Researchers announced the breakthrough discovery of a new type of antibiotic compound that is capable of killing many types of harmful bacteria, including deadly antibiotic-resistant strains, and published their findings in Cell on February 20. What makes this remarkable is that the researchers, from the Massachusetts Institute of Technology (MIT), Harvard, and McMaster University, used machine learning (a form of artificial intelligence) to discover the new antibiotic--an achievement that heralds the disruption of traditional research and drug development processes deployed by pharmaceutical industry behemoths. Antibiotic resistance is a global threat that is exacerbated by the overuse of antibiotics in livestock, the proliferation of antimicrobials in consumer products, and over-prescription in health care. Though estimating the future impact is challenging, one report predicted that by 2050, 10 million deaths per year could result from antimicrobial-resistant (AMR) infections. Combating the problem of antimicrobial resistance requires bringing novel compounds to market.
Co-founder and chief strategy officer David Hunt says their technology allows farmers to see what is happening on their dairy "in high resolution in real time…without anyone needing to go into the barn." Based in California, Canada and Ireland, the company launched their first product in late January. Alus Nutrition focuses on "all things related to feed bunk management," according to portfolio growth lead Tyler Bramble. This includes when feed is delivered to cows or when the cows have cleaned out the feed and need more. Cainthus' smart cameras monitor cows, while their software interprets what the cameras see.
This is an updated version. The Godfathers of AI and 2018 ACM Turing Award winners Geoffrey Hinton, Yann LeCun, and Yoshua Bengio shared a stage in New York on Sunday night at an event organized by the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020). The trio of researchers have made deep neural networks a critical component of computing, and in individual talks and a panel discussion they discussed their views on current challenges facing deep learning and where it should be heading. Introduced in the mid 1980s, deep learning gained traction in the AI community the early 2000s. The year 2012 saw the publication of the CVPR paper Multi-column Deep Neural Networks for Image Classification, which showed how max-pooling CNNs on GPUs could dramatically improve performance on many vision benchmarks; while a similar system introduced months later by Hinton and a University of Toronto team won the large-scale ImageNet competition by a significant margin over shallow machine learning methods.
The demographics of Canada are changing quickly. By 2050, 26% of Canada's population is expected to be aged 65 or better, up from 18% today. With smaller families, busier schedules, and tighter budgets, the pressure is on to find solutions to ensure this growing group of people receives quality care. Fortunately, artificial intelligence is helping the retirement industry serve up innovative solutions to meet the burgeoning need. Though results from our 2019 Sklar Wilton AI tracker* indicate that 57% of people aged 65 and older don't understand the current state of artificial intelligence, 71% believe AI may affect them.
According to the Polish Economics Institute (PIE), the first coronavirus warnings were issued on December 31 by a Canada-based health monitoring startup. The Canadian company, BlueDot, even correctly predicted the cities outside of China coronavirus would next appear: Tokyo, Seoul, Taipei and Bangkok. PIE said: "Algorithms using artificial intelligence solutions identified the onset of the coronavirus epidemic a few days earlier than reported in the official information from international organisations such as the WHO or the CDC." BlueDot's AI predicted the spread of coronavirus by analysing airline data, international news stories and reports of coronavirus animal infections.
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.
As the North American banking landscape continues to evolve, many consumers have noted the growing presence of Toronto-based TD Bank Group in major U.S. cities across the East Coast. TD Bank, which brands itself as "America's Most Convenient Bank", is now the 8th largest U.S. bank by deposits and the 10th largest bank in the United States by total assets. I recently spoke with TD executive, Michael Rhodes, who serves as Group Head, Innovation, Technology, and Shared Services for the bank, and I began our conversation with a direct question -- given that only 37.8% of leading firms report being data-driven, and only 26.8% claim to have established a data culture, would he characterize TD as being "data-driven". His response was quick and emphatic. "Yes, we are data-driven", Rhodes replied, "We have made substantial investments in data and AI capabilities that are providing customer value today".
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.
Eddy Travels, an AI-powered travel assistant bot which can understand text and voice messages, has closed a pre-seed round of around $500,000 led by Techstars Toronto, Practica Capital and Open Circle Capital VC funds from Lithuania, with angel investors from the U.S., Canada, U.K. Launched in November 2018, Eddy Travels claims to have more than 100,000 users worldwide. Travelers can send voice and text messages to the Eddy Travels bot and get personalized suggestions for the best flights. Because of this ease of use, it now gets 40,000 flight searches per month -- tiny compared to the major travels portals, but not bad for a bot that is available on Facebook Messenger, WhatsApp, Telegram, Rakuten Viber, Line and Slack chat apps. The team is now looking to expand into accommodation, car rentals and other travel services. Eddy Travels search is powered by partnerships with Skyscanner and Emirates Airline.
In this paper, we propose a winner-take-all method for learning hierarchical sparse representations in an unsupervised fashion. We first introduce fully-connected winner-take-all autoencoders which use mini-batch statistics to directly enforce a lifetime sparsity in the activations of the hidden units. We then propose the convolutional winner-take-all autoencoder which combines the benefits of convolutional architectures and autoencoders for learning shift-invariant sparse representations. We describe a way to train convolutional autoencoders layer by layer, where in addition to lifetime sparsity, a spatial sparsity within each feature map is achieved using winner-take-all activation functions. We will show that winner-take-all autoencoders can be used to to learn deep sparse representations from the MNIST, CIFAR-10, ImageNet, Street View House Numbers and Toronto Face datasets, and achieve competitive classification performance.