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Ben Goertzel converses with Hugo de Garis on his transhumanist argument for the reality of a Creator.

BEN GOERTZEL: Hugo, you’ve recently published an article on KurzweilAI.net titled “From Cosmism to Deism”, which essentially posits a transhumanist argument that some sort of “God” exists, i.e. some sort of intelligent creator of our universe – and furthermore that this “creator” is probably some sort of mathematician. I’m curious to ask you some questions digging a little deeper into your thinking on these (fun, albeit rather far-out) issues.

The Transhumanist FAQ was developed in the mid-1990s and in 1998 became a formal FAQ through the inspirational work of transhumanists, including Alexander Chislenko, Max More, Anders Sandberg, Natasha Vita-More, Eliezer Yudkowsky, Arjen Kamphius, and many others. Greg Burch, David Pearce, and Anders Sandberg kindly offered extensive editorial comments. The presentation in the cryonics section was, and still is, directly inspired by an article by Ralph Merkle. Ideas, criticisms, questions, phrases, and sentences to the original version were contributed by (in alphabetical order): Alex ([email protected]), Brent Allsop, Brian Atkins, Scott Badger, Doug Bailey, Harmony Baldwin, Damien Broderick, Greg Burch, David Cary, John K Clark, Dan Clemensen, Damon Davis, Jeff Dee, Jean-Michel Delhotel, Dylan Evans, [email protected], Daniel Fabulich, Frank Forman, Robin Hanson, Andrew Hennessey, Tony Hollick, Joe Jenkins, William John, Michelle Jones, Arjen Kamphius, Henri Kluytmans, Eugene Leitl, Michael Lorrey, [email protected], Peter C. McCluskey, Erik Moeller, J. R. Molloy, Max More, Bryan Moss, Harvey Newstrom, Michael Nielsen, John S. Novak III, Dalibor van den Otter, David Pearce, [email protected], Thom Quinn, Anders Sandberg, Wesley R. Schwein, [email protected], Allen Smith, Geoff Smith, Randy Smith, Dennis Stevens, Derek Strong, Remi Sussan, Natasha Vita-More, Michael Wiik, Eliezer Yudkowsky, and [email protected].

Over the years, this FAQ has been updated to provide a substantial account of transhumanism. Extropy Institute (ExI) was a source of information for the first version of the Transhumanist FAQ, version 1.0 in the 1990s. The Transhumanist Manifesto, conceived by Natasha Vita-More in 1983 and revised in 1998–2020 to include advances of the growing worldview, was published in the CD placed onboard the Cassini-Huygens spacecraft in its mission to Saturn.

Humanity+, also known as WTA, adopted the FAQ in 2001 and Nick Bostrom added substantial information about future scenarios. With the contributions of close to hundred people from ExI, Aleph, DeTrans, Transcedo, WTA, and the UK Transhumanist Association, new material has been added and many old sections have been substantially reworked. In the preparation of version 2.0, the following people have been especially helpful: Eliezer Yudkowsky, who provided editorial assistance with comments on particular issues of substance; Dale Carrico who proofread the first half of the text; and Michael LaTorra who did the same for the second half; and “Reason” who then went over the whole document again, as did Frank Forman, and Sarah Banks Forman. Useful comments of either substance or form have also been contributed by (in alphabetical order): Michael Anissimov, Samantha Atkins, Milan Cirkovic, José Luis Cordeiro, George Dvorsky, James Hughes, G.E.

face_with_colon_three circa 2021.


Think about where our energy comes from: drilling rigs and smokestacks, windmills and solar panels. Lithium-ion battery packs might even come to mind.

We probably don’t think about the farms that comprise over one-third of Earth’s total land area. But farms can also be an energy source. Barcelona-based battery company Bioo is generating electricity from the organic matter in soil and creating biological batteries that can power agricultural sensors, a growing 1.36 billion dollar global market.

Bioo’s tech eliminates the need for single-use chemical batteries, which have to be replaced frequently. The company will work with large players such as Bayer Crop Science to pilot its sensor tech on farms, while also experimenting with using bio-batteries to power lighting installations. Eventually, Bioo envisions a future where biology may even help power our largest cities.

Improving the efficiency of algorithms for fundamental computations is a crucial task nowadays as it influences the overall pace of a large number of computations that might have a significant impact. One such simple task is matrix multiplication, which can be found in systems like neural networks and scientific computing routines. Machine learning has the potential to go beyond human intuition and beat the most exemplary human-designed algorithms currently available. However, due to the vast number of possible algorithms, this process of automated algorithm discovery is complicated. DeepMind recently made a breakthrough discovery by developing AplhaTensor, the first-ever artificial intelligence (AI) system for developing new, effective, and indubitably correct algorithms for essential operations like matrix multiplication. Their approach answers a mathematical puzzle that has been open for over 50 years: how to multiply two matrices as quickly as possible.

AlphaZero, an agent that showed superhuman performance in board games like chess, go, and shogi, is the foundation upon which AlphaTensor is built. The system expands on AlphaZero’s progression from playing traditional games to solving complex mathematical problems for the first time. The team believes this study represents an important milestone in DeepMind’s objective to improve science and use AI to solve the most fundamental problems. The research has also been published in the established Nature journal.

Matrix multiplication has numerous real-world applications despite being one of the most simple algorithms taught to students in high school. This method is utilized for many things, including processing images on smartphones, identifying verbal commands, creating graphics for video games, and much more. Developing computing hardware that multiplies matrices effectively consumes many resources; therefore, even small gains in matrix multiplication efficiency can have a significant impact. The study investigates how the automatic development of new matrix multiplication algorithms could be advanced by using contemporary AI approaches. In order to find algorithms that are more effective than the state-of-the-art for many matrix sizes, AlphaTensor further leans on human intuition. Its AI-designed algorithms outperform those created by humans, which represents a significant advancement in algorithmic discovery.

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