The Navy is progressively phasing artificial intelligence (AI) into its ship systems, weapons, networks, and command and control infrastructure as computer automation becomes more reliable and advanced algorithms make once-impossible discernments and analyses. Previously segmented data streams on ships, drones, aircraft, and even submarines are now increasingly able to share organized data in real-time, in large measure due to breakthrough advances in AI and machine learning. AI can, for instance, enable command and control systems to identify moments of operational relevance from among hours or days or surveillance data in milliseconds, something which saves time, maximizes efficiency, and performs time-consuming procedural tasks autonomously at an exponentially faster speed. "Multiple data bytes of information will be passed around on the networks here in the near future. So as we think about big data, and how do we handle all that data and turn it into information without getting overloaded, this will be a key part of AI, then we're talking about handling decentralized systems," Nathan Husted of the Naval Surface Warfare Center, Carderock told an audience at the 2022 Sea Air Space Symposium.

We have also seen that despite the discrepancies between countries from North to South, to Western countries to Eastern countries, there is a domino effect according to which major ethical issues and tendencies are almost simultaneously faced by every country at the same time, whatever their place in the AI race. It was the case for the AI tracking applications and the facial recognition applications during #COVID-19, and it will probably continue because AI is questioning the equilibrium of geopolitics worldwide. It also questions our ability to face fundamental and crucial questions as of the future of multilateralism. Below the translation of an article published in January, 2021 that highlights some trends I have foreseen in December 2020 for AI globally with some insights on the French market. Carrying out a prospective exercise is never easy and is even less so in the current context, which reminds us of the impermanence of all things and the need to adapt with agility, both individually and collectively, while keeping a long-term vision and without giving in to the call of falsely obvious and short-term choices.

There are opportunities across all functional areas for AI to augment and support human decision making, even the boardroom. Back in 2014, Deep Knowledge Ventures (DKV) made headlines when it appointed the first artificial intelligence (AI) algorithm, aptly named Vital, to its board of directors. Now more than three years out, I was curious to see how Vital fared – did it retain its seat at the boardroom table or get relegated back to the lab due to a failed investment? To my surprise, Vital has not only retained its seat, but has been credited with keeping DKV out of bankruptcy. Through its pattern recognition algorithm, Vital assessed risk factors across more than 50 parameters and recommended investments not identified by its human counterparts.

Here's what others have to say about DeepMind Montreal: "DeepMind's exceptional research accomplishments have helped focus the world's attention on AI and to propel new scientific discoveries. The mission of DeepMind, solving intelligence, is perfectly aligned with my own research work and goals. I am really excited to join forces with DeepMind and to help build the new Montreal team. At the same time, I look forward to continue training the next generations of machine learning researchers at MILA and McGill, fostering diversity and inclusion in the research community, through AI projects for social good, and building further the Montreal AI ecosystem." "I am very excited to be working with Doina, Rich Sutton, Mike Bowling, Patrick Pilarski and the rest of our incredible research team to grow DeepMind's research labs in Edmonton and Montreal - two cities with a vibrant AI ecosystem. Key to the health of this ecosystem is the collaboration between academic institutions and industry and I look forward to building strong and enduring ties between the two right here in Canada. I am a big admirer of Doina's work and her focus on AI for social good, which clearly aligns with DeepMind's mission, and I am looking forward to supporting her in her future efforts."

Artificial intelligence (AI) technology has the potential to boost productivity but increase wealth inequality and wipe out millions of jobs, a research report by the White House claimed on Tuesday. An increasing number of industries are set to be impacted by automation technology over the coming years which could displace jobs, a fear that has been voiced by academics and business leaders. Auto companies are developing driverless cars while factories could are seeing the increased use of robotics, which has the ability to eat into jobs. But many developments are at the early stage and the impact of automation technology could affect different industries are varying speeds. "Because AI is not a single technology, but rather a collection of technologies that are applied to specific tasks, the effects of AI will be felt unevenly through the economy. Some tasks will be more easily automated than others, and some jobs will be affected more than others--both negatively and positively," the White House report said.

"Because AI is not a single technology, but rather a collection of technologies that are applied to specific tasks, the effects of AI will be felt unevenly through the economy. Some tasks will be more easily automated than others, and some jobs will be affected more than others--both negatively and positively," the White House report said. "Some jobs may be automated away, while for others, AI-driven automation will make many workers more productive and increase demand for certain skills. Finally, new jobs are likely to be directly created in areas such as the development and supervision of AI as well as indirectly created in a range of areas throughout the economy as higher incomes lead to expanded demand." Researchers across the world have given varying estimates about the size of job losses.

A grounding of a formula $\phi$ over a given finite domain is a ground formula which is equivalent to $\phi$ on that domain. Very effective propositional solvers have made grounding-based methods for problem solving increasingly important, however for realistic problem domains and instances, the size of groundings is often problematic. A key technique in ground (e.g., SAT) solvers is unit propagation, which often significantly reduces ground formula size even before search begins. We define a "lifted" version of unit propagation which may be carried out prior to grounding, and describe integration of the resulting technique into grounding algorithms. We describe an implementation of the method in a bottom-up grounder, and an experimental study of its performance.