Lifeboat Foundation

“I believe we can train the algorithm not only to picture accurately a face you’re looking at, but also any face you imagine vividly, such as your mother’s,” explains Dado.

“By developing this technology, it would be fascinating to decode and recreate subjective experiences, perhaps even your dreams,” Dado says. “Such technological knowledge could also be incorporated into clinical applications such as communicating with patients who are locked within deep comas.”

Continue reading “‘Mind-Reading’ Technology Translates Brainwaves into Photos” »

In recent years, deep learning algorithms have achieved remarkable results in a variety of fields, including artistic disciplines. In fact, many computer scientists worldwide have successfully developed models that can create artistic works, including poems, paintings and sketches.

Researchers at Seoul National University have recently introduced a new artistic deep learning framework, which is designed to enhance the skills of a sketching robot. Their framework, introduced in a paper presented at ICRA 2022 and pre-published on arXiv, allows a sketching robot to learn both stroke-based rendering and motor control simultaneously.

Continue reading “A deep learning framework to enhance the capabilities of a robotic sketching agent” »

Millions of children log into chat rooms every day to talk with other children. One of these “children” could well be a man pretending to be a 12-year-old girl with far more sinister intentions than having a chat about “My Little Pony” episodes.

Inventor and NTNU professor Patrick Bours at AiBA is working to prevent just this type of predatory behavior. AiBA, an AI-digital moderator that Bours helped found, can offer a tool based on behavioral biometrics and algorithms that detect sexual abusers in online chats with children.

And now, as recently reported by Dagens Næringsliv, a national financial newspaper, the company has raised capital of NOK 7.5. million, with investors including Firda and Wiski Capital, two Norwegian-based firms.

Most materials—from rubber bands to steel beams—thin out as they are stretched, but engineers can use origami’s interlocking ridges and precise folds to reverse this tendency and build devices that grow wider as they are pulled apart.

Researchers increasingly use this kind of technique, drawn from the ancient art of origami, to design spacecraft components, medical robots and antenna arrays. However, much of the work has progressed via instinct and trial and error. Now, researchers from Princeton Engineering and Georgia Tech have developed a general formula that analyzes how structures can be configured to thin, remain unaffected, or thicken as they are stretched, pushed or bent.

Continue reading “Researchers unfold elegant equations to explain the enigma of expanding origami” »

An art collective is trying to get an AI-supported candidate into Danish Parliament in 2023. Could we have a fully virtual candidate one day?

With all the political rancor that has become a part of our everyday reality, maybe it’s time to admit that humans may not be the best at forging agreements. Our egos are always in play, and emotions often rule our political choices more than reason. Maybe artificial intelligence (AI) could do a better job, or at least that’s what the creators of The Synthetic Party, the world’s first AI-based political party, think. The party hopes to run an AI candidate in Denmark’s general election in 2023.

Full Story:

Continue reading “An AI-based party vows to win Denmark’s general election in 2023. Can it succeed?” »

This is Episode 7 in a series of videos discussing the General Theory of General Intelligence as overviewed in the paper.
Goertzel, Ben. “The General Theory of General Intelligence: A Pragmatic Patternist Perspective.“
https://arxiv.org/pdf/2103.15100
This episode overviews ideas regarding how the particular nature and requirements of *human-like-ness* can be used guide the design and education of AGI systems. This is where cognitive science and computer science richly intersect. Core architectural ideas of OpenCog along with numerous other AGI systems (MicroPsi, LIDA, Aaron Sloman’s work,…) are reviewed in this context.
Some additional references relevant to this episode are:
Goertzel, Ben. “The Embodied Communication Prior: A characterization of general intelligence in the context of Embodied social interaction.” In 2009 8th IEEE International Conference on Cognitive Informatics, pp. 38–43. IEEE, 2009.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.352…1&type=pdf.
Bengio, Yoshua. “The consciousness prior.” 2017
https://arxiv.org/pdf/1709.08568
Goertzel, Ben, Matt Iklé, and Jared Wigmore. “The architecture of human-like general intelligence.” In Theoretical foundations of artificial general intelligence, pp. 123–144. Atlantis Press, Paris, 2012.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.352.1548
Ben Goertzel, Cassio Pennachin, and Nil Geisweiller. Engineering.
General Intelligence, Part 1: A Path to Advanced AGI via Embodied Learning and Cognitive Synergy. Springer: Atlantis Thinking Machines, 2013.
https://1lib.us/book/2333263/7af06e?id=2333263&secret=7af06e.
Ben Goertzel, Cassio Pennachin, and Nil Geisweiller. Engineering.
General Intelligence, Part 2: The CogPrime Architecture for Integrative, Embodied AGI. Springer: Atlantis Thinking Machines, 2013.
https://1lib.us/book/2333264/207a57?id=2333264&secret=207a57
–
SingularityNET is a decentralized marketplace for artificial intelligence. We aim to create the world’s global brain with a full-stack AI solution powered by a decentralized protocol.
We gathered the leading minds in machine learning and blockchain to democratize access to AI technology. Now anyone can take advantage of a global network of AI algorithms, services, and agents.
Website: https://singularitynet.io.
Forum: https://community.singularitynet.io.
Telegram: https://t.me/singularitynet.
Twitter: https://twitter.com/singularity_net.
Facebook: https://facebook.com/singularitynet.io.
Instagram: https://instagram.com/singularitynet.io.
Github: https://github.com/singnet

Hyperparameter tuning is important for algorithms. It improves their overall performance of a machine learning model and is set before the learning process and happens outside of the model. If hyperparameter tuning does not occur, the model will produce errors and inaccurate results as the loss function is not minimized.

Hyperparameter tuning is about finding a set of optimal hyperparameter values which maximizes the model’s performance, minimizes loss and produces better outputs.

In recent years, roboticists have been trying to improve how robots interact with different objects found in real-world settings. While some of their efforts yielded promising results, the manipulation skills of most existing robotic systems still lag behinds those of humans.

Fabrics are among the types of objects that have proved to be most challenging for robot to interact with. The main reasons for this are that pieces of cloth and other fabrics can be stretched, moved and folded in different ways, which can result in complex material dynamics and self-occlusions.

Researchers at Carnegie Mellon University’s Robotics Institute have recently proposed a new computational technique that could allow robots to better understand and handle fabrics. This technique, introduced in a paper set to be presented at the International Conference on Intelligent Robots and Systems and pre-published on arXiv, is based on the use of a tactile sensor and a simple machine-learning algorithm, known as a classifier.

Unstable black holes would require a rewrite of Einstein’s gravitational theory.

An international group of scientists finally proved that slowly rotating Kerr black holes are stable, a report from Quanta Magazine

In 1963, mathematician Roy Kerr found a solution to Einstein’s equations that accurately described the spacetime around what is now known as a rotating black hole.

Continue reading “Black Holes Finally Proven Mathematically Stable” »