Lifeboat Foundation

According to this guy, the argument will be that the AI is needed to make split second decisions, and will gradually increase from there.

Retired U.S. Army General Stanley McChrystal joins ‘Influencers with Andy Serwer’ to share his biggest fears regarding artificial intelligence.

ANDY SERWER: I want to ask you about AI, artificial intelligence, because you wrote, “ceding the ability to manage relationships to an algorithm, we rolled a dangerous die.” What are the specific uses of AI that concern you and then we can talk about AI weapons and that’s really scary stuff. But let’s talk about it generally and then specifically with regard to the military.

Continue reading “Retired U.S. Army General: ‘We absolutely will’ give control over lethal strike to A.I.” »

Using AI to analyze your income and expenses regularly is a great way to help you better understand where your money goes each month. Most modern financial institutions have apps that will automatically categorize your spending into expense types, making it easy for you to see how much of your paycheck ends up going toward rent/mortgage, food, transportation, entertainment, etc.

Technology is empowering women to build wealth through AI-assisted financial management. Women are now able to invest and manage their finances by using technology that automatically invests and manages money for them. This software provides a unique algorithm for each woman with personalized goals, risk tolerance, income, and age.

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Continue reading “Can Artificial Intelligence Help To Close The Financial Equality Gap For Women?” »

International diplomacy has traditionally relied on bargaining power, covert channels of communication, and personal chemistry between leaders. But a new era is upon us in which the dispassionate insights of AI algorithms and mathematical techniques such as game theory will play a growing role in deals struck between nations, according to the co-founder of the world’s first center for science in diplomacy.

Michael Ambühl, a professor of negotiation and conflict management and former chief Swiss-EU negotiator, said recent advances in AI and machine learning mean that these technologies now have a meaningful part to play in international diplomacy, including at the Cop26 summit starting later this month and in post-Brexit deals on trade and immigration.

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.