Mint’s All About AI Tech4Good Awards recognised impactful AI solutions at the Jio World Centre in Mumbai. The event emphasised purpose-driven innovation, with discussions on ethical AI and community empowerment, showcasing how technology can address pressing social and environmental issues.
Eric Berger expects NASA and the public support for the SpaceX Mars city will surge with the first unmanned landings on Mars.
Originally published on Towards AI.
ABSTRACT: The fundamental problem of causal inference defines the impossibility of associating a causal link to a correlation, in other words: correlation does not prove causality. This problem can be understood from two points of view: experimental and statistical. The experimental approach tells us that this problem arises from the impossibility of simultaneously observing an event both in the presence and absence of a hypothesis. The statistical approach, on the other hand, suggests that this problem stems from the error of treating tested hypotheses as independent of each other. Modern statistics tends to place greater emphasis on the statistical approach because, compared to the experimental point of view, it also shows us a way to solve the problem. Indeed, when testing many hypotheses, a composite hypothesis is constructed that tends to cover the entire solution space. Consequently, the composite hypothesis can be fitted to any data set by generating a random correlation. Furthermore, the probability that the correlation is random is equal to the probability of obtaining the same result by generating an equivalent number of random hypotheses.
Using generative artificial intelligence, a team of researchers at The University of Texas at Austin has converted sounds from audio recordings into street-view images. The visual accuracy of these generated images demonstrates that machines can replicate human connection between audio and visual perception of environments. The research team describes training a soundscape-to-image AI model using audio and visual data gathered from a variety of urban and rural streetscapes and then using that model to generate images from audio recordings.
In a Stanford study, a two-hour interview was all it took for an AI to accurately predict people’s responses to a barrage of questions.
Cold Spring Harbor Laboratory scientists developed an AI algorithm inspired by the genome’s efficiency, achieving remarkable data compression and task performance.
In a sense, each of us begins life ready for action. Many animals perform amazing feats soon after they’re born. Spiders spin webs. Whales swim. But where do these innate abilities come from? Obviously, the brain plays a key role as it contains the trillions of neural connections needed to control complex behaviors.
However, the genome has space for only a small fraction of that information. This paradox has stumped scientists for decades. Now, Cold Spring Harbor Laboratory (CSHL) Professors Anthony Zador and Alexei Koulakov have devised a potential solution using artificial intelligence.
Some researchers propose that advancing AI to the next level will require an internal architecture that more closely mirrors the human mind. Rufin VanRullen joins Brian Greene to discuss early results from one such approach, based on the Global Workspace Theory of consciousness.
This program is part of the Big Ideas series, supported by the John Templeton Foundation.
Continue reading “Can Models of Human Consciousness Enhance AI Capabilities?” »
Anthropic, a leading AI model provider, has proposed a protocol and architecture for providing language models with the necessary context obtained from external systems.
Physicists from the University of the Witwatersrand (Wits) have developed an innovative computing system using laser beams and everyday display technology, marking a significant leap forward in the quest for more powerful quantum computing solutions.
The breakthrough, achieved by researchers at the university’s Structured Light Lab, offers a simpler and more cost-effective approach to advanced quantum computing by harnessing the unique properties of light. This development could potentially speed up complex calculations in fields such as logistics, finance and artificial intelligence. The research was published in the journal APL Photonics as the editor’s pick.
“Traditional computers work like switchboards, processing information as simple yes or no decisions. Our approach uses laser beams to process multiple possibilities simultaneously, dramatically increasing computing power,” says Dr. Isaac Nape, the Optica Emerging Leader Chair in Optics at Wits.
As a simple illustration, let’s say someone wanted to create a tomato sauce recipe, optimizing vitamin C and using sustainable tomatoes within a certain cost range. Journey Foods then taps into its database to generate an optimal recipe, and will continually push recommendations of top suppliers.
“Essentially, when people go to ChatGPT or something, and they’re asking them, ‘write this paper for me, or give me a social media post, speak to this audience,’ or whatever, right? It’s the same thing with our generative recipe recommendations,” Lynn said.
Except Lynn doesn’t use ChatGPT. Systems such as ChaptGPT gather data from the open internet, but Journey Foods gets its data from research institutions, academic journals, suppliers and manufacturers. Lynn said her business uses a lot of private, hard data that’s unstructured, with her company then giving it structure and doing so globally.
