If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
From car insurance quotes to which posts you see on social media, our online lives are guided by invisible, inscrutable algorithms. They help private companies and governments make decisions -- or automate them altogether -- using massive amounts of data. But despite how crucial they are to everyday life, most people don't understand how algorithms use their data to make decisions, which means serious problems can go undetected. The New Zealand government has a plan to address this problem with what officials are calling the world's first algorithm charter: a set of rules and principles for government agencies to follow when implementing algorithms that allow people to peek under the hood. By leading the way with responsible algorithm oversight, New Zealand hopes to set a model for other countries by demonstrating the value of transparency about how algorithms affect daily life.
FreshBooks has an ambitious vision. We launched in 2003 but we're just getting started and there's a lot left to do. We're a high-performing team working towards a common goal: building an elite online accounting application to help small businesses better handle their finances. Known for extraordinary product and customer service experiences and based in Toronto, Canada, FreshBooks serves paying customers in over 120 countries. As a Business Analyst for Data, you will drive discussions with business stakeholders to gather requirements, identify problems, and define functional solutions.
Nitin Alabur is an iOS developer from India who lived in the US and dreamed of creating a tech startup. "I had a zillion ideas," he tells me. But he'd been hired by a US firm under an H-1B visa, which ties you to your employer. A green card that would make self-employment possible was years away. "It felt like shackles," he says.
Toronto and the corridor that stretches west to Kitchener and Waterloo is already Canada's capital of finance and technology--and naturally, the region's leaders want to set an example for the rest of the world. That's part of the reason why in 2017, municipal organizations in Toronto tapped Google's sister company Sidewalk Labs to redevelop a disused waterfront industrial district as a high-tech prototype for the "smarter, greener, more inclusive cities" of tomorrow. But within three years the deal had collapsed, a victim of conflicting visions, public concerns over privacy and surveillance, and (to hear Sidewalk Labs tell it) pandemic-era economic change. Journalist Brian Barth, who trained in urban planning and spent seven years living and working in Toronto before returning to the US this summer, says the Sidewalk fiasco also symbolizes a larger difference: the contrast between Silicon Valley's hard-charging, individualist, libertarian ethos and a Canadian business style that emphasizes collaboration, respect, and social responsibility. In this edition of Deep Tech, Barth talks about the tensions that led to Sidewalk Labs' departure and the strategies Canadian CEOs are following to build a more open and inclusive tech sector. Toronto would like to be seen as the nice person's Silicon Valley, if that's not too much trouble, June 17, 2020 Wade Roush: Is Toronto like Silicon Valley for nice people?
You must use this link to sign up: https://hckrn.st/MTS1 Don't miss out, join us as we explore different ways of connecting our tech nerd communities in the coming months! "HackerNest feels like coming home" - attendees First timer? While this Mega Tech Social - North America Edition features our (surprise, surprise!) North American communities, all are welcome, regardless of geography.
As a machine learning researcher in the biology field, I have been keeping an eye on the recently emerging field of AI in drug discovery. Living in Toronto myself, where many "star" companies in this field were founded (Atomwise, BenchSci, Cyclica, Deep Genomics, ProteinQure… just to name a few!), I talked to many people in this field, and attended a few meetup events about this topic. What I learned is that this field is growing at such a rapid speed, and it is becoming increasing hard to keep track of all companies in this field and get a comprehensive view of them. Therefore, I decide to use my data science skills to track and analyze the companies in this field, and build an interactive dashboard (https://ai-drug-dash.herokuapp.com) to visualize some key insights from my analysis. The Chief Strategy Officer of BenchSci (one of the "star" AI-drug startups in Toronto), Simon Smith, is an excellent observer and communicator in the AI-drug discovery field.
But even by the usual standards, artificial intelligence has had a turbulent run. Is AI a society-renewing hero or a jobs-destroying villain? As always, the truth is not so categorical. At more than 1,000 pages, Thomas Piketty's doorstop sequel to his previous opus, Capital in the Twenty-First Century, does not disappoint. But whether it will fundamentally change the global debate about inequality is an open question.
On January 6th, the US Centers for Disease Control and Prevention (CDC) notified the public that a flu-like outbreak was propagating in Wuhan City, in the Hubei Province of China. Subsequently, the World Health Organization (WHO) released a similar report on January 9th. While these responses may seem timely, they were slow when compared to an AI company called BlueDot. BlueDot released a report on December 31st, a full week before the CDC released similar information. Even more impressive, BlueDot predicted the Zika outbreak in Florida six months before the first case in 2016.
In this work, we examine the extent to which embeddings may encode marginalized populations differently, and how this may lead to a perpetuation of biases and worsened performance on clinical tasks. We pretrain deep embedding models (BERT) on medical notes from the MIMIC-III hospital dataset, and quantify potential disparities using two approaches. First, we identify dangerous latent relationships that are captured by the contextual word embeddings using a fill-in-the-blank method with text from real clinical notes and a log probability bias score quantification. Second, we evaluate performance gaps across different definitions of fairness on over 50 downstream clinical prediction tasks that include detection of acute and chronic conditions. We find that classifiers trained from BERT representations exhibit statistically significant differences in performance, often favoring the majority group with regards to gender, language, ethnicity, and insurance status. Finally, we explore shortcomings of using adversarial debiasing to obfuscate subgroup information in contextual word embeddings, and recommend best practices for such deep embedding models in clinical settings.