Lifeboat Foundation

Deepfake videos are well-known; many examples of what only appear to be celebrities can be seen regularly on YouTube. But while such videos have grown lifelike and convincing, one area where they fail is in reproducing a person’s voice. In this new effort, the team at UoC found evidence that the technology has advanced. They tested two of the most well-known voice copying algorithms against both human and voice recognition devices and found that the algorithms have improved to the point that they are now able to fool both.

The two algorithms— SV2TTS and AutoVC —were tested by obtaining samples of voice recordings from publicly available databases. Both systems were trained using 90 five-minute voice snippets of people talking. They also enlisted the assistance of 14 volunteers who provided voice samples and access to their voice recognition devices. The researchers then tested the two systems using the open-source software Resemblyzer—it listens and compares voice recordings and then gives a rating based on the similar two samples are. They also tested the algorithms by using them to attempt to access services on voice recognition devices.

The researchers found the algorithms were able to fool the Resemblyzer nearly half of the time. They also found that they were able to fool Azure (Microsoft’s cloud computing service) approximately 30 percent of the time. And they were able to fool Amazon’s Alexa voice recognition system approximately 62% of the time.

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Microsoft’s blog post on Megatron-Turing says the algorithm is skilled at tasks like completion prediction, reading comprehension, commonsense reasoning, natural language inferences, and word sense disambiguation. But stay tuned—there will likely be more skills added to that list once the model starts being widely utilized.

GPT-3 turned out to have capabilities beyond what its creators anticipated, like writing code, doing math, translating between languages, and autocompleting images (oh, and writing a short film with a twist ending). This led some to speculate that GPT-3 might be the gateway to artificial general intelligence. But the algorithm’s variety of talents, while unexpected, still fell within the language domain (including programming languages), so that’s a bit of a stretch.

Continue reading “Microsoft’s Massive New Language AI Is Triple the Size of OpenAI’s GPT-3” »

AI startups can rake in investment by hiding how their systems are powered by humans. But such secrecy can be exploitative.

The nifty app CamFind has come a long way with its artificial intelligence. It uses image recognition to identify an object when you point your smartphone camera at it. But back in 2015 its algorithms were less advanced: The app mostly used contract workers in the Philippines to quickly type what they saw through a user’s phone camera, CamFind’s co-founder confirmed to me recently. You wouldn’t have guessed that from a press release it put out that year which touted industry-leading “deep learning technology,” but didn’t mention any human labelers.

The practice of hiding human input in AI systems still remains an open secret among those who work in machine learning and AI. A 2019 analysis of tech startups in Europe by London-based MMC Ventures even found that 40% of purported AI startups showed no evidence of actually using artificial intelligence in their products.

Despite the continued progress that the state of the art in machine learning and artificial intelligence (AI) has been able to achieve, one thing that still sets the human brain apart — and those of some other animals — is its ability to connect the dots and infer information that supports problem-solving in situations that are inherently uncertain. It does this remarkably well despite sparse, incomplete, and almost always less than perfect data. In contrast, machines have a very difficult time inferring new insights and generalizing beyond what they have been explicitly trained on or exposed to.

How the brain evolved to achieve these abilities and what are the underlying ‘algorithms’ that enable them to remain poorly understood. The development and investigation of mathematical models will lead to a deep understanding of what the brain is doing and how are not mature and remain a very active area of research.

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Continue reading “Sorry AI — The Brain Is Still The Best Inference Machine Out There” »

Thanks to artificial intelligence, drones can now fly autonomously at remarkably high speeds, while navigating unpredictable, complex obstacles using only their onboard sensing and computation.

This feat was achieved by getting the drone’s neural network to learn flying by watching a sort of “simulated expert” – an algorithm that flew a computer-generated drone through a simulated environment full of complex obstacles. Now, this “expert” could not be used outside of simulation, but its data was used to teach the neural network how to predict the best trajectory, based only on the data from the sensors.

AI.Reverie offered APIs and a platform that procedurally generated fully annotated synthetic videos and images for AI systems. Synthetic data, which is often used in tandem with real-world data to develop and test AI algorithms, has come into vogue as companies embrace digital transformation during the pandemic. In a recent survey of executives, 89% of respondents said synthetic data will be essential to staying competitive. And according to Gartner, by 2,030 synthetic data will overshadow real data in AI models.

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Facebook has quietly acquired AI.Reverie, a New York-based startup creating synthetic data to train machine learning models, VentureBeat has learned.

A Japanese startup at CES is claiming to have solved one of the biggest problems in medical technology: Noninvasive continuous glucose monitoring. Quantum Operation Inc, exhibiting at the virtual show, says that its prototype wearable can accurately measure blood sugar from the wrist. Looking like a knock-off Apple Watch, the prototype crams in a small spectrometer which is used to scan the blood to measure for glucose. Quantum’s pitch adds that the watch is also capable of reading other vital signs, including heart rate and ECG.

The company says that its secret sauce is in its patented spectroscopy materials which are built into the watch and its band. To use it, the wearer simply needs to slide the watch on and activate the monitoring from the menu, and after around 20 seconds, the data is displayed. Quantum says that it expects to sell its hardware to insurers and healthcare providers, as well as building a big data platform to collect and examine the vast trove of information generated by patients wearing the device.

Quantum Operation supplied a sampling of its data compared to that made by a commercial monitor, the FreeStyle Libre. And, at this point, there does seem to be a noticeable amount of variation between the wearable and the Libre. That, for now, may be a deal breaker for those who rely upon accurate blood glucose readings to determine their insulin dosage.

The Sustainable Development Goals (SDGs) include a call for action to halve the annual rate of road deaths globally and ensure access to safe, affordable, and sustainable transport for everyone by 2030.

According to the newly launched initiative, faster progress on AI is vital to make this happen, especially in low and middle-income countries, where the most lives are lost on the roads each year.

According to the World Health Organization (WHO), approximately 1.3 million people die annually as a result of road traffic crashes. Between 20 and 50 million more suffer non-fatal injuries, with many incurring a disability.

Continue reading “Artificial intelligence can help halve road deaths by 2030” »

Circa 2020

Artificial intelligence (AI) is evolving—literally. Researchers have created software that borrows concepts from Darwinian evolution, including “survival of the fittest,” to build AI programs that improve generation after generation without human input. The program replicated decades of AI research in a matter of days, and its designers think that one day, it could discover new approaches to AI.

“While most people were taking baby steps, they took a giant leap into the unknown,” says Risto Miikkulainen, a computer scientist at the University of Texas, Austin, who was not involved with the work. “This is one of those papers that could launch a lot of future research.”

Continue reading “Artificial intelligence is evolving all by itself” »