Hi, everyone. It is so good to be here. I would like to start by thanking Product School Life for giving me this great opportunity. A little about me. I'm a Senior Product Manager at Microsoft in M365 Customer Support Engineering Team. Things we will cover in today's session. Who is a mentor? What are you looking for from a mentorship program? How can you find and approach potential mentors? How can mentors help in shaping your career? Who is a mentor? A mentor is more experienced, a knowledgeable person who helps in nurturing and development of a less experienced person. Mentor is like a guide. A mentor is someone who is willing to walk with you, help you by moving beside you in the path, and support encourage you along the way. Someone who can guide you to leverage your core strengths, someone who will boost your confidence and will make you believe in yourself. Mentor is someone who is curious. The mentor should allow the mentees to set the agenda. As mentees should own and drive the discussion, it gives them a sense of power. It makes them feel more autonomous on what they want to discuss and bring it to the table. At the end of the day, it is the responsibility of mentee to bring the agenda, as they are the ones who are seeking for guidance to shape up their career from the mentors. A common question that I have heard is what is the difference between a coach, a mentor and sponsor? Before I move ahead and talk about what are the differences between coach, mentor and sponsor, I would like to say that they all share the same common code. They all intend the best for you. They want you to flourish and be successful in your career. Now let's look at the differences between coach, mentor and sponsor. Who is a coach? Coach can be anybody, as it's very formal, semi-informal setting. A coach is someone who stays curious for a longer time. Your manager can be your coach, your colleague can be your coach, your friend can be your coach. Coach is someone you are looking for guidance for any moment or any time in your life. Who is a mentor? Mentor falls into a more formal setting. A mentor is someone who has walked a very, very similar path to their mentees. Mentors are a combination of a guide and someone who needs to be curious, something what I've already covered in one of my previous slides. Mentors are someone who will have very similar experiences and stories to share with their mentees from their past experiences. Who is a sponsor? Sponsor is someone who advocates and champion with champion people as they are more externally focused. Sponsor is something that you earn over the course of time. It's not in a day that you build up that relationship with your hard work, with your dedication, with your commitment towards your work. That's how when you start showcasing those skills to your mentors, that's when they transition to become your sponsor or your allies. Now the key question that comes in is, why do you need a mentor? Mentor is someone who can help you to boost up your career success. Mentors can profoundly support you, guide you and help you in growing your career. Based on the survey conducted by Olivetti University back in 2019, they found out that there are about 37% of people who have mentors. Mentors are someone who can guide you in your journey. They can share their own learnings, their experiences from the past and help you to move forward and march forward in your career. Mentors are someone who help you in keeping content in your job. Based on some of the surveys, it has been found out that people who have mentors are more happy and content in their job as they get the right clarity and support to address the problems that they face in their career as they move along. Now, what are you looking for a mentorship program? The first thing is to develop yourself awareness. Reflect and see how other people see you and your strengths and weaknesses. I'll share an example from a personal experience. One of the type of feedback I got from one of my previous managers at Microsoft was that I need to work on my documentation skills. I need to learn how to level up the docs when presenting to higher leadership. This was definitely an area for improvement for me, but I was really slacking behind. I ensured to take up that as a challenge. I went ahead and tried looking for a mentor within the organization who could help me to come through this challenge. I went ahead and found a mentor who had immense experience of presenting to the higher leadership, someone who was able to give me the right tools and guidance and suggestions on how I could level up my documentation. And that really helped me to move along in my career as well. The second key thing that you need to make sure is define your goals, your short term one to two years goal and then your long term goals five years and above. Defining your career goals will really help you to get clarity on what kind of a mentor do you need. I'll again share an example that is something I'm really working towards. I'm looking for a long term career goal as a people manager. I have ensured that my mentors are someone who are in the people manager role. They are the ones who have been in my position back a couple of years. Someone who was a very strong individual contributor and now has been a very successful people manager. They are the ones who are guiding me with the right tools, right skills that a leadership really needs to build upon. Someone who is guiding me to help move forward in my career journey. And last but not the least is define the skills you're looking for in a mentor. Are you looking for a mentor with strong leadership skills? Are you looking for a very analytical mentor if you are really analytical in taking all the approaches in your career? Or are you looking for someone with immense experience working in different industries and domains? How can you find and approach potential mentors? I'm gonna split this into two halves. The first is how can you find potential mentors? It's obviously the first key thing is know and believe in yourself. You must know who you are and what you want out of the mentorship program. Be courageous, don't be scared. Try to expand on your network. And the other thing that you need to think about is what are you bringing to the table