Ivan Ortega

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How AI Fails Us

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Divya Siddarth, Daron Acemoglu, Danielle Allen, Kate Crawford, James Evans, Michael Jordan, E. Glen Weyl The dominant vision of artificial intelligence imagines a future of large-scale autonomous systems outperforming humans in an increasing range of fields. This “actually existing AI” vision misconstrues intelligence as autonomous rather than social and relational. It is both unproductive and dangerous, optimizing for artificial metrics of human replication rather than for systemic augmentation, and tending to concentrate power, resources, and decision-making in an engineering elite.  Alternative visions based on participating in and augmenting human creativity and cooperation have a long history and underlie many celebrated digital technologies such as personal computers and the internet.  Researchers and funders should redirect focus from centralized autonomous general intelligence to a plurality of…

The Turing Test Is Bad for Business

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Originally posted on Wired: Written by Daron Acemoglu, Michael I. Jordan, and E. Glen Weyl on November 8, 2021 FEARS OF ARTIFICIAL intelligence fill the news: job losses, inequality, discrimination, misinformation, or even a superintelligence dominating the world. The one group everyone assumes will benefit is business, but the data seems to disagree. Amid all the hype, US businesses have been slow in adopting the most advanced AI technologies, and there is little evidence that such technologies are contributing significantly to productivity growth or job creation. This disappointing performance is not merely due to the relative immaturity of AI technology. It also comes from a fundamental mismatch between the needs of business and the way AI is currently being conceived by many in the technology…

Secure computation: Homomorphic encryption or hardware enclaves?

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Originally posted on Medium: Written by Raluca Ada Popa on September 16, 2021 How to collaborate with confidential data without sharing it. Secure computation has become increasingly popular for protecting the privacy and integrity of data during computation. The reason is that it provides two tremendous advantages. The first advantage is that it offers “encryption in use” in addition to the already existing “encryption at rest” and “encryption in transit”. The “encryption in use” paradigm is important for security because “encryption at rest” protects that data only when it is in storage and “encryption in transit” protects the data only when it is being communicated over the network, but in both cases the data is exposed during computation, namely, while it…

EPIC Lab receives $2M NSF grant to build tools for criminal justice big datasets

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Originally posted on EECS NEWS: Submitted by Magdalene L. Crowley on September 7, 2021 – 1:55pm EPIC Lab receives $2M NSF grant to build tools for criminal justice big datasets CS Prof. Joseph Hellerstein, and Assistant Profs. Aditya Parmeswaran and Sarah Chasins, are among the principal investigators of a new lab that has just received a $2M grant from the National Science Foundation to make big datasets used by the criminal justice system more accessible to non-technical researchers. The Effective Programming, Interaction, and Computation with Data (EPIC) Lab will create tools that utilize machine learning, program synthesis, and human-centered design, to improve the ability of public defenders, investigators and paralegals to research police misconduct, judicial decision-making, and related issues, for their…