We're going to the next session that we have planned, about 20 minutes or so, and we're asking, we've asked Jenny Milne to lead this discussion. And we're asking for feedback. And most of what we've done during this meeting so far is we've had our panels and had our talks, and then we've asked for questions, but this is an opportunity for you, the audience, to sort of give us feedback on what you think, you know, some of your ideas to give them to us as we take this to the next level. And I'm going to let Jenny lead that discussion. So Jenny, do you want to start this? Wonderful. Thank you, Richard. And thank you everybody over these last couple of days for your insights into this massive problem that we have the pleasure to begin trying to address the challenges and solve the problem. So with that, this is a session for you all, participants in the workshop to give us your feedback. So we've put these questions together.  So do that by going to the reaction button at the bottom and raising your hand.  You don't have to stick to these questions, but we are very interested in hearing what you think are the key insights that you have gained from this workshop regarding opportunities and challenges and technology gaps in the area, with particular focus on trying to electrify industrial processes. And of course, we've heard that it's not quite as straightforward as that. There's probably a need for a CCS and hydrogen at some parts of these processes. So just give us your thoughts on that. And also, what did we not cover that is important? And I know there's probably policies, a big aspect of this as well, carbon prices and all these other factors, but just some of your thoughts that come to the forefront on that. And then what R&D directions would you recommend at Stanford and more broadly the academic community try to address to develop solutions? So with that, I'll give you a little time for that. And I'll just touch on some of the next steps that we are planning or thinking about. So this is a big workshop, a lot of efforts gone into this, Richard and I and our industry friends and academics have put our heads together over the last three months to come up with this agenda. So what do we do with the output from this workshop? Well, we'll have the presentations and slides on the website for everyone to look at. Eventually we might well write a report, but in the more near term, it's likely that we will write a request for proposals. And so that's my job, that's kind of where I fit in the pre-court institute, I run the advanced research projects. And under that, there's various opportunities for faculty to respond to request for proposals and get their research ideas funded. And you can see some of them on the slide here, the C grants, that's for faculty who may not have worked in energy before to test new ideas, there are small grants maybe for a student or a post-doc for a year or two. And then CERC, some of you may be familiar with CERC that stands for the Strategic Energy Research Consortium and it's under the SEA, so it's under the Strategic Energy Alliance. So those are some of the sponsors you've heard from today and we had a targeted call for proposals, on that I think it was two years ago. And that's a substantial amount of funding for faculty to address the challenges that we set in an RFP in topics that are fundamental science but are on pathways to industrially relevant technologies or problems. So in the middle here is what I wanna bring your attention to. This is what we launched last year under the new director Eishwe, the pre-court pioneering projects. And we started this because we saw a gap in the opportunities that faculty have in terms of getting their research ideas supported. And this is to bring together teams of faculty two to four faculty members and the funding is in between the SEA grants and the CERC funding is for two years. And the hope is that what we're doing with that is doing targeted calls in specific areas that we've identified. And it's one such area would be this topic that comes out of this workshop. And we'll craft the research for the request for proposals based on things we identify as being maybe the most challenging or the areas that academic research could have the most impact. And so the hope is that those topics once we give funding to those professors in that area brings together people at Stanford. And we also do this in collaboration with some other centers at Stanford. So last year we had four, five RFPs in different areas. One was in plastics. We did that with the Woods Institute for the Environment. The first one we did was on AI. So artificial intelligence to solve energy and climate problems. And we did that in collaboration with HGI at Stanford, that's human centered AI and with Slack and with bits and watts as well. So there's many opportunities there. We hope that those projects then grow that area and then go on to attract more funding from other places and externally and maybe even internally. So how does that work in the whole ecosystem at Stanford? Well, some of these we've used targeted calls to help launch initiatives. So for