Collaborating Authors


AI Predicts What You Can Eat


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, 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.

[D] Germany vs Canada for MS in Machine Learning


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).

Photographer captures highest resolution shots of snowflakes ever

Daily Mail - Science & tech

A renowned photographer has captured the highest resolution shots of snowflakes ever using a homemade prototype described as one part microscope and one part camera. Nathan Myhrvold, an American scientist, inventor, photographer and ex-chief technology officer of Microsoft, took 18 months to build the 100 megapixel camera capable of capturing a snowflake's microscopic detail. Using the camera, which he describes as the'highest resolution snowflake camera in the world', he took 100 frames of each snowflake in quick succession then stacked them for the whole image to be in focus. The results show the lush variety of snowflakes measuring only a few tens of millimetres in diameter, captured when Myhrvold was in Alaska and Canada. Pictured, stellar dendrite captured in Yellowknife, Canada.

TextBox: A Unified, Modularized, and Extensible Framework for Text Generation Artificial Intelligence

We release an open library, called TextBox, which provides a unified, modularized, and extensible text generation framework. TextBox aims to support a broad set of text generation tasks and models. In TextBox, we implements several text generation models on benchmark datasets, covering the categories of VAE, GAN, pre-trained language models, etc. Meanwhile, our library maintains sufficient modularity and extensibility by properly decomposing the model architecture, inference, learning process into highly reusable modules, which allows easily incorporating new models into our framework. It is specially suitable for researchers and practitioners to efficiently reproduce baseline models and develop new models. TextBox is implemented based on PyTorch, and released under Apache License 2.0 at

A Distributional Approach to Controlled Text Generation Artificial Intelligence

We propose a Distributional Approach to address Controlled Text Generation from pre-trained Language Models (LMs). This view permits to define, in a single formal framework, "pointwise" and "distributional" constraints over the target LM -- to our knowledge, this is the first approach with such generality -- while minimizing KL divergence with the initial LM distribution. The optimal target distribution is then uniquely determined as an explicit EBM (Energy-Based Model) representation. From that optimal representation we then train the target controlled autoregressive LM through an adaptive distributional variant of Policy Gradient. We conduct a first set of experiments over pointwise constraints showing the advantages of our approach over a set of baselines, in terms of obtaining a controlled LM balancing constraint satisfaction with divergence from the initial LM (GPT-2). We then perform experiments over distributional constraints, a unique feature of our approach, demonstrating its potential as a remedy to the problem of Bias in Language Models. Through an ablation study we show the effectiveness of our adaptive technique for obtaining faster convergence.

Principle Machine Learning Engineer - VDart, Inc. - Plano, TX


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How Large Companies Can Grow Their Data and Analytics Talent


While many companies are hiring data scientists and other types of analytical and artificial intelligence talent, there is little consensus within and across companies about the qualifications for such roles. The term data scientist might mean a job with a heavy emphasis on statistics, open-source coding, or working with executives to solve business problems with data and analysis. The idea of data scientist "unicorns" who possess all these skills at high levels was never very realistic. As the job has grown more popular and sought-after, an increasing number of professionals have begun to use it to describe their role. Colleges and universities have responded to the demand as well by offering hundreds of new programs on data science and analytics.

Gatik Raises Funding for Autonomous Delivery in Canada


Gatik, a Palo Alto and Toronto based autonomous technology company deploying autonomous vehicles for B2B short-haul middle-mile logistics, announced today it has raised $25 million in Series A funding. The round was co-led by Wittington Ventures and Innovation Endeavors with participation from FM Capital and Intact Ventures. Existing investors like Dynamo Ventures, Fontinalis Partners, AngelPad and others participated as well. Gatik's investors bring a wealth of deep experience in automotive, artificial intelligence and supply chain, making them a strong strategic fit for the company's rapid growth. Gatik will use the funding to further expand its operations across North America, its team size in Silicon Valley and growing presence in Canada.

Artificial Intelligence Activity On The Enforcement Front - Technology - Canada


Artificial Intelligence ("AI") is clearly on the horizon of the regulatory landscape. Alongside the use of technology to assist with navigating the regulatory process, regulators are now digitizing their enforcement efforts. The Canadian Securities Administrators ("CSA")1 have approached this challenge head-on. In 2018, the CSA put the capital markets on notice that they were strengthening their technological capabilities to assist in fighting securities misconduct.2 The CSA confirmed they would rely on AI technology to analyze large data sets, allowing them to detect misconduct faster and earlier, through the Market Analysis Platform ("MAP"), an automated centralized solution that the CSA believed could handle the size of the current market practices.

Canada punches above its weight with AI researchers in new Element AI report


The number of artificial intelligence (AI) researchers in Canada's private sector is proportionally higher than that of other countries, according to Montreal-based Element AI's 2020 Global AI Talent Report. The report found that Canada has 367 AI researchers, making it second only to the US. The Global AI Talent Report measured the size of the available talent pool in the AI industry through self-reported data on social media and demand via the monthly total job postings for the same role up to August 2020. The goal of the report is to assess the most current global patterns for the worldwide AI talent pool. The report tracked 477,956 people worldwide working in the AI industry, of which 61 percent worked in productization, 38 percent in engineering, and a mere one percent in research.