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In a short period of time, AI has already become a driver for emerging technologies such as the Internet of Things (IoT) as well as generative AI. This led to tools such as ChatGPT, which broke the record of the fastest-growing user base in 2022 and currently has around 180 million users. As artificial intelligence tools increasingly permeate our daily and professional lives, with businesses incorporating AI into their operations, Data Sciences surveyed Canadians about their feelings towards big tech’s growing influence and the changes that come with it. In the inaugural part of our two-part series on big tech and AI, we present 5 top findings about public sentiment towards major tech companies. 

February 11th marked the International Day for Women and Girls in STEM, and we're thrilled to showcase some remarkable women in STEM at Data Sciences. We sat down with Jenn, Tayrine, Fatima, and Yujie to delve into their experiences in the world of science. Jenn holds a Ph.D. in Human Genetics from McGill University. She is our Vice President of Data and Analytics at Data Sciences. Tayrine holds a Ph.D. in Information and Knowledge Society from the Universitat Oberta de Catalunya in Spain. She is a Research Analyst on the Insights team at Data Sciences. Fatima Smaili holds a Ph.D. in Computer Science from KAUST (King Abdullah University of Science and Technology in Thuwal, Saudi Arabia) and is a Data Scientist at DS. Yujie, a Data Scientist at DS holds a Ph.D. in Biochemical Engineering from Western University.

As we may start to feel the impact of climate change at home, we investigated public sentiment in Canada towards climate change and flagship policies discussed during COP28, to understand how Canadians position themselves towards these global negotiations. Data shown in this memo was collected among a representative sample of 1,000 Canadians surveyed online in October 2023, before the start of COP28.

Have you ever found yourself in a tricky situation where friends or family ask you about your job as a data engineer, and it feels like you're speaking an entirely different language? The world of data engineering is rich, complex, and incredibly technical, making it challenging to tell what we do in a way that non-technical individuals can easily grasp. But fret not! I've been there too, faced with puzzled looks and perplexed expressions when trying to explain my role. That's why I've come up with something special, an analogy that transforms the often mysterious world of data engineering into something far more digestible for the non-techies in our lives.

Canada is in the midst of a housing affordability crisis. Renters and owners alike have seen their housing costs rise consistently, leading many to question how we got here, and what we can do to make housing more affordable. We conducted an online, nationally representative survey among 1,000 Canadians in March 2023 to gauge how Canadians feel about the housing affordability crisis, and which policies they think are most effective at addressing it.

It’s not enough for a product to be top of the line, better than the ones that came before it, or utilize the newest technological advances. A product must be tailored to its users and meet their needs. In the past decade, the concept and practice of User Experience (UX) has changed the way we design and interact with digital products.

In the previous blog, I discussed the impact of Machine Learning (ML) in our current world. We ended with two questions: 1. In this brave new world of AI and machine learning (ML), how do we operate? 2. As individuals, groups, businesses, and governments, how do we navigate a landscape filled with ML? We need to understand what we’re working with before we can plan how to move forward. This blog will dive into some of the machinery that makes AI and machine learning work.

For the purposes of this blog, we’re going to be focusing in on machine learning (ML), a subset of AI that focuses on understanding and building methods that let machines “learn”: training data is leveraged by algorithms to improve the performance of models. These models can describe anything that you are trying to predict or classify based on underlying characteristics.

The new guidelines made headlines in Canada and abroad, generating a public debate about what this meant for people’s relationships with drinking, social habits and perceptions of risk associated with it. In light of these new guidelines, DS conducted an online survey among Canadians in March to understand their perceptions of these new rules and their intent to follow them in the future. Will Canadians really limit themselves to two weekly alcohol drinks?