Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Expressive architecture encourages application and innovation.

These are some the most elite academic journals in the world. And last year, one tech company, Alphabet's Google, published papers in all of them. The unprecedented run of scientific results by the Mountain View search giant touched on everything from ophthalmology to computer games to neuroscience and climate models. For Google, 2016 was an annus mirabilis during which its researchers cracked the top journals and set records for sheer volume. Behind the surge is Google's growing investment in artificial intelligence, particularly "deep learning," a technique whose ability to make sense of images and other data is enhancing services like search and translation (see "10 Breakthrough Technologies 2013: Deep Learning").

These are some the most elite academic journals in the world. And last year, one tech company, Alphabet's Google, published papers in all of them. The unprecedented run of scientific results by the Mountain View search giant touched on everything from ophthalmology to computer games to neuroscience and climate models. For Google, 2016 was an annus mirabilis during which its researchers cracked the top journals and set records for sheer volume. Behind the surge is Google's growing investment in artificial intelligence, particularly "deep learning," a technique whose ability to make sense of images and other data is enhancing services like search and translation (see "10 Breakthrough Technologies 2013: Deep Learning").

'Some of AlphaGo's moves puzzled observers, because they did not fit usual human patterns of play. DeepMind CEO Demis Hassabis explained the reason for this difference as follows: "unlike humans, the AlphaGo program aims to maximize the probability of winning rather than optimising margins". If this binary logic – in which the only thing that matters is winning while the margin of victory is irrelevant – were built into an autonomous weapons system, it would lead to the violation of the principle of proportionality, because the algorithm would see no difference between victories that required it to kill one adversary or 1,000.'

These are three terms that are heard all the time now, but often people still get confused about what each one really entails. Below is a quick rundown of each that will hopefully things out a little and give you a real insight as to what these interchangeable terms mean. Artificial Intelligence, or AI for short, is the broadest way in which to describe computer intelligence. Back in1956 it was described as "Every aspect of learning or any other feature of intelligence can in principle is that a machine can be made to simulate it" at the Dartmouth Artificial Intelligence Conference. AI can come in various forms including game-playing computer programs and voice recognition systems.

Progress in artificial intelligence causes some people to worry that software will take jobs such as driving trucks away from humans. Now leading researchers are finding that they can make software that can learn to do one of the trickiest parts of their own jobs--the task of designing machine-learning software. In one experiment, researchers at the Google Brain artificial intelligence research group had software design a machine-learning system to take a test used to benchmark software that processes language. What it came up with surpassed previously published results from software designed by humans. In recent months several other groups have also reported progress on getting learning software to make learning software.

Analysis Yahoo! last month married clustered compute to Google's machine learning. The firm's engineers released TensorFlowOnSpark (TFoS), getting the Google Brain Team's machine-learning framework up and running on Spark and Hadoop clusters. Spark is the open-source cluster framework overseen by Apache and employed by Yahoo!, Netflix and others processing petabytes of data across thousands of nodes. TFoS code is available on GitHub under an Apache licence and for use on Amazon's EC2. The idea of TFoS is deep learning on massively clustered systems – and all the benefits of processing and storage that entails – only in a Google-free setting and using an architecture that's "easy" to build and that also delivers fast throughput.

Fei Fei Li, chief scientist of AI/ML for cloud services at Google Inc., speaks at Cloud Next '17 in front of an image of one of sister company Waymo's driverless cars. Deep learning has become the technology du jour of late and few companies have advanced the field as much across as many areas or integrated the technology as completely into their operations as Google and its Alphabet affiliates. In keeping with Google's push to externalize its innovations, the company's Next '17 cloud conference featured a number of AI-related announcements and a general theme of democratizing access to the world's most powerful deep learning systems. In recent years Google and its sister companies have become synonymous with advancing the AI revolution at a frenzied pace and infusing deep learning across the company's services. Perhaps most famously, last year Deep Mind's AlphaGo became the first machine to beat a top Go player, while Waymo's driverless cars have become symbols of the autonomous driving revolution.

Objective: We investigate whether deep learning techniques for natural language processing (NLP) can be used efficiently for patient phenotyping. Patient phenotyping is a classification task for determining whether a patient has a medical condition, and is a crucial part of secondary analysis of healthcare data. We assess the performance of deep learning algorithms and compare them with classical NLP approaches. Materials and Methods: We compare convolutional neural networks (CNNs), n-gram models, and approaches based on cTAKES that extract pre-defined medical concepts from clinical notes and use them to predict patient phenotypes. The performance is tested on 10 different phenotyping tasks using 1,610 discharge summaries extracted from the MIMIC-III database. Results: CNNs outperform other phenotyping algorithms in all 10 tasks. The average F1-score of our model is 76 (PPV of 83, and sensitivity of 71) with our model having an F1-score up to 37 points higher than alternative approaches. We additionally assess the interpretability of our model by presenting a method that extracts the most salient phrases for a particular prediction. Conclusion: We show that NLP methods based on deep learning improve the performance of patient phenotyping. Our CNN-based algorithm automatically learns the phrases associated with each patient phenotype. As such, it reduces the annotation complexity for clinical domain experts, who are normally required to develop task-specific annotation rules and identify relevant phrases. Our method performs well in terms of both performance and interpretability, which indicates that deep learning is an effective approach to patient phenotyping based on clinicians' notes.

We explore the use of deep learning hierarchical models for problems in financial prediction and classification. Financial prediction problems – such as those presented in designing and pricing securities, constructing portfolios, and risk management – often involve large data sets with complex data interactions that currently are difficult or impossible to specify in a full economic model. Applying deep learning methods to these problems can produce more useful results than standard methods in finance. In particular, deep learning can detect and exploit interactions in the data that are, at least currently, invisible to any existing financial economic theory.