Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are again top technology topics for 2018. In the past several months, these strategic technologies have rapidly gained traction in financial markets, big data analytics, and consumer devices to improve trend analysis, automation, and user learning. Furthermore, the accessibility of AI tools to the general public has vastly improved with the introduction of AI-optimized neural chips and open-source AI solutions, e.g. TensorFlow (Google), Turi Create (Apple), and Gluon (Amazon, Microsoft). While interrelated and often used interchangeably, AI, ML, and DL refer to distinct aspects of making machines smarter. Artificial intelligence, for instance, is the over-arching term that refers to technologies that mimic aspects of human intelligence, such as recognizing an image without significant instruction.

Long-Short Term Memory networks are state-of-the-art tools for long sequence modeling. However, there is a problem with understanding what they have learned and investigating why they are making particular mistakes. Many articles and papers do it for convolutional neural networks, but for LSTM we do not have many tools to look inside and debug them. In this article we try to partially fill this gap. We visualize LSTM network activations from Australian sign language (Auslan) sign classifying model.

It is hard to imagine a more scathing indictment of our ability to read another's thoughts and intentions than our inability to predict Donald Trump's next move. From the gross pre-election misjudgments to postelection bafflement, the best pundits are at a loss to accurately anticipate his response to matters like North Korean military aggressiveness or his moment-by-moment political gyrations and opinion reversals. Labeling Trump a narcissist, psychopath, megalomaniac or attention-impaired, or all of the above, might feel explanatory, but even when armed with the best psychoanalytic insights, we have no idea what he will do when presented with a new or unforeseen circumstance. If conventional psychology isn't up to the task, perhaps we should step back and consider a tantalizing sci-fi alternative -- that Trump doesn't operate within conventional human cognitive constraints, but rather is a new life form, a rudimentary artificial intelligence-based learning machine. When we strip away all moral, ethical and ideological considerations from his decisions and see them strictly in the light of machine learning, his behavior makes perfect sense. Consider how deep learning occurs in neural networks such as Google's Deep Mind or IBM's Deep Blue and Watson.

Editor's note: This post is only one part of a far more thorough and in-depth original, found here, which covers much more than what is included here. Working with unstructured text data is hard especially when you are trying to build an intelligent system which interprets and understands free flowing natural language just like humans. You need to be able to process and transform noisy, unstructured textual data into some structured, vectorized formats which can be understood by any machine learning algorithm. Principles from Natural Language Processing, Machine Learning or Deep Learning all of which fall under the broad umbrella of Artificial Intelligence are effective tools of the trade. Based on my previous posts, an important point to remember here is that any machine learning algorithm is based on principles of statistics, math and optimization.

With more and more businesses looking to scale up their operations, it has become integral for them to imbibe both machine learning as well as predictive analytics. AI coupled with the right deep learning framework has truly amplified the overall scale of what businesses can achieve and obtain within their domains. The machine learning paradigm is continuously evolving. The key is to shift towards developing machine learning models that run on mobile in order to make applications smarter and far more intelligent. Deep learning is what makes solving complex problems possible. Given that deep learning is the key to executing tasks of a higher level of sophistication, building and deploying them successfully proves to be quite the Herculean challenge for data scientists and data engineers across the globe.

I've found the review on the first three courses by Arvind N very useful in taking the decision to enroll in the first course, so I hope, maybe this can also be useful for someone else. Taking the five courses is very instructive. The content is well structured and good to follow for everyone with at least a bit of an understanding on matrix algebra. Some experience in writing Python code is a requirement. The programming assignments are well designed in general.

Plans for smart houses in the future are slowly becoming more and more plausible. A house that does all the manual labor for the occupants, where dinner is ready on the kitchen table and all the amenities in a house are included in these plans. Thanks to Baylor University's School of Electrical and Computer Engineering and deep learning research, a future with smart houses is getting closer. Listed among the research opportunities in the School of Electrical and Computer Engineering is deep learning. This research is helping Artificial Intelligence (AI) to develop into what is presented in science fiction novels and television shows.

Existing inefficient traffic light control causes numerous problems, such as long delay and waste of energy. To improve efficiency, taking real-time traffic information as an input and dynamically adjusting the traffic light duration accordingly is a must. In terms of how to dynamically adjust traffic signals' duration, existing works either split the traffic signal into equal duration or extract limited traffic information from the real data. In this paper, we study how to decide the traffic signals' duration based on the collected data from different sensors and vehicular networks. We propose a deep reinforcement learning model to control the traffic light. In the model, we quantify the complex traffic scenario as states by collecting data and dividing the whole intersection into small grids. The timing changes of a traffic light are the actions, which are modeled as a high-dimension Markov decision process. The reward is the cumulative waiting time difference between two cycles. To solve the model, a convolutional neural network is employed to map the states to rewards. The proposed model is composed of several components to improve the performance, such as dueling network, target network, double Q-learning network, and prioritized experience replay. We evaluate our model via simulation in the Simulation of Urban MObility (SUMO) in a vehicular network, and the simulation results show the efficiency of our model in controlling traffic lights.

Extracting accurate attribute qualities from product titles is a vital component in delivering eCommerce customers with a rewarding online shopping experience via an enriched faceted search. We demonstrate the potential of Deep Recurrent Networks in this domain, primarily models such as Bidirectional LSTMs and Bidirectional LSTM-CRF with or without an attention mechanism. These have improved overall F1 scores, as compared to the previous benchmarks (More et al.) by at least 0.0391, showcasing an overall precision of 97.94%, recall of 94.12% and the F1 score of 0.9599. This has made us achieve a significant coverage of important facets or attributes of products which not only shows the efficacy of deep recurrent models over previous machine learning benchmarks but also greatly enhances the overall customer experience while shopping online.

This essay examines how what is considered to be artificial intelligence (AI) has changed over time and come to intersect with the expertise of the author. Initially, AI developed on a separate trajectory, both topically and institutionally, from pattern recognition, neural information processing, decision and control systems, and allied topics by focusing on symbolic systems within computer science departments rather than on continuous systems in electrical engineering departments. The separate evolutions continued throughout the author's lifetime, with some crossover in reinforcement learning and graphical models, but were shocked into converging by the virality of deep learning, thus making an electrical engineer into an AI researcher. Now that this convergence has happened, opportunity exists to pursue an agenda that combines learning and reasoning bridged by interpretable machine learning models.