Big data in healthcare can now be measured in exabytes, and every day more data is being thrown into the mix in the form of patient-generated information, wearables and EHR systems. Traditional methods of analysis are no longer enough to handle, let alone take proper advantage of, the potential that healthcare data holds. This is where deep machine learning (or simply, "deep learning") comes in. However, its greatest power lies in its ability to extract value from data in ways that humans and traditional machine learning methods cannot. Deep machine learning has applications in a number of healthcare areas.

Big data in healthcare can now be measured in exabytes, and every day more data is being thrown into the mix in the form of patient-generated information, wearables and EHR systems. Traditional methods of analysis are no longer enough to handle, let alone take proper advantage of, the potential that healthcare data holds. This is where deep machine learning (or simply, "deep learning") comes in. However, its greatest power lies in its ability to extract value from data in ways that humans and traditional machine learning methods cannot. Deep machine learning has applications in a number of healthcare areas.

For those of you that can remember the 80s TV series Knight Rider, it was about a guy and his self-driving car, which had an Artificial Intelligence called KITT and some cool secret agent-like gadgets (for you millennials….think Google Car with Amazon Alexa built by James Bond's Q). The hero of the show, Michael Knight could connect and talk with the car via his smartwatch and give KITT some instructions on how to stop the bad guys – though from time to time the car would disagree with him and do what it felt was statistically better. Today the technologies of science fiction from Knight Rider are fast becoming mainstream, and the insurance industry is faced with a glut of new issues and challenges caused by the ever evolving world of connected devices, artificial intelligence, and autonomous technology. Consider a scenario where KITT makes a decision and causes a car accident that results in an insurance claim.

The world's first AI healthcare companion for the home can store, dispense and even order medication The rise of fitness trackers and exercise programs with a cult following have made it clear that our society is desperate to get and remain healthy. Those looking for a high-tech way to keep track of their well-being might be interested in a new home health robot called Pillo. A combination of face recognition, machine learning, automation and video conferencing make Pillo a personal health assistant that can even dispense your daily vitamins and medication. According to the company responsible for the device, the system uses facial recognition and can identify the face and voice of every user in a household, and dispense the right pills at the appropriate time. The medication is stored in a tamper-proof casing that can fit up to 250 medium-sized pills.

After decades of stagnation in research and development of artificial intelligence solutions, machine learning has blossomed again – and its seeds are spreading to financial services. Sofia Lotto Persio reports on how AI is improving the field. Almost 20 years later, in March 2016, Google's AlphaGo programme beat South Korean champion Lee Sedol at four matches of Go, a strategy game, in what represents another historic breakthrough for artificial intelligence (AI). In the two-decade span between these achievements, AI has progressed tremendously. Go is a much more complex game than chess, as there are more possible positions on the board than there are atoms in the universe.

So Brexit turned out to be the non-event for markets we expected with asset prices booming to record highs while support for remaining inside the single currency free-trade zone has risen within other European Union countries. As is often the case, the trade was to "fade" – or bet against – the apocalyptic hyperbole both before and after the referendum. Remarkably one boutique Australian quant shop did exactly this by leveraging academic studies of "blue green algae", which helped it generate enormous 32 per cent returns on the day of the vote. Taaffeite Capital Management (TCM) claim their "artificial intelligence" (aka computer code operating autonomously of humans) figured out that there were massive financial market mispricings that warranted shorts on equities and long positions on government bonds without actually knowing that a referendum was being held. The intellectual property underlying this "systematic" – or automated – trading strategy belongs to Massachusetts Institute of Technology PhD Desmond Lun, who is a 36-year old Australian professor of computer science at Rutgers University in the US, and a Melbourne University alumnus.

Looking for something to read? Check out:There was a good amount of eye-rolling in the Engadget Slack channel when Shervin Pishevars mobile app response to recent police shootings was brought up (Okay, most of it was mine). Maybe his heart was in the right place, or maybe it was just his pocketbook, but the fact remains: It might be time to admit that not everything needs an app or a Silicon Valley-style "disruption" to be solved. Because Verizon is our parent company, people often think we can't write about them objectively. Cherylnn Low's honest coverage of Verizon's new data plans is sure to ruffle some feathers on both sides of the fence (and has already provoked quite a few thoughts in the comment section).

Nowadays you look around and it seems like everyone at the gym or the park is wearing the coloured band of a FitBit, FuelBand, Apple Watch, Jawbone Up or some other activity tracker on their arm. You might very well have one on as you read this. These tools work to collect huge amounts of data about our personal activity, relatively inexpensively and easily. This allows us to quantify how much we do to improve our health by taking measurements of ourselves performing activites such as running, swimming and lifting weights. However, rarely do we try to quantify how well we perform an activity.

That's what the most ubiquitous genre of video games are colloquially called. They are the reason critics constantly say that video games are obsessed with guns. Since the early 1990s when titles like "Doom" and "Wolfenstein 3-D" popularized the concept of a gun-based game from a first-person perspective, shooters have dominated. It's a gun-obsessed medium that mirrors a gun-obsessed society. But as mass shootings become an increasingly regular part of the news cycle, it's fair to ask: Did the industry create a monster?

The legality of and public's view towards marijuana is rapidly changing as more states decriminalize and legalize the drug. As such, how have the words associated with marijuana in news articles changed over time? I developed a two-step approach to to try to answer this question. First, I wanted to identify distinct eras characterized by the use of key words associated with marijuana. Second, I aimed to assess whether words in each era represented larger themes regarding the public's view toards marijuana and the the drug's legality.