HOW LANGUAGE MODELS COULD CHANGE DISINFORMATION
A new Georgetown Center for Security and Emerging Technology report examines how OpenAI’s language model GPT-3 could generate content for disinformation campaigns., finding that should adversaries choose to pursue automation in their disinformation campaigns, we believe that deploying an algorithm like the one in GPT-3 is well within the capacity of foreign governments, especially tech-savvy ones such as China and Russia. It will be harder, but almost certainly possible, for these governments to harness the required computational power to train and run such a system, should they desire to do so. Read More
Monthly Archives: May 2021
Cecilia.ai The First Robotic Interactive Bartender
High-performance speech recognition with no supervision at all
Whether it’s giving directions, answering questions, or carrying out requests, speech recognition makes life easier in countless ways. But today the technology is available for only a small fraction of the thousands of languages spoken around the globe. This is because high-quality systems need to be trained with large amounts of transcribed speech audio. This data simply isn’t available for every language, dialect, and speaking style. Transcribed recordings of English-language novels, for example, will do little to help machines learn to understand a Basque speaker ordering food off a menu or a Tagalog speaker giving a business presentation.
This is why we developed wav2vec Unsupervised (wav2vec-U), a way to build speech recognition systems that require no transcribed data at all. It rivals the performance of the best supervised models from only a few years ago, which were trained on nearly 1,000 hours of transcribed speech. We’ve tested wav2vec-U with languages such as Swahili and Tatar, which do not currently have high-quality speech recognition models available because they lack extensive collections of labeled training data.
Wav2vec-U is the result of years of Facebook AI’s work in speech recognition, self-supervised learning, and unsupervised machine translation. It is an important step toward building machines that can solve a wide range of tasks just by learning from their observations. We think this work will bring us closer to a world where speech technology is available for many more people. Read More
The race to understand the exhilarating, dangerous world of language AI
Hundreds of scientists around the world are working together to understand one of the most powerful emerging technologies before it’s too late.
On May 18, Google CEO Sundar Pichai announced an impressive new tool: an AI system called LaMDA that can chat to users about any subject.
To start, Google plans to integrate LaMDA into its main search portal, its voice assistant, and Workplace, its collection of cloud-based work software that includes Gmail, Docs, and Drive. But the eventual goal, said Pichai, is to create a conversational interface that allows people to retrieve any kind of information—text, visual, audio—across all Google’s products just by asking. Read More
This AI Makes Robert De Niro Perform Lines in Flawless German
When films are dubbed in another language, an actor’s facial movements may clash with his lines. Technology related to deepfakes can help smooth things over.
You talkin’ to me … in German?
New deepfake technology allows Robert De Niro to deliver his famous line from Taxi Driver in flawless German—with realistic lip movements and facial expressions. The AI software manipulates an actor’s lips and facial expressions to make them convincingly match the speech of someone speaking the same lines in a different language. The artificial-intelligence-based tech could reshape the movie industry, in both alluring and troubling ways. Read More
‘Where the action is’: Vice Media Group now produces more Stories than text or video
Vice Media Group Cory Haik doesn’t want to call it a pivot.
But a new mobile app developed in house last year, called Stories Studio, has turned Vice Media Group’s feet, hips and eyes in the direction of more stories-style content, the mobile-native content format that can easily be distributed across platforms including TikTok, Instagram, Facebook, Google and, for some reason, LinkedIn. Read More
Google’s Cinematic Moments is like Apple’s Live Photos — but a lot creepier
Google Photos will see several new features later this year — but some could be a bit creepier than others.
Arriving this summer, Cinematic Moments is a Google Photos tool that yields similar results to Apple’s Live Photos. The difference is that Cinematic Moments uses artificial intelligence (AI) to fill in the gaps of a few photos, rather than record short video, to produce in-motion media.
With a handful of still images, Cinematic Moments can create a complete and animated action shot. It uses neural networks to synthesize the moment, materializing frames from thin air, practically. Read More
MUM: A new AI milestone for understanding information
…People issue eight queries on average for complex tasks. Today’s search engines aren’t quite sophisticated enough to answer the way an expert would. But with a new technology called Multitask Unified Model, or MUM, we’re getting closer to helping you with these types of complex needs. So in the future, you’ll need fewer searches to get things done.
MUM has the potential to transform how Google helps you with complex tasks. Like BERT, MUM is built on a Transformer architecture, but it’s 1,000 times more powerful. MUM not only understands language, but also generates it. It’s trained across 75 different languages and many different tasks at once, allowing it to develop a more comprehensive understanding of information and world knowledge than previous models. And MUM is multimodal, so it understands information across text and images and, in the future, can expand to more modalities like video and audio. Read More
Markov models and Markov chains explained in real life: probabilistic workout routine
Markov defined a way to represent real-world stochastic systems and processes that encode dependencies and reach a steady-state over time.
Andrei Markov didn’t agree with Pavel Nebrasov, when he said independence between variables was necessary for the Weak Law of Large Numbers to be applied.
The Weak Law of Large Numbers states something like this:
When you collect independent samples, as the number of samples gets bigger, the mean of those samples converges to the true mean of the population.
But Markov believed independence was not a necessary condition for the mean to converge. So he set out to define how the average of the outcomes from a process involving dependent random variables could converge over time. Read More
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