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

@machinelearnbot

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.


AI, Data Science, Machine Learning: Main Developments in 2016, Key Trends in 2017

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At KDnuggets, we try to keep our finger on the pulse of main events and developments in industry, academia, and technology. We also do our best to look forward to key trends on the horizon. Over the past few weeks, we published a series of posts outlining expert opinions in data science, machine learning, artificial intelligence, and related fields. In an encore post of this series, we bring you the collected responses to an amalgam question -- including experts from all of the previous posts' fields -- while adding a second dimension this time around. I'd like to thank one of my researchers, Alekh Agarwal, for great input here.


Let's Talk About Self-Driving Cars – The Startup

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This one is simple, it's when you completely drive yourself. Cars that we mostly drive today belong here, those are the ones that have anti-lock brakes and cruise-control, so they can take over some non-vital processes involved in driving. When the system can take over control in some specific use cases but driver still has to monitor system all the time is here, it's applicable to situations when the car is self-driving the highway and you just sit there and expect it to behave well. This level means that driver doesn't have to monitor the system all the time but has to be in a position where the control can quickly be resumed by a human operator. That means no need to have hands on a steering wheel but you have to jump in at the sounds of the emergency situation, which system can recognize efficiently.


Focus AI: What to expect of AI in 2017?

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Overview: An event series which helps startup, corporate, and tech investment professionals sift through the noise, and understand the true happenings in the Artifical Intelligence industry. Guests from various industries will use this new found Focus to learn how to prepare for, and even implement Artificial intelligence into their businesses. Matt Greenwood - Chief Innovation Officer at Two Sigma Investments: Two Sigma Ventures is a division of Two Sigma that seeks to invest in companies that push the boundaries of an industry and bring real progress to the world, by harnessing the power of data science, machine learning, distributed computing, artificial intelligence, advanced hardware, and related fields. Vincent Tang - Lead Machine Learning Engineer at Samsung Accelerator: The accelerator partners with innovators to build ideas into products, grow products into businesses, and scale businesses that leverage and transform the Samsung ecosystem.Started in 2013 on a mission to create breakout software and services and foster a startup culture at Samsung. Jake Soffer - Co-Founder & CEO at Rollio: Rollio is Artificial Intelligence (AI) built into your Sales Team's core.


What to expect of artificial intelligence in 2017

#artificialintelligence

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.