What do you do if decisions that used to be made by humans, with all their biases, start being made by algorithms that are mathematically incapable of bias? If you’re rational, you should celebrate. If you’re a militant liberal, you recognize this development for the mortal threat it is, and scramble to take back control.
You can see this unfolding at AI conferences. Last week I attended the 2020 edition of NeurIPS, the leading international machine learning conference. What started as a small gathering now brings together enough people to fill a sports arena. This year, for the first time, NeurIPS required most papers to include a ‘broader impacts’ statement, and to be subject to review by an ethics board. Every paper describing how to speed up an algorithm, for example, now needs to have a section on the social goods and evils of this obscure technical advance. ‘Regardless of scientific quality or contribution,’ stated the call for papers, ‘a submission may be rejected for… including methods, applications, or data that create or reinforce unfair bias.’ Read More
Daily Archives: December 24, 2020
2020 in Review: 10 AI Podcasts You Need to Know
The term “podcast” first appeared in the ‘00s, coined by a British journalist as a portmanteau of “iPod” and “broadcast.” Podcasts have since evolved into a popular entertainment and information source, and with 2020 emptying offices and curtailing nights out at the club or cinema, podcasts have become more attractive than ever.
… Synced has selected 10 AI-related podcasts for readers to check out over the holiday season. Read More
Advanced Analytics and AI: Two Divergent But Synergistic Capabilities
Two commonly cited use cases for advanced analytics across financial services entail risk management and fraud detection; specifically, the use of advanced analytics to detect and reduce incidents of false positives.
Many financial services institutions (FSIs) are still working on optimizing these solutions and contrary to what some may believe, artificial intelligence has not rendered advanced analytics obsolete. In fact, many robust AI solutions rely on insights weaned through advanced analytics, and those organizations that are not yet ready to hand off their reins entirely to AI may find solace in mastering advanced analytics first. Read More
DeepMind researchers claim neural networks can outperform neurosymbolic models
So-called neurosymbolic models, which combine algorithms with symbolic reasoning techniques, appear to be much better-suited to predicting, explaining, and considering counterfactual possibilities than neural networks. But researchers at DeepMind claim neural networks can outperform neurosymbolic models under the right testing conditions. In a preprint paper, coauthors describe an architecture for spatiotemporal reasoning about videos in which all components are learned and all intermediate representations are distributed (rather than symbolic) throughout the layers of the neural network. The team says that it surpasses the performance of neurosymbolic models across all questions in a popular dataset, with the greatest advantage on the counterfactual questions. Read More
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