.”Change Shopping Forever” … that’s a pretty bold statement to make from two small cap companies in the early stages of commercialization. But when you watch this interview, it becomes pretty apparent why Loop Insights (MTRX:TSXV) and ImagineAR (IP:CSE / IPNFF:OTCQB) realized within a couple of hours of speaking they were creating the “Minority Report” of shopping. How? Well, this is the part where we write a carefully crafted synopsis of the interview to provide viewers with enough context to dive into the video. But this is the first time in our existence where a synopsis just isn’t possible because it’s tough enough trying to describe the benefits of one paradigm shifting technology, let alone two of them that have joined forces. If we tried, we’d give away the entire interview and still not do it justice. Trust us that this is a video you have to watch – and then share. In fact, you’ll probably end up watching it a couple of times. These two companies are onto something we’ve never seen before. At Agoracom, we believe the convergence of AI and AR is going to change the world this decade in many applications – and we just found the first two companies who may very well be doing by closing the loop between E-commerce and Bricks & Mortar. Get your popcorn ready. Watch this interview or listen by Podcast on Apple, Google, Spotify or your favourite podcaster.
Paco Nathan (O’Reilly Media) reviews use cases where Jupyter provides a frontend to AI as the means for keeping humans in the loop. This process enhances the feedback loop between people and machines, and the end result is that a smaller group of people can handle a wider range of responsibilities for building and maintaining a complex system of automation. Subscribe to O’Reilly on YouTube: http://goo.gl/n3QSYi Follow O’Reilly on: Twitter: http://twitter.com/oreillymedia Facebook: http://facebook.com/OReilly Instagram: https://www.instagram.com/oreillymedia LinkedIn: https://www.linkedin.com/company-beta/8459/
Zachary Lipton (Carnegie Mellon University) https://simons.berkeley.edu/talks/tba-79 Emerging Challenges in Deep Learning
Human in the loop Machine learning and AI for the people Paco Nathan is a unicorn. It’s a cliche, but gets the point across for someone who is equally versed in discussing AI with White House officials and Microsoft product managers, working on big data pipelines and organizing and part-taking in conferences such as Strata in his role as Director, Learning Group with O’Reilly Media. Nathan has a mix of diverse background, hands-on involvement and broad vision that enables him to engage in all of those, having been active in AI, Data Science and Software Engineering for decades. The trigger for our discussion was his Human in the Loop (HITL) framework for machine learning (ML), presented in Strata EU. Human in the loop HITL is a mix and match approach that may help make ML both more efficient and approchable. Nathan calls HITL a design pattern, and it combines technical approaches as well as management aspects. HITL combines two common ML variants, supervised and unsupervised learning. In supervised learning, curated (labeled) datasets are used by ML experts to train algorithms by adjusting parameters, in order to make accurate predictions for incoming data. In unsupervised learning, the idea is that running lots of data through an algorithm will reveal some sort of structure. The less common ML variant that HITL builds on is called semi-supervised, and an important special case of that is known as “active learning.” The idea is to take an ensemble of ML models, and let them “vote” [More]
O’Reilly 2019 AI Conference – Speaker Submission http://jsonai.org
Human in the Loop is a social enterprise based in Bulgaria which provides employment to refugees in the field of annotating training data for Machine Learning and Artificial Intelligence
Lets utilize Deep Learning to annotate thouthands of images in hours! More here: https://supervise.ly
CS547: Human-Computer Interaction Seminar Human in the Loop Reinforcement Learning Speaker: Emma Brunskill
How to build blood vessel segmentation in retina images when you only have 6 images in training set. Read the whole blog post on Hackernoon: https://hackernoon.com/deep-learning-in-medicine-advancing-medical-image-analysis-with-supervisely-33e936159206 More at https://supervise.ly