In radiology, we'd like deep learning models to identify patterns in imaging that suggest disease. For example, to detect pneumonia (lung infection), we'd like them to identify patterns in the lung that indicate the presence of an active infection. But do we know that is what they're actually doing? My collaborators and I recently released a preprint on arXiv examining how confounding variables may degrade the generalization performance of a CNN trained to identify pneumonia. Let me take a step back and give some examples of the problem that motivates this work.

Americans spend 8 billion hours stuck in traffic every year. Deep neural networks can help! DeepTraffic is a deep reinforcement learning competition. The goal is to create a neural network to drive a vehicle (or multiple vehicles) as fast as possible through dense highway traffic. What you see above is all you need to succeed in this competition.

Dez Blanchfield speaks with business leaders about artificial intelligence and deep learning adoption in the "From Here to AI" podcast series from IBM Power Systems. When you start to investigate artificial intelligence (AI), or branch out to buy a couple AI servers to tinker with for your organization, the process of implementing a full AI solution can seem daunting. With the help of four business executives and AI leaders and digital transformation expert and avid podcaster Dez Blanchfield, we set out to outline the natural progression of implementing AI in the data center. No matter what stage of the journey you are on, these podcasts should help you get "from here to AI." Below is a quick overview of each session. We've posted them as a series so you can binge-listen if you have the time, or you can tee them up separately to plug into the ones that interest you most.

One of the factors often credited for this latest boom in artificial intelligence (AI) investment, research, and related cognitive technologies, is the emergence of Deep Learning AI algorithms, and the corresponding large volumes of big data and computing power that makes Deep Learning a reality. However, deep learning is just an approach to machine learning (ML), that while having proven much capability across a wide range of problem areas, is still just one particular approach. Increasingly, we're starting to see news and research showing the limits of deep learning, and some of the downsides to the deep learning approach. So we have to ask, are people's enthusiasm of AI tied to their enthusiasm of deep learning, and is deep learning really able to deliver on many of its promises? AI researchers have struggled to understand how the brain learns from the very beginnings of the development of the field of artificial intelligence.

It may seem that the time when artificial intelligence (AI) fully integrates into our lives is in the distant future. However, if you look to those pioneering technological advancements in telecommunications, retail, or the financial services industry, you'll find that AI is already changing the way these companies do business. AI - specifically deep learning, the fastest growing segment of AI - is quickly moving from its academic roots to the forefront of business and industry. This report, developed in partnership with O'Reilly Media, examines how companies in three market sectors--telecommunications, retail, and financial services--are tackling new challenges and opportunities by incorporating AI into their products and operations.

This post is co-authored by Animesh Singh, Nicholas Png, Tommy Li, and Vinod Iyengar. Deep learning frameworks like TensorFlow, PyTorch, Caffe, MXNet, and Chainer have reduced the effort and skills needed to train and use deep learning models. But for AI developers and data scientists, it's still a challenge to set up and use these frameworks in a consistent manner for distributed model training and serving. The open source Fabric for Deep Learning (FfDL) project provides a consistent way for AI developers and data scientists to use deep learning as a service on Kubernetes and to use Jupyter notebooks to execute distributed deep learning training for models written with these multiple frameworks. Now, FfDL is announcing a new addition that brings together that deep learning training capability with state-of-the-art machine learning methods.

The International Conference on Machine Learning (ICML) 2018 will be held July 10–15 in Stockholm, Sweden. Yesterday, from more than 600 accepted papers, the prestigious conference announced its Best Paper Awards. Two papers shared top honours. Researchers Anish Athalye of MIT and Nicholas Carlini and David Wagner of UC Berkeley's Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples; and Delayed Impact of Fair Machine Learning, from a UC Berkeley research group led by Lydia T. Liu and Sarah Dean. The Best Paper Runner Up Awards go to Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices, from Professor Zengfeng Huang of Fudan University; The Mechanics of n-Player Differentiable Games from DeepMind and University of Oxford's David Balduzzi and Sebastien Racaiere, James Martens, Jakob Foerster, Karl Tuyls and Thore Graepel; and Fairness Without Demographics in Repeated Loss Minimization, from a Stanford research group including Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang.

There are so many amazing ways artificial intelligence and machine learning are used behind the scenes to impact our everyday lives and inform business decisions and optimize operations for some of the world's leading companies. Here are 27 amazing practical examples of AI and machine learning. Using natural language processing, machine learning and advanced analytics, Hello Barbie listens and responds to a child. A microphone on Barbie's necklace records what is said and transmits it to the servers at ToyTalk. There, the recording is analyzed to determine the appropriate response from 8,000 lines of dialogue.

A couple of months ago I left Pivotal to join idealo.de Besides the usual tasks like building out the data science team, setting up the infrastructure and many more administrative stuff, I had to define the ML powered product roadmap. And associated with this was also the definition of a Minimum Viable Product (MVP) for machine learning products. The question I often face though, here at idealo and actually also back at my time at Pivotal, is what actually a good MVP means? In this article, I will shed some lights on the different dimensions of a good MVP for machine learning products drawing in the experiences that I've gained so far.

Three, it turns out, is better than one. At least that's how it worked for a trio of former rivals who teamed up to claim the just-announced top prize in this year's Data Science Bowl. The fourth annual event focused on one of healthcare's most pressing problems -- the soaring cost and time needed to discover new drugs. A record-setting 18,000 participants battled over 90 days to deliver a deep learning algorithm to accelerate a crucial step in the drug-discovery pipeline: identifying the nucleus of each cell. This year's Data Science Bowl was "driven by a very real need to develop new treatments faster and more accurately," said Anne Carpenter, director of the imaging platform at the Broad Institute of MIT and Harvard, the nonprofit partner for the contest.