Disclaimer: The following is based on my observations of machine learning teams -- not an academic survey of the industry. If you're a developer, you probably have at least a passing interest in machine learning. The concept of algorithms that can teach themselves to make predictions is just kind of… cool. However, if you do decide to study ML and follow the canonical recommendations for getting started, there's a decent chance you'll spend two weeks learning linear algebra and multivariable calculus before giving up. The reason for this is that most introductory material for machine learning isn't geared towards developers, it's geared to ML researchers--and this is an issue for developers who just want to build products with machine learning.

I was super happy that I had the opportunity to present at a world class Machine Learning event in Warsaw, Poland. People from research organizations from all over the world attended ML in PL. I had been looking forward to all of the deeply technical talks, but I was grateful to the organizers that we could start the day by taking a step back and reflecting a bit on the ethics of what we do. It's an important topic and doesn't receive the attention that it should. As Machine Learning people, we work on technologies that are super powerful.

This talk was presented at PyBay2019 - 4th annual Bay Area Regional Python conference. Description Through a series of case studies, I will illustrate different types of algorithmic bias, debunk common misconceptions, and share steps towards addressing the problem. About the speaker Rachel Thomas is a professor at the University of San Francisco Data Institute and co-founder of fast.ai, She was selected by Forbes as one of 20 Incredible Women in AI, earned her math PhD at Duke, and was an early engineer at Uber. Rachel is a popular writer and keynote speaker.

Stanford Libraries will host the 2nd International Conference on AI for Libraries, Archives, and Museums over three days, December 4, 5 & 6, 2019. The first'Fantastic Futures' conference, which took place in December 2018 at the National Library of Norway in Oslo, initiated a community-focused approach to addressing the challenges and possibilities for libraries, archives, and museums in the era of artificial intelligence. The Stanford conference will expand that charge, adding to the plenary gathering a full day of workshops and a half day'unconference' shaped by the interests of those assembled. Wednesday, December 4, will be a day of plenary sessions to introduce attendees to a range of topics in AI, from the concerns of algorithmic bias and data privacy to the exciting developments in transforming discovery and digital content curation (see the full program). The two keynote addresses reflect Stanford Library's position as an academic center in close proximity to Silicon Valley: Bryan Catanzaro, the Vice President of Applied Deep Learning at Nvidia, will speak to the important contribution he thinks libraries can make in AI.

By Rachel Thomas, Co-founder at fast.ai Editor's note: This is part 3 in a series. To announce Google's AutoML, Google CEO Sundar Pichai wrote, "Today, designing neural nets is extremely time intensive, and requires an expertise that limits its use to a smaller community of scientists and engineers. That's why we've created an approach called AutoML, showing that it's possible for neural nets to design neural nets. We hope AutoML will take an ability that a few PhDs have today and will make it possible in three to five years for hundreds of thousands of developers to design new neural nets for their particular needs."

By Rachel Thomas, Co-founder at fast.ai Q: What 3 things would you most like the general public to know about AI?

By Rachel Thomas, Co-founder at fast.ai. An all-too-common scenario: a seemingly impressive machine learning model is a complete failure when implemented in production. The fallout includes leaders who are now skeptical of machine learning and reluctant to try it again. One of the most likely culprits for this disconnect between results in development vs results in production is a poorly chosen validation set (or even worse, no validation set at all). Depending on the nature of your data, choosing a validation set can be the most important step.

Most of us have come across a form of bias when we interact with others. These biases can make their way to a machine learning system, leading to unfair decisions. Rachel Thomas, co-founder of fast.ai and researcher in residence at The University of San Francisco explains the origins and implications of bias in machine learning. We also talked about solutions to limit bias. Rachel also explained the role of linear algebra in machine learning and how to teach it effectively for people working in ML applications.

When Sahil Singla joined the social impact startup Farmguide, he was shocked to discover that thousands of rural farmers in India commit suicide every year. When harvests go awry, desperate farmers are forced to borrow from microfinance loan sharks at crippling rates. Unable to pay back these predatory loans, victims kill themselves – often by grisly methods like swallowing pesticides – to escape the threats and violence of their ruthless debt collectors. Singla and his team are tackling this social injustice with one unexpected but powerful tool: deep learning. Recent growth of computational power and structured data sets has allowed deep learning algorithms to achieve extraordinary results.