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Adaptability is one of the key cognitive abilities that defined us as humans. Even as babies, we can intuitively shift between similar tasks even if we don't have prior training on them. This contrasts with the traditional train-and-test approach of most artificial intelligence(AI) systems which require an agent to go through massive amounts of training before it can master a specific task. By definition, train-and-test systems are not very adaptable and, consequently, they are not very applicable to scenarios that operate in real word environments. Improving the adaptability of AI systems has been one of the core areas of research of an increasingly popular discipline known as meta-learning that focuses on improving the learning abilities of AI agents.

An introduction to different methods for Interpretability can be found here. During the ICIAM Theoretical advances of deep learning mini-symposia, there were some talks on interpretability, perhaps the most interesting ones were by Wojciech Samek, Fraunhofer Heinrich Hertz Institute, and by Stephan Waeldchen, Technische Universität Berlin. The first talk debated how LRP can be understood as a deep Taylor decomposition of the prediction. Some more information and tutorials on these can be found on their webpage. One of the methods to study the interpretability of a net is sensitivity analysis. For this, the changes of the gradient are used to decompose the neural net, however, the gradient is unreliable.

An introduction to different methods for Interpretability can be found here. During the ICIAM Theoretical advances of deep learning mini-symposia, there were some talks on interpretability, perhaps the most interesting ones were by Wojciech Samek, Fraunhofer Heinrich Hertz Institute, and by Stephan Waeldchen, Technische Universität Berlin. The first talk debated how LRP can be understood as a deep Taylor decomposition of the prediction. Some more information and tutorials on these can be found on their webpage. One of the methods to study the interpretability of a net is sensitivity analysis. For this, the changes of the gradient are used to decompose the neural net, however, the gradient is unreliable.

In this video, I will show you a step by step guide on how you can compute the derivative of a Sigmoid Function. Sigmoid function is a widely used activation function Deep Learning & Machine Learning. If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer those. If you enjoy these tutorials & would like to support them then the easiest way is to simply like the video & give it a thumbs up & also it's a huge help to share these videos with anyone who you think would find them useful. Please consider clicking the SUBSCRIBE button to be notified for future videos & thank you all for watching.

First of all, apologies for not posting as frequently as I used to. As you might imagine, blogging is not my full time job and I'm currently extremely involved in a very exciting startup (something I'm going to write about soon). On weekends and evening I'm busy with 7mo infant to help care for and altogether that leaves me with very little time. But I'll try to make it better soon, since a lot is going on in the AI space and signs of cooling are visible now all over the place. In this post I'd like to focus on the recent book by Gary Marcus and Ernest Davis, Rebooting AI.

The ACM Symposium on Operating Systems Principles (SOSP) has a long history and a great reputation in Operating Systems (OS) research. This year SOSP was held in Huntsville, a charming town located in lake country, some 200km north of Toronto. On a rainy Sunday, Synced visited Huntsville to check out the SOSP AI System Workshop. The growing and widespread deployment of AI has motivated OS researchers to develop novel system engineering for AI. The SOSP AI System Workshop explored these efforts to advance research in AI and operating systems.

It can take years of birdwatching experience to tell one species from the next. But using an artificial intelligence technique called deep learning, Duke University researchers have trained a computer to identify up to 200 species of birds from just a photo. The real innovation, however, is that the A.I. tool also shows its thinking, in a way that even someone who doesn't know a penguin from a puffin can understand. The team trained their deep neural network -- algorithms based on the way the brain works -- by feeding it 11,788 photos of 200 bird species to learn from, ranging from swimming ducks to hovering hummingbirds. The researchers never told the network "this is a beak" or "these are wing feathers."

The expression "Artificial Intelligence" (AI) is stacked with a lot of suppositions taken from many years of sci-fi motion movies about robots taking control over the world. A long way from being a part of a whimsical anecdote about your microwave creating emotions however, AI is as of now a common truth of modern life. Despite the fact that fantasies of robots who can have our spot at work while we go through our days golfing and going for long snacks might be some distance later on, what computer scientists call "narrow" or "weak" AI is as of now integrated into our everyday lives. Progressively, the issues presented by counterfeiting are on the web. The size of the issue fluctuates by brand, nation and platform, however, plainly a few brands and products are being disproportionately affected by counterfeit online deals such that they wouldn't be by physical shops or merchants.

The expression "Artificial Intelligence" (AI) is stacked with a lot of suppositions taken from many years of sci-fi motion movies about robots taking control over the world. A long way from being a part of a whimsical anecdote about your microwave creating emotions however, AI is as of now a common truth of modern life. Despite the fact that fantasies of robots who can have our spot at work while we go through our days golfing and going for long snacks might be some distance later on, what computer scientists call "narrow" or "weak" AI is as of now integrated into our everyday lives. Progressively, the issues presented by counterfeiting are on the web. The size of the issue fluctuates by brand, nation and platform, however, plainly a few brands and products are being disproportionately affected by counterfeit online deals such that they wouldn't be by physical shops or merchants.