YouSaid · the spoken record

Richard Karp

lines on the record: 126
first: 2020-07-26
most recent: 2020-07-26
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“I would say. And you also have the observation that you might ask, who is better off the boys who are doing the proposing or the girls who are reacting to proposals? And it turns out that it's the boys who are doing the best, that as each boy is doing at least as well as he could do in any other stable matching. So there's a sort of lesson for the boys that you should go out and be proactive and make those proposals. Go for broke”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“They don't try again. They don't try again because the girls are always improving their status as they get more as they receive. better and better proposals. The boys are going down their list starting with their top preferences. One can prove that That the process will come to an end. Where everybody will get matched with somebody, and you won't have any pair that want to abscond from each other.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“And moreover, it can be computed by a simple algorithm. in which each boy starts making proposals to girls. And if a girl receives the proposal, she accepts it tentatively, but she can. Drop it if she can drop it later if she gets a better proposal from her point of view The boys start going down their lists proposing to their first, second, third choices until stopping when A proposal is accepted. But the girls, meanwhile, are watching the proposals that are coming into them. And the girl will drop her current partner. if she gets a better proposal.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“Imagine that you want to marry off N boys and girls. And each boy has an ordered list of his preferences among the girls, his first choice, his second choice through her nth choice. And we'll say that a matching, a one-to-one matching of the boys with the girls is stable. If there are No two couples in the matching, such that the boy in the first couple prefers the girl in the second couple to her mate, and she prefers the boy to her current mate. In other words, if the matching is stable, if there is no pair who want to run away with each other, leaving their partners behind. Actually, this is relevant to matching residents with hospitals and some other real life problems, although not quite in the form that I described. So it turns out that for any set of preferences, a stable matching exists.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, I think it would have enormous impact on the world in either way case. If P is unequal to NP, which is what we expect. Then we know that for the great majority of the combinatorial problems that come up since they're known to be NP complete, we're not going to be able to solve them by efficient algorithms. However, there's a little bit of hope in that it may be that we can solve most instances. All we know is that if a problem is not in P, then it can't be solved efficiently on all instances. But basically, If we find that P is unequal to NP, it will mean that we can't expect always to get the optimal solutions to these problems. And we have to depend on heuristics that perhaps work most of the time or give us good approximate solutions, but not.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“I think that if there is a proof that P is equal to NP or that P is not equal to NP, It'll depend on concepts that are now outside the box.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“Oh, that's just the small technicality. So when we're talking about decision problems, that means that the answer is just yes or no. There is a clique of size 15 or there's not a clique of size 15. On the other hand, an optimization problem would be asking, find the largest clique. The answer would not be yes or no. It would be 15. So when you're asking for the... When you're putting a valuation on the different solutions and you're asking for the one with the highest valuation, that's an optimization problem. And there's a very close affinity between the two kinds of problems. But the counterpart of being the hardest decision problem, the hardest yes, no problem, the counterpart of that. Is to minimize or maximize an objective function. And so a problem that's hardest in the class”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“They're all the same, but not exactly. They're all the same in terms of whether they are. rich enough to express any of the others. But that doesn't mean that they have the same computational complexity. But what we can say is that either all of these problems or none of them are solvable in polynomial time.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“Right. And what I did in the 1971 paper was to take 21 fundamental problems that commonly occurring problems of packing, covering, matching, and so forth, lying in the class NP, and show that the satisfiability problem can be re-expressed as any of those, that any of those have the same expressive power.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, if they just have the same expressive power, you can take. Any one of them and translated into the terms of the other.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“Also, an edge between them if they represent opposite values of the same variable because you can't make a variable both true and false. And so you get a graph where you have all of these occurrences of variables. You have edges, which mean that you're not allowed to choose both ends of the edge, either because they're in the same clause or their negations of one another.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“one term in the clause must be. Must be true So now to convert this problem to something called the independent set problem where you're just sort of asking for a set of vertices in a graph such that no two of them are adjacent, sort of the opposite of the clique problem. So we've seen that we can now express that as finding Set of terms one in each clause without picking Both the variable and the negation of that variable. Because if the variable is assigned the truth value, Negated variable has to have the opposite truth value. And so we can construct the graph where the vertices are the Terms in all of the clauses. And you have an edge between two Terms if An edge between two occurrences of terms, either if they're both in the same clause because you're only picking one element from each clause.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“The satisfiability problem is whether those clauses can be simultaneously satisfied. To satisfy all those clauses, you have to find one of the terms in each clause. which is going to be given Is going to be true in your truth assignment. You can't make the same variable both true and false. So if you have the variable a in one clause and you want to satisfy that clause by making a true, you can't also make. complement of A, true in some other clause.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“It is a big leap, yeah. Well, let me give you another example. Another problem in NP is whether a graph contains a clique of a given size. And now The question is. Can we reduce the propositional logic problem to problem of whether there's a clique of a certain size. If you look at the propositional logic problem, it can be expressed as a number of clauses, each of which is a Of the A or B or C where A is either one of the variables in the problem or the negation of one of the variables. And An instance of the propositional logic problem. can be rewritten using operations of Boolean logic. can be rewritten as the conjunction of a set of clauses, the and of a set of ors, where each clause is a disjunction, an or of variables or negated variables. So the question of”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, that makes the problem much harder and it was not difficult to show that the satisfiability problem can be restated as an integer programming problem.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“So, the P versus NP can be Solvable in polynomial But there's more. I encountered Cook's paper when he published it in a conference in 1971. Yeah. So when I saw Cook's paper and saw this reduction of each of the problems in NP by a uniform method to the satisfiability problem of propositional logic. Meant that the satisfiability problem was a universal combinatorial problem. And it occurred to me through experience I had had in trying to solve other combinatorial problems that there were many other problems which seemed to have that universal structure. And so I began looking for. deductions from the satisfiability to other problems. One of the other problems would be the so-called integer programming problem of solving determining whether there's a solution to a set of linear inequalities involving integer variables.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“It's astonishing when you look at Cook's proof, it's not too difficult to sort of figure out why this is the thing. Why is this so, but the implications are staggering. It tells us that of all the problems in NP, all the problems where solutions are easy to check, they can all be rewritten in terms of the satisfiability problem.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“possible computation. Now one of these so-called Turing machines is too simple to be useful in practice. But for theoretical purposes, we can depend on the fact of an algorithm for any computer can be translated into one that would run on a Turing machine. And then using that fact, he could sort of describe Any possible non-deterministic polynomial time algorithm, any algorithm for a problem in NP could be expressed as a sequence of. Moves of the Turing machine described in terms of Reading a symbol on the tape. While you're in any given state and moving to a new state and leaving behind a new symbol, and given that the fact that any non-deterministic polynomial time algorithm can be Described by a list of such instructions, you could translate the problem into the language of the satisfiability problem.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“Data on a tape, and you have basic instructions, a finite list of instructions, which say if you're reading a particular symbol on the tape and you're in a particular state, then you can move to A different state and change the state of the number or the element that you were looking at, the cell of the tape that you were looking at.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“Nice statement, a really hard problem. And what Cook showed is that every problem in NP Can be re expressed as an instance of the satisfiability problem. So to do that, he used the observation that a very simple abstract machine called the Turing machine can be used to describe any algorithm, an algorithm for any realistic computer can be translated into an equivalent algorithm on one of these Turing machines, which are extremely simple.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“Determined that no assignment of truth values to the variables A and B will allow that conjunction of what are called clauses to be true. So that's an example of a formula in Propositional logic involving expressions based on the operations and or and not. That's an example of a problem which is not satisfiable. There is no solution that satisfies all of those constraints”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“The first and most important stage of progress was a result by Stephen Cook who showed that a certain problem called the satisfiability problem of propositional logic is as hard as any problem in the class P. So the propositional logic problem is expressed in terms of expressions involving the logical operations and or and not operating on variables that can be either true or false. So an instance of the problem would be some formula involving and or and not. And the question would be whether there is an assignment of truth values to the variables in the problem that would make the formula true. So for example, if I take the formula A or B and A or not B and not A or B and not A or not B and take the conjunction of all four of those so called expressions you can”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“Right. And in fact, we have some results that I was instrumental in obtaining following up on work by the mathematician Stephen Cook. to show that within the class NP of easy to check problems, there's a huge number that are equivalent in the sense that either all of them or none of them lie in P. And this happens only if P is equal to NP. So if P is unequal to NP, we would also know that Virtually all the standard combinatorial problems, if P is unequal to NP, none of them can be solved in polynomial time.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“Once you have found the factors, express the number as a product of the two. Smaller numbers, you can quickly verify that they are factors of the number.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“I would bet that P is unequal to NP simply because there are problems that have been around for centuries and have been studied intensively in mathematics and even more so in the last 50 years since the P versus NP was stated. And no polynomial time algorithms have been found for these easy-to-check problems. So one example is a problem that goes back to the mathematician Gauss who was interested in factoring large numbers. So we know what a number is prime if it doesn't be written as the product of two or more numbers unequal to one. So if we can factor a number like 91 at 7 times 13. But if I give you 20 digit or 30 digit numbers, you're probably going to be at a loss to have any idea whether they can be factored. So the problem of factoring very large numbers is. Does not appear to have an efficient solution.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“We don't know for sure. So the theoretical question, which is considered to be the Most central problem in theoretical computer science, or at least computational complexity theory. combinatorial algorithm theory. The question is whether P is equal to NP. If P were equal to NP, it would be amazing. It would mean that every problem where a solution can be rapidly checked. Can actually be solved in polynomial time. We don't really believe that's true. If you're scheduling classes at a school, We expect that if somebody hands you a satisfying schedule, you can verify that it works. That doesn't mean that you should be able to find such a schedule. So intuitively, NP encompasses a lot more problems than P.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“is going through is easier, checking is easier, and therefore the class of problems that can be checked appears to be much larger than the class of problems that can be solved.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“Okay, let's talk about problems where you're getting a yes, no answer rather than a numerical value. So either there is a perfect matching of the boys with the girls or there isn't. It's clear that every problem in P is also in NP. If you can solve the problem exactly, then you can certainly verify On the other hand, the There are problems in the class NP. This is the class of problems that are easy to check, although they may be hard to solve. It's not at all clear that problems in NP lie in P. So, for example, if we're looking at scheduling classes out of school The fact that you can verify when handed a schedule for the school, whether it meets all the requirements, that doesn't mean that you can find the schedule rapidly. So intuitively, NP, non-deterministic polynomial, checking rather than finding Is going to be harder than.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“That's a polynomial. So there, the problem of finding the clique Appears to be extremely hard, but the problem of verifying a clique to see if it reaches a target number of vertices is easy to verify. So finding the clique is hard, checking it is easy. Problems of that nature are called non-deterministic polynomial time algorithms. And that's the class NP.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“So the problem might be to determine whether in a given graph there exists a clique of a certain size. That turns out to be a very hard problem. But if somebody hands you a clique and asks you to check whether it hands you a set of vertices and asks you to check whether it's a clique, you could do that simply by exhaustively looking at all of the edges between the vertices and the clique and verifying that they're all there.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“So for example, if the input was a graph, we might want to find the largest clique in the graph, or a clique is a set of vertices such that any vertex, each vertex in the set is adjacent to each of the others. So the clique is a complete subgraph.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“Which may be hard to solve, but when confronted with a solution, you can check it in polynomial time. Let me give you an example there. So if we look at the assignment problem, so you have n boys, you have n girls, the number of numbers that you need to write down to specify the problem instances n squared. And the question is. How many steps are needed to solve it? And Jack Edmonds and I were the first to show that it could be done in time and cubed. Earlier algorithms required into the fourth. So as a polynomial function of the size of the input, this is a fast algorithm. Now, to illustrate the class NP, the question is how long would it take to verify that a solution is optimal?”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“So for every case of the problem. And that's very important that in this theory, when we measure the complexity of an algorithm, we really measure the number of growth of the number of steps in the worst case. So you may have an algorithm that runs very rapidly in most cases. But if there is any case where it gets into a very long computation, that would increase the computational complexity by this measure. And that's a very important issue because there are, as we may discuss later, there are some very important algorithms. Standing from the point of view of their worst case performance and yet are very effective. So theoreticians are interested in P, the class of problem solvable in polynomial time Then there's NP, which is the class of problems.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“Right. So a polynomial time algorithm is one who's running time is bounded by a polynomial in the size of the input. Then the class of such algorithms is called P.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, that's also true, especially as we get very large networks, the size can be in the millions and then anything above. and log in where n is the size would be too much for practical solution.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“That's right. Theoreticians take that to be The definition of an algorithm being efficient. And we're interested in which problems can be solved by such efficient algorithms. One can argue whether that's the right definition of efficient because you could have an algorithm who's running time is the 10th thousandth power of the size of the input, and that wouldn't be really efficient.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“A function of the size of the input, the number of vertices, the number of edges, and so on The number of basic computational steps grows only as some fixed power of that size. A linear algorithm would. Execute a number of steps linearly proportional to the size quadratic algorithm would be steps proportional to the square of the size and so on. In algorithms whose running time is bounded by some fixed power of the size are called polynomial algorithms.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“In a problem like the assignment problem or scheduling schools or any of these applications, you have a set of input data. Which might, for example, be Set of vertices connected by edges are given for each edge the capacity of the edge. And you have algorithms which think of them as computer programs with operations such as addition subtraction, multiplication, division, comparison of numbers, and so on. And you're trying to construct an algorithm Based on those operations, which will determine in a minimum number of computational steps the answer to the problem, in this case the computational step, is one of those operations. And the answer to the problem is, let's say, the The configuration of the network that carries the maximum amount of flow. And an algorithm is said to run in polynomial time.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“Products flowing from one operation to another. and the edges have a capacity which is the rate at which the commodity can flow. And a central problem is to determine given a network of these channels, in this case the edge is a communication channels the challenge is to find the maximum rate at which the information can flow along these channels to get from a source to a destination. And that's a fundamental combinatorial problem that I've worked on jointly with the scientist Jack Edmonds. I think we're the first to give a formal proof that this maximum flow problem through a network can be solved in polynomial time.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so there the edges represent channels along which some commodity can flow, it might be gas, it might be water, it might be information.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“Are the object of combinatorial algorithms. So it could be scheduling classes at a school where the vertices, the nodes of the network are the individual classes and the edges indicate the constraints which say that certain classes cannot take place at the same time or certain teachers are available only at certain for certain classes etc. Or I talked earlier about the assignment problem of matching the boys with the girls where you have the error graph with an edge from each boy to each girl with a weight indicating the cost. or in logical design of computers, you might want to find a set of so-called gates switches that perform logical functions, which can be interconnected to realize some function. You might ask, how many gates do you need in order to”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“They can be directed or undirected. You can think of them as if a graph were representing a communication network, then the edge could be undirected, meaning that information could flow along it in both directions, or it could be directed with only one way communication. A road system is another example of a graph with weights on the edges. And then a lot of problems of optimizing the efficiency of such networks or learning about the performance of such networks.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“That's simple. It's a set of points. Certain pairs of which are joined by lines called edges. And they sort of represent the Different applications represent the interconnections between discrete objects so they could be the interactions, interconnections between switches in a digital circuit or interconnections indicating the communication patterns of a human community”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“Combinatorial algorithm is one which deals with a system of discrete objects that can Occupy various states or take on various values from a discrete set of values and need to be arranged or selected in such a way as to achieve some. Minimize some cost function or to prove the existence of some combinatorial configuration. So an example would be coloring the vertices of a graph.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“I don't think any constant factor improvement could change things. Given our current comprehension of how the what cognition requires. It seems to me that multiplying the speed of the switches by a factor of a thousand or a million. Will not be useful until we really understand the organizational principle behind the network of switches.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“Just because none of the achievements in Speech or robotics or natural language processing or creation of Flexible computer assistants or any of that comes anywhere near close to that level of cognition.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“So I know that there are many who believe that General intelligence can be achieved, and there are even some who feel certain that the singularity will come and we will be surpassed by the machines which will then learn more and more about themselves and reduce humans to an inferior breed. I am doubtful that this will ever be achieved.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“Is just a network of neurons operating by rules. I guess you could say that that's an existence proof of the ability, the capabilities of a mechanism. But it would be almost impossible to acquire the information. unless we got enough insight into the operation of the brain.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“I am doubtful about that. Yes, the argument in favor of it is that the human brain seems to achieve We call intelligence, cognitive abilities of different kinds. And if you buy the premise that the human brain is just an enormous interconnected set of switches, so to speak, then in principle you should be able to diagnose what that interconnection structure is like, characterize the individual switches and build a simulation outside. Why that may be true in principle? That cannot be the way we're eventually going to tackle this problem. That does not seem like a feasible way to go about it. So there is, however, an existence proof that If you believe that the brain”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source
“It's an open question. It seems to me that Most of the achievements have Operate within a very limited set of ground rules and for a very limited precise task, which is a quite different situation from the Processes that go on in the minds of humans, where they have to sort of function in changing environments. They have emotions. They have... Physical attributes for exploring their environment. They have intuition. They have desires. emotions. I don't see anything in the current achievements of what's called AI that come close to that capability. I don't think there's any. Computer program which surpasses a six-month-old child in terms of comprehension of the world.”
2020-07-26 · Lex Fridman Podcast · #111 – Richard Karp: Algorithms and Computational Complexity · IDENTIFIED FROM THE TRANSCRIPT · source