Amii brings Edmonton’s AI community together with new meetups

The Alberta Machine Intelligence Institute (better known as Amii) held its first AI Meetup at Startup Edmonton on September 10. The organization has been hosting “Tea Time Talks” and other small events at the University of Alberta for a while now, but the new AI Meetup is an attempt to reach a broader audience. Judging by the turnout to the first event, they’re on to something!

AI Meetup

Melissa Woghiren, an Amii grad student, was one of two speakers at the packed event. She spoke about her work using machine learning to assist physicians in the timely diagnosis of stroke. “No one cares about the algorithms,” she said. “They care about the ML tools, generally speaking.” Melissa dazzled the crowd with details on what AI can do now to detect stroke and heard attacks, but also discussed the risks of algorithmic bias. Her take home points were that AI can be useful in medicine, we care more about the ‘what’ than the ‘how’, and the goal is not to replace doctors but to assist them.

Jobber co-founder Forrest Zeisler was the second speaker. He focused on “Applied AI Myths and Misconceptions”, a topic that really seemed to resonate with the crowd. The biggest myth is that most of a machine learning project involves machine learning – it doesn’t, he said. The bulk of the work is planning, infrastructure, UX, training, etc. Forrest also dispelled the myth that you need a research lab to do AI. “That’s like hiring people for your restaurant who can build new microwaves,” he said. Instead, use an off-the-shelf model and “you can have a pretty big impact.”

AI Meetup

After the talks, there was an opportunity for questions as well as a DemoCamp-style call for anyone in the room who is hiring.

The next AI Meetup is coming up on October 10 at Startup Edmonton from 5:15pm to 7:15pm:

“Discuss the latest topics in AI and machine learning, learn about the latest tools and techniques in machine learning, discover how companies are using AI to drive value, and network with thought leaders from Amii, local AI companies, service providers, and corporate labs.”

Register for the free event here.

Machine Learning 101

Just a few days later Amii held a Machine Learning 101 meetup at Startup Edmonton and once again the event was standing room only. Geoff Kliza, a Project Manager at Amii, delivered a modified version of an ML101 talk he has given to dozens of organizations recently. Here’s my Twitter thread from the event.

Machine Learning 101

“You don’t have to work with our 14 world-leading researchers” to use ML and to do it well, he started. Geoff talked about how the cost of prediction is getting cheaper thanks to cheaper computing power and storage, and more efficient algorithms. A common question that comes up is how AI relates to ML, data science, and other terms, and he showed a great Venn diagram to help explain it. He also defined some common terms in AI such as unsupervised learning (learning about your data), supervised learning (learning from examples), deep learning (neural networks), reinforcement learning (learning via experience), and transfer learning (learning from analogous situations).

Geoff also shared 8 key takeaways reinforcing similar points made at the AI Meetup, including that machine intelligence projects often involve very little ML, that data changes destroy models, that no one cares about your algorithm only what it can do, and that machine intelligence and humans work best together.

On that last point he shared the example of metastatic breast cancer diagnosis. Doctors are about 96.6% accurate and machines are about 92.5% accurate, Geoff said. But together, they are 99.5% accurate, which is an 85% decrease in the human error rate. That’s the power of working together.

Machine Learning 101

The next Machine Learning 101 event is coming up on October 17 at Startup Edmonton from 4-5pm:

“Heralded by many as the fourth industrial revolution, artificial intelligence has inspired countless news articles, novels, and films. With this deluge of information comes hopes and aspirations, fears and misconceptions – some justified and others not. How can we make sense of it all? “

Register for the free event here.

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Edmonton is a world leader in the science of artificial intelligence

Though he works in perhaps the most hyped field of science there is, Dr. Richard Sutton comes across as remarkably grounded. I heard him described at the 2018 AccelerateAB conference on Tuesday as “the Wayne Gretzky of artificial intelligence” and he’s often called a global pioneer in the field of AI. Sutton has spent 40 years researching AI and literally wrote the textbook on Reinforcement Learning. But he spent the first part of his closing keynote discussing the tension between ambition and humility. “It’s good to be ambitious,” he told the audience tentatively. “I’m keen on the idea of Alberta being a pioneer in AI.” But he tempered that by discussing the risk of ambition turning to arrogance and affecting the work of a scientist.

