With much of the latest discussion focused on the latest techniques in machine learning and in particular deep learning, the significant benefits of machine learning and deep learning are now a public reality. Yet, machine learning in effect represents the predictive analytics techniques that have been used for many years by data scientists. Furthermore, data scientists and their end users have always recognized the huge economic advantages of predictive analytics. But the significant advances of deep learning in the last 5 years have just expanded the application of predictive analytics to other areas which were technically not feasible at the time. The market for these solutions is huge and the competition is fierce.

Researchers from NVIDIA, led by Guilin Liu, introduced a state-of-the-art deep learning method that can edit images or reconstruct a corrupted image, one that has holes or is missing pixels. The method can also be used to edit images by removing content and filling in the resulting holes.

Source: What AI can really do for your business (and what it can't) InfoWorld. Artificial intelligence, machine learning, and deep learning are no silver bullets. A CIO explains what every business should know before investing in AI. How can you tell whether an emerging technology such as artificial intelligence is worth investing time into when there is so much hype being published daily? We're all enamored by some of the amazing results such as AlphaGo beating the champion Go player, advances in autonomous vehicles, the voice recognition being performed by Alexa and Cortana, and the image recognition being performed by Google Photos, Amazon Rekognition, and other photo-sharing applications.

Hitherto the present, there has been a post floating around the internet detailing multiple "types" of artificial intelligence, purportedly written by someone named "Yuli Ban". If you see this post, know that it wasn't written by me at all, absolutely not, I take no responsibility for the cringy contents of that post, and you are likely remembering something that never existed or perhaps was written by my evil twin, Tali. In all seriousness, I've been meaning to update that post for a while now thanks to some greater understanding of how AI works. I recall mentioning how it was a smorgasbord of buzzwords without much meaning, written by someone in 2016 with no experience in AI whatsoever. This one, I hope, provides greater usefulness. Artificial intelligence has a problem: no one can precisely tell you what it is supposed to be.

In the last post I tried to determine what is creating the AI Buzz now? The answer to that was development of Machine Learning using Deep Learning techniques vis-à-vis traditional brute force approach to programming. It was also aided by development of Natural Language Parsing/Processing, referred commonly as NLP. In this post I will try to explain the concept of Machine Learning using Deep Learning. To understand this concept simply, assume that computer program is a child that is to be educated. How was / would knowledge imparted when there were no books?

Microscope images are information rich. In this issue of Cell, Christiansen et al. show that label-free images of cells can be used to predict fluorescent labels representing cell type, state, and organelle distribution using a deep-learning framework. This paves the way for computationally multiplexed assays derived from inexpensive label-free microscopy.

We present below initial performance data from multiple frameworks that reflects the optimizations done so far on the IA transformer. On the latest Intel Xeon Platinum 8180 processor, in conjunction with MKLDNN v0.13, we are able to meet or greatly exceed the performance of previously optimized frameworks such as MXNet-MKLDNN-CPU (MXNet optimized with MKLDNN) and neon-MKLML-CPU (neon optimized with MKLML). We also deliver better performance than the TensorFlow XLA compiler (TF-XLA-CPU), but there are significantly more optimizations that can be done with XLA both on the default CPU implementation and on nGraph.

I am sure that almost everybody who is reading this article has heard about deep learning before. For example, you could have read some article about how convolutional neural networks work (CNN), or maybe it was about recurrent neural nets (RNN). If it is your case, well, you already know the science of deep learning and the theoretical part of it. If, on the other hand, you never heard or read some article about that before, don't worry, it is a good one to start on this beautiful world. In contrast with the previous paragraph, I am also sure, that more than one of you have never heard about the data engineering existing before every machine learning project in the real world.

Reinforcement learning is an important type of Machine Learning where an agent learn how to behave in a environment by performing actions and seeing the results. In recent years, we've seen a lot of improvements in this fascinating area of research. Examples include DeepMind and the Deep Q learning architecture in 2014, beating the champion of the game of Go with AlphaGo in 2016, OpenAI and the PPO in 2017, amongst others. In this series of articles, we will focus on learning the different architectures used today to solve Reinforcement Learning problems. These will include Q -learning, Deep Q-learning, Policy Gradients, Actor Critic, and PPO.

Recent blog posts by Andrej Karpathy at Medium.com and Pete Warden at PeteWarden.com have caused a paradigm shift in the way I think about neural nets. Instead of thinking of them as powerful machine learning tools, the authors instead suggest that we should think of neural nets, and in particular, convolution deep nets, as'self-writing programs.' It turns out that a large portion of real-world problems have the property that it is significantly easier to collect the data than to explicitly write the program. A large portion of programmers of tomorrow do not maintain complex software repositories, write intricate programs, or analyze their running times. They collect, clean, manipulate, label, analyze and visualize data that feeds neural networks.