Artificial intelligence – We've all heard about it. It's everywhere, in your cell phone, your social media apps, Amazon Alexa, Google Assistant – you name it. Let's just agree that it's become a norm in the technology space and is destined to make an appearance in almost every area of our lives in the coming years. The thing is, AI is a computer program that has the ability to learn. With a regular software, once you're done typing in commands, the software stops operating.

No two cultural touchstones better illustrate our diametrically opposed feelings about voice systems. Star Trek's always listening, ever-helpful Computer represents the highest ideal of a digital assistant, while Orwell's Telescreen, with its "Big Brother is watching" messages, was emblematic of our darkest fears. With each passing year and digital assistant breakthrough, we vacillate wildly between these two perspectives. Bloomberg recently reported that Amazon was employing thousands of humans to comb through utterances and transcribe what we say to Amazon's Echo-based voice assistant. The revelation broke an unspoken agreement between Amazon and the millions of Echo and Alexa-enabled device owners who assumed that only algorithms, not humans in a back office somewhere, would be analyzing our words for meaning.

Machine learning is the most important technology for the business of the future. That's because AI-driven software is already helping companies increase efficiency, improve customer relationships, and boost sales. Researchers estimate that machine learning has the potential to add $2.6 trillion in value to the marketing and sales industry by 2020, as well as another $2 trillion to manufacturing and logistics fields. The International Data Corporation estimates that spending on machine learning will reach $77.6 billion by 2022. This is why companies of all sizes are collaborating with Python development outsourcing firms to source experienced data scientists as-needed and develop custom data analytics software.

John Borthwick (above) believes the convenience of today's smart assistants comes at a price far higher than the cost paid for the devices. 'It's hard to call it anything but surveillance,' says the former Time Warner exec Tech investor John Borthwick believes the convenience of today's smart assistants from Amazon, Google and Apple comes at a price far higher than the cost paid for the devices. 'From a consumer standpoint, user standpoint, is that these, these devices are being used for what's -- it's hard to call it anything but surveillance,' Borthwick says, warning that government regulation may be the only safeguard to user privacy. Borthwick, a venture capitalist who started out in the technology industry with a web content studio that was bought by AOL, and who later headed tech strategy for Time Warner, tells Yahoo that he expects regulators will hand over more control of privacy to device users. As it stands now, he warns tech companies that manufacture and sell popular smart speakers, like Amazon's Echo, Google Assistant and Apple's HomePod, are having much more than they're audible responses recorded.

As artificial intelligence (AI) and machine learning become ubiquitous, we will soon be hard-pressed to find any industry not capitalizing on the benefits they can provide. Telecommunications is one of the fastest-growing industries as well as one that uses artificial intelligence and machine learning in many aspects of their business from enhancing the customer experience to predictive maintenance to improving network reliability. The largest telecoms in the world rely on artificial intelligence and machine learning in a number of ways. Here are the most common applications. Nearly every telecom uses artificial intelligence and machine learning to improve its customer service primarily by using virtual assistants and chatbots.

Well, when the president does it, that means that it is not illegal," marks one of the famous lines from US President Nixon's interviews, where Nixon implicated himself in the Watergate scandal. At the time it was well-known that the government was tapping phones of its opponents, Nixon's confession gave it the much-needed propriety. What is different about today's scenario is that tech companies are well within their confines to listen to your conversations. It's just that they are being obscure about the way they do it. The latest to join this is Cupertino giant Apple.

As more and more people shift their movie watching online, competition between movie viewing websites are getting more and more intense. Therefore, it has become incredibly important to accurately predict a given user's watching list to maximize the chances of keeping the user on the platform. Recent studies have suggested that the novelty-seeking propensity of users can impact their viewing behavior. In this paper, we aim to accurately model and describe this novelty-seeking trait across many users and timestamps driven by data, taking into consideration user forgetfulness. Compared to previous studies, we propose a more robust measure for novelty. Our model, termed Deep Forgetful Novelty-Seeking Model (DFNSM), leverages demographic information about users, genre information about movies, and novelty-seeking traits to predict the most likely next actions of a user. To evaluate the performance of our model, we conducted extensive experiments on a large movie rating dataset. The results reveal that DFNSM is very effective for movie recommendation.

In 2006, Netflix, which was then a DVD rental service, announced a data science competition for movie rating predictions. The company would offer a $1 million grand prize to the team that could improve their existing recommender system's prediction accuracy by 10%. The competition garnered much interest from researchers and engineers in both academia and industry. Within the first year of the competition, over 40,000 teams from more than 100 countries had entered the competition [1]. In June 2009, the prize was awarded to BellKor's Pragmatic Chaos, a team of AT&T engineers, who submitted the winning algorithm a few minutes earlier than the second-place team [2].

I wrote about recommender systems last week, but there is so much discussion around their effects right now in the mainstream tech press that they deserve a second issue. As a recap, I said that there were two things that made recommender systems super ineffective, and that YouTube, one of the premier companies tech using recommendations, suffers from both a lot of the first and a lot of the second. Recommender systems today have two huge problems that are leading companies (sometimes at enormous pressure from the public) to rethink how they're being used: technical bias, and business bias. The real problem is YouTube's business model. YouTube is THIRSTY for advertising money, at all times.

It will no longer keep audio recordings of Siri users by default, though it will retain automatically generated transcripts of the requests. Users will be able to opt in to sharing their recordings with Apple. "We hope that many people will choose to help Siri get better," the company said. Only Apple employees will be allowed to listen to those audio samples. The company had previously outsourced the work to contracting firms.