Researchers at Washington University in St. Louis are developing a new imaging technique that can reportedly provide accurate, real-time, computer-aided diagnosis of colorectal cancer. Using deep learning, a type of machine learning, the team used the technique on more than 26,000 individual frames of imaging data from colorectal tissue samples to determine the method's accuracy. Compared with pathology reports, they were able to identify tumors with 100% accuracy in this pilot study. This is the first report ("Real-time colorectal cancer diagnosis using PR-OCT with deep learning") using this type of imaging combined with machine learning to distinguish healthy colorectal tissue from precancerous polyps and cancerous tissue. Results appear in advance online publication in the journal Theranostics.

The speed of AI progress is accelerating at breakneck speed. This year, we saw some very cool industry breakthroughs with AI - and we're excited to share them with you. The objective of Artificial Intelligence is to enhance the ability of machines to process copious amounts of data and by doing so, automate a broad range of tasks. Despite this benign objective, AI also lends itself to nefarious ends, and in our increasingly digitising world, AI has the potential to cause an unprecedented degree of damage. In the same way that human intelligence can be used towards positive, benign or detrimental purposes, so can artificial intelligence.

Analytics India Magazine is dedicated to championing and promoting the analytics and AI ecosystem in India. As a part of this process, each year AIM produces and publishes numerous videos ranging from events, interviews and web-series, to explainers, deep dives and news. In this article, we list down the top 10 videos produced by Analytics India Magazine in 2019. About: Analytics India Guru: What is a Convolutional Neural Network is a Hindi explainer video which helps you understand Convolutional Neural Network by digging deeper into understanding what is CNN and how does it work. The video includes interactive visuals which help you understand the basic of CNN in an easy manner.

In particular, the more potential inputs you have to an algorithm, the more out of control your scaling problem gets when analyzing its problem space. This is where MACH, a research project authored by Rice University's Tharun Medini and Anshumali Shrivastava, comes in. MACH is an acronym for Merged Average Classifiers via Hashing, and according to lead researcher Shrivastava, "[its] training times are about 7-10 times faster, and... memory footprints are 2-4 times smaller" than those of previous large-scale deep learning techniques. In describing the scale of extreme classification problems, Medini refers to online shopping search queries, noting that "there are easily more than 100 million products online." This is, if anything, conservative--one data company claimed Amazon US alone sold 606 million separate products, with the entire company offering more than three billion products worldwide.

As we come to the end of 2019, we reflect on a year whose start already saw 100 machine learning papers published a day and its end looks to see a record-breaking funding year for AI. But the path getting real value from data science and AI can be a long and difficult journey. To paraphrase Eric Beinhocker from the Institute for New Economic Thinking, there are physical technologies that evolve at the pace of science, and social technologies that evolve at the pace at which humans can change -- much slower. Applied to the domain of data science and AI, the most sophisticated deep learning algorithms or the most robust and scalable real-time streaming data pipelines ('physical technology') mean little if decisions are not effectively made, organizational processes actively hinder data science and AI, and AI applications are not adopted due to lack of trust ('social technology'). With that in mind, my predictions for 2020 attempt to balance both aspects, with an emphasis on real value for companies, and not just'cool things' for data science teams.

Deepfakes are spreading fast, and while some have playful intentions, others can cause serious harm. We stepped inside this deceptive new world to see what experts are doing to catch this altered content. Chances are you've seen a deepfake; Donald Trump, Barack Obama, and Mark Zuckerberg have all been targets of the computer-generated replications. A deepfake is a video or an audio clip where deep learning models create versions of people saying and doing things that have never actually happened. A good deepfake can chip away at our ability to discern fact from fiction, testing whether seeing is really believing.

Introduced in 2014, GANs (Generative Adversarial Networks) was first presented by Ian Goodfellow and other researchers at the University of Montreal. It comprises of two deep networks, the generator which generates data instances, and the discriminator which evaluates the data for authenticity. GANs works not only as a form of generative model for unsupervised learning, but also has proved useful for semi-supervised learning, fully supervised learning, and reinforcement learning. In this article, we are in conversation with Greg Walters, one of the authors of the book'Hands-On Generative Adversarial Networks with PyTorch 1.x', where we discuss some of the real-world applications of GANs. According to Greg, facial recognition and age progression will one of the areas where GANs will shine in the future.

On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game. The game of Dota 2 presents novel challenges for AI systems such as long-time horizons, imperfect information, and complex, continuous state-action spaces, all challenges which will become increasingly central to more capable AI systems.

Deep learning will radically change aspects of our medical care. How well do we need to understand how AI tools work? In clinics around the world, a type of artificial intelligence called deep learning is starting to supplement or replace humans in common tasks such as analyzing medical images. Already, at Massachusetts General Hospital in Boston, "every one of the 50,000 screening mammograms we do every year is processed through our deep learning model, and that information is provided to the radiologist," says Constance Lehman, chief of the hospital's breast imaging division. In deep learning, a subset of a type of artificial intelligence called machine learning, computer models essentially teach themselves to make predictions from large sets of data.

Are you interested in the field of Deep Learning? Here is the short and useful Review of Deep Learning A-Z Hands-On Artificial Neural Networks. If you are in the intermediate level people who know the basics of Deep Learning and Machine Learning, including the classical algorithms like linear regression or logistic regression and more advanced topics like Artificial Neural Networks, but who want to learn more about it and explore all the different fields of Deep Learning. This is one of the Best Seller courses on Udemy where students enrolled more than 157K with 21K reviews and 4.5 average star rating. With this top-selling Deep Learning tutorial, you will learn how to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts.