Daniel Mulet, Investor and Leah Morris, Associate
Photo Source: Invest for Good
In CBRE Group’s latest report scoring tech talent across 50 North American markets, Canadian cities took 3 of the top 5 spots, with Toronto ranked #1 fastest growing market. Montreal and Vancouver were third and fourth, respectively.
Toronto is increasingly known as a powerhouse for attracting top-tier AI and tech talent. The city has the third largest pool of tech talent, next to the Bay Area (which has the benefit of San Francisco and Silicon Valley being grouped together) and New York. Toronto’s totals in this survey do not include its talent-rich neighbouring region, Kitchener-Waterloo.
Toronto-based talent increased by 43% from 2015 to 2020. Over the same timeframe, there was a doubling of the VC capital raised by founders in Canada’s largest city. While the growth in investment has broken records the past few years, the aggregate amount raised over the past five years ($10Bn) still lags behind the Bay Area ($264Bn) and even the smaller talent market of Los Angeles ($23Bn).
Source: CBRE 2021; Radical analysis of Tracxn data, July 14, 2021
In the top tech talent markets, Toronto ranks last on dollars raised-per-technical employee in 2020, with approximately $4,300 raised per head, compared to $130,000 in the Bay Area.
Closing this gap in capital was a fundamental reason for creating Radical Ventures. We witnessed first-hand many of the brightest minds in AI flocking to Toronto and Canada after the creation of the Vector Institute and the world’s first national AI strategy, but struggling to raise VC investment locally.
Toronto outperforms on talent, and over the next ten years we expect capital to follow. There are tremendous investment opportunities at all stages and we are incredibly excited to continue investing and partnering with exceptional founders who are building global companies in Toronto.
5 Noteworthy AI and Deep Tech Articles: week of July 19, 2021
1) New artificial intelligence software can compute protein structures in 10 minutes (Science Daily) and Highly accurate protein structure prediction with AlphaFold (Nature)
Two important advances in the field of computational biology with the open source release of the Alphafold protein folding code by Deepmind, and the competing RoseTTAFold system by researchers at the University of Washington School of Medicine. Both systems reduce the timeframe of determining the structure of a protein from several years down to a few weeks.
2) Machine learning model from the largest US COVID-19 dataset predicts disease severity (The Verge)
Machine learning can be a powerful tool when combined with large datasets. In this case, a machine learning model accurately predicted the clinical severity of SARS-CoV-2, in a study of more than 174,000 adults. The model has access to the largest centralized repository of COVID-19 health records to date. The repository, called N3C for short, includes data from 73 health institutions and has records from over 2 million COVID-19 patients. While standardizing data across states and hospitals is a challenge, N3C enables the model to pull from a large and diverse dataset. More than 200 research projects using the data are underway, including studies examining risk factors for COVID-19 re-infection and the disease’s impact on pregnancy.
3) Researchers use machine learning to translate brain signals from a paralyzed patient into text (STAT)
In a pioneering demonstration, researchers at the University of California harnessed the brainwaves of a paralyzed man unable to speak and turned what he intended to say into sentences on a computer screen. It will likely take years of additional research to scale, but this is an early example of a powerful natural language model application. While this is a medical first, the device’s speed, accuracy, and vocabulary size will need to be improved before users are able to communicate with a computer-generated voice rather than text.
4) Time for AI to pull up a chair to the negotiating table (Financial Times)
Could AI increase the likelihood of a peaceful outcome from negotiations? Applying AI to broker peace and avoid conflict is an under discussed topic for military applications of AI. Machine learning-powered polling platforms are currently helping inform negotiations and build consensus where humans have difficulty.
5) Four ways artificial intelligence is helping us learn about the universe (Space)
Astronomy is all about data. The universe is getting bigger and so is the amount of information we have about it. Figuring out just how we’re going to study all the data we are collecting is one of the biggest challenges in astronomy today. AI can help researchers search, identify, and synthesize data to rapidly uncover breakthroughs.
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