Here are five important AI stories from the week.
Today’s machine learning applications need a lot of labeled data to have good performance, but most of the world’s data is not labeled. For machine learning to advance, algorithms will need to learn from unlabeled data and make sense of the world from pure observation, much like how children learn to operate in the real world after birth without too much guidance.
According to Yann LeCun, one of the fathers of machine learning and currently the chief AI scientist at Facebook, the future of machine learning will be driven by unsupervised or self-supervised learning systems. For more, please turn to this article in the MIT Technology Review or my book on unsupervised learning.
Compared to a few years ago, solutions for a lot of the common machine learning tasks have been commoditized; companies have built robust solutions to help developers set up cloud infrastructure for machine learning, acquire data to train their models, clean and prepare the data, apply machine learning algorithms and perform hyper-parameter tuning, and deploy their trained models. The best way for startups to provide value in machine learning is not by trying to reinvent what has already been commoditized but rather by developing domain-specific solutions to high value business problems. In other words, let’s focus obsessively on solving the business problem not just on the latest and greatest tech.
Most people consume machine learning applications throughout the day without ever realizing it. Machine learning is not some high tech that will come in the future; it’s already here. This articles explores machine learning applications in smartphones, transportation, web services, sales and marketing, security, and finance.
Amazon is one of the leaders in machine learning today, and it recognizes just how disruptive the technology will be to the existing labor force. In preparation, Amazon has set aside $700 million to retrain a third of its U.S. workforce — nearly 100,000 people. Non-corporate workers will be transitioned to IT support roles and non-technical corporate workers will retrain as software engineers.
Companies that build computer vision applications need lots and lots of photos to power their facial recognition technology. Over the past several years, these companies have crawled photos online to build these massive datasets and, in some cases, installed cameras in public spaces to capture this data. This article does a great job exploring just how that data gets collected and used, often without consent from the users.
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