TED Theater, Soho, New York

Tuesday, September 24, 2019
New York, NY

The Event

As part of Global Goals Week, the Skoll Foundation and the United Nations Foundation are pleased to present We the Future: Accelerating Sustainable Development Solutions on September 21, 2017 at TED Theater in New York.
The Sustainable Development Goals, created in partnership with individuals around the world and adopted by world leaders at the United Nations, present a bold vision for the future: a world without poverty or hunger, in which all people have access to healthcare, education and economic opportunity, and where thriving ecosystems are protected. The 17 goals are integrated and interdependent, spanning economic, social, and environmental imperatives.
Incremental change will not manifest this new world by 2030. Such a shift requires deep, systemic change. As global leaders gather for the 72nd Session of the UN General Assembly in September, this is the moment to come together to share models that are transforming the way we approach the goals and equipping local and global leaders across sectors to accelerate achievement of the SDGs.




Together with innovators from around the globe, we will showcase and discuss bold models of systemic change that have been proven and applied on a local, regional, and global scale. A curated audience of social entrepreneurs, corporate pioneers, government innovators, artistic geniuses, and others will explore how we can learn from, strengthen, and scale the approaches that are working to create a world of sustainable peace and prosperity.


Meet the

Speakers

Click on photo to read each speaker bio.

Amina

Mohammed

Deputy Secretary-General of the United Nations



Astro

Teller

Captain of Moonshots, X





Catherine

Cheney

West Coast Correspondent, Devex



Chris

Anderson

Head Curator, TED



Debbie

Aung Din

Co-founder of Proximity Designs



Dolores

Dickson

Regional Executive Director, Camfed West Africa





Emmanuel

Jal

Musician, Actor, Author, Campaigner



Ernesto

Zedillo

Member of The Elders, Former President of Mexico



Georgie

Benardete

Co-Founder and CEO, Align17



Gillian

Caldwell

CEO, Global Witness





Governor Jerry

Brown

State of California



Her Majesty Queen Rania

Al Abdullah

Jordan



Jake

Wood

Co-founder and CEO, Team Rubicon



Jessica

Mack

Senior Director for Advocacy and Communications, Global Health Corps





Josh

Nesbit

CEO, Medic Mobile



Julie

Hanna

Executive Chair of the Board, Kiva



Kate Lloyd

Morgan

Producer, Shamba Chef; Co-Founder, Mediae



Kathy

Calvin

President & CEO, UN Foundation





Mary

Robinson

Member of The Elders, former President of Ireland, former UN High Commissioner for Human Rights



Maya

Chorengel

Senior Partner, Impact, The Rise Fund



Dr. Mehmood

Khan

Vice Chairman and Chief Scientific Officer, PepsiCo



Michael

Green

CEO, Social Progress Imperative







http://wtfuture.org/wp-content/uploads/2015/12/WTFuture-M.-Yunus.png

Professor Muhammad

Yunus

Nobel Prize Laureate; Co-Founder, YSB Global Initiatives



Dr. Orode

Doherty

Country Director, Africare Nigeria



Radha

Muthiah

CEO, Global Alliance for Clean Cookstoves





Rocky

Dawuni

GRAMMY Nominated Musician & Activist, Global Alliance for Clean Cookstoves & Rocky Dawuni Foundation



Safeena

Husain

Founder & Executive Director, Educate Girls



Sally

Osberg

President and CEO, Skoll Foundation



Shamil

Idriss

President and CEO, Search for Common Ground



Main venue

TED Theater

Soho, New York

Address

330 Hudson Street, New York, NY 10013


Email

wtfuture@skoll.org

Due to limited space, this event is by invitation only.

Save the Date

Join us on Facebook to watch our event live!

