In 1998, an engineer in Sony’s computer science lab in Japan filmed a lost-looking robot moving trepidatiously around an enclosure. The robot was tasked with two objectives: avoid obstacles and find objects in the pen. It was able to do so…
Category: robotics/AI – Page 1671
The National Science Foundation has awarded a highly competitive $5 million grant to Vanderbilt University that greatly expands a School of Engineering-led project for creating novel AI technology and tools and platforms that train and support individuals with Autism Spectrum Disorder in the workplace.
The significant federal investment follows a successful $1 million, nine-month pilot grant to the same team that forged partnerships with employers and other stakeholders and produced viable prototypes through immersive, human-centric design. The multi-university team includes Yale University, Cornell University, Georgia Institute of Technology and Vanderbilt University Medical Center as academic partners.
The grant, made through NSF’s Convergence Accelerator program, advances the School of Engineering’s focus on Inclusion Engineering,® which uses the disciplines within engineering to broaden meaningful participation for people who have been marginalized.
Deep neural networks (DNNs) are essentially formed by having multiple connected perceptrons, where a perceptron is a single neuron. Think of an artificial neural network (ANN) as a system which contains a set of inputs that are fed along weighted paths. These inputs are then processed, and an output is…
Circa 2018 aerodrums.
LAS VEGAS (PRWEB) January 09, 2018.
Aerodrums today celebrates the live playing of an experimental variant of its air drumming instrument as part of a ground breaking musical performance introducing Intel’s keynote at CES 2018.
Percussionist Sergio Carreño accompanied pianist Kevin Doucette in a jazz improvisation featuring artificially intelligent avatars playing guitar and bass.
Circa 2018
Tesla is known for its electric vehicles — but now it’s hoping to add trucks to its roster of roadsters. Here’s everything we know so far.
When the COVID-19 shutdown began in March throughout the United States, my team at Adobe had to face a stark reality: Business as usual was no longer an option. Suddenly, over just a single weekend, we had to shift our global workforce of over 22,000 people to working remotely. Not surprisingly, our existing processes and workflows weren’t equipped for this abrupt change. Customers, employees, and partners — many also working at home — couldn’t wait days to receive answers to urgent questions.
We realized pretty quickly that the only way to meet their needs was to completely rethink our support infrastructure.
Our first step was to launch an organization-wide open Slack channel that would tie together the IT organization and the entire Adobe employee community. Our 24×7 global IT help desk would front the support on that channel, while the rest of IT was made available for rapid event escalation.
Teeny-tiny living robots made their world debut earlier this year. These microscopic organisms are composed entirely of frog stem cells, and, thanks to a special computer algorithm, they can take on different shapes and perform simple functions: crawling, traveling in circles, moving small objects — or even joining with other organic bots to collectively perform tasks.
The world’s first living robots may one day clean up our oceans.
“Of chess, it has been said that life is not long enough for it,” chess master William Napier once said, “but that is the fault of life, not chess.”
The game of chess itself has had a gloriously long life, with earliest recovered relics of the ancient game dating to the ancient Persian Sasanian Empire in 600 AD.
The game has gone through hundreds of modifications, tweaks and enhancements over the centuries. Of an estimated 2,000 variations of the game, most have been developed only in recent years. One single version itself, known as Chess960 (created by world chess champion Bobby Fischer), has 960 variations of the game, with each version rearranging the standard positioning of all game pieces.
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.”
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.