Innovative technologies have regularly reshaped the world. In the last few decades, inventions like the personal computer, the internet, and the smartphone have dramatically enhanced human productivity, while creating tremendous wealth in the process. And artificial intelligence (AI) promises to be the next transformative technology. In fact, research company McKinsey estimates that AI could boost global economic output by 16% (or $13 trillion) between 2018 and 2030. Companies like Nvidia (NVDA 1.74%) and Lemonade (LMND -6.03%) could be major beneficiaries of that trend because both are using AI to shape the future of technology.

The insurance industry was at a standstill before the development of insurtech – now the field has been revolutionised, with old insurance companies having to digitise or fear being left behind. Founded: New York, NY – 2015. Lemonade is a provider of a peer-to-peer insurance platform designed for renters and homeowners. It is powered by artificial intelligence and behavioral economics and utilizes bots and machine learning to create an insurance experience. However, as of April 2021, Lemonade has announced its plans to branch out into other insurance lanes as it aims to be a one-stop-shop.

The Palo Alto-based startup launched a car insurance comparison service using artificial intelligence and machine learning in January 2019.

We would all like to think we're impeccable drivers, but the reality is most of us are far from perfect on the road. Even if self-driving car tech improves and your vehicle is equipped with the best version, chances are you'll eventually end up in an accident. You may even be at fault. Though accidents can't be completely prevented, the enormous property damage bills that come along with car accidents can be partially minimized with the right insurance. Auto insurance is required in most states, yet this incredibly important protection is often overly complicated to understand and annoying to secure.

After three years speaking about, writing about and training in AI Ethics, organizations I speak with report that many of the students come back with a good understanding of the elements and remedies for ethical issues. But they continue to work as before. Part of the problem is the use of the term "ethics." It's too ineffable for most people to grasp. Some groups have tried "trustworthy."

Turns out that AI is not able to be a patent holder, plus other thorny topics. Can AI be an inventor? According to a recent decision by the U.S. Patent and Trademark Office (USPTO), the answer seems to be no. There is more to this story, though, and we'll need to push past the surface to understand the full nuances involved. Perhaps a more apt way to depict the situation is whether AI can be formally granted a U.S. patent, and for that the answer appears to unequivocally and emphatically be a razor-sharp no.

In a country with more than 230 million vehicles and half a million auto accidents every year, scheduling damage inspections can keep cars and policyholders off the road for days or longer. A more convenient way was needed. To ease the pain, ICICI Lombard partnered Microsoft to develop India's first AI-enabled car inspection feature in its mobile app, "Insure." Policyholders can simply take images of their vehicle and upload them to the app. AI analyses the images, identifies damages and provides an estimated repair cost.

Getting Over Ranking Factor Studies in the Era of Machine Learning September 25, 2018 Posted by Mordy Oberstein Just admit it, SEO is scary. Between the inherent complexity of what we do and Google not exactly being the epitome of clarity, the ground that is doing SEO can be a bit shaky at times. That's pretty much why we're obsessed with what works and what doesn't work and are vigilantly on the lookout for content that offers a bit of light at the end of the tunnel. In the not too distant past, I wrote a piece highlighting how machine learning has impacted rank volatility (in that rank is considerably more volatile). At the time, we touched on what machine learning means for understanding how ranking works and how the process directly influences rank. Here, we'll get into the nitty-gritty of it all by analyzing the holy of holies of optimization information, ranking factor studies, particularly niche ranking studies by asking one very simple question .... Do ranking factors studies still apply in a world where machine learning and intent reign supreme, and if so, to what extent? Recap of Machine Learning's Impact on Rank The increase in rank volatility aside, in what for all intents and purposes was "Part I" of this post we discussed how machine learning impacts rank qualitatively, i.e., what rank "looks like" as a result of RankBrain and the like. Since I'm a nice guy, let me recap (and expand on) what we said there so that you don't have to comb through the last piece trying to glue together all of the pieces to the puzzle. Machine Learning Sets Site Proportions In serving up results that align to user intent, Google uses machine learning to determine the proportion of sites to meet that intent or those intents. OK, Mordy, say that in English, please?! If you'll remember, in the last post I took a very straightforward search term, buy car insurance, and showed that Google sees two (or really more than two) intents embedded in that phrase: to buy an actual insurance policy and to get information about doing just that. How should Google handle these two intents?

It's hard to imagine what life was like before the peak of AI hype in which we currently find ourselves. But it was just a few years ago, in 2012, that Apple gave the world the first integrated version of Siri on the iPhone 4S, which people used to impress their friends by asking it banal questions. Google was just beginning to test its self-driving cars in Nevada. And the McKinsey Global Institute had recently released "Big data: The next frontier for innovation, competition, and productivity." On the starting blocks of the race to release the next big AI-powered thing, no one was talking about explainable AI.

We all know that artificial intelligence (AI) is getting smarter and that software tooling to drive increasingly useful AI chatbots has improved massively. But one CIO I met recently refuses to adopt any chatbot development altogether. He says that his firm will exclusively use human interactions instead and champion that factor as a Unique Selling Point. A neat enough business idea I thought, at least initially. He was one of a group of around fifteen Chief Information Officers I had met to discuss the challenges they face on a daily basis.