This is what Latent semantic analysis (LSA) does. It is based on how frequently you see the word on the exact topic. Like, there are more tech terms in tech articles, for sure. The names of politicians are mostly found in political news, etc. Yes, we can just make clusters from all the words at the articles, but we will lose all the important connections (for example the same meaning of battery and accumulator in different documents). LSA will handle it properly, that's why its called "latent semantic".

On a good day, Android TV, Google's Android OS for TVs, allows users to display photos from their Google Photos albums as screensavers. Over the weekend, a disturbed Android TV owner took to Twitter when he realized, through the Google Home app, he could access a massive list of random accounts, as well as photos they'd added to their Google Photos albums. If someone were to click on "linked accounts" while setting your Google Photos screensaver, the Google Home bug apparently showed a giant, scrolling list of users. From there, the bug allowed limited access to users' personal images in Google Photos, which could then be displayed as Ambient Mode screensavers. That is, someone could have theoretically displayed your photos as screensavers on their Android TV without you knowing it.

Artificial intelligence and machine learning are an increasingly integral part of many industries, including marketing. But while we often talk about using or incorporating AI in marketing, what do we really mean by that? What does it look like in practice? Here are 15 examples of AI and machine learning in action in the marketing industry (P.S. remember to check out Econsultancy's Marketer's Guide to Machine Learning and AI). The practice of clustering customer behaviours to predict future behaviours began way back in 1998, with a report on'digital bookshelves' by Jussi Karlgren, a Swedish computational linguist at Columbia University. In the same year, Amazon began using "collaborative filtering" to enable recommendations for millions of customers. Fast forward to 2019, and some of the most successful digital companies have built their product offerings around the ability to provide highly relevant and personalised product or content recommendations – including Amazon, Netflix and Spotify.

Celent has released a new report titled Artificial Intelligence in Insurance: Use Cases from Early Adopters. The report was written by Wenli Yuan, a senior analyst with Celent's Insurance practice. Leveraging artificial intelligence, insurers are able to engage with consumers in a faster and more consistent manner. They can use virtual customer service representatives at contact centers to free up staff to deal with more complicated inquiries; use robo advisors to provide consistent, rule-based advisory services at an affordable cost; and use virtual assistants to guide processes and related transactions. Insurers can also upgrade authentication mechanisms to biometrics authentication, such as voice recognition.

Dhananjay Motwani is thinking of an animal, and his 20 Questions opponent is, question by question, trying to figure out what it is. "Is it larger than a microwave oven?" "Yes." "No." "Is it a vegetarian?" "Yes." What's impressive here isn't that the questioner is a computer; that's old hat. It's that the machine and Motwani are chatting in his blue Hyundai Sonata, trundling along one of Silicon Valley's many freeways.

Amazon was one of the first companies to pioneer personalized shopping recommendations, and over the years its algorithms have become increasingly sophisticated. Suggestions are now based not only on past purchases, but also items that other customers have bought, searching and browsing behavior, and many other factors.

Some worry about the emergence of job-destroying intelligent robots, or even a conscious AI that would replace humanity. While others see rather a new possibility of progress for humankind. The rapid progress of artificial Intelligence (AI) heralds a new era, one of machines that are capable of learning by themselves ( machine learning) and of mimicking the human brain's network of neurons for what is called deep learning. Admittedly, today, AI is still limited. AI algorithms are very effective for certain specific tasks, but are far from equalling the highly diverse cognitive abilities of a small child.

Microsoft has Cortana, Amazon has Alexa, and Google has … well, Google Assistant. That last name doesn't give it away, but get it talking and the common link between all three AI assistants is revealed: they are all supposed to be women. Providing assistance has long been considered a woman's role, whether virtual or physical, fictional or real. The robots that men voice, meanwhile, tend to be in positions of power – often dangerously so. Think Hal 9000, or the Terminator: when a robot needs to be scary, it sounds like a man.

Artificial Intelligence (AI) is everywhere. It quietly integrated itself in nearly every aspect of our lives. Still, this technology is in its infancy and we don't entirely understand its full potential. We cringe at the idea of data being mined by governments, as reported in an amazing story on CBS's 60 Minutes. We worry about Big Brother, but excitedly lap up every Cortana or Siri update hoping it makes life just a little bit more interesting and easier.

Recommender systems are practically a necessity for keeping a site's content current, useful, and interesting to visitors. Recommender systems are everywhere, helping you find everything from movies to jobs, restaurants to hospitals, even romance. Practical Recommender Systems goes behind the curtain to show readers how recommender systems work and, more importantly, how to create and apply them for their site. This hands-on guide covers scaling problems and other issues they may encounter as their site grows.