Yesterday, Apple launched its Worldwide Developer's Conference (WWDC) and had its public keynote address. While many new things were announced, the one thing that caught my eye was the dramatic expansion of Apple's use of artificial intelligence (AI) tools. I talked a bit about AI with Hilary Parker on the latest Not So Standard Deviations, particularly in the context of Amazon's Echo/Alexa, and I think it's definitely going to be an area of intense competition between the major tech companies. Pretty much every major tech player is involved in AI--Google, Facebook, Amazon, Apple, Microsoft--the list goes on. Recently, a some commentators have suggested that Apple in particular will never catch up with the likes of Google with respect to AI because of Apple's strict stance on privacy and unwillingness to gather/aggregate data from all its users.

At Build 2016, Microsoft CEO, Satya Nadella, outlined our approach for the new era of conversational intelligence, based on a belief that the most impactful data-driven solutions will go beyond analytics, and utilize the best of big data, cloud, and intelligence capabilities. Microsoft Azure Machine Learning, now part of Cortana Intelligence Suite, is democratizing data and intelligence. Its best-in-class algorithms and simple drag-and-drop interface let data scientists quickly and easily go from idea to deployment. Since Build, I have been working with Azure Machine Learning and the Azure Machine Learning Studio, and thinking about the opportunities for partners to add more value to business intelligence, reporting, SharePoint, and data engagements. This is really a new monetary stream for your customer where they can provide their IP and domain expertise as a service to their customers. In this age of technologies, business decision makers are looking for ways to bring in other sources of revenue.

The dating app has rolled out verified profiles, which means now you'll know if "celebrity" profiles on the app are for real – instead of having to rely on whether or not the profile picture is a heavily pixellated crop from a website, complete with watermark still attached. Tinder announced the move on its blog: "Now when notable public figures, celebrities and athletes appear in your recommendations, you'll know it's for real." Verified profiles for celebrities have long been in development, and were first mooted back in March 2014, when Tinder's chief executive, Sean Rad, said: "This will allow celebrities to enter Tinder in a different way." The app's chief marketing officer, Justin Mateen, added: "Tinder gives them [celebrities] the control to filter through the noise and communicate with people they want to know." Back in May 2014, singer Ed Sheeran said he believed he was the first celebrity to be approached by Tinder for a verified profile.

Netflix does not usually jump to the top of the list when one thinks of leaders in artificial intelligence, but Netflix's VP of Product Innovation, Carlos Uribe-Gomez, and Chief Product Officer Neil Hunt published a paper informing investors that some of its algorithms help them save 1 billion each year. In the paper, the two executives detailed how the company's recommendation engine impacts its churn rate. The video streaming giant does not report its churn rate, but the paper mentions that the Netflix's retention rates "are already high enough that it takes a very meaningful improvement to make a retention difference of even 0.1%." This year, the streaming giant plans to spend 6 billion on content. With such a big investment, it could get all sorts of TV series and movies, but if it just presents the most popular selections to everyone, many titles would remain unseen.

Facebook is well known for its early and increasing use of artificial intelligence. The social media site uses AI to pinpoint its billion-plus users' individual interests and tailor content accordingly by automatically scanning their newsfeeds, identifying people in photos and targeting them with precision ads. And now behind the scenes the social network's AI researchers are trying to take this technology to the next level--from pure data-crunching logic to a nuanced form of "common sense" rivaling that of humans. AI already lets machines do things like recognize faces and act as virtual assistants that can track down info on the Web for smartphone users. But to perform even these basic tasks the underlying learning algorithms rely on computer programs written by humans to feed them massive amounts of training data, a process known as machine learning.

When you think of leaders in artificial intelligence, Netflix (NASDAQ:NFLX) doesn't usually jump to the top of the list. But the streaming video service's VP of Product Innovation Carlos Uribe-Gomez and Chief Product Officer Neil Hunt published a paper that says some of its AI algorithms save Netflix 1 billion each year. In their paper, the two Netflix execs detail how the company's recommendation engine impacts its churn rate. Netflix no longer reports its churn rate, but the paper notes that Netflix's "retention rates are already high enough that it takes a very meaningful improvement to make a retention difference of even 0.1%." Let's dive into how the recommendation engine saves Netflix money -- and what the return on investment looks like.

"AI is the new mobile" is a phrase we might as well start getting used to hearing. It's infuriating that, just as the marketing world finally starts to think "mobile first", not one but two technology giants signal loudly that they're over mobile and on to the next thing. Witness Google chief executive Sundar Pichai, who recently spoke about moving from a mobilefirst to an AI-first world. Make no mistake, he sees artificial intelligence as the future of search, best exemplified by what Google calls the Google assistant: less a product and more an artificially intelligent, conversational interface to all things Google. Earlier in the year, at Facebook's developer conference F8, Mark Zuckerberg made AI one of the three pillars of Facebook's ten-year roadmap (alongside – yes, you guessed it – connectivity and virtual reality).

A robotic employee will be deployed instead of human council workers to answer customer queries. IPsoft said Amelia, its technology platform, will be deployed to work for Enfield Council in North London. Capable of analyzing natural language, she understands context, applies logic, learns, resolves problems and even senses emotions. IPsoft said Amelia will be deployed to work for Enfield Council in North London. Capable of analyzing natural language, she understands context and even senses emotions.

Microsoft (NASDAQ:MSFT) co-founder Bill Gates recently called artificial intelligence "the holy grail that anyone in computer science has been thinking about" during Vox Media's Code Conference. Gates discussed the rapid progress of speech recognition and computer vision technologies over the past five years, and noted that "the dream is finally arriving." If that dream arrives, tech investors should recognize the major trends and players in this market. To get started, let's examine 10 fascinating facts about the AI industry. Research firm Markets and Markets estimates that the AI market will grow from 420 million in 2014 to 5.05 billion by 2020, thanks to the rising adoption of machine learning and natural language processing technologies in the media, advertising, retail, finance, and healthcare industries.

Microsoft (NASDAQ:MSFT) co-founder Bill Gates recently called artificial intelligence "the holy grail that anyone in computer science has been thinking about" during Vox Media's Code Conference. Gates discussed the rapid progress of speech recognition and computer vision technologies over the past five years, and noted that "the dream is finally arriving." If that dream arrives, tech investors should recognize the major trends and players in this market. To get started, let's examine 10 fascinating facts about the AI industry. Research firm Markets and Markets estimates that the AI market will grow from 420 million in 2014 to 5.05 billion by 2020, thanks to the rising adoption of machine learning and natural language processing technologies in the media, advertising, retail, finance, and healthcare industries.