As stated in my previous article, I want to show next where AI is taking us in the future. However, I need to describe first how AI has evolved during its short life. I have written three articles that develop this theme. In this first article, I briefly outline the background context with what has been achieved up until the start of the millennium. In the next article, I describe the impact that machine learning paradigms such as genetic algorithms and neural networks have made in the last 20 years. Finally, in the third article, I outline our future in a world dominated by AI.

For the last 5 to 10 years, connectivity and mobility have been on top of the agenda for many industries, both are essential enablers for a digital transformation. But while mobility and connectivity continue to be important enablers to expand the internet of things, there is something much bigger at stake that starts to emerge now. As more devices get connected you also start to collect more data and gain more insights and intelligence on how to optimize and automate tasks and processes. So here are a few questions related to the impact these changes could have on our society. How many of the tasks done today by various types of employees can be fully automated?

So the UK, it seems, is headed out of the European Union's single market, perhaps also out of the customs union. Prime Minister Theresa May has said she wants to preserve barrier-free trade between the UK and the EU as far as possible. One option that has been floated, if the two sides can't agree a comprehensive free trade agreement, is sectoral deals. They might cover cars, for example, or perhaps financial services. But there is a problem with this approach: World Trade Organization rules.

Artificial intelligence is everywhere, at times obscured and sometimes fully hidden. It lurks in the Facebook newsfeed algorithm that curates the news you see, it's being implemented in the programs of semi-autonomous vehicles that decide who lives in case of an accident, and it spectacularly beat the top Go champions in the world with its deep neural network technology. The applications of AI are evolving with increased sophistication, sparking considerable, complex questions related the social impact, governance, and ethics of its technology. These questions are particularly salient as accountability mechanisms for algorithms are yet in a nascent stage, where the balance of power is skewed towards industry giants who control these technologies. In this particular moment, the research, development, and deployment of AI is primarily taking place in the private sector, while governments around the world are increasingly contracting out their own use of these powerful technologies. In this context, the future role of universities emerges as one that is particularly meaningful when it comes to addressing these questions of social impact, ethics, and governance of AI.

Try typing "the machines" into Google and chances are that one of the top results the artificial intelligence-powered search engine will return is the phrase: "The Machines are Coming". After a 2016 filled with high-profile advances in artificial intelligence (AI), leading technologists say this could be a breakout year in the development of intelligent machines that emulate humans. Asia, until now lagging Silicon Valley in AI, will play a bigger role as the field cements itself at the pinnacle of the technology world in 2017, the experts say. AI – technically, a computing field that involves the analysis of large troves of data to predict outcomes and patterns – is as old as modern computers but its esoteric nature means it has long endured caricatures of its actual potential – think for example, the 1960s space age cartoon The Jetsons, which featured a sentient robot maid and automated flying cars (both of which we are still waiting for, even 50 years on). Now, a confluence of factors has given rise to hopes that computers with human-like cognitive ability may soon be a reality.

Back in 2014 an Accenture report predicted that investment in the Industrial Internet of Things would reach $500 billion by 2020. A combination of cheap sensors, powerful data processing and machine learning has enabled companies to make their industrial processes significantly smarter and more efficient. A good example of this in action comes in the rail industry, where a number of fascinating use cases have emerged in the past year. For instance, Finnish company Sharper Shape have been using drones to map utility networks. They use machine learning to identify trees that are at risk of falling onto power lines.

The capabilities of artificial intelligence and machine learning are accelerating, and many cybersecurity tasks currently performed by humans will be automated. There will still be plenty of work to go around so job prospects should remain good, especially for those who keep up with technology, broaden their skill sets, and get a better understanding of their company's business needs. Cybersecurity jobs won't go the way of telephone operators. Take, for example, Spain-based antivirus company Panda Security. When the company first started, there were a number of people reverse-engineering malicious code and writing signatures.

You're young and want to get ahead. You work weekends and message colleagues with inspired trading ideas at all hours. You've just marked yourself as a potential rogue trader. Welcome to the brave new world of trader surveillance, where former Goldman Sachs research analyst Erkin Adylov is building a library of banking villainy based on the behaviors of hundreds of past miscreants such as UBS Group's Tom Hayes and Societe Generale's Jerome Kerviel. Using thousands of inputs, from stress levels in voice recordings to the frequency of visits to the staff canteen, Adylov and his team at startup Behavox grade employees on how likely they are to go bad before they do anything wrong. While that may sound like something out of a Philip K. Dick science-fiction novel, hedge fund Marshall Wace and interdealer broker TP ICAP are already using the software to monitor employees, and some of the biggest investment banks and commodities dealers in the world have begun testing it.

Mr. HUANG talked about the challenges facing the industry in the era of AI and how it could translate those challenges into opportunities. He pointed out that the emergence of neural machine translation, the application of speech interactive technology and the rapid development of the Internet Era had a huge impact on traditional human translation. Though the industry has begun to realize the importance of AI and other technologies, the mechanism and platform to promote their application is still lacking. Mr. HUANG believed that governments, language colleges, language and AI service providers, experts and freelance translators should join hands in exploring this topic and advise the industry on development in the new era. In addition to the guidance of government and industrial organizations, an effective cooperative mechanism shall be established to ensure the maximum synergy between colleges, research institutes and language service providers.

Marketers will need to transform from campaigns to real-time, continuous interaction with customers via intelligent agents. So says Forrester Research VP and Principal Analyst Brian Hopkins, co-author (with Adam Silverman) of a new Forrester Research report, The Top Emerging Technologies to Watch: 2017 to 2021 ($499 for individual purchase). It's about the top 15 developing technologies that will help businesses become more customer-obsessed over the next years. Forrester is obsessed with customer-obsession, which it says is essential to a modern brand and which is characterized by several key principles. According to the research firm, such a customer-focused company is led by insights from and about customers, responds quickly and is thoroughly connected everywhere. The report chose 15 technologies for their impact on companies employing these principles.