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.
Source: Caffe | Deep Learning Framework
Tensors and Dynamic neural networks in Python
with strong GPU acceleration.
PyTorch is a deep learning framework that puts Python first.
We are in an early-release Beta. Expect some adventures.
Source: PyTorch
Machine learning is cropping up more and more in research papers – does it work?
Source: From drugs to galaxy hunting, AI is elbowing its way into boffins’ labs • The Register
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.
Source: Image-to-Image Translation with Conditional Adversarial Networks
This is the pix2pix implementation in Tensorflow
The above link also has concrete examples allowing you to play with the data yourself on level = easy.
This is the man who made the tensorflow port and also uses it to fill in drawings of cats.
ETSI is pleased to announce the creation of the Industry Specification Group ‘Experiential Network Intelligence’ (ISG ENI).
The purpose of the group is to define a Cognitive Network Management architecture that is based on the “observe-orient-decide-act” control model. It uses AI (Artificial Intelligence) techniques and context-aware policies to adjust offered services based on changes in user needs, environmental conditions and business goals. The system is experiential, in that it learns from its operation and from decisions given to it by operators to improve its knowledge of how to act in the future. This will help operators automate their network configuration and monitoring processes, thereby reducing their operational expenditure and improving the use and maintenance of their networks.
Source: New ETSI group on improving operator experience using Artificial Intelligence
you choose a sculpture and a painting and the Artificial Intelligence shows you how they are related through other art works. Invisible inspiration!
Gamalon uses a technique that it calls Bayesian program synthesis to build algorithms capable of learning from fewer examples. Bayesian probability, named after the 18th century mathematician Thomas Bayes, provides a mathematical framework for refining predictions about the world based on experience. Gamalon’s system uses probabilistic programming—or code that deals in probabilities rather than specific variables—to build a predictive model that explains a particular data set. From just a few examples, a probabilistic program can determine, for instance, that it’s highly probable that cats have ears, whiskers, and tails. As further examples are provided, the code behind the model is rewritten, and the probabilities tweaked. This provides an efficient way to learn the salient knowledge from the data.
Source: AI Software Juggles Probabilities to Learn from Less Data
Graph Engine – Open Source
Microsoft Graph Engine is a distributed in-memory data processing engine, underpinned by a strongly-typed in-memory key-value store and a general distributed computation engine.
This repository contains the source code of Graph Engine and its graph query language — Language Integrated Knowledge Query (LIKQ). LIKQ is a versatile graph query language on top of Graph Engine. It combines the capability of fast graph exploration and the flexibility of lambda expression: server-side computations can be expressed in lambda expressions, embedded in LIKQ, and executed on the server side during graph traversal. LIKQ is powering Academic Graph Search API, which is part of Microsoft Cognitive Services.
Source: GitHub – Microsoft/GraphEngine: Microsoft Graph Engine
As described by Hadley Wickham, tidy data has a specific structure:
each variable is a column
each observation is a row
each type of observational unit is a tableThis means we end up with a data set that is in a long, skinny format instead of a wide format. Tidy data sets are easier to work with, and this is no less true when one starts to work with text. Most of the tooling and infrastructure needed for text mining with tidy data frames already exists in packages like dplyr, broom, tidyr, and ggplot2. Our goal in writing the tidytext package is to provide functions and supporting data sets to allow conversion of text to and from tidy formats, and to switch seamlessly between tidy tools and existing text mining packages.
Source: The Life-Changing Magic of Tidying Text
text2vec is an R package which provides an efficient framework with a concise API for text analysis and natural language processing (NLP).
Goals which we aimed to achieve as a result of development of text2vec:
Concise – expose as few functions as possible
Consistent – expose unified interfaces, no need to explore new interface for each task
Flexible – allow to easily solve complex tasks
Fast – maximize efficiency per single thread, transparently scale to multiple threads on multicore machines
Memory efficient – use streams and iterators, not keep data in RAM if possible
Source: text2vec
Initially used to improve the experience for visually impaired members of the Facebook community, the company’s Lumos computer vision platform is now powering image content search for all users. This means you can now search for images on Facebook with key words that describe the contents of a photo, rather than being limited by tags and captions.
To accomplish the task, Facebook trained an ever-fashionable deep neural network on tens of millions of photos. Facebook’s fortunate in this respect because its platform is already host to billions of captioned images. The model essentially matches search descriptors to features pulled from photos with some degree of probability.
[…]
Facebook isn’t the only one racing to apply recent computer vision advances to existing products. Pinterest’s visual search feature has been continuously improved to let users search images by the objects within them. This makes photos interactive and more importantly it makes them commercializable.Google on the other hand open sourced its own image captioning model last fall that can both identify objects and classify actions with accuracy over 90 percent. The open source activity around TensorFlow has helped the framework gain prominence and become very popular with machine learning developers.
Facebook is focused on making machine learning easy for teams across the company to integrate into their projects. This means improving the use of the company’s general purpose FBLearner Flow.
“We’re currently running 1.2 million AI experiments per month on FBLearner Flow, which is six times greater than what we were running a year ago,” said Joaquin Quiñonero Candela, Facebook’s director of applied machine learning.
Lumos was built on top of FBLearner Flow. It has already been used for over 200 visual models. Aside from image content search, engineers have used the tool for fighting spam.
Source: Facebook’s AI unlocks the ability to search photos by what’s in them | TechCrunch
Average compensation for staff in sales, trading, and research at the 12 largest global investment banks, of which Goldman is one, is $500,000 in salary and bonus, according to Coalition. Seventy-five percent of Wall Street compensation goes to these highly paid “front end” employees, says Amrit Shahani, head of research at Coalition.
For the highly paid who remain, there is a growing income spread that mirrors the broader economy, says Babson College professor Tom Davenport. “The pay of the average managing director at Goldman will probably get even bigger, as there are fewer lower-level people to share the profits with,” he says.
[…]
Goldman Sachs has already begun to automate currency trading, and has found consistently that four traders can be replaced by one computer engineer, Chavez said at the Harvard conference. Some 9,000 people, about one-third of Goldman’s staff, are computer engineers.
[…]
Goldman’s new consumer lending platform, Marcus, aimed at consolidation of credit card balances, is entirely run by software, with no human intervention, Chavez said. It was nurtured like a small startup within the firm and launched in just 12 months, he said. It’s a model Goldman is continuing, housing groups in “bubbles,” some on the now-empty trading spaces in Goldman’s New York headquarters: “Those 600 traders, there is a lot of space where they used to sit,” he said.
Source: As Goldman Embraces Automation, Even the Masters of the Universe Are Threatened
This site features a curated selection of data visualization tools meant to bridge the gap between programmers/statisticians and the general public by only highlighting free/freemium, responsive and relatively simple-to-learn technologies for displaying both basic and complex, multivariate datasets. It leans heavily toward open-source software and plugins, rather than enterprise, expensive B.I. solutions.
Why?Well, information visualization, or InfoVis, has for the past three decades been mostly regarded as a specialty skill relegated to the ranks of researchers and scientists. But in recent years, the proliferation of Big Data combined with a surge of new, open-source tools for data display have given rise to the democratization of “data visualization” and “data journalism.” It’s something anyone can do. As such, all resources that may require basic programming knowledge are labeled as such.
As Simon Rogers of The Guardian so artfully stated in 2008, “Anyone can do it. Data journalism is the new punk.”
Source: dataviz.tools
As the “Brains vs. Artificial Intelligence: Upping the Ante” poker competition nears its halfway point, Carnegie Mellon University’s AI program, Libratus, is opening a lead over its human opponents — four of the world’s best professional poker players.One of the pros, Jimmy Chou, said he and his colleagues initially underestimated Libratus, but have come to regard it as one tough player.”The bot gets better and better every day,” Chou said. “It’s like a tougher version of us.”
[…]
In the first Brains vs. AI contest in 2015, four leading pros amassed more chips than the AI, called Claudico. But in the latest contest, Libratus had amassed a lead of $459,154 in chips in the 49,240 hands played by the end of Day Nine.“I’m feeling good,” Sandholm said of Libratus’ chances as the competition proceeds. “The algorithms are performing great. They’re better at solving strategy ahead of time, better at driving strategy during play and better at improving strategy on the fly.”
Source: CMU AI Is Tough Poker Player | Carnegie Mellon School of Computer Science
A bunch of highly funded researchers
Source: The Google Brain Team
Publications subdivided into subjects
Machine Intelligence publications subset
Big Picture does a lot of high level visualisation of datasets and their relationships
When we look very closely at images generated by neural networks, we often see a strange checkerboard pattern of artifacts. It’s more obvious in some cases than others, but a large fraction of recent models exhibit this behavior.
Mysteriously, the checkerboard pattern tends to be most prominent in images with strong colors. What’s going on? Do neural networks hate bright colors? The actual cause of these artifacts is actually remarkably simple, as is a method for avoiding them.
Source: Deconvolution and Checkerboard Artifacts — Distill
A popular method for exploring high-dimensional data is something called t-SNE, introduced by van der Maaten and Hinton in 2008. The technique has become widespread in the field of machine learning, since it has an almost magical ability to create compelling two-dimensonal “maps” from data with hundreds or even thousands of dimensions. Although impressive, these images can be tempting to misread. The purpose of this note is to prevent some common misreadings.
