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IntuitionMachine
@IntuitMachine
, 7 tweets, 3 min read Read on Twitter
Philosophically, it (Deep Learning) is every rationalist’s nightmare: the triumph of the inexplicable over the clear, and that of the probabilistic over the deterministic... thenewcentre.org/seminars/parad…
"Here Charles Sanders Peirce’s tychism and Georg Simmel’s dynamism will help us understand what these (Deep Learning) developments mean for the future of human (and machine) subjectivity." #deeplearning #ai
en.wikipedia.org/wiki/Tychism - Peirce formulates four reasons in support of his hypothesis: Growth and increasing complexity, Variety and diversity, Regularity (laws of nature),
Mind/consciousness/feeling
en.wikipedia.org/wiki/Georg_Sim… - Simmel dealt with forms and interaction. The forms include subordination, superordination, exchange, conflict and sociability.
Encapsulates the history of AI: "approximate periods of dominance: (1) Speculative (to 1940), (2) Cybernetic (1940-1955), (3) Symbolic AI (1955-1985), (4) Subsymbolic AI (1985-2010), and (5) Deep Learning (2010-present)."
Disregarding the speculative part, it is Symbolic AI (30 years) that is philosophically distinct from the other 3. "Symbolic AI often goes unrecognized as the most significant attempt to apply logical positivist analysis to reality itself." @AuerbachKeller
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IntuitionMachine
@IntuitMachine
OpenAI's experiments in co-evolution (deep reinforcement learning and self-play) openai.com/blog/emergent-… reveals the emergent complexity of intentional agents competing and cooperating among themselves.
This kind of technology cannot be undersold. It's a paradigm shift not only in technological terms but also in scientific terms. Detractors who keep pitching expectation moderation are in fact performing a disservice to society as a whole.
Many investors lost their shirt when the dot-com burst. Yet society as a whole benefited wildly from the bust. Society discovered an extreme surplus of cheap fiber optics that accelerated progress in many other fronts.
Read 10 tweets
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IntuitionMachine
@IntuitMachine
Deep Learning (DL circa 2012) big breakthrough is that it demonstrated a new kind of Artificial Intuition. Prior to DL, computers provided programmable Artificial Logic (circa 1945).
AlphaGo big breakthrough is that it demonstrated that with the use of DL one could beat the best human in a game of intuition (i.e. Go). .
AlphaZero big breakthrough is that you could train a system from scratch (without human knowledge) and it could be the best human system Stockfish that was programmed using logic. medium.com/intuitionmachi…
Read 6 tweets
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IntuitionMachine
@IntuitMachine
The entire history of computer science can be framed from the perspective of scaling specification into robust implementation.
Attempts using analog circuitry had difficulty scaling due to accumulative errors in their iterative operations.
This was solved through the transition of digital based computation, however the original sequential behavior of algorithms remained.
Read 10 tweets

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Jonny
@Onkel_Jo
#Nightmare/#weirddream: can't get out of this one. Get sucked into a realistic video game world at a school. First game: Counterstrike. Usually not my game, try to leave the dream, but can't. Shock. Need to find an exit. Playing the game to conclusion maybe? We give curbstomp. ~
At the score of 11-0, we are suddenly on a deserted version of the map. Can we get the other team to spawn in? We run around and try to find them. I run into a building that had been boarded up before. Inside is a fancy modern party going on. Relief. Game apparently over. Then, ~
a group of four men from a single table causes a ruckus. I make a wide berth around them. They see me in the corner, freeze, and sit down again as if nothing happened. A woman that seems familiar steps up to me and tells me that the bus is ready. Maybe I can finally get out now.~
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François Chollet
@fchollet
Deep learning is capable of performing reasoning tasks, in the sense that it's pattern recognition engine, and any task (including ones we would categorize as involving reasoning) can be cast as pattern recognition, given a sufficient dense sampling of the input/output space.
It's also technically possible to train deep learning models to perform specific symbolic tasks (say, arithmetic, sorting...) using *few* data points, if you hard-code that task in the architecture space (replacing learning with a prior)...
Neither "train on a dense sampling of the data" nor "practically hard-code a solution template and then tune a few parameters using SGD" are particularly good options.
Read 3 tweets
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Dr. Linda Bui
@Lindambui
Learning about surface vs deep learners. @lsu Teaching Colloquium. Thinking about implications and applications of learned information.
Learner needs to regard info as important, intriguing, or beautiful. Then deep learning occurs. Learner needs to be in charge of the question. But, few students will not take a deep approach to learning.
Students are conditioned to take a surface approach to learning. School is the biggest place that conditions surface learning. #KenBains #learning #whatTheBestCollegeTeachersDo
Read 22 tweets
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Cik JamNa
@jamnafaziran88
Aku nak share skit kisah aku bawak grab kene tahan polis bersama 3 orang warga asing yang membawa senjata.

Ini thread
Arini aku ade open house kt umah mmber, so aku siap² la berbaju kurung bagai nk ke open house..tpi sblm tu ak bercadang nk buat la grab sementara ada mase kan..then ak pun kluar dri umah, pegi isi minyak then on apps grab..
Then job pertama masuk berdekatan dgn tmpt aku tu.. Ak ambik rider ak ni kt brem mall ke mutiara apatment jalan klang lama.. mse ak smpai tu, ak xnmpk rider..ak call xangkt tp lps tu dy muncul then suruh ak tggu jp lg 2 org kawn dy.. Sorg laki sorg pmpn.. Yg suruh ak tggu ni laki
Read 20 tweets
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François Chollet
@fchollet
Deep learning is useful because it enables us to create programs that we could not otherwise code by hand. But the space of programs you can learn via deep learning models is a minuscule slice of the space of programs that we may be interested in.
Most interesting processes cannot be learned by a stack of affine transforms (plus squashing functions) mapping one vector space to another point-by-point. Even given large amounts of data.
Perhaps more importantly, although human engineers can cover a dramatically larger section of the space of interesting programs (compared to deep learning models), many useful programs remain inaccessible even to humans -- we cannot develop them by hand.
Read 4 tweets

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