"AI is transforming the entire world of technology. Much of this progress is due to the ability of learning algorithms to spot patterns in larger and larger amounts of data. Today this is powering everything from web search to self-driving cars. This insatiable hunger for processing data has caused the bleeding edge of machine learning to shift from CPU computing, to cloud, to GPU, to HPC," observes Andrew Ng, the Chief Scientist at Baidu. Ng will describe this in more detail at his much-anticipated upcoming talk, How HPC in Supercharging Machine Learning at the June ISC High Performance conference in Frankfurt, Germany.

As the tech world pivots on game-changing applications, data scientists rise to the occasion. Such is the case with Holden Karau, principal software engineer of Big Data at IBM and coauthor of Learning Spark. When asked about the current renovations within Spark, Karau said she sees this time as an "opportunity to get rid of dead weight" by streamlining certain processes. For example, she cited getting functional and relative queries to talk to each other within Spark. Two area of expansion include sequencing and machine learning.

The true data enthusiast has a lot to read about: big data, machine learning, data science, data mining, etc. Besides these technology domains, there are also specific implementations and languages to consider and keep up on: Hadoop, Spark, Python, and R, to name a few, not to mention the myriad tools for automating the various aspects of our professional lives which seem to pop up on a daily basis. There are a lot of topics to keep abreast of. There are a lot of lists available of the top books in particular categories related to data. In fact, KDnuggets has previously, and rather recently, put together such lists on data mining, databases & big data, statistics, AI & machine learning, and neural networks.

There's a saying among futurists that a human-equivalent artificial intelligence will be our last invention. After that, AIs will be capable of designing virtually anything on their own -- including themselves. Here's how a recursively self-improving AI could transform itself into a superintelligent machine. When it comes to understanding the potential for artificial intelligence, it's critical to understand that an AI might eventually be able to modify itself, and that these modifications could allow it to increase its intelligence extremely fast. Once sophisticated enough, an AI will be able to engage in what's called "recursive self-improvement."

Much of today's technology reporting is focused on the potential threats posed by developments. Dangers are seen in everything from robots to flying drones and two-wheeled "hoverboards". Physicist Stephen Hawking has even warned that full artificial intelligence "could spell the end of the human race". Such concerns are not new, according to Carl Benedikt Frey, co-director of the Oxford Martin programme on technology and employment at Oxford university. "Fears about technology, and certainly fears that technology will destroy our jobs, have been with us for as long as jobs have existed," he says.

After seeing recent industry presentations on bots, machine learning and artificial intelligence (AI), I see the application of these technologies changing the practice of project management. The question is, is this future desirable or will we have a choice? Much of the daily work of a project manager has not dramatically changed over the last 30 years. We may use different management methodologies, but we spend a great deal of time manually collecting and disseminating information between the various roles on a project. This effort directly results from the need to fill the information gaps caused by systems that can't capture what is truly happening within the organization. In a recent PMI sponsored roundtable discussion, missing or incorrect data was highlighted as a significant issue.

At the International Solid State Circuits Conference in San Francisco this week, MIT researchers presented a new chip designed specifically to implement neural networks. It is 10 times as efficient as a mobile GPU, so it could enable mobile devices to run powerful artificial-intelligence algorithms locally, rather than uploading data to the Internet for processing. Neural nets were widely studied in the early days of artificial-intelligence research, but by the 1970s, they'd fallen out of favor. In the past decade, however, they've enjoyed a revival, under the name "deep learning." "Deep learning is useful for many applications, such as object recognition, speech, face detection," says Vivienne Sze, an assistant professor of electrical engineering at MIT whose group developed the new chip.

It's been more than 20 years since IBM's Deep Blue won its first match against world chess champion Garry Kasparov, marking the first time an artificial intelligence machine defeated a reigning champion. Deep Blue eventually lost the match 2-4, but evened the score in a May 1997 rematch. Fourteen years later, AI made its television debut in grand style, when IBM's Watson took down a pair of former "Jeopardy!" In milliseconds, the machine culled the most probable answer to each question from more than 200 million pages of content, including the complete Wikipedia catalog. Now, Google's AI system, AlphaGo, is making cognitive computing history.

Introduction to Bots A BOT is the most basic example of a weak AI that can do automated tasks on your behalf. Chat bots were one of the first automated programs to be called as'bots'. Web crawlers used by Search Engines like Google are a perfect example of sophisticated and advanced BOT.

The China Computer Go team could throw down the gauntlet to the Anglo-American program at the end of 2016. The news emerged on Thursday, during a Beijing event organized by the Chinese Go Association and the Chinese Association for Artificial Intelligence. Predictably, AlphaGo, which won a game against professional South Korean player Lee Sedol in March, was the main topic of discussion at the event. The victory of the computer, a brainchild of UK company DeepMind, came as a shock for many computer science experts, who thought the current state of AI technology would not be up to the task of beating a top-class Go player. Coincidentally, Google's CEO Sundar Pichai was also in China on Thursday, and he visited a renowned Go training school to better understand the game.