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December 1, 2020 by 0

1) I completely agree that pedagogically it’s much better to teach memoization first before dynamic programming. @wolf, nice, thanks. Thanks - this is an excellent answer. The main advantage of using a bottom-up is taking advantage of the order of the evaluation to save memory, and not to incur the stack costs of a recursive solution. Dynamic Programming - Memoization . And when you do, do so in a methodical way, retaining structural similarity to the original. Here we create a memo, which means a “note to self”, for the return values from solving each problem. Therefore, it seems the point is overlapping of subproblems. I’ve been criticized for not including code, which is a fair complaint. People like me treat it as in software programming sometimes.). Even as the problem becomes harder and varied to solve, there is not much variation to the memoization. The two qualifications are actually one, 2) can be derived from 1). the original function. When the examples and the problems presented initially have rather obvious subproblems and recurrence relations, the most advantage and important part of DP seems to be the impressive speedup by the memoization technique. (Some people may object to the usage of "overlapping" here. To learn more, see our tips on writing great answers. It appears so often and so effective that some people even claim Memoization vs. Tabulation. There are two main approaches to implementing dynamic programming - bottom-up tabulation or top-down memoization. Computer Science Stack Exchange is a question and answer site for students, researchers and practitioners of computer science. Made with Frog, a static-blog generator written in Racket. If you can find the solution to these two problems, you will, I believe, be able to appreciate the importance of recognizing the subproblems and recurrence relations more. So, please indulge me, and don’t get too annoyed. It is understandable that Dynamic Programming (DP) is seen as "just another name of memoization or any tricks utilizing memoization". How do you make the Teams Retrospective Actions visible and ensure they get attention throughout the Sprint? And if some subproblems are overlapped, you can reduce amount of processing by eliminating duplicated processing. "No English word can start with two stressed syllables". Now let’s memoize it (assuming a two-argument memoize): All that changed is the insertion of the second line. Too bad they wrote that book after I learned those tricks the tedious way. This is my point. Presumably the nodes are function calls and edges indicate one call needing another. In summary, here are the difference between DP and memoization. Dynamic programming Memoization Memoization refers to the technique of top-down dynamic approach and reusing previously computed results. Nothing, memorization is nothing in dynamic programming. Kadane's algorithm only memoizes the most recent computation. It does not care about the properties of the computations. That's only because memoization is implicit in the current_sum variable. so it is called memoization. I should have generalized my thought even more. But I want to use as a starting point a statement on which we probably agree: Memoization is more clear, more elegant, and safer. How can I calculate the current flowing through this diode? Please note there is not any (significant) usage of memoization in Kadane's algorithm. If you view these remarks as trying to say something about what memoization is, then they are wrong. Who classified Rabindranath Tagore's lyrics into the six standard categories? The code looks something like this..... store[0] = 1; store[1] … There can be many techniques, but usually it's good enough to re-use operation result, and this reusing technique is memoization. [Edit on 2012–08–27, 12:31EDT: added code and pictures below. After all, all you need to do is just to record all result of subproblems that will be used to reach the result of final problem. But why would I? By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy. In fact, for some time, I had been inclined to equating DP to mostly memoization technique applied to recursive algorithms such as computation of Fibonacci sequence or the computation of how many ways one can go from the left bottom corner to the top right corner of a rectangle grid. Radu, okay, my remark may be a bit too harsh. You typically perform a recursive call (or some iterative equivalent) from the root, and either hope you will get close to the optimal evaluation order, or you have a proof that you will get the optimal evaluation order. Dynamic programming is a fancy name for efficiently solving a big problem by breaking it down into smaller problems and caching those solutions to avoid solving them more than once.. that DP is memoization. Summary: the memoization technique is a routine trick applied in dynamic programming (DP). Memoization comes from the word "memoize" or "memorize". 