2013 – Immortal minds are a matter of time
everything made of atoms.. so make a machine.. same materials brains are made.. but either organized same or diff… no one knows much about way brain does knowledge/reasoning.. to hard to predict how long will take to do things like that..
i grew up talking to people like asimov…. who turned out many novels about their image of the future.. hard to make predictions..
2 min – it’s hard to see how many very similar minds could become particularly more powerful than a small number..
3 min – a large number of people in democratic org.. make better decisions that typical individual.. but my impression is that psychologists are proud to demo such things and they’re really quite rare..
4 min – 100 very good mathematicians doesn’t usually produce an einstein .. or einstein like community… that gets new ideas that none of the individuals could get
5 min – we can’t keep growing way we are.. but if can if we can make ourselves more efficient and smaller.. no reason we can’t have trillion very small people rather than 20 billion large ones eating up all the world’s resources.. so there’s lots of possibilities in future that no one seriously discusses..
intrigue with wolfram‘s thinking in nks
6 min – my impression is that progress has slowed in last few decades.. learning less and less in recent years.. because people are trying to understand brain itself.. rather than make good theories of how brains might work.. doesn’t help to look at a brain unless your head is already full of powerful ideas about what might be going on there.. the only people who have thought about the future.. in my opinion of humanity.. are the science fiction writers…
Marvin Lee Minsky (born August 9, 1927) is an American cognitive scientist in the field of artificial intelligence (AI), co-founder of Massachusetts Institute of Technology’s AI laboratory, and author of several texts on AI and philosophy.
He developed, with Seymour Papert, the first Logo”turtle”. Minsky also built, in 1951, the first randomly wired neural network learning machine, SNARC.
Minsky wrote the book Perceptrons (with Seymour Papert), which became the foundational work in the analysis of artificial neural networks.
In the early 1970s at the MIT Artificial Intelligence Lab, Minsky and Seymour Papert started developing what came to be called The Society of Mind theory. The theory attempts to explain how what we call intelligence could be a product of the interaction of non-intelligent parts.
what problems we need to solve: health, ed, …..
the question i think people – #of people, ie: 100 mill people, 6 in tall
we’re not smart enough to figure out the problems we should be thinking about
maybe if we live 200 yrs – we could solve enough problems…
turn out sights higher.. to – can we use our resources … for different types of problems
from #ml30 –
Marvin Minsky suggests that the best way to start a speech is to ask if there are any questions #ML30
Original Tweet: https://twitter.com/cinemakinoeye/status/660094886541176832
Nikola interviews Marvin (2013):
5 min – lunch w/eisntein et al.. like an everyday event.. between harvard and mit at that time
7 min – at first i thought.. figure out how neurons work. then put to gether and you’d figure out whole thing.. then seemed to me.. many people had tried that.. and yet the theories in 1950s were if anything worse than in 1860s
8 min – on freud thinking not getting published form 1895, finally published in 1950 on how neurons might learn..
11 min – somehow.. by 1980s research in ai declined.. prior to had been leaders in defense dept.. which had almost continuous inheritance..
13 min – some politicians.. 70s 80s.. decided paying for basic research instead of war was immoral… so that… ibm and bell labs had been able to do basic research w/very little competition.. but when liberals got more power than capitalists.. quality of basic science declined..
16 min – society of mind (book) had 1 page chapters… so could skip if you didn’t understand.. result was that great many of hs students knew theories.. and new more than profs when got to college… next book.. didn’t do that.. perhaps a mistake… next book i write i hope to write so students can understand
18 min – frankenstein was the opposite… created and judgmental people destroyed..
20 min – on asimov not wanting to see minsky’s robots.. because it would spoil his imagination..
21 min – on singularity.. well there’s progress.. but i’ve watched it slow down the last 8-10 yrs.. this phenomenon.. that these young people that show promise have been unable to get jobs
22 min – siri is pretty good.. but it’s quite old
23 min – ai depends on how many smart people get to work on it.. hard to predict.. i would have never predicted so few are working on it now.. but there are no jobs..
24 min – turing test is a joke about saying a machine would be intelligent if it does things that an observer would say must be being done by a human.. turing never intended it as a way to determine if a machine is intelligent.. so not a serious question..
