Before this year's E3, the annual video games event where publishers descend on Los Angeles to unveil and promote their wares for the next year and beyond, anticipation was high for Square Enix's new Avengers game – an action-adventure for one to four players, in which you can fight as Hulk, Black Widow, Thor and plenty of others. In the year that Endgame grossed more than $2.7bn at the box office worldwide, surely not much could go wrong for a game proffering a personalised Marvel superhero fantasy. As it turned out, however, the Avengers game's big reveal fell rather flat (and was rather eclipsed by Keanu Reeves, who made a surprise appearance to reveal his top-secret cameo in the forthcoming Cyberpunk 2077 the day before). That is Iron Man, right? Why does he look nothing like Robert Downey Jr? The Avengers characters in this online action-adventure game share absolutely no likeness with the ones we know from the Marvel Cinematic Universe.

The newly organized research project "MELLODDY" (Machine Learning Ledger Orchestration for Drug Discovery), involving ten large pharma companies and seven technology providers, is that kind of deals which can catalyze a transition of the pharmaceutical industry to a new level -- a "paradigm shift", as one might refer to it in terms of Thomas Kuhn's "The Structure of Scientific Revolutions". The project aims at developing a state-of-the-art platform for collaboration, based on Owkin's blockchain architecture technology, which would allow collective training of artificial intelligence (AI) algorithms using data from multiple direct pharmaceutical competitors, without exposing their internal know-hows and compromising their intellectual property -- for the collective benefit of everyone involved. While artificial intelligence (AI) already proved to be a groundbreaking thing in many industries (robotics, finance, surveillance, cyber security, self-driving cars to name just a few), drug discovery still seems like a hard case for machine learning practitioners. A major reason for that is the lack of quality data to train models properly. It might seem surprising, as pharmaceutical research generates enormous amounts of data daily.

Sooner or later, everyone working in applied forms of machine learning goes after a use case that is going to yield tons of data, examples off of which to train a neural network. It's the data, many believe, that very often is the biggest deciding factor in making a network useful. That's the premise of Owlcam, a Palo Alto-based startup that sells a $349 camera for your car dashboard. It has been able to gather millions of videos from its users to refine its ability to detect crashes, to know when to capture video that can be used to handle insurance claims, or to detect an intruder to potentially solve car theft. The product, in other words, is the young company's entrée into a big problem where there's lots to learn.

The saying "a picture is worth a thousand words" does something of an injustice to the medium of language. It suggests that words are an inefficient form of communication when in fact the opposite is true. When humans use language to communicate, so much is left out because the speaker and listener share experience of the same world, which makes explicit statements about that shared world unnecessary in everyday speech. For example, if I say to you "the vase is on its side, rolling along the table," I don't need to also tell you that the vase is made of fragile stuff (it's a reasonable assumption that it is), or that the table doesn't have edges that will stop the vase's rolling, or that as a result the vase will likely roll off the table, or that gravity will make the vase to fall to the floor, which is hard and will therefore cause the fragile vase to shatter. It's enough for me to say "the vase is on its side, rolling along the table" for you to know the vase will likely smash to pieces unless someone intervenes.

Data helps to drive every industry now. When used effectively, it can lower operational costs and utilize resources in a more effective manner. The U.S. legal cannabis market was valued at $11.9 billion in 2018 and is expected to be worth $66.3 billion by the end of 2025. With this kind of growth, data collection and use are essential to the Cannabis industry in many ways. The access to a vast amount of data, allows growers to optimize for environmental changes and variables and can even change the strain of the product.

AI and Machine Learning job postings on Indeed rose 29.10% over the last year between May 2018 and May 2019. Indeed found the increase is significantly less than it was for the previous two years. During the same period, May 2017 to May 2018 AI job postings on Indeed rose 57.91%, and a whopping 136.29% between May 2016 and May 2017. Indeed is seeing a leveling off of candidate-initiated searches for AI & Machine Learning (ML) jobs, dropping 14.5% between May 2018 and May 2019. In comparison, searches increased 32% between May 2017 and May 2018 and 49.1% between May 2016 and May 2017.

Products/Services Visa agreed to acquire the token and electronic ticketing business of Rambus for $75 million in cash. The business involved is part of the Smart Card Software subsidiary of Rambus. It includes the former Bell ID mobile-payment businesses and the Ecebs smart-ticketing systems for transit providers. Meanwhile, Rambus expanded its CryptoManager Root of Trust product line. "Security is a mission-critical imperative for SoC designs serving virtually every application space," Neeraj Paliwal, vice president of products, cryptography at Rambus, said in a statement.

In the world of AI research, Europe has drawn a line in the sand, declaring that R&D must focus squarely on "Edge AI." This proclamation draws a stark contrast to "Cloud-based AI," the model aggressively pursued by China and the United States. During "Innovation Days" hosted here by French research institute CEA-Leti this past week, Emmanuel Sabonnadiere, CEA-Leti's CEO, discussed the "two schools of AI research" that have split the world in two. Both the U.S. and China have been collecting massive amounts of data which they use for training AIs, the basis for their claims they lead the world AI race. Strict data privacy regulations in Europe might be seen as impeding European companies' progress in AI, but that's not necessarily the case.

Welcome to the now "annual" Canadian AI Ecosystem Map. What a year it's been. The report also goes to feed the excellent (and searchable!) directory at Canada.ai. The point of creating this map was to emphasize that the strength lies in the Canadian AI Ecosystem, as opposed to just one city's. This year, we've seen ties strengthen, but also some weaknesses exposed.

The MIT Machine Intelligence Community began with a few friends meeting over pizza to discuss landmark papers in machine learning. Three years later, the undergraduate club boasts 500 members, an active Slack channel, and an impressive lineup of student-led reading groups and workshops meant to demystify machine learning and artificial intelligence (AI) generally. This year, MIC and MIT Quest for Intelligence joined forces to advance their common cause of making AI tools accessible to all. Starting last fall, the MIT Quest opened its offices to MIC members and extended access to IBM and Google-donated cloud credits, providing a boost of computing power to students previously limited to running their AI models on desktop machines loaded with extra graphics processors. The MIT Quest and MIC are now collaborating on a host of projects, independently and through MIT's Undergraduate Research Opportunities Program (UROP).