The story of machine learning starts in 1943 when neurophysiologist Warren McCulloch and mathematician Walter Pitts introduced a mathematical model of a neural network. The field gathered steam in 1956 at a summer conference on the campus of Dartmouth College. There, 10 researchers came together for six weeks to lay the ground for a new field that involved neural networks, automata theory and symbolic reasoning. The distinguished group, many of whom would go on to make seminal contributions to this new field, gave it the name artificial intelligence to distinguish it from cybernetics, a competing area of research focused on control systems. In some ways these two fields are now starting to converge with the growth of IoT, but that is a topic for another day.

Artificial Intelligence (AI) has arrived. It is not science fiction anymore. Computers already recognize objects in images and understand speech and language at least as well as, if not better than, humans. This has been made possible with rapid advances in hardware, vast amounts of training data, and innovations in machine learning algorithms such as deep neural networks. Deep learning is the driving force behind the current AI revolution and is giving intelligence to today's self-driving cars, smartphone and smart speakers, and making deep inroads into radiology and even gaming.

Computer systems that can automatically generate image captions have been around for several years. While many of these techniques perform considerably well, the captions they produce are typically generic and somewhat uninteresting, containing simple descriptions such as "a dog is barking" or "a man is sitting on a bench." Alasdair Tran, Alexander Mathews and Lexing Xie at the Australian National University have been trying to develop new systems that can generate more sophisticated and descriptive image captions. In a paper recently pre-published on arXiv, they introduced an automatic captioning system for news images that takes the general context behind an image into account while generating new captions. The goal of their study was to enable the creation of captions that are more detailed and more closely resemble those written by humans.

Scalable Deep Learning services are contingent on several constraints. Depending on your target application, you may require low latency, enhanced security or long-term cost effectiveness. Hosting your Deep Learning model on the cloud may not be the best solution in such cases. Deep Learning on the edge alleviates the above issues, and provides other benefits. Edge here refers to the computation that is performed locally on the consumer's products.

I think you will not argue with me when I state that data science is becoming one of the most popular fields to work at, especially given that Harvard Business Review named "data scientist" the sexiest job of the 21st century. In the field, we have come a long way, from the times when terms like data science and machine learning were still unknown and everything was gathered under the umbrella of statistics. However, we are far from the end of the journey. That can also be a dividing aspect of data science -- the field is developing so rapidly that it can be difficult to even follow all the new algorithms, techniques, and approaches. So working in data science, similarly to software engineering, often requires constant learning and development.

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. Advances in artificial intelligence in the early 2010s, particularly in deep learning, triggered a new wave of panic and fear about technological unemployment. Further intensifying those fears were a host of sensational articles about the magical capabilities of AI algorithms and ambiguous statements by company executives creating the impression that human-level AI is just around the corner. But the past few years have only highlighted the limits of current AI technologies. At the turn of the decade, as the world locked down to prevent the spread of the novel coronavirus, we got to see whether the promises of artificial intelligence and robots replacing humans would materialize.

On prem data science solution based on AdventureWorks - MachineLearningWithHuman/Projects

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SARS-CoV-2 virus RNA sequence classification and geographical analysis with convolutional neural networks approach. Abstract Covid-19 infection, which spread to the whole world in December 2019 and is still active, caused more than 250 thousand deaths in the world today. Researches on this subject have been focused on analyzing the genetic structure of the virus, developing vaccines, the course of the disease, and its source. In this study, RNA sequences belonging to the SARS-CoV-2 virus are transformed into gene motifs with two basic image processing algorithms and classified with the convolutional neural network (CNN) models. The CNN models achieved an average of 98% Area Under Curve(AUC) value was achieved in RNA sequences classified as Asia, Europe, America, and Oceania. The resulting artificial neural network model was used for phylogenetic analysis of the variant of the virus isolated in Turkey. The classification results reached were compared with gene alignment values in the GISAID database, where SARS-CoV-2 virus records are kept all over the world. Our experimental results have revealed that now the detection of the geographic distribution of the virus with the CNN models might serve as an efficient method. Keywords: Deep Learning, Bioinformatics, Convolutional neural network, SARS-Cov-2, Pattern Classification Introduction Artificial intelligence practices and particularly deep learning studies are a widely used discipline in many research fields, including medicine and bioinformatics. The CNN models, especially in the field of medical imaging, are very successful in lesions and disease diagnosis. In addition to the success of deep learning methods in the fields of image processing, natural language processing, also has a lot of usage on a time scale with approaches such as Long-Short Term memory. In deep learning practices, low-level features such as DNA sequence, pathology images, and tomography scans can be learned from the data, by largely eliminating the need for engineering applications.

Although deep RL models have shown a great potential for solving various types of tasks with minimal supervision, several key challenges remain in terms of learning rapidly from limited experience, adapting to environmental changes, and generalizing learning from a single task. Recent evidence in decision neuroscience has shown that the human brain has an innate capacity to resolve these issues, leading to optimism regarding the development of neuroscience-inspired solutions toward sample-efficient, adaptive, and generalizable RL algorithms. We show that the computational model, adaptively combining model-based and model-free control, which we term the prefrontal RL, reliably encodes the information of highlevel policy that humans learned, and this model can generalize the learned policy to a wide range of tasks. First, we trained the prefrontal RL, deep RL, and meta RL algorithms on 82 human subjects' data, collected while human participants were performing two-stage Markov decision tasks, in which we experimentally manipulated the goal, state-transition uncertainty, and state-space complexity. In the reliability test, which is based on a combination of the latent behavior profile and the parameter recoverability test, we showed that the prefrontal RL reliably learned the latent policies of the human subjects, while all the other models failed to pass this test. Second, to empirically test the ability to generalize what these models learned from the original task, we situated them in the context of environmental volatility. Specifically, we ran large-scale simulations with 10 different Markov decision tasks, in which latent context variables change over time. Our information-theoretic analysis showed that the prefrontal RL showed the highest level of adaptability and episodic encoding efficacy. To the best of our knowledge, this is the first attempt to formally test the possibility that computational models mimicking the way the brain solves general problems can lead to practical solutions to key challenges in machine learning.