Why did I bother writing this? Well, here is one of the most trivial yet life-changing insights and worldly wisdoms from my former professor that has become my mantra ever since: "If you have to do this task more than 3 times just write a script and automate it." By now, you may have already started wondering about this blog. I haven't written anything for more than half a year! Okay, musings on social network platforms aside, that's not true: I have written something -- about 400 pages to be precise. This has really been quite a journey for me lately. And regarding the frequently asked question "Why did you choose Python for Machine Learning?"

Deep learning is a technique used to solve complex problems such as natural language processing and image recognition. We are now able to solve these computational problems quickly, thanks to a component called the Graphics Processing Unit (GPU). Originally used to generate high-resolution computer images at fast speeds, the GPU's computational efficiency makes it ideal for executing deep learning algorithms. Analysis which used to take weeks can now be completed in a few days. While all modern computers have a GPU, not all GPUs can be programmed for deep learning.

When IBM's Watson computer triumphed over human champions in the quiz show "Jeopardy!" it was a stunning achievement that suggested limitless horizons for artificial intelligence. Soon after, IBM's leaders moved to convert Watson from a celebrated science project into a moneymaking business, starting with health care. Yet the next few years after its game show win proved humbling for Watson. Today, IBM executives candidly admit that medicine proved far more difficult than they anticipated. Costs and frustration mounted on Watson's early projects. They were scaled back, refocused and occasionally shelved.

In Forrester's US B2B eCommerce Forecast: 2015 to 2020 they quote that "74% of B2B buyers research, at least one-half of their work purchases online. With that percentage nearly doubling to 56% by 2017, B2B sellers will see a significant volume of offline business move online in the next few years." Taking those facts further, at the Forrester Sales Enablement Forum, a study by Andy Hoar, Principal Analyst at Forrester, revealed that he expected 22% of B2B Sales jobs go by 2020. With Enterprise purchases taking place more and more online This means the traditional B2B sales person is being replaced by Search Engines, YouTube, websites etc. The Diagram above is Forester's view on what will replace the B2B Salesperson.

Robots are coming for our jobs. These doom-laden predictions probably sound familiar to anyone who's read or seen any movies lately involving artificial intelligence. Sometimes they're invoked with genuine alarm, as in the case of Elon Musk and Stephen Hawking warning against the danger of killer automatons. Other times, the anxiety comes across as a kind of detached, ironic humor masking the true depths of our dread, as if tweeting nervous jokes about #Skynet will somehow forestall its rise. AI raises unsettling questions about our place in the economy and society; even if by some miracle 99 percent of employers agree not to use robots to automate labor, that still leaves many hardworking people potentially in the lurch.

Most people don't seem to want to click on Web ads. Do they want to talk to them? Maybe if Watson-- IBM IBM 0.65 % 's supercomputer perhaps best known for its Jeopardy prowess--was the one talking back? The media company, all of which was acquired by IBM last October except for its traditional TV channel, is planning to roll out digital ads that people can communicate with this fall. Using Watson's artificial intelligence software, advertisers will be able to produce display ads that invite people to ask Watson questions about their products, either by speaking or typing into a search box.

On June 6, 2015, Kalief Browder took his own life at his home, in the Bronx. He was twenty-two years old. He had been released from Rikers Island two years earlier, ending an ordeal that had begun on a spring night in 2010, when he had been arrested for robbery, at sixteen. He spent the next three years in jail trying to prove his innocence, and, for about two of those years, he was held in solitary confinement, where he attempted suicide several times. The charges against him were eventually dropped.

Tesla Motors CEO Elon Musk has brushed aside concern that Google could become a rival in self-driving cars. Instead, he practically confirmed that Apple will be Tesla's self-driving car rival. Google and major automakers like Ford, Audi and General Motors have been testing self-driving cars in the wild while Apple's electric car project is still an unconfirmed but widely accepted open secret that has Apple fanatics salivating. Speaking Wednesday night at an annual gathering of Silicon Valley royalty known as the Code Conference in Rancho Palos Verdes, California, Tesla's futurist co-founder was asked if Google is considered Tesla's crosstown tech industry rival. Tesla co-founder and CEO Elon Musk said Wednesday that Apple, not Google, is a potential competitor to his electric car company's efforts to be a leader in autonomous driving.

Tianqi Chen will be in Santa Monica, June 2, 2016 and besides a meetup talk in the evening (already sold out, sorry) I'm also organizing an advanced workshop in the afternoon (3:00-6:00pm) to do more advanced stuff. There will be only 10 spots for the workshop and you'll have to apply by filling out this form (Update: workshop is full.). The workshop will be a mix of Tianqi talking about more advanced stuff and participants interacting, asking questions etc. (partly hands-on, bring your laptop and your specific questions/problems/datasets). We can use this github repo (issues, PR) for setting up questions/problems/topics etc. to be discussed in the workshop, feel free to participate. Location disclosed only to the selected participants.

We're happy to announce Magenta, a project from the Google Brain team that asks: Can we use machine learning to create compelling art and music? Soon we'll begin accepting code contributions from the community at large. If you'd like to keep up on Magenta as it grows, you can follow us on our GitHub and join our discussion group. First, it's a research project to advance the state of the art in machine intelligence for music and art generation. Machine learning has already been used extensively to understand content, as in speech recognition or translation.