The pace of change in the artificial intelligence (AI) and machine learning arena is already breathtaking, and it promises to continue to upend conventional wisdom and surpass some of our wildest expectations as it proceeds on what appears at times to be an unalterable and pre-ordained course. Along the way, much of what we now consider to be "normal" or "acceptable" will change. Some technology companies are already envisioning what our collective AI future will look like and just how far the boundaries of normality and acceptability can be stretched. In 2016, for example, Google produced a video that provided a stunningly ambitious and unsettling look at how some people within the company envision using the information it collects in the future. Shared internally at the time within Google, the video imagines a future of total data collection, where Google subtly nudge users into alignment with the company's own objectives, custom-prints personalized devices to collect more data, and even guides the behavior of entire populations to help solve global challenges such as poverty and disease. Entitled "The Selfish Ledger," the nine-minute film maintained that the way we use our smartphones creates a constantly evolving representation of who we are, which it terms a "ledger," positing that these data profiles can be built up, used to modify behaviors and transferred from one user to another.

The human sense of taste is the result of millennia of evolution. And its astoundingly good at letting us enjoy pleasant foods and beverages as well as warning us against ingesting harmful substances. Man-made sensors, on the other hand, have yet to approach the ease with which our taste buds recognize substances. This is a significant technological gap, as there are many substances out there that we would like to "taste" without actually putting them in our mouth. For the rapid and mobile fingerprinting of beverages and other liquids less fit for ingestion, our team at IBM Research is currently developing Hypertaste, an electronic, AI-assisted tongue that draws inspiration from the way humans taste things.

In almost all of the 30 courses in'Crash Team Racing: Nitro Fueled' you'll find chickens doing funny things. But don't keep your eyes off the track too long. The Easter holiday may have come and gone, but that doesn't mean the hunt for eggs is over. "Easter eggs" – as it pertains to video games – are hidden treats you can find by taking the time to explore your virtual surroundings. In other cases, you might need to press a combination of buttons on the game controller (or computer keyboard) to unlock the secret.

Sure, world is crying out loud that big-data's biggest problem will be resources. Demand has skyrocketed and everyone in the world is going into tailspin in meeting that demands. Companies are going frantic and overspending to hire data scientists to secure themselves from any upcoming shortfall. This is nothing but a sign that world needs our robot algorithm friends to pacify some demand and increase credibility to new paradigms. Who could forget Steve Balmer's famous quote comparing Big Data as a Machine Learning problem.

From automated eye scans to analysing the cries of new-born babies, faster drug development to personalised medicine, artificial intelligence (AI) promises huge advances in the field of healthcare. At the recent AI for Good Summit in Geneva, Switzerland, we were told how AI could speed up the development of new drugs, lead to personalised medicine informed by our genomes, and help diagnose diseases in countries suffering from underdeveloped health services and a chronic shortage of doctors. But there are two main obstacles preventing access to this utopian destination. One is that the AI being applied to the world's health problems isn't quite good enough yet. The other related issue is the lack of good quality digital data - less than 20% of the world's medical data is available in a form that AI machine learning algorithms can ingest and learn from, the WHO estimates.

What do we mean when we say'context'? In essence, context is the information that frames something to give it meaning. Taken on its own, a shout could be anything from an expression of joy to warning. In the context of a structured piece of on-stage Grime, it's what made Stormzy's appearance at Glastonbury the triumph it was. The problem is that context doesn't come free – it has to be discovered.

Elon Musk has a predilection for grandeur. The billionaire tech provocateur made his fortune as a founder of the revolutionary online-payments company PayPal, and since then, he has announced his intention to revolutionize cars, trains, space travel, intercontinental flight, and city driving. SpaceX, his aerospace company, has begun work on the infrastructure to beam internet access down to Earth from satellites in orbit around the planet. And this week, Musk shifted his public ambitions to his next target: the human brain. Neuralink, a neurotechnology company owned by Musk, crept out of the corporate shadows Tuesday with a live-stream that included one of the founder's signature big promises: The company is developing a device to implant inside the brain that supposedly will allow people to control computers and other devices with their mind.

At its crudest, most reductive, we could sum up the future of artificial intelligence as being about robot butlers v killer robots. We have to get there eventually, so we might as well start with the killer robots. If we were to jump forward 50 years to see what artificial intelligence might bring us, would we – Terminator-style – step into a world of human skulls being crushed under the feet of our metal and microchip overlords? No, we're told by experts. It might be much worse.

This week's furor over FaceApp has largely centered on concerns that its Russian developers might be compelled to share the app's data with the Russian government, much as the Snowden disclosures illustrated the myriad ways in which American companies were compelled to disclose their private user data to the US government. Yet the reality is that this represents a mistaken understanding of just how the modern data trade works today and the simple fact that American universities and companies routinely make their data available to companies all across the world, including in Russia and China. In today's globalized world, data is just as globalized, with national borders no longer restricting the flow of our personal information - trend made worse by the data-hungry world of deep learning. Data brokers have long bought and sold our personal data in a shadowy world of international trade involving our most intimate and private information. The digital era has upended this explicit trade through the interlocking world of passive exchange through analytics services.

The use of facial recognition in the United States public sector has received a great deal of press lately, and most of it isn't positive. There's a lot of concern over how state and federal government agencies are using this technology and how the resulting biometric data will be used. Many fear that the use of this technology will lead to a Big Brother state. Unfortunately, these concerns are not without merit. We're already seeing damaging results where this technology is prevalent in countries like China, Singapore, and even the United Kingdom where London authorities recently fined a man for disorderly conduct for covering his face to avoid surveillance on the streets. In the United States, San Francisco recently banned the use of facial recognition by law enforcement and other agencies due to its impression of "spying" on residents.