Source: How to Use t-SNE Effectively — Distill
Recurrent neural networks are one of the staples of deep learning, allowing neural networks to work with sequences of data like text, audio and video. They can be used to boil a sequence down into a high-level understanding, to annotate sequences, and even to generate new sequences from scratch!
Source: Attention and Augmented Recurrent Neural Networks — Distill
The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free.
Source: Deep Learning
Neural Networks and Deep Learning is a free online book. The book will teach you about:
Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you many of the core concepts behind neural networks and deep learning.
Source: Neural networks and deep learning
Several interactive visualizations of a generative model of handwriting. Some are fun, some are serious.
Source: Experiments in Handwriting with a Neural Network — Distill
Embeddings are ubiquitous in machine learning, appearing in recommender systems, NLP, and many other applications. Indeed, in the context of TensorFlow, it’s natural to view tensors (or slices of tensors) as points in space, so almost any TensorFlow system will naturally give rise to various embeddings.
To learn more about embeddings and how to train them, see the Vector Representations of Words tutorial. If you are interested in embeddings of images, check out this article for interesting visualizations of MNIST images. On the other hand, if you are interested in word embeddings, this article gives a good introduction.
TensorBoard has a built-in visualizer, called the Embedding Projector, for interactive visualization and analysis of high-dimensional data like embeddings. It is meant to be useful for developers and researchers alike. It reads from the checkpoint files where you save your tensorflow variables. Although it’s most useful for embeddings, it will load any 2D tensor, potentially including your training weights.
Source: TensorBoard: Embedding Visualization
There’s a projector as well, which you can use seperately from tensorflow here
You can use this to see what your AI is thinking…
DeepMind Lab is a fully 3D game-like platform tailored for agent-based AI research. It is observed from a first-person viewpoint, through the eyes of the simulated agent. Scenes are rendered with rich science fiction-style visuals. The available actions allow agents to look around and move in 3D. The agent’s “body” is a floating orb. It levitates and moves by activating thrusters opposite its desired direction of movement, and it has a camera that moves around the main sphere as a ball-in-socket joint tracking the rotational look actions. Example tasks include collecting fruit, navigating in mazes, traversing dangerous passages while avoiding falling off cliffs, bouncing through space using launch pads to move between platforms, playing laser tag, and quickly learning and remembering random procedurally generated environments.
Source: Open-sourcing DeepMind Lab | DeepMind
We’re releasing Universe, a software platform for measuring and training an AI’s general intelligence across the world’s supply of games, websites and other applications.
Universe allows an AI agent to use a computer like a human does: by looking at screen pixels and operating a virtual keyboard and mouse. We must train AI systems on the full range of tasks we expect them to solve, and Universe lets us train a single agent on any task a human can complete with a computer.
In April, we launched Gym, a toolkit for developing and comparing reinforcement learning (RL) algorithms. With Universe, any program can be turned into a Gym environment. Universe works by automatically launching the program behind a VNC remote desktop — it doesn’t need special access to program internals, source code, or bot APIs.
Source: Universe
It uses OpenAI Gym for Reinforcement Learning
Reinforcement learning (RL) is the subfield of machine learning concerned with decision making and motor control. It studies how an agent can learn how to achieve goals in a complex, uncertain environment. It’s exciting for two reasons:
RL is very general, encompassing all problems that involve making a sequence of decisions: for example, controlling a robot’s motors so that it’s able to run and jump, making business decisions like pricing and inventory management, or playing video games and board games. RL can even be applied to supervised learning problems with sequential or structured outputs.
RL algorithms have started to achieve good results in many difficult environments. RL has a long history, but until recent advances in deep learning, it required lots of problem-specific engineering. DeepMind’s Atari results, BRETT from Pieter Abbeel’s group, and AlphaGo all used deep RL algorithms which did not make too many assumptions about their environment, and thus can be applied in other settings.However, RL research is also slowed down by two factors:
The need for better benchmarks. In supervised learning, progress has been driven by large labeled datasets like ImageNet. In RL, the closest equivalent would be a large and diverse collection of environments. However, the existing open-source collections of RL environments don’t have enough variety, and they are often difficult to even set up and use.
Lack of standardization of environments used in publications. Subtle differences in the problem definition, such as the reward function or the set of actions, can drastically alter a task’s difficulty. This issue makes it difficult to reproduce published research and compare results from different papers.OpenAI Gym is an attempt to fix both problems.
source
The Gym homepage
The Gym github page