The debate was moderated by Prof. David Chalmers and recorded Oct 5th, 2017. A "nature-nurture" debate took place in the foundations of artificial intelligence. Advocates of deep learning, including Yann LeCun, held that to create advanced artificial intelligence systems, general mechanisms for learning from the environment would play the most important role.

The research was led by Marco Zorzi at the University of Padova and funded with a starting grant from the European Research Centre (ERC). The project – GENMOD – demonstrated that it is possible to build an artificial neural network that observes the world and generates its own internal representation based on sensory data. For example, the network was able by itself to develop approximate number sense, the ability to determine basic numerical qualities, such as greater or lesser, without actually understanding the numbers themselves, just like human babies and some animals. "We have shown that generative learning in a probabilistic framework can be a crucial step forward for developing more plausible neural network models of human cognition," Zorzi says. Tests on visual numerosity show the network's capabilities, and offer insight into how the ability to judge the amount of objects in a set emerges in humans and animals without any pre-existing knowledge of numbers or arithmetic.

Gal is a research engineer at the Intel Nervana algorithms team. He has a great passion for AI, and specifically for training and implementing Reinforcement Learning agents. He has optimized and trained low precision neural networks, enabling deep learning inference, on various Intel devices. He has been with Intel for 10 years, and before joining Intel Nervana, was mainly focused on power management algorithms optimization for Intel CPUs. In his spare time, Gal enjoys baking sourdough breads, hiking and watching movies with his wife.

A description of the main concepts that permitted the goals achieved in the last decade, an introduction of image classification and object localization problems, ILSVRC and the models that obtained best results from 2012 to 2015 in both the tasks.

In recent years, the field of object detection has seen tremendous progress, aided by the advent of deep learning. Object detection is the task of identifying objects in an image and drawing bounding boxes around them, i.e. localizing them.

Everyone now is learning, or claiming to learn, Deep Learning (DL), the only field of Artificial Intelligence (AI) that went viral. Paid and free DL courses count 100,000s of students of all ages. Too many startups and products are named "deep-something", just as buzzword: very few are using DL really. Most ignore that DL is the 1% of the Machine Learning (ML) field, and that ML is the 1% of the AI field. Remaining 99% is what's used in practice for most tasks.

Just in time for Halloween, a research team from the MIT Media Lab's Scalable Cooperation group has introduced Shelley: the world's first artificial intelligence-human horror story collaboration. Shelley, named for English writer Mary Shelley -- best known as the author of "Frankenstein: or, the Modern Prometheus" -- is a deep-learning powered artificial intelligence (AI) system that was trained on over 140,000 horror stories on Reddit's infamous r/nosleep subreddit. She lives on Twitter, where every hour, @shelley_ai tweets out the beginning of a new horror story and the hashtag #yourturn to invite a human collaborator. Anyone is welcome to reply to the tweet with the next part of the story, then Shelley will reply again with the next part, and so on. The results are weird, fun, and unpredictable horror stories that represent both creativity and collaboration -- traits that explore the limits of artificial intelligence and machine learning.

Yann LeCun is one of AI's most accomplished minds, so when he says that even recent advances in the field aren't taking us closer to super-intelligent machines, you need to pay attention. LeCun has been working in AI for decades, and is one of the co-creators of convolutional neural networks -- a type of program that's proved particularly adept at analyzing visual data, and powers everything from self-driving cars to facial recognition. Now, as head of Facebook's AI research facility FAIR, he helps AI make the journey from the lab to the real world. His team's software automatically captions photos for blind users and performs 4.5 billion AI-powered translations a day. "We had a bigger impact on products than Mark Zuckerberg expected," LeCun told The Verge over Skype recently.

The co-founder of online education platform Coursera has made it his mission to build talent for AI through his new project, deeplearning.ai. Andrew is preparing courses on deep-learning--advanced AI inspired by the human brain's neural networks--that will be available on Coursera. In an interview with ET's J Vignesh, the former chief scientist at Baidu also spoke about how technology disruption can help countries like India leapfrog and take a lead in the new world. Edited excerpts: How are we progressing towards the concept of singularity, or general intelligence, from sector-specific artificial intelligence? That is hard to project.