for your mentor? Couple of things that you think of that you can add to the value of learnings of your mentor is maybe you are helping them in expanding their network. You are sharing the insights of different streets of businesses that are being worked across in an organization. And last but not the least is the appreciation and acknowledgement of all the effort that they are putting out for your career success. Second is people in your network. Reflect back, see who you admire the most. Who do you think you want to aspire to become one day? Someone who is really successful in their career journey and growth. And don't be hesitant or intimidated by their title. We as humans normally are very, very skeptical about reaching out to people at high position thinking, will they have enough bandwidth to entertain people like us? The answer is, of course, yes. They are more than willing to support you and guide you to be successful in your career. Now, how can you approach your mentors? First, you really need to do a good job in researching about your potential mentors. That is really like an icebreaker because you would have a sense of what your mentor is all about. A very good tool to start with is LinkedIn. You can go to the LinkedIn profile, see their past experiences, their qualification, their education, their skill set. And then you can see, are those the things that align with what you are looking for your mentor? And then start the conversation by sending out an email. Make sure you are very, very formal in the language of the email that you are sending out to them. You need to make sure that you are giving some background and context onto why you are approaching them, why are you reaching out to them. And also communicate how the mentorship program is gonna be beneficial for them as well. It's not just always about a mentee, it is also about how are you adding value to your mentor? And then request for an introductory meeting and make sure you are asking them what schedule and time works for them. Because especially for people at higher positions, it gets very tricky and you need to be really respectful of their time. How can mentors help in shaping your career? Mentors can help in accelerating your career by giving your guidance and right tools to move forward. Clarity, when you are stuck in a situation, mentors, guidance and advice really comes in handy as they are sharing their own experiences from the past, from their own learnings. And those are the guidance that come in really, really handy and give you more clarity on how you should unblock yourself if you are running into any challenges. Mentors play a crucial role in building out a broader community of courage. Example, my mentors have guided me and supported me whenever I have thought of moving ahead or changing my career or changing my domain within the organization. I've got immense support from them. They have acted as my ally and have helped me in giving some great recommendations to the hiring managers. The key thing I want to call out here is when you enter to a mentorship program, please don't enter with this expectation that a mentor is someone who's gonna help you in getting you a job or is someone who's gonna help you in getting referred. This is something you earn over the period of time and that's like once you start investing into the program, once the mentor starts looking at your hard work, your commitment, you're honesty towards the work you are doing and when they see you are dedicated and invested in the mentorship program, that's when they gradually move from being a mentor to a sponsor or an ally. But having been said, never start the mentorship program with this intention that, oh, mentor is someone who is gonna right away help me in getting a job. That is not the right intent or the program calls out for. Now let's summarize with some of the key takeaways that we take from today's session. First, define your goals, create a list of things, what you would like to learn from your mentors, expand your network, be courageous, go to as many networking events as possible.  And become a mentor. You learn a lot when you teach others. Do your bit by mentoring others, pass on all the learnings and great wisdom that you are gonna get from your mentors. I'm doing my bit, are you? Mentorship can come in many forms at the end of the day. It is structured in a way of a mutually beneficial, supportive relationship where two people help each other to be successful in their career. Thank you.


Good morning. I'll have to end a few minutes early today. I hope you'll bear with me about that. So today we're looking at Crip Keys naming a necessity again on Monday. We'll again be looking at naming a necessity and going back over the question is it true as Frege and Serral thought that descriptions fix the references of ordinary proper names. Crip Keys got a quite different take on this question. I'm a quite different picture of how the connect between the sign and the object gets set up. It's probably the view of reference that's the most common today. It's taken for granted I think by most people working on language today. So I'll set that out in the second section and then assuming time permits we'll move on to look at questions about informative identities once more. There's no class to be complete without us looking at informative identities right? Okay so last time we were looking at that. What fixes the reference of the name Gödel relative to different possible worlds? And what I was saying was if you take the name of someone famous like Gödel there are two questions. One is how is this reference fixed in the actual world? How does the name get hooked up to an object in the actual world? And then the other question is when you talk about what could have happened to Gödel, what would have happened to Gödel under various counterfactual circumstances is it still a description that's fixing the reference? And last time I was arguing no it's always the same object that the name is referring to in different possible worlds even though the descriptions that the thing is satisfying might be quite different. How about that? That's all right so far. Any questions on that? Okay Plain is dead? Okay. So you get the cluster of descriptions associated with Gödel, the