Over the years, deep learning has evolved causing a massive disruption into industries and business domains. Deep learning is a branch of machine learning that deploys algorithms for data processing and imitates the thinking process and even develops abstractions. Deep learning uses layers of algorithms for data processing, understands human speech and recognizes objects visually. In deep learning, Information is passed through each layer, and the output of the previous layer acts as the input for the next layer. The first layer in a network is referred as the input layer, while the last is the output layer the middle layers are referred to as hidden layers where each layer is a simple, uniform algorithm consisting of one kind of activation function.

This is that Latent semantic analysis (LSA) do. It is based on how frequent you see the word on the exact topic. Like, there are more tech terms in tech articles, for sure. The names of politicians are mostly found in political news, etc. Yes, we can just make clusters from all the words at the articles, but we will lose all the important connections (for example the same meaning of battery and accumulator in different documents). LSA will handle it properly, that's why its called "latent semantic". So we need to connect the words and documents into one feature to keep these latent connections.

Just a few weeks ago, I read that the three top British AI companies to watch are Alphabet subsidiary DeepMind, cyber security firm DarkTrace and Blippar. Not sure why Blippar was on the list, it was after-all an augmented reality company. But what we can now say is that Blippar is in trouble -- it may not have gone to meet its maker, but it has met its administrators. I can see an argument that follows on from this which suggests that the future of AI is worrisome. After-all, there are clear parallels with boo.com.

Solving a challenge: This was the first task set out by the Mount Sinai AI Consortium, a group of scientists, physicians and researchers at New York City–based Mount Sinai Health System dedicated to developing artificial intelligence in medicine. "We wanted to [apply AI] in the healthcare context and tackle a problem that is clinically impactful and relevant to our practices," says Eric Karl Oermann, instructor in the department of neurosurgery at the Icahn School of Medicine and director of the AI program, dubbed AISINAI. The challenge the group landed on was to identify markers of acute neurological illnesses, such as hemorrhages and strokes. Time matters because a patient's "clinical condition is something that worsens, in some cases, by the minute," says Oermann. "They're extremely time-sensitive." With this in mind, the group set out to see if they could find a way to use AI and deep learning to save some of those precious minutes.

Mixed precision combines different numerical precisions in a computational method. The Volta and Turing generation of GPUs introduced Tensor Cores, which provide significant throughput speedups over single precision math pipelines. Deep learning networks can be trained with lower precision for high throughput, by halving storage requirements and memory traffic on gradient and activation tensors. The following NVIDIA tools can enable you to analyze your model and maximize Tensor Cores utilization. NVIDIA Nsight Systems provides developers with a system-wide performance analysis tool, offering a complete and unified view of how their applications utilize a computer's CPUs and GPUs.

Reinforcement learning (RL) problems often feature deceptive local optima, and learning methods that optimize purely for reward signal often fail to learn strategies for overcoming them. Deep neuroevolution and novelty search have been proposed as effective alternatives to gradient-based methods for learning RL policies directly from pixels. In this paper, we introduce and evaluate the use of novelty search over agent action sequences by string edit metric distance as a means for promoting innovation. We also introduce a method for stagnation detection and population resampling inspired by recent developments in the RL community that uses the same mechanisms as novelty search to promote and develop innovative policies. Our methods extend a state-of-the-art method for deep neuroevolution using a simple-yet-effective genetic algorithm (GA) designed to efficiently learn deep RL policy network weights. Experiments using four games from the Atari 2600 benchmark were conducted. Results provide further evidence that GAs are competitive with gradient-based algorithms for deep RL. Results also demonstrate that novelty search over action sequences is an effective source of selection pressure that can be integrated into existing evolutionary algorithms for deep RL.

This paper presents a new algorithm, Evolutionary eXploration of Augmenting Memory Models (EXAMM), which is capable of evolving recurrent neural networks (RNNs) using a wide variety of memory structures, such as Delta-RNN, GRU, LSTM, MGU and UGRNN cells. EXAMM evolved RNNs to perform prediction of large-scale, real world time series data from the aviation and power industries. These data sets consist of very long time series (thousands of readings), each with a large number of potentially correlated and dependent parameters. Four different parameters were selected for prediction and EXAMM runs were performed using each memory cell type alone, each cell type with feed forward nodes, and with all possible memory cell types. Evolved RNN performance was measured using repeated k-fold cross validation, resulting in 1210 EXAMM runs which evolved 2,420,000 RNNs in 12,100 CPU hours on a high performance computing cluster. Generalization of the evolved RNNs was examined statistically, providing interesting findings that can help refine the RNN memory cell design as well as inform future neuro-evolution algorithms development.

In January 2019, DeepMind revealed AlphaStar to the world-the first artificial intelligence (AI) system to beat a professional player at the game of StarCraft II-representing a milestone in the progress of AI. AlphaStar draws on many areas of AI research, including deep learning, reinforcement learning, game theory, and evolutionary computation (EC). In this paper we analyze AlphaStar primarily through the lens of EC, presenting a new look at the system and relating it to many concepts in the field. We highlight some of its most interesting aspects-the use of Lamarckian evolution, competitive co-evolution, and quality diversity. In doing so, we hope to provide a bridge between the wider EC community and one of the most significant AI systems developed in recent times.

Controlled generation of text is of high practical use. Recent efforts have made impressive progress in generating or editing sentences with given textual attributes (e.g., sentiment). This work studies a new practical setting of text content manipulation. Given a structured record, such as `(PLAYER: Lebron, POINTS: 20, ASSISTS: 10)', and a reference sentence, such as `Kobe easily dropped 30 points', we aim to generate a sentence that accurately describes the full content in the record, with the same writing style (e.g., wording, transitions) of the reference. The problem is unsupervised due to lack of parallel data in practice, and is challenging to minimally yet effectively manipulate the text (by rewriting/adding/deleting text portions) to ensure fidelity to the structured content. We derive a dataset from a basketball game report corpus as our testbed, and develop a neural method with unsupervised competing objectives and explicit content coverage constraints. Automatic and human evaluations show superiority of our approach over competitive methods including a strong rule-based baseline and prior approaches designed for style transfer.

Deep image clustering is a recent research area, but with exciting published works [15]. The approaches use the most diverse architectures varying the structure of the deep networks, theclustering algorithms and the combination of both parts. Approachessuch as the Deep Clustering Network (DCN) [9] use a pretrained autoencoder combined with the k-means algorithm. Methods such as Joint Unsupervised Learning (JULE) [10] combines deep convolutional networks with hierarchical clustering. Deep Embbed Cluster (DEC) [11], also uses a pretrained autoencoder, then removes the decoder part and uses the encoder as a feature extractor to feed the clustering method. After that, the network is fine-tuned using the cluster assignment hardening loss. Meanwhile, the clusters are iteratively tuned by minimizing the KL-divergence between the distribution of soft labels and the auxiliary target distribution.