Artificial General Intelligence (AGI) News and Discussions

[firestar464]

I say by the beginning of next year

[spryfusion]

[weatheriscool]

AI outperforms humans in standardized tests of creative potential
https://techxplore.com/news/2024-03-ai- ... ative.html
by University of Arkansas

In a recent study, 151 human participants were pitted against ChatGPT-4 in three tests designed to measure divergent thinking, which is considered to be an indicator of creative thought.

Divergent thinking is characterized by the ability to generate a unique solution to a question that does not have one expected solution, such as "What is the best way to avoid talking about politics with my parents?" In the study, GPT-4 provided more original and elaborate answers than the human participants.

The study, "The current state of artificial intelligence generative language models is more creative than humans on divergent thinking tasks," was published in Scientific Reports and authored by U of A Ph.D. students in psychological science Kent F. Hubert and Kim N. Awa, as well as Darya L. Zabelina, an assistant professor of psychological science at the U of A and director of the Mechanisms of Creative Cognition and Attention Lab.

[Yuli Ban]

Excuse me, it did what?

[firestar464]

Has it merely learned from big brain people online? Or is this something else?

[Yuli Ban]

[Nero]

Absolutely incredible stuff.

[weatheriscool]

Microsoft's small language model outperforms larger models on standardized math tests
https://techxplore.com/news/2024-03-mic ... arger.html
by Bob Yirka , Tech Xplore

A small team of AI researchers at Microsoft reports that the company's Orca-Math small language model outperforms other, larger models on standardized math tests. The group has published a paper on the arXiv preprint server describing their testing of Orca-Math on the Grade School Math 8K (GSM8K) benchmark and how it fared compared to well-known LLMs.

Many popular LLMs such as ChatGPT are known for their impressive conversational skills—less well known is that most of them can also solve math word problems. AI researchers have tested their abilities at such tasks by pitting them against the GSM8K, a dataset of 8,500 grade-school math word problems that require multistep reasoning to solve, along with their correct answers.

In this new study, the research team at Microsoft tested Orca-Math, an AI application developed by another team at Microsoft specifically designed to tackle math word problems, and compared the results with larger AI models.

[weatheriscool]

Balancing training data and human knowledge to make AI act more like a scientist
https://techxplore.com/news/2024-03-hum ... ntist.html
by Cell Press

When you teach a child how to solve puzzles, you can either let them figure it out through trial and error, or you can guide them with some basic rules and tips. Similarly, incorporating rules and tips into AI training—such as the laws of physics—could make them more efficient and more reflective of the real world. However, helping the AI assess the value of different rules can be a tricky task.

Researchers report March 8 in the journal Nexus that they have developed a framework for assessing the relative value of rules and data in "informed machine learning models" that incorporate both. They showed that by doing so, they could help the AI incorporate basic laws of the real world and better navigate scientific problems like solving complex mathematical problems and optimizing experimental conditions in chemistry experiments.

"Embedding human knowledge into AI models has the potential to improve their efficiency and ability to make inferences, but the question is how to balance the influence of data and knowledge," says first author Hao Xu of Peking University. "Our framework can be employed to evaluate different knowledge and rules to enhance the predictive capability of deep learning models."

Generative AI models like ChatGPT and Sora are purely data-driven—the models are given training data, and they teach themselves via trial and error. However, with only data to work from, these systems have no way to learn physical laws, such as gravity or fluid dynamics, and they also struggle to perform in situations that differ from their training data.

[spryfusion]