Autumn is as good a season to learn natural language processing as any other, and why not do so with quality, free online courses? This is a collection of just such free, quality online NLP courses, from such esteemed institutions of learning as Stanford, Oxford, University of Washington, and UC Berkeley. There are also offerings from independent sources like Yandex Data School, and even a short practical course on spaCy by one of its creators and co-founder of the company which steers its development. So whether you are looking for theoretical or practical, or are a beginner or an advanced learner, the content included herein won't fail on living up to the promise of being 10 free top notch natural language processing courses. So dig in and learn NLP today.

At the Deep Learning Summit conference here in London, DeepMind Research Scientist Ali Eslami presented a very interesting project, entitled "Artificial Intelligence and Creativity". Eslami's team at DeepMind set an AI agent (a neural network which takes some kind of action) the challenge of drawing a human face, given a computer drawing program (Mypaint) and allowing it to experiment with variables including brush selection and placement, and line pressure and colour. The agent was fed an unlabelled training dataset of photographs of faces. The results were startlingly realistic. Eslami's group used a technique called reinforcement learning. With most AI systems today, we use supervised learning, where we feed in data that is labelled in some way, so the system can compare its results with the right answer.

GPT-2 generates text that is far more realistic than any text generation system before it. OpenAI was so shocked by the quality of the output that they decided that the full GPT-2 model was too dangerous to release because it could be used to create endless amounts of fake news that could fool the public or clog up search engines like Google. How easy it is for an average person to generate fake news that could trick a real person and how good are the results? Let's explore how a system like this could work and how much of a threat it is. Let's try to build a newspaper populated with fake, computer generated news: To populate News You Can't Use, we'll create a Python script that can'clone' a news site like the New York Times and generate artificial news stories on the same topics.

Update (August 19): We've released TabNine Local, which lets you run Deep TabNine on your own machine. TL;DR: TabNine is an autocompleter that helps you write code faster. We're adding a deep learning model which significantly improves suggestion quality. You can see videos below and you can sign up for it here. There has been a lot of hype about deep learning in the past few years.

Recent surveys, studies, forecasts and other quantitative assessments of the progress of AI highlighted US consumers' interest in AI working alongside their physicians, the current porn-heavy state of deepfakes, the potential business benefits of Robotic Process Automation (RPA), and the impact of automation on incomes and employment. The internet is home to at least 14,678 deepfakes, according to a new report by DeepTrace. Funds run by computers that follow rules set by humans account for 35% of America's stockmarket, 60% of institutional equity assets and 60% of trading activity. Exchange-traded funds (etfs) and mutual funds automatically track indices of shares and bonds--in September 2019 these vehicles had $4.3trn invested in American equities, exceeding the sums actively run by humans for the first time [The Economist] Only 16% expect to reduce training budgets; 52% of the U.S. employees surveyed believe they have the necessary skills to be successful in an AI-enabled workplace, 20% saying they do not possess the right skills, and 28% reporting they aren't sure; 25% of employers and 20% of employees see a definite gap in workers' skills; 63% of U.S. employees expressed willingness to use a virtual or digital assistant to help them self-manage tasks and deadlines [Genesys surveys of 303 employers and 1,001 employees] Only 29% of finance departments that have deployed Robotic Process Automation (RPA) have utilized the technology for financial reporting; the average amount of avoidable rework in accounting departments can take up to 30% of a full-time employee's overall time. The first comprehensive review of studies published since 2012 comparing analysis of medical images by healthcare professionals and deep learning systems found humans and machines are on a par.

With the advancement of artificial intelligence in medical diagnostics, many reader studies have been carried out to determine whether man or machine is the better diagnostician [1x[1]Esteva, A. et al. Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. In these past studies, in particular in the field of skin cancer detection, man and machine were always regarded as competitors. However, this setting does not reflect clinical reality, where team-based diagnoses are considered to be more accurate than individual diagnoses [5x[5]Barnett, M.L. et al. Collective intelligence meets medical decision-making: the collective outperforms the best radiologist.

We are looking for a deep learning scientist to help us improve our AI trading system. Magic Carpet AI is a research and technology company focused on artificial intelligence applied to investment solutions. We have seen the power of our AI engine and are building a world-class asset management system. MCAI is a strong predictive analytics unit with a tech oriented foundation. We built the ideal playground for AI research in the financial market.

Deep Learning (DL) models are revolutionizing the business and technology world with jaw-dropping performances in one application area after another -- image classification, object detection, object tracking, pose recognition, video analytics, synthetic picture generation -- just to name a few. However, they are like anything but classical Machine Learning (ML) algorithms/techniques. DL models use millions of parameters and create extremely complex and highly nonlinear internal representations of the images or datasets that are fed to these models. They are, therefore, often called the perfect black-box ML techniques. We can get highly accurate predictions from them after we train them with large datasets, but we have little hope of understanding the internal features and representations of the data that a model uses to classify a particular image into a category.

Since deep learning regained prominence in 2012, many machine learning frameworks have clamored to become the new favorite among researchers and industry practitioners. From the early academic outputs Caffe and Theano to the massive industry-backed PyTorch and TensorFlow, this deluge of options makes it difficult to keep track of what the most popular frameworks actually are. If you only browsed Reddit, you might assume that everyone's switching to PyTorch. Judging instead by Francois Chollet's Twitter, TensorFlow/Keras may appear as the dominant framework while PyTorch's momentum is stalling. In 2019, the war for ML frameworks has two remaining main contenders: PyTorch and TensorFlow. My analysis suggests that researchers are abandoning TensorFlow and flocking to PyTorch in droves.

Artificial Intelligence is still a nascent technology; much of the groundbreaking work moving the industry forward is done inside AI research labs. It's often from those labs that open source projects are started. Institutes like Open AI, NASA's JPL, Google Deepmind, MIT CSAIL, BAIR, The Turing Institute, and Max Planck -- to name just a handful -- are presenting at ODSC in 2019, helping us bring our community to the leading edge of AI. To learn more about the labs' role at ODSC, visit ODSC West. Since our next conference is in San Francisco, we're looking west at a few exciting research labs in the area that are participating in ODSC this year.