Sounds like some scary future education method from George Orwell's 1984, right? Well, machine learning is not even remotely close to being as scary as it sounds and can even be used to help improve your business' marketing strategy. According to Optimove, a customer retention automation platform, machine learning is "a discipline combining science, statistics and computer coding that aims to make predictions based on patterns discovered in data." In other words, machine learning helps individuals make better decisions and predictions through large sets of data. Unlike rule-based decision systems, "which follow an explicit set of instructions known by the developers in advance, machine learning algorithms are designed to analyze data and discover patterns that people cannot find by themselves."

For resources, the single best thing you can do is find people who can challenge you and make you think. These can be collaborators that you work with in "real life," or folks online (say, for example, contributing to open source projects). I've also found that the projects that turn out the best for me are the ones that I find most interesting or exciting, so I've grown to put a lot of effort into reading about many different things so I can find out what seems most cool or fun and then go after that--at first it felt a little backward, like instead I should be reading up to find out what I "should" be excited about and then letting that guide my choices, but I've found that thinking about it instead from the perspective of "what makes me excited, and let's think of a way to apply machine learning or data science to that" is way more fun for me. That's not really a resource, sorry, but I think it's important. For resources, I love online courses (like Udacity of course, but there are lots of good ones out there), podcasts (I have to say that, since I host one as a side project–Linear Digressions), and there are some excellent blogs out there too.

I've always had a thing for Mario's brother Luigi. So when I recently heard of another Luigi--also tall, thin, and willing to drop into sewers--I had to meet him. At MIT's Senseable City Lab, a team of researchers recently premiered a second-generation robot named Luigi who sifts through sewage as a way to improve public health. Luckily, I didn't have to brave the smells of the sewer to meet him. In a small campus makerspace stacked to the ceiling with batteries, wires, and glue, architects Newsha Ghaeli and Alaa AlRadwan introduce me to Luigi, then proceed to do what all good inventors do: repeatedly try to turn the robot on.

If you had the opportunity to vote for a politician you totally trusted, who you were sure had no hidden agendas and who would truly represent the electorate's views, you would, right? What if that politician was a robot? Futures like this have been the stuff of science fiction for decades. And, if so, should we pursue this? Recent opinion polls show that trust in politicians has declined rapidly in Western societies and voters increasingly use elections to cast a protest vote.

In Facebook Messenger, for example, several news organization such as CNN and The Wall Street Journal are already using bots and some level of automation to deliver news through the platform. Artificial intelligence understands the environment it operates in and performs certain actions as a result of it. AI seeks to learn what its users want and how they want it. In the specific case of news media, articles can be processed through algorithms that analyze readers' locations, social media posts and other publicly available data. They can then be served content tailored to their personality, mood and social economic status, among other things.

The size of the digital universe will grow to 176 zettabytes by 2025–leading to a future of machine learning that could have significant ramifications for the defense and intelligence communities, officials said. Speaking at an event hosted by DefenseOne, James Harris, chief technology officer of the Defense Intelligence Agency (DIA), and Jason Matheny, director of the Intelligence Advanced Research Projects Activity (IARPA), said speed and automation will be key to the future of intelligence collection and analysis. DIA is testing machine learning to understand the full range of capabilities. One example is a machine that runs through all of the resumes a company has received, and matches the resumes with job vacancy announcements, said Harris. While this is a small-scale example, it opens up the possibility of machines running all open-source data.

Silicon Valley veteran Jerry Kaplan came to the Collision tech conference in New Orleans to talk about artificial intelligence, human intelligence, the likelihood of singularity and the future of technology and our lives. But his message was a little more tangible than all that: Don't let the hype around artificial intelligence distract you from the tremendous opportunities and potential pitfalls that lie before us as human beings. We are a long way away from humans merging with machines or machines taking over human existence. "We need to get rid of, in this field, this gee-whiz, apocalyptic gloss," Kaplan, an artificial intelligence pioneer who co-founded Go and Onsale, said speaking from the conference's main stage. "I'm not worried about super-intelligent machines or whether I'm going to live long enough to be uploaded into cyberspace. Such concerns only distract us from the real threats and opportunities that mankind is likely to face."

The next big trend in AI looks likely to be computers and robots that teach themselves through trial and error. Elon Musk and Sam Altman (of Y Combinator) caused a stir last December by luring several high-profile researchers to join OpenAI, a billion-dollar nonprofit dedicated to releasing cutting-edge artificial intelligence research for free. Today the nonprofit released the first fruits of its work, and it suggests that kind of learning will be important for the future of AI. The nonprofit has released a tool called OpenAI Gym for developing and comparing different so-called reinforcement learning algorithms, which provide a way for a machine to learn through positive and negative feedback. This week OpenAI also announced two new recruits, including Pieter Abbeel, an associate professor at Berkeley and a leading expert on applying reinforcement learning to robots. OpenAI Gym includes code and examples to help others get started with reinforcement learning.

A California start-up called View, which has raised a whopping 500 million from investors including Corning, General Electric and Khosla Ventures, is making high-tech windows that have the potential to bring to buildings what high-resolution touchscreens did for smartphones. View's windows eliminate glare, change hue, moderate internal temperature -- and at some point, could show entirely different views of the outside world -- via a process that uses a pane of glass sprayed with electrochromic material, which alters light transmission. The result is smart glass that increases energy efficiency and promises better worker productivity, via technology accessed through an app. "When you look at smart glass, the only smart surface we saw was on our phones," says Ben Bajarin, an analyst for Creative Strategies who follows the industry. "Now, we believe consumers are moving toward an age where smart glass can do almost anything -- for example, project images of the sun on your windows during a rainy day or viewing data on the window." While elements of the technology have been around on a smaller scale, such as car windows, View is the first company to commercially produce such glass at a large scale.

Stanford's five-foot "virtual diver" was originally built for studying coral reefs in the Red Sea where a delicate touch is necessary, but the depths go well beyond the range of meat-based divers. The "tail" section contains the merbot's onboard batteries, computers and array of eight thrusters, but it is the front half that looks distinctly humanoid with two eyes for stereoscopic vision and two nimble, articulated arms. Those arms are what make OceanOne ideal for fragile reef environments or priceless shipwrecks like La Lune, which sank off the coast of France over 350 years ago and hasn't been touched until now. Force sensors in each wrist transmit haptic feedback to the pilot, allowing them to feel the object's weight while staying high and dry on a dive ship. The robot's "brain" works with the tactile sensors to ensure the hands don't crush fragile objects, while the navigation system can automatically keep the body steady in turbulent seas.