25 min – jobs about ai.. but not for basic research
27 min – 98% chance they won’t map right aspects.. should spend money on house fly before wasting money on wrong theories of what to map on human brain..
28 min – current ways of trying to represent the nervous system in terms of probabilities… not any better than in 60s
29 min – i’m not interested in everything.. i’m really interested in good researchers.. what i’ve learned.. find the person who’s thinking you admire most and go and meet that person and see if you can copy him…
music, mind, and meaning
“[People] like themselves just as they are,” says Marvin Minsky. “Perhaps they are not selfish enough, or imaginative or ambitious. Myself, I don’t much like how people are now. We’re too shallow, slow, and ignorant. I hope that our future will lead us to ideas that we can use to improve ourselves.”
“Marvin Minsky is the smartest person I’ve ever known,” computer scientist and cognitive researcher Roger Schank points out. “He’s absolutely full of ideas, and he hasn’t gotten one step slower or one step dumber. One of the things about Marvin that’s really fantastic is that he never got too old. He’s wonderfully childlike. I think that’s a major factor explaining why he’s such a good thinker. There are aspects of him I’d like to pattern myself after. Because what happens to some scientists is that they get full of their power and importance, and they lose track of how to think brilliant thoughts. That’s never happened to Marvin.”
Most words we use to describe our minds (like “consciousness”, “learning”, or “memory”) are suitcase-like jumbles of different ideas. Those old ideas were formed long ago, before ‘computer science’ appeared. It was not until the 1950s that we began to develop better ways to help think about complex processes.
Computer science is not really about computers at all, but about ways to describe processes
Unfortunately, that new domain was mainly dominated by continuous mathematics and feedback theory. This made cybernetics slow to evolve more symbolic computational viewpoints, and the new field of Artificial Intelligence headed off to develop distinctly different kinds of psychological models.
There are two quite different reasons why “something” might seem hard to explain. One is that it appears to be elementary and irreducible-as seemed Gravity before Einstein found his new way to look at it. The opposite case is when the ‘thing’ is so much more complicated than you imagine it is, that you just don’t see any way to begin to describe it.
The brain, after all, is built by processes that involve the activities of several tens of thousands of genes. A human brain contains several hundred different sub-organs, each of which does somewhat different things. To assert that any function of such a large system is irreducible seems irresponsible-until you’re in a position to claim that you understand that system. We certainly don’t understand it all now. We probably need several hundred new ideas-and we can’t learn much from those who give up. We’d do better to get back to work.
It all seems so basic and immediate that there seems no room for analysis. The feelings of being seem so direct that there seems to be nothing to be explained. I think this is what leads those philosophers to believe that the connections between seeing and feeling must be inexplicable. Of course we know from neurology that there are dozens of processes that intervene between the retinal image and the structures that our brains then build to represent what we think we see. That idea of a separate world for ‘subjective experience’ is just an excuse for the shameful fact that we don’t have adequate theories of how our brains work.
Our old ideas about our minds have led us all to think about the wrong problems. We shouldn’t be so involved with those old suitcase-ideas like consciousness and subjective experience. It seems to me that our first priority should be to understand “what makes human thought so resourceful”.
the emotional machine (limit reached on my recommends to overdrive) – book about the above
If an animal has only one way to do something, then it will die if it gets in the wrong environment. But people rarely get totally stuck. We never crash like computers do. If what you’re trying to do doesn’t work, then you find another way
A ‘meaning’ is not a simple thing. It is a complex collection of structures and processes, embedded in a huge network of other such structures and processes. The ‘secret’ of human resources lies in the wealth of those alternative representations.
Consequently, the sorts of explanations that work so well in other areas of science and technology are not appropriate for psychology-because our minds rarely do things in only one way.
If the problem is to explain our resourcefulness, then we shouldn’t expect to find this in any small set of concise principles. Indeed, whenever I see a ‘theory of knowledge’ that can be explained in a few concise statements, then I assume that it’s almost sure to be wrong. Otherwise, our ancestors could have discovered Relativity, when they still were like worms or anemones.
memory’ is a suitcase word that we use to describe-or rather, to avoid describing-perhaps dozens of different phenomena.
