Training deep neural networks regularly is a never-ending challenge in artificial intelligence(AI) projects. If training can be overwhelming for simple machine learning scenarios, can you imagine the efforts for systems such as self-driving vehicles that need to perform a large variety of tasks in real time in constantly-changing environments? Recently, Alphabet's subsidiaries Waymo and DeepMind partnered to find a more efficient process to train self-driving vehicles algorithms and their work took them back to one of the cornerstones of our history as species: evolution. Self-driving vehicles can be categorized as some of the most complex AI systems ever built. They need to operate safely in highly populated cities, they work in incomplete environments in which unknown factors appear real time, they need to perform a large number of intelligence tasks cohesively as a single system and the list of challenges never seems to end.

Moorfields Eye Hospital doctors with no prior artificial intelligence (AI) expertise have developed their own accurate digital diagnosis models for a range of afflictions using Google software. Allowing clinicians who are not AI experts to develop algorithms which can be used to identify potential symptoms in patients could greatly speed up the diagnostic process, leading to earlier detection and treatment of disease. The team used a range of tools from Google Cloud AutoML, software developed specifically for people with limited experience in machine learning technology, to build five diagnostic systems. Medical images including eye scans, chest x-rays and skin lesions were categorised into databases to'train' the systems to provide diagnoses. Four of the five doctor-created models performed as well as diagnostic algorithms developed by AI professionals for simple tasks, making them comparable to state-of-the-art systems according to the study published in medical journal The Lancet Digital Health.

JavaScript library and wanted a project to build up my skills with it! I ended up going with a simple web app for visualising and editing network models which I've named netbrix.ml. I've wanted to build something like this for a while since it seemed like a really good project to improve my web development skills and my understanding of the process of building deep learning models. After reading this post on /r/deeplearning where the writer gives insight into the modular nature of deep learning and gives the analogy of a deep learning'lego set' it gave me the motivation to start work on this with that sort of vision in mind and I've now got a decent working web app! I'm sure there are existing tools similar to this in existence, so I wanted to keep it as simple as possible and not try to over-engineer it. It's meant to be easy and simple to use!

Hyderabad: Researchers from the Indian Institute of Technology Hyderabad (IIT-H) have developed a method by which the inner workings of Artificial Intelligence (AI) models can be understood in terms of causal attributes. Modern AI models, also known as Deep learning (DL), can arrive at decisions by in a more human-like manner, but how they arrive at those decisions is unknown, said the IIT-H in a statement, adding that it makes DL less useful when needed to understand that aspect. Artificial Neural Networks (ANN) are AI models and programs that mimic the working of the human brain so that machines can learn to make decisions in a more human-like manner. Modern ANNs, often also called Deep Learning (DL), have increased tremendously in complexity such that machines can train themselves to process and learn from data that has been supplied to them as input, and almost match human performance in many tasks. However, how they arrive at decisions is unknown, making them less useful when the reason to understand that is necessary.

For the Vision AI Developer Kit, Microsoft and Qualcomm have partnered to simplify training and deploying computer vision-based AI models. Developers can use Microsoft's cloud-based AI and IoT services on Azure to train models while deploying them on the smart camera edge device powered by a Qualcomm's AI accelerator. Let's take a close look at Vision AI Developer Kit. The Vision AI Developer Kit not only looks stylish and sophisticated, but also boasts of an impressive configuration. The kit is powered by a Qualcomm Snapdragon 603 processor, 4GB of LDDR4X memory and 16GB of eMMC storage.

Written by Rakesh Chada and Marcos Jimenez, data scientists at x.ai. At x.ai we strive to make pain associated with scheduling meetings a thing of the past. We've built a virtual assistant (it goes by the name of Amy or Andrew) who can be cc'd into your typical request to meet with people over email. Amy will "understand" the hand-over and just take it from there with your guests, following up with them to nail the time and location details for the meeting. Under the hood this means that Amy must automatically extract meeting-related pieces of information from your email and, mashing that up with your calendar and overall preferences, proceed to get your guests to agree to a time that works for you and them, plus gather whatever other details are needed for the meeting (phone conference number, meeting room, address, google hangout link, etc …). Now the hard, cool, data-science part. Amy "understanding" all the pieces of information from free-form human text presents us with a number of formidable and fascinating data science challenges. This is the realm of natural language processing (NLP), where recent strides in deep learning have made tackling these problems viable. The problem goes far beyond simply detecting words related to times and locations, or named entity recognition (NER).

Computer vision is one of the most popular applications of artificial intelligence. Image classification, object detection and object segmentation are some of the use cases of computer vision-based AI. These techniques are used in a variety of consumer and industrial scenarios. From face recognition-based user authentication to inventory tracking in warehouses to vehicle detection on roads, computer vision is becoming an integral part of next-generation applications.

OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and sequential, strictly turn-taking and simultaneous-move, perfect and imperfect information games, as well as traditional multiagent environments such as (partially- and fully- observable) grid worlds and social dilemmas. OpenSpiel also includes tools to analyze learning dynamics and other common evaluation metrics. Games are represented as procedural extensive-form games, with some natural extensions. The core API and games are implemented in C and exposed to Python.

Sam Charrington is an industry analyst, specialized in Machine Learning and Artificial Intelligence. In this conversation with Sam, we plunge into how and why businesses are using ML and AI, the biggest learnings Sam has had after doing nearly 300 episodes, who was his favorite guest, as well as the outlook for AI/ML in 2020. Please send me your questions -- as an audio file if you'd like -- to nminterdial@gmail.com. Otherwise, below, you'll find the show notes and, of course, you are invited to comment. If you liked the podcast, please take a moment to go over to iTunes to rate it.