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Canadian Cloud region brings efficiency, agility and AI to businesses



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Canada’s first Google Cloud region is open for business in Montreal. The new region, called “northamerica-northeast1”, is the 15th for Google worldwide, and the fifth in North America

With its Canadian offering Google joins Amazon Web Services (AWS), which has run a Canada-based region since 2016.

The new region could boost Google Cloud use, and cloud infrastructure in general, for Canadian businesses that have yet to make the switch.

Global Head of Solutions for Google Cloud Miles Ward said the new region will improve latency for Google Cloud’s Canadian customers.

“To be able to put this infrastructure into Montreal improves not just the performance in Montreal, but for all customers here in Canada,” said Ward.

Ottawa-based company Pythian provides consultations and services for international companies, and CEO of Pythian, Paul Vallée, spoke of the advantages that the new cloud region brings to companies looking to build their North American presence.

“You don’t need to put in us-east1 anymore, you can put it right here (in Montreal) and still have access to the entire low latency dynamics, and to the entire North American economy which is I think something new and something really exciting.”

Making the switch to cloud

Google’s new region adds to Canada’s ongoing innovation in the cloud infrastructure arena. According to Forbes, Canada is a major player in the push towards public cloud strategies. The federal government recently released its plan to move all unclassified data to the cloud.

But some hesitation remains. Many business owners will be weighing whether the benefits outweigh the costs and effort to transition to cloud options.

For Ward, the public cloud is all about agility, efficiency and remaining competitive in an innovative market.

“The reality is that the efficiency of these centralized resources is orders of magnitude higher. As a result, the companies that are able to take advantage of those tools just are more agile — able to make choices more quickly with lower risk, able to operate at lower cost. The result is this is the opportunity of this generation to leapfrog their competitors, to outcompete and to operate on a global stage. “

Vallée stressed that the speed of cloud-based project work is the key advantage for businesses — and that this new method of storing and sharing work outmaneuvers old ways of defining success.

“A lot of companies need to adopt cloud because of a velocity or a business agility imperative. They’re adopting the technology in order to win by beating their competitors to market — not win by saving pennies, and not win through a more efficient capital structure, but win because you beat them there and you built it before they could.”

Google Cloud offers modern, AI-friendly option to businesses

Having a data centre located in Montreal could also sway businesses with concerns about data sovereignty or latency to embrace Google Cloud.

According to Vallée, the new region is just the latest reason businesses will want to get on board with Google for their public cloud services, over competitors like AWS or Microsoft Azure.

“They have differentiated in two major ways versus the other cloud vendors,” said Vallée. “The first one is they have a very simplified, platform-as-service-oriented cloud. That’s their roots, that’s their DNA. The other cloud platforms really started with infrastructure-as-a-service and started tacking on platform features after the fact.

“Whereas Google went the opposite route. Their Cloud started with Google apps for MyDomain and… they’ve been expanding that so that now they’re roughly comparable (to other vendors), but have very much a platform centre, which is a much more modern approach to cloud infrastructure.”

Vallée also sees a big bonus to companies looking to develop AI-related products and tools within Google Cloud.

“The other dynamic that I think is really important is Google is really a leader in machine learning, and has the most compelling demonstrations of their machine learning capability in terms of their road map. Google is, I think by general concensus, far in the lead in terms of their machine learning innovation, what with their Google Brain project and Google DeepMind intiative.

“And what we’re seeing coming out of Google Brain and Google DeepMind is all being built into the Google Cloud API support over time. Which means that for future proofing your cloud investment, if you are doing an analytics-oriented, data science-oriented, machine learning or AI-oriented adoption of the public cloud, Google is really a nicely differentiated platform to make that kind of investment on.”

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5G wireless telecommunication corridor coming to Canada



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Ontario and Quebec are partnering with major digital technology providers to construct a corridor of 5G wireless test beds throughout the two most populated provinces in Canada.

The goal of the $400-million public-private investment is to bring technological innovation to Canadian businesses — by allowing established players and startups to experiment with new products and innovative ideas within geographic concentrations of interconnected businesses, suppliers, and associated institutions.

What is 5G?

5th generation wireless systems (5G) offer performance as high as 20 gigabits per second, said to be up to ten times faster than current 4G networks. As well as promising faster download speeds, 5G is also expected to usher in lower latency, which is the time it takes for the item to actually start downloading.

