You might have seen a recent article from The Guardian written by "a robot." "I know that my brain is not a'feeling brain.' But it is capable of making rational, logical decisions. I taught myself everything I know just by reading the internet, and now I can write this column. My brain is boiling with ideas!" Read the whole thing and you may be astonished at how coherent and stylistically consistent it is.

Scientific workloads have traditionally exploited high levels of sparsity to accelerate computation and reduce memory requirements. While deep neural networks can be made sparse, achieving practical speedups on GPUs is difficult because these applications have relatively moderate levels of sparsity that are not sufficient for existing sparse kernels to outperform their dense counterparts. In this work, we study sparse matrices from deep learning applications and identify favorable properties that can be exploited to accelerate computation. Based on these insights, we develop high-performance GPU kernels for two sparse matrix operations widely applicable in neural networks: sparse matrix-dense matrix multiplication and sampled dense-dense matrix multiplication. Using our kernels, we demonstrate sparse Transformer and MobileNet models that achieve 1.2-2.1x

It is not news that machine learning and deep learning is expensive. While the business value of incorporating AI into organizations is extremely high, it often does not offset the computation cost needed to apply these models into your business. Machine learning and deep learning are very compute-intensive, and it has been argued that until cloud or on-premises computing costs decrease -- AI innovation will not be worth the cost, despite its unprecedented business value. In an article on WIRED, Neil Thompson, a research scientist at MIT and author of "The Computational Limits of Deep Learning" mentions numerous organizations from Google to Facebook that have built high-impact, cost-saving models that go unused due to computational cost making the model not profitable. In some recent talks and papers, Thompson says, researchers working on particularly large and cutting-edge AI projects have begun to complain that they cannot test more than one algorithm design, or rerun an experiment because the cost is so high.

What in the name of Paypal and/or Palantir did you just say about me, you filthy degenerate? I'll have you know I'm the Crown Prince of Silicon Valley, and I've been involved in numerous successful tech startups, and I have over $1B in liquid funds. I've used that money to promote heterodox positions on human enhancement, control political arenas, and am experimenting with mind uploading. I'm also trained in classical philosophy and was recently ranked the most influential libertarian in the world by Google. You are nothing to me but just another alternative future. I will wipe you out with a precision of simulation the likes of which has never been seen before, mark my words.

How to understand the history of artificial intelligence in the popular press in five easy steps - 1. This technology is amazing! 2. We thought it was amazing, but it's actually terrible! We've moved on to something else. 5. Repeat. I've seen this for data mining, big data, machine learning and deep learning. What's the next AI technology that will be run through the cycle?

OpenAI's new immensely convincing language generator, GPT-3, recently demonstrated its rhetorical prowess when it argued the case for why it's harmless. Now, research scientist Janelle Shane has used the tool to generate something a bit more lighthearted. Namely, ideas on how to make nuclear waste sites safe for thousands upon thousands of years. Are you not terrified and repulsed?? I prompted GPT-3 with some human proposals for marking a nuclear waste site, in a way that will still be forbidding millennia from now.https://t.co/3v8uPJ98mo

For several years, there has been a lot of discussion around AI's capabilities. Many believe that AI will outperform humans in solving certain areas. As the technology is in its infancy, researchers are expecting human-like autonomous systems in the next coming years. OpenAI has a leading stance in the artificial intelligence research space. Founded in December 2015, the company's goal is to advance digital intelligence in a way that can benefit humanity as a whole.

Deep Learning and Machine Learning are one of the hottest tech fields to be in right now! The field is exploding with opportunities and career prospects. Machine/Deep Learning techniques are widely used in several sectors nowadays such as banking, healthcare, transportation and technology. Machine learning is the study of algorithms that teach computers to learn from experience. Through experience (i.e.: more training data), computers can continuously improve their performance. Deep Learning is a subset of Machine learning that utilizes multi-layer Artificial Neural Networks. Deep Learning is inspired by the human brain and mimics the operation of biological neurons. A hierarchical, deep artificial neural network is formed by connecting multiple artificial neurons in a layered fashion. The more hidden layers added to the network, the more

An artificial intelligence equipped robot named'Curly' beat one of the world's best curling teams by quickly adapting to changes in the ice, its developers claim. The sport of curling involves constantly changing and uncontrollable environmental conditions - providing the perfect'test bed' for an AI-driven robot. Curly, who delivers the stone but doesn't sweep, won three out of four official matches against the Korean Olympic silver-medal winning women's team. The robot was developed by researchers from Korea University, who said this development narrows the gap between computer simulators and the real world. It's hoped the deep learning techniques developed for Curly could be applied to other robots that need to work'in the real world' and adapt to changing conditions.

Speech Recognition is the ability of a machine or program to identify words and phrases in spoken language and convert them to textual information. You have probably seen it on Sci-fi, and personal assistants like Siri, Cortana, and Google Assistant, and other virtual assistants that interact with through voice. In order to understand your voice these virtual assistants need to do speech recognition. Speech Recognition is a complex process, so I'm not going to teach you how to train a Machine Learning/Deep Learning Model to do that. Instead, I will instruct you how to do it using google speech recognition API.