Here's the latest news and events from the RE.WORK team!Machines, Humans and Artificial Intelligence We're only two weeks away from our Boston edition of the Deep Learning Summit, taking place on 26-27 May. Deep Learning is an emerging and rapidly advancing topic in artificial intelligence. At the event in Boston, over 200 experts from industry and academia will come together to explore advances in topics including speech recognition, natural language processing and image classification. At the summit, you'll not only learn about the most recent developments in deep learning algorithms and methods, but also how advancements in areas such as object perception and voice recognition will solve challenges in healthcare, drug discovery, security and even environmental issues. Speakers include:

  • Andrew Ng, Chief Scientist at Baidu
  • Hassan Sawaf, Senior Director of Human Language Technology at eBay
  • Aude Oliva, Principal Research Scientist at MIT
  • Richard Socher, Co-Founder and CTO at MetaMind
  • Tara Sainath, Senior Research Scientist at Google

Companies attending include: Apple, Toyota, Autodesk, BMW, Dropbox, Thomson Reuters, Ford, PwC, Shutterstock and MIT.   The Science of Traffic: Can IoT Help End Congestion?

We spoke with Bryan Mistele, Co-Founder and CEO of INRIX - a company at the forefront of connecting cars to smarter cities by leveraging big data analytics to reduce the individual, economic and environmental toll of traffic congestion. Bryan will be presenting his insights and developments at the Internet of Things Summit in Boston on May 28 & 29.Q: What are the practical applications of your work and what sectors are most likely to be affected?BM: What continues to excite us at INRIX is how many new industries we are finding that can be assisted by the data we collect and the analytics we provide. When we started our goals were to solve travel and traffic problems for drivers but now companies are using our data to make investment decisions, to decide where to build their next store or restaurant and to think about how their sales are affected by traffic patterns. Governments and public service agencies are using INRIX insight to plan future roads and public transportation systems, synchronize traffic signals and to plan emergency response due to weather or other events increasing not only efficiency but public safety. Q: What industries do you think will be disrupted by IoT in the future?BM: All of them. We’re nearing an inflection point of innovation around IoT that will have an impact on the world that we haven’t seen since the dawn of the Internet itself. Just as every company has become a technology company, soon every company will be an IoT company. Previously, companies were forced to rely on slow, inaccurate and often costly data to drive business decisions. Through access to accurate and inexpensive real-time data from a variety of connected devices, IoT companies already have created massive breakthroughs in a variety of industries. I expect the pace of innovation only to increase as network effects around connectivity and data increase over time. Read the full interview here. Bryan Mistele will be speaking on Day 2 of the IoT Summit, alongside speakers from Xively, Zubie, Misfit Wearables, Soofa, SafeLogic & more.  View the IoT Summit schedule here.Pushing the Frontiers of Computer Vision: A Q&A with Google's Christian Szegedy

Due to the inroads of deep learning, computer vision appears to be on the verge of being solved. However, current methods are extremely data hungry and getting high quality labelled data is both expensive and cumbersome. Instead of letting humans do the hard work, can we turn our computers into couch potatoes and program them to figure out our visual world by watching decades of videos? The team at Google has set out to push the frontiers of computer vision by giving an affirmative answer to this question. Christian Szegedy is Senior Research Scientist at Google, working on deep learning for computer vision, including image recognition, object detection and video analysis. We caught up with Christian ahead of his presentation at the Deep Learning Summit in Boston this month.Q: What are the main types of problems now being addressed in the deep learning space? CS: Deep learning is applied successfully to machine perception and large scale data analysis. Prime examples of the former are all kinds of computer vision, speech recognition and music classification tasks. A major section of the computer vision literature of the last two years is dedicated to the utilization of learned deep convolutional network features to a large variety of computer vision problems with huge success. Recurrent neural networks have just started to revolutionize the field of machine translation and text understanding as well. Q: What are the practical applications of your work and what sectors are most likely to be affected? CS: My recent work is focused on various fundamental computer vision tasks: on image annotation, object detection, segmentation and pose estimation. This has laid the ground-work for a lot of the computer vision systems used in Google products. For example, Inception network architectures are at the core of several vision-heavy Google services: personal photo search by image content, face tagging in social photos, business detection/recognition in StreetView imagery. Advances in deep learning pave the way for a future in which utilization of visual signals will be as easy, efficient and ubiquitous as textual processing by computers today. Continue reading here.

Until next time!