The terminology humans have conceived to explain and study our own brain may be mis-aligned with how these constructs are actually represented in nature. For example, in many human societies, when a baby is born either a "male" or a "female" box is checked on the birth certificate. Reality, however, may be less black and white. In fact, the assumption of dichotomic differences between only two sex/gender categories may be at odds with our endeavors that try to carve nature at its joints. Such is the case with a new paper, published recently in the journal Cerebral Cortex, where researchers argue that there are at least nine directions of brain-gender variation.
Oracle supercharged its efforts to take on cloud giants Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) and today launched a data science platform that runs as a native service on Oracle Cloud Infrastructure. The announcement marks the company's second cloud push of the new year. Last week Oracle announced its Generation 2 Cloud was available in five new regions including Jeddah, Saudi Arabia; Melbourne, Australia; Osaka, Japan; Montreal; and Amsterdam. The new Oracle's Cloud Infrastructure Data Science Platform uses elements of DataScience.com, The vendor claims the new offering can bring data scientists together and aid analysis with capabilities like shared projects, model catalogs, team security policies, reproducibility, and auditability.
Mila is happy to announce the nomination of five new faculty members to the Mila team! Guillaume Lajoie was appointed Core Academic Member. Guillaume is an assistant professor in the Department of Mathematics and Statistics (DMS) at the Université de Montréal. His research is rooted at the intersection of AI and neuroscience where he pursues questions surrounding neural network dynamics and computations, with some applications to neuroengineering.
MONTREAL, Quebec, Feb. 12, 2020 -- Element AI, a global developer of artificial intelligence-powered (AI) services and software that helps enterprises'operationalize AI', today announced Element AI Orkestrator, the latest addition to the company's portfolio of AI-powered software solutions. Designed for AI practitioners and the IT teams that support them, Element AI Orkestrator is a workload scheduling tool built as a result of the company's need to optimize its own computing resources. A software-as-a-service (SaaS) version is available now, with an on-premises version expected for Spring 2020. Element AI Orkestrator helps customers future-proof their IT infrastructures through optimized utilization of GPU clusters, allowing AI practitioners to build quality models and remove most of the engineering heavy lifting required to efficiently use GPUs. By maximizing these specialized and in-demand resources, Orkestrator will play a pivotal role in supporting the structural backbone of organizations deploying AI solutions for increased overall productivity.
Yoshua Bengio: Yoshua Bengio OCFRSC (born 1964 in Paris, France) is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning. He was a co-recipient of the 2018 ACM A.M. Turing Award for his work in deep learning. He is a professor at the Department of Computer Science and Operations Research at the Université de Montréal and scientific director of the Montreal Institute for Learning Algorithms (MILA). Geoffrey Hinton: Geoffrey Everest HintonCCFRSFRSC (born 6 December 1947) is an English Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks. Since 2013 he divides his time working for Google (Google Brain) and the University of Toronto.
Sharing AI related news has become crucial in this era of digital transformation. Given the current wave, many AI researchers have turned into AI influencers to drive value and success to their respective field. These are the people who are driving conversations about AI across social media and other platforms. Please note: This is not a ranking article. Gregory Piatetsky is a well-known expert in Big Data, Business Analytics, Data Mining, Data Science, and Machine Learning and is among top influencers in those fields.
The world's tech powers are sending giant sums of money spinning into Canada, but while many see this as a sign of success, others are worried about researchers and intellectual property being swallowed wholesale. The country is in the midst of an artificial intelligence (AI) boom, with Google, Microsoft, Facebook, Huawei and other global heavyweights spending millions or even hundreds of millions of dollars on research hubs in Quebec, Ontario and Alberta. Canadian doors are open – some fear too open. Jim Hinton, an IP lawyer and founder of the Own Innovation consultancy, reckons that more than half of all AI patents in Canada end up being owned by foreign companies. What we need to be doing is getting money out of our ideas ourselves, instead of seeing foreign talent scoop it all up," said Hinton. "Otherwise we'll never have a Canadian champion." The country is home to hundreds of fledgling AI companies, including much-talked-about start-ups like Element AI and Deep Genomics, but they remain relatively small. "They don't have a strong market position yet," Hinton says. Deep learning pioneers such as Yoshua Bengio and Geoffrey Hinton (no relation to Jim) have nurtured top-notch talent in AI in Canada for years, back when AI was an emerging field. But despite Canadian inheriting this brilliant AI lead from the country's AI "godfathers", big foreign players have an unassailable advantage over homegrown efforts, Hinton said. "It's not an easy go for the average company to make a business out of AI.
Researchers at McGill University showed that analysis of blood samples using artificial intelligence (AI) could predict and provide a more comprehensive explanation for the progression of neurodegenerative diseases. The findings were published in the journal Brain. The results were gathered from analyzing the blood-brain samples of over 1,900 patients with the presence of late-onset Alzheimer's and Huntington's disease. Researchers used a novel gene expression contrastive trajectory inference (GE-cTI) method able to unveil enriched temporal patterns, while also predicting neuropathological severity among affected participants. Spanning decades, the machine learning algorithm identified how the patients' genes expressed themselves uniquely, a first study of which revealed how molecular changes underlies neurodegeneration.
After years in the (mostly Canadian) wilderness followed by seven years of plenty, Deep Learning was officially recognized as the "dominant" AI paradigm and "a critical component of computing," with its three key proponents, Geoffrey Hinton, Yann LeCun, and Yoshua Bengio, receiving the Turing Award in March 2019. Turing Award winners (from left to right) Yoshua Bengio, Yann LeCun, and Geoffrey Hinton at the ... [ ] ReWork Deep Learning Summit, Montreal, October 2017. In October 2012, a deep neural network achieved an error rate of only 16% in the ImageNet Large Scale Visual Recognition Challenge, a significant improvement over the 25% error rate achieved by the best entry the year before. Yann LeCun: "The difference there was so great that a lot of people, you could see a big switch in their head going'clunk.' Now they were convinced;" Geoffrey Hinton: "Until we could produce results that were clearly better than the current state of the art, people were very skeptical;" Yoshua Bengio: "[Anyone hoping to make the next Turing-winning breakthrough in AI] should not follow the trend--which right now is deep learning." Deep Learning is a "critical component of computing"… or biology? As customary for Turing Awards laureates, Hinton, LeCun and Bengio delivered the A. M. Turing Lecture.
Evaluating the effectiveness of therapies for neurodegenerative diseases is often difficult because each patient's progression is different. A new study shows artificial intelligence (AI) analysis of blood samples can predict and explain disease progression, which could one day help doctors choose more appropriate and effective treatments for patients. Scientists at The Neuro (Montreal Neurological Institute and Hospital) of McGill University and the Ludmer Centre for Neuroinformatics and Mental Health used an AI algorithm to analyze the blood and post-mortem brain samples of 1969 patients with Alzheimer's and Huntington's disease. Their goal was to find molecular patterns specific to these diseases. The algorithm was able to detect how these patients' genes expressed themselves in unique ways over decades.