Lifeboat Foundation

A neural-network-based artificial intelligence outperforms ChatGPT at quickly folding new words into its lexicon, a key aspect of human intelligence.

The power of human language and thought arises from systematic compositionality—the algebraic ability to understand and produce novel combinations from known components. Fodor and Pylyshyn1 famously argued that artificial neural networks lack this capacity and are therefore not viable models of the mind. Neural networks have advanced considerably in the years since, yet the systematicity challenge persists. Here we successfully address Fodor and Pylyshyn’s challenge by providing evidence that neural networks can achieve human-like systematicity when optimized for their compositional skills. To do so, we introduce the meta-learning for compositionality (MLC) approach for guiding training… More.

Over 35 years ago, when Fodor and Pylyshyn raised the issue of systematicity in neural networks¹, today’s models¹⁹ and their language skills were probably unimaginable. As a credit to Fodor and Pylyshyn’s prescience, the systematicity debate has endured. Systematicity continues to challenge models^{11,12,13,14,15,16,17,18} and motivates new frameworks^{34,35,36,37,38,39,40,41}. Preliminary experiments reported in Supplementary Information 3 suggest that systematicity is still a challenge, or at the very least an open question, even for recent large language models such as GPT-4. To resolve the debate, and to understand whether neural networks can capture human-like compositional skills, we must compare humans and machines side-by-side, as in this Article and other recent work^7,42,43. In our experiments, we found that the most common human responses were algebraic and systematic in exactly the ways that Fodor and Pylyshyn¹ discuss. However, people also relied on inductive biases that sometimes support the algebraic solution and sometimes deviate from it; indeed, people are not purely algebraic machines^3,6,7. We showed how MLC enables a standard neural network optimized for its compositional skills to mimic or exceed human systematic generalization in a side-by-side comparison. MLC shows much stronger systematicity than neural networks trained in standard ways, and shows more nuanced behaviour than pristine symbolic models. MLC also allows neural networks to tackle other existing challenges, including making systematic use of isolated primitives^11,16 and using mutual exclusivity to infer meanings⁴⁴.

Our use of MLC for behavioural modelling relates to other approaches for reverse engineering human inductive biases. Bayesian approaches enable a modeller to evaluate different representational forms and parameter settings for capturing human behaviour, as specified through the model’s prior⁴⁵. These priors can also be tuned with behavioural data through hierarchical Bayesian modelling⁴⁶, although the resulting set-up can be restrictive. MLC shows how meta-learning can be used like hierarchical Bayesian models for reverse-engineering inductive biases (see ref. ⁴⁷ for a formal connection), although with the aid of neural networks for greater expressive power. Our research adds to a growing literature, reviewed previously⁴⁸, on using meta-learning for understanding human^49,50,51 or human-like behaviour^52,53,54. In our experiments, only MLC closely reproduced human behaviour with respect to both systematicity and biases, with the MLC (joint) model best navigating the trade-off between these two blueprints of human linguistic behaviour. Furthermore, MLC derives its abilities through meta-learning, where both systematic generalization and the human biases are not inherent properties of the neural network architecture but, instead, are induced from data.

Continue reading “Human-like systematic generalization through a meta-learning neural network” »

According to Alphabet’s CEO, Google’s Gemini is just the first of a series of next-generation AI models that Google plans to bring to market in 2024.

With the multimodal Gemini AI model, Google wants to at least catch up with OpenAI’s GPT-4. The model is expected to be released later this year. In the recent quarterly earnings call, Alphabet CEO Sundar Pichai said that Google is “getting the model ready”.

Gemini will be released in different sizes and with different capabilities, and will be used for all internal products immediately, Pichai said. So it is likely that Gemini will replace Google’s current PaLM-2 language model. Developers and cloud customers will get access through Vertex AI.

VentureBeat presents: AI Unleashed — An exclusive executive event for enterprise data leaders. Network and learn with industry peers. Learn More

A group of artificial intelligence researchers from the University of Science and Technology of China (USTC) and Tencent YouTu Lab have developed an innovative framework, coined as “Woodpecker”, designed to correct hallucinations in multimodal large language models (MLLMs).

The research paper outlining this groundbreaking approach was published on the pre-print server arXiv, under the title Woodpecker: Hallucination Correction for Multimodal Large Language Models.

Magnets are magnificent. Made of iron, aluminum, nickel, cobalt, and various other metals, they’re used in compasses for navigation, in medical imaging machines to see inside the human body, in kitchens to keep cabinets and refrigerators closed, in computers to store data and in new high-speed “hyperloop” trains that can travel at speeds of up to 76 miles per hour.

For environmentalists, however, the most exciting use yet for magnets might be a newly discovered application out of Australia’s Royal Melbourne Institute of Technology, otherwise known as RMIT University: Using magnets, researchers there have discovered a novel way of removing harmful microplastics from water.

“[Microplastics] can take up to 450 years to degrade, are not detectable and removable through conventional treatment systems, resulting in millions of tons being released into the sea every year,” co-lead research Nasir Mahmood said in a statement. “This is not only harmful for aquatic life, but also has significant negative impacts on human health.”

🦠🔬💊 https://www.news-medical.net/news/20231025/How-strep-bacteri…-treatment

Researchers discover that Group A streptococcal infections alter immunoglobulin G (IgG) homeostasis to evade the immune system, affecting the transition from local to systemic infections. The study also raises concerns about the effectiveness of antibody-based therapies, as the bacteria’s virulence factors can degrade therapeutic antibodies.

The AI guys aren’t alright.

Anaconda co-founder and CEO Peter Wang predicts that books will soon be replaced by AI-generated, interactive content trips dubbed “thunks.”

Scientists from the University of Science and Technology of China and Tencent’s YouTu Lab have developed a tool to combat “hallucination” by artificial intelligence (AI) models.

Year 2009 This is actually proof of string theory existence through magnetic monopoles.

Neutron scattering measurements on two spin-ice compounds show evidence for magnetic monopoles.