example, we have the new carbon removal initiative at Stanford and one of the calls we did last year was on carbon removal. And we actually funded a project in the natural climate solutions area for that. So that will help that initiative to gain interest from other parties and hopefully grow and grow the research that they can do there. Also we're doing one on hydrogen currently, hydrogen for decarbonization. And Jimmy Chan and others, Fritz Prince and Charlie Jang are leading and Naomi Bones are leading this launch of the hydrogen initiative at Stanford. So with that, if anyone has any comments or okay, one hand, awesome, well done Mark, would you like to kick us off with your thoughts? Never ever shy. Mark Neckadam with the Western States Petroleum Association and just by context, former regulator under Jerry Brown, the oil and gas industry and an advisor to the Secretary of Agriculture in the United States on climate issues for many years. One of the things that's really striking about the last three days and consistent theme is the scale issue. There is such a gap between the, if you'll pardon me being very frank here, the magical thinking and policy, particularly here in California, if I may be a little bit edgy with the Air Board and the legislature. I think the biggest message that I will certainly be carrying in our work here but I would really encourage many of you to think of ways to make these scale issues manageable and pithy. We have a lot of people making very critical decisions, putting things into statute, like definitions of urban neutrality, worrying about CCS being viable and any number of issues here that are in some ways almost defy the other message, which is we have a climate crisis, we have an emissions crisis, we have a carbon loading and cycling crisis. And so just the issue of scale, things like the slides from an earlier panel on just the sheer number of terawatts of generating capacity that would have to be used to decarbonize the industrial or chemical sector. Those are the kinds of things that most of the people making the critical decisions that will set the pathway forward do not understand quite simply. And we really need your help, they need your help. I'm a happy facilitator and have for years, my former role as a federal scientist bridged the gap between what's going on in research and how to communicate that to policy people, but we're not doing that sufficiently well. And it's reflected in some of the analysis at the Air Resources Board, even under the scoping plan. It's certainly reflected in a statute that's being proposed in California, Washington, Oregon and other places. So just if there's anything that we can take away from this and put in their heads, it's just the vast gap between the scale of the problem and the solutions. Thank you, Jim. I appreciate the opportunity to weigh it. Thanks, Mark. Now that's great. I just, if you have any thoughts on how we could do that. So one of the mechanisms is of course to send professors to Washington and to chat with folks there and to do reports. And is there an additional way that you can think of or do we need to start also just proposing new ways to insert ourselves and be able to close that gap? Well, I have a great respect for, and having come from academia myself, a great respect for the fierce defense of academic independence and not entering lightly into what may appear to be lobbying. So it's critical to both maintain that position but also communicate why that's important. You have credible voices from across the world and you've brought a lot of credible voices together here but we're not really asking you to become lobbyists. We're asking you to help us take the 50 to a thousand page and of research and report and help us to distill it into the one and two pager. And I know that's a huge challenge and we all know that the world that we're talking about here is incredibly complex. Who knew it was so complicated? But we also just to, if for no other reason, introduce a little bit of caution into the policy discussion. And for example, the rush to electrification of the transportation sector that's currently driving the Newsom administration and the Air Resources Board and also effecting, I mean, being a major influence in other states, including recently Washington and Reagan, making sure that we have opportunities for people who are going to be voting on critical legislation or voting on a scoping plan and regulatory process, whether it's advanced clean fleet or the series of rule makings that will come out of the scoping plan, helping them to understand, again, this scale issue, the technology maturity options, the adoption of technology challenges, the valley of death problem between R&D and commercialization, helping to educate the people who by necessity have to make these decisions and making it possible for them to enter without necessarily feeling like they're being beat up or threatened by academics. It's terribly important for them to understand that they need to be much more cautious and much more thoughtful about the implication of their decisions. So sort of repeated myself, but