Why not being actively involved in AI carries risks for government agencies (and us all) and what to do about it. A few months ago I introduced Alexa into my home – the voice controlled AI system made by Amazon. For those of you living in the US, UK or Germany this may not seem a big feat, and perhaps even somewhat late given that Alexa was first introduced in 2014, but outside of its designated markets, Amazon is still actively discouraging people from using it. I cannot buy an Alexa device, such as the Echo, on Amazon as it won't ship to the Netherlands. And the app that is required to install and use it is geoblocked in my appstore – quite meticulously I might add. For a company who se main purpose is to sell stuff, I find this quite odd. Nonetheless I have been able to get it up and running, and my Echo Dot has re-joined the fast growing population of Alexa devices. Amazon announced a couple of weeks ago that Alexa devices such as the Echo Dot had been the best-sel ling products overall during last year's holiday season, selling more than 20 million of them.

By all accounts, "Maple Valley" is thriving. Based on available data to date, it is estimated that funding raised by Canadian AI companies in 2017 will exceed US$250 million, representing an almost two-fold increase from the previous record historical high of US$143 million in 2015.[ii] This healthy injection of private-sector funding has been accompanied by significant public investment. Notably, the 2017 federal budget provided for C$125 million in research and development funds earmarked for AI initiatives and nearly C$1 billion over 5 years to promote innovation superclusters.[iii] Access to unprecedented levels of capital, a strong network of academic institutions, improving infrastructure and availability of talent facilitated by open immigration rules have fuelled the development of a burgeoning industry north of the border.

In 2016, an estimated 65.6 million people across the globe were forced from their homes by everything from war to human rights violations. Climate change and global warming are exacerbating the problem of displaced persons, with millions of people expected to be forced to relocate to other -- often cooler -- countries. The problem is becoming so widespread that New Zealand is even considering creating a new visa specifically for those displaced by climate change. Once they make the difficult decision to leave their home, refugees face a slew of other questions: To which country do they flee? Where in that country should they go?

We usually think of surveillance cameras as digital eyes, watching over us or watching out for us, depending on your view. But really, they're more like portholes: useful only when someone is looking through them. Sometimes that means a human watching live footage, usually from multiple video feeds. Most surveillance cameras are passive, however. They're there as a deterrence, or to provide evidence if something goes wrong.

We usually think of surveillance cameras as digital eyes, watching over us or watching out for us, depending on your view. But really, they're more like portholes: useful only when someone is looking through them. Sometimes that means a human watching live footage, usually from multiple video feeds. Most surveillance cameras are passive, however. They're there as a deterrence, or to provide evidence if something goes wrong. But this is changing -- and fast.

Tue 23 Jan 2018 12.47 EST Last modified on Tue 23 Jan 2018 12.48 EST The computer scientist Mary Lee Berners-Lee, who has died aged 93, was on the programming team for the computer that in 1951 became the first in the world to be sold commercially: the Ferranti Mark I. She led a successful campaign at Ferranti for equal pay for male and female programmers, almost two decades before the Equal Pay Act came into force. As a young mother in the mid-1950s she set up on her own as a home-based software consultant, making her one of the world's first freelance programmers. Modest about her own pioneering achievements, she is on record (in an interview with computer historian Janet Abbate) as saying that her biggest contribution was to be "the grandmother of the web". In 1989 Tim Berners-Lee (now Sir Tim), the eldest of her four children, proposed a system to access and exchange documents across the internet, and soon afterwards built the first web server, website and browser.

Yesterday's leak might have spoiled the surprise, but DJI was still enthusiastic about unveiling its new Mavic Air drone in rainy New York this morning. While last year's Spark was all about making drones accessible to a new generation of pilots, the $799 Mavic Air is a high-performance creative machine that just happens to be incredibly small. When folded up, the Mavic Air is about as tall and wide as a smartphone -- it's so tiny, in fact, that a spokesperson had a few crammed into his vest without looking the least bit frumpy. Despite its diminutive size, the Mavic Air can float around for up to 21 minutes on a single charge. DJI claims that's the longest flight time you'll find in a drone this small.

The development of self-learning and independent computers has long captured our imagination. The HAL 9000 computer, in the 1968 film, 2001: A Space Odyssey, for example, assured, "I am putting myself to the fullest possible use, which is all I think that any conscious entity can ever hope to do." Machine learning raises many challenging legal and ethical questions as to the relationship between man and machine, humans' control -- or lack of it -- over machines, and accountability for machine activities. While these issues have long captivated our interest, few would envision the day when these developments (and the legal and ethical challenges raised by them) would become an antitrust issue. Sophisticated computers are central to the competitiveness of present and future markets. With the accelerating development of AI, they are set to change the competitive landscape and the nature of competitive restraints.

By all accounts, "Maple Valley" is thriving. Based on available data to date, it is estimated that funding raised by Canadian AI companies in 2017 will exceed US$250 million, representing an almost two-fold increase from the previous record historical high of US$143 million in 2015.[ii] This healthy injection of private-sector funding has been accompanied by significant public investment. Notably, the 2017 federal budget provided for C$125 million in research and development funds earmarked for AI initiatives and nearly C$1 billion over 5 years to promote innovation superclusters.[iii] Access to unprecedented levels of capital, a strong network of academic institutions, improving infrastructure and availability of talent facilitated by open immigration rules have fuelled the development of a burgeoning industry north of the border.

We have seen how the internet of things and the growing phenomenon of'big data' will throw up major problems for consumers and citizens, problems that have as yet barely been grasped by most policy-makers. In this world of growing complexity, the potential for an unintended consequence becomes greater and greater from machines performing an action that was not anticipated. There are key issues, too, about our reliance on data at a time of massive data generation, data storing and data preservation which have the potential to both obscure results and generate injustices. Perhaps the greatest issue that we now face is caused by our blind faith in machines. We have invested them with certainty and – as we have pointed out – we trust them. Part of the reason for this is an odd confusion that has conflated the machines of the industrial age with the machines of the information age.