Packaging in Python has a bit of a reputation for being a bumpy ride. This is mostly a confused side effect of Python's versatility. Once you understand the natural boundaries between each packaging solution, you begin to realize that the varied landscape is a small price Python programmers pay for using the most balanced, flexible language available. Tomas is blogging an algorithm / data structure a day. Find fix broken code fast!

Apple's director of artificial intelligence, Ruslan Salakhutdinov, believes that the deep neural networks that have produced spectacular results in recent years could be supercharged in coming years by the addition of memory, attention, and general knowledge. Speaking at MIT Technology Review's EmTech Digital conference in San Francisco on Tuesday, Salakhutdinov said these attributes could help solve some of the outstanding problems in artificial intelligence. Salakhutdinov, who retains a post as an associate professor at Carnegie Mellon University in Pittsburgh, pointed in his talk to limitations with deep-learning-driven machine vision and natural-language understanding. Deep learning--a technique that involves using vast numbers of roughly simulated neurons arranged in many interconnected layers--has produced dramatic progress in machine perception over recent years, but there are many ways in which these networks are limited. Salakhutdinov showed, for example, how image captioning systems based on the technology can label images incorrectly because they tend to focus on everything in the image.

It was/is a lot material to cover in 90 minutes, and conceptual understanding / developing intuition was the main point. Of course, there is great online material to make use of, and you'll see my preferences in the cited sources;-). This year, having covered the basics, I hope to be developing use cases and practical applications showing applicability of Deep Learning even in non-Google-size (resp: Facebook, Baidu, Apple...) environments.

The pace of machine-learning advancement is often underestimated, according to Massachusetts Institute of Technology principal research scientist Andrew McAfee. Massachusetts Institute of Technology principal research scientist Andrew McAfee says the pace of machine-learning advancement is often underestimated, and he cites data center management and the game of Go as several areas in which machines have lately beaten humans. McAfee notes Google DeepMind's AlphaGo algorithm has gained in both these fields, making a data center's energy efficiency 15% better on average, and beating world-class Go players even though experts predicted such a milestone would never occur. He says these examples illustrate the hazard of relying on the "highest-paid person's opinion," or following the views of experienced professionals' bosses, who are "chronically second-guessing the geek" and ignoring bold, rational, iterative, transparent, and experimental mindsets that are more likely to accurately predict smart-machine advances and cushion disruptions. McAfee also urges companies to experiment with advanced technology ideas, warning, "If an organization isn't ramping up the successful experiments they aren't doing their job."

This is the first installment in our blog series about deep learning. In this series, we will discuss the deep learning technology, available frameworks/tools, and how to scale deep learning using big data architecture. Machine learning is a branch of computer science that studies the algorithms that learn with the help of observational data but without explicit programming. In other words, the goal is to design algorithms that can learn automatically without the intervention of humans. In general, Machine Learning can be considered as a subfield of AI.

AlphaGo has again defeated Ke Jie, the world's number one Go player, in their second game, meaning the AI has secured victory in the three-part match. The win over Ke, universally considered the best Go player in the world, essentially confirms that AlphaGo has surpassed human Go ability a little over a year after the AI first beat Lee Se-dol. Ke played "perfectly" for about the first 50 moves, according to AlphaGo's analysis, and evolved his strategy to engage in a series of complex battles across the board. But AlphaGo handled the multi-angled assault well to simplify the engrossing game and eventually forced Ke to resign. "For the first 100 moves it was the closest we've ever seen anyone play against the Master version of AlphaGo," DeepMind CEO Demis Hassabis said in the post-game press conference.

I don't need to know exactly why Netflix recommends certain movies to me -- if it looks like a fit, I'm happy to take their recommendation. On the other hand, if your AI tells me that I should undergo an invasive medical treatment because a deep neural network (DNN) recommends it -- well, I'm going to want to understand why before I take your recommendation. AI deployed in military tools, financial tools such as loan assessments, or self-driving cars may use DNNs without being able to establishing culpability -- if we can't understand how an algorithm works, who's responsible when something goes wrong? As long as breakthroughs in artificial intelligence (AI) are common, researchers and startups will probably focus most of their effort into making new, flexible AI models. Maybe we can't explain how these models work, but if x.ai's Amy or Andrew can miraculously figure out how and when to schedule meetings for me, do I even care?

Google's artificial intelligence program AlphaGo has beaten a master of the ancient Chinese strategy game Go for the second time. The victory was part of a three match event taking place this week that is meant to test the limits of computers in taking on humans at complex tasks. Ke Jie the 19-year-old Chinese world number one, anointed the program as the new'Go god' after his defeat. It is a feather in the cap for Google's parent company Alphabet's ambitions in the artificial intelligence arena, as it looks to woo Beijing to gain re-entry into the country. AlphaGo beat Ke Jie, 19, (left) taking an unassailable 2-0 lead in a best of three series being held at in the eastern Chinese water town of Wuzhen.

In the latest round of man versus machine, machine has come out on top. Google's AlphaGo beat Go world champion Ke Jie for a second time in as many days, taking an unassailable lead in the three-part series. By rights, Deepmind's AI can now be considered the world's best Go player, having beaten the game's two biggest names in a little under a year. Although today's result wasn't as "close" as the first match, where AlphaGo won by just half a point, Deepmind CEO Demis Hassabis said that Ke Jie played "perfectly" for much of the contest before he resigned, at least according to AlphaGo's evaluations. "For the first 100 moves it was the closest we've ever seen anyone play against the Master version of AlphaGo," Hassabis confirmed in the post-game press conference. "Today's game was different from the first," Ke said, reported by The Verge.

A top-selling holiday gift was the AI-powered Amazon Echo; IBM Watson was used to diagnose cancer; and Google DeepMind's system AlphaGo cracked the ancient and complex Chinese game Go sooner than expected. And progress continues in 2017. Neil Jacobstein, faculty chair of Artificial Intelligence and Robotics at Singularity University, hit the audience at Singularity University's Exponential Manufacturing Summit with some of the more significant updates in AI so far this year. DeepMind, for example, recently outlined a new method called Elastic Weight Consolidation (EWC) to tackle "catastrophic forgetting" in machine learning. The method helps neural networks retain previously learned tasks.