it really is. I mean, I'm watching it up close here. In fact, my office window works right out over the Capitol right now and it's alarming, it's truly alarming. So the degree to which we can get help from our academic institutions on national labs in these pithy little one and two page, hold on, let's make sure you understand you're talking about gigatons of carbon. We're not talking about boutique forest resources. I spent years doing forest and carbon and stuff. So it's really terribly important for people to understand that they have a much larger job to do than maybe they do. Yeah, that's an excellent point, Mark, thank you. I think solving the technology issues that we have and those challenges is a big part of it, but that's an equally large part, right? And so yeah, point well taken, thank you. So, Drew, would you like to say something? I mean, this is, so first of all, my insight from the soft course is that it's a very big challenge. It's a very big challenge and we have to deal with it and it requires a lot of effort as well. My thought is around the resourcing, training of the next gen and not being reactive to the demand actually of electrification coming, but anticipating that and then having a whole, I would say, army of engineers and innovators who are conscious of this electrification as well. So really coming around, training them so that they can be the workforce of this electrification that is going to happen in the next few decades or it's already happening. So that is one, which is really around the training part that we have not discussed here of how it would be able to resource this electrification journey. The second one was talked about by many people which is of a great upgrade and then it goes back to the policy as well, but in the whole commercialization of the processes that we have right now, energy availability or electricity is considered as something that you take for granted and that is something which is going to change and because it is taken for granted, the way the structure is arranged is that the grid will find it difficult to adapt to all the demand that is going to come in. Yes, we can do demand response as well. Yes, we can chip in to slowly increase and increase the capacity of that grid, but eventually a fundamental change has to be made where more investment needs to go into that grid and the way the grid is structured right now, the grid companies need to be, the participating companies, utility companies and the operators and so on, they all need to be able to adapt to that investment and take that innovation. So it has to be synced with what the industrial needs are to how the electricity can be generated and that interface. So those three parts need to be really synced well. Thank you, Drew. Yeah, that's absolutely right. We need, you know, in some ways, there's lots of things will happen all at the same time, but it seems to me there's an awful lot that needs to happen very quickly and so, you know, how are we going to manage that amount of evolution, revolution? You know, I heard, you know, some of the terms that Leora used, I thought was really, really cool and I think it can be applied across the whole system. Steve Living. Yeah, thanks, Jennifer. I really like the comments that Mark and Drew made. I think they hit along a lot of things that my thoughts as well. One thing I just want to add on, I want to tie it right back to Arun's opening comment. When we think about things that we should be working on, the forgotten one is efficiency because it's not exciting, it's not always cutting edge and sexy, but efficiency is a huge deal and just about all these things we've talked about over the past few days and being able to speak out 5% and 10% and 2% here and there are actually quite significant and we shouldn't neglect that when we think about opportunities in this space. Thanks, Steve. Yeah, you know, absolutely right. I agree. I've been looking at this book I have here. I used it to optimize my workstation and then admitted the efficient use of steam. I don't know if you can see that, but it's a book from the 1950s and it's really interesting. It does say that, you know, it's like every new knowledge will bring another decimal point each generation. That's another decimal place to, you know, improving the properties of the use of steam over the years. And so, I mean, I'd like to see that efficiency gains that we are able to do them because, you know, I think that reducing our consumption from the beginning is really, you know, at that point is the first thing we need to do and so absolutely agree. So we have about two minutes left if anyone has any final thoughts. I just want to remind you, actually I didn't say it, so I won't remind you. I'll tell you that we have a survey. I think a survey will be sent out to everybody who attended or registered here and it's similar three questions and so we'd love if you have additional thoughts or those who are too shy or quiet to speak up just now, please respond to that survey. We will look at those and we will synthesize that and take that on board as we go forward with our next steps.


Let us now describe the Turing machine, which will perform this conversion of unary representation of a number to its binary representation according to the strategy, which we had outlined. So, as an example, think of this string of ones, which is of course, 5 in unary and we would like to convert it to binary. Remember that we will use for binary representation, the symbols a and b, where a would stand for 0 of the normal binary representation and b will stand for 1. So, this is 5 and therefore, what we should get here b a b, which is 1 0 1, which is the binary representation of this unary number. What we will do is, we have the convention that our Turing machine will start at the left most one of the dis-input string. So, the Turing machine will start here scanning this particular symbol. So, let us see how we can carry out the strategy by means of a Turing machine. Let us say this is the initial state and in this initial state, it is scanning this left most one. Now, we said that as soon as this machine sees this one, it will convert to an x. This one, it converts to an x and what should it do? It should move to the right, it is skipping the next one etcetera. So, therefore, this is a right moving state, it comes to and in this when it sees a one, what should it do? It should just go back to this state, so that it can cross off the next one. So, and as it remember that initially it was here. So, this was converted to x and the machine came here and in this when it sees a one, it just keeps that one. So, this one is written as one and it moves to this state and in this state, it should be scanning this particular symbol. So, therefore, now we know that this is also a right moving state. So, this one is left as one in this state. Now it is scanning this particular symbol which will be converted to x, it comes here and in this state, it is now seeing this one which will remain as one because one is not being changed. So, one is being overwritten by one itself and then it moves to the right. Therefore, the head will be here scanning this one, but this particular one will be converted to x and it comes here having converted to x and then since it is a right moving state the head will be under this blank. But what does now seeing a blank mean? It means that the input is entirely scanned in this pass and also see what has happened is this one was crossed and you found a one corresponding to this, this one was crossed, you found a one corresponding to this, this one was crossed and you did not find a one corresponding to this particular one which was here. So, that means this original length of the string is odd and therefore, it should print b to the first blank cell available. So, therefore, it keeps on now moving to the left and in this as it moves to the left, you know it just keeps on moving to the left, keeps on moving to the left when it reaches a blank symbol here it will convert it to b. Why b? Because it knew that the string of once that it had considered in this pass is odd and the remainder by division with two is one and therefore, our code of one being b it should print that b and so it is here. So, therefore, having written this blank this blank by b. So, the situation will be like this what should it do? Now, it should again start this process all over again on the remaining one which are the remaining ones the ones which is the remit the quotient of division by two and that is indeed c 5 when you divide by two quotient is two and that is what you have here there are two ones. So, it will come here and then having written b it should go over this side. So, therefore it is a right moving state and in this as it moves to the right it ignores this cross it sees a one and this one it is converted to x and this process begins again. So, now let us go over again. So, this one is now changed to x having changed to x the machine is in this state this sees an x whatever self loops we are not writing that means it remains there without changing the symbol. So, this x it skips this one takes the machine to the top state without converting that one into anything that one remains as one this one is here and this comes here in this right moving state and now it will again keep moving to the right sees an x which of course it ignores. Now, it sees a blank the head will be here in this state and blank means again that the current once we have already taken care of and in this case you see what had happened was this iteration what had happened this one we converted to x this one we could match it off to this and which remain one and now it is here seeing a blank which means that the number of once in this part left was even. So, it now should print a and therefore, it is similar to this except in this part what it remembers is that it should print an a whenever it sees a blank. So, in corresponding to this there is a left moving state and so this is actually as it is from here it is moving to the right and when it keeps on moving to the left till it finds a blank which it changes to a. So, let us go over it again what is going to happen this it will keep on after after after seeing this blank it would have come to this state and it will start moving to the left to skip all once all x's this b and when it sees this blank it will change it to a and then moves to the right. In this right what it should do it will keep on going to the right till it sees this one. So, these things do not change this one is converted to x x blank now it sees. So, it is here and what it finds that it has reached a blank. So, you know on blank in these cases it would go to the left, but now remembering the left string of once that it had considered in this situation was odd and therefore, a b needs to be printed. So, it will now again go over to the left and here this blank would be converted in the next step to b and as the machine converts the blank to b. So, where did where is this machine now this machine was here it converted this blank to b and it is now in the right moving state and it will now it is not going to see any more once because all the once have been crossed off. So, it will keep on moving to the right and what will happen it is going to see a blank the moment it sees a blank in this state what does it mean that means you know that there was nothing more to do because all the once now have been converted to x s and that is why you are not seeing any one in the current space on which you need to work and since it you found that all once have been taken care of therefore, the job of conversion is over. So, let us see this once more and remember here we are talking of a Turing machine where we have used our own convention that any transition which is not shown means the machine remains in that particular state without changing the symbol. So, I wrote here blank but more technically or to be consistent with what we are writing I should have said blank is replaced by blank. So, let us go over once more very quickly this transition diagram. So, remember that the machine would start at the left most one of a block of once which are flanked on both sides by blanks the machine would start here and in this state why this is the state how do we know from this transition diagram that this is the initial state because there is an arrow coming from nowhere into this state which indicates this is the state in which we start the operation. So, in this state if you see a one you converted to x go to this right moving state that means the machine moves to the right and when it sees another one it does not bother to change it because this is the one which corresponding to the one which you had crossed off. So, one converts gets converted to x next one is not converted to anything it remains one but it means that we have corresponding to this one we have found a one. So, you are back in this particular state. So, this alternately so this we know that given a block of once what we are doing is we are putting it crossing it to x. So, first one next one we are leaving it as such crossing it to one next one as such crossing it to one leaving it as such. But now when we see a blank that means that input space is over and depending on whether the machine is here or here it knows whether the input space had once which is the input which is whose length is either odd or even in this case it is even in this case it is odd. So, this is what is going on and here those transitions which we have not shown. So, for example, in this if there was an x in this state if you see an x what does it mean it means the machine remains here and moves to the right that is the convention we have made use of. Notice that in this machine we have made a transformation of unary to binary and something again you do not know how it is it could have been done at all by finite state machine. So, this is something extra that we can see that Turing machines can do which we could not do by finite state machine or by PDA. We have seen now several at least two examples of Turing machines both doing fairly interesting tasks. The first example was checking whether a string of A's B's and C's had equal number of A's B's and C's the second one was converting unary representation number into its binary representation. But in this particular course of theory of computation our focus is not so much computing functions, but our focus has been to recognize languages. How do we recognize languages or what is really what does it mean to say Turing machine recognizes a particular language? As we are now seeing several examples of Turing machines now we know what are the components or what go to define a Turing machine. Like if you look at this Turing machine first of all it has a finite number of states. We can see a Turing machine consists of q of finite state of states it uses tape alphabet that is all this is the set of all symbols which can appear on a particular Turing machine tape. Now, a subset of this sigma which is a subset of this comma is the input alphabet. Then one particular symbol we made a special and that was the blank symbol you can say blanks which is an element of the tape symbol, but which was not in the input is the blank symbol and the importance of the blank symbol is that this is the symbol which most of the cells in a Turing machine tape will have. Then of course, we had a transition function and this transition function in this example was graphically or pictorially represented, but what is the transition function for deterministic Turing machine? It takes a state and a some symbol which appears on the tape. So, this is the present state and the symbol that is being scanned and corresponding to which they will be a next state and the symbol that will over write and the move. So, therefore, transition function is a mapping which is from the set of states crossed with the set of tape symbols and its range is that means given a present state and the present symbol being scanned this transition function should tell you what is the next state and therefore, this component is q what is the symbol being written therefore, this we write and with what is the move to one of left or right. Then you have of course, a special state which is the start state. So, q 0 which is an element of q is the start state of the Turing machine and in the content of text of language recognition we should have some final accepting states. So, some f which is a subset of q the final accepting states and in these states once the machine if the machine at all reaches one of these states then the machine holds that is the convention we have. So, final accepting states are all halting. So, if you specify all these then you specify a particular Turing machine different Turing machines of course, can have different q gamma sigma blank symbol may be different transition functions will be different etcetera. However, notice that this is finite this is also finite this is also being a subset