‘consciousness’ is only a name for a suitcase of methods that we use for thinking about our own minds. Inside that suitcase are assortments of things whose distinctions and differences are confused by our giving them all the same name.
I don’t consciousness as holding one great, big, wonderful mystery. Instead it’s a large collection of useful schemes that enable our resourcefulness. Any machine that can think effectively will need access to descriptions of what it’s done recently, and how these relate to its various goals. For example, you’d need these to keep from getting stuck in a loop whenever you fail to solve a problem. You have to remember what you did-first so you won’t just repeat it again, and then so that you can figure out just what went wrong-and accordingly alter your next attempt.
Such an instrument (cap reading neurons – giving too much info) won’t be of much use until we can also equip it with a Semantic Personalizer for translating its output into forms that are suited to your very own individual internal representations. Then, for the first time, we’ll become capable of some ‘genuine introspection.‘ For the first time we’ll be really self-conscious. Only then will we be able to wean ourselves from dualism.
output – document everything
Then for the first time, we could really become ‘self-conscious.’ For the first time, you’ll really be able to know (sometimes for better, and sometimes for worse) what actually caused you to do what you did.
Computers are not so resourceful, yet. This is because those programs don’t yet have good enough ways to exploit that information. It’s a popular myth that consciousness is almost the same thing as thinking. Having access to information is not the same as knowing how to use it.
philosophers are too often seen as impractical bumblers, because of being ahead of their time, and not getting credit for previous accomplishments.
emotions are not alternatives to thinking; they are simply different types of thinking.
The big mistake comes from looking for some single, simple, ‘essence’ of hurting, rather than recognizing that this is the word we use for complex rearrangement of our disposition of resources.
My conjecture is that this process employs an adaptation of the ancient imprinting mechanism, which first evolved mainly to promote the offspring’s physical safety. The baby animal becomes disturbed when not in the presence of the parent, and this serves to make it quickly learn behavior that makes it stay close by. In humans though, it seems to me, this mechanism later became involved with two new types of learning, whose activities we recognize as emotions called pride and shame.
I maintain that the type of learning connected with pride is used to establish new high-level goals-or what we call positive values. The point is that pride is only evoked when a child is praised by the a person to whom it’s attached. So it’s not quite the same as conventional “positive reinforcement”-which can only reinforce sub-goals. Similarly, if a child is scolded by an attachment person, then that child’s current intentions acquire the negative character of a shameful taboo.
I don’t think that most people will bother with this, because they like themselves just as they are. Perhaps they are not selfish enough, or imaginative or ambitious. Myself, I don’t much like how people are now. We’re too shallow, slow, and ignorant. I hope that our future will lead us to ideas that we can use to improve ourselves.
RIP Marvin Minsky. The “foolish reason to stop” caught up with him. https://t.co/Sds8kfXPNm via @YouTube
Original Tweet: https://twitter.com/tds153/status/691900348382998528
more from roger…
A story to explain how I felt about Marvin Minsky. It was 1986. The Mets were playing the Red Sox. Me and my son Joshua had tickets and stayed at Marvin’s house since he didn’t live far from Fenway. Marvin didn’t like baseball. He made a snarky comment about Joshua Schank‘s “outfit.” (He was wearing a Met’s uniform.) When we returned from the game that night, Marvin was waiting for us. He had watched the entire game, so we could all discuss it.
Don’t pay any attention to the critics. Don’t even ignore them. – Marvin Minsky
.@edge “Remembering Minsky” with 1/2 hr 2002 video never shown before: “The Emotion Machine”https://t.co/V29B3OpQMg
Original Tweet: https://twitter.com/edge/status/692146440521515009
i usually start lectures by saying.. are there any questions..
4 min – the real question is why the universe has to have laws at all..
8 min – on the nature of thinking… and emotions
9 min – on couple thousand year gap w/little growth in psychology – aristotle to spinoza..
10 min – why didn’t science have ideas about thinking until recently… .