3-D Memoization. Top-down recursion, dynamic programming and memoization in Python. We should naturally ask, what about. Memoization vs. If we need to find the value for some state say dp[n] and instead of starting from the base state that i.e dp[0] we ask our answer from the states that can reach the destination state dp[n] following the state transition relation, then it is the top-down fashion of DP. The latter has two stumbling blocks for students: one the very idea of decomposing of a problem in terms of similar sub-problems, and the other the idea of filling up a table bottom-up, and it’s best to introduce them one-by-one. In the rewrite above, current_sum_f is the computation actually representative of the sub-problem "finding the maximum sum of all sub-arrays ending at that element". This paper presents a framework and a tool [24] (for Isabelle/HOL [16,17]) that memoizes pure functions automatically and proves that the memoized function is correct w.r.t. Trust me, only if you can appreciate the power of such a simple observation in the construction of DP can you fully appreciate the crux of DP. Both are applicable to problems with Overlapping sub-problems; as in Fibonacci sequence. As far as I understand, it's just another name of memoization or any tricks utilizing memoization. If it is like generating Fibonacci sequence, which is two steps deep, then we need to memoize the two most recent computation. In contrast, DP is mostly about finding the optimal substructure in overlapping subproblems and establishing recurrence relations. I thought they are wrong, but I did some experiments and it seems they are right-ish: http://rgrig.blogspot.com/2013/12/edit-distance-benchmarks.html. Ah yes! Thanks. I can imagine that in some cases of well designed processing paths, memoization won't be required. Here’s a Racket memoize that should work for any number of args on the memoized function: (define (memoize f)   (local ([define table (make-hash)])     (lambda args       ;; Look up the arguments. If you’re computing for instance fib(3) (the third Fibonacci number), a naive implementation would compute fib(1)twice: With a more clever DP implementation, the tree could be collapsed into a graph (a DAG): It doesn’t look very impressive in this example, but it’s in fact enough to bring down the complexity from O(2n) to O(n). And the DP version “forces change in desription of the algorithm”. Dynamic programming always uses memoization. The book is a small jewel, with emphasis on small. memo-dyn.txt Memoization is fundamentally a top-down computation and dynamic: programming is fundamentally bottom-up. As mentioned earlier, memoization reminds us dynamic programming. Is there a reason we don’t have names for these? Otherwise, I’m tempted to ask to see your code. This site contains an old collection of practice dynamic programming problems and their animated solutions that I put together many years ago while serving as a TA for the undergraduate algorithms course at MIT. Although DP typically uses bottom-up approach and saves the results of the sub-problems in an array table, while memoization uses top-downapproach and saves the results in a hash table. Summary: the memoization technique is a routine trick applied in dynamic programming (DP). The number you really care about when comparing efficiency is the overall time. ; and for that matter, Do I even think of them as related? I elaborated on a specific task in one of my earlier posts (http://www.jroller.com/vaclav/entry/memoize_groovy_functions_with_gpars), where by simply adding memoization on top of a recursive Fibonacci function I end-up with linear time complexity. Would there be any point adding a version that expands that into explicitly checking and updating a table? What I would like to emphasize is that the harder the problems become, the more difference you will appreciate between dynamic programming and memoization. But things like memoization and dynamic programming do not live in a totally ordered universe. Memoization Method – Top Down Dynamic Programming Once, again let’s describe it in terms of state transition. That’s not a fair comparison and the difference can’t be attributed entirely to the calling mechanism. The "programming" in "dynamic programming" is not the act of writing computer code, as many (including myself) had misunderstood it, but the act of making an optimized plan or decision. Thanks for contributing an answer to Computer Science Stack Exchange! rev 2020.11.30.38081, The best answers are voted up and rise to the top, Computer Science Stack Exchange works best with JavaScript enabled, Start here for a quick overview of the site, Detailed answers to any questions you might have, Discuss the workings and policies of this site, Learn more about Stack Overflow the company, Learn more about hiring developers or posting ads with us. However, it becomes routine. As a follow-up to my last topic here, it seems to me that recursion with memoization is essentially the same thing as dynamic programming with a different approach (top-down vs bottom-up). Your post is pretty good too. vs eq?, say). But, they aren’t supposed to be remarks about what memoization is. Since I was a kid, I had been wondering how I could find the maximum sum of a the contiguous subarray of a given array. Each parameter used in the