Disclaimer: This episode of Chips with Everything covers topics that some listeners might find disturbing. Back in April 2017, The Guardian produced a film called Rise of the Sex Robots, a documentary by journalist Jenny Kleeman. In it, Jenny travelled to California to see a new prototype for an artificially intelligent sex robot. Its name, or her name, if you prefer, is Harmony and it can fulfil each user's particular desires through customisable appearance and personality. Fast forward 18 months, and in September 2018 the company behind this sex robot, Abyss Creations, has finally started shipping Harmony to customers.

In the early, optimistic days of the internet, we thought it would be a repository of high-quality information. Instead, it's starting to feel like a bottomless ocean of lies that rewards attention-grabbing disinformation and pollutes the political process. That's the note of alarm that three members of Congress sounded in a letter this week to Daniel Coats, the U.S. director of national intelligence. In it, the lawmakers warned specifically about the technology called deepfake, which lets computer users with little tech savvy create convincing footage of people doing and saying things that they never actually did. "Hyper-realistic digital forgeries -- popularly referred to as'deep fakes' [sic] -- use sophisticated machine learning techniques to produce convincing depictions of individuals doing or saying things they never did, without their consent or knowledge," read the letter.

Artificial intelligence (AI) company Krzana is providing journalists at the UK's largest news publisher Reach (formerly Trinity Mirror) with an AI news gathering platform. Krzana's platform monitors 60,000 sources of news from the major social networks including Twitter, YouTube, local news sites and blogs and identifies newsworthy events before they trend on social media. The user of Krzana sets up search criteria in advance, creating personalised newsfeeds based on interests and geographical locations. The system then sources text, video and images that match each of these newsfeeds as soon as they're uploaded, including posts with zero views. Krzana will enable journalists in Reach's local newsrooms to uncover pre-trending news stories without requiring individuals to constantly monitor social media to search for breaking news events.

I'm trying to train an LSTM to generate song lyrics. For my input data, I downloaded a bunch of song lyrics (1-D list where each entry is one line of a song) and used Keras Tokenization w one-hot. This is is where I'm having trouble setting up the structure. Once I have converted the lyric-lines to one-hot, what should the input to the LSTM look like? When I fit the model, what should I use for my target?

Has anyone tried to use Stable-Baselines? How does it compare to the official Baselines from OpenAI in your experience? Stable Baselines is a set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines. You can read a detailed presentation of Stable Baselines in the Medium article. These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of.

The real-world application of this algorithm used a series of iterative calculations to determine the best routes for traveling salespeople to use to fully and most efficiently cover their territory. Through the iterative process, this appeared to learn routes and improve efficiency over time. Many scholars considered this a little bit of a stretch on machine learning and artificial intelligence. To quote the introduction in the book, Artificial Intelligence: A Modern Approach (2016 edition): "We call ourselves Homo sapiens – man the wise – because our intelligence is so important to us." And while we consider ourselves intelligent, many people think only living, breathing beings can be intelligent and learn. I would have to disagree with that – thanks to the introduction of neural networks, natural language processing and all the other disciplines that touch artificial intelligence, machine learning is a real thing. Machines learn from the data that passes through them and are capable of processing vast amounts of data faster than us humans can. And those machines have the power to remember all that data, find the hidden patterns, the missing links, etc. – better than people can, and even faster than groups of people working together in enterprise businesses. Think about that device in your back pocket for a moment.

When the original Echo Show debuted last year, plenty of people (including us) made fun of its chunky and bulky design. Well, Amazon must've taken those comments to heart, because the new Echo Show is decidedly much better looking. While the older Echo Show might've reminded us of mall kiosks, the newer Show looks a bit more like a smaller, cuter TV. I tried it out briefly, following the Amazon announcement earlier today, and the first thing I noticed is that it's much more minimalist in design, with a beautiful 10-inch HD display dominating the front. It even takes up less counter space than the Google-powered 10-inch Lenovo Smart Display.

Amazon is improving its Alexa voice assistant with each new update, enabling more natural conversation patterns and upgrading the system's ability to understand complex or vague questions. All of this is great for people who are sick of screaming, "ALEXA," and repeating commands a dozen different ways before the device actually does what it's supposed to. However, these upgrades are also good for advertisers -- and, by association, Amazon. Amazon hasn't laid out a clear, public plan for Alexa-enabled ads, but the company showed off the future of voice-assisted product placement during its event on Thursday. Head of all things Alexa, David Limp, explained that Amazon's voice assistant sometimes doesn't have the answer itself, but one of its skills (connected apps) might.

A friendly introduction to recommender systems with matrix factorization and how it's used to recommend movies in Netflix.

Neo4j, the market leader in connected data, announced today the upcoming release of Neo4j 3.5, the native graph platform designed to drive the success and adoption of real-time business applications, including artificial intelligence (AI) and machine learning (ML) systems. Neo4j customers – including eBay and Caterpillar – have demonstrated that connected graph datasets are a foundational element of enterprise AI applications. Graph-based data models provide the necessary context for AI applications by capturing facts related to and relationships among people, processes, applications, data and machines. Informed by successful AI customer deployments – including knowledge graphs, fraud detection, recommendation systems and conversation engines – Neo4j 3.5 delivers foundational features for AI-powered systems of connection to generate bottom-line business value. "The way we organize and represent knowledge in AI-powered systems has a profound effect on what and how they can learn," said Bowles.