Cortex just released V 0.10, which includes their new Predictor Interface for serving models. It lets you take models from any framework and implement them in simple Python, before deploying them with a single terminal command. V 0.10 also still includes out-of-the-box support for TensorFlow Serving and ONNX Runtime.

Researchers say computer systems are learning from lots and lots of digitized books and news articles that could bake old attitudes into new technology. Researchers say computer systems are learning from lots and lots of digitized books and news articles that could bake old attitudes into new technology. Researchers say computer systems are learning from lots and lots of digitized books and news articles that could bake old attitudes into new technology. Researchers say computer systems are learning from lots and lots of digitized books and news articles that could bake old attitudes into new technology. Last fall, Google unveiled a breakthrough artificial intelligence technology called BERT that changed the way scientists build systems that learn how people write and talk.

It's been 7 months since my last commentary on the field, and as it became regular appearance in this blog (and in fact many people apparently enjoy this form and keep asking for it), it is a time for another one. For those new to the blog, here we generally strip the AI news coverage out of fluff and try to get to the substance, often with a fair dose of sarcasm and cynicism. The more pompous and grandiose the PR statement, the more sarcasm and cynicism - just to provide some balance in nature. The field of AI never fails to deliver on pompous and grandiose fake news hence I predict there will be a material for this blog for many years to come. Now that the introductory stuff is behind and you've been warned, let us go straight to what happened in the field since May 2019.

This repository contains my personal notes and summaries on DeepLearning.ai I've enjoyed every little bit of the course hope you enjoy my notes too. This is by far the best course series on deep learning that I've taken. If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Today, artificial intelligence is mostly about artificial neural networks and deep learning. But this is not how it always was. In fact, for most of its six-decade history, the field was dominated by symbolic artificial intelligence, also known as "classical AI," "rule-based AI," and "good old-fashioned AI." Symbolic AI involves the explicit embedding of human knowledge and behavior rules into computer programs. The practice showed a lot of promise in the early decades of AI research.

As we already know, large and effective deep learning models are data-hungry. They require training with thousands or even millions of data points before making a plausible prediction. Training is very expensive, both in time and resources. For example, the popular language representation model BERT, developed by Google, has been trained on 16 Cloud TPUs (64 TPU chips total) for 4 days. Put in perspective, this is 60 desktop computers running non-stop for 4 days.

Despite the success of supervised machine learning and deep learning, there's a school of thought that says that unsupervised learning has even greater potential. The learning of a supervised learning system is limited by its training; i.e., a supervised learning system can learn only those tasks that it's trained for. By contrast, an unsupervised system could theoretically achieve "artificial general intelligence," meaning the ability to learn any task a human can learn. If the biggest problem with supervised learning is the expense of labeling the training data, the biggest problem with unsupervised learning (where the data is not labeled) is that it often doesn't work very well. Nevertheless, unsupervised learning does have its uses: It can sometimes be good for reducing the dimensionality of a data set, exploring the pattern and structure of the data, finding groups of similar objects, and detecting outliers and other noise in the data.

KP Labs, a NewSpace company based in Poland, specializes in machine learning applications for the space industry and hyperspectral imagery acquisition and processing. Its flagship product is the Intuition-1 satellite mission. It is 6U satellite, equipped with a hyperspectral optical instrument and an on-board computer "Leopard" that advanced data processing through convolutional neural networks. In downstream, the company concentrates on the use of hyperspectral images for industrial and agricultural purposes. For this application, it has developed a lightweight, hyperspectral imager "Zebra" A few months back KP Labs signed a contract with ACC Clyde Space for the delivery of a satellite bus and placing the satellite into low Earth orbit. The launch of Intuition-1 is expected by 2023.

While the majority of us are'wow'ing the early applications of machine learning, it continues to evolve at quite a promising pace, introducing us to more advanced algorithms like Deep Learning. This branch, by the way, is attracting even more attention than all other ML-algorithms combined. Of course, I don't have to declare it. It is simply great in terms of accuracy when trained with a huge amount of data. Also, it plays a significant role to fill the gap when a scenario is challenging for the human brain.

Deep learning has proved its supremacy in the world of supervised learning, where we clearly define the tasks that need to be accomplished. But, when it comes to unsupervised learning, research using deep learning has either stalled or not even gotten off the ground! There are a few areas of intelligence which our brain executes flawlessly, but we still do not understand how it does so. Because we don't have an answer to the "how", we have not made a lot of progress in these areas. If you liked my previous article on the functioning of the human brain to create machine learning algorithms that solve complex real world problems, you will enjoy this introductory article on Hierarchical Temporal Memory (HTM). I believe this is the closest we have reached to replicating the underlying principles of the human brain. In this article, we will first look at the areas where deep learning is yet to penetrate.