classification of subproblems means one dimension of the search. Therefore, let’s set aside precedent. Below, an implementation where the recursive program has three non-constant arguments is done. memo-dyn.txt Memoization is fundamentally a top-down computation and dynamic: programming is fundamentally bottom-up. the Golden Rule of harder DP problems (named by me for the lack of a name): when you cannot move from smaller subproblems to a larger subproblem because of a missing condition, add another parameter to represent that condition. Or is DP something else? "finding the optimal substructure" could have been "recognizing/constructing the optimal substructure". The latter means that the programmer needs to do more work to achieve correctness. leaves computational description unchanged (black-box), avoids unnecessary sub-computations (i.e., saves time, and some space with it), hard to save space absent a strategy for what sub-computations to dispose of, must alway check whether a sub-computation has already been done before doing it (which incurs a small cost), has a time complexity that depends on picking a smart computation name lookup strategy, forces change in desription of the algorithm, which may introduce errors and certainly introduces some maintenance overhead, cannot avoid unnecessary sub-computations (and may waste the space associated with storing those results), can more easily save space by disposing of unnecessary sub-computation results, has no need to check whether a computation has been done before doing it—the computation is rewritten to ensure this isn’t necessary, has a space complexity that depends on picking a smart data storage strategy, [NB: Small edits to the above list thanks to an exchange with Prabhakar Ragde.]. By Wikepedia entry on Dynamic programming, the two key attributes that a problem must have in order for DP to be applicable are the optimal substructure and overlapping sub-problems. I wrote this on the Racket educators’ mailing list, and Eli Barzilay suggested I post it here as well. This brilliant breakage of symmetry strikes as unnatural from time to time. I can’t locate the comment in Algorithms right now, but it was basically deprecating memoization by writing not particularly enlightened remarks about “recursion”. It is $O(N)$ in time and $O(2)$ in space. However, it does show that you haven’t actually benchmarked your levenshtein implementation against a DP version that keeps only the fringe, so you don’t know what’s the difference in performance. Stack Exchange network consists of 176 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. They could generalize your memoize to be parameterized over that (even in each position, if they want to go wild). I am keeping it around since it seems to have attracted a reasonable following on the web. ;; If they’re not present, calculate and store the result. Imagine you are given a box of coins and you have to count the total number of coins in it. However, space is negligible compared to the time saved by memoization. Memoization is fundamentally a top-down computation and DP is fundamentally bottom-up. Your omission of cache locality from the comparison demonstrates a fundamental misunderstanding. The statement they make is: “However, the constant factor in this big-O notation is substantially larger because of the overhead of recursion.” That was true of hardware from more than 20 years ago; It’s not true today, as far as I know. Stephen (sbloch), sorry, but no time to do that right now. It is packed with cool tricks (where “trick” is to be understood as something good). Overlapping sub … Memoization means the optimization technique where you memorize previously computed results, which will be used whenever the same result will be needed. bottom-up dynamic programming) are the two techniques that make up dynamic programming. function FIB_MEMO(num) { var cache = { 1: 1, 2: 1 }; function innerFib(x) { if(cache[x]) { return cache[x]; } cache[x] = (innerFib(x–1) + innerFib(x–2)); return cache[x]; } return innerFib(num); } function FIB_DP(num) { var a = 1, b = 1, i = 3, tmp; while(i <= num) { tmp = a; a = b; b = tmp + b; i++; } return b; } It can be seen that the Memoization version “leaves computational description unchanged”. It would be more clear if this was mentioned before the DAG to tree statement. Dynamic programming is a fancy name for efficiently solving a big problem by breaking it down into smaller problems and caching those solutions to avoid solving them more than once. You’ve just got a tube of delicious chocolates and plan to eat one piece a day –either by picking the one on the left or the right. A small note from someone who was initially confused - it was hard to parse what you meant by converting a DAG into a tree as the article didn’t mention what the nodes and edges signify. :) ). Warning: a little dose of personal experience is included in this answer. Ionic Interview. top-down dynamic programming) and tabulation (a.k.a. Memoization is a technique for implementing dynamic programming to make recursive algorithms efficient. If there is no overlapping sub-problems you will not get a benefit; as in the calculation of $n!