Sharing AI related news has become crucial in this era of digital transformation. Given the current wave, many AI researchers have turned into AI influencers to drive value and success to their respective field. These are the people who are driving conversations about AI across social media and other platforms. Please note: This is not a ranking article. Gregory Piatetsky is a well-known expert in Big Data, Business Analytics, Data Mining, Data Science, and Machine Learning and is among top influencers in those fields.
Paige Dickie develops Artificial Intelligence (AI) and digital strategy for Canada's banking sector at the Vector Institute for Artificial Intelligence in Toronto. She began her career in management consulting -- much to the disappointment of her father, an engineer -- because she had earned advanced engineering degrees in biomedical and mechanical engineering. Dickie initially worked at McKinsey, the global consulting firm, helping multinational financial institutions across a range of fields from data strategy and digital transformation to setting up innovation centers. She recently joined Vector to lead what she describes as "an exciting project with Canada's banking industry. It's an industry-wide, sector-wide, country-wide initiative where we have three different work streams -- a consortium work stream, a regulatory work stream, and a research-based work stream."
The AI Times is a weekly newsletter covering the biggest AI, machine learning, big data, and automation news from around the globe. If you want to read A I before anyone else, make sure to subscribe using the form at the bottom of this page. Toronto-based Daisy Intelligence, which has created an AI-powered platform for retail and insurance, has raised $10 million in Series A financing. "Microsoft and Eros are partnering to take Bollywood's $5B movie industry global by developing a new platform, creating new offerings, and delivering personalized content – all using Azure." You don't see a startup get a $50 million seed round all that often, but such was the case with Vianai, an early-stage startup launched by Vishal Sikka, former Infosys managing director and SAP executive.
Wealthsimple is democratizing financial investing to make smart investing easy, accessible, and transparent for everyone. With $2 Billion in assets under management and over 80,000 clients, we're the market leader in Canada and are fast growing in the US and UK. Our team is working together to build one of the largest and most innovative fintech companies in the world. Come join us and change the future of fintech. You can read about our mission and our team to learn more.
Akelius buys, upgrades and manages residential properties. The company owns 47,000 apartments in Sweden, Denmark, Germany, France, Canada, England and the United States. We are a rapidly growing international company more than eight hundred employees around the world. An integral part of our company is the Technology department. The Development team consists of more than one hundred employees mostly based in Berlin.
TORONTO, ONTARIO – According to a new research report by the market research and strategy consulting firm, Global Market Insights, Inc, penetration of AI technologies in the retail market will exceed 8 billion USD by 2024. The AI in retail market is driven by increasing investments across the globe. This growing interest can be attributed to the wide applications of machine learning, predictive analytics, and deep learning. Furthermore, AI is set to unleash the next phase of the digital disruption in retail – and the major players in the sector are ramping up their digitalization efforts as a result. With a storied history in the AI and data analytics space, the founder and CEO of Daisy Intelligence, Gary Saarenvirta, is today working at the forefront of the disruption taking place globally in retail.
Sure, no data scientist role is the same, and that's the reason for the inquiry. So many potential data scientists are interested in knowing what it is that those on the other side keep themselves busy with all day, and so I thought that having a few connections provide their insight might be a useful endeavor. What follows is yet another round of some of the great feedback I received via email and LinkedIn messages from those who were interested in providing a few paragraphs on their daily professional tasks. The short daily summaries are presented in full and without edits, allowing the quotes to speak for themselves. Rogelio Cuevas is a Data Scientist at the Data Science and Model Innovation team at Scotiabank, Toronto.
About Datameer Datameer is changing the way companies do business by enabling them to get value and insights from their data at the speed of thought to make better, more trustworthy decisions and drive better business outcomes. Datameer offers a unified way to simplify the time-consuming, cumbersome process of turning complex, multi-source data into valuable, business-ready information in a matter of minutes and hours, rather than weeks or months. Leading global organizations, including Citibank, Royal Bank of Canada, Aetna, Optum, HSBC, National Instruments, Vivint and more use our secure and scalable enterprise-grade platform to streamline and simplify data integration, preparation and exploration so subject matter experts can leverage trusted data to cultivate innovation, creativity and efficiency for competitive advantage.
Correlation matrices are omnipresent in multivariate data analysis. When the number $d$ of variables is large, the sample estimates of correlation matrices are typically noisy and conceal underlying dependence patterns. We consider the case when the variables can be grouped into $K$ clusters with exchangeable dependence; an assumption often made in applications in finance and econometrics. Under this partial exchangeability condition, the corresponding correlation matrix has a block structure and the number of unknown parameters is reduced from $d(d-1)/2$ to at most $K(K+1)/2$. We propose a robust algorithm based on Kendall's rank correlation to identify the clusters without assuming the knowledge of $K$ a priori or anything about the margins except continuity. The corresponding block-structured estimator performs considerably better than the sample Kendall rank correlation matrix when $K < d$. Even in the unstructured case $K = d$, though there is no gain asymptotically, the new estimator can be much more efficient in finite samples. When the data are elliptical, the results extend to linear correlation matrices and their inverses. The procedure is illustrated on financial stock returns.