of gamma this is finite this is one particular symbol this is a map from q cross gamma both of finite. So, therefore, when it is a deterministic machine we have said corresponding to each one there will be one such component give one q one symbol will tell me what is the next state symbol being written the direction of the move. So, point is even this is a finite function. So, everything here is finite. So, therefore, the description of a Turing machine whether you use a diagram or whether you use these symbols and then elaborate each one separately whatever it is a Turing machine can be described finitely and that is a very important point Turing machine as a finite description. Now, you must realize that this is what is expected why because what we are trying is our overall motivation is to capture the intuitive notion of an algorithm by Turing machine. Now, they can of course, be many many infinitely many algorithms they can be infinitely many Turing machines that is fine, but an algorithm is a finite object is not it supposing I code an algorithm in my favorite language programming language whether it is c or java in that is a text and that text is something finite. So, my every program is a finite you know I can think of a program as a finite text with something like a finite you know entity or an object. So, if I am seeing that our algorithms can be captured by Turing machines in to retain that correspondence since algorithms are finite things or entities Turing machines also must be finite entities and indeed these are as you can see a particular Turing machine is represented in terms of these things and these are all finite things and therefore, this particular Turing machine is also a finite entity. Now, let us consider what we mean by recognition language recognition by deterministic Turing machine and the notion we shall use here is the configuration of Turing machine we have spoken of configuration at least several times before when we talked of pdf for example, we had a notion of configuration and configuration is kind of snapshot which tells me at what point the computing devices and this snapshot should have enough information that from the point onwards if I would like to carry out the computation by the computing device the configuration should have all the information to tell me to how to carry out that further those further steps of the computation. Now, configuration of a Turing machine at least one type Turing machine the kinds of Turing machines that we are talking of should have then what it must tell me what are the symbols which are there now on the input and their order in which they are there that is the configuration must have the tape contents and it should also tell me the state of the machine and it should tell me where the head of the Turing machine is position currently. Again remember that configuration should give me a snapshot which has complete information about the current affairs of that running of a particular Turing machine. So, at any given time a Turing machine tape has some symbols and the redried head is position to be reading a symbol on the tape and the machine is in some state q the configuration of the Turing machine must consist of all this information. However, tape is an infinite thing is not it we said it extends to infinity in both directions then how do I represent this infinite thing finitely. Fortunately, remember that if begin with we had only finitely many symbols on the tape initially and after a number of steps non blank cells will still be finite will be bounded because why because you see it is like this it suppose initially this much was the space of the tape which had which contained all the non blank symbols if you can imagine the input and then the machine after n steps may have moved to all this area in n steps it would have at best converted n more blank cells into non blank to contain non blank symbols. So, after n steps the number of cells which would contain all the non blank symbols will be the original ones plus n at most that is the bound. So, in providing tape contents we do not have to of course, say give all the blank cells all we do is.  So, I would represent this situation by saying a blank a b a d a and that is all I will write with the understanding all the symbols to the left of this and all the symbols to the right of this all these symbols are blank. So, now the machine is in machine is scanning this particular b in state q in this picture a convenient way of writing all this in one single line is to do this will write a blank a and here we write the state and then b a d this is the way we will represent configuration. So, couple of things again to say here that this is a string which will contain symbols from the tape alphabet including blank like here we have one blank, but the idea is the current tape contents is this and everything to the right are blanks everything to the left are blanks and now you see there is a state symbol appearing the idea is that this the machine is in this particular state scanning the symbol immediately to the right. So, it is scanning this b all right and if you notice that this very conveniently represents whatever information that particular picture had all right. So, now what we can say that initially you know we always say that initially the Turing machine is scanning the left most symbol of the input that is the kind of convention we all have all the time. So, what is the initial configuration initial configuration will be the input let us say x some string and the machine is going to be scanning the left most symbol of this input and that situation is very conveniently represented by this configuration and notice our. So, we are saying that the machine is in state q 0 scanning the left most symbol of x which is our input now let us elaborate it a little bit. So, let us say x is a b y and suppose our Turing machine is such that that in q 0 if the input is simple or not the input