One of the driving forces behind much of my work over the past quarter century has been how we can use massive datasets and computing platforms to help us understand global society, from the patterns that underlie our behavior to the narratives and emotions that make us human. From cataloging our past and visualizing our history to finding the patterns of history and weaving all of those narratives and stories together, I've long been fascinated with what becomes possible when our digital and digitized history is coupled with massive computing power and directed at the grand challenge questions around who we are, where we've been and where we're heading. Perhaps the greatest contribution of the web era has been the speed with which it has ushered in the digital revolution. By creating a medium and business model for the production and curation of born-digital content at global scale, the web has reshaped the way we think about the production and consumption of information, encouraging everyone from businesses and governments down to ordinary citizens to publish their thoughts, beliefs, emotions, ideas, information and experiences to the world. The web collects all of this, serving as a single access point to society's present thoughts and past knowledge.

Artificial Intelligence and its associated technologies have become the buzzwords of companies, individuals and even governments. This begs the question; How did AI gain the prominence and attention it has today? What forced companies to stop and take notice of a technology that has effectively changed every walk of life? According to Google Trends, this phenomenon occurred around the beginning of 2016, with indexed search volume results almost doubling over just one month. However, the building blocks for the widespread adoption of AI had already been set. In 2011, IBM had debuted their Watson AI on the reality TV show Jeopardy!, where it was pitted against two of the best players in the world.

A survey by Tata Consultancy Services reveals that while some jobs have been lost to machine intelligence, that's not the major way companies are using AI today. Companies are more likely to be using AI to improve computer-to-computer tasks while employing the same number of people. The 170-year-old news service Associated Press offers a case in point. In 2013, demand for quarterly earnings stories was insatiable, and staff reporters could barely keep up. So that year, AP began working with an AI firm to train software to automatically write short earnings news stories. By 2015, AP's AI system was writing 3,700 quarterly earnings stories – 12 times the number written by its business reporters.

Cruce Saunders This is "Towards a Smarter World," and I'm your host Cruce Saunders. Very pleased to be joined today by Elizabeth McGuane, who is the content strategy lead at Intercom where she is part of the product design team, and owns the language of the core product including its messenger app. Elizabeth's been working in UX for 10 years and before that was a journalist. I'm really glad she could be with us today. She recently wrote an amazing article to check out on TechCrunch, called "On Bots, Language, and Making Technology Disappear." Elizabeth, with that article, would you summarize some of your thinking behind how you ended up arriving the conclusion that actually naming a bot is not necessarily the best strategy? We did it through research, but I think where we started was through a really careful and considered approach to testing the language. When I started, this was one of the first projects I worked on at Intercom when I joined just over a year ago, and we were looking at introducing bot-like, very simple bot, into our messenger. We make a B2B messenger, so not to get to complicated in terms of the UX of our product, but we always have to think of our users in terms of two layers: we have our customers and then our customers' customers, and we were really creating a bot that businesses would use to communicate concepts or to get data from their customers. I knew that we need to be really careful about how we express things so that we would marry with the business' tone of voice so that we wouldn't be overstepping the bounds of what we could say on their behalf. I had a feeling, and this was really just my gut instinct, that having a very chatty personality would not necessarily marry with the tone of voice of every single business that wanted to use our messenger. It was a very practical consideration on that front. When we went into testing, we tested with a name and without out a name. We also did testing with different tones of voice because going into this I think the design leads were interested to see whether a more friendly tone of voice or a more functional tone of voice would work. That was the initial consideration of "let's just try different kinds of copy, and see what works." I felt that I wanted to take a more structured approach and try names, no names, functional, more friendly, then we also tried with a pronoun, without a pronoun. Once we realized that names didn't work we also tried removing the first person "I", and removing an introduction so that the bot didn't say, "Hi, I'm so-and-so's digital assistant," or what have you to see what impact that had. That's really where it started was with an actual structured approach to research.

Hello future, you are weird. At least it is here at CES 2019, the tech industry's biggest annual conference. It's where companies big and small come to launch gadgets and test new ideas. Not all of them make it to stores. But there's an eternal optimism that technology can solve, well, almost any problem -- even ones you never knew you had.

Welcome to day three of our CES 2019 liveblog. Wednesday is the mid-point of the big show. The expo halls have been open for a full day, and most attendees are heading back for seconds. We saw a great number of wacky gadgets yesterday and Monday--some of them innovative, some of them ridiculous. Expect more of the same today.

A video software firm has come up with a way to prevent people from sharing their account details for Netflix and other streaming services with friends and family members. UK-based Synamedia unveiled the artificial intelligence software at the CES 2019 technology trade show in Las Vegas, claiming it could save the streaming industry billions of dollars over the next few years. Casual password sharing is practised by more than a quarter of millennials, according to figures from market research company Magid. Separate figures from research firm Parks Associates predicts that by $9.9 billion (£7.7bn) of pay-TV revenues and $1.2 billion of revenue from subscription-based streaming services will be lost to credential sharing each year. The AI system developed by Synamedia uses machine learning to analyse account activity and recognise unusual patterns, such as account details being used in two locations within similar time periods. Netflix's recommendation algorithm is pretty sophisticated these days, to the point where it can probably determine not only what you want to watch next, but what you'll eat for breakfast 13 years on Wednesday and the thread count of your sheets. And yet, it still has a tendency to spit out some peculiar recommendations.

Sharing your Netflix password could soon be much harder to get away with. At CES 2019 in Las Vegas, video software provider Synamedia unveiled a creepy new AI-powered system designed to crack down on account sharing. The firm points to recent research from Magid, which found that roughly 26 percent of millennials give out the credentials for video streaming services to other people. Its new software will be able to analyze which users are logged in and where to quickly flag shared accounts. Sharing your Netflix password could soon be much harder to get away with.

AI plays a huge role in how Spotify delivers personalized playlists to users, so it's somewhat fitting that the company's new partnership with Microsoft is focused on messages about how AI can impact all aspects of life -- including education, healthcare and philanthropy. Those messages are going to be showing up in the Discover Weekly playlist for free users, the first time that Spotify has lets brands have full customization and control over advertising in that feed. We talked with Spotify's Danielle Lee, VP and Global Head of Partner Solutions, about how the company uses AI to power its personalized content, how it can apply that in the future to things like video and podcasts and how the company can offer branding options for Discover Weekly while keeping things relevant to listeners. Follow all the latest news from CES 2019 here!

Dataset pruning is the process of removing sub-optimal tuples from a dataset to improve the learning of a machine learning model. In this paper, we compared the performance of different algorithms, first on an unpruned dataset and then on an iteratively pruned dataset. The goal was to understand whether an algorithm (say A) on an unpruned dataset performs better than another algorithm (say B), will algorithm B perform better on the pruned data or vice-versa. The dataset chosen for our analysis is a subset of the largest movie ratings database publicly available on the internet, IMDb [1]. The learning objective of the model was to predict the categorical rating of a movie among 5 bins: poor, average, good, very good, excellent. The results indicated that an algorithm that performed better on an unpruned dataset also performed better on a pruned dataset.