We are experiencing a digital revolution. Machine learning, artificial intelligence, and big data are the buzzwords of the day. Yet most corporations are having a hard time to start implementing machine learning solutions or even see the usefulness of these new technologies. Unfortunately, organizations which don't get to grips with concepts like deep learning and big data soon will be left behind. Truth be told, most companies face challenges similar to what their own clients are facing.

THE car's computer saw Elaine Herzberg pushing her bicycle across the highway a full six seconds before it struck her. Travelling at just under 70 kilometres per hour, it had more than enough time to stop or swerve. But it did neither, hitting her head on. Herzberg died in hospital, the first pedestrian to be killed by an autonomous vehicle. A preliminary investigation by the US National Transport Safety Board into the collision, which happened in Tempe, Arizona, in March, found that the emergency braking procedure of the Uber-operated car was designed to be disabled when driving autonomously to ensure a smoother ride.

An unprecedented wave of rank-and-file rebellion is sweeping Big Tech. At one company after another, employees are refusing to help the US government commit human rights abuses at home and abroad. At Google, workers organized to shut down Project Maven, a Pentagon project that uses machine learning to improve targeting for drone strikes – and won. At Amazon, workers are pushing Jeff Bezos to stop selling facial recognition to police departments and government agencies, and to cut ties with Immigration and Customs Enforcement (Ice). At Microsoft, workers are demanding the termination of a $19.4m cloud deal with Ice.

Google services such as its image search and translation tools use sophisticated machine learning which allow computers to see, listen and speak in much the same way as human do. Machine learning is the term for the current cutting-edge applications in artificial intelligence. Basically, the idea is that by teaching machines to "learn" by processing huge amounts of data they will become increasingly better at carrying out tasks that traditionally can only be completed by human brains. These techniques include "computer vision" – training computers to recognize images in a similar way we do. For example, an object with four legs and a tail has a high probability of being an animal.

Canada's rankings in innovation has lagged that of other peer nations for decades despite government efforts to address this issue. Considering its success in developing research programs at its universities, its mediocre rankings overall in technology development is disappointing. Those programs alone have not been enough to translate into entrepreneurial innovation. A 2017 C.D. Howe Institute study points out that, even though Canadians have been at the forefront of breakthroughs in emerging technologies, in many cases, the chief beneficiaries of those breakthroughs have been other nations' economies. Canada needs to take a stronger role in building an environment in which Canadian know-how spurs Canadian business growth. According to a 2017 PwC global survey, Canadian companies stand significantly ahead of their global counterparts in having a dedicated team for digital innovation, with 54% of Canadian respondents reporting that their company does, as opposed to 43% of global respondents. Looking deeper, though, shows a far less innovative spirit, as 47% of respondents said that their pursuit of digital innovation takes the form of seeking to copy others' innovations rather than pursuing their own. Already a decade ago, experts recognized factors that constrain Canadian innovation growth. A 2009 study by the Council of Canadian Academies pointed to two key issues that have held Canadian businesses back from prioritizing innovation in their business strategies. The first issue deals with what has been called "the resource curse." Canada is largely "upstream" in the international supply chain, providing raw materials for other businesses that create products that are in turn passed down the value chain until they reach the stage of finished products sold to end customers. That places Canada in a position far distant from end customers, whose evolving needs spur businesses at the downstream end of the supply chain to adapt, which, in turn, spurs innovation.

If Google is planning a search engine for China, how is it planning to obey the country's strict censorship laws from day one? The Intercept has obtained documents reportedly showing that Google has been using 265.com, a hybrid information and search portal it acquired in 2008, as a "honeypot" that would help it develop a blacklist for search terms in China. Google has supposedly been collecting info about search queries, which technically redirect to Baidu, to see if they would be censored. According to the report, Google has been using a tool nicknamed BeaconTower to see if the final destinations of these searches would survive the Great Firewall. If they didn't, Google would exclude them from the first page of results in its prototype Chinese search engine.

The Great Firewall of China is the largest-scale internet censorship operation in the world. The Chinese state says the firewall is there to promote societal harmony within an increasing population of billions of people. It considers the internet in China as part of its sovereign territory. Eight years ago, Google withdrew from China, pulling its search and other services out because of country's limits to freedom of speech. But it is now planning to relaunch a heavily censored version of its services in China, according to a whistleblower who spoke to online news website The Intercept.

BOGOTA/CARACAS – A former Venezuelan municipal police chief and anti-government activist says he helped organize an operation to launch armed drones over a military rally on Saturday that President Nicolas Maduro has called an assassination attempt. In an interview, Salvatore Lucchese, a Venezuelan activist who was previously imprisoned for his role in past protests, told Reuters he orchestrated the attack with a loose association of anti-Maduro militants known generally in Venezuela as the "resistance." The "resistance" referred to by Lucchese is a diffuse collection of street activists, student organizers and former military officers. It has little formal structure, but is known in the country mostly for organizing protests in recent years in which demonstrators have clashed with police and soldiers. Reuters could not independently verify Lucchese's claims about the attack, in which drones flew over the rally in central Caracas.

Its proposed solutions, while excellent, are partial and create the impression that the underlying purpose of this report was to make academia's voice heard in Whitehall and secure new long-term funding. No one doubts the stature of the UK's universities and research institutes, or the world-leading AI and robotics expertise within them, but by appearing to regard AI solely as an academic discipline, the report misses nearly all of the most important challenges facing the UK. As noted above, this report is interesting because it pushes the voices of workers to the front of the debate on the impact of AI. Too often we get drawn into the excitement around the potential of automation, the opportunities that could be gained, without thinking about the people that could be left behind as a result. Let's hope that this new commission can apply real pressure to the government to come up with an effective strategy and some new practical policies around addressing the forthcoming changes – working with citizens, employees and trade unions, rather than against them.

Almost no one knows how to utilize the technology at scale. More precisely, only a very small handful of organizations truly understand how to manage the risks of machine learning (ML) when implemented widely. Those risks include navigating the legal, reputational, and ethical issues ML can create – from wildly offensive chatbots and image classifiers to furthering racial disparities amongst zip codes, and much, much more. And that's not even taking into account the deceptively complex requirement of being able to predict how ML models will behave over long periods of time, or new laws like the EU's GDPR and their impact on ML. That's why we're thrilled to partner with the Future of Privacy Forum to release the first-ever guide to managing risk in machine learning, written specifically for practitioners.