Researchers and entrepreneurs from the University of Toronto are set to showcase their innovative work to a global audience during Elevate 2019, Canada's largest technology and innovation festival. The week-long event, which kicks off Friday, features hundreds of speakers and is expected to draw tens of thousands of attendees. U of T will play a central role thanks to its contributions to Toronto's thriving technology ecosystem and research that underpins key advances in fields like artificial intelligence (AI) and precision medicine. At this year's festival, U of T experts will be featured prominently at Elevate AI, a day-long program on Sept. 25 devoted to conversations around AI research, applications and commercialization held at the MaRS Discovery District. The scheduled speakers include Brendan Frey, the founder and CEO of Deep Genomics, which is using AI to build life-saving genetic therapies.

Google has completed a controversial take-over of the health division of its UK AI acquisition, DeepMind. The personnel move had been delayed as National Health Service (NHS) trusts considered whether to shift their existing DeepMind contracts -- some for a clinical task management app, others involving predictive health AI research -- to Google. In a blog post yesterday Dr Dominic King, formerly of DeepMind (and the NHS), now UK site lead at Google Health, confirmed the transfer, writing: "It's clear that a transition like this takes time. Health data is sensitive, and we gave proper time and care to make sure that we had the full consent and cooperation of our partners. This included giving them the time to ask questions and fully understand our plans and to choose whether to continue our partnerships. As has always been the case, our partners are in full control of all patient data and we will only use patient data to help improve care, under their oversight and instructions."

Apple has struggled in recent years to establish a robust artificial intelligence (A.I.) practice. This partially stems from the company's ironclad privacy policies--it's more difficult to analyze datasets for insights when internal rules prevent the company from using every piece of user data it can vacuum up. Nonetheless, Apple's newest projects show that it's powering ahead anyway--including one platform that, if it's ever released, could change how you use A.I. and machine learning (ML). It seems unlikely that Apple's stance on data and privacy will change during Cook's tenure.) How does Overton go about this herculean task?

Researchers at the Queensland University of Technology (QUT) have applied artificial intelligence (AI) to develop a more accurate and detailed method for analysing images of the back of the eye to help clinicians better detect and track eye diseases. In the study, the group of researchers explored a range of deep learning techniques to analyse Optical Coherence Tomography (OCT) images, said David Alonso-Caneiro, QUT senior research fellow and study lead author. OCT, which takes cross-sectional images of the eye to show different tissue layers, is a common instrument used by optometrists and ophthalmologists. These images are around four microns in size and can help clinicians detect eye diseases such as glaucoma and age-related macular degeneration. The team collected OCT chorio-retinal eye scans from an 18-month longitudinal study of 101 children with good vision and healthy eyes, and used these images to train the AI program to detect patterns and define the choroid boundaries.

Researchers at the Queensland University of Technology (QUT) have applied artificial intelligence (AI) to develop a more accurate and detailed method for analysing images of the back of the eye to help clinicians better detect and track eye diseases. In the study, the group of researchers explored a range of deep learning techniques to analyse Optical Coherence Tomography (OCT) images, said David Alonso-Caneiro, QUT senior research fellow and study lead author. OCT, which takes cross-sectional images of the eye to show different tissue layers, is a common instrument used by optometrists and ophthalmologists. These images are around four microns in size and can help clinicians detect eye diseases such as glaucoma and age-related macular degeneration. The team collected OCT chorio-retinal eye scans from an 18-month longitudinal study of 101 children with good vision and healthy eyes, and used these images to train the AI program to detect patterns and define the choroid boundaries.

Debugging is one of the most difficult aspects in the lifecycle of deep learning problems. The recent advancements in deep learning frameworks have lowered the entry point for creating really sophisticated models that are both effective and hard to interpret at the same time. Very often, researchers need to understand why the metrics of a specific model are trending in certain direction and they rely on relatively subjective techniques to do so. Additionally, the ecosystem of debugging and visualization tools hasn't evolved at the same speed of the development stacks so very often engineers end up creating models that are next to impossible to debug. Recently, Microsoft Research open sourced TensorWatch, a new tools that takes a new approach to solve the debugging and visualization of deep learning programs.

"Every year we're developing new technology -- like deep learning, which has improved the performance of pattern recognition," he added.

Competition is one of the socio-economic dynamics that has influenced our evolutions as species. The vast amount of complexity and diversity on Earth evolved due to co-evolution and competition between organisms, directed by natural selection. By competing against a different party, we are constantly forced to improve our knowledge and skills on a specific subject. Recent developments in artificial intelligence(AI) have started to leverage some of the principles of competition to influence learning behaviors in AI agents. Specifically, the field of multi-agent reinforcement learning(MARL) has been heavily influenced by the competitive and game-theoretic dynamics.

For the past couple of years, researchers and companies have been trying to make deep learning more accessible to non-experts by providing access to pre-trained computer vision or machine translation models. Using a pre-trained model for another task is known as transfer learning, but it still requires sufficient expertise to fine-tune the model on another dataset. Fully automating this procedure allows even more users to benefit from the great progress that has been made in ML to date. This is called AutoML, and it can cover many parts of predictive modelling such as architecture search and hyperparameter optimization. In this post, I focus on the former, as there has been a recent explosion of methods that search for the "best" architecture for a given dataset.