Aug 28, 2022
AI Creating ‘Art’ Is An Ethical And Copyright Nightmare
Posted by Kelvin Dafiaghor in category: robotics/AI
If a machine makes art, is it even art? And what does this mean for actual artists?
If a machine makes art, is it even art? And what does this mean for actual artists?
Summary: A newly developed artificial intelligence model can detect Parkinson’s disease by reading a person’s breathing patterns. The algorithm can also discern the severity of Parkinson’s disease and track progression over time.
Source: MIT
Parkinson’s disease is notoriously difficult to diagnose as it relies primarily on the appearance of motor symptoms such as tremors, stiffness, and slowness, but these symptoms often appear several years after the disease onset.
Meta is developing a machine learning model that scans these citations and cross-references their content to Wikipedia articles to verify that not only the topics line up, but specific figures cited are accurate.
This isn’t just a matter of picking out numbers and making sure they match; Meta’s AI will need to “understand” the content of cited sources (though “understand” is a misnomer, as complexity theory researcher Melanie Mitchell would tell you, because AI is still in the “narrow” phase, meaning it’s a tool for highly sophisticated pattern recognition, while “understanding” is a word used for human cognition, which is still a very different thing).
Meta’s model will “understand” content not by comparing text strings and making sure they contain the same words, but by comparing mathematical representations of blocks of text, which it arrives at using natural language understanding (NLU) techniques.
A new study in Science overthrew the whole gamebook. Led by Dr. David Baker at the University of Washington, a team tapped into an AI’s “imagination” to dream up a myriad of functional sites from scratch. It’s a machine mind’s “creativity” at its best—a deep learning algorithm that predicts the general area of a protein’s functional site, but then further sculpts the structure.
As a reality check, the team used the new software to generate drugs that battle cancer and design vaccines against common, if sometimes deadly, viruses. In one case, the digital mind came up with a solution that, when tested in isolated cells, was a perfect match for an existing antibody against a common virus. In other words, the algorithm “imagined” a hotspot from a viral protein, making it vulnerable as a target to design new treatments.
The algorithm is deep learning’s first foray into building proteins around their functions, opening a door to treatments that were previously unimaginable. But the software isn’t limited to natural protein hotspots. “The proteins we find in nature are amazing molecules, but designed proteins can do so much more,” said Baker in a press release. The algorithm is “doing things that none of us thought it would be capable of.”
Well, now we have a robot version of this classic Serengeti scene.
The fawn in this case is a robotic dog at the University of California, Berkeley. And it’s likewise a surprisingly quick learner (relative to the rest of robot-kind). The robot is also special because, unlike other flashier robots you might have seen online, it uses artificial intelligence to teach itself how to walk.
Continue reading “This Robot Dog Has an AI Brain and Taught Itself to Walk in Just an Hour” »
Findings from a machine learning study suggest that some of the speech differences associated with autism are consistent across languages, while others are language-specific. The study, published in the journal PLOS One, was conducted among separate samples of English speakers and Cantonese speakers.
Autism spectrum disorder (ASD) is often accompanied by differences in speech prosody. Speech prosody describes aspects of speech, like rhythm and intonation, that help us express emotions and convey meaning with our words. Atypical speech prosody can interfere with a person’s communication and social abilities, for example, by causing a person to misunderstand others or be misunderstood themselves. The reason these speech differences commonly present among autistic people is not fully understood.
Study author Joseph C. Y. Lau and his team wanted to shed light on this topic by studying prosodic features associated with autism across two typologically distinct languages.
This is just one of many military advancements the nation has made against its arch-rival.
Back in July, South Korea undertook a 33-minute flight of its homegrown KF-21 fighter jet for the first time flaunting its military might and perhaps sending a message to North Korea.
South Korea is pursuing stealth drones that could take out North Korean air defenses as part of a “manned-unmanned teaming system.”
Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Watch here.
We’re in the midst of a data revolution. The volume of digital data created within the next five years will total twice the amount produced so far — and unstructured data will define this new era of digital experiences.
Unstructured data — information that doesn’t follow conventional models or fit into structured database formats — represents more than 80% of all new enterprise data. To prepare for this shift, companies are finding innovative ways to manage, analyze and maximize the use of data in everything from business analytics to artificial intelligence (AI). But decision-makers are also running into an age-old problem: How do you maintain and improve the quality of massive, unwieldy datasets?
WEST LAFAYETTE, Ind. — When the human brain learns something new, it adapts. But when artificial intelligence learns something new, it tends to forget information it already learned.
As companies use more and more data to improve how AI recognizes images, learns languages and carries out other complex tasks, a paper published in Science this week shows a way that computer chips could dynamically rewire themselves to take in new data like the brain does, helping AI to keep learning over time.
“The brains of living beings can continuously learn throughout their lifespan. We have now created an artificial platform for machines to learn throughout their lifespan,” said Shriram Ramanathan, a professor in Purdue University’s School of Materials Engineering who specializes in discovering how materials could mimic the brain to improve computing.