serral style generation of the dossier and then you get the idea that that is so that's perfectly well reasonably well defined. This is well defined as anything in this area tends to be. So then the question is is that what's fixing the reference? Now Sel admits that there can be, indeed he says it's an advantage of his account that it acknowledges types of indeterminacy about which descriptions are in the cluster. You know if only one or two people put descriptions into the cluster does that get in? If some descriptions get in that conflict with other descriptions do those get in? What kind of weight do you give to particular descriptions in the cluster and how many descriptions in the cluster does an object have to match to be the reference of the term? So there's kind of indeterminacy about all these things. So that gives the view a lot of flexibility. It's difficult to get a knock down objection to the view when it has that kind of flexibility in it. But Kripke's argument is nothing like that is right. That whole picture is wrong from start to finish and it can't be fixed by juggling with the pieces here. As I say there's room for flexibility but Kripke is going to argue none of this is right. Ordinary proper names don't have their references fixed by descriptions at all. And one key example he gives you really have to read the whole thing partly because it consolidates everything we've done so far. That's kind of a sound effect. Here comes the causal theory of reference. Okay here it comes. And here's a key example. Kripke gave a very influential example. Goudal, Kurt Goudal, very famous as discovering the incompleteness of arithmetic. Proving that for any finitely stateable set of axioms for arithmetic there are going to be truths of arithmetic that can be recognized to be true but not provable in that system. Okay so we all know who Goudal is. He's a person who discovered the incompleteness of arithmetic. So can we say that that's what fixes the reference of the term? Well and Kripke outlines this picture in which Goudal who was of course an unassuming and immensely distinguished Princeton mathematician. Goudal and Kripke's picture may actually have been a somewhat desperate character. Here he is. And you can see what Kripke means. These are his accomplices in I guess early mid-century Austria and suppose that someone else, not Goudal, was the brilliant and hard-working mathematician who carried out the proof of the incompleteness of arithmetic. Goudal never did any of that stuff. Goudal was simply the brains of the outfit that stole this other guy's discovery. You can imagine that scenario. As Kripke says in the lecture I hope Professor Goudal is not present. So Schmidt let us call him the guy who did prove the incompleteness of arithmetic. Here's Kripke's scenario. Suppose Goudal was not in fact the author of this theorem. A man named Schmidt whose body was found in Vienna under mysterious circumstances many years ago actually did the work in question. His friend Goudal somehow got hold of the manuscript and it was thereafter attributed to Goudal. So this is the scenario. Poor old Schmidt lying there somewhere on the rain swept street. It's still true that even if Goudal did not discover the incompleteness of arithmetic the name still refers to the person who stole the credit. So for any set of descriptions you could get this phenomenon. You could get this with Aristotle. Maybe back in Athens Aristotle actually stole the credit for all those works. It makes perfect sense to say perhaps Aristotle was a fraud. Perhaps Goudal was an imposter. He passed himself off as having done this proof. That makes perfect sense. The story is not a contradiction. When I describe the scenario to you I am not describing a scenario in which it turns out that Goudal was not this man. Goudal was actually Schmidt. If you see what I mean. Was that a bit too fast? If you assume that the name Goudal has its reference fixed by the discoverer of the incompleteness of arithmetic then in this scenario what you're discovering is that Schmidt was Goudal. But that's not right. What you're discovering is that Goudal was a fraud. Was that too fast? Okay so if I've explained that correctly that should be completely obvious. But if that's right then it really is over for the description theory of the way names get their references fixed. You can always find out that the set of descriptions you associate with a name they do not actually apply to that person. Those descriptions got associated with that person as a result of some error. Kripke goes on to point out that it's also true and it's almost trivial really that point we've made many times before in this class that if you take the descriptions most people associate with a name they may be wildly off the mark. Maybe most people do think that Einstein insofar as you've heard of him at all was the inventor of the atomic bomb. That does not show that Einstein was not the guy who showed that E equals MC squared. That just shows that the reference is not being fixed by those descriptions that are popularly associated with the name. One other example people say there are people who say well after all those shape plays commonly attributed to Shakespeare they were not written by Shakespeare. Shakespeare stole the credit for them from Bacon. But when people say this they are saying Shakespeare was a fraud. They are not saying Shakespeare was Bacon. As you can see these are two quite different people. One of them wears a hat. You see what I mean here we have two people one is Shakespeare one is Bacon. If Shakespeare stole the credit for Bacon's plays so that the descriptions we commonly associate with the name Bacon. So that the descriptions we commonly associate with the name Shakespeare are all in fact true of Bacon. That does not show that Shakespeare and Bacon are one in the same person. They are two different people. It's just that we associated all the wrong descriptions with the name Shakespeare. Therefore the reference of the name Shakespeare is not being fixed by the set of descriptions we associate with the term. The reference of the name Einstein is not being associated with the totality descriptions we associate with the term. The reference of the name Gödel is not being fixed by the totality