AccelerateAB

“I think you should say whatever strong thing is true,” he said. Then: “Edmonton is a world leader in the science of AI.”

Sutton made sure to highlight the word “science” and noted that we fall behind when it comes to the application of AI. And of course, he backed up his claim with sources, citing DeepMind’s decision to open an international AI research office here at the University of Alberta, and pointing to the csrankings.org site which ranks the U of A at #2 in the world for artificial intelligence and machine learning.

So how did Edmonton come to be such a leader?

It started with Jonathan Schaeffer’s work in the 1990s on Chinook, the first computer program to win the world champion title in checkers. The U of A’s growing expertise in game AI helped to attract a number of AI/ML professors and funding from the provincial and federal governments throughout the early 2000s. Edmonton’s rise to AI prominence was cemented with DeepMind’s recent decision to locate here.

Sutton showed the following timeline to help illustrate Edmonton’s path to AI-science leadership:

AccelerateAB

Sutton then outlined some of the key advances that have happened in the field of artificial intelligence over the last seven years:

  • IBM’s Watson beats the best human plays of Jeopardy! (2011)
  • Deep neural networks greatly improve the state of the art in speech recognition, computer vision, and natural language processing (2012-)
  • Self-driving cars becomes a plausible reality (2013-)
  • DeepMind’s DQN learns to play Atari games at the human level, from pixels, with no game-specific knowledge (~2014)
  • University of Alberta program solves Limit Poker (2015) and then defeats professional players at No-limit Poker (2017)
  • DeepMind’s AlphaGo defeats legendary Go player Lee Sedol (2016) and world champion Ke Jie (2017), vastly improving over all previous programs
  • DeepMind’s AlphaZero decisively defeats the world’s best programs in Go, chess, and shogi (Chinese chess), with no prior knowledge other than the rules of each game

Though the research taking place here in Edmonton and elsewhere has helped to make all of that possible, “the deep learning algorithms are essentially unchanged since the 1980s,” Sutton told the audience. The difference, is cheaper computation and larger datasets (which are enabled by cheaper computation). He showed a chart illustrating Ray Kurzweil’s Law of Accelerating Returns to make the point that it is the relentless decrease in the price of computing that has really made AI practical.

“AI is the core of a second industrial revolution,” Sutton told the crowd. If the first industrial revolution was about physical power, this one is all about computational power. As it gets cheaper, we use more of it. “AI is not like other sciences,” he explained. That’s because of Moore’s Law, the doubling of transistors in integrated circuits every two years or so. “It feels slow,” he remarked, and I found myself thinking that only in a room of tech entrepreneurs would you see so many nodding heads. “But it is inevitable.”

Given this context, Sutton had some things to say about the future of the field:

  • “Methods that scale with computation are the future of AI,” he said. That means learning and search, and he specifically called out prediction learning as being scalable.
  • “Current models are learned, but they don’t learn.” He cited speech recognition as an example of this.
  • “General purpose methods are better than those that rely on human insight.”
  • “Planning with a learned model of a limited domain” is a key challenge he sounded excited about.
  • “The next big frontier is learning how the world works, truly understanding the world.”
  • He spoke positively about “intelligence augmentation”, perhaps as a way to allay fears about strong AI.

Recognizing the room was largely full of entrepreneurs, Sutton finished his talk by declaring that “every company needs an AI strategy.”

I really enjoyed the talk and was happy to hear Sutton’s take on Edmonton and AI. It’s a story that more people should know about. You can find out more about Edmonton’s AI pedigree at Edmonton.AI, a community-driven group with the goal of creating 100 AI and ML companies and projects.

If you’re looking for more on AI to read, I recommend Wait But Why’s series: here is part 1 and part 2.