Artificial Intelligence is poised to solve world-peace, poverty and hunger as well as the climate crisis. At least that's what one could think when reading the headlines. In that respect I really liked this article from MIT Sloan and BCG: What managers need to know about artificial intelligence. This all fits very well with what I have learned so far about how DigitalCX (The customer experience platform that enterprises use to power chatbots, virtual assistants and in-app engagement) is helping clients to improve their customer's experience and reduce costs or improve revenues. Clients that understand what their customers want to know (and don't know) benefit greatly from our natural language processing capabilities.

This week Stanford was the center of attention in the artificial intelligence community after it published news that it trained a deep learning model that diagnoses skin cancer as accurately as a dermatologist. The algorithm apparently can identify a cancerous mole with nothing more than a picture, meaning it could be put into the hands of anyone with a simple smartphone -- otherwise known as a pocket supercomputer. Deep learning is revolutionizing the way innovators can apply AI and data science to solve real-world problems. Image classification, facial recognition, computational linguistics, translation, augmented reality, self-driving cars -- all of these fields have made huge leaps in the last several years as computer scientists apply the rapidly-developing machine learning models that empower them. With all the excitement around these developments, one starts to wonder…what does a future with advanced AI look like?

If you're a business executive (rather than a data scientist or machine learning expert), you've probably been exposed to the mainstream media coverage of artificial intelligence or AI. You've seen articles in The Economist and Vanity Fair, you've seen emotional stories about Tesla Autopilot and the threat of AI to mankind by such luminaries as Stephen Hawking, and you might even have seen Dilbert make jokes about Artificial Intelligence and Human Intelligence. So if you're an executive who cares about growing your business, all this AI media coverage may prompt two nagging questions. First, is the business potential of AI real or not? The answer to the first question is that the business potential of AI is real.

Things, not Strings Entity-centric views on enterprise information and all kinds of data sources provide means to get a more meaningful picture about all sorts of business objects. This method of information processing is as relevant to customers, citizens, or patients as it is to knowledge workers like lawyers, doctors, or researchers. People actually do not search for documents, but rather for facts and other chunks of information to bundle them up to provide answers to concrete questions. Strings, or names for things are not the same as the things they refer to. Still, those two aspects of an entity get mixed up regularly to nurture the Babylonian language confusion.

Machine learning-based natural language processing systems are amazingly effective, when plentiful labeled training data exists for the task/domain of interest. Unfortunately, for broad coverage (both in task and domain) language understanding, we're unlikely to ever have sufficient labeled data, and systems must find some other way to learn. I'll describe a novel algorithm for learning from interactions, and several problems of interest, most notably machine simultaneous interpretation (translation while someone is still speaking). This is all joint work with some amazing (former) students He He, Alvin Grissom II, John Morgan, Mohit Iyyer, Sudha Rao and Leonardo Claudino, as well as colleagues Jordan Boyd-Graber, Kai-Wei Chang, John Langford, Akshay Krishnamurthy, Alekh Agarwal, Stéphane Ross, Alina Beygelzimer and Paul Mineiro.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

Systematic improvements are the best way for a marketing organization to make consistent gains in performance. To have an impact over the long-run, changes should be made at the fundamental level to how the budget gets allocated, how content is created, how performance is measured, etc. That's why marketing performance management (MPM) has increased as a focus for so many B2B marketing departments. What marketing performance management means to B2B organizations varies quite a bit. For those who are just developing marketing performance management practices, it can be as simple as tracking channel performance and trying to base budget allocation decisions on demand generation goals.

Sound is an incredibly valuable means of communicating information. Most motorists are familiar with the alarming noise of a slipping belt drive. And many other experts can detect problems with common machines in their respective fields just by listening to the sounds they make. If we can find a way to automate listening itself, we would be able to more intelligently monitor our world and its machines day and night. We could predict the failure of engines, rail infrastructure, oil drills and power plants in real time -- notifying humans the moment of an acoustical anomaly.

There is tremendous potential for facial recognition technology, such as informing visually impaired persons if someone they know is approaching them. I find it difficult to believe just how fast things are moving with regard to using artificial neural networks (ANNs) and deep learning techniques (for example, see Deep learning machine vision system aids blind and visually impaired, Deep learning hits a sweet note, Machine learning platform speeds optimization of vision systems, Unlocking the power of AI for all developers, and Push-button generation of deep neural networks). Of course, one really interesting application is to perform object detection and identification, including the really tricky task of recognizing and identifying faces in images and videos. This sort of task benefits from the extreme parallelism offered by FPGAs. Of particular interest are Intel's current generation of FPGAs, whose hard-core DSP slices offer both fixed-point and floating-point capabilities, making them suitable for a wide range of artificial intelligence (AI) and embedded vision applications.

Selecting a time series forecasting model is just the beginning. Using the chosen model in practice can pose challenges, including data transformations and storing the model parameters on disk. In this tutorial, you will discover how to finalize a time series forecasting model and use it to make predictions in Python. How to Make Predictions for Time Series Forecasting with Python Photo by joe christiansen, some rights reserved. A lot is written about how to tune specific time series forecasting models, but little help is given to how to use a model to make predictions. We will take a look at each of these elements in this tutorial, with a focus on saving and loading the model to and from file and using a loaded model to make predictions.