Click on the first attribute in the dataset in the "Attributes" panel. Take note of the details in the "Selected attribute" panel.

Insilico Medicine will unveil a newly-developed Artificial Intelligence (AI) drug discovery engine at the Re-Work Machine Intelligence Summit in Berlin, Germany, to be held June 29-30, 2016. The AI engine is capable of predicting therapeutic use, toxicity, and adverse effects of thousands of molecules. Insilico says that this drug-discovery engine has the potential to transform the pharmaceutical industry and double the number of drugs on the market by "developing multi-modal deep-learned and parametric biomarkers as well as multiple drug-scoring pipelines for drug discovery and drug repurposing, and hypothesis and lead generation." By using AI coupled with a deep understanding of pharmaceutical R&D processes, Insilico hopes to overcome hurdles to drug discovery, such as failure rates due to irreproducible experiments with poor choices of animal models and the inability to translate the results from animal models directly to humans. Up until now, Insilico has dealt mainly with nutraceuticals and cosmetics, signing an exclusive agreement with Life Extension, a vendor of major nutraceutical products, to develop a set of geroprotectors, which are natural products that mimic a young, healthy state in multiple old tissues.

When we hear about Convolutional Neural Network (CNNs), we typically think of Computer Vision. CNNs were responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today, from Facebook's automated photo tagging to self-driving cars. More recently we've also started to apply CNNs to problems in Natural Language Processing and gotten some interesting results. In this post I'll try to summarize what CNNs are, and how they're used in NLP. The intuitions behind CNNs are somewhat easier to understand for the Computer Vision use case, so I'll start there, and then slowly move towards NLP.

DESCRIPTION You have hundreds of thousands of hosts, hundreds of millions of lines of code, billions of online transactions, and one of the most visited sites on the Internet. Now go build systems to secure it. The Application Security team is charged with building automated software that ingests hundreds of gigabytes of information daily, then interprets, transforms, and catalogs it into concrete and actionable information that is used to to drive the highest security standards possible. We are looking for a Senior Software Engineer that wants to write applications to import and analyze big data, use that information to drive Machine Learning solutions, push findings through a workflow system, and create tools that integrate with Amazons build and operations systems to ensure security every step through the development process. An engineer that enjoys personal responsibility, big problems, lots of influence on the development process, and an iterative approach to finding the right solution will thrive on our team.

We have lot of amazing frameworks for deep learning which allow us easy and fast prototyping and learning complex architectures even not thinking about what happening inside of them. But sometimes you need to deploy your model somewhere… let's say where you can't use your favorite I recently faced this problem, when I had to deploy recurrent neural network for action recognition trained in Keras in Java. My client doesn't want to use some microservices architecture, he wants everything in Java and basta cosi:) Embedding is vector length of 11, hidden units 15. First, let's load our weights from .hdf5 And if we check one of the most popular tutorials in LSTMs… We are just lucky!

We could explicitly unroll the loops ourselves, creating new graph nodes for each loop iteration, but then the number of iterations is fixed instead of dynamic, and graph creation can be extremely slow. Let's go over two examples. First, we'll create a simple cumulative-sum operation using scan. For example, [1, 2, 2, 2] as input will produce [1, 3, 5, 7] as output. Second, we'll build a toy RNN from scratch, and we'll have it learn the cumulative-sum operation from example input, target sequences.

The line between fiction and reality is blurring. Some years ago, driverless cars and drones delivering packages at our doorsteps would have seemed like science fiction. However, these and other new technological advances continue to astound us and make our lives easier. For instance, Dag Kittlaus, who created the virtual assistant Siri, recently showcased another artificial intelligence (AI) platform, Viv, at TechCrunch Disrupt, New York. Based on the demo shown, Viv seems to be a more sophisticated and powerful a virtual assistant.

Twitter has made no secret of its interest in machine learning in recent years, and on Monday the company put its money where its mouth is once again by purchasing London startup Magic Pony Technology, which has focused on visual processing. "Magic Pony's technology -- based on research by the team to create algorithms that can understand the features of imagery -- will be used to enhance our strength in live [streaming] and video and opens up a whole lot of exciting creative possibilities for Twitter," Twitter cofounder and CEO Jack Dorsey wrote in a blog post announcing the news. The startup's team includes 11 Ph.Ds with expertise across computer vision, machine learning, high-performance computing, and computational neuroscience, Dorsey said. They'll join Twitter's Cortex group, made up of engineers, data scientists, and machine-learning researchers. The acquisition follows several related purchases by the social media giant, including Madbits in 2014 and Whetlab last year.

BOSTON, June 30, 2016 (GLOBE NEWSWIRE) -- indico, an innovator in the machine learning and artificial intelligence space, will make a presentation on deep learning at the Sentiment Analysis Symposium, which takes place in New York, July 12th. Dr. Daniel Kuster, a researcher at indico, will focus on the differences between deep learning and traditional machine learning approaches, and how the advantages of deep learning can be exploited to quickly gain new insights about what people say online, and how they say it. The presentation will take place at Fordham University's Lincoln Center Campus in New York City. Machine learning is becoming the tool of choice for analyzing text and image data. While traditional text processing solutions rely on the ability of experts to encode domain knowledge, machine learning models learn directly from the data.

Kaname Hayashi is known as the "father of Pepper." Hayashi is the "father of Pepper," the charming humanoid robot from Japanese carrier SoftBank Mobile and French company Aldebaran Robotics. Pepper, with its circular doe eyes and welcoming smile, is billed as a robot that can read your emotions. It's available for sale and has even enrolled in school. Like any proud parent whose kids leave home, Hayashi had a void to fill.