For anyone that hasn't yet joined the Becoming a Data Scientist Podcast Data Science Learning Club, I thought I'd write up a summary of what we've been doing! The first activity involved setting up a development environment. Some people are using R, some using python, and there are several different development tools represented. In this thread, several people posted what setup they were using. I posted a "hello world" program and the code to output the package versions.

Diffbot, an artificial intelligence company that helps clients extract and combine data from multiple Web sources, announced today it raised 10 million from investors including Tencent and Felicis Ventures to expand its "knowledge-as-a-service" offerings to businesses and consumer apps. The Palo Alto, CA-based startup, founded in 2009, still has a tiny staff of 14. But Diffbot's ambition is huge: to catalog trillions of facts across the Web--many of them drawn from page elements such as comment forums, which can't be mined by traditional search engines. The startup says it has made a significant start on that goal, having indexed 1.2 billion entities such as people, products, and places since the middle of last year. Its Global Index also encompasses 10 to 20 times that number of facts, says Diffbot founder and CEO Mike Tung.

It was probably the HAL 9000 from the movie 2001: A Space Odyssey, that introduced the concept of AIs to the general public. But that was almost 40 years ago, and examining the more recent times, we have to look no further than in our own pockets to find the AIs that paved the way for the current hype: smartphones' digital assistants. Sure, there has been things like Cleverbot around earlier, but nothing has been as widely spread as these digital assistants. The main difference between a chatbot and a digital assistant is that former responds (be default) only to written queries, and the latter is capable to understand (at least to some extend) more natural, spoken queries. Things like voice activated searches and speech recognition softwares have been around for quite some time, but these digital assistants take the concept a step further by engaging in dialogue, performing tasks such as booking flights or setting up location based reminders, and they can even tell you a joke if you ask one.

AlphaGo, Google's artificially intelligent computer program, beat a professional human Go player last October in 4 out of 5 matches. Go is a very complex board game to master and has been around since the 1000s. The game requires the players to think ahead a few moves and consider what the opponent might or might not do. Tasks that involve a lot of decision-making or snap decisions have typically been difficult for computers to achieve. AlphaGo is helping to break that barrier.

Poaching is one of the greatest threats in the conservation of wildlife, and even patrol rangers' extreme efforts are not enough to completely fend off poachers, especially in very large protected areas. "In most parks, ranger patrols are poorly planned, reactive rather than pro-active, and habitual," said Fei Fang, a Ph.D. candidate in the computer science department at the University of Southern California, in a statement. With these in mind, researchers, in collaboration with the National Science Foundation and the Army Research Office, have developed a new Artificial Intelligence (AI)-based application employing game theory to efficiently map out patrol routes and areas. According to the press release of National Science Foundation, game theory uses mathematical and computer models of conflict and cooperation between rational decision-makers to predict the behavior of adversaries and plan optimal approaches for containment. The application, dubbed "Protection Assistant for Wildlife Security" or PAWS, uses mathematical models to effectively analyze data from previous patrols and evidence of poaching.

So, you bought into the current machine learning craze and went on to collect millions/billions of records from this promising new data source. Now, what do you do with them? Too often, the abundance of data quickly turns into an abundance of problems. How do you extract that "magic essence" from your data without falling into the common pitfalls? In her session at @ThingsExpo, Natalia Ponomareva, Software Engineer at Google, will provide tips on how to be successful in large scale machine learning.

Jia Yeuting may be a relative unknown in the rarefied world of Silicon Valley but the billionaire Chinese businessman in charge of the conglomerate LeEco -- which is also funding Faraday Future -- is not afraid to ruffle some pretty big feathers, taking aim at both Apple and Tesla in his first interviews with Western media. Jia called Apple "outdated," claiming the reason for Apple's slowing growth in the key Chinese market was down to a lack of innovation from the iPhone maker, pointing to the launch of the iPhone SE as an example. "From an industry insider's perspective, this is a product with a very low level of technology," Jia told CNBC. "We think this is something they just shouldn't have done." Not content with take aim at Apple, Jia, who last week launched LeEco's first self-driving car, also took aim at Tesla, saying his company would eclipse Elon Musk's electric vehicle efforts.

Last month, Google DeepMind's AlphaGo programme famously defeated professional Go player, Lee Sedol, in what has been described as a breakthrough for artificial intelligence research. Unlike previous gaming computers, such as IBM's chess-playing Deep Blue which defeated Garry Kasparov in 1997 and IBM's Watson which won Jeopardy! in 2011, AlphaGo implements a fundamentally different type of AI search algorithm that leverages neural networks trained with a combination of supervised and reinforcement learning. Previous game-playing computers relied heavily on deterministic search techniques custom built for a narrow problem domain. For example, IBM's Deep Blue, though expert at chess, would have to be entirely reprogrammed to play checkers. The novelty of AlphaGo's search algorithm lies in its use of deep neural networks, a method of programming that does not rely on any specific domain information.

According to Gartner, "Big Data Discovery" is the next big trend in analytics. Each of these areas has seen explosive growth, but there are clear upsides and downsides to each. For example, Data Discovery excels in ease of use, but allows only limited depth of exploration, while Data Science provides powerful analysis but is slow, complex, and difficult to implement. Since the disadvantages of the three technologies map to nicely to the advantages of the others, they are now starting to blend, and Gartner believes Big Data Discovery will be a distinct new market category by 2017. The emerging Big Data Discovery tools will be simpler to use than data science products and accessible to a wider ranger of users, with more powerful manipulation of a wider variety of data sources. According to Gartner Analyst Joao Tapadinhas, these tools will be used by new "Citizen Data Scientists" who marry the skills of traditional business analysts with some of the expertise of expert statisticians.

I'm impressed with Tyler Vigen for his work popularizing Spurious Correlations. He has found an effective way to convey an important message. Namely that correlation does not equal causation. Lots of things are correlated but that doesn't mean that they have anything to do with each other. To create his graphs Vigen indulges in "Data Dredging… a technique used to find something that correlates with one variable by comparing it to hundreds of other variables" (Vigen, 2015, page xii).