Where is Deep Learning applicable? This is one of the more fleeting ideas to understand about Deep Learning and related A.I. technologies. It is all too easy to fall in the trap that a "Artificial Intelligence" application can solve your problem. The usual coverage of this problem involves the question of "do you have enough data?" Unfortunately, that is too vague in that to answer this you have to at least understand your problem domain.

No knowledge of machine learning is required for using Signals model builder. Just choose from a variety of indicators, ranging from traditional technical analysis to deep learning or sentiment analysis based on media monitoring and combine them together. However, if you happen to be a developer or a data scientist you can develop new trading indicators from scratch and monetize your data science skills through Signals indicator marketplace.

As you may probably know, DeepMind has recently published a paper on AlphaZero [1], a system that learns by itself and is able to master games like chess or Shogi. Before getting into details, let me introduce myself. I am a researcher in the broad field of Artificial Intelligence (AI), specialized in Natural Language Processing. I am also a chess International Master, currently the top player in South Korea although practically inactive for the last few years due to my full-time research position. Given my background I have tried to build a reasoned opinion on the subject as constructive as I could.

Recurrent Neural Networks (RNNs) are powerful sequence modeling tools. However, when dealing with high dimensional inputs, the training of RNNs becomes computational expensive due to the large number of model parameters. This hinders RNNs from solving many important computer vision tasks, such as Action Recognition in Videos and Image Captioning. To overcome this problem, we propose a compact and flexible structure, namely Block-Term tensor decomposition, which greatly reduces the parameters of RNNs and improves their training efficiency. Compared with alternative low-rank approximations, such as tensor-train RNN (TT-RNN), our method, Block-Term RNN (BT-RNN), is not only more concise (when using the same rank), but also able to attain a better approximation to the original RNNs with much fewer parameters. On three challenging tasks, including Action Recognition in Videos, Image Captioning and Image Generation, BT-RNN outperforms TT-RNN and the standard RNN in terms of both prediction accuracy and convergence rate. Specifically, BT-LSTM utilizes 17,388 times fewer parameters than the standard LSTM to achieve an accuracy improvement over 15.6\% in the Action Recognition task on the UCF11 dataset.

Cooperative multi-agent systems can be naturally used to model many real world problems, such as network packet routing and the coordination of autonomous vehicles. There is a great need for new reinforcement learning methods that can efficiently learn decentralised policies for such systems. To this end, we propose a new multi-agent actor-critic method called counterfactual multi-agent (COMA) policy gradients. COMA uses a centralised critic to estimate the Q-function and decentralised actors to optimise the agents' policies. In addition, to address the challenges of multi-agent credit assignment, it uses a counterfactual baseline that marginalises out a single agent's action, while keeping the other agents' actions fixed. COMA also uses a critic representation that allows the counterfactual baseline to be computed efficiently in a single forward pass. We evaluate COMA in the testbed of StarCraft unit micromanagement, using a decentralised variant with significant partial observability. COMA significantly improves average performance over other multi-agent actor-critic methods in this setting, and the best performing agents are competitive with state-of-the-art centralised controllers that get access to the full state.

Last week, during the Deep Learning Summit at AWS re:Invent 2017, Terrence Sejnowski (a pioneer of deep learning) succinctly said "Whoever has more data wins". He was echoing a premise that has been repeated many times in many ways by many people: machine learning requires big data to work. Without large, well maintained training sets, machine learning algorithms--especially deep learning algorithms--fall far short of their potential. That's why here at Qubole we believe that enabling data scientists starts with giving them a platform to quickly select, clean, and aggregate datasets on a massive scale. The recent surge in impactful applications of deep learning algorithms has misled many people to believe that there has been a corresponding upswell in innovation in this field.

This article is part of an ongoing "Humans of AI" series consisting of interviews with AI experts and visionaries around the world. When he's not working on one of his several projects, machine learning developer Jorge Muñoz spends much of his time trying to keep up to date on the cutting edge of artificial intelligence research. "Every month there is something new; every month there are people doing something interesting," he said. "The field is getting really huge, so it's getting really difficult to stay informed on what everyone is doing." The AI explosion of the last few years "as it's actually started working" has created a huge demand for resources to learn about AI, Muñoz said.

The AMIs also come with improved framework support for NVIDIA Volta. They include PyTorch v0.3.0, and support NVIDIA CUDA 9 and cuDNN 7, with significant performance improvements for training models on NVIDIA Volta GPUs. As well, they include a version of TensorFlow built from the master and merged with NVIDIA processors for Volta support. We've also added Keras 2.0 support on the CUDA 9 version of the AWS Deep Learning AMIs to work with TensorFlow as the default backend.

Interest in machine learning has exploded over the past decade. You see machine learning in computer science programs, industry conferences, and the Wall Street Journal almost daily. For all the talk about machine learning, many conflate what it can do with what they wish it could do. Fundamentally, machine learning is using algorithms to extract information from raw data and represent it in some type of model. We use this model to infer things about other data we have not yet modeled.

One of today's most overused buzzword is "Artificial Intelligence". Both technical and general press is full of articles talking about machines that drive autonomous cars and invent new languages. Machine Learning is an essential part of the AI puzzle and Deep Learning is one of the most popular approaches to implement Machine Learning. Interestingly, Deep Learning is not new. Geoffrey Hinton demonstrated the use of back-propagation of errors for training multi-layer neural networks in 1986, more than 30 years ago. Even earlier, in the 60's, Kelley, Bryson and Ho published research papers about dynamic optimization which many consider as the basis for back-propagation.