This post is authored by Sudarshan Raghunathan, Principal Development Lead for modules in the Microsoft Azure ML Studio team based in Cambridge, MA. In his blog post last month, John Langford wrote about the open source Vowpal Wabbit (VW) machine learning (ML) system. He highlighted some of the main advantages of VW, e.g. its performance and ability to handle large sparse datasets, which make it particularly popular both within and outside Microsoft for applications such as sentiment analysis and recommendation systems. When we initially released the public preview of Azure ML in July this year, we exposed a small subset of VW functionality as part of our Feature Hashing module. The latter transforms datasets with text features into binary using the feature hashing algorithm (Murmur hash) implemented in VW.

Virtual agents and chatbots will be the top consumer applications of artificial intelligence over the next five years, according to a consensus poll released today by TechEmergence, a marketing research firm for AI and machine learning. The emphasis on virtual agents and chatbots is in many ways not surprising. After all, the tech industry's 800-pound gorillas have all made big bets: Apple with Siri, Amazon with Alexa, Facebook with M and Messenger, Google with Google Assistant, Microsoft with Cortana and Tay. However, the poll's data also suggests that chatbots may soon be viewed as a horizontal enabling technology for many industries. "The most unexpected result was that so many founders who were not directly involved in the chatbot space or smart home/device space were very excited about these areas," wrote Daniel Faggella, founder of TechEmergence, in an email interview.

AI (or, artificial intelligence) is a topic which is becoming increasingly prevalent. Whilst it is becoming more common within the market research industry, I don't believe it can't totally replace a researcher and how we think and work (thankfully or we'd all be looking for new jobs!); and on top of that I don't think that it should. Companies these days are generating more data than ever before so, don't get me wrong, anything that makes our lives easier is great - and as Helene Protopapas discussed the fact that AI can help speed up research is advantageous to us all. However, there are some reasons why I strongly believe that AI isn't the'produce insight' button we've all secretly been wishing for and that you still need a researcher to deliver the insight that clients want and expect. As Harmony Crawford points out, if you just give someone a load of numbers, they'll drown in them.

Since the advent of Siri, personal digital assistants have been an exciting thing. We later saw Google Now and then came Microsoft's Cortana. While digital assistants can do a lot more there is another category, the bots. Many of us would have observed that replies to customers from telcos in social networks appear to be robotic in nature. According to our sources in the telecom industry, they aren't handled by bots yet.

A Passion for Research Internet of Things, Machine Learning & Robotics Are High Priorities For Developers In 2016 by Louis Columbus on June 25, 2016 56.4% of developers are building robotics apps today. These and many other insights are from the Evans Data Corporation Global Development Survey, Volume 1 (PDF, client access) published earlier this month. The methodology was based on interviews with developers actively creating new applications with the latest technologies. The Evans Data Corporation (EDC), International Panel of Developers, were sent invitations to participate and complete the survey online. Please see page 17 of the study for additional details on the methodology.

Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. Join Jennifer Marsman as she welcomes Sonja Knoll to the show as they take a deep dive into Deep Learning as well as apply some real-world scenarios for you to try out on your own. If you're interested in learning more about the products or solutions discussed in this episode, click on any of the below links for free, in-depth information:

Procurement's Digital Transformation Artificial Intelligence and Machine Learning are changing the ball game in Supply Chain & Procurement! Artificial Intelligence and Procurement Can AI improve Procurement? Modern Technology and Talents Whatever the industry, attracting talents is the key to your success! This is Procurement's Golden Age Procurement: The revolution of supplier relationships! Artificial Intelligence & Human Interaction… It's probably not likely, but if you need to stop by a physical…

Recent observations of galaxies at z \gtrsim 7, along with the low value of the electron scattering optical depth measured by the Planck mission, make galaxies plausible as dominant sources of ionizing photons during the epoch of reionization. However, scenarios of galaxy-driven reionization hinge on the assumption that the average escape fraction of ionizing photons is significantly higher for galaxies in the reionization epoch than in the local Universe. The NIRSpec instrument on the James Webb Space Telescope (JWST) will enable spectroscopic observations of large samples of reionization-epoch galaxies. While the leakage of ionizing photons will not be directly measurable from these spectra, the leakage is predicted to have an indirect effect on the spectral slope and the strength of nebular emission lines in the rest-frame ultraviolet and optical. Here, we apply a machine learning technique known as lasso regression on mock JWST/NIRSpec observations of simulated z 7 galaxies in order to obtain a model that can predict the escape fraction from JWST/NIRSpec data.

The End of Talent Management as We Know It.

I've been really interested in AI and bots lately. I decided to think about a bot experience for YouTube.