descriptions we associate with the term. Here ends the first lesson. Yes that's right. I think that is Charles idea. You could always cast off some of the descriptions but the whole boat could still keep a float. Something like that. Yeah but the thing about this Gödel example is that Gödel is quite well known in academic life. Are you quite well known anyway. But that's the stuff that everybody knows about him. I mean there isn't anything left. You see what I mean there isn't anything left to fix the reference. Once you've thrown out those descriptions these are the central things we know about Gödel. On any kind of picture where you have some kind of weighted vote being given to a whole bunch of descriptions anyone would say these mathematical discoveries they're the most important things. Or if you take Shakespeare you know very little is known about Shakespeare's private life. The vast majority of what's known about Shakespeare has to do with him writing those plays. So the thing about these examples is there's nothing left when you subtract those descriptions. It's fair enough for some to say I can juggle with it a bit at the fringes. But this is the center. Yes you could find a new description that's right. But the thing is that is coming after the fact. What's happening is that you've got the name. You've got the object. You've got the connect between the name and the person. You are discovering that all the descriptions you associated with that name are not true of the person. And now you say well what is true of this guy. Well he was a fraud and so on. And so you now revise what descriptions you associate with the name. That is what would happen. But the point is that that's assuming that there's a connect between the name and the person that has been set up independently of the descriptions. And that connect is what you're using to drive which descriptions you associate with the name. Yeah. Yeah. So the description that actually applies to Bacon when I say Shakespeare, am I talking about Shakespeare or am I talking about Bacon? Right. Well on a description theory you'd be talking about Bacon. But the claim is that's the wrong answer. You're still talking about Shakespeare. I mean Shakespeare is Shakespeare. Oh you see what I mean. That's the argument anyhow. That's very important I think. Yeah. I mean if you say Shakespeare. I will actually give some examples of that kind. But if you said look Shakespeare was born just two months ago. Yeah. He's only two months old. He didn't write any of those plays. Yeah. Then is it really the same person? Yeah. That is a point for the description theory. I agree. And I agree to that Kripke's formulations leave it wide open that that possibility could be correct. That you could still be referring to Shakespeare even though you were so wildly wrong about the descriptions. And that doesn't seem right. Yeah. The positive thing that Kripke does get though is if you say I mean how should I put this. If you say well I've got to get there are boundaries right. That's the way of putting the point. There are boundaries. So it can't be kind of lived all that long after the time we think he lived at. He kind of lived all that long before. If you said well Shakespeare was born on the stone edge. They couldn't even write then. I mean he couldn't have written those. But yeah. That's the same thing.  I mean the animal. Yeah. Then you say well those boundaries around which descriptions could be true. And Kripke doesn't really have any place for those boundaries around the descriptions that can be true. But it seems right that there are boundaries. But just saying it's got to be something within those boundaries isn't going to be enough to fix a reference. Yeah. That's the key thing that Kripke still has. Yeah. So I think your point is extremely important. But it doesn't bring back the description theory. Yeah. It says there was something right about the description theory. Yeah. One two. But we should move on because I will try not to talk for so long. But we should move on after this because I will have to stop a little bit carry on. Yes. Yes. Yes. Yes. Well I mean even with family members I mean this is more like a kind of spooky movie. But even with family members can't you imagine this kind of thing. I mean your best friend says do you remember that walk we took when we were children by this by the cliffs at the seaside so long ago. And you say yes that's one of my most precious memories of you. That's right at the heart of the cluster of descriptions I associate with your name. And he says nah that was my evil twin. You never knew about my evil twin. You see what I mean.  You see what I mean. I don't want to make your spine tingle too much but these things could happen. Yeah. You could make those discoveries. Yeah. You still be talking about that same person despite all these revelations. Yeah. That's the idea anyway. Yeah. Last one. Is it is not quick. Okay. Let's talk about it later. Yeah. Okay. So Kripke's positive picture. Well one way to think about what Kripke's positive picture is is to think about photographs. So I have here a simple goose. I think that is a goose isn't it. Yeah. It's a bird. Let's just call it a bird. Right. I have here a picture of a simple bird. Now there are tons of birds quite like that. Yeah. So let us ask the question for photographs. You can ask the question that's parallel to the question about reference. Which thing is this a photograph of. So there's a question of often is for photographs as of the question of reference for signs. Yeah. So you can think well one theory you could have that would be like the description theory for photographs would be that a photograph is a photograph of the thing that is most like it. So in order to find which bird this is a photograph of what you'd have to do is go among the geese and discover the one that looks most like that. So that's what you might call a match theory of who the photograph is of. But of course that is not actually how we think of photographs. I mean it could turn out that around this goose this has taken a civic center in San Rafael where there are literally just thousands of geese all very similar. And let us suppose that among all those geese there is one that is just a bit lighter than