We know that the money launderers are winning. We know that law enforcement is losing. By now, you've probably heard the UN's statistics on this, that we catch less than 1% of these crimes, which add up to an estimated $1.6 trillion a year. At the heart of this problem is asymmetrical technology. The bad guys have sophisticated, networked, rapidly improving technologies, and the good guys are stuck in the past.

According to media reports, China and the US are leading the way in adopting AI, with the former's percentage of AI patents granted growing by 190 per cent in a five-year period and the latter investing close to $10 billion in venture capital. Not to be left behind by the other two superpowers though, Russia's president has announced his intentions to make 30 per cent of its military equipment robotic by 2025. The compound annual growth rate of AI has hit 60 per cent and is still growing. The UK is itself funding over £603 million investment in AI and one industry here that stands to benefit from AI is field and customer service. Field service organisations increasingly feel the pressure to maximise the productivity and efficiency of their workforce so they get every job right on the first try, keeping customers happy and reducing costs through increased productivity and SLA compliance.

How to ensure human autonomy over our computational "screens, scenes, and unseens." Digital assistants such as Alexa and Siri and Google Assistant can be quite helpful -- but their actual allegiance is to Amazon and Apple and Google, not to the ordinary people who use them. By introducing AI-based digital agents that truly represent and advocate for us as individuals, rather than corporate or government institutions, we can make the Web a more trustworthy and accountable place. In the 2004 film "I Robot," Will Smith's character, the enigmatic Detective Del Spooner, harbors an animosity toward the humanoid-like robots operating in his society. Over the course of the film we learn why.

How will emerging autonomous and intelligent systems affect the international landscape of power and coercion two decades from now? Will the world see a new set of artificial intelligence (AI) hegemons just as it saw a handful of nuclear powers for most of the twentieth century? Will autonomous weapon systems make conflict more likely or will states find ways to control proliferation and build deterrence, as they have done (fitfully) with nuclear weapons? And importantly, will multilateral forums find ways to engage the technology holders, states as well as industry, in norm setting and other forms of controlling the competition? The answers to these questions lie not only in the scope and spread of military applications of AI technologies but also in how pervasive their civilian applications will be.

Deep down inside, you know your worth! You recognize that there's only one of you on this big blue marble, and there's no one exactly like you. Oh, maybe you get a little down sometimes, worried about the world around us; but that DNA is yours alone; and it's special. You can, and will, succeed in the brave new world of machine learning and artificial intelligence. You'll solve challenges that are interesting for you, and valuable for those around you.

Yoshua Bengio is a Professor at the University of Montreal, and the Scientific Director of both Mila (Quebec's Artificial Intelligence Institute) and IVADO (the Institute for Data Valorization). He is Co-director (with Yann LeCun) of CIFAR's Learning in Machines and Brains program. Bengio received a Bachelor's degree in electrical engineering, a Master's degree in computer science and a Doctoral degree in computer science from McGill University. Bengio's honors include being named an Officer of the Order of Canada, Fellow of the Royal Society of Canada and the Marie-Victorin Prize. His work in founding and serving as Scientific Director of the Quebec Artificial Intelligence Institute (Mila) is also recognized as a major contribution to the field.

If you are not a representative of the company being nominated please skip this section. Those nominations that are shortlisted by our Judging panel will have an opportunity to Showcase their AI product at one of our Showcase events. We will be having a three Showcases, one will be in the UK at an AI Conference, another two virtual events will be scheduled for USA and APAC timezones. These Showcases will be attended by our Judges and will help us make final decisions on the winners for each Award category. We will also be Awarding Special Best in Show awards for one of the companies at each Showcase.

A new neural network developed by researchers from the Massachusetts Institute of Technology is capable of constructing a rough approximation of an individual's face based solely on a snippet of their speech, a paper published in pre-print server arXiv reports. The team trained the artificial intelligence tool--a machine learning algorithm programmed to "think" much like the human brain--with the help of millions of online clips capturing more than 100,000 different speakers. Dubbed Speech2Face, the neural network used this dataset to determine links between vocal cues and specific facial features; as the scientists write in the study, age, gender, the shape of one's mouth, lip size, bone structure, language, accent, speed and pronunciation all factor into the mechanics of speech. According to Gizmodo's Melanie Ehrenkranz, Speech2Face draws on associations between appearance and speech to generate photorealistic renderings of front-facing individuals with neutral expressions. Although these images are too generic to identify as a specific person, the majority of them accurately pinpoint speakers' gender, race and age.

The robot was programmed to stop momentarily if a person passed by. But the researchers noticed that the robot would often freeze in place, overly cautious, long before a person had crossed its path. If this took place in a real manufacturing setting, such unnecessary pauses could accumulate into significant inefficiencies. The team traced the problem to a limitation in the robot's trajectory alignment algorithms used by the robot's motion predicting software. While they could reasonably predict where a person was headed, due to the poor time alignment the algorithms couldn't anticipate how long that person spent at any point along their predicted path -- and in this case, how long it would take for a person to stop, then double back and cross the robot's path again.

SURGERY performed with the help of a robot has been billed as the next revolution in healthcare: such procedures can be carried out through an incision the width of a finger, causing less scarring and often allowing people to return to their homes more quickly. The UK's National Health Service recently announced plans to spend £50 million on more robotic surgical equipment for operating theatres, and yet the benefits of this high-tech approach are debated.