Report Ocean has come up with a study on Artificial Intelligence (AI) Hardware Market and the report is laden with information that can be utilized by …

Invented by Geoffrey Hinton in 1985, Restricted Boltzmann Machine which falls under the category of unsupervised learning algorithms is a network of symmetrically connected neuron-like units that make stochastic decisions. This deep learning algorithm became very popular after the Netflix Competition where RBM was used as a collaborative filtering technique to predict user ratings for movies and beat most of its competition. It is useful for regression, classification, dimensionality reduction, feature learning, topic modelling and collaborative filtering. Restricted Boltzmann Machines are stochastic two layered neural networks which belong to a category of energy based models that can detect inherent patterns automatically in the data by reconstructing input. They have two layers visible and hidden.

Will Artificial Intelligence vs Humans become a reality instead of just seeing it in movies? Will artificial intelligence ever become super-intelligent so that it no longer needs humans? The possibility of clashing with robots and artificial intelligence in the future is a real possibility. This is right out of science fiction movies many of us grew up watching. If someone would have said this 5 years ago would have said not a chance.

Fairy tales are mostly fictions decorated with fantasy. But sci-fic movies are often taken into serious account. The reason behind this is that machines and robotics-related movies from the past are slowly turning to be a reality now. Henceforth, people are curious about what technologies movies might unravel to the mindsets of scientists. Familiar topics like advanced Artificial Intelligence (AI) systems, humanoid robots, self-driving cars, and a whole world full of digital growth is what the movie sector has portrayed so far.

Using attention weights to identify information that is important for models' decision making is a popular approach to interpret attention-based neural networks, which is commonly realized via creating a heat-map for every single document based on attention weights. However, this interpretation method is fragile. In this paper, we propose a corpus-level explanation approach, which aims to capture causal relationships between keywords and model predictions via learning importance of keywords for predicted labels across a training corpus based on attention weights. Using this idea as the fundamental building block, we further propose a concept-based explanation method that can automatically learn higher-level concepts and their importance to model prediction task. Our concept-based explanation method is built upon a novel Abstraction-Aggregation Network, which can automatically cluster important keywords during an end-to-end training process. We apply these methods to the document classification task and show that they are powerful in extracting semantically meaningful keywords and concepts. Our consistency analysis results based on an attention-based Na\"ive Bayes Classifier also demonstrate these keywords and concepts are important for model predictions.

Decisions made by machine learning systems have increasing influence on the world, yet it is common for machine learning algorithms to assume that no such influence exists. An example is the use of the i.i.d. assumption in content recommendation. In fact, the (choice of) content displayed can change users' perceptions and preferences, or even drive them away, causing a shift in the distribution of users. We introduce the term auto-induced distributional shift (ADS) to describe the phenomenon of an algorithm causing a change in the distribution of its own inputs. Our goal is to ensure that machine learning systems do not leverage ADS to increase performance when doing so could be undesirable. We demonstrate that changes to the learning algorithm, such as the introduction of meta-learning, can cause hidden incentives for auto-induced distributional shift (HI-ADS) to be revealed. To address this issue, we introduce `unit tests' and a mitigation strategy for HI-ADS, as well as a toy environment for modelling real-world issues with HI-ADS in content recommendation, where we demonstrate that strong meta-learners achieve gains in performance via ADS. We show meta-learning and Q-learning both sometimes fail unit tests, but pass when using our mitigation strategy.

This paper reviews the current state of the art in Artificial Intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically Machine Learning (ML) algorithms, is provided including Convolutional Neural Network (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement Learning (DRL). We categorise creative applications into five groups related to how AI technologies are used: i) content creation, ii) information analysis, iii) content enhancement and post production workflows, iv) information extraction and enhancement, and v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, machine learning-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of machine learning in domains with fewer constraints, where AI is the `creator', remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human centric -- where it is designed to augment, rather than replace, human creativity.

One of the challenges in the NLP field is training large classification models, a task that is both difficult and tedious. It is even harder when GPU hardware is unavailable. The increased availability of pre-trained and off-the-shelf word embeddings, models, and modules aim at easing the process of training large models and achieving a competitive performance. We explore the use of off-the-shelf BERT models and share the results of our experiments and compare their results to those of LSTM networks and more simple baselines. We show that the complexity and computational cost of BERT is not a guarantee for enhanced predictive performance in the classification tasks at hand.

Liam Porr, a computer science student at the University of California,Berkeley, used the new machine learning model to generate the post with the …

PCCI's data science and clinical team worked diligently in collaboration with the Parkland Informatics team to develop an innovative machine learning …