this one. But because of some mistake I mean some error in the photo processing this coloration here is a bit lighter than the coloration of the bird that was actually in front of the camera when the picture was taken. Was that too fast? No it was too slow. So let us suppose there is a goose there round about that matches the photo better than the one that was in front of the camera when the picture was taken. That is not sure that this is really a picture of that other goose that I never ever pointed the camera at. Do you see what I mean? If you ask what makes a photograph a photograph of one thing rather than another what matters for it being a photograph of one thing rather than another is that that is the thing light bouncing off which entered the camera lens and affected the camera sensor resulting in the production of this photograph. So it's the thing that was causally involved in the production of the photograph. Some goose that matches this photograph very well but was often the other side of the planet that's not the goose that this photograph is of. Photographs are of the things that were causally involved in the generation in the production of the photograph.  I look much better than that you think. That's not a photograph of me. I mean if the match theory were right you could actually argue no that's a photograph of someone else because it doesn't match me at all. You see what I mean?  So I think that is similar to Kripke's picture of how it works for names that Kripke's picture is what goes on in the fixing of the reference of a name is you have some kind of initial dubbing I would call this child Saul and then you have a chain of communication in which gossip news information about that person circulate in a community. You got a chain of communication in which the name is passed from speaker to speaker. So what happens with you is that there's some kind of initial dubbing of you and then as time goes on you radiate information about yourself into the community. Your name gets passed from person to person and there is a cluster of descriptions connected to you but what matters is not who best matches the cluster of descriptions what matters is how that cluster of descriptions was generated in the first place.  So what matters is how the cluster of descriptions is generated not what best matches the cluster of descriptions. That's like the case with photographs it's not what best matches the photograph it's how it was generated how the photograph was made. So you get two stages here you get the initial baptism in which you maybe point to the thing or you say by description I'll call my oldest son Saul you get that initial description in which you say low Saul or whatever and then you get the second stage that chain of communication and there is a question what's meant by chain of communication here and after all if you say what he's got in mind here is something like this you got the talk of he's talking about the physicist Feynman suppose the name is spread from link to link as if by a chain and a certain passage of communication reaching ultimately to the man himself does reach the speaker the speaker is then referring to Feynman there's been that chain of talk from the initial dubbing through to you or I right now and then we're referring to whoever it is that was at the other end of that chain even though you or I can't identify them uniquely a chain of communication going back to Feynman himself has been established by virtue of his membership in a community which passed the name on from link to link is not that sitting alone in your study you said I suppose out there somewhere or other there is a great physicist I shall call this guy Feynman that's not what happened that can happen but it's a very unusual case usually what happens is there's just that actual chain of talk that you pick up on there's stuff going out there in the in the community that you pick up on and using the name and you are referring to whoever's at the end of the chain that you picked up on think of the cluster so you can think of the cluster of descriptions circulating in the community around Feynman as a kind of communal photograph of Feynman so if you take cells dossier of descriptions cells cluster of descriptions think of that as a kind of communally generated photograph it might be a it might be a good or an imperfect photograph of Feynman but that cluster of descriptions is like the content of the photograph and what you're talking about when you say Feynman is not whoever best matches the photograph what you're talking about is whoever was causally involved in generating that cluster of descriptions so that's where the cluster of descriptions the idea that description six reference turns out to be completely wrong not just wrong in some point of detail but completely wrong yes that's right yeah I mean this is this is the tragedy of life right that people can spread rumors about you they're still talking about you yeah that's what's so terrible about it the stories I could tell you but you see what I mean and when people sue because somebody's spreading scandal about them you can't defend yourself by saying well the description didn't fit so naturally I was talking about someone else see settle you see what I mean it doesn't work like that yeah uh one yeah yeah yes right absolutely true I mean the problem of informative identities does not go away it just gets worse on this picture and I'll bring this out towards the end I mean that's really why I started out with it it may seem like a kind of well surely you can handle that kind of question right at the start but actually the depth of the problem just gets more and more apparent the more you think about these questions so I'll come on to that at the end but you're completely right unicorns yes right right the yeah I mean you're aware that there are no unicorns yeah I mean that's the way you you put it by saying and the unicorns started all off there was no unicorn right right right good okay right I'm sorry okay but you're right the name unicorn seems to have some kind of meaning even though there's no causal chain going back to anything yeah how did that happen yeah that's a question yeah that I mean the two arguments for the description theory were one informativeness of