For years, the Sonos Wi-Fi speakers have been beloved by music fans for the ease of listening to streaming music at home with great sound. Also, add the ability to add multiple speakers for even better sound without having to resort to stringing speaker wire all over. The one drawback: Sonos speakers can be pricey, topping off at $499 per speaker and even higher for the $699 TV Playbar. So good news consumers: The most affordable way to get into the Sonos system goes on sale Thursday. But you won't find the speakers at Best Buy, Amazon or any of the other retailers which usually stock Sonos products.

If you ever get the creepy feeling you're being monitored when you use your computer, smartphone or smart speaker, our guest Geoffrey Fowler is here to tell you you are. Fowler writes a consumer-oriented technology column for The Washington Post. He's been investigating the ways our browsers and phone apps harvest personal information about us even while we're sleeping. And he discovered that Amazon had kept four years' worth of recorded audio from his home, captured by his Alexa smart speaker, including family conversations about medications and a friend doing a business transaction. Geoffrey Fowler joined the Post in 2017 after 16 years with the Wall Street Journal, writing about consumer technology, Silicon Valley, national affairs and China. He writes his technology column from San Francisco. He spoke with FRESH AIR's Dave Davies. You have a recent column. The headline is "I Found Your Data. It's For Sale." What kind of personal data did you find available for sale on the Internet? GEOFFREY FOWLER: I found all kinds of things that normal people would consider secrets and that corporations spend a lot of money - millions and millions of dollars - to try to keep out of the hands of their competitors and criminals. I found people's flight records. I found people's records from their doctors prescribing them medications. I found people's tax documents that they were - thought they were only sharing with their tax preparer. And they were available with one click. I could have opened them up and downloaded them. And where did this data come from?

A team of U.S. researchers has developed a program that weeds-out fake news. The Giant Language Model Test Room is devised by IT experts at Harvard University and the Massachusetts Institute of Technology (MIT) in a bid to counter inauthentic journalism. Based around predictive language models, which allow computers and bots to write copy, the system aims to machine algorithms. According to results of their own research, GLTR helped to improve the detection-rate of forged text from 54 percent to 72 percent - meaning the days of misinformation could potentially be numbered. Due to their modeling power,automated language models have the potential to generate textual output that is indistinguishable from the real thing - AKA it's often fake news The Giant Language Model Test Room enables forensic analysis of how likely an automatic system generated a text.

This paper proposes a recommender system to alleviate the cold-start problem that can estimate user preferences based on only a small number of items. To identify a user's preference in the cold state, existing recommender systems, such as Netflix, initially provide items to a user; we call those items evidence candidates. Recommendations are then made based on the items selected by the user. Previous recommendation studies have two limitations: (1) the users who consumed a few items have poor recommendations and (2) inadequate evidence candidates are used to identify user preferences. We propose a meta-learning-based recommender system called MeLU to overcome these two limitations. From meta-learning, which can rapidly adopt new task with a few examples, MeLU can estimate new user's preferences with a few consumed items. In addition, we provide an evidence candidate selection strategy that determines distinguishing items for customized preference estimation. We validate MeLU with two benchmark datasets, and the proposed model reduces at least 5.92% mean absolute error than two comparative models on the datasets. We also conduct a user study experiment to verify the evidence selection strategy.

With convolutional neural networks and state-of-the-art image recognition techniques it is possible to make old movie classics shine again. Neural networks polish the image, reduce the noise and apply colors to the aged images. The first movies were created in the late nineteenth century with celluloid photographic film used in conjunction with motion picture cameras. Skip ahead to 2018, when the global movie industry was worth $41.7 billion globally. Serving entertainment, cultural and social purposes, films are a hugely important heritage to protect.

Tesla cars will be able to stream Netflix and YouTube according to CEO, Elon Musk who announced the addition in a recent tweet. Musk said that the services will be coming to the company's line of electric vehicles'soon,' albeit with one major caveat -- the apps may only be used when the car is fully stopped. 'Ability to stream YouTube & Netflix when car is stopped coming to your Tesla soon!,' Musk wrote in a tweet over the weekend. 'Has an amazingly immersive, cinematic feel due to the comfy seats & surround sound audio.' Tesla CEO Elon Musk announced that cars will'soon' be able to stream videos from Netflix and YouTube when the car is stopped'When full self-driving is approved by regulators, we will enable video while moving,' said Musk in an additional tweet. In anticipation of full autonomy, Tesla already started to add several features to its breed of semi-autonomous cars which include high-profile video games like Mario Kart and, more recently, indie favorites like Cuphead.

As a marketer, you could use AI in a variety of ways, enhancing customer experiences and evaluating consumer behavior. Over the last few years, its acceptance, impact and widespread popularity have revolutionized how businesses look at everyday processes. Between now and 2024, the global AI space will grow at a 26% Compound Annual Growth Rate (CAGR), reaching a staggering USD 71 billion. It is astonishing how AI is deeply embedded across our daily lives -- play music on your Spotify app, and there's AI to curate a special playlist for you; craving ice cream? Your preferred delivery app will suggest options to order from, and if you back out, it will insist that you proceed with the "sweet deal."

AI algorithms can generate text convincing enough to fool the average human--potentially providing a way to mass-produce fake news, bogus reviews, and phony social accounts. Thankfully, AI can now be used to identify fake text, too. The news: Researchers from Harvard University and the MIT-IBM Watson AI Lab have developed a new tool for spotting text that has been generated using AI. Called the Giant Language Model Test Room (GLTR), it exploits the fact that AI text generators rely on statistical patterns in text, as opposed to the actual meaning of words and sentences. In other words, the tool can tell if the words you're reading seem too predictable to have been written by a human hand.

Harry Potter fans will remember Professor Albus Dumbledore's nifty memory reviewer - the Pensieve. Throughout the series, several characters used it to store their memories and rewatch them to derive insights. Taking this concept from fiction to reality, Gaurav Shrivastava, Co-founder of Zimmber, built a transparent data machine - called Pensieve - AI with Co-founder Prahlad Routh. The two were ecstatic about implementing text analytics in the legal domain and the ample growth of Machine Learning (ML) and Artificial Intelligence (AI). The duo identified that legal tech could be a thing if Natural Language Processing (NLP) is used rightly, and started Pensieve in Mumbai in 2017.

Well, you know, maybe you are a robot, I don't know. We'll have to see this. I don't think you are, but we aren't quite there yet. But yes there is a journalism bot chapter -- I couldn't help that as a journalist -- and I was surprised at how far automation is already you know entered the newsroom. In 2016, actually some of the basic election coverage that year was done by A.I. programs that had certain phrases and words and data that had been programmed in.