Lifeboat Foundation

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.

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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
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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” »

I have been invited to participate in a quite large event in which some experts and I (allow me to not consider myself one) will discuss about Artificial Intelligence, and, in particular, about the concept of Super Intelligence.

It turns out I recently found out this really interesting TED talk by Grady Booch, just in perfect timing to prepare my talk.

No matter if you agree or disagree with Mr. Booch’s point of view, it is clear that today we are still living in the era of weak or narrow AI, very far from general AI, and even more from a potential Super Intelligence. Still, Machine Learning bring us with a great opportunity as of today. The opportunity to put algorithms to work together with humans to solve some of our biggest challenges: climate change, poverty, health and well being, etc.

Near-term quantum computers, quantum computers developed today or in the near future, could help to tackle some problems more effectively than classical computers. One potential application for these computers could be in physics, chemistry and materials science, to perform quantum simulations and determine the ground states of quantum systems.

Some quantum computers developed over the past few years have proved to be fairly effective at running quantum simulations. However, near-term quantum computing approaches are still limited by existing hardware components and by the adverse effects of background noise.

Researchers at 1QB Information Technologies (1QBit), University of Waterloo and the Perimeter Institute for Theoretical Physics have recently developed neural error mitigation, a new strategy that could improve ground state estimates attained using quantum simulations. This strategy, introduced in a paper published in Nature Machine Intelligence, is based on machine-learning algorithms.

Van der Waerden’s conjecture mystified mathematicians for 85 years. Its solution shows how polynomial roots relate to one another.

Google AI announced the launch of the Google Research YouTube channel today. The channel is set to focus on a wide range of subjects like AI/ML, robotics, theory and algorithms, quantum computing, health and bioscience.