The development is a further sign of Canada’s commitment to place itself as a leader in new technologies, particularly with the promotion of startups offering connected services. The U.S., South Korea, Japan and China are also racing to establish hub areas with 5G services. Those who establish first are most likely to be ahead of this next-generation series of technological developments.

Public-private partnership to power new network

The project is called ‘Evolution of Networked Services through a Corridor in Quebec and Ontario for Research and Innovation’ — or ENCQOR for short.

According to The Globe and Mail, the two provincial governments will provide $67 million, with the remainder coming from five private-sector partners, including:

  • Ericsson
  • Ciena Canada
  • Thales Canada
  • IBM Canada
  • CGI

The partners will work with provincial coordinators Prompt, CEFRIO, and Ontario Centers of Excellence.

Over the course of five years, the money will go towards supporting 1,000 small and medium-sized enterprises connecting to the advanced 5G platform. Other parts of the funding will go towards providing support in terms of research and technology.

5G could power smart cities and streets

Discussing the new plans, Innovation Minister Navdeep Bains told City News that the types of developments that companies could usher under the 5G arrangements cover the expanse from:

  • autonomous vehicles
  • smart cities
  • improved traffic control and reducing accidents
  • retail innovations, like food deliveries
  • smart home technologies

“5G is the gateway to the future and we are just on the brink of this technological revolution,” says Bains. He said the news will help strengthen employment, with more than 4,000 jobs being created of which 1,800 of which will be specialized in 5G systems.

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The Global Artificial Intelligence Talent Report: 2018





Jean-François Gagné, CEO of Element AI
Jean-François Gagné, CEO of Element AI
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The demand for AI experts has grown exponentially over the last few years. As companies increasingly adopt AI solutions for their businesses, the need for highly experienced, PhD-educated, and technically-adept talent shows no signs of stopping anytime soon.


This guest post was contributed by Jean-François Gagné, CEO of Element AI and originally appeared on his site here. For further commentary beyond the report, see the accompanying blog post.
For a table of the full list of countries and their numbers, or to submit information about the talent pool in your region, send a message using the contact form.

This report summarizes our research into the scope and breadth of the worldwide AI talent pool. Although these data visualizations map the distribution of worldwide talent at the start of 2018, we want to acknowledge that this is a predominantly Western-centric model of AI expertise.

We are submitting our work amidst similar, though much broader, reports such as Tencent’s recent “2017 Global AI Talent White Paper,” which focused primarily on China in comparison to the United States. Tencent’s research found that currently “200,000 of the 300,000 active researchers and practitioners” are already employed in the industry, while some 100,000 are researching or studying in academia. Their number far exceeds the high-end of our measure at 22,000, primarily because it includes the entire technical teams and not just the specially-trained experts. Our report, however, focuses on finding out where the relatively small number of “AI experts” currently reside around the world.

We drew on two popular data sources for this line of inquiry. First, we used the results from several LinkedIn searches, which showed us the total number of profiles according to our own specialized parameters. Second, for an even more advanced subset, we captured the names of leading AI conference presenters who we consider to be influential experts in their respective fields of AI. Finally, we relied on other reports and anecdotes from the global community to put our numbers in greater context and see how the picture may develop in the near future.

Even though we relied primarily on English-language data sources, our view of the talent pool provides a good global representation of the best experts that the field currently has to offer. For this reason, the second half of the report focuses on a qualitative assessment of talent and funding in Asia and Africa, where the reliability of our numbers drops off significantly and does not match the industry or academic output of these hotspots.

According to our broadest LinkedIn measures, we have found that there are roughly 22,000 PhD-educated researchers in the entire world who are capable of working in AI research and applications, with only 3,074 candidates currently looking for work. In the smaller, more advanced subset, we have found that there are currently 5,400 AI experts in the world who are publishing and presenting at leading AI conferences across the globe and who are well-versed enough in the technology to work with teams taking to take it from research to application.

How We Defined “Talent”

Building transformative AI applications for enterprises requires teams of people who have proven technical competency in Machine Learning/Deep Learning, several years of work experience, and can collaborate and thrive in an interdisciplinary environment.

The critical shortage of “talent” in the current AI job market suggests that there are currently not enough people with the strong grasp of academic research and applied software development required to mediate the worlds of business, science, and engineering.

The teams that need to be filled should be able to identify a problem that can be solved with modern machine learning techniques, build and implement that solution from scratch, and then optimizing the solution to work efficiently.

In our search, our hypothetical expert must be either highly talented or very experienced in order to capture the most elite leaders, seniors, and top juniors who would be able to work on such an effort. We used two different approaches to accurately size the pool of people in the world: LinkedIn searches and identifying participants in academic conferences.


Using LinkedIn, we broke down these search criteria to capture a broad view of what it means to be an AI specialist.

These search parameters were built to find candidates who were awarded a PhD no later than 2015, to account for several years of work experience.

Although a PhD is not technically required to be considered an AI expert (since experience applying AI solutions in a real-world setting is more important than a degree), we’ve nonetheless found that having a PhD is a good proxy for assessing the technical ability of the talent pool across different nations.To qualify for this subset, these profiles must also have mentioned “AI” or Artificial Intelligence in addition to one or more advanced concepts, such as deep learning, artificial neural networks, machine learning, computer vision, natural language processing, or robotics. These candidates must also be technically adept: we filtered our numbers to include only people who have a solid grasp of either Python, Tensorflow, or Theano, to make sure they have some experience developing real-world applications. Using these very broad parameters, we identified a total of 22,064 experts.

We also ran a more advanced subset that did not include the (“AI” OR “Artificial Intelligence”) qualifier, omitted “python”, and included more specific AI-only frameworks.

The idea behind this search was to capture candidates who listed very specific frameworks that we typically employ in our own work (these include torch, caffe, and nltk) and omit candidates who are using “AI” as a buzzword. In this search, we identified a number that comes very close to that of our conference presenter numbers: we found 6,138 experts using this search, with 1,735 indicating that they are available for work.For our visualization, we decided to plot the less conservative estimate, in order to capture talent with potentially interdisciplinary skills.

Academic Conferences

In addition to these narrower LinkedIn searches, we counted authors of published papers or posters to estimate other high-level influencers and “rising stars” in the field. In theory, In theory, these candidates are required to apply AI theories established in controlled environments to messier real-world settings. In this talent pool, we found 5,400 experts who have presented a research paper in the last few years.

The following conferences were prioritized in our research: the Conference on Neural Information Processing Systems (NIPS), the International Machine Learning Society (ICML) conference, and finally the International Conference on Learning Representations (ICLR). We scraped researcher names from these conferences and filled out their location, experience, and education profiles using Mechanical Turk.

Dataset Biases

According to our data, European and Asian countries have significantly fewer researchers than the US, the UK, or Canada, but we are the first to acknowledge that this is most likely due to LinkedIn being a predominantly Western platform. Our searches found 413 candidates in China, 291 in Singapore, 204 in Japan, and 147 in Korea.

recent tabulation of LinkedIn users by country done by Meenakshi Chaudhary points out a large discrepancy in LinkedIn user penetration rates, even among developed countries. Chaudhary mentions that “after [the] US, India, Brazil, Great Britain, and Canada have the highest number of LinkedIn users,” which suggests that LinkedIn’s adoption in certain countries and markets heavily skews the representations within our sample. To that effect, while the quantities of LinkedIn experts found in Asia are much lower than in North America or Europe, these numbers are still very high given the fact that LinkedIn’s penetration rates are lower in Asia.

The same goes for the careful examination of presentations at academic conferences. By limiting our search to several English-speaking conferences in the Western world, we risk missing other institutions where AI research and development is done: research centres, private labs, think tanks, smaller universities and institutes, independent researchers and consultants. These people, although experts and domain-leaders, might not be engaging with the global community when they are working at a smaller scale or privately.

AI Talent Hotspots Across the Globe

North America

Out of our 22,000 LinkedIn profiles, almost half of all candidates (9,010) are living and working in the United States. Most of the LinkedIn experts listed their field of study as either Computer Science (12,856) or Computer Engineering (3,879) –– less common fields of study included Mathematics (2,592), Physics (2,157), and IT (1,175). A substantial portion of these experts have worked, at some point, for either Google (756), Microsoft (357), or IBM (265), and have anywhere between three and 10 years of experience working.

The dominance of the U.S. in the AI talent markets is not at all surprising. Paysa, in a recent studyof artificial intelligence talent, found that nearly $650 million is slated to be spent in the United States on annual AI-related salaries alone, with several U.S. companies, having raised an additional $1 billion to fund AI development, making it hard for smaller countries to compete with the U.S.

Nonetheless, Canada came in third place for the number of researchers in our LinkedIn and conference presenter searches, making it a viable competitor to the U.S., with 1,154 high-level profiles, which is high given Canada’s small population and GDP. The Canadian AI talent pool has been refilling with former students and new international researchers alike, with Montreal leading the charge (Facebook, Google, Uber, Samsung, DeepMind have all set up labs there, among others) and Toronto, Edmonton and Vancouver close in tow.


The United Kingdom was the runner-up to the U.S. with a total of 1,861 high-profile candidates. Industry has been a big player in the UK, which has led to significant brain drain: as Ian Sample at The Guardian has recently pointed out, AI professors have been leaving for the industry primarily because demand for talent has been “heavily outstripping supply.”

Germany, on the other hand, has had the opposite problem. As Yasser Jadidi, head of AI research at the Bosch Center for Artificial Intelligence pointed out to The Financial Times, Germany has a strong presence of “young professionals and academics” which has remained “sort of hidden.”  With a strong academic presence of 276 conference presenters, Germany has been thinking of ways to commercialize AI expertise for business. Emerging tech hubs such as Cyber Valley in Southern Germany, are looking to give a shared space to industry and academia.

Other European countries also had significant numbers of experts: France had 797 eligible LinkedIn profiles, while Spain came up with 606 profiles. Overall, it is fairly clear that in recent years, Europe has steadily become a competitive location for finding AI talent.


The North American and European AI dominance that we have covered so far does not, however, paint the full picture of global talent. Asia has been vastly underrepresented in our LinkedIn and conference presentation data, primarily due to our Anglo-centric approach. Despite the fact that our searches turned up lower numbers in Asia, paper publications and funding show a different story.

Below, we have summarized the incredible growth that China, Singapore, Japan, and South Korea are exhibiting in their respective markets. We will also cover the reasons why the West-East divide in the talent markets is so prominent and has been so hard to bridge.

In general, we have found that the Asian countries are much more focused on developing applications of AI technology rather than investing into academic research.


China’s AI market growth has been staggering. The United States-China Economic and Security Review Commission has recently stated, in its 2017 Annual Report, that “local [Chinese] governments have pledged more than $7 billion in AI funding, and cities like Shenzhen are providing $1 million for AI startups. By comparison, the U.S. federal government invested $1.1 billion in unclassified AI research in 2015 largely through competitive grants.”

According to this report, Chinese tech companies Baidu, Alibaba, and Tencent have become “global leaders in AI,” a trend that is reinforced by the Chinese government making AI a national priority. Just last July, CNN reported that China’s State Council is planning to build an AI industry worth $150 billion in the next few years.

Despite these big leaps in funding, the West has been largely unaware of the work going on in China. As Andrew Ng pointed out in an interview for The Atlantic, “China has a fairly deep awareness of what’s happening in the English-speaking world, but the opposite is not true.” While Chinese researchers speak English and have access to the Western-world of research, the English-speaking community is cut off from Chinese research due to the language barrier.

As a result, China has been able to make big leaps in academia below the radar of the West. While our LinkedIn searches only picked up 413 profiles, 206 of which are also conference presenters, China has recently jumped ahead of the U.S. in artificial intelligence paper publications according to an AI report done by the Obama Whitehouse in late 2016. Traditionally seen as a reliable marker of research activity, published papers are a good indicator of talent growth in the region, although the influence and quality of these papers is contested by some.

In a well-sourced report at The Aleph, Alex Barrera points to the rising quality of education as one of the big reasons that China now has two universities, Peking and Tsinghua, that have recently been categorized among the top 30 universities in the world by the Times Higher Education rankings. Barrera sees the potential for this trend to continue: “While institutions like Stanford still hold onto their perch of the global ranking, universities like Peking University, are closing in. Stanford outranks them in specific scores but lags in others like technology transfer.”

While AI education in China has been growing rapidly, serious AI faculty are still hard to find. Many AI practitioners in China have transitioned from a field like Electrical Engineering or another branch of Computer Science. In short, while the growth of the Chinese talent pool shows no intention of stopping anytime soon, the country still needs some time to build up a rigorous market that rivals that of the United States.


Recent reports have also emphasized the extent to which Singapore is quickly becoming an artificial intelligence research hub. According to a 2017 report from Channel News Asia, The National Research Foundation will be investing $110 million USD into “a new national programme aimed at boosting Singapore’s artificial intelligence capabilities over the next five years.”

Our own data has identified at least 291 highly-qualified AI profiles in the country, along with 21 high-level experts who are publishing papers in leading conferences. Michael James Milne, director at Kaishi Partners, estimated in correspondence that there are more likely to be around 1,500 qualified AI experts currently in Singapore and Southeast Asia.

These numbers are supported by the growing number of research centres that are starting to take hold in the small, cosmopolitan city-state. Joel Ko, of Marvelstone Ventures, recently confirmed to the South China Morning Post that Marvelstone plans to set up an AI hub “which would incubate 100 startups every year.”

The increasing government and private funding means only one thing: Singapore AI is bound to grow significantly over the next few years as these changes pull in more talent.

South Korea

After Google’s DeepMind program “AlphaGo” defeated South Korean Go champion Lee Sedol in 2016, the South Korean government announced that it would invest $863 million USD in AI research over the next five years.

Since then, Korean news reports, which were graciously translated translated and shared with us by Rufina K. Park, have documented the Korean government’s heavy investments in AI infrastructure. On December 22, 2017, the Ministry of Science and ICT announced “The Plan for Innovation Growth” whereby the government committed to spend 1.56 trillion won (approx. 1.53 billion USD) on AI and related sectors that will prepare Korea for the “fourth industrial revolution” in 2018. Similarly, the Council for Intelligent Knowledge Society aims to spend 244 billion won (approx. 22.6 million USD) on AI in 2018. In total, 7.96 Trillion Won will be spent on the 13 Innovation Growth areas from 2018-2022. Korea’s current plan is to create 550,000 new jobs in the innovative sectors by 2025.

This funding has come in addition to two existing AI research projects, says Mark Zastrow at Nature, noting two specific undertakings that are currently in progress: “Exobrain, which is intended to compete with IBM’s Watson computer, and Deep View, a computer vision project.” Korea has pulled ahead as an industry leader in the area, taking third place in the number of AI patents in 2017.

In our own data, we found a sizeable subset of 147 AI experts currently working in South Korea with 21 recent conference presenters hailing from that area. While having a strong industry presence in AI, it is clear that academic research in Korea, ranked 7th in number of AI dissertations, is not yet quite as strong as in China or Japan.


Unlike China, Japan has a long history of robotics and artificial intelligence research which has largely gone undiscussed in the media. Part of the problem, as some outlets have noted, is Japan’s notorious industry-level insularity, which results from a stiff “language barrier and rigid business practices.” Japan’s academic AI footprint, however, is notably stronger than either South Korea or Singapore, since Japan has roughly 117 active researchers presenting at NIPS and other leading conferences.

Artificial intelligence academics have noted the difficulty of keeping up with other Asian countries: Mitsuru Ishizuka, professor emeritus in AI at the University of Tokyo, noted that Japanese research has fallen behind the work “that is being done in China.” While Japan’s talent footprint is significant, it is clear that their ratios are skewed towards academia: 117 conference presenters versus 204 LinkedIn profiles, significantly lower than China, Singapore, and South Korea. Anita Pan, the Second Secretary and Trade Commissioner of Canada to Japan, pointed out in an email that Japan’s AI talent shortages are well known: “of the 15,659 students enrolled in graduate studies in advanced information technologies, 619 are specifically related to AI, and of those, 123 are expected to complete doctoral degrees.”

Last August, however, the Japanese government announced that it is “planning to invest billions of yen to fund next-generation semiconductors and other technologies critical to AI development.” Pan expects that the funding for the fiscal 2018 will most likely double 2017’s allocation of 51.7 billion yen ($575 million CAD), resulting in a funding package that exceeds 100 billion yen ($1.1 billion CAD). Such advances in funding could spur an industry which has the history and research power to harness home-grown talent. These pecuniary advances have already netted some results: just this August, deep learning startup Preferred Networks Inc. raised $95 million USD from Toyota to work on self-driving technology.


Although not as prolific as either the East or the West, African countries have recently been been growing significantly in AI research and development.

Jacques Ludik, the President of the Machine Intelligence Institute of Africa (MIIA), estimates that there are roughly 1,500 members in his association, 70% of whom can be classified as experts in their respective fields. Ludik pointed out that funding is difficult to come by, but the continent has nonetheless been able to implement AI applications in agriculture and the mobile space.

Timnit Gebru, a postdoctoral researcher at Microsoft Research and a member of the FATE (Fairness Transparency Accountability and Ethics in AI) group, has pointed out in correspondence that machine learning in Africa is privy to a wealth of different kinds of funding: B4 Capital Group, for instance, specializes solely in African and Latin American AI initiatives. The by-product of this kind of funding are AI solutions that are tailored to the problems of each area. Ethiopia, for instance, which has 88 active and individual languages, has been actively developing Natural Language Processing solutions to improve communications.

Nouha Abardazzou, writing for How We Made it in Africa, supports the claim that AI has largely manifested itself in agriculture and healthcare (partially by way of mobile development). One recent AI-driven application has been the ECX e-Trade Platform, which uses Internet of Things (IoT) devices and AI in order to create a coffee-traceability solution that works through all the stages of the supply chain. In the healthcare industry, the SOPHiA artificial intelligence analyses “genomic data to identify disease-causing mutations in patient’s genomic profiles.”

Public awareness about Africa’s role in AI has grown significantly in 2017. Gebru has recently hosted the very first Black in AI workshop at NIPS 2017, which focused not only on the research currently being done in Africa, but also AI work done by black researchers all over the world. Similarly, MIIA recently hosted the very first AI Africa Conference in Johannesburg, South Africa, in October of 2017. This conference was a big success, drawing in expert researchers from all over Africa and the rest of the world to talk about real-world applications of deep learning in the continent.

Analysis of Global Trends

Major Movements

Our conference researcher data also allowed us to make some observations about the ways in which researchers have moved, either for work or school. By looking at the discrepancies between the location of the candidates’ alma mater and their current work location, we have found that candidates are likely to move to the USA for their education and then move to another country for work.

The arc-map above shows candidates who, despite being educated in Canada, the UK, Germany, France, or China, were more likely to move to the USA for professional work. Furthermore, these connected countries hold the highest numbers of talent exchanges: “inbound” researchers indicates the number of researchers who moved to that country for work, “outbound” indicates the amount of people who got their PhD in that country and then ended up going somewhere else for employment.

These arcs suggest that the U.S. acts as the “hub” for AI research and education, serving as the link for both the academic and business worlds where AI intersects. Aligning this finding with our previous assessment that Israel’s and Japan’s scholar to LinkedIn profile ratios are nearly 75% and 57%, respectively, we can see how transnational and global collaboration is a key to  sharing AI knowledge and expertise in both industry and in academia.

Interestingly enough, the flows between Asia and Europe are almost non-existent. The AI talent phenomenon is global insofar as it is mediated by the West.

Academia vs Industry

The interplay between the LinkedIn and the conference presenter data allows us to make some interesting observations. Assuming that the conference researchers in this subset all have LinkedIn profiles that were captured in our searches above, we can say, with some approximation, that roughly one third of all AI specialists have at some point presented their research at one of these large academic conferences.

Presenting at conferences understandably skews towards academia. However, there remains an industry presence at these events. The NIPS author demographics indicate that the 2017 conference consisted of 88% academic presenters and 12% industry presenters. While the one-third ratio can seem high, we found that it is a strong global proxy: some countries, such as Israel and Japan, have much higher rates of academics in AI.

This ratio of academics to industry-experts is higher for countries like Germany, where 44% of all LinkedIn candidates are likely to have at one point been conference presenters, and much lower for countries like the UK, where only 14% of AI experts are active in conferences. These trends reflect the journalistic findings that we outlined above: in Germany, it looks like most AI work is happening in academic institutions, while in the UK, AI is more industry-driven, poaching talent from academia in the process.

Israel (75%) and Japan (57%) have the highest ratios between conference researchers and LinkedIn profiles, meaning that their AI work is heavily driven by the academy, which is consistent with the various reporting on these trends. Though, it seems that industry is still a large driver of AI development.

Ireland (1.7%), Brazil (3.3%), and Spain (4.4%) had the lowest ratio of conference presenters to LinkedIn profiles, which suggests that most of the candidates in those countries work in the industry-driven sector of AI research and development.


Although artificial intelligence talent is predominantly U.S.-centric, it is apparent that there are large global hotspots of AI talent in the European, African, and Asian markets. These areas are nonetheless slowly getting tied into the largely Western English-speaking community of academic conferences and LinkedIn industry searches and are set to grow significantly in the coming years.


Written with Fedor Karmanov and Simon Hudson

Research by Yoan Mantha and Julien-Pier Boisvert

Many thanks to all those from the community who reached out and contributed stats, anecdotes, translations and other useful information from their regions:

Bayo Adekanmbi | Ade Akin-Aina | Zainab Bawa | Adel Bibi | Valentine Goddard | Ian Goodfellow | Timnit Gebru | Ahmed Mamdouh A. Hassanien | Kiran Jonnalagadda | Jacques Ludik | Daniel McCormack | Michael James Milne | Shakir Mohamed | Anmol Mohan | Adeyemi Odeneye | Anita Pan | Rufina K. Park | Arjun Ram | Maged Shalaby | Yu Shao | Daniel Shinun | Ahmed Yousef

And special thanks to those at Element AI who provided invaluable commentary:

Jeremy Barnes | Philippe Beaudoin | Nicolas Chapados | Wonchang Chung | Sébastien Paquet | Anqi Xu

Jean-François Gagné is the Chief Executive officer of Element AI.
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Insurtech and disruption bring change to Canadian insurance



Chris Gory
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Canadian insurance companies have yet to fully embrace the potential of blockchain and digital authentication. Meanwhile, other industries are pushing ahead with innovative, enterprise-scale services related to these technologies.

Chris Gory has seen big changes in the insurance industry over the course of his 23-year career. But the founder of Insurance Portfolio Financial Services (IFPS) thinks Canadian insurance companies need to do more to embrace the digital capabilities that are changing businesses across multiple industries.

“As an industry, especially on the benefits side, we really need to embrace change more. When you look at other industries, there’s change going on, and things are changing pretty quick. And we really need to keep up,” he says.

The beginnings of enterprise-scale transformation

What sort of changes are the larger insurance providers undertaking? It’s about more than just putting a shiny new face on the same services. To make real headway, companies need to adopt what EY calls ‘enterprise-scale digital services’.

According to Gory, big providers are already taking steps in the right direction, but the best is yet to come.

The transformation taking place in Canadian insurance is clear when looking at new technologies targeted to improve the customer experience.

  • Manulife has developed Alexa integration, allowing customers to access account information via voice recognition.
  • Sun Life’s new digital coach Ella works with Google Assistant to provide customer support in a similar fashion.
  • Sun Life has also opened up the playing field for blockchain and benefits providers with the recent announcement that the the company is teaming up with SecureKey to help customers verify their credentials.

Gory notes that blockchain innovation has big potential for the insurance industry, especially for benefits — reining in the debilitating paper trail of claims.

Startups and legacy companies push ahead together

The presence of startups within the insurance market has become too large to ignore.

According to the 2017 World Insurance Report, 31.4% of customers rely on insurtech options — either on their own or paired with more established insurance provider offerings.

At IPFS, a company that specializes in providing employee benefits to startups around the world, Gory does business with a lot of the exciting, young companies that are reshaping Canada’s business landscape.

Gory says he has never seen the scale of change currently moving through the insurance space.

“This is something awesome. Before, you wouldn’t see such engagement, such traction with the tech startups, with the insurtechs. You saw some before, but definitely not on the same scale. They’re being more widely accepted than they were say in the Dot Com era. The Dot Com era was pretty limited as to what you could do. There was a certain number of vendors, but you didn’t have the number of insurtechs out there.”

Insurtechs pointing a way forward

“In the benefits space, you’re seeing a lot of companies that are trying to really gain traction in Canada,” says Gory. “They’re competitive, but they’re also pushing the insurance companies to make changes.”

Gory points to a couple of Canadian insurtechs as sources of inspiration and collaboration for larger providers.

  • Insurtech platform League has partnered with business leaders like RBCI, AETNA and Humana to broaden its reach and improve its ability to offer customer solutions.
  • Newcomer Honeybee — a group working under the umbrella of insurance provider Benicaid — has also garnered buzz recently for its non-traditional benefits platform. The online app allows employers to allocate benefits to employee healthcare spending accounts, allowing the employees to choose what they spend their benefits on.

These developments are helping to transform the insurance industry, adapting the wealth of technology that can upgrade outdated systems of file keeping and customer onboarding. And according to Gory, it’s this collaboration and ability to engage with enterprise-scale solutions that is beginning to transform the insurance industry in Canada.

“I think we’re starting to see that they’re realizing, they’ve got to do big picture,” says Gory. “It’s not just small-scale innovation that’s going to set them apart.”

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