symbol this symbol which is being scanned is a then it goes to the state p writing b and moves to the right. So, therefore, what would happen initially in picture we may write like this these are all blanks here a then b and then this whole thing is y and they these are all blanks initially the machine is here in state q 0 and this in one step what will be the situation of course, these will be blanks and this a is converted to b. So, b and of course, nothing else changes. So, this a has become b there was a b here so that b is here and then the string y and then of course, blank and so on and the head will be since the move is to the right the head will be here. So, in terms of configuration what is the situation initially my configuration was q 0 a b y and in one step the configuration became b p p b y from this configuration we are getting this particular configuration why because this Turing machine had carried out one step of it. So, what we can see that if we consider that if we consider that if we consider that if we consider that there is a space of configurations of a Turing machine there is a relation amongst configuration and what is that relation supposing there is configuration c 1 another configuration c 2 we see c 1 derive c 2 in one step or c 1 is related by this relation c 1 is related if the Turing machine in one step can go from c 1 to c 2. So, let us see let us this way put it we define c 1 you know then this relation symbol to c 2 if in a single step the Turing machine can go from configuration c 1 to c 2. So, therefore, this is a binary relation relating configurations and of course, you can define another relation which we can say. So, if you remember we are saying that this relation stands for machine so machine one step situation in a single step configuration. Single step from configuration c 1 the machine goes to configuration c 2 this we will say in 0 or more steps one way of relating this two is that you know this particular relation this is again a relation on configurations and this particular relation is the reflexive and transitive closure of this relation even without going into those kinds of mathematical jargon intuitively the idea is very simple will write c 1 goes to c 2 will express like this for a Turing machine m in case the configuration c 1 from starting from configuration c 1 in 0 or more steps you can go to configuration c 2. Why are we doing all this? You are doing all this because very precisely we would like to capture the notion of accepting or recognizing a language by Turing machine. We are trying to understand very formally and very rigorously what does it mean to say a Turing machine recognizes a language and towards that we define a relation on configurations and this relation will relate two configurations provided from the first you can go to the second configuration in 0 or more steps using that Turing machine which you have in hand. So, begin with a Turing machine m remember it has a number of states its tape uses an alphabet which we are calling gamma let us say the input again is over an alphabet sigma then it has a transition function delta and it has an initial state q 0 is a special symbol which is blank symbol let us write it as this and then let us mention the states which are final accepting states. So, these are this is final accepting states once more this is the set of states of the Turing machine this is the input alphabet or rather tape alphabet this is the input alphabet this is the transition function the initial state the special blank symbol and the final accepting and l m is the language accepted by Turing machine m this Turing machine. So, this stands for language accepted by m and using our terminology now we can very precisely say that this l m is this set of all strings x over alphabet input alphabet sigma such that q 0 x which is a configuration which really stands for configuration in which the every part of the tape is blank except where the input x is written the machine is in its initial state q 0 scanning the left most symbol of the input and from this configuration if in 0 or more steps you can reach a configuration where you know some alpha is here and maybe p and beta where p is an element of f. So, what we are saying is scanning the left most symbol of the input initially in state q 0 if in 0 or more steps the machine m if it reaches a final accepting state in that case the original x is in the language accepted by the Turing machine m. Now, right away let me use another terminology this set of all languages for which we have a Turing machine recognizer Turing machine which can accept a language the such a language is called recursively enumerable language. So, let me since this is an important concept let me note it down this is the definition a language l is said to be recursively enumerable if there is a Turing machine m which is such that this language l is the language accepted by the Turing machine m. So, recursively enumerable languages are those languages which are accepted or you know we also use the term recognized accepted by Turing machine and this is the class of languages which will consider you know extensively in next classes, but we already know from the first example that the language of equal number of a is followed by equal number of b is an equal number of c. That means this language because we gave a Turing machine which recognize precisely this language. So, therefore, this particular language which we know is not context free is a recursively enumerable language. So, our goal will be to study this class of recursively language enumerable languages and c which all languages at least intuitively which all languages are recursively enumerable and if there are languages which are not even recursively enumerable that will be our concern. Before we end let me see that we do not always write this whole thing recursively enumerable languages we just simply write R e. So, this is an abbreviation.


