Machine learning has historically been successful at dealing with structured datasets, such as tabular data. With recent advances, particularly in deep learning, there are now also well-established and powerful methods for working with image, text and speech data. However, a lot of real-world data does not easily fit into one of these categories. One important class of such data is network or graph data, which can be used to model concepts such as social networks, transaction flow, computer networks and even molecular interactions. Using graphs we can easily represent and capture the complex interactions and dependencies between objects, but it also opens up a question: How can we apply machine learning to graph structured data?

Yoshua Bengio is recognised as one of the world's leading experts in artificial intelligence and a pioneer in deep learning. Following his studies in Montreal, culminating in a PhD in computer science from McGill University in 1991, Professor Bengio did postdoctoral studies at the Massachusetts Institute of Technology (MIT) in Boston. In 2019, he was awarded the Killam Prize as well as the 2018 Turing Award, considered to be the Nobel prize for computing. These honours reflect the profound influence of his work on the evolution of our society. Yoshua Bengio is also known for collecting the largest number of new citations in the world in the year 2018.

It's been 7 years since my first interview with Gary Marcus and I felt it's time to catch up. Gary is the youngest Professor Emeritus at NYU and I wanted to get his contrarian views on the major things that have happened in AI as well as those that haven't happened. Prof. Marcus is an interesting interviewee not only because he is an expert in the field but also because he is a skeptic on the current approaches and progress towards Artificial General Intelligence but an optimist that we will eventually figure it all out. I can honestly say that I have learned a lot from Gary and hope that you will too. During this 90 min interview with Gary Marcus we cover a variety of interesting topics such as: Gary's interest in the human mind, natural and artificial intelligence; Deep Mind's victory in Go and what it does and doesn't mean for AGI; the need for Rebooting AI; trusting AI and the AI chasms; Asimov's Laws and Bostrom's paper-clip-maximizing AI; the Turing Test and Ray Kurzweil's singularity timeline; Mastering Go Without Human Knowledge; closed vs open systems; Chomsky, Minsky and Ferrucci on AGI; the limits of deep learning and the myth of the master algorithm; the problem of defining (artificial) intelligence; human and machine consciousness; the team behind and the mission of Robust AI.

Artificial intelligence offers a lot of advantages for organisations by creating better and more efficient organisations, improving customer services with conversational AI and reducing a wide variety of risks in different industries. Although we are only at the start of the AI revolution, we can already see that artificial intelligence will have a profound effect on our lives, both positively and negatively. The financial impact of AI on the global economy is estimated to reach US$15.7 trillion by 2030, with 40% of jobs expected to be lost due to artificial intelligence, and global venture capital investment in AI is growing to greater than US$27 billion in 2018. Such estimates of AI potential relate to a broad understanding of its nature and applicability. AI will eventually consist of entirely novel and unrecognisable forms of intelligence, and we can see the first signals of this in the rapid developments of AI. In 2017, Google's Deepmind developed AlphaGo Zero, an AI agent that learned the abstract strategy board game Go with a far more expansive range of moves than chess. Within three days, by playing thousands of games against itself, and without the requirement of large volumes of data (which would normally be required in developing AI), the AI agent beat the original AlphaGo, an algorithm that had beaten 18-time world champion Lee Sedol.

Artificial intelligence is a branch of engineering that has traditionally ignored brains, but recent advances in biologically inspired deep learning have dramatically changed AI and made it possible to solve problems in vision, speech planning and natural language. If you talk to Alexa or use Google Translate, you have experienced deep learning in action. In this lecture, explore the past, present and future of deep learning with Terrence J. Sejnowski from the Salk Institute for Biological Studies. Arntzen Grand Challenges Lecture Series: The Deep Learning Revolution Presented by Terrence J. Sejnowski Tuesday, November 5, 2019 Lecture: 5 p.m. Reception: 6 p.m. Interdisciplinary Science and Technology Building IV (ISTB4) Marston Exploration Theater, Tempe campus [map] Register to attend! Light hors d'oeuvres and an open bar will be provided.

Deep learning is a technique which teaches computers to do what we, as humans, do so naturally: learn by example. There are deep learning workstations available with tremendous capabilities too. The car that doesn't need a driver, the ability to know that a person is a person and not a bollard. The voice-controlled devices that we all enjoy so much. And still, deep learning is something that we all enjoy, yet know relatively little about.

Today we will discuss how to launch semantic segmentation and style transfer models in your browser using Tensorflow.js and ONNX.js. The purpose of this article is to determine if relatively large models can be used in a browser on your PC and mobile device. TensorFlow.js is a library for machine learning in JavaScript. It allows us to run existing models or train your own in the browser. The current version of Tensorflow.js, 1.2.7, supports quite a wide range of operations, while most of them are almost the same as in Tensorflow, others such as tf.browser.fromPixels

Sad not to attend NeurIPS (previously NIPS) this year? Thus I have written this papers selection for the 2018 edition that I want to share with you! NeurIPS (Neural Information Systems Processing, previously called NIPS) is more popular than a Beyoncé concert. The biggest AI conference in the world sold out in just a few minutes this year. Moreover, the number of accepted papers this year breaks all records (more than one thousand).

Biological systems are inherently complex. Identifying patterns in biological systems is a daunting, time consuming endeavor. Biomedical engineers at Duke University have created a novel artificial intelligence (AI) machine learning methodology that can predict behaviors of biological circuits in orders of magnitude faster than standard computational methods, and published their findings in Nature Communications on September 25, 2019. In scientific research for pharmaceuticals, disease treatments, and biomedicine, mathematical modeling is used to understand the processes for the particular biological system. Different systems require a separate approach.

This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.