Lifeboat Foundation

The human brain is probably the most complex thing in the universe. Apart from the human brain, no other system can automatically acquire new information and learn new skills, perform multimodal collaborative perception and information memory processing, make effective decisions in complex environments, and work stably with low power consumption. In this way, brain-inspired research can greatly advance the development of a new generation of artificial intelligence (AI) technologies.

Powered by new machine learning algorithms, effective large-scale labeled datasets, and superior computing power, AI programs have surpassed humans in speed and accuracy on certain tasks. However, most of the existing AI systems solve practical tasks from a computational perspective, eschewing most neuroscientific details, and tending to brute force optimization and large amounts of input data, making the implemented intelligent systems only suitable for solving specific types of problems. The long-term goal of brain-inspired intelligence research is to realize a general intelligent system. The main task is to integrate the understanding of multi-scale structure of the human brain and its information processing mechanisms, and build a cognitive brain computing model that simulates the cognitive function of the brain.

The Schwartz Reisman Institute for Technology and Society and the Department of Computer Science at the University of Toronto, in collaboration with the Vector Institute for Artificial Intelligence and the Cosmic Future Initiative at the Faculty of Arts & Science, present Geoffrey Hinton on October 27, 2023, at the University of Toronto.

0:00:00 — 0:07:20 Opening remarks and introduction.
0:07:21 — 0:08:43 Overview.
0:08:44 — 0:20:08 Two different ways to do computation.
0:20:09 — 0:30:11 Do large language models really understand what they are saying?
0:30:12 — 0:49:50 The first neural net language model and how it works.
0:49:51 — 0:57:24 Will we be able to control super-intelligence once it surpasses our intelligence?
0:57:25 — 1:03:18 Does digital intelligence have subjective experience?
1:03:19 — 1:55:36 Q&A
1:55:37 — 1:58:37 Closing remarks.

Continue reading “Geoffrey Hinton | Will digital intelligence replace biological intelligence?” »

We present a new route to ergodicity breaking via Hilbert space fragmentation that displays an unprecedented level of robustness. Our construction relies on a single emergent (prethermal) conservation law. In the limit when the conservation law is exact, we prove the emergence of Hilbert space fragmentation with an exponential number of frozen configurations. These configurations are low-entanglement states in the middle of the energy spectrum and therefore constitute examples of quantum many-body scars. We further prove that every frozen configuration is absolutely stable to arbitrary perturbations, to all finite orders in perturbation theory.

Wouldn’t it be wonderful if we could produce happiness by neuro modulation.

Shared with Dropbox.

Shared with Dropbox.

Sarcasm, a complex linguistic phenomenon often found in online communication, often serves as a means to express deep-seated opinions or emotions in a particular manner that can be in some sense witty, passive-aggressive, or more often than not demeaning or ridiculing to the person being addressed. Recognizing sarcasm in the written word is crucial for understanding the true intent behind a given statement, particularly when we are considering social media or online customer reviews.

While spotting that someone is being sarcastic in the offline world is usually fairly easy given facial expression, body language and other indicators, it is harder to decipher sarcasm in online text. New work published in the International Journal of Wireless and Mobile Computing hopes to meet this challenge. Geeta Abakash Sahu and Manoj Hudnurkar of the Symbiosis International University in Pune, India, have developed an advanced sarcasm detection model aimed at accurately identifying sarcastic remarks in digital conversations, a task crucial for understanding the true intent behind online statements.

The team’s model comprises four main phases. It begins with text pre-processing, which involves filtering out common, or “noise,” words such as “the,” “it,” and “and.” It then breaks down the text into smaller units. To address the challenge of dealing with a large number of features, the team used optimal feature selection techniques to ensure the model’s efficiency by prioritizing only the most relevant features. Features indicative of sarcasm, such as information gain, chi-square, mutual information, and symmetrical uncertainty, are then extracted from this pre-processed data by the algorithm.

Issues such as abrupt changes in speed limits and incomplete lane markings are among the most influential factors that can predict road crashes, finds new research by University of Massachusetts Amherst engineers. The study then used machine learning to predict which roads may be the most dangerous based on these features.

Published in the journal Transportation Research Record, the study was a collaboration between UMass Amherst civil and environmental engineers Jimi Oke, assistant professor; Eleni Christofa, associate professor; and Simos Gerasimidis, associate professor; and civil engineers from Egnatia Odos, a publicly owned engineering firm in Greece.

The most influential features included road design issues (such as changes in speed limits that are too abrupt or guardrail issues), pavement damage (cracks that stretch across the road and webbed cracking referred to as “alligator” cracking), and incomplete signage and road markings.

The Internet is abuzz with talk about Sora, a new AI model that brings text-to-video generation to a whole new level.

Analysis of the terms used for emotions across a sample of 2,474 spoken languages reveals low similarity across cultures.