The Google Brain Team looks back at a year of research ranging wrong their efforts to automate machine learning, understanding and generating speech and applying deep learning in computer systems and privacy and security.

As we all know, technology is playing its cards right in changing the present-day business scenario, with Artificial intelligence (AI) becoming the "not-so-hidden" ace here. From picking out valuable details from the mountain of big data to utilizing that information to empower the process through intelligent, machine-learning bots, AI is already bolstering sales efforts. And there's so much that this maverick aid can do, through the following ways: The Internet has become a wizard of sorts which can predict your demands based on the requirements you have put. This is due to the adoption of Artificial Intelligence (AI). Deep learning algorithms have already started working their bit to forever uplift the world of automated ads, to that extent that they can now predict the online behaviour of an average user.

The new Business Intelligence Lab (BIL) will focus on effective and efficient data analysis technology for emerging data intensive applications, while the Robotics and Autonomous Driving Lab (RAL) will concentrate on computer vision, particularly in autonomous driving in order to solidify Baidu's base technologies in this field. The new labs will bring Baidu Research's presence to a total of five labs, adding to the existing Institute of Deep Learning (IDL), Big Data Lab (BDL) and Silicon Valley Artificial Intelligence Lab (SVAIL). Together, the labs will continue to focus on fundamental research in their specialized areas to work toward the long-term innovation of fundamental AI technologies. They will also work with corresponding Baidu AI technology departments to drive Baidu's AI development and accelerate commercialization.

This is the first in a multi-part series by guest blogger Adrian Rosebrock. Adrian writes at PyImageSearch.com about computer vision and deep learning using Python, and he recently finished authoring a new book on deep learning for computer vision and image recognition. I had two goals when I set out to write my new book, Deep Learning for Computer Vision with Python. The first was to create a book/self-study program that was accessible to both novices and experienced researchers and practitioners -- we start off with the fundamentals of neural networks and machine learning and by the end of the program you're training state-of-the-art networks on the ImageNet dataset from scratch. Along the way I quickly realized that a stumbling block for many readers is configuring their development environment -- especially true for those wanted to utilize their GPU(s) and train deep neural networks on massive image datasets (such as ImageNet).

Women in AI is a biweekly podcast from RE•WORK, meeting with leading female minds in AI, Deep Learning and Machine Learning. We will speak to CEOs, CTOs, Data Scientists, Engineers, Researchers and Industry Professionals to learn about their cutting edge work and advances, as well as their impact on AI and their place in the industry.

The new artificial intelligence technology is programmed to pay attention to particular words while creating images from text descriptions. This technique of focused attention has allowed about three times increase in image quality as compared to previous text-to-image generation. Xiaodong He, a principal researcher in Deep Learning Technology at Microsoft, says that the pictures produced by the bot are created pixel by pixel, from scratch. "These birds may not exist in the real world -- they are just an aspect of our computer's imagination of birds," he added. At the core of this artist bot, there's a technology named Generative Adversarial Network, or GAN.

In August 2015, a number of carefully selected Facebook users in the Bay Area discovered a new feature on Facebook Messenger. Known as M, the service was designed to rival Google Now and Apple's Siri. A personal assistant that would answer questions in a natural way, make restaurant reservations and help with Uber bookings, M was meant to be a step forward in natural language understanding, the virtual assistant that – unlike Siri – wasn't a dismal experience. Fast forward a couple of years, and the general purpose personal assistant has been demoted within Facebook's product offering. Poor M. The hope was that it would tell users jokes and act as a guide, life coach and optimisation tool.

Machines are beginning to outperform humans at highly-specific tasks that often take humans years to master. A Deep Learning algorithm at Stanford, for example, recently was trained to be able to detect pneumonia from x-rays of patients, and the accuracy of the machine predictions exceeded that of four trained radiologists. The algorithm was trained on 100,000 x-ray images released from the National Institutes of Health. In addition to pneumonia, the algorithm also was able to more accurately identify from x-rays 14 additional diseases which include fibrosis, hernias, and cell masses. Pranav Rajpurkar, a graduate student in the Stanford Machine Learning Group, said that "interpreting X-ray images to diagnose pathologies like pneumonia is very challenging, and we know that there's a lot of variability in the diagnoses radiologists arrive at." Andrew Ng, former chief scientist at Chinese tech company Baidu, said that "I've been encouraged by how quickly people are accepting the idea that deep learning can diagnose at an accuracy superior to doctors in select verticals.

At KDnuggets, we try to keep our finger on the pulse of main events and developments in industry, academia, and technology. We also do our best to look forward to key trends on the horizon. To close out 2017, we recently asked some of the leading experts in Big Data, Data Science, Artificial Intelligence, and Machine Learning for their opinion on the most important developments of 2017 and key trends they 2018. This post considers what happened in Machine Learning & Artificial Intelligence this year, and what may be on the horizon for 2018. "What were the main machine learning & artificial intelligence related developments in 2017, and what key trends do you see in 2018?"

We've been running Kubernetes for deep learning research for over two years. While our largest-scale workloads manage bare cloud VMs directly, Kubernetes provides a fast iteration cycle, reasonable scalability, and a lack of boilerplate which makes it ideal for most of our experiments. We now operate several Kubernetes clusters (some in the cloud and some on physical hardware), the largest of which we've pushed to over 2,500 nodes. This cluster runs in Azure on a combination of D15v2 and NC24 VMs. On the path to this scale, many system components caused breakages, including etcd, the Kube masters, Docker image pulls, network, KubeDNS, and even our machines' ARP caches.