Lifeboat Foundation

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” »

An advanced artificial intelligence created by OpenAI, a company founded by genius billionaire Elon Musk, recently penned an op-ed for The Guardian that was so convincingly human many readers were astounded and frightened. And, ew. Just writing that sentence made me feel like a terrible journalist.

That’s a really crappy way to start an article about artificial intelligence. The statement contains only trace amounts of truth and is intended to shock you into thinking that what follows will be filled with amazing revelations about a new era of technological wonder.

Here’s what the lede sentence of an article about the GPT-3 op-ed should look like, as Neural writer Thomas Macaulay handled it earlier this week:

The promise of artificial intelligence for cybersecurity is that it will free security professionals at government agencies from menial tasks and allow them to focus on threat hunting and higher-level work. Another benefit that might get lost in the shuffle, but is no less important, is that automation in cybersecurity can actually lead to enhanced security for agencies.

Five governments are testing that proposition. Last month, the states of Arizona, Louisiana, Massachusetts and Texas, along with Maricopa County, Ariz., announced a partnership with the Multi-State Information Sharing and Analysis Center and the Johns Hopkins Applied Physics Laboratory (APL) to pilot a cybersecurity automation program.

The agencies will be using security orchestration, automation and response (SOAR) tools, which “enable organizations to collect security-threat data through multiple sources and perform triage response actions significantly faster than with manual processes,” according to a Johns Hopkins press release. The hope is that it will enable the agencies to “quickly and broadly share information — in near real time — and leverage automation to prevent or respond to cyberattacks,” the release states.

GPT-3, an advanced language generator developed by OpenAI, wrote a frighteningly humanlike essay for The Guardian on how humanity shouldn’t fear AI.

Singapore-based blockchain data firm CyberVein has become one of 12 firms participating in the construction of China’s Hainan Wenchang International Aerospace City. Construction commenced last month, with the site previously hosting a satellite launch center. Described as “China’s first aerospace cultural and tourism city,” it will be a hub for the development of aerospace products and support services intended for use in Chinese spacecraft and satellite launch missions. The 12-million-square-meter facility will host the country’s first aerospace super-computing center, and will focus on developing 40 technological areas including big data, satellite remote sensing and high precision positioning technology. CyberVein will work alongside major Chinese firms, including Fortune 500 companies Huawei and Kingsoft Cloud, and will leverage its blockchain, artificial intelligence and big data technologies to support the development of the city’s Smart Brain Planning and Design Institute.”

Blockchain firm CyberVein is partnering with the Chinese government to build a blockchain-powered governance system for its aerospace ‘smart city.’

Listen to article.

Plastics, plastics everywhere! From the water that we drink to the Great Pacific Garbage Patch, plastics and all sorts of trash are quite literally everywhere; so the fact that the seabed houses even more of those probably won’t come off as a surprise for you.

Thankfully, the Fraunhofer Center for Maritime Logistics and Services (CML) and an international network of partners are willing to tackle this problem with the use of autonomous robots in a project called SeaClear.

Freeing the Earth from underwater waste is not an easy feat, but this project might actually tidy up our mess.

Continue reading “New Project to Clear the Seabed of Plastic with Autonomous Robots” »

Like a comic book come to life, researchers at Stanford University have developed a kind of X-ray vision—only without the X-rays. Working with hardware similar to what enables autonomous cars to “see” the world around them, the researchers enhanced their system with a highly efficient algorithm that can reconstruct three-dimensional hidden scenes based on the movement of individual particles of light, or photons. In tests, detailed in a paper published Sept. 9 in Nature Communications, their system successfully reconstructed shapes obscured by 1-inch-thick foam. To the human eye, it’s like seeing through walls.

“A lot of imaging techniques make images look a little bit better, a little bit less noisy, but this is really something where we make the invisible visible,” said Gordon Wetzstein, assistant professor of electrical engineering at Stanford and senior author of the paper. “This is really pushing the frontier of what may be possible with any kind of sensing system. It’s like superhuman vision.”

This technique complements other vision systems that can see through barriers on the microscopic scale —for applications in medicine—because it’s more focused on large-scale situations, such as navigating self-driving cars in fog or heavy rain and satellite imaging of the surface of Earth and other planets through hazy atmosphere.

Future wireless networks of the 6th generation (6G) will consist of a multitude of small radio cells that need to be connected by broadband communication links. In this context, wireless transmission at THz frequencies represents a particularly attractive and flexible solution. Researchers at Karlsruhe Institute of Technology (KIT) have now developed a novel concept for low-cost terahertz receivers that consist of a single diode in combination with a dedicated signal processing technique. In a proof-of-concept experiment, the team demonstrated transmission at a data rate of 115 Gbit/s and a carrier frequency of 0.3 THz over a distance of 110 meters. The results are reported in Nature Photonics.

5G will be followed by 6G: The sixth generation of mobile communications promises even higher data rates, shorter latency, and strongly increased densities of terminal devices, while exploiting Artificial Intelligence (AI) to control devices or autonomous vehicles in the Internet-of-Things era. “To simultaneously serve as many users as possible and to transmit data at utmost speed, future wireless networks will consist of a large number of small radio cells,” explains Professor Christian Koos, who works on 6G technologies at KIT together with his colleague Professor Sebastian Randel. In these radio cells, distances are short such that high data rates can be transmitted with minimum energy consumption and low electromagnetic immission. The associated base stations will be compact and can easily be mounted to building facades or street lights.

To form a powerful and flexible network, these base stations need to be connected by high-speed wireless links that offer data rates of tens or even hundreds of gigabits per second (Gbit/s). This may be accomplished by terahertz carrier waves, which occupy the frequency range between microwaves and infrared light waves. However, terahertz receivers are still rather complex and expensive and often represent the bandwidht bottleneck of the entire link. In cooperation with Virginia Diodes (VDI) in Charlottesville, U.S., researchers of KIT’s Institute of Photonics and Quantum Electronics (IPQ), Institute of Microstructure Technology (IMT), and Institute for Beam Physics and Technology (IBPT) have now demonstrated a particularly simple inexpensive receiver for terahertz signals. The concept is presented in Nature Photonics.