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It's a fact that Artificial technology is increasingly making our lives easier. If we think about it, every second component is now attached with some sort of machine learning tool that makes it work by minimum human interference. AI technology is transforming every sequence of our lives, therefore machine learning is also growing with a newer speed, and so are the innovations of artificial intelligence development companies. Transportation has grown a lot more than the commutation methods and assisting the communication requirements of the clients. The customers are gradually becoming addicted to handling complex tasks from mobile phones. The proliferation of various machine learning frameworks has justified the huge demand of industries to hire app AI developers who can work with their esteemed AI-enabled apps and solutions.

Our new @nature paper: AlphaStar is the first learning system to reach the top tier of a major esport without any game restrictions, achieving Grandmaster status in StarCraft II. Researchers have been working on the StarCraft series for over 15 years.

PULP Platform Youtube channel (subscribe it!): PULP-DroNet is a deep learning-powered visual navigation engine that enables autonomous navigation of a pocket-size quadrotor in a previously unseen environment. Thanks to PULP-DroNet the nano-drone can explore the environment, avoiding collisions also with dynamic obstacles, in complete autonomy -- no human operator, no ad-hoc external signals, and no remote laptop! This means that all the complex computations are done directly aboard the vehicle and very fast. The visual navigation engine is composed of both a software and a hardware part.

A team of Google researchers has used a deep-learning algorithm to predict lung cancer accurately from computed scans. The work demonstrates the potential for Artificial Intelligence (AI) to increase both accuracy and consistency, which could help accelerate adoption of lung cancer screening worldwide. Lung cancer is the deadliest of all cancers worldwide -- more than breast, prostate, and colorectal cancers combined -- and it's the sixth most common cause of death globally, according to the World Health Organization. "Using advances in 3D volumetric modelling alongside datasets from our partners (including Northwestern University), we've made progress in modelling lung cancer prediction as well as laying the groundwork for future clinical testing," Shravya Shetty, M.S. Technical Lead at Google explained in a blog post late Monday. Google researchers created a model that can not only generate the overall lung cancer malignancy prediction (viewed in 3D volume) but also identify subtle malignant tissue in the lungs (lung nodules).

Sign in to report inappropriate content. Why do more layers help? Why does the neuron activation function matter? We will answer these questions and more.

Cloud and datacenter architects searching for new ways to pack more artificial intelligence horsepower into already constrained spaces will want to take a close look at Intel's new Nervana Neural Network Processors. Depending on the application, the processors may offer four times the performance or one-fifth the power draw as commercially available alternatives. The new processors are Intel's first ASIC offerings tailored specifically for deep learning workloads. The company announced last week the processors are shipping now. In addition to the NNP-T1000 for training and the NNP-I1000 for inference, Intel also announced the coming generation of the Movidius Myriad Vision Processing Unit, which is designed for AI vision and inference processing at the edge. The ASIC, code-named Keem Bay, is scheduled to ship in the first half of next year.

This post demonstrates a *basic* example of how to build a deep learning model with Keras, serve it as REST API with Flask, and deploy it using Docker and Kubernetes. This is NOT a robust, production example. This is a quick guide for anyone out there who has heard about Kubernetes but hasn't tried it out yet. To that end, I use Google Cloud for every step of this process. The reason is simple -- I didn't feel like installing Docker and Kubernetes on my Windows 10 Home laptop. The additional benefit to you all following along is reproducing my steps should be much easier as you can run everything using the exact specifications I used.

Argonne researchers have created a neural architecture search that automates the development of deep-learning-based predictive models for cancer data. While increasing swaths of collected data and growing scales of computing power are helping to improve our understanding of cancer, further development of data-driven methods for the disease's diagnosis, detection and prognosis is necessary. There is a particular need to develop deep-learning methods -- that is, machine learning algorithms capable of extracting science from unstructured data. Researchers from the U.S. Department of Energy's (DOE) Argonne National Laboratory have made strides toward accelerating such efforts by presenting a method for the automated generation of neural networks. As detailed in a paper for presentation at the SC19 conference, the researchers, utilizing resources from the Argonne Leadership Computing Facility (ALCF), a DOE Office of Science User Facility, have established a neural architecture search (NAS) that, for a class of representative cancer data, automates the development of deep-learning-based predictive models.

There's an old saying in the Artificial Intelligence community: once software starts working people stop calling it AI. You could make the argument that the opposite has taken place during the last 6 years of the neural network renaissance with Machine Learning researchers returning to the term AI as the old stigma of exaggerated hype wears off. However, it does point to an interesting guideline for technological maturity: it's mature when you stop noticing it. That's why the old Palm Pilots were a conversation piece but modern smartphones go completely unnoticed. One particularly powerful implementation of Deep Learning is in the proliferation of Voice assistants.