11 min – fav ie: late 1930s – piaget & next 10 yrs of watching kids growing up.. wrote several hundred little theories of processes going on in brain… about 20 books.. all based on observing 3 children carefully… strangely enough …holding up… question isn’t .. is piaget right/wrong.. but why wasn’t there someone like piaget 2000 yrs ago… that no one thought of observing children… and try to figure how they worked…
13 min – papert – research assignment for him.. i liked his (thinking) better… worked w/him
people didn’t have ideas of data structure.. ie: physics not good at describing in detail that happened in context with dozens of laws… ie: if you take 20 assumptions.. mathematics is dead… ie: group theory made from 5 assumptions… if you make 5 assumptions about same things… you’re on the edge of what people can understand.. if you write computer program w/100 lines of code.. no way to figure out consequences in general… 1950s – in appearance of computer science… several hundred new ideas appear that no one ever had.. i think of cs – as a new way to describe/think about complicated systems.. comes with a huge library of new ideas…
17 min – computer slow memory… fast memory – ram.. avg computer .. cashe.. instructions for last few things it did… dozens of kinds of memory… ie: as if then rules; as semantic networks; neuro networks.. links are dumb – rest of machine can’t figure out what neural net knows.. – the more of brain used in that particular way.. less brain can think/reason about what it has learning (book by papert and i)… machine doesn’t understand what it knows..
19 min – we’re in an era where people are starting to get an idea about what thinking means.. but many people still burdened by old way of thinking..
the world is very thick with old pre-computational theories of how mind works…
22 min – keeps referencing early 1950s
24 min – 1954 – on writing programs that would write new programs.. begged languages… many didn’t see the power of programs that could save… and taken over by bad languages… in which impossible to write programs that change themselves…
26 min – in early days of ai… we started to make programs that would do very advanced things.. maybe that was a mistake… ie: prove theorems in euclidean geom… curious how many kids learn that today… what euclid geom used to be… you’d learn a dozen assumptions.. you were in a world where assumptions simple, logic clear, beautiful thing..
for some reason a large fraction of humans find it very hard to deal with a subject where you don’t have to know much.. math is the simplest of all things…
how could a person be bad at math.. it must be that they get a wrong model of what something is… when of course the way you do things.. like prove theorems.. of course the way to do it.. you use common sense…. often..
it’s the thinking there’s something you don’t know that you need to know that’s (keeping people in the dark, thinking they don’t know ie: math).. if you didn’t know you were supposed to be a little bit original.. maybe it’s hard…
32 min – by 1980 – 10s of thousands of programs..doing some very special thing… but no program that could do things you expect 5 yr old to do… we’ve regressed from calc/geom/algebra and trying to get people to work on common sense problems.. the sort that every 4-5 yr old can do.. though millions working on ai et al…. i’ve only been able to find about a dozen interested in these kinds of problems.. that children can do..
feb 2016 – Marvin #Minsky and The Beginning of #AI [short documentary] – by Nikola
2 min – on getting to meet many of the great people that got displace from ww2
7 min – generally when something new… a fear… good because it stabilizes a society… perhaps a culture can only generate a few new ideas every generation…
8 min – living near isaac isomov..
language of self-awareness.. highest of any
Roger Schank (@rogerschank) tweeted at 5:21 AM – 25 Jul 2018 :
‘The discourse is unhinged’: how the media gets AI alarmingly wrong https://t.co/OlFLUaLQJ4 (http://twitter.com/rogerschank/status/1022079344016060417?s=17)
While the giddy hype around AI helped generate funding for researchers at universities and in the military, by the end of the 1960s it was becoming increasingly obvious to many AI pioneers that they had grossly underestimated the difficulty of simulating the human brain in machines. In 1969, Marvin Minsky, who had pronounced only eight years earlier that machines would surpass humans in general intelligence in his lifetime, co-authored a book with Seymour Papert proving that Rosenblatt’s perceptron could not do as much the experts had once promised and was nowhere near as intelligent as the media had let on.
Minsky and Papert’s book suffused the research community with a contagious doubt that spread to other fields, leading the way for an outpouring AI myth debunking. In 1972, the philosopher Hubert Dreyfus published an influential screed against thinking machines called What Computers Can’t Do, and a year later the British mathematician James Lighthill produced a report on the state of machine intelligence, which concluded that “in no part of the field have the discoveries made so far produced the major impact that was then promised
About that @BostonGlobe story… On me, and deciding to leave @MIT @medialab: https://t.co/LP4Y9HKm9o
Original Tweet: https://twitter.com/EthanZ/status/1163989963706380288