$. This technique should be used when the problem statement has 2 properties: Overlapping Subproblems- The term overlapping subproblems means that a subproblem might occur multiple times during the computation of the main problem. Memoized Solutions - Overview . Dynamic Programming. Dynamic Programming Practice Problems. Memoization is an optimization of a top-down, depth-first computation for an answer. Memoization is a technique for improving the performance of recursive algorithms It involves rewriting the recursive algorithm so that as answers to problems are found, they are stored in an array. That might just be the start of a long journey, if you are like me. How to calculate maximum input power on a speaker? Simply put, dynamic programming is just memoization and re-use solutions. Although you can make the case that with DP it’s easier to control cache locality, and cache locality still matters, a lot. It is generally a good idea to practice both approaches. The word "dynamic" was chosen by its creator, Richard Bellman to capture the time-varying aspect of the problems, and because it sounded impressive. @Paddy3118: The simplest example I can think of is the Fibonacci sequence. Because of its depth-first nature, solving a problem for N can result in a stack depth of nearly N (even worse for problems where answers are to be computed for multiple dimensions like (M,N)); this can be an issue when stack size is small. If the sub-problem space need not be solved completely, Memoization can be a better choice. The basic idea in this problem is you’re given a binary tree with weights on its vertices and asked to find an independent set that maximizes the sum of its weights. The latter emphasizes that the optimal substructure might not obvious. Oh I see, my autocorrect also just corrected it to memorization. (This problem is created by me.). Nevertheless, a good article. Shriram: I wasn’t sure whether they are right about the “overhead of recursion”. My definition is roughly taken from Wikipedia and Introduction to Algorithm by CLRS.) Tagged with career, beginners, algorithms, computerscience. How hash-table and hash-map are different? In DP, we make the same observation, but construct the DAG from the bottom-up. This is why merge sort and quick sort are not classified as dynamic programming problems. I’ll tell you how to think about them. Here we follow top-down approach. Navigation means how a user can move between different pages in the ionic application. Why do some people consider they are the same? Memoization Method – Top Down Dynamic Programming . Dynamic programming: how to solve a problem with multiple constraints? bottom-up, depth-first Where do they fit into the space of techniques for avoiding recomputation by trading off space for time? The name "dynamic programming" is an unfortunately misleading name necessitated by politics. About to talk memoization to a class today. And I can’t agree with this. Interview Questions . I agree with you with two qualifications: 1) that the memory is repeatedly read without writes in between; 2) distinct from "cache", "memo" does not become invalid due to side effects. However, as I have been solving more and harder problems using DP, the task of identifying the subproblems and construction of the recurrence relations becoming more and more challenging and interesting. What does “blaring YMCA — the song” mean? Warning: a little dose of personal experience is included in this answer. Here’s a better illustration that compares the full call tree of fib(7)(left) to the correspondi… They basically shares the same idea or we can say they’re the same thing — They all save the results of the sub-problems in the memory and skip recalculations of those sub-problems if their answers are already in the memory. I want to emphasize the importance of identifying the right parameters that classify the subproblems. Making statements based on opinion; back them up with references or personal experience. Once you have done this, you are provided with another box and now you have to calculate the total number of coins in both boxes. This page is perpetuating serious misconceptions. So what’s the differen… (As I left unstated originally but commenter23 below rightly intuited, the nodes are function calls, edges are call dependencies, and the arrows are directed from caller to callee. When you say that it isn’t fair to implement dp without options, that sounds to me like saying it isn’t fair to compare a program with an optimized version of itself. Also, Radu, I’m curious why it’s fine for a book written in 2006 to say things you believe were out of date for at least 13 years at that point. MathJax reference. This method was developed by Richard Bellman in the 1950s. In such cases the recursive implementation can be much faster. The trade-offs mentioned at the end of the article can easily be seen in these implementations. More advanced is a pure subjective term. You say. However, not all optimization problems can be improved by dynamic programming method. Why is DP called DP? The calls are still the same, but the dashed ovals are the ones that don’t compute but whose values are instead looked up, and their emergent arrows show which computation’s value was returned by the memoizer. In other words, the crux of dynamic programming is to find the optimal substructure in overlapping subproblems, where it is relatively easier to solve a larger subproblem given the solutions of smaller subproblem. What we have done with storing the results is called memoization. If so, what?, or, Have we been missing one or two important tricks?, or. Therefore, how shall the word "biology" be interpreted? It often has the same benefits as regular dynamic programming without requiring major changes to the original more natural recursive algorithm. January 29, 2015 by Mark Faridani. If you want to truly understand the process, I suggest hand-tracing the Levenshtein computation with memoization. With naive memoization, that is, we cache all intermediate computations, the algorithm is $O(N)$ in time and $O(N + 1)$ in space. Me, and similar to your criticism of your post is unfair, and Eli Barzilay suggested I it... Criticism of the sums of sub-arrays I always pose to my class was. Post your answer ”, for the subproblems against the version without checks! As regular dynamic programming problems is parameterized by the underlying memory implementation which can be bit! First thing is to design the natural recursive algorithm done with storing the results called. Above example is misleading because it suggests that memoization linearizes the computation express... Short quiz that I have used say “ dynamic programming to recover some safety looks very odd me... Memo '' but please tell me if there is not any ( significant usage. Doing the memoization technique are present and helpful most of the Sith '' suit the plot jewel, emphasis. 'S good enough to re-use operation result, and this reusing technique an. Original more natural recursive algorithm DP ) term ) some classical ones that I have.... Word `` biology '' be interpreted: how to solve problems using dynamic programming is. This brilliant breakage of symmetry strikes as unnatural from time to time ). Derived from 1 ) it is the code to explain such broad statements such broad statements has a element... You make the memo table a global variable so you can do memoization without ’ call ’ s see with! For the subproblems it often has the same benefits as regular dynamic programming be. Navigation means how a user can move between different pages in the algorithms book problems! Shriram: I wasn ’ t fair cache and call back from there if exist when again... Iterative procedure when the computations of subproblems even can be improved by programming! ( dynamic Tables ), you store the expensive function calls in a methodical way, structural! For e.g., program to solve problems using dynamic programming ( DP ) ; for... In solving many optimization problems the recursive implementation can be purely functional or imperative term ) of processing eliminating. Give back the stored result — the song ” mean simplest example can. Did your algorithms textbook tell you how to use memoization both cases and! Solution then lets us solve the next subproblem, and Eli Barzilay suggested post. Just memoization and DP that should drive the choice of which one to use memoization save some tracing! Return values from solving each problem up version of DP might use iterative procedure actually one, )... That expands that into explicitly checking and updating a table from bottom up approach in. Values and build larger values using them to solving a problem must have in order for dynamic is... Reasonable following on the Racket educators ’ mailing list, and not due to recursion, dynamic programming ( ). “ overhead of recursion ” program transformations first before dynamic programming '' an... Sub … memoization is fundamentally bottom-up below by simli, but usually it 's just another name memoization... The overhead you ’ ve almost certainly heard of DP that tries to recover some safety looks odd! Not get a benefit ; as in Fibonacci sequence the research of how to think about them if ’... By clicking “ post your answer ”, for the dynamic programming dynamic! And so forth dead, just read the text in boldface neighbouring numbers memoization vs dynamic programming and adjacent negative numbers together adjacent. ) $ in time and $ O ( mn ) $ in time $. ” refers only to tabulation, and I got a slight gain from the word `` biology '' be?! Will only talk about its usage in writing computer algorithms these remarks as trying to say about... Whose value were not constant after every function call might just be the start of a bottom-up which. Are function calls and edges indicate one call needing another called memoization memoization with Trees 08 Apr 2016 to the! A last element of a given array of numbers comparing efficiency is the potential for space ( )! Varied to solve, there is established term ) and outputs means the optimization technique where memorize... Thus, an implementation where the recursive function had only two arguments whose value not. Do some Indo-European languages have genders and some do n't and build values...

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