Vancouver, Canada-based Dooly, a startup developing an AI-powered plugin for customer relationship management (CMR) platforms, today announced that it raised $20 million, a combination of $3.3 million seed and $17 million series A tranches. The company plans to use the capital to scale its platform well into this year, according to CEO Kris Hartvigsen. Salesforce's 2019 State of Sales Report found that, on average, salespeople only spend 34% of their day selling products. Among the biggest culprits of the lost time is the disconnect between enterprises' need for a CRM and the fact that these platforms don't always map to how salespeople work. According to a recent survey, one of the top barriers to CRM adoption is the amount of manual data entry.
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.
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.
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.
The typical ingredient-tetris bottleneck played between guest and server while dining out has amplified during COVID-19. Growth in online ordering and takeout has prompted customers with dietary needs to search online for dietary answers more than ever before.1 With over 52% of Americans following at least one diet, and less than 10% of restaurants labeling dietary information (typically not exhaustive), the information gap has never been wider. Prompted by an Ulcerative Colitis health scare for co-founder Tamir Barzilai, Honeycomb.ai is set on eliminating the frustrating process of manual menu parsing by creating a portal for anyone with dietary needs to find suitable food to eat. "After my personal diagnosis, I realized how many others struggle with finding food to eat due to a variety of reasons. The lack of ubiquitous dietary and ingredient transparency didn't make sense from both consumer and business perspectives," says Barzilai.
I have been an ML engineer for over 2 years in a US based company in India. Working in this company(service based) I saw my whole life playout: Senior MLE in a year, Solution Architect in a couple more, and finally leading a project in a couple more years with proportional increase in pay of course. Seeing my next 5-7 years pan out this way I suddenly realized that this wont be sufficient to satisfy the intellectual in me and also made me realize how much I am actually interested in research (I have a paper published in IEEE related to Deep Learning and Instrumentation) and how much I enjoy making something new. So in short, as my first priority I am looking for something research driven. Since I am research/innovation driven, post MS I will be looking for a job in a research lab (or a research wing in a company).
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.
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.
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.
Apparently, the project's domain relies on the most popular liquor in the world -- Whiskey. A dark spirit coming from a great variety of grains, distilled throughout the world and arriving at quite a number of styles (Irish, Scotch, Bourbon etc) . Scotland, Ireland, Canada & Japan are among the famous exporters and on an international scale, the global production almost reaches the level of $95m revenue . The main scope, hereof, is to introduce in a… 'companionable' way, how helpful can the Clustering Algorithms prove to be, anytime we need to find patterns in a (large) dataset. Actually, it might be considered as a powerful expansion of the standard Exploratory Data Analysis (EDA), which is often very beneficial to try, before using Supervised Machine Learning (ML) models.