27. Alayna Kennedy - AI safety, AI ethics and the AGI debate
Towards Data Science
03/30/20
•45m
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Most of us believe that decisions that affect us should be made rationally: they should be reached by following a reasoning process that combines data we trust with a logic that we find acceptable.
As long as human beings are making these decisions, we can probe at that reasoning to find out whether we agree with it. We can ask why we were denied that bank loan, or why a judge handed down a particular sentence, for example.
Today however, machine learning is automating away more and more of these important decisions, and as a result, our lives are increasingly governed by decision-making processes that we can’t interrogate or understand. Worse, machine learning algorithms can exhibit bias or make serious mistakes, so a black-box-ocracy risks becoming more like a dystopia than even the most imperfect human-designed systems we have today.
That’s why AI ethics and AI safety have drawn so much attention in recent years, and why I was so excited to talk to Alayna Kennedy, a data scientist at IBM whose work is focused on the ethics of machine learning, and the risks associated with ML-based decision-making. Alayna has consulted with key players in the US government’s AI effort, and has expertise applying machine learning in industry as well, through previous work on neural network modelling and fraud detection.
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26. Jeremy Howard - Coronavirus: the data behind the disease
March 20, 2020
•43m
In mid-January, China launched an official investigation into a string of unusual pneumonia cases in Hubei province. Within two months, that cluster of cases would snowball into a full-blown pandemic, with hundreds of thousands — perhaps even millions — of infections worldwide, with the potential to unleash a wave of economic damage not seen since the 1918 Spanish influenza or the Great Depression.
The exponential growth that led us from a few isolated infections to where we are today is profoundly counterintuitive. And it poses many challenges for the epidemiologists who need to pin down the transmission characteristics of the coronavirus, and for the policy makers who must act on their recommendations, and convince a generally complacent public to implement life-saving social distancing measures.
With the coronas in full bloom, I thought now would be a great time to reach out to Jeremy Howard, co-founder of the incredibly popular Fast.ai machine learning education site. Along with his co-founder Rachel Thomas, Jeremy authored a now-viral report outlining a data-driven case for concern regarding the coronavirus.
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28. Emily Robinson - Building a Career in Data Science
April 7, 2020
•43m
It’s easy to think of data science as a purely technical discipline: after all, it exists at the intersection of a number of genuinely technical topics, from statistics to programming to machine learning.
But there’s much more to data science and analytics than solving technical problems — and there’s much more to the data science job search than coding challenges and Kaggle competitions as well. Landing a job or a promotion as a data scientist calls on a ton of career skills and soft skills that many people don’t spend nearly enough time honing.
On this episode of the podcast, I spoke with Emily Robinson, an experienced data scientist and blogger with a pedigree that includes Etsy and DataCamp, about career-building strategies. Emily’s got a lot to say about the topic, particularly since she just finished authoring a book entitled “Build a Career in Data Science” with her co-author Jacqueline Nolis. The book explores a lot of great, practical strategies for moving data science careers forward, many of which we discussed during our conversation.
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