I’ve felt this might be true for many years. There’s obviously nothing inherently biological about neural networks. It could even explain the development of intelligent life when so many things work against that development — the universe is driven to try to create intelligence — a form of “intelligent design.”
We live inside a neural network, he says, not a simulation — “but we might never know the difference.”
The search is on to discover new states of matter, and possibly new ways of encoding, manipulating, and transporting information. One goal is to harness materials’ quantum properties for communications that go beyond what’s possible with conventional electronics. Topological insulators—materials that act mostly as insulators but carry electric current across their surface—provide some tantalizing possibilities.
“Exploring the complexity of topological materials—along with other intriguing emergent phenomena such as magnetism and superconductivity—is one of the most exciting and challenging areas of focus for the materials science community at the U.S. Department of Energy’s Brookhaven National Laboratory,” said Peter Johnson, a senior physicist in the Condensed Matter Physics & Materials Science Division at Brookhaven. “We’re trying to understand these topological insulators because they have lots of potential applications, particularly in quantum information science, an important new area for the division.”
For example, materials with this split insulator/conductor personality exhibit a separation in the energy signatures of their surface electrons with opposite “spin.” This quantum property could potentially be harnessed in “spintronic” devices for encoding and transporting information. Going one step further, coupling these electrons with magnetism can lead to novel and exciting phenomena.
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…
Massive multi-gigapixel images are starting to become a little more common now, with today’s computing power being what it is. But they still rarely fail to impress. Especially when they cover vast distances and include a lot of detail to zoom in on. This massive 195-Gigapixel image comes from Shanghai, shot from the top of Shanghai’s Oriental Pearl Tower.
Chinese fighter jets approached Taiwan on Thursday for a second day in a row, the island’s defence ministry said, urging China to stop “destroying regional peace” in a further ratcheting up of tension across the sensitive Taiwan Strait.
China, which claims Taiwan as its own territory, has held numerous military exercises up and down its coast and near the island in recent weeks.
The defence ministry said Su-30 fighters and Y-8 transport aircraft were among the Chinese aircraft that entered Taiwan’s air identification zone to its southwest on Thursday morning.
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:
