And implicit bias can show up in other forms of artificial intelligence software. A ProPublica investigation found that software by Northpoint, a consulting and research firm, used to predict the likelihood that criminal defendants would become repeat offenders overestimated risk for Black people and underestimated risk for white people. Black defendants were "77 percent more likely to be pegged as at higher risk of committing a future violent crime" than white defendants, according to the organization's research.
Edge computing provides groundbreaking innovations to enterprise cloud organizations, including nearly instant code transfer, reduced latency, and enhanced performance. The lightning speed of edge compute is due to the placement of the platform. Unlike public cloud, edge compute is placed as close as possible to the point of interaction with humans, electronics, and various connected devices. Edge compute becomes more and more relevant to companies as applications evolve, including virtual reality, augmented reality, and video analytics, which rely on artificial intelligence. With real-time code transfer that AI needs to be extremely precise, and as AI evolves, every millisecond counts, according to Paul Savill (pictured), senior vice president of core network and technology solutions at CenturyLink Inc.
Easy enough to abstract information from someone's mind, but you'll know you're getting somewhere when you put information "in." Like maybe if you can get a monkey to "get the red ball" and they routinely do after having the thought put in their mind. Or for human trials have then be given a question they could know the answer to if the thought insertion worked. You shouldn't be trying to get a brain and a computer to work directly in tandem. Not at all compatible, but you can translate thoughts into computer code, have the computer do the processing and then insert the thought back.
This is getting really crazy... I wonder if a discussion about this topic with both of them is possible. Something where all the evidence is presented and discussed. While I feel like there is a lot of damning evidence I feel like we mostly hear about the Schmidhuber side of things on this subreddit. I would like to hear what Bengio et al. have to say for themselves.
In general, the hyperparams are related - if you perturb one hyperparam, you need to perturb some other hyperparams also to get satisfactory results. Some people do a random search on their hyperparam grid but if one hyperparam is very sensitive to changes in the other hyperparams, then the search will be more difficult. Personally, I've had OK results using Cyclic Learning Rate together with batchnorm and only have 3 values for the max-learning-rate hyperparam in my hyperparam grid. However, you probably won't find many papers on CLR because its efficacy and the details of the right way to use it is probably quite problem-specific and there's very little theory behind it even by deep-learning standards.
The AI Index Report tracks, collates, distills, and visualizes data relating to artificial intelligence. Its mission is to provide unbiased, rigorously-vetted data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI. Expanding annually, the Report endeavors to include data on AI development from communities around the globe.
I called for "call for contributions" recently, but it didn't end well. People were too obsessed with keeping their secrets and know little outside of ML. So I searched myself for challenging problems in science, with high meaningful impact, potential for ML to make breakthrough, ready dataset and benchmark, and I found this ProteinNet for protein folding. These scientists seem to think for the sake of science as a whole, and want to see how ML can help advance their field. You are welcome to use it for your side project if you are already tired of old time CV or NLP tutorials.