identities and the other meaning without reference yeah so this second question meaning without reference is also a hard question and I don't actually want to suggest that it just goes away the question how a name like unicorn kind of meaning even though it doesn't refer because there is no just as you say there is no causal chain going back to you we've got an account of meaning here that doesn't doesn't on the face of it explain that yeah so I don't want to try and close off what you're raising there with a sound bite there isn't a way of closing that off with a sound bite that is still a hard problem yeah yeah right yeah yeah okay someone's gonna capital P in capital right right right yeah okay these are two different cases right with it with the pink hat example um well I assume the girl exists it's not just a kind of joint hallucination right you yeah that is another case but in that case you write it's not her agency it's not that she collaborated in this but still light was bouncing off her hat and hitting you retina you see what I mean she was causally involved even though she wasn't deliberately bringing it about so causation here is not the same thing as intentionally making it happen yeah um but the the other case of yeah the artist who imagines something or yeah someone with an imaginary friend right we've all got them they've all got imaginary friends right and you give them a name right so so what is going on there um well I don't want to just shut up the question so let me say something brief about this in the unicorn case and one thing you might say is well in an imaginary kind of case what you're doing is after all you're imagining that there is an object there you're imagining that you are causally connected to something out there um so you're imagining that the name has a meaning in the case of unicorn or in the case of uh the imaginary friend the thing the name doesn't really have a meaning but you've just invented a kind of vivid game in the context of which you're imagining that there are all the trappings there the object the causal chain in virtue of which the name would have a meaning there's a pretty radical view but it says in talking about unicorn or talking about imaginary friends always involves some element of pretence or make-believe and it's only in that context a pretence or make-believe that you can have the idea of meaning or reference at all in these cases it doesn't really mean anything we will discuss this further okay okay um but they are both these are both just the questions to be raising the informativeness and the non-existence okay um I see now that I think of it you guys are actually anticipating everything I was going to say uh so let me um let me shape up to the example that was given earlier of uh Shakespeare being a French peasant or whatever it was not every sort of this is Kripke not every sort of causal chain reaching from me to a certain man will do for me to make a reference and it says you can hear a name and decide to use it to refer to some unrelated object and after all you get your new puppy you say what do I call a puppy and you say Wittgenstein what a great name for a puppy um you can do that and that's causally connected to your knowledge of the philosopher right that doesn't mean that when you say Wittgenstein needs feeding now or whatever that doesn't mean um isn't isn't Wittgenstein adorable um that doesn't mean you're talking about the philosopher even though there is a causal chain going back from your use of the name to the philosopher yeah um so what's the right kind of causal chain some kind of causal chain of the right ones and some are not um and Kripke addresses this by saying when the name is passed from link to link is it as you go on in the chain the receiver of the name must I think intend when he learns it to use it with the same reference as a man from whom he heard it so this is like um that would block the Wittgenstein case because when you use Wittgenstein as a name for your dog you don't intend to use it with the same reference yeah but let me give you another couple of examples this is an example that was actually suggested to me by someone in this class a little no this when I was teaching this class a couple of years ago um suppose you have a family this is kind of a heart-rending story but suppose you have a family with a beloved dog dear old lopured spot yeah greatly loved by the children greatly loved by everyone and one day the family is out and the father comes home alone and what should he find the poor old spot dead as a doornail and he is concerned about the tragedy he's concerned about how upset everyone should be so he um takes a shovel and um disposes of spot um and very rapidly finds another dog that looks exactly like lopured spot and gets the new dog in place before the rest of the family return so over the years what happens here is everyone talks about spot all the way through no one else realizes there's been that unnoticed substitution so when this is not like the I'm going to call my dog vittgenstein case this is a case in which everyone intends to use the name with the same reference all the way through now just think about what happens in this scenario and at first when the children come back and say spot dear old spot if they knew the truth the right answer would be that's not spot that's an imposter right yes I mean you know that would be kind of a stubborn reaction but that would be true yes okay but now suppose that um uh suppose that the new spot lives with the family for another 15 years they have many happy memories of all their rumps together those evenings by the fireside um all that stuff at the end of the 15 years when people say spot which dog are they referring to the new dog or the old dog I mean at that point it seems absurd to say well spots an imposter what they're talking about is is a new spot even though they intended to use the name with the same reference all the way along the reference has shifted so even though there's a causal connect back to the original dog and even though they intended to use the name with the same reference all the way through by kripke's lights the name should be referring all the time to the original dog but it's not here's um suppose you just came upon this suppose that you work on ancient philosophy I mean guys I mean my impression is it with the pre-socratics you very you don't get the pre-socratics you don't have books by them right you don't have even have articles you just have these fragments that are found right so suppose you think a lot about pre-socratic philosophy and then you come upon this passage there are no unknowns there are things that we know that we know there are no unknowns that is to say there are things that we now know we don't know but they're also unknown unknowns there are things we do not know we don't know and you think that's that's wonderful um that's probably about 3000 bc um this yeah and and you think I what a discovery and you see the author's name is rumsfeld and you think okay this is a pre-socratic philosopher all right um and fits right in and you speculate about his influence on Aristotle um you you wonder whether it's which community he was really central in right that's what you do with a fragment like that right so um you speculate that this was a pre-socratic epistemologist um laying down some of the foundational thoughts for um uh succeeding centuries of epistemology um now I mean this to be like the Shakespeare example earlier that if that's what you think rumsfeld is right if you think rumsfeld lived um thousands of years ago at the dawn of uh philosophy are you referring to the politician so is this the guy you're referring to if you say I think he was a pre-socratic philosopher um this is the same point really I mean you were ahead of me that um there's that box of limitations on how far wrong you can be in the description you are you are if you pick up the name here you are intending to use the name rumsfeld with the same reference as everybody else you know it's not like you're just calling your dog rumsfeld you mean to be talking about the same thing as everyone else but you just made up such a radical mistake that it seems crazy to say you have very unsound views about um uh a late 20th century politician you see what I mean it seems crazy to say that that's who you're referring to so this is the same point that there are limitations in how far wrong you can go in your use of descriptions and that this oops sorry got the wrong way this two-stage thing of um kripke's passing the name from link to link with the intention to preserve reference that's not enough there's some other kind of conditions on what the right causal chain is okay let me um finally I just quickly want to go through um uh a quick point of contact between um russell and kripke remember russell's got this picture there's that basic class of names that is getting hooked up to the objects but not by means of descriptions and is connected to your sense data or whatever and um you say well uh uh that read doesn't sound right that gives us this picture on which each of us is fundamentally alone trapped behind our sense data unable to think and talk ultimately about anything else couldn't you keep a picture like russell's where you have a basic class of names and they refer but they refer to concrete objects to things in the public world to things in the shared world on the face of it that's just what kripke is doing kripke's names like when kripke's talking about names is just like russell in that he's saying these are not defined in terms of descriptions I mean you see I got a basic class of names here and the connect to the world this was driving the rest of language well the causal chain is what is hooking up the names to the world and driving the rest of language that seems like a better clearer answer than russell's as to what the basic connect is between names in the world is not us associating descriptions it's the stuff out there causing the use of the symbols by us it's really not a matter of convention or anything like that this I mean if you think of um what a photograph is of that's not a matter of us laying down conventions about what we take a photograph to be of who a photograph is of is just an objective fact about the way the world works or what the causal connections are in the world it's nothing to do with um conventions we lay down and similarly for his basic class of names and what fixes reference is just this objective fact about the way the world causally operates so you could think well russell's acquaintance you should really just russell should really just have interpreted that in terms of a causal connection because it's through a causal connection to the world that you get knowledge of what's going on around you that basic impacting on you by the external objects that's what makes um thought and talk possible the things that are impacting on you um it's not a description theory make it seem as though what goes on in your head is kind of autonomous from what goes on in the world you just dream up all these conceptions as to what's out there and then the question is does the world shape up does the world match up but this picture turns out in its head and says what's going on in your mind is driven by what's out there oh i should have warned you if you're not if you're of a nervous disposition you might want to shut your eyes okay so okay here we have here we have one um unfortunate individual here we have another individual not so unfortunate are they the same is this person that person well suppose i told you that's one in the same person is that an informative identity i mean not at the same age of course is that an informative identity yes that was highly informative right um and what it's saying is not whoever meets this description meets that description what it's saying is whoever was at the end of the causal chain generating this photo was at the end of the causal chain generating that photo yep that's a comments informative because these are two different photos two different things causally generating them yeah that's all right yeah that's how that's a comments informative but now consider these identities if your nerves are up to it um now consider the identity of this person with this person is that informative aha why not because i agree that this photo matches this photo the descriptive content of this photo is the same as the descriptive content of that photo but that doesn't guarantee that they're one in the same suppose