Businesses know that rich analytics capabilities are critical to remain competitive in today's reality. While Canadian executives may feel challenged by the immense amount of data they already have access to, they plan to continue investing in new and expanded data sources to enable them to better mitigate risk – and meet constantly shifting consumer expectations,

Machine learning is a race. Those companies that can put machine learning models into production, on a large scale, first, will gain a huge advantage over their competitors and billions in potential revenue. But, there is a huge issue with the usability of machine learning -- there is a significant challenge around putting machine learning models into production at scale. Organisations can create incredibly complex machine learning models, but it's problematic to take huge datasets, apply them to different iterations of ML models and then deploy those successful iterations into production. Machine learning is becoming the phrase that data scientists hide from CVs, putting a data science model into production is the biggest data challenge, and companies are still not getting it.

RS Energy Group, Inc. (RSEG) is an advanced analytics and technology firm that delivers industry-leading, comprehensive insights to those operating, investing in or servicing the energy space. RSEG's work environment is positive, supportive, innovative, and dynamic, with interdisciplinary teams focused on leveraging the latest in technology, machine learning, data science and AI. Headquartered in Calgary, RSEG also has offices in Houston, New York and Conshohocken.

About the Author: Alexandre Hubert began his career as a trader in the city of London, and shifted to become a data scientist after four years. He has worked on a wide range of use cases, from creating models that predict fraud to building specific recommendation systems. Alex has also worked on loan delinquency for leasing and refactoring institutions as well as marketing use cases for retailer bankers. Alex is a lead data scientist at Dataiku, located in Singapore.

At Criteo, we define the word "relevance" as being products that people purchase after having been shown an ad. With AI, we focus on the causal relationship between the ads we show and the products people end up buying. Our business models are designed to align incentives across the whole chain: to maximize our profit, we need to maximize relevance and reduce waste and noise. Only a machine could perform this task a million times per second for a billion users and this machine is AI-driven. Criteo is first and foremost a technology company.

Financial services organisations that have adopted artificial intelligence (AI) expect to see a 41% improvement in competitiveness within three years, according to a new study by Microsoft Asia and IDC Asia-Pacific. More than half (52%) of the financial services organisations in Asia-Pacific have already started on their AI journeys. This is higher than the average of 41% for all industries, indicating the sector is more advanced than others in the region. The findings are contained in "Future Ready Business: Assessing Asia-Pacific's Growth with AI". "The digital economy has resulted in demands for organisations to reinvent themselves to remain relevant to their customers," said Connie Leung, senior director and financial services business lead with Microsoft Asia.

All the roads in France lead to Paris! As we all know, Paris is home to some of the cutting edge startups, especially in artificial intelligence. In the way of acknowledging it, tech giants like Facebook and Google have opened research centers devoted primarily to artificial intelligence. As per the latest report, the number of AI startups in France continues to soar from 312 last year to 432 this 2019. This report was produced by Roland Berger, a global consulting firm in collaboration with France Digitale, an association that represents venture capitalists and entrepreneurs.

Machine learning can help us to improve human health in many ways, like predicting and preventing musculoskeletal injuries, personalizing rehabilitation, and developing antibodies to thwart quickly-mutating pathogens. It can also help us to enhance the analysis of medical imaging, model the complex geometry of neurons, and design synthetic biological systems. Our mechanical engineers bring their unique expertise to these biomedical challenges.

Hyderabad: Micron Technology Inc. on Friday launched its Global Development Centre in the IT corridor or Hi-Tech city in Hyderabad. The new site will be used to develop technologies in a wide range of areas such as artificial intelligence and machine learning. The new centre was inaugurated by Telangana IT minister K. T. Rama Rao, National Institute for Transforming India Aayog CEO Amitabh Kant and other officials. The company's new centre is located in the heart of Hyderabad's IT corridor and spans across an area of 350,000 square feet. "We're delighted to launch our Global Development Centre in Hyderabad and expand our team of engineers, researchers, developers and IT specialists," said Mehrotra.

It seems like only yesterday that artificial intelligence (AI) was the stuff of science fiction - a concept, rather than grounded in reality. Marketers in particular have waxed lyrical about the potential of AI for perhaps five years or so, but during that time much of the conversation around AI has been the manifestation of a sort of'shiny new tech syndrome'. To suggest – as many do – that AI is still a buzzword, is to vastly underestimate how it, when paired with the right data and the increase in demand for intelligent virtual assistants, is already radically altering aspects of marketing. At The Drum Arms at Advertising Week New York, The Drum co-founder and editor-in-chief, Gordon Young, took the stage with Microsoft Advertising, the American basketball league NBA and digital agency Digitas, to discuss how this combination of data and AI will define – and in some cases is already defining – the future of marketing. Jorge Urrutia del Pozo, head of fan audience strategy and engagement at the NBA, discussed how he and his team utilize the huge and various amounts of data that the NBA creates.