Most companies struggle to capture the enormous potential of their data. Typically, they launch massive programs that try to meet the needs of every data end user or have individual application-development teams set up customized data pipelines that can’t easily be repurposed. Firms instead need to figure out how to craft data strategies that deliver value in the near term and at the same time lay the foundations for future data use. Successful companies do this by treating data like a commercial product. When a business develops a product, it tries to maximize sales by addressing the needs of as many kinds of customers as possible with it—often by creating a standard offering that can be tailored for different users. A data product works similarly. It delivers a high-quality, easy-to-use set of data that people across an organization can apply to various business challenges. It might, say, provide 360-degree views of customers, of employees, or of a channel. Because they have many applications, data products can generate impressive returns. The customer data product at one large bank, for instance, has nearly 60 use cases, and those applications generate $60 million in incremental revenue and eliminate $40 million in losses annually.

Element AI has closed a much-anticipated venture round, raising a $200 million CAD ($151.4 million USD) Series B from the Government of Quebec, pension fund Caisse de dépôt et placement du Québec (CDPQ), and McKinsey & Company, among others. The $200 million in funding is one of the largest venture capital rounds in Canadian history, and follows a record-breaking year for Canadian tech. New investors included CDPQ, a long-term institutional investor, McKinsey & Company, a global management consulting firm and owner of advanced analytics company QuantumBlack, and Gouvernement du Québec. Existing investors from this round include DCVC (Data Collective), Hanwha Asset Management, BDC Capital, Real Ventures, among many others, the startup said, bringing the total amount raised so far to $340 million CAD ($257 million USD). "Operationalizing AI is currently the industry's toughest challenge, and few companies have been successful at taking proofs-of-concept out of the lab, imbedding them strategically in their operations, and delivering actual business impact," said Element AI CEO Jean-François Gagné. "We are proud to be working with our new partners who understand this challenge well, and to leverage each other's expertise in taking AI solutions to market."

Element AI has closed a much-anticipated venture round, raising a $200 million CAD ($151.4 million USD) Series B from the Government of Quebec, pension fund Caisse de dépôt et placement du Québec (CDPQ), and McKinsey & Company, among others. The $200 million in funding is one of the largest venture capital rounds in Canadian history, and follows a record-breaking year for Canadian tech. New investors included CDPQ, a long-term institutional investor, McKinsey & Company, a global management consulting firm and owner of advanced analytics company QuantumBlack, and Gouvernement du Québec. Existing investors from this round include DCVC (Data Collective), Hanwha Asset Management, BDC Capital, Real Ventures, among many others, the startup said, bringing the total amount raised so far to $340 million CAD ($257 million USD). "Operationalizing AI is currently the industry's toughest challenge, and few companies have been successful at taking proofs-of-concept out of the lab, imbedding them strategically in their operations, and delivering actual business impact," said Element AI CEO Jean-François Gagné. "We are proud to be working with our new partners who understand this challenge well, and to leverage each other's expertise in taking AI solutions to market."

Fear, uncertainty and doubt are tainting the banking industry's views of artificial intelligence. There's so much noise about AI, it's reminiscent of irrational fears about electricity or even the microwave -- it's going to take away our jobs, is more dangerous than nuclear weapons and will have a negative impact on our cities. From my perspective, it's important to be cautious when we evaluate new technologies, but I'm an optimist at heart. I believe in the power of technology to create value and transform lives. The individuals who are responsible for AI have the capacity to create guardrails and ensure that these new approaches to data science do not have a negative impact.

National Bank is pleased to announce that Manuel Morales will join its team in the position of Chief Scientist, Artificial Intelligence. The main task of this accomplished researcher from the Université de Montréal will be to head the research and implementation of technological solutions in the area of artificial intelligence at National Bank in order to improve customer experience. Manuel Morales is currently an Associate Professor in the Department of Mathematics and Statistics at the Université de Montréal. He has received several awards for his applied research in machine learning, and has contributed to the publication of numerous articles in this field. Mr. Morales will devote half of his time to National Bank, while pursuing his role at the university.

This week, Silicon Valley-based Harvesting Inc launched an Artificial Intelligence (AI)-backed platform that uses satellite images of farms to gauge credit risk for banks that lend to farmers. The technology can be used to capture data on farms in the developing world as well as in mature markets like the US. Harvesting Inc generates "credit scorecards" using satellite imagery of farms and other data points, already in the works in the African market. "Think of us as someone who calculates the language of agriculture [into] the language of finance," Garg said. Garg told deBanked that because small farmers in emerging markets don't have credit cards, let alone smartphones, they have no credit history and their creditworthiness can't be evaluated through a traditional Western banking system.

I recently completed Andrew Ng's computer vision course on Coursera. Ng does an excellent job at explaining many of the complex ideas required to optimize any computer vision task. My favourite component of the course was the neural style transfer section (see lesson 11), which allows you to create artwork which combines the style of Claud Monet with the content of whichever image you would like. In this article, I will discuss 11 key lessons that I learned in the course. Note that this is the fourth course in the Deep Learning specialization released by deeplearning.ai.

The Bank and IVADO will promote innovation in the financial services industry, work to increase networking between universities and the private sector in AI, and encourage philanthropic data analysis initiatives. National Bank will collaborate with IVADO partners like HEC Montréal's Tech3Lab and create targeted AI internships. "With National Bank signing on as a founding member, the IVADO will receive significant support in terms of research and the transfer of technology in the field of finance," said Valérie Bécaert, director of partnerships and knowledge mobilization at IVADO. "We're extremely pleased with this new collaboration which will no doubt give rise to several innovative development projects." The two organizations plan to encourage the sharing of best practices in data management, analysis, and governance with not-for-profit organizations.