they are identical twins one could have been at the causal chain at the end of the causal chain generating one photo the other a moment later at the end of the causal chain generating another photo i mean we're so sensitive to humans that you might think well it's very unlikely they'd be just so similar but it's not impossible and if i did it with geese it would clearly be possible yeah so this this identity isn't um uninformative because it's not a priori or analytic that what was causally involved in generating this image was causally involved in generating that image and similarly here i mean that after all might be two different people on different occasions they just posed exactly the same way so neither of these identities are uninformative but then once you think well that's to be a causal theorist in general thinks about identity how could you sorry thinks about reference how could you ever get an uninformative identity on a causal theory of reference on that point you know i i have i have to go okay thank you


Thank you for joining us for the 2021 Distinguished Engineering Alumni-Alumni Awards. I'm Cole Heald, a junior from Fortville, Indiana, majoring in mechanical engineering. As an impact maker, the opportunity to serve as president of the Purdue Engineering Student Council, PESC, has fostered my persistent pursuit of the next giant leap by developing my leadership abilities and opportunities to connect with faculty, industry, and students. In this role, I also collaborate with other groups on campus, advocating for student well-being and mental health awareness. In addition to PESC, I serve as a troop mentor for STEM Power, providing hands-on STEM activities for local girls. And I'm a member of the College of Engineering Grade Appeals Committee, which is the final authority in any disputes about final grades among professors and students, especially in the case of capricious or inconsistent grading. Tonight, I'm honored to introduce the 2021 Distinguished Engineering Alumni Award recipients from the School of Mechanical Engineering. I begin with Angela Barbie, an international engineering executive and champion of diversity and inclusion in STEM. Her impressive 23-year career at General Motors culminated as director of global creative design operations, leading five business units, a $400 million budget, and 120 team members in North America, China, Korea, Australia, Brazil, and Germany. An engaged boiler maker, Barbie was the GM engineer in residence in mechanical engineering for a year, teaching three courses, mentoring students, and growing collaborative research and cultural understanding between GM and Purdue. Moving from cars to kitchens, she joined Kohler Company as the vice president of advanced development for global kitchen and bath, innovating in water and health care technology, smart home, and sustainable engineering. In her persistent pursuit of the next giant leap, Barbie recently moved to a new role with Weber Grill in Chicago, serving as the senior vice president of technology and global R&D, where she will make her mark for the world's premier manufacturer of charcoal, gas, and electric grills. She has been featured in the publication Driven, a tribute to African-American achievements in the auto industry, and the Engineering or Future exhibit at the Chicago Museum of Science and Industry. Recognitions for her career achievements include the Black Engineer of the Year, corporate promotion of education, and the Career Communications Group Hall of Fame. For her visionary leadership in business management, product development, and manufacturing expertise of large engineering enterprises directly impacting consumer products while advocating for diversity and inclusion within the STEM disciplines, the College of Engineering is proud to present the Distinguished Engineering Alumnus Award to Angela Barbie. Next I'd like to introduce Juan Manuel Debedu, an industry leader in long-term energy and aerospace technology strategy who holds more than 10 patents. After obtaining his master's degree, he set aside industry opportunities in favor of obtaining a Purdue PhD, not because the path was necessarily better, but because it provided a broader set of options to choose from. Doctorate in hand, he joined General Electric Global Research, Central R&D Division, subsequently becoming the youngest global technology leader for power conversion systems, developing advanced technologies including a $27 million wind turbine partnership. His proven track record in shaping strategic technology planning drew him to Raytheon Technologies, the third largest aerospace and defense company in the world. In his new role as the Vice President of Aerospace Technologies, Debedu's persistent pursuit of the next giant leap will take him in the direction of leading the development of electric propulsion systems for the Air Lander 10 airship, aiming to deliver the hybrid turbine system within the next three years. He received the Alumni Association 40 Under 40 Award, the Hispanic and Technology Award from the Society of Hispanic Professional Engineers, and was recognized in Diversity MBA Magazine's Top 100 Under 50. For his established success in shaping business strategy and driving development across diverse product lines, creating impactful innovations in the energy and aerospace industries through long-term technology roadmaps and strategic and transformational industry segments, the College of Engineering is proud to present the Distinguished Engineering Alumnus Award to Juan Manuel Debedu. Our final recipient is Manohar R. Shah, who chiseled his dreams from startup to global enterprise. While an engineer international harvester, he and his wife, Rika, founded the Natural Stone Company MS International Inc, MSI, out of the basement of their Fort Wayne, Indiana home. In his persistent pursuit of the next giant leap, Shah revolutionized the hard surface industry by recognizing an opportunity in commercial and residential use. He pioneered the importation and sale of natural stone in the 1970s, turning the business into a global leader by expanding into porcelain and other materials. Today, MSI has 2,000 employees, more than 25 distribution centers in North America and Canada, and Shah has helped create more than 130,000 jobