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The Global Artificial Intelligence Talent Report: 2018

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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.

Summary

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.

LinkedIn

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.

Europe

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.

Asia

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

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.

Singapore

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.

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.

Africa

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.

Conclusion

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.

Acknowledgments

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

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The risks and rewards of big data and tech in agriculture

Farmers are turning to high-tech solutions in the face of climate change and rising costs, but are met by cybersecurity dilemmas — revealing the tightrope between tech resilience and potential pitfalls

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The threats to global food security are immense. Climate change is wreaking havoc, costs are increasing on everything from equipment to fertilizer, war rages in the breadbasket of Ukraine and there are fewer farmers tilling the fields, to name a few. 

Farming itself can contribute to greenhouse gas emissions or water contamination through fertilizers, and the way a field is managed can help sequester carbon for wildlife habitat, or emit that carbon and sterilize the landscape. 

It’s all part of a complex balance of production, protection, cost and benefits that farmers must contend with, and which directly impacts global food supply and costs at the till. 

In the face of these threats, farmers do what they have always done — adapt. 

Farming has always been a sector of experimentation, change and technology — setting the stage for civilization as we know it if you go back far enough. But the pace of the changes and the challenges stacking up makes this moment in time different. 

And like most things in our complex contemporary world, the potential solutions to some of these threats raise issues of their own. As farms and farmers embrace new digital tools, including AI, they must then grapple with questions of data ownership, cybersecurity and the impacts of corporate consolidation.

Getting it wrong can leave farmers in a more precarious spot, while getting it right can help them navigate a changing world and climate while reducing costs. 

“I think there’s some potential, but just like other technologies, it’s not the technology itself that necessarily raises concerns, but it’s often who controls the technology,” said Kelly Bronson, Canada research chair in Science and Technology at the University of Ottawa, who studies the intersection of data and agriculture.

The complexity of farming

With the stereotype of farmers being stuck in the past, some assume farming isn’t overly complex. You grow the product, sell it, and wait for the next season.

The reality is much more complicated.

The interplay of weather, climate, soil, and pests along with the cost of equipment, fertilizer, herbicide, and pesticide — not to mention international markets and the increasing use of agriculture for things like fibres and fuels — are just some of the reasons a contemporary North American farm is a significant enterprise.

It also means turning a profit could be challenging. One bad crop, too much fertilizer, or equipment failure can mean the difference between making it or losing out at a time when a modern combine can cost $1 million.

“We think, actually, it’s the constraints that are going to drive and continue to drive the adoption of technology and innovation to continue to try and push the risk down and to find margin where margins are otherwise tight,” says Wilson Acton, a managing partner at Tall Grass Ventures, which invests in agrifood technology companies. 

Tall Grass’ portfolio includes companies that use machine learning and big data to manage fruit production, track the health and well being of livestock, and maintain real-time tracking and monitoring of grain quality as it’s collected. 

Those investments are geared towards improving profits, but they also help mitigate contributions to climate change and better understand how to farm in a more unpredictable environment. 

“How can we replace some of the things that we are using today to produce food, fuels and fibres with things that are more sustainable, natural or, you know, less polluting — with less risk associated with them?” says Acton.

Those solutions extend beyond traditional concepts of agriculture as a source of food. 

Powerful tools

One company in Tall Grass’ portfolio makes a bio epoxy resin from vegetable oils that can help make products like snowboards or sunglasses more sustainable.

Those sorts of tools and technologies can be invaluable for a farmer trying to make the best decisions about which crops to grow and market, not to mention where and when to plant them. It can help greenhouses find optimal conditions. It can reduce pollution and it can help drive a giant combine in a straight line — no small thing.

Automated combine. Photo by Antony Trivet on Pexels

The end result should be more, produced for less, but all of that innovation relies on, and generates, a new critical resource: data.

Big data, machine learning and applications geared towards agriculture can have significant impacts, says University of Ottawa’s Bronson. More automation decreases labour needs and results in more efficient supply chains. It can also help reduce the use of gas and fertilizers through optimization, subsequently reducing the environmental footprint of a farm.

But she warns agriculture has a long history of power consolidation and work needs to be done to democratize data in the interest of food security and food sovereignty.

“I think we need to be really careful and notice that the same companies that historically have controlled agricultural technologies, are, and have been for about 15 years, really dominating in this new sort of digital era with these new technologies,” says Bronson.

In a recent article in The Conversation, she points to Bayer as one example, noting it has “the capability to access data from almost half of all farmers in North America.”

So while farmers struggled in the past with consolidated control of seeds, the transport of grain by the railways, or market control by powerful corporate interests, today they also have to contend with control of data and information.

“There are a bunch of ways that the companies can profit from these data, and that’s not necessarily a bad thing,” says Bronson. 

“But there are some, maybe, misuses of farm data that can happen in the name of profit, for example, the sale of data to insurance or reinsurance companies.”

That sort of knowledge can be powerful. 

Insurance companies can profit off loss that follows a predictive model, or chemical companies can look to those same models and set prices accordingly, she says. 

Farmers can also benefit, but that knowledge will cost them while further increasing the bottom line of the companies collecting their data.

That value of the data also makes it an alluring target for nefarious political and criminal actors.

Cybersecurity

Anytime you link a device to the wider world, there is risk. When you’re talking about the global food system, and the needs of billions, the risk is more acute. 

Writing this summer in Modern Farmer, Charles Eagan, the chief technology officer for Blackberry, says the threat of malicious forces taking control of farm infrastructure isn’t hypothetical. 

“Hackers are jail-breaking tractors and they’re using ransomware to go after individual farms,” he wrote in August. “Earlier this month, a Quebec agricultural group, l’Union des producteurs agricoles, dealt with a ransomware attack that impacted its more than 40,000 members.” 

Eagan says there are end points that can be exploited at all levels of the agricultural sector, from refrigeration units to combines, and throughout the global supply chain. 

If enough of the new digital infrastructure for farming went down, it would pose an enormous threat.

“I spent a lot of time farming, before that stuff existed,” says Acton with Tall Grass Ventures, who grew up working the land in Saskatchewan without the help of GPS-guided combines. 

“So you had to learn how to drive straight lines, which is actually a real skill that takes time to develop. If the satellite goes out, that’s not really a big deal. But you can see how that starts to compound.”

Those same combines often operate on licensed technology that’s owned by the manufacturer. 

“That presents some risks to the, I would say, the food system at scale in terms of food security, because what if a nefarious actor were to hack all the John Deere tractors?” says Bronson. 

“If we think about the food system in terms of bioweaponry, I think that’s a real security risk that we should be aware of, in this new digital era for farming.”

The future of food security

According to the Food Security Information Network, approximately 238 million people in 48 countries faced acute levels of food insecurity due largely to war, economic shocks and extreme weather in 2023.

But even in areas where a food crisis hasn’t taken hold, the challenges are immense. In Canada, abnormally dry or drought conditions are present across the country and the summer could be devastating for crops and livestock. Extreme drought conditions currently exist throughout much of southern Alberta and into Saskatchewan. 

Beyond the farm, everyone has noticed the ever increasing cost of groceries as paycheques stagnate. 

In addition to stresses felt across the globe, the age of farmers in Canada continues to rise, while the number of farms and farmers continues to decrease, according to the latest agricultural census.

All of these factors as a whole means agriculture will increasingly rely on technologies in order to maintain production and face down some daunting challenges — taking proven technologies from other sectors and applying them in fields and greenhouses. 

Acton calls it a generational shift. 

“There’s risk and we have to make sure that we’re building resilient technology around it,” he says, specifically referencing security. “But because there’s risk in it is, in our opinion, not a good enough reason to just not adopt.”

The benefits of doing so are plentiful, it’s just a question of for whom it’s plentiful for.

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How connected technologies trim rework and boost worker safety in hands-on industries

A look into the practical shifts underway in industries like construction and manufacturing as digital technologies spark a new era of efficiency and adaptation

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Long before David Mitchell founded XYZ Reality, he was entrenched in the construction industry — and developing an obsession.

Having worked in residential construction with his father on the west coast of Ireland, Mitchell was well-versed in the sector from a young age, and eventually became an experienced builder with commercial projects around Europe.

But in the background of a seasoned career, Mitchell became fixated on paperless construction, XYZ Reality’s mission critical director Waleed Zafar told DX Journal. He wanted to find ways for companies to skip the 2D drawing process altogether.

The UK-based software development company would go on to introduce Augmented Reality (AR) to the building sector in 2019 with the Atom headset, which has workers build from holograms.

It’s a wearable technology that allows for “connected workers” — on-site or remote employees who, according to Visual Capitalist’s Katie Jones, use digital technologies to assist them with day-to-day duties.

“We task our trades today to look at a 2D drawing, and conceptualise a 3D asset from that,” said Zafar. “And the most challenging part is to position that information out on site, within millimetre accuracy.”

If the process were streamlined, Zafar recalls Mitchell saying, “it will change the game forever.”

The award-winning headset allows construction companies to increase their accuracy, efficiencies, and workplace safety while decreasing margins for error and the likelihood of rework, Zafar said.

It’s just one example of how hands-on industries are being transformed by connected worker technologies — and a market for devices that is reportedly set to “explode” within the next 20 years.

Endless potential, game-changing solutions

The proliferation of smart devices transformed remote work and allowed employees to “remain fully connected” through technology, noted Visual Capitalist’s report.

In businesses like construction, engineering, or manufacturing, those remote workers can include operators, field workers, engineers, and executives who are connected to data in real time — and through technologies like platforms, interfaces, and wearable devices.

According to a 2021 article by Forbes contributor Sundeep Ravande, companies are using strategies to connect workers so they can “promote mobile collaboration between front-line workers and decision makers.”

The goal is to help workers get jobs done faster, better, more safely, and “let management and front-line workers use real-time operational data gathered digitally in the field to make informed, knowledge-based decisions,” he wrote.

As for which industries are expected to provide the bulk of the market for connected worker technologies, Visual Capitalist predicted that the top five will include oil and gas, chemical production, construction, mining and minerals, and airlines by 2039 — and there’s data to help explain the interest and demand.

Connected workers are reported to reduce operational spending by 8%, it said, while wearable devices are reported to increase productivity by 8.5%.

“With seemingly endless potential, these devices have the ability to provide game changing solutions to ongoing challenges across dozens of industries,” the report said.

Solving the golden triangle

Forbes contributor Alana Rudder and editor Kelly Main wrote in 2023 that the “golden triangle” of project management is defined by three constraints that must be in balance: cost, time, and quality. 

These constraints also helped guide the challenges XYZ Reality looked to solve with its headset, Zafar said, and the first the team sought to address was quality. 

To improve the accuracy of installations, they had to make sure the headset met construction tolerances in positioning the 3D model on site.

“We’re pleased to say we can position models with three-millimetre accuracy,” said Zafar.

In 2020, that accuracy would prove its worth when a presentation solved a real-world problem: the headset was demonstrated for a quality manager on a job site that was early in development, where concrete foundation pads had been poured.

But the headset displayed a hologram of cement that was perfectly overlaid, and the newly poured concrete was about 500 millimetres over the AR hologram.

“They’re like, ‘Wait a second. Have they done an overpour of concrete on that pad?’” Zafar recalled.

They had — and most critically, Zafar said the headset alerted the construction team to the overpour immediately. Without this, it likely wouldn’t have been discovered until later in the project’s development when steel was to be placed on top.

“It wouldn’t have fit, and that would have actually caused a three-week delay to the entirety of the project,” he said. “But because we caught it the moment the pour happened, it meant that they could actually fix it real time, without … a huge problem in terms of logistics.”

The example highlights the device’s ability to improve accuracy and, by eliminating the need for rework, improve efficiencies — and also, Zafar says, safety.

Photo by Sandy Millar on Unsplash

Safety, visibility, and ‘a lifeline to a real person’

About 30% of all construction activities are rework, which means a third of human capital — or “time” in the golden triangle — is allocated to fixing issues that wouldn’t exist if initially done properly, Zafar says.

Meanwhile, over three-quarters of health and safety issues are related to fixing rework problems.

“Building things right the first time … produces that product faster, more cheaply, and more importantly, the production process is safer,” Zafar said. “And that’s what we ultimately need as an industry.”

While some connected worker technologies indirectly make projects safer, others are being developed to directly enhance worker safety — and Blackline Safety’s Christine Gillies says that in some instances, they could mean the difference between life and death.

The company produces gas detectors, area monitors, and lone worker devices that can provide real-time visibility into the wellbeing of employees, according to chief product and marketing officer Gillies.

They also provide immediate situational awareness as incidents are progressing, which facilitates quicker reaction times in emergencies.

With connected safety technologies, Gillies said there’s approximately one minute and 40 seconds between the time a device detects a hydrogen sulphide emergency to the time a site evacuation is initiated — including sending help for a downed worker.

But without them, it takes up to two hours for someone to notice a worker is missing and initiate a search, and even longer to find them.

“An increase in connected workers means [they] will feel more confident and safety incidents will be addressed sooner, with fewer catastrophic outcomes and consequent labour disruptions,” Gillies said.

“Lone workers are [also] less isolated, with connected safety tech giving them a lifeline to a real person when working out of sight.”

How companies can implement connected technologies

When it comes to implementing connected technologies, Gillies said it’s key for companies to secure buy-in from workers on the need or rationale.

This could mean emphasising life-saving benefits, or immediately addressing employee concerns, like privacy.

As for tech like XYZ Reality’s headset, Zafar said companies need to walk before they can run, and step one is making sure to first have a decent model and schedule.

“Provided you have that … you’re good to run, basically,” he said.

“You’re good to be able to adopt these new technologies that can then kind of help bridge the gap, and connect the two data pieces together.”

And while neither Gillies nor Zafar had concerns about over-reliance on connected worker technologies, Forbes’ Sundeep Ravande cautioned that digital worker platforms generally function through wifi — so unstable connections are a potential issue to be mindful of.

“Unless the platform offers an offline mode that syncs once a connection is made, a connected worker platform will be of little use in such a situation,” he said.

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Tech’s gender gap persisted in 2023 — how has AI played a role and how might it be a solution?

A look at gender disparity in the tech workforce and how a Canadian AI software addresses it

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Corporate and industry giants have noted gender disparity in tech for the past decade and beyond. Yet, despite all the diversity and inclusion initiatives, technological innovations, and women studying STEM subjects, not much has changed. 

One Business Today India article highlights that while 43% of STEM graduates are women, women make up a mere 14% of engineers, scientists, and technologists. Plus, you’ll only find women in 7% of all executive leadership roles in tech. 

This disparity extends lower down the hierarchy as well, with women making up only 13% of director positions and 17% of mid-level manager roles. 

And if we look at women’s general participation in tech in all roles, representation was at 30% in 2013, and 36% in 2023, which is just a six-point percentage jump in ten years. And to add fuel to the fire, female tech workers in Canada are paid an average of $20,000 less per year than male counterparts in the same roles.

The dark side of AI seems to be playing a role in the disparity. Recently, Amazon introduced AI to their hiring process and since most of Amazon’s past recruits were male and white, the algorithm reflected that bias in its candidate recommendations. 

Another factor stems from the culture. Diversity targets might place some women in executive leadership roles later in their careers, but career nurturing should start earlier when they first enter the workforce. 

Still, not all hope is lost. 

A closer eye on AI can reverse the damage, as Forbes points out in this article. Brands can use it to identify and reduce bias from:

  • Marketing campaigns
  • Recruitment materials
  • Job advertisements and descriptions
  • Resumes (blind hiring)

The gender disparity in tech plus limitations to AI inspired two Canadian women to found Toast, an AI talent software that’s dedicated to increasing female representation in tech companies. 

The software’s algorithms are trained to assess diverse datasets without gender and racial bias. Co-founder Marissa McNeelands created those algorithms herself, backed with robust expertise from a master’s degree in AI. 

While the platform offers practical fixes to gender bias like name removals off resumes, it’s more than just a software. Toast runs a membership club for women to connect with and support one another, share best practices, and find job opportunities.

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