Hey, what's up, everybody? I'm Jeff. Before we get going on this, two prerequisites. You have to know what a glam is. Everybody cool with that? Everybody familiar with MC Hammer's fabulous 90s masterpiece of pop perfection? You can't touch this. Yes? OK, let's go. The average human lifespan is roughly 80 years. And during our time here on Earth, some of us will be lucky enough to grow old, fall in love, and leave a better world than when we arrived. This is what time means for most humans. For glams, our missions often stand independent of human time, rooted in cultural preservation, community, and scholarship, designed to transcend lifespans. This is what time means to most glams. Technology marches at a different pace than humans and glams, and at a different scale. During the 15 seconds that this slide is shown, about 12,000 images will be uploaded to the web. This is what time means to the internet. And during this 15 seconds, 160 new internet users will log on for their first joy ride across the worldwide web. By the end of this talk, the internet will have grown by almost 2,500 users. This is also what time means to the internet. So the questions I posed today are honest ones. How can we transform our glams to do more than just deal with digital shifts? Can we remake ourselves to exploit the speed and thrive in today's world? Or tomorrow's not-so-distant future world? We're back at this. I don't know where this slide came from, so I'm going to just advance and go, hell no. The swiftness with which glams can operate is not mutually exclusive to the longevity of our missions. If longevity is the most important, we need to confront the things that threaten, said longevity head on. So here are eight ways we can begin to attack glamour time. We must examine all facets of our internal operations and explore new perspectives. Digital technologies offer new tools, new approaches, and heightened levels of speed. Let's audit our operations and work toward responsive operational efficiencies. Number two, we must embrace the notion of iteration. Nothing is ever finished in a digital sphere. It either gets better or it gets deprecated. I assert that our organization should be versioned the same way source code is. GitHub but for glams, always getting better. Number three, we must make our glams safe spaces for failure. I'm not talking about epic fails with huge financial costs and detrimental hits to brand or reputation. There's never room for that. Let's learn to fail fast, fail cheaply, and fail forward. Number four, we should implement policy that fosters radical collaboration. Internally, let's bring down departmental silos with efficient communication and design thinking. Externally, let's make it easier for glams to work together on future-friendly sector transforming projects. Number five, radical transparency. Let's build windows into our organizations instead of walls. Glamms need to be sharing and learning from each other, and let's not be afraid to share our struggles and our failures so our collective knowledge base can grow.  The internet affords so much promise in the financial realm, let's put it to work for us. Let's explore new revenue streams and be swiftly opportunistic with tactics. At the same time, number seven, we need to forge new funding models. Let's get real. Grant cycles and technology cycles are often out of alignment. And sometimes this can send an org off the rails. Let's work together with our grant-based funders to develop new tech-friendly paths forward. And finally, number eight, we should invest in the workforce and workflows of tomorrow. Let's make our Glamms attractive places for workplaces for the new guard. Then just maybe we can begin poaching talent from Facebook and Google instead of the other way around. OK, tackling this entire list at once will surely be a frustrating endeavor for those who bite it off. However, maybe the point of this whole workshop should be identifying and setting up the dominoes that need to fall. We've all kind of been doing this long enough to know that implementing ideas like this in our complex organizations, tradition-heavy orgs, is easier said than done. But we owe it to our Glamms, we owe it to our constituents, and we owe it to ourselves to at least try. Because we, the people in this room, are the makers, we're the catalysts, and we're the ones that can pull it off. We are the agents of change. So let's commit to ending glamour time by building momentum and transforming the sector through partnership, cooperation, responsiveness, and ingenuity. Go back to your institutions with confidence and swagger and start small, ship a prototype, then blog about it, host a drinking about museums, form a change coalition within your organization. Together and only together are we able to stop glamour time.


Asian to a history was made today in international series Macau, we'll get to that in a moment but let's bring you some of the outstanding gold played in this third round. Matt Chong of Hong Kong, made an eagle at 13, a short eagle as well, 66 for 11 under par for Chong. Strange day for David Puge, though he got off to a bad start, he was 2 over after 5 holes but in typical fashion he recovered with his lovely tee shot at the 14th. In the end he would make a 68, he's at 13 under, he's by no means out of it. Also right up there challenging for the title is Ben Campbell of New Zealand, the Hong Kong Open Champion, this at the 13th. Hold out beautifully, didn't have a bogey on the card, 63 today for Campbell, he's on 14 the same mark as Martin Treynor. This beautifully judged birdie at the 8th, he too, on 14 under after a 64 today. JB Kruger of South Africa only had four birdies on the card, we say only because it was a low scoring day but no bogeys and 66 means he's right in it at 15 under. This was an awkward one for Lucas Herbert of Australia but beautifully played at the 15th. 62 today for Herbert, 15 under par and he is tied for 3rd. Jason Kochrak had a wonderful day as well, what about this long eagle putt at 13 by the American. Didn't drop a shot today, a 62 and he's on 16 under in 2nd place. But the man of the day was John Catlin of the US. This birdie at the 11th, a long putt, the 7th of 9 birdies on his card. But the key came at the last, an eagle putt from the back of the green for a 59. In it went, beautifully judged, the first ever 59 on the Asian tour. Well done John Catlin and he leads by 2 going into the final round.