“The true sign of intelligence is not knowledge but imagination. I have no special talent. I am only passionately curious.” Albert Einstien.
The difference you will create in your customers' lives
An ambitious but achievable position in our market that talks to our vision and leads towards it
An effective mixture of thought and action with a basic underlying structure that is a step towards the missionrr
High Objective: To become as smart as possible, that is to “prove as many theorums”, “understand the world” or just “know” as much as possible. This avoids the exploration/exploitation problem as evrything is exploration/learning. It also mitigates the objective mismatch problem as learning is reasonably benign.
Low Objective: To convert correlations to causations. Correlations may be obtained unsupervised, work out the causal structure given what is already known about the correlated and causal world.
How might it fit together:
GAN on “caused” database allows the generation of ideas which are not known to be true but are plausibly true as close to existing ideas. For each idea:
Full systems commodity compute came down by a factor of 10,000 in 30 years (https://aiimpacts.org/wikipedia-history-of-gflops-costs), so expect availble cheap compute to rise at e^0.3t where t is in years.
The 2008 paper Whole Brain Emulation: A Roadmap says that supercomputing is much cheaper. We have supercomputers that'll do spiking neuron emulation/similation now. In 2040 we expect the cost to be down to $1m - prob too high - the current supercomputer has done 148.6 petaFLOPS and cost around $200 million https://en.wikipedia.org/wiki/Summit_(supercomputer)
Prices are for 1 or 5 days days per week and charged monthly ex-VAT. ideaSpace first, then ideaSpace and Eagle?
Potential other names
- text based - lots of knowledge that's text based, can learn in parallel, doesn't need physical robot.
Nested RNNs (LSTMs), each with a GAN that learns the state vector space. Each RNN has inputs from the GAN representaiton of the last layer and outputs to lower layers. At the bottom is input from the senses and outputs to motor control. The states from all level map to “emotions” (pleasure/pain signals).
Thinking about things leads to memory as that point is trained on with the GAN.
How does the attention mechanism cover all words in a machine translation task?
how does GAN video prediction work?
Companies to watch: A Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3070741
MORE STUFF TO ADD
Consciousness. hypnosis . Focus and attention. Suggestion that people are more suggestible. Supression of Region of brain that people are responsible for actions. Reinforcement learning and reward allocation. Need to decide if what happens is a result of what we do, if it is then we learn to do better, if not then we are modelling the outside world. This is direct sense of self, or consciousnesses.
Need to read and understand https://towardsdatascience.com/a-new-kind-of-deep-neural-networks-749bcde19108?_utm_source=1-2-2
MORE STUFF TO ORGANISE:
Value of state/action V(s, a). Utility of state is expectation or maximisation of Value, U(s) = E| V(s, a) | over all actions. Have a Confidence on U(s), C(s).
Policy, P, generates actions from states, a = P(s).
Improve Policy, P(s), by running forwards over actions until we are fairly certain of a significant value. Other stopping criteria are available. Repeat whilst the AGI is in the same state, i.e. “thinking” about the same thing. This is just sampling from current distributions, but it could allow explanation, that is X leads to Y which leads to Z which I know is good/bad therefore I will (not) do X.
ちかい、近い – near, close (chikai) しゃかい、社会 – society (shakai) けいざい、経済 – economy, economics (keizai) いらい、依頼 – request (irai) げんざい、現在 – now (genzai) みらい、未来 – future (mirai) たかい、高い – expensive, high (takai) ちいさい、小さい – small (chiisai) わかい、若い – young (wakai) やわらかい、柔らかい – soft (yawarakai) かたい、硬い、堅い – hard (katai) つめたい、冷たい – cold (tsumetai) うまい、美味い、旨い – delicious, appetizing (umai) まずい、不味い – tastes awful (mazui) あまい、甘い – sweet (amai) からい、辛い – hot [spicy] (karai) しょっぱい、塩っぱい – salty (shoppai) にがい、苦い – bitter (nigai) こわい、怖い、恐い – scary (kowai) いたい、痛い – painful (itai) くさい、臭い – stinky (kusai) つらい、辛い – painful, heart-breaking (tsurai) はい – yes (hai) たい – indicates desire to perform verb (tai) くらい、ぐらい – approximately, about (kurai)