Last Week in AI #95

Making AI more energy efficient, regulating AI hiring tools, and more!

Mini Briefs

Tiny four-bit computers are now all you need to train AI

In a recent paper published at NeurIPS, the largest annual AI conference, researchers from IBM unveiled an algorithm that can train deep neural networks with only 4-bit precision numbers. For context, neural networks can be trained with numbers of varying precisions, from double (64 bit) to single (32 bit), and more recently half (16 bit). "Precision" roughly corresponds to how many possible numbers each variable in the neural network can represent, and consequently how much memory and computational resources the neural network needs. With 4 bits, each variable can only take on 2^4 = 16 values, which is tiny compared to 2^16 = 65536 values for half precision training.

The IBM researchers simulated 4-bit precision training on a variety of deep learning tasks, from image recognition to natural language processing. Their algorithm did not perform significantly worse than half-precision training and was 7 times faster and more energy efficient. While 4-bit precision training in the real world will not happen for another few years (this requires 4-bit precision hardware, which is in development), this is still an exciting result with many potential applications. Low-precision training could make deep learning more resource and energy efficient.

It could also make training powerful AI models possible on smartphones and other small devices, which would improve privacy by helping to keep personal data on a local device. And it would make the process more accessible to researchers outside big, resource-rich tech companies.

New York City Considers Regulating AI Hiring Tools

AI hiring tools have gained popularity in recent years. While they have the potential to make the hiring process more efficient and effective for both employers and job-seekers, there are real concerns over recent "cases of discrimination based on gender, race and disability during candidate sourcing, screening, interviewing and selecting using automated tools." As such, New York City Council introduced a bill that would require employers inform candidates if automated-decision systems were used and also require AI technology vendors to provide bias audits before the products can be sold. If passed, the bill would take effect in 2022.

Julia Stoyanovich, professor of computer science at New York University's Center for Responsible AI, suggests that employers should also allow "the job applicant to understand, and, if necessary, correct and contest the information" given to automated hiring systems. With regard to the bill, she notes how "if left unchecked, automated hiring tools will replicate, amplify and normalize results of historical discrimination in hiring and employment."

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Expert Opinions & Discussion within the field

  • On the Moral Collapse of AI Ethics - Innovation and technological progress, in the context of racial capitalism, are predicated on profound extraction so the next disaster is inevitable, our failure to respond and offer an alternative worldview is not. We can do better."

  • Give the A.I. Economy a Human Touch - "Embracing artificial intelligence can help us create a new, equitable social contract - but only if we remember what makes us human. "


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