We have actual people being affected by these algorithms. We see things in the news such as algorithms that predict recidivism -- whether someone will re-commit a particular crime -- and set a bail amount or pass that information on to a judge who decides whether or not to set bail. The algorithms used to make these predictions end up relying on correlations with socioeconomic status, or race, or gender. So someone who might have a very similar background to you but differs across race or gender might have a very different outcome because of what the algorithm predicts. Do you think people are generally aware of the degree to which these algorithms are already part of everyday life?

It's very abstract and it's something that deep neural networks can't do even in the simplest form. They can't recognize same… If you give them an example of something in which two things are the same versus two things are different in general, it can't do that task.

The School of Science has announced that 14 of its faculty members have been appointed to named professorships. The faculty members selected for these positions receive additional support to pursue their research and develop their careers. Riccardo Comin is an assistant professor in the Department of Physics. He has been named a Class of 1947 Career Development Professor. This three-year professorship is granted in recognition of the recipient's outstanding work in both research and teaching.

In this Q&A on Explainable AI, Andrea Brennen speaks with In-Q-Tel's Peter Bronez about descriptive vs. prescriptive models, "white box" vs. "black box" explanation techniques, and why some models are easier to explain than others. Peter also discusses the reproducibility crisis in Psychology and why good experiment design is so important. Peter is a VP on the technical staff at IQT. Could you tell me about your experience with machine learning and AI? PETER: As an undergraduate, I studied econometrics and operations research, so my exposure to machine learning was in the context of designing models of the world that you could test mathematically -- basically, doing hypothesis testing using statistics. Afterwards, I worked at the Department of Defense and used a lot of the same techniques. From there, I went to the private sector and [worked on] social media and data mining in marketing applications, trying to create mathematical models to categorize people, activities, and messages in order to understand them better.

Accurate simulation of cancer-implicated proteins holds enormous promise for basic biomedical science and development of effective therapies, but the high computational cost required has long slowed progress. Recently a multi-institution research team developed a machine learning-based simulation for next-generation supercomputers capable of modeling protein interactions and mutations that play a role in many forms of cancer. Their work on simulating the RAS protein family will be published at SC19 and is a finalist for the Best Paper award. RAS proteins are implicated in roughly one third of cancers, and research to obtain a more detailed understanding of how they interact with the cell's lipid membranes and influence signaling pathways has long been pursued. One way to shortcut the simulations needed and to reduce the computational cost is to use ML to zoom in on areas of interest.

Tech leader Elon Musk is known for sounding the alarm bells on the risks of artificial intelligence. Musk has said that he believes that AI will soon manipulate social media if it hasn't already -- a concern that pales in comparison to his previous predictions of a future humanity governed by an intelligent machine dictator. A year ago, he told Recode Decode that the relative intelligence ratio between such a dictator and the rest of humanity would resemble the ratio between a person and a cat. The great Musk doesn't stand alone in fearing the risks of AI gone wrong. Stephen Hawking and other researchers have said that intelligent machines could become very dangerous.

Philippe Starck is a name that became a brand. From the habitation module on the new international space station to Steve Jobs' yacht, Philippe Starck's list of achievements speaks volumes. I interviewed one of most emblematic figures of design in the Peninsula's bar Felix in Hong Kong–a bar that he designed, of course. Philippe Branche: How do you see the future of design? Philippe Starck: I don't see one.

I have been trying yo reproduce the results of a SOTA paper regarding object detection. I have reimplemented their method and trained on the same dataset, based on the paper, however I was not able to achieve their results on the datasets they use for evaluation, no matter what I have tried. Then I also studied their referenced papers and realised that many of them use a train-test split strategy for evaluating their models. This means that they use a part of the evaluation dataset for finetuning their already trained model and then evaluate it on the testing part of the same dataset. In the case of these papers, this fact was explicitly mentioned.

A Chinese wildlife park has sparked outcry after making visitors submit to facial recognition scanning, with one law professor taking it to court. Professor Guo Bing is taking action against Hangzhou safari park, after it replaced its existing fingerprinting system with the new technology. "I [filed this case] because I feel that not only my [privacy] rights are being infringed upon but those of many others," Guo, from Zhejiang University of Sci-Tech, said according to an audio recording of an interview posted by state-run Beijing News. Guo is attempting to force the park to return the money he paid for an annual pass and highlight its misuse of data gathered by the software. A court in Fuyang has accepted his case.

Every technology revolution has a unique inflection point. The spark that ignited the artificial intelligence movement was a statistical data analysis system developed by Jim Goodnight when he was a statistics professor at North Carolina State University 45 years ago. He never imagined that the technology he created to improve crop yields would evolve into sophisticated data analytics software, a precursor to modern day AI. Back then computers could only compute 300 instructions a second and had 8K of memory. Today they can execute 3 billion instructions a second and contain multiple terabytes of memory.