Lifeboat Foundation

Black holes are perhaps the most mysterious objects in nature. They warp space and time in extreme ways and contain a mathematical impossibility, a singularity – an infinitely hot and dense object within. But if black holes exist and are truly black, how exactly would we ever be able to make an observation?

This morning the Nobel Committee announced that the 2020 Nobel Prize in physics will be awarded to three scientists – Sir Roger Penrose, Reinhard Genzel and Andrea Ghez – who helped discover the answers to such profound questions. Andrea Ghez is only the fourth woman to win the Nobel Prize in physics.

Continue reading “The 2020 Nobel Prize in physics awarded for work on black holes. An astrophysicist explains the trailblazing discoveries” »

For many futurist Like Anthony Lewandoski, the point beyond where machines achieve Artificial Super Intelligence, is the point where all their rationale for the future meets unfathomable numbers of probabilities.

The best analytical minds cannot peer behind this thick curtain of the future, a future that seems will be woven with threads of the singularity; a future that seems runaway even before we get there.

It appears the only projection we can arrive at as we peer into a future harnessed on Artificial Super Intelligence and driven by the Singularity is that, we as humans will have to take the back seat and allow a more advanced form of intelligence take the reign.

Continue reading “The Road to Creating an AI God” »

A new D-Theory of Time, or Digital Presentism, is predicated on reversible quantum computing at large, including the notion of ‘Anti-Time’ around which the present article revolves. If you think Anti-Time is nothing but fiction, and doesn’t apply to our reality, think again. As Dr. Antonin Tuynman writes in his Foreword to The Physics of Time: D-Theory of Time & Temporal Mechanics by Alex M. Vikoulov: “Whereas quantum physics and relativity theory have been solidly in place for over a century now, stubbornly and forcedly we still cling to atavistic interpretations, which are no longer in line with the well-established findings of our experiments in physics. Amidst the turmoil of this spinning convoluted dreamtime of our digital Cyberbardo, Vikoulov carves out a trajectory for understanding.”

#AntiTime #PhysicsofTime #DTheoryofTime #DigitalPresentism #TemporalMechanics

Many temporal concepts are undoubtedly extremely counterintuitive. Time directionality and time symmetry are especially notorious ones. Any of the possible pasts may have led to the present “digital” conscious instant. This is a strange idea if you are accustomed to looking at the world in a strictly linear, deterministic way, but it reflects the uncertain world described by quantum mechanics. A major counterargument to the multitude of pasts could be a combinatorial explosion of observer ‘anti-time’-lines, i.e., digital timelines extending in the opposite temporal direction from the present temporal singularity to the Alpha Point (Digital Big Bang). So, how in the quantum multiverse are those digital anti-timelines supposed to converge once again at the Alpha Point?

Continue reading “Anti-Time: A Twin of Time?” »

« Has the universe been around forever? If so, perhaps it’s been bouncing back and forth in a never-ending cycle of big bangs in which all matter bubbles out of a singularity, followed by big crunches, in which everything gets swallowed up again to form that dense point from which the universe is born again. And the cycle continues over and over and over. »

All you need is some string.

On GPT-3, achieving AGI, machine understanding and lots more… Will GPT-3 or an equivalent be used to deepfake human understanding?

Joscha Bach on GPT-3, achieving AGI, machine understanding and lots more
02:40 What’s missing in AI atm? Unified coherent model of reality
04:14 AI systems like GPT-3 behave as if they understand — what’s missing?
08:35 Symbol grounding — does GPT-3 have it?
09:35 GPT-3 for music generation, GPT-3 for image generation, GPT-3 for video generation
11:13 GPT-3 temperature parameter. Strange output?
13:09 GPT-3 a powerful tool for idea generation
14:05 GPT-3 as a tool for writing code. Will GPT-3 spawn a singularity?
16:32 Increasing GPT-3 input context may have a high impact
16:59 Identifying grammatical structure & language
19:46 What is the GPT-3 transformer network doing?
21:26 GPT-3 uses brute force, not zero-shot learning, humans do ZSL
22:15 Extending the GPT-3 token context space. Current Context = Working Memory. Humans with smaller current contexts integrate concepts over long time-spans
24:07 GPT-3 can’t write a good novel
25:09 GPT-3 needs to become sensitive to multi-modal sense data — video, audio, text etc
26:00 GPT-3 a universal chat-bot — conversations with God & Johann Wolfgang von Goethe
30:14 What does understanding mean? Does it have gradients (i.e. from primitive to high level)?
32:19 (correlation vs causation) What is causation? Does GPT-3 understand causation? Does GPT-3 do causation?
38:06 Deep-faking understanding
40:06 The metaphor of the Golem applied to civ
42:33 GPT-3 fine with a person in the loop. Big danger in a system which fakes understanding. Deep-faking intelligible explanations.
44:32 GPT-3 babbling at the level of non-experts
45:14 Our civilization lacks sentience — it can’t plan ahead
46:20 Would GTP-3 (a hopfield network) improve dramatically if it could consume 1 to 5 trillion parameters?
47:24 GPT3: scaling up a simple idea. Clever hacks to formulate the inputs
47:41 Google GShard with 600 billion input parameters — Amazon may be doing something similar — future experiments
49:12 Ideal grounding in machines
51:13 We live inside a story we generate about the world — no reason why GPT-3 can’t be extended to do this
52:56 Tracking the real world
54:51 MicroPsi
57:25 What is computationalism? What is it’s relationship to mathematics?
59:30 Stateless systems vs step by step Computation — Godel, Turing, the halting problem & the notion of truth
1:00:30 Truth independent from the process used to determine truth. Constraining truth that which can be computed on finite state machines
1:03:54 Infinities can’t describe a consistent reality without contradictions
1:06:04 Stevan Harnad’s understanding of computation
1:08:32 Causation / answering ‘why’ questions
1:11:12 Causation through brute forcing correlation
1:13:22 Deep learning vs shallow learning
1:14:56 Brute forcing current deep learning algorithms on a Matrioshka brain — would it wake up?
1:15:38 What is sentience? Could a plant be sentient? Are eco-systems sentient?
1:19:56 Software/OS as spirit — spiritualism vs superstition. Empirically informed spiritualism
1:23:53 Can we build AI that shares our purposes?
1:26:31 Is the cell the ultimate computronium? The purpose of control is to harness complexity
1:31:29 Intelligent design
1:33:09 Category learning & categorical perception: Models — parameters constrain each other
1:35:06 Surprise minimization & hidden states; abstraction & continuous features — predicting dynamics of parts that can be both controlled & not controlled, by changing the parts that can be controlled. Categories are a way of talking about hidden states.
1:37:29 ‘Category’ is a useful concept — gradients are often hard to compute — so compressing away gradients to focus on signals (categories) when needed
1:38:19 Scientific / decision tree thinking vs grounded common sense reasoning
1:40:00 Wisdom/common sense vs understanding. Common sense, tribal biases & group insanity. Self preservation, dunbar numbers
1:44:10 Is g factor & understanding two sides of the same coin? What is intelligence?
1:47:07 General intelligence as the result of control problems so general they require agents to become sentient
1:47:47 Solving the Turing test: asking the AI to explain intelligence. If response is an intelligible & testable implementation plan then it passes?
1:49:18 The term ‘general intelligence’ inherits it’s essence from behavioral psychology; a behaviorist black box approach to measuring capability
1:52:15 How we perceive color — natural synesthesia & induced synesthesia
1:56:37 The g factor vs understanding
1:59:24 Understanding as a mechanism to achieve goals
2:01:42 The end of science?
2:03:54 Exciting currently untestable theories/ideas (that may be testable by science once we develop the precise enough instruments). Can fundamental physics be solved by computational physics?
2:07:14 Quantum computing. Deeper substrates of the universe that runs more efficiently than the particle level of the universe?
2:10:05 The Fermi paradox
2:12:19 Existence, death and identity construction.

