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Who Should Stop Unethical A.I.?

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In computer science, the main outlets for peer-reviewed research are not journals but conferences, where accepted papers are presented in the form of talks or posters. In June, 2019, at a large artificial-intelligence conference in Long Beach, California, called Computer Vision and Pattern Recognition, I stopped to look at a poster for a project called Speech2Face. Using machine learning, researchers had developed an algorithm that generated images of faces from recordings of speech. A neat idea, I thought, but one with unimpressive results: at best, the faces matched the speakers’ sex, age, and ethnicity attributes that a casual listener might guess. That December, I saw a similar poster at another large A.I. conference, Neural Information Processing Systems (Neur ....

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Artificial Intelligence Ecosystem: Ethics to Exascale

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Artificial Intelligence Ecosystem: Ethics to Exascale
Artificial intelligence, or AI, has become a household word. Once reserved for research and national security circles, AI now permeates our everyday existence.
From smart appliances and social media to medical research and electricity delivery, the applications for AI seem limitless.
In recent forums, multidisciplinary experts from Pacific Northwest National Laboratory (PNNL) joined other international leaders to discuss opportunities, as well as challenges, for the future of neural information processing-the foundation for AI.
Svitlana Volkova (top left) joined a panel with scientists from Amazon AI, Facebook, Apple, and Carnegie Mellon University to discuss the state of NLP research, as well as challenges and opportunities presented by the COVID-19 outbreak. (Image capture by Svitlana Volkova | Pacific Northwest National Laboratory.) ....

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Who needs a teacher? Artificial intelligence designs lesson plans for itself

Who needs a teacher? Artificial intelligence designs lesson plans for itself
Jan. 19, 2021 , 3:15 PM
Unlike human students, computers don’t seem to get bored or frustrated when a lesson is too easy or too hard. But just like humans, they do better when a lesson plan is “just right” for their level of skill. Coming up with the right curricula isn’t easy, though, so computer scientists wondered: What if they could make machines design their own?
That’s what researchers have done in several new studies, creating artificial intelligence (AI) that can figure out how best to teach itself. The work could speed learning in self-driving cars and household robots, and it might even help crack previously unsolvable math problems. ....

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Supporting Responsible Use Of AI And Equitable Outcomes In Financial Services – Federal Reserve Governor Lael Brainard At The AI Academic Symposium Hosted By The Board Of Governors Of The Federal Reserve System, Washington, D.C. (Virtual Event)

Supporting Responsible Use Of AI And Equitable Outcomes In Financial Services â Federal Reserve Governor Lael Brainard At The AI Academic Symposium Hosted By The Board Of Governors Of The Federal Reserve System, Washington, D.C. (Virtual Event)
Date
12/01/2021
Today s symposium on the use of artificial intelligence (AI) in financial services is part of the Federal Reserve s broader effort to understand AI s application to financial services, assess methods for managing risks arising from this technology, and determine where banking regulators can support responsible use of AI and equitable outcomes by improving supervisory clarity.1
The potential scope of AI applications is wide ranging. For instance, researchers are turning to AI to help analyze climate change, one of the central challenges of our time. With nonlinearities and tipping points, climate change is highly complex, and quantification for risk assessments requires the analysis of vast amounts o ....

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Incremental Risk Minimization Algorithm

Incremental Risk Minimization Algorithm
Incremental Regression with Polynomials ↑
Incremental (or on-line) learning regression is the process of adapting a model one example at a time without accumulating a batch of data. It has the advantages of allowing continuous adaptation to non-stationary environments, easily handling big data through stream processing, and a fixed low computation and memory demand.
The easiest solution is to perform a gradient descent on a squared error metric with each new training example. But this solution does not work well for complex model structures. Especially, the influence of a non-linear transformation of the inputs through a fixed model structure has long been an open problem. During my PhD I worked on an approach which is able to deal with a broad class of non-linear model structures. Its emphasis is on minimizing the effect of local training examples on changes of the global model. Thus, it yields a robust behavio ....

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