing infrastructure," wrote Sense founders Tristan Zajonc and Anand Patil in a blog post.

Here we discuss general applications of statistical models, whether they arise from data science, operations research, engineering, machine learning or statistics. We do not discuss specific algorithms such as decision trees, logistic regression, Bayesian modeling, Markov models, data reduction or feature selection. Instead, I discuss frameworks - each one using its own types of techniques and algorithms - to solve real life problems. Most of the entries below are found in Wikipedia, and I have used a few definitions or extracts from the relevant Wikipedia articles, in addition to personal contributions. Spatial dependency is the co-variation of properties within geographic space: characteristics at proximal locations appear to be correlated, either positively or negatively. Methods for time series analyses may be divided into two classes: frequency-domain methods and time-domain methods.

Machine learning explores the study and construction of algorithms that learn and make predictions based on data. In the field of machine learning, data scientists, who specialize in analyzing data, are responsible for writing and modifying such algorithms. Initially, a data scientist writes an algorithm based on a set of data features. This is generally an iterative process in which the data scientist explores different algorithms for predictive purpose. In this process, the amount of data and the number of features chosen for analysis may change.

One of the biggest issues in the presidential race is voter anger over lost middle-income jobs, real and perceived damage from trade deals, and rising inequality. But none of the candidates is talking about the elephant pushing its way into the room: a new wave of job-eating information technology, advanced automation, robots and artificial intelligence. The elites have been discussing what's coming for some time, notably a 2014 speech by Eric Schmidt, the executive chairman of Google's parent Alphabet. Huge numbers of middle-class jobs were going to be automated, and few new positions would replace them. He called it the "defining" issue of the next two or three decades. A study from the previous year by Carl Benedikt Frey and Michael Osborne examined the vulnerability of more than 700 occupations.

There's a big difference between what a person wants and what they need. On one hand we need healthy food, a good night's rest, and decent medical care. But a little voice inside our heads has us craving dinner at Gordon Ramsay's, an overnight stay at the Ritz Carlton, and a spa weekend at the St. Regis in Aspen to fix whatever is wrong. The same is true with countries. There's a big difference between what a country wants and what it needs.