In the continuation of our conversation from last week with Technologist William Ammerman, we explore how modern digital marketing is changing the way people are persuaded. We're already at a point where we are empathetically relating to our devices, and by empathetically relating to our devices, we are opening ourselves up to persuasion at a very deep level. Studies have shown that the more human-like a relationship with a device is, the more empathetic we are and the more vulnerable we are to persuasion. You have to start pondering: What does it mean that we're all talking to our devices; that we're asking these devices questions? Today, it's something simple like, you know, "show me a scary movie," and your TV providing a recommendation.

Ever hear the one about how a group of monkeys could, given enough time, produce the entire works of Shakespeare? Well, apparently an AI being forced to watch 1,000 hours of Batman can be coerced into creating a script. Honestly though it looks like something that might cause the Joker to grimace in utter distaste since it's about one of the most nonsensical things around. Seriously, it's laughable enough that someone might want to pick it up and make a movie out of it, a parody at least. This kind of proves that as much as AI might be a perceived threat to some, there's not much chance that it's going to be much of a threat to writers in the near future since at this point computers still aren't quite expert at thinking around corners.

Danamica, which was founded in 2016, has built machine learning tools and "business intelligence capabilities" to specialize in dynamic pricing of …

The Australian Broadcasting Corporation is using machine learning to extract metadata from text, podcasts and other forms of media, making them easier to find via a new search engine. Machine learning engineer Gareth Seneque told the YOW! Data 2019 conference that the ABC moved out of beta in February this year with a new search engine based on technology from US startup Algolia (which also runs search for the likes of Twitch and Stripe). The search domain still sports beta labelling but is in full production use. "There are reasons for [the url] behind the scenes - stuff involving CMS migrations and the like that I won't detour into - but we're very much in the scaling up and out phase of things," Seneque said. But Seneque said user feedback on search was poor. "Specifically, content types were not supported, indexing speeds were slow stuff as the stuff would take a while to show up in the index, and the relevance of results was poor," he said.

Probably the worst error is thinking there is a correlation when that correlation is purely artificial. Take a data set with 100,000 variables, say with 10 observations. You are almost guaranteed to find one above 0.999999. This is best illustrated in may article How to Lie with P-values (also discussing how to handle and fix it.) This is being done on such a large scale, I think it is probably the main cause of fake news, and the impact is disastrous on people who take for granted what they read in the news or what they hear from the government.

Recent years have witnessed a significant increase in the online sharing of medical information, with videos representing a large fraction of such online sources. Previous studies have however shown that more than half of the health-related videos on platforms such as YouTube contain misleading information and biases. Hence, it is crucial to build computational tools that can help evaluate the quality of these videos so that users can obtain accurate information to help inform their decisions. In this study, we focus on the automatic detection of misinformation in YouTube videos. We select prostate cancer videos as our entry point to tackle this problem. The contribution of this paper is twofold. First, we introduce a new dataset consisting of 250 videos related to prostate cancer manually annotated for misinformation. Second, we explore the use of linguistic, acoustic, and user engagement features for the development of classification models to identify misinformation. Using a series of ablation experiments, we show that we can build automatic models with accuracies of up to 74%, corresponding to a 76.5% precision and 73.2% recall for misinformative instances.

We study the problem of source separation for music using deep learning with four known sources: drums, bass, vocals and other accompaniments. State-of-the-art approaches predict soft masks over mixture spectrograms while methods working on the waveform are lagging behind as measured on the standard MusDB benchmark. Our contribution is two fold. (i) We introduce a simple convolutional and recurrent model that outperforms the state-of-the-art model on waveforms, that is, Wave-U-Net, by 1.6 points of SDR (signal to distortion ratio). (ii) We propose a new scheme to leverage unlabeled music. We train a first model to extract parts with at least one source silent in unlabeled tracks, for instance without bass. We remix this extract with a bass line taken from the supervised dataset to form a new weakly supervised training example. Combining our architecture and scheme, we show that waveform methods can play in the same ballpark as spectrogram ones.

On any given day we can find a story in the media about technology and its impact on society. But of equal importance are the ways in which new technologies like artificial intelligence (AI) and data analytics are shaping the media itself. The volume of content and the speed at which it is disseminated have both increased dramatically in the past ten years because of new technology platforms like Facebook and Twitter. The types of news pushed through these platforms or what we see when visiting them is being orchestrated by algorithms underpinned by advanced data analytics. And the ways in which we process, cite, and assess stories are all influenced by the types of screens we use and company we keep online. Together, these technologies can spread information and disinformation equally and in real time.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Last week, artificial intelligence research lab OpenAI decided to release a more expanded version of GPT-2, the controversial text-generating AI model it first introduced in February. At the time, the lab refrained from releasing the full AI model, fearing it would be used for malicious purposes. Instead, OpenAI opted for a staged release of the AI, starting with a limited model (124 million parameters), and gradually releasing more capable models. In May, the research lab released the 355-million-parameter version of GPT-2, and last week, it finally released the 774-million-model, at 50 percent capacity of the text generator.

The latest AI project from the MIT Media Lab is demonstrating just how terrifying the prospects of deep learning can go. Welcome to the Nightmare Machine: an algorithm that has been trained to generate horrifying images. It is attempting to find the scariest faces and locations possible, and gets humans to tell it which are the worst. The first aspect of the project, Haunted Faces, is truly terrifying. The team behind the project, led by Iyad Rahwan, associate professor at MIT Media Lab, used deep learning to generate new faces, before dropping "a hint of scariness" onto the generated faces in the spirit of Halloween.