This week, McGill’s School of Continuing Studies welcomed the Stradigi AI team for an influencer talk about bridging the theory of AI into practical applications for a group of eager students and young professionals.
Our tall task was to introduce the topic of artificial intelligence, discuss our current ecosystem, all while giving concrete career advice about our industry in a 60 minute session.
We tackled it the same way we do everything, we rounded up a group of experts with diverse backgrounds and broke down the conversation into clear topics. We assigned roles, extracted our knowledge and developed a final presentation that offered the best user experience possible.
Here’s how we did it:
Montreal’s AI Ecosystem by Phil Mitsopoulos
“Montreal is an AI hub”, have you heard the expression lately? Everywhere in the news, people keep referring to our city as a breeding ground for incredible talent and a cluster of innovation composed of startups and AI labs led by some of the most well-known global tech leaders in the world, such as Facebook, IBM, Microsoft and Google… just to name a few. On top of this, both the provincial and federal governments have committed major financial investments to foster AI development in our city.
This concentration of AI leadership has created a dynamic environment for businesses and allowed for a unique interconnected and collaborative ecosystem.
Along with talent and investment, Montreal is home to some of the best accelerators and incubators in the world. Across the board, Montreal AI players have been stealing the spotlight during some of the biggest tech events over the last few years. C2 Montreal hosts its own AI Forum, which showcases our innovative spirit and NIPS, which is the biggest scientific conference in the industry (and whose tickets sold out in 11 minutes), will be taking place at Palais des congrès in a few weeks.
Key Takeaway: The Montreal AI ecosystem is one of the deepest and most welcoming AI talent pools in the world and that’s something we should all be proud of.
An Introduction to Data Science, Machine Learning and Deep Learning by Nicolas Simard, Research Scientist
Albert Einstein once said: “If you can’t explain it simply, you don’t understand it well enough.” This is was our challenge to Nicolas Simard. Yet in roughly 10 minutes, he was able to give a very clear and concise explanation to a question most people in the room were excited to have answered: “What is artificial intelligence, exactly?”
He first began with the “data” in data science. Data is information, it’s what you post on social media, the content of your emails, your buying history, movies you watch on Netflix and even your DNA. This is your digital footprint, the equivalent of your biography, but containing more detail than you are probably comfortable with. The “science” refers to the various tasks you can accomplish with your data, such as prediction, classification, data exploration and data clustering.
For Machine Learning, instead of breaking down a technical explanation, a simple analogy to human learning made understanding the process a lot simpler.
Think of a model as a student who is in the process of being educated. By using various datasets (exercises), the model will learn parameters (patterns, or lessons) and by repeating this process over and over, the model will not only be able to perform a task (the exam), but will use what it’s learned to transform the input into an output (in the most effective manner). Deep Learning is Machine Learning on steroids. Instead of 1 layer, 2 parameters and 1 input, you now have 467 layers, 54,339,810 parameters, 268, 203 input and the model is trained for days on multiple computers.
Key Takeaway: With the emergence of big data and supercomputers, we aren’t simply dealing with artificial intelligence, but big intelligence.
The Misconceptions of AI by Michael Rokos, Director of Business Development
Killer robots. Skynet. The Terminator. These are all scare tactics that make great movie scripts and generate a lot of clicks. The next time you hear this narrative, bring up the concept of Narrow AI vs General AI, it’s a great way to win over the conversation.
The misconceptions surrounding artificial intelligence are simply staggering. Right now, we are living with Narrow AI, technology that is created to perform tasks a human could do, but algorithms do better. We haven’t reached the stage where robots have developed a conscience, so rest assured that the plot line for your favourite sci-fi movie isn’t something you need to concern yourself with.
Right now, the idea is to create technology that is useful to solve problems and can be used as a collaboratively as a form of human and machine interaction, augmenting productivity. Among other things, Narrow AI can recommend, predict and forecast, recognize patterns and anomalies, understand natural language, pictures and videos. Ultimately, you should see current models or solutions as a means to assist on specific mundane and repetitive tasks and allows you to focus on higher-level thinking, or tasks that require creativity and human-to-human interactions.
Key Takeaway: Remember, it isn’t Skynet…it’s just math.
Exploring Legal & Ethical Policy by Hélène Beauchemin, Legal Counsel
The key legal issues of ethics in AI is something we as a company care very deeply about. Not only do we discuss and consider the impact this technology will have on businesses, consumers and society as a whole, but we implement these considerations into our framework whenever we start a project.
The distinct nature of artificial intelligence and its uses will have concrete impacts on human rights, liability, contracts, data privacy and intellectual property. Well defined processes to protect people from algorithmic bias, cybersecurity and managing the protection of sensitive information are crucial to ensure our values are protected.
Considering AI’s immense power to transform human lives, we recognize the need for ethical guidelines based on five pillars: fairness, transparency & interpretability, incorruptibility, and accountability. There are already a few solutions to these key issues, including algorithmic impact assessments, stronger technical safeguards and of course, due diligence. As AI continues to evolve at the speed of light, the conversation and efforts being put into promoting a just and fair development process should be a core value for everyone in the industry.
Key Takeaway: It isn’t enough to simply believe in ethics, making a conscious effort to integrate your values in to your processes and procedures is how ideas turn into actions.
Career Paths Into AI by Ben Tang, Business Analyst
It’s rare to find a person who has a real background in AI, but that doesn’t mean you can’t forge a path within the industry. At Stradigi AI we speak a great deal about the importance of the strength of our diversity, whether it be gender, ethnicity, and this certainly includes our teams’ backgrounds.
Our research team is composed of over 30 PhDs with expertise in mathematics, physics, computer science and electrical engineering. Full stack developers, mobile developers, UX/UI designers, project managers and analysts make up our solutions department. Finally, we have Human Resources, Marketing and Client Services! The most important thing to remember is everyone has a part to play. Deep dive into your interests, take the time to network, draw connections between different domains and leverage their relationship and of course, cultivate humility, curiosity, and kindness.
Key Takeaway: Non traditional career paths aren’t as uncommon as they used to be and the tech industry has fewer barriers. Make yourself adaptable and never stop learning new skills.
AI has a way of inspiring incredible conversations. As human beings, we are inherently curious and this curiosity is the driving force in our industry. We want to push the boundaries, make new discoveries and find a way to make the world a better place.
Stradigi AI is currently in hyper growth and looking for smart, driven and enthusiastic people to join our team. Contact us today!