According to many thought leaders, analysts and early adopters, Artificial Intelligence (AI) and Machine Learning (ML) technologies are rapidly making their way into every industry, geography, system and process. This means that B2B sales and marketers need a primer for artificial intelligence and machine learning to quickly catch up on how they can benefit. This primer on AI and ML will explain how. In general, artificial intelligence is the simulation of human intelligence processes by machines. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction (continuous and tireless learning).

IBM today announced the launch of its new Deep Learning as a Service (DLaaS) program for AI developers. With DlaaS, users can train neural networks using popular frameworks such as TensorFlow, PyTorch, and Caffe without buying and maintaining costly hardware. The service lets data scientists train models using only the resources they need, paying only for GPU time. Each cloud processing unit is set up for ease-of-use and prepared for programming deep learning networks without the need for infrastructure management from users. Users can choose from a set of supported deep learning frameworks, a neural network model, training data, and cost constraints and then the service takes care of the rest, providing them an interactive, iterative training experience.

For years, Swami Sivasubramanian's wife has wanted to get a look at the bears that come out of the woods on summer nights to plunder the trash cans at their suburban Seattle home. So over the Christmas break, Sivasubramanian, the head of Amazon's AI division, began rigging up a system to let her do just that. So far he has designed a computer model that can train itself to identify bears--and ignore raccoons, dogs, and late-night joggers. He did it using an Amazon cloud service called SageMaker, a machine-learning product designed for app developers who know nothing about machine learning. Next, he'll install Amazon's new DeepLens wireless video camera on his garage. The $250 device, which will go on sale to the public in June, contains deep-learning software to put the model's intelligence into action and send an alert to his wife's cell phone whenever it thinks it sees an ursine visitor.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

WEBINAR DESCRIPTION This 1 hour session will provide an overview on Multi-layer Artificial Neural Networks (ANNs). Artificial Neural Networks (ANNs) are the building blocks of modern Deep Learning applications, such as image processing, speech recognition, text analytics, driverless cars etc. This session will cover the basics of multi-layer ANNs, discuss forward propagation and backpropagation logic, cost function in a multi-layer ANN and how to achieve convergence. This session will also include demonstration of small python programs which implement such Multi-Layer ANNs. WARNING:- This is an advanced Deep Learning topic.

Everyone knows the answer: Data. All of these companies have managed to leverage the vast amounts of information they get from their multitude of users - whether it be their search habits, the posts they share, the products they buy, or the music they listen to - into major revenue streams. It's not just the fact that these companies have been able to gather data on millions (or billions, in the case of some of these companies); it's that those companies have managed to effectively utilize that data to better understand and market to their users. All of these companies are using artificial intelligence (or, more accurately, deep learning) to do this. Of course, it's important to note that you don't have to be a dominating enterprise like Amazon or Google to turn data into a competitive advantage.

Over at the Lenovo Blog, Dr. Bhushan Desam writes that the company just updated its LiCO tools to accelerate AI deployment and development for Enterprise and HPC implementations. The newly updated Lenovo Intelligent Computing Orchestration (LiCO) tools are designed to overcome recurring pain points for enterprise customers and others implementing multi-user environments using clusters for both HPC workflows and AI development. LiCO simplifies resource management and makes launching AI training jobs in clusters easy. LiCO currently supports multiple AI frameworks, including TensorFlow, Caffe, Intel Caffe, and MXNet. Additionally, multiple versions of those AI frameworks can easily be maintained and managed using Singularity containers.

Interventional clinical cancer trials are generally too restrictive and cancer patients are often excluded from them on the basis of comorbidity, past or concomitant treatments and the fact that they are over a certain age. The efficacy and safety of new treatments for patients with these characteristics are not, therefore, defined. In this work, we build a model with which to automatically predict whether short clinical statements were considered inclusion or exclusion criteria. We used clinical trials protocols on cancer that have been available in public registries for the last 18 years to train word embeddings, and constructed a dataset of 6M short free-texts labeled as eligible or not eligible. We then trained and validated a text classifier, using deep neural networks with pre-trained word-embedding as its inputs, to predict whether or not short free-text statements describing clinical information were considered eligible. The best model achieved an F-measure of 0.92 and an almost perfect agreement when employing a validation set of 800K labeled statements. The trained model was also tested on an independent set of clinical statements mimicking those used in routine clinical practice, yielding a consistent performance. We additionally analyzed the semantic reasoning of the word embedding representations obtained, and were able to identify equivalent treatments for a type of tumor in an analogy with the drugs used to treat other tumors. The present work shows that representation learning using neural networks can be successfully leveraged to extract the medical knowledge available on clinical trial protocols and potentially assist practitioners when prescribing treatments.

Generative adversarial networks (GANs) are powerful tools for learning generative models. In practice, the training may suffer from lack of convergence. GANs are commonly viewed as a two-player zero-sum game between two neural networks. Here, we leverage this game theoretic view to study the convergence behavior of the training process. Inspired by the fictitious play learning process, a novel training method, referred to as Fictitious GAN, is introduced. Fictitious GAN trains the deep neural networks using a mixture of historical models. Specifically, the discriminator (resp. generator) is updated according to the best-response to the mixture outputs from a sequence of previously trained generators (resp. discriminators). It is shown that Fictitious GAN can effectively resolve some convergence issues that cannot be resolved by the standard training approach. It is proved that asymptotically the average of the generator outputs has the same distribution as the data samples.

Though Convolutional Neural Networks (CNNs) have surpassed human-level performance on tasks such as object classification and face verification, they can easily be fooled by adversarial attacks. These attacks add a small perturbation to the input image that causes the network to mis-classify the sample. In this paper, we focus on neutralizing adversarial attacks by compact feature learning. In particular, we show that learning features in a closed and bounded space improves the robustness of the network. We explore the effect of L2-Softmax Loss, that enforces compactness in the learned features, thus resulting in enhanced robustness to adversarial perturbations. Additionally, we propose compact convolution, a novel method of convolution that when incorporated in conventional CNNs improves their robustness. Compact convolution ensures feature compactness at every layer such that they are bounded and close to each other. Extensive experiments show that Compact Convolutional Networks (CCNs) neutralize multiple types of attacks, and perform better than existing methods in defending adversarial attacks, without incurring any additional training overhead compared to CNNs.