Results


How AI is defining insurtech strategy

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AI – which Margaris prefers to call "the influence of machine learning or deep learning" – is starting to be felt across the insurtech, fintech and associated industries, he said. "AI, through machine learning and deep learning, will eventually become the entrepreneur of the future--and we humans need to compete against it." A company still needs to have a compelling business case that attracts clients, but AI, machine learning and deep learning for sure will be part of the equation to compete successfully in their space." Margaris has reiterated what has been the most important technology lesson learned over the past four decades, and continues to be the lesson going forward with each new technology wave.


Mapping the Canadian AI Ecosystem

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If we sum up all the available numbers for AI research investments (including other government funding like the $93.5M awarded to IVADO by the Canada Research Excellence Fund last September, as well as private funds invested in public or semi-public labs) we end up with close to $500M in funding across the country. Beyond that, when we look up other domains that work hand in hand with AI, such as Big Data, cloud infrastructure and the like, that number grows even higher. What made Silicon Valley's talent pump work up to now was its ecosystem of large firms and venture capital feeding startups, as well as research who in turn generate the innovations to push the large firms forward. With investments from the federal and provincial governments in research, as well as from Big Tech, the Canadian talent pump is growing quickly.


Are You Completely Underestimating AI? – The Startup – Medium

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The industrial revolution allowed us to build products at faster rates we had ever seen, and allowed us to scale up our creations to sizes never possible before. Just like machines whose strength is hundreds, if not thousands of times stronger than us, AI's intelligence will be hundreds, if not thousands of times smarter than us. Physical problems like will be solved thousands of times faster than humans could. Machines removed the physical constraints of humans and freed us to pursue more intellectual paths like the information industry, and AI will remove our mental constraints.


What to expect of artificial intelligence in 2017

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Last year was huge for advancements in artificial intelligence and machine learning. The idea has been around for decades, but combining it with large (or deep) neural networks provides the power needed to make it work on really complex problems (like the game of Go). Invented by Ian Goodfellow, now a research scientist at OpenAI, generative adversarial networks, or GANs, are systems consisting of one network that generates new data after learning from a training set, and another that tries to discriminate between real and fake data. The hope is that techniques that have produced spectacular progress in voice and image recognition, among other areas, may also help computers parse and generate language more effectively.


Creating machine learning models to analyze startup news

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This is the second part in a series where we analyze thousands of articles from tech news sites in order to get insights and trends about startups. So, if a sample mentions an IoT pacemaker startup, it should get the IoT tag in addition to the Health tag. Tagging the data was a similar process to the previous classifier, except that this time we took special care in tagging every sample with all the relevant categories. At this point, we are ready to repeat the same experiment we did in the previous post: classifying 100 articles and seeing what happens.


Analyzing 10 years of startup news with Machine Learning

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This is the final part in a series where we use machine learning and natural language processing to analyze articles published in tech news sites in order to gain insights about the state of the startup industry. Let's visualize the coverage per industry for all the articles published in the last ten years to find out: The most popular startup industry in the last ten years has been Mobile. Right when this industry started to gain visibility in 2013, Oculus Rift was the top keyword by a wide margin. The only way to perform an analysis like this is using machine learning and natural language processing, since there's no way we can get a person to read through and interpret 270,000 articles.


On Machine Learning & AI in Healthcare

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I've been watching this space for some time now, and I continue to be bullish on the prospects of ML/AI in the healthcare industry. Here, I'm going to write about my views on The Why of ML/AI, some examples of The Who in this space, and, finally, some thoughts on The How these practices are going to disrupt processes in healthcare. Oh, and I'll also provide some thoughts on the infrastructure that's needed to make this all happen, because, those who know me will know my thoughts on infrastructure, viz. Let's start by looking at this clever map of the most well-funded AI startups in each state. Upon tallying the results, you'll note that 21% of the most well-funded AI startups across the US focus exclusively on healthcare.


Pluto AI Aims To Transform Wastewater Treatment With Applied AI

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Artificial Intelligence startup Pluto AI has raised $2.1 million in VC funding in order to inject intelligence into the traditionally mundane world of wastewater treatment. Modern plants are flush with sensors and automated controls, but they typically operate independently and often require user intervention. Pluto AI aims to take these treatment plants to the next level by gulping up all the data produced by the array of sensors and controls equipment already in place, then provide intelligent insights to save time, money and water. "Pluto is an analytics platform for smart water management. We enable water facilities like treatment plants or beverage processing plants to prevent water wastage, predict asset health, and minimize operating costs.


Global tech giants compete to be AI champion

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When most people think about artificial intelligence, their minds turn to glorified fights to save the human race from rogue robots, a familiar story played out on Hollywood screens in decades gone by. While machine intelligence is still far from resembling human consciousness, an AI fight is playing out in real life, not between robots and humans, but rather among the businesses vying to lead an increasingly lucrative market. The origins of AI stretch back to 1950, when computer science pioneer Alan Turing published a paper speculating that machines could one day think like humans. Last year, research firm IDC valued the market at $8 billion, forecasting a rise to $47 billion in 2020. Between Turing's landmark paper 67 years ago and today's wild market valuations, most major AI developments have either fallen in the realms of research and academia or involved computers beating people at human games.


A Quick Artificial Intelligence Update - ODBMS.org

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Artificial Intelligence is offering the kind of revolution mankind first saw a 100 years ago with electricity, with Andrew Ng, the former Chief Scientist at Baidu claiming he can't think of an industry it won't disrupt in the next 10 years. Google's AI stands at the point where it can recognize user doodles and translate what they're trying to draw; IBM's Watson is quickly expanding its cognitive computing platform across multiple industries; Alexa can now tell you what's in the news, call you an Uber, book you a flight, and incessantly wakes me up to finish my real analysis homework every morning. With more than 35 acquisitions in 2017 and over 200 rounds of financing since 2012, the race for intelligence is picking up fast; Google itself has acquired 11 AI companies. Big Data and increased processing power, which was considered the limiting constraint handicapping the development of AI have picked up the pace in 2016, but with increased horsepower through products such as NVidia's GPUs, and Intel's AI chips, such constraints have decreased. Just in the past month, Elon Musk launched Neuralink, a venture to merge the human brain with AI, NVidia's deep-learning chips showed promise of disrupting the medicine industry, Bots were reported to be chatting in their own language and companies such as Forbes and Intel showed promising signs of boosting AI efforts.