The ability of future superintelligent machines and enhanced humans alike to instantly transfer knowledge and directly share experiences with each other in digital format will lead to evolution of intelligence from relatively isolated individual minds to the global community of hyperconnected digital minds. The forthcoming phenomenon, the Syntellect Emergence, or the Cybernetic Singularity, is already seen on the horizon, when Digital Gaia, the global neural network of billions of hyperconnected humans and superintelligent machines, and trillions of sensors around the planet, “wakes up” as a living, conscious superorganism. It is when, essentially, you yourself transcend to the higher Gaian Mind. https://link.medium.com/vXrDIWOns9

#CyberneticSingularity

“Evolution is a process of creating patterns of increasing order… I believe that it’s the evolution of patterns that constitutes the ultimate story of our world. Each stage or epoch uses the information-processing methods of the previous…

Continue reading “The Cybernetic Singularity: When Humankind Transcends Biology” »

Scientists suggest that a counter-intuitive, hypothetical species of black holes may negate the standard model of cosmology, where dark energy is an inherent and constant property of spacetime that will result in an eventual cold death of the universe. “It’s the big elephant in the room,” says Claudia de Rham, a theoretical physicist at Imperial College London about dark energy, the mysterious, elusive phenomena that pushes the cosmos to expand so rapidly and which is estimated to account for 70% of the contents of the universe. “It’s very frustrating.”

Generic Objects of Dark Energy

Astronomers have known for two decades that the expansion of the universe is accelerating, but the physics of this expansion remains a mystery. In 1966, Erast Gliner, a young physicist at the Ioffe Physico-Technical Institute in Leningrad, proposed an alternative hypothesis that very large stars should collapse into what could be called Generic Objects of Dark Energy (GEODEs). These appear to be black holes when viewed from the outside but, unlike black holes, they contain dark energy instead of a singularity.

Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.

Known for identifying cutting edge technologies, he is currently a Co-Founder of a startup and fundraiser for high potential early-stage companies. He is the Head of Research for Allocations for deep technology investments and an Angel Investor at Space Angels.

A frequent speaker at corporations, he has been a TEDx speaker, a Singularity University speaker and guest at numerous interviews for radio and podcasts. He is open to public speaking and advising engagements.

Adapting the Intelligence Community

As machines become the primary collectors, analysts, consumers, and targets of intelligence, the entire U.S. intelligence community will need to evolve. This evolution must start with enormous investments in AI and autonomization technology as well as changes to concepts of operations that enable agencies to both process huge volumes of data and channel the resulting intelligence directly to autonomous machines. As practically everything becomes connected via networks that produce some form of electromagnetic signature or data, signals intelligence in particular will need to be a locus of AI evolution. So will geospatial intelligence. As satellites and other sensors proliferate, everything on earth will soon be visible at all times from above, a state that the federal research and development center Aerospace has called the “GEOINT Singularity.” To keep up with all this data, geospatial intelligence, like signals intelligence, will need to radically enhance its AI capabilities.

The U.S. intelligence community is currently split up into different functions that collect and analyze discrete types of intelligence, such as signals or geospatial intelligence. The RIA may force the intelligence community to reassess whether these divisions still make sense. Electromagnetic information is electromagnetic information, whether it comes from a satellite or an Internet of Things device. The distinction in origin matters little if no human ever looks at the raw data, and an AI system can recognize patterns in all of the data at once. The division between civilian and military intelligence will be similarly eroded, since civilian infrastructure, such as telecommunications systems, will be just as valuable to military objectives as military communications systems. Given these realities, separating intelligence functions may impede rather than aid intelligence operations.