As we welcome in 2019 and look ahead to another busy year of RE•WORK Summits, we thought we'd put together some of our blog posts from the last 12 months. As always, interviews with expert speakers have been particularly popular, as have more technical pieces as well as articles highlighting diversity in AI. Here are the articles that received the most reads in 2018:
For the first time ever, RE•WORK brought together the ‘Godfathers of AI’ to appear not only at the same event but on a joint panel discussion. At the Deep Learning Summit in Montreal, we saw Yoshua Bengio, Yann LeCun and Geoffrey Hinton come together to share their most cutting-edge research progressions as well as discussing the landscape of AI and the deep learning ecosystem in Canada.
Written by Catherine Lu, Principal at Spike Ventures, the post explores the enormous promise of AI. The jury is still out on who the biggest AI winners in the enterprise space will be. So far, applying AI to enterprise has not made as much impact as people have expected. Cloud computing, for instance, has had far greater impact in the enterprise space than AI has.
Having won several notable awards, and speaking at several RE•WORK Summits, Ilya Sutskever has become a well-known name in the deep learning field. Since being announced as Research Director at OpenAI, a non-profit company formed with Elon Musk, Sam Altman and Greg Brockman, Ilya has been working on the goal to advance artificial intelligence that benefits humanity.
Last May 24 - 25, RE•WORK returned to Boston for the Deep Learning Summit & Deep Learning in Healthcare Summit. In the months leading up to the event, we had some really exciting conversations with experts working in deep learning from both academia and industry. We wanted to share with you some of the most influential women behind advancements in AI, focusing on Boston and the surrounding area.
Due to the positive response we received from the Boston Women in AI list, we decided to create a similar article for each new location we visited with our Summits. As a strong advocate for supporting female entrepreneurs and women working towards advancing technology and science, we also hold Women in AI Dinners to champion progressions in AI, with a focus on leading women in the field. We spoke with women at these events and dinners to hear who they thought was leading the way in the field.
Ever popular in the media the terms artificial intelligence, deep learning and machine intelligence are constantly popping up. The first thing we tend to think of are the big names in tech like Amazon, Facebook, and Google as having a clear use for these tools. Whilst this is true, there are also huge advantages for their applications in businesses and everyday enterprises. DL is constantly being refined to provide solutions and increase business efficiency in a number of areas including predictive maintenance, risk analysis, demand and supply optimization, sentiment analysis, and market targeting.
This guest post from Reena Shaw from KDnuggets explores the types of machine learning algorithms as well as quantifying the popularity of each of these algorithms. In a technical and in-depth article, Reena explains how logistic regression, linear regression, classification and regression trees and naïve bayes all play their part in machine learning.
Last year at the AI Assistant Summit in San Francisco, we were joined by Pararth Shah, Research Engineer at Google, who we spoke with about his current work as well as AI and deep learning more generally. Pararth is currently working to improve the process in which conversational AI interacts with the user by creating an AI that actually learns from its conversation and interaction with the user.
In the lead up to each event, we used this time as an opportunity to look into some of the most exciting work within AI taking place in the cities, and wanted to share with you some of the most influential women behind these progressions across multiple industries. Hear how Beena Ammanath, Carol Reiley, Daphne Koller and more have contributed to AI.
Both in the summer and the Christmas break, it's the time of year when you pick up a book and actually realise how much you enjoy reading. Twice this year, we've made it it our mission at RE•WORK to suggest the top books in AI and DL to encourage you to read more and keep learning. We spoke to some of our AI community to ask what Deep Learning books, journals and papers they’d recommend, and we’ve compiled a list.Keen to hear more topics covered by RE•WORK? Suggest them in the comments.