It's not an exaggeration to say that your smartphone is the most personal device you have. The fact it's always with you, however, sure does generate a lot of information about your habits. Yes, your phone and the Google services powering it are incredibly useful in many tangible ways. And if you use a strong password and two-factor authentication, your information is likely safer on Google's servers than just about anywhere else. But it's not paranoia to take stock from time to time of just how much you're handing over to Google or if you'd rather not place all your digital privacy eggs in the same basket. Here are some simple tips about how you can stay in control of all that critical information and ensure that privacy isn't something you have to surrender.

HTC has unveiled two new smartphones with artificial intelligence that can learn a user's phone habits and support them. The firm revealed the HTC U Play and U Ultra, both of which house AI software called HTC Sense Companion. The U Ultra features a small second screen at the top of the display that is designed to stay on, offering a faster window to important notifications. Like Apple, HTC has controversially ditched the headphone jack, meaning users will need to rely on wireless headphones or use an adapter. HTC's new smartphones contain artificial intelligence that can learn a user's phone habits and support them, the technology giant said.

Just when you thought HTC might be ready to hang up on its smartphone efforts, the Android underdog is turning up the volume and announcing what it describes as a "new direction" -- in the form of a series of smartphones preloaded with its own AI assistant. While mobile phones were originally a device for talking to other humans, before smartphones plus touchscreens turned devices (and people) into texting machines, analysts are spying signs of a renaissance for voice -- as a control mechanism to speed up interacting with increasingly complex devices. Every major smartphone device and OS maker has their own AI these days, from Apple's Siri, to Microsoft's Cortana, to Samsung's Viv, to Amazon's Alexa, to Google's Assistant. HTC is finally following suit, unveiling what it's calling the "HTC Sense companion" at a launch event today. The company teased the launch of the new U series smartphones last month, hinting at the "For U" personalization it had in the pipeline.

Investment in the sector is declining, but tech developments are accelerating. According to data from Thomson Reuters, U.S. fintech investments reached $4.27 billion in 2016, which was down 30 percent from 2015. The amount of investment declined in large part because of uncertainty about the presidential election and smaller deal sizes, according to data provider CB Insights and consultancy KPMG. Payments and lending remain the leading fintech subsectors across the globe, say CB Insights and KPMG, and continue to earn considerable attention from venture capital investors despite some signs of market saturation. Other areas, including blockchain, data and analytics, regulatory tech and insurance tech are gaining more attention.

Businesses and IT leaders concerned about cybersecurity have a lot of potential threats to deal with, from spear phishing attacks to ransomware, and may soon have another item on their worry list: artificial intelligence. Although AI has the potential to boost productivity and handle rote office tasks, freeing employees to work on more complex assignments, there is a looming downside in the security realm. Someday in the near future, hackers may be able to use AI tools to find new vulnerabilities and then create new exploits and attacks in a fraction of the time it would take a human. In August, the Defense Advanced Research Projects Agency, the Defense Department's research arm, sponsored the Cyber Grand Challenge hacking competition in Las Vegas. The contest pitted seven autonomous machines against each other to find and exploit bugs in each other's systems.

A small fraction of individuals account for the bulk of population healthcare expenditures in the USA, Denmark and other industrialised countries.1–4 Although many high-cost patients show consecutive high-cost years, the majority experience a'cost bloom', or a surge in healthcare costs that propels them from a lower to the upper decile of population-level healthcare expenditures between consecutive years.4 Proactively identifying and managing care for high-cost patients--especially cost bloomers, who may disproportionately benefit from interventions to mitigate future high-cost years--can be an effective way to simultaneously improve quality and reduce population health costs.5–16 However, since the Centers for Medicare and Services (CMS) commissioned the Society of Actuaries to compare leading prediction tools more than 10 years ago, scant progress has been made in improving cost-prediction tools.17 Overcoming these and other challenges associated with the management and care of high-cost patients is essential to achieving a higher value healthcare system.

Like it or not, robots and AIs already make a lot of decisions about our lives, and that's only likely to increase in the future. Computer algorithms have been used for decades for credit scoring and insurance assessments among many other things. Often this process had been shrouded in secrecy – to the point where "computer says no" is a well-understood joke. In the near future, increasingly complex algorithmic decision-making is likely to creep into every field, as machine learning and predictive modelling become more sophisticated. Will this lead to a future where we as individuals have little hope of understanding the complex web of decision-making we are subjected to when we apply for finance, healthcare or employment?

In a landmark achievement for artificial intelligence, a poker bot developed by researchers in Canada and the Czech Republic has defeated several professional players in one-on-one games of no-limit Texas hold'em poker. Perhaps most interestingly, the academics behind the work say their program overcame its human opponents by using an approximation approach that they compare to "gut feeling." "If correct, this is indeed a significant advance in game-playing AI," says Michael Wellman, a professor at the University of Michigan who specializes in game theory and AI. "First, it achieves a major milestone (beating poker professionals) in a game of prominent interest. Second, it brings together several novel ideas, which together support an exciting approach for imperfect-information games."

What programming language should one learn to get a machine learning or data science job? It is debated in many forums. I could provide here my own answer to it and explain why, but I'd rather look at some data first. After all, this is what machine learners and data scientists should do: look at data, not opinions. So, let's look at some data.

Let us start with the simple stuff - showing word similarity in a vector space.