Lifeboat Foundation

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?” »

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.

Drug discovery is being transformed by advances in computational protein structure prediction and protein design.

It’s electric! A startup emerged from stealth this week with grand plans to pioneer a new form of neurotech dubbed “electric medicine.”

Elemind’s approach centers on artificial intelligence-powered algorithms that are trained to continuously analyze neurological activity collected by a noninvasive wearable device, then to deliver through the wearable bursts of neurostimulation that are uniquely tailored to those real-time brain wave readings.

Machine learning algorithms play important roles in medical imaging analysis but can be affected by biases in training data. Jones and colleagues discuss how causal reasoning can be used to better understand and tackle algorithmic bias in medical imaging analysis.

The interior of black holes remains a conundrum for science. In 1916, German physicist Karl Schwarzschild outlined a solution to Albert Einstein’s equations of general relativity, in which the center of a black hole consists of a so-called singularity, a point at which space and time no longer exist. Here, the theory goes, all physical laws, including Einstein’s general theory of relativity, no longer apply; the principle of causality is suspended.

This constitutes a great nuisance for science—after all, it means that no information can escape from a black hole beyond the so-called event horizon. This could be a reason why Schwarzschild’s solution did not attract much attention outside the theoretical realm—that is, until the first candidate for a black hole was discovered in 1971, followed by the discovery of the black hole in the center of our Milky Way in the 2000s, and finally the first image of a black hole, captured by the Event Horizon Telescope Collaboration in 2019.

In 2001, Pawel Mazur and Emil Mottola proposed a different solution to Einstein’s field equations that led to objects that they called gravitational condensate stars, or gravastars. Contrary to black holes, gravastars have several advantages from a theoretical astrophysics perspective.

Tech giant Google has finally unveiled its much-hyped Gemini AI, a series of generative AI models it claims are its “largest and most capable” to date.

“This new era of models represents one of the biggest science and engineering efforts we’ve undertaken as a company,” said Google CEO Sundar Pichai.

Continue reading “Google’s new Gemini AI beats GPT-4 in 30 of 32 tests” »

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Researchers from NYU discovered that classical computers could keep up with or even surpass quantum computers in certain circumstances. Classical computers can get a boost in speed and accuracy by adopting a new innovative algorithmic method, which could mean that they still have a future in a world of quantum computers.

Many experts believe that quantum computing is the future, and that we are veering away from classical computing, primarily because classical computers are significantly slower and weaker than their quantum-based counterparts. However, turns out that quantum computers are delicate and prone to information loss, and even if information is preserved it is difficult to convert it to classical information necessary for practical computation.

Summary: Researchers developed an innovative AI tool, DeepGO-SE, that excels in predicting the functions of unknown proteins, marking a significant advance in bioinformatics. Leveraging large language models and logical entailment, this tool can deduce molecular functions even for proteins without existing database matches, offering a groundbreaking approach to understanding cellular mechanisms.

Its precision has placed DeepGO-SE among the top algorithms in an international function prediction competition, demonstrating its potential in drug discovery, metabolic pathway analysis, and beyond. The team aims to apply this tool to explore proteins in extreme environments, opening new doors for biotechnological advancements.