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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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Artificial General Intelligence — Short for AGI is a trending and recent topic of debate among AI researchers and computer scientists. A pressing issue for AI or artificial Intelligence is the AI alignment problem. The AI control problem could be the most important task for humanity to solve. There have been many suggestions from AI researchers to avoid the dangers of artificial general intelligence or a digital super-intellgience. It seems among the best solutions to this problem has been a merging scenario with AGI. Elon Musk has suggested we regulate artificial intelligence and we should proceed very carefully if humanity collectively decides that creating a digital super-intelligence is the right move. Elon Musk is the founder of many high tech companies, including Neuralink. Which develops implantable brain–machine interfaces. Elon Musk warns that AI is probably the biggest existential threat for humanity. AGI is probably even more dangerous than nuclear warheads and nobody would suggest we allow anyone to build nuclear weapons if they want. The pressing issue for a potential AGI development and eventually the creation of a digital super-intelligence is going to be increasingly relevant in the coming years. Dr. Ben Goertzel, CEO & Founder, of SingularityNET Foundation, is one of the world’s foremost experts in Artificial General Intelligence. According to him these reactions are probably going to look very silly to people a few decades from now, as they go about their lives which have been made tremendously easy and happy and fascinating compared to 2020 reality, via the wide rollout of advanced AGI systems to handle manufacturing service, and all the other practical jobs that humans now spend their time doing. Elon musk suggested, the merge scenario with A.I. is the one that seems like probably the best,” or as he put it on the Joe Rogan Experience. “If you can’t beat it, join it.

#AGI #AI #Artificialintelligence.

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Continue reading “Can We Merge With Artificial General Intelligence? — The AGI Symbiosis” | >

We recently caught up with Dr. Aubrey de Grey and talked to him about the upcoming Dublin Longevity Summit and how things are looking on the advocacy landscape.

The return of face-to-face conferences means a great deal to me, because ever since my first one in 2003, I have found that they are the absolute most effective way to (a) bring capable newcomers into the field and (b) connect established people in synergistic ways.

The COVID-driven demise of the Berlin Undoing Aging series, which had been a revival of the Cambridge series, was a tragedy that I am delighted to be consigning to history.

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A Sydney Harbor Tunnel explosion showcases the work of UNSW researchers using wireless signals and artificial intelligence to more accurately identify dangerous fire situations.

Engineers from UNSW Sydney have developed a new fire detection system that could help save lives by monitoring the changes in Wi-Fi signals.

And a controlled test detonation of a car, planned by the Sydney Harbor Tunnel Company, recently provided further data to demonstrate the effectiveness of the technology.

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Pawpaw varieties are assessed on their flavor, yield, fruit size, texture, and disease resistance, Crabtree says. She adds that the “best varieties” would be high yield trees that produce a pawpaw with “firmness and/or creaminess that’s not watery, mushy, or gritty” as well as a lower percentage of seeds.

Hunting for pawpaw

Native to 26 states, pawpaw can be found along the East Coast between Ontario, Canada, and northern Florida west to Kentucky, Ohio, Michigan, Nebraska, Kansas, and even Texas.

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SpaceX says it has revised plans for its next-generation Starlink Gen2 constellation to allow the upgraded satellites to launch on its workhorse Falcon 9 rocket in addition to Starship, a new and unproven vehicle.

Set to be the largest and most powerful rocket ever flown when it eventually debuts, SpaceX’s two-stage Starship launch vehicle is also intended to be fully reusable, theoretically slashing the cost of launching payloads into and beyond Earth orbit. Most importantly, SpaceX says that even in its fully-reusable configuration, Starship should be capable of launching up to 150 tons (~330,000 lb) to low Earth orbit (LEO) – nearly a magnitude more than Falcon 9. However, once said to be on track to debut as early as mid-2021 to early 2022, it’s no longer clear if Starship will be ready for regular Starlink launches anytime soon.

In August 2021, SpaceX failed a major Starlink Gen2 revision with the FCC that started the company along the path that led to now. That revision revealed plans to dramatically increase the size and capabilities of each Gen2 satellite, boosting their maximum throughput from about 50 gigabits per second (Gbps) to ~150 Gbps. Just as importantly, SpaceX’s August 2021 modification made it clear that the company would prefer to launch the entire constellation with Starship, although it included an alternative constellation design that would lend itself better to Falcon 9 launches.

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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.

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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 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 and a simple machine-learning algorithm, known as a classifier.

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