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Canadian Agritech Startup Farmers Edge Inc. Files IPO to Raise CAD 100 Million

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Farmers Edge Inc, an AI startup to help growers increase crop yields, plans to go public on Canada's largest Toronto Stock Exchange under the ticker symbol "FDGE". The company seeks to raise CAD 100 million (approximately USD 79 million). Founded in 2005, Farmers Edge uses AI technology to collect and analyze local weather, soil moisture and satellite data to help farmers improve crop efficiency and yield. Besides the Canadian Prairie, the company currently hosts offices in the United States, Australia, Russia, Brazil and Ukraine. As of the end of 2020, more than 3,000 growers have used the Farmers Edge products, covering more than 23 million acres of land in six countries.


Snapcommerce raises $85M for AI-powered ecommerce messaging

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Toronto, Canada-based ecommerce startup Snapcommerce today announced that it raised $85 million in a funding round led by Inovia Capital and Lion Capital, bringing its total venture capital raised to over $100 million. Snapcommerce, which is on the path to an initial public offering, says it plans to use the funds to expand into new consumer verticals while scaling its product and team. While consumer spending in the U.S. dipped last month year-over-year, on the whole, the pandemic has supercharged ecommerce. According to data from IBM's U.S. Retail Index, business closures and shelter-in-place orders accelerated the shift to digital shopping by five years, with online shopping projected to grow nearly 20% in 2020. Based on the survey data from BMC and Mercatus, ecommerce grocery orders alone totaled $5.9 billion, up 3.6% from $5.7 billion in August.


Gatik raises $9 million to winterize self-driving box trucks

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Gatik, a startup developing an autonomous vehicle stack for B2B short-haul logistics, today announced it has raised $9 million, with $1 million coming from a partnership with Ontario's Autonomous Vehicle Innovation Network (AVIN). Gatik says the AVIN collaboration -- part of an Ontario government program providing R&D, business, technical, and talent support, as well as vehicle test tracks -- will help it understand how inclement weather affects its vehicles' movements Some experts predict the pandemic will hasten adoption of autonomous vehicles for delivery. Self-driving cars, vans, and trucks promise to minimize the risk of spreading disease by limiting driver contact. This is particularly true with regard to short-haul freight, an estimated 30% of which takes place in snowy and icy conditions. The producer price index for local truckload carriage jumped 20.4% from July to August, according to the U.S. Bureau of Labor Statistics, most likely propelled by demand for short-haul distribution from warehouses and distribution centers to ecommerce fulfillment centers and stores.


U of T researchers train AI to read difficult-to-decipher medieval texts

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In a move that could transform manuscript studies, University of Toronto researchers have partnered with a team in the United Kingdom to develop a program that can read and transcribe the handwritten Latin found in 13th-century legal manuscripts. While scholars have been making digital images of these manuscripts for years, transcribing and comparing these texts is painstaking and tedious work that can take years or even decades to complete. That's because medieval handwriting can often look crabbed and unintelligible, with non-standardized spellings, hyphenations, abbreviations, calligraphic flourishes and any number of distinct "hands." But machine-reading software called Transkribus promises to change the field. Using artificial intelligence (AI), the software can theoretically be trained to read any type of handwriting, in any language – and Michael Gervers, a professor of medieval social and economic history at U of T Scarborough, says it could eventually be applied across medieval studies.


Now DeepMind's New AI Agent Outperforms Humans

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Recently, a team of researchers from DeepMind, Google Brain and the University of Toronto unveiled a new reinforcement learning agent known as DreamerV2. This reinforcement learning agent learns behaviours purely from the predictions in the compact latent space of a powerful world model. According to the researchers, DreamerV2 is the first agent to achieve human-level performance on the Atari benchmark. DreamerV2, a collaboration between DeepMind, @GoogleAI and the @UofT, is the first RL agent based on a world model to achieve human-level performance on the Atari benchmark. From driverless cars to beating Go world champions, reinforcement learning has come a long way.


Cape Breton-founded AI business merges with Swiss International Exchange

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A major stake in a Cape Breton-created company known for its work in developing artificial intelligence and language understanding technology has been sold to the Swiss International Exchange (SIX). The merger via growth investment involves Orenda Software Solutions, started in 2015 in Sydney by Tanya Seajay, which specializes in environment, social and governance (ESG) and alternative data sets. "SIX has a strong commitment to both innovation and sustainability," stated Seajay, Orenda founder and CEO, in a news release. "We had previously announced a sales partnership with SIX, this new step enables Orenda to accelerate its expansion to a much broader global customer base and to develop new solutions that combine the vast securities database of SIX and Orenda unique skillset." Orenda now operates out of Ontario and has an office in Membertou.


Get real about artificial intelligence

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In recent years, artificial intelligence has been attracting increasing attention, money and talent. But much of the hype is the result of myths and misconceptions being peddled by people outside of the field. For many years, the field was growing incrementally, with existing approaches performing around 1-2 percent better each year on standard benchmarks. But there was a real breakthrough in 2012, when computer scientist Geoffrey Hinton and his colleagues at the University of Toronto showed that their "deep learning" algorithms could beat state-of-the-art computer vision algorithms by a margin of 10.8 percentage points on the ImageNet Challenge (a benchmark dataset). At the same time, AI researchers were benefiting from ever-more powerful tools, including cost-effective cloud computing, fast and cheap number-crunching hardware (GPUs), seamless data sharing through the internet, and advances in high-quality open-source software.


How to Manage AI Teams for Success

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A few months ago, I had a conversation with several researchers from a prominent AI company in Toronto, and their company philosophy was that everybody should write production-grade code and even be able to deploy it. It made me think about a lot of stuff. AI teams are specifically interesting because they simultaneously require at least two disciplines: software/hardware engineering and scientific discovery. So how does one work towards success and creating a cohesive team, or teams, of researchers and software engineers who work together and create great products? The product being either pure research in the context of an enterprise trying to gain a competitive edge in terms of intellectual property, or applied research geared more towards a commercial product in a given vertical, or a hybrid of both.


Artificial Neural Nets Finally Yield Clues to How Brains Learn

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In 2007, some of the leading thinkers behind deep neural networks organized an unofficial "satellite" meeting at the margins of a prestigious annual conference on artificial intelligence. The conference had rejected their request for an official workshop; deep neural nets were still a few years away from taking over AI. The bootleg meeting's final speaker was Geoffrey Hinton of the University of Toronto, the cognitive psychologist and computer scientist responsible for some of the biggest breakthroughs in deep nets. He started with a quip: "So, about a year ago, I came home to dinner, and I said, 'I think I finally figured out how the brain works,' and my 15-year-old daughter said, 'Oh, Daddy, not again.'" Hinton continued, "So, here's how it works."


Thomson Reuters to stress AI, machine learning in a post-pandemic world

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NEW YORK (Reuters) - Thomson Reuters Corp will streamline technology, close offices and rely more on machines to prepare for a post-pandemic world, the news and information group said on Tuesday, as it reported higher sales and operating profit. The Toronto-headquartered company will spend $500 million to $600 million over two years to burnish its technology credentials, investing in AI and machine learning to get data faster to professional customers increasingly working from home during the coronavirus crisis. Thomson Reuters' New York- and Toronto-listed shares each gained more than 8%. It aims to cut annual operating expenses by $600 million through eliminating duplicate functions, modernizing and consolidating technology, as well as through attrition and shrinking its real estate footprint. Layoffs are not a focus of the cost cuts and there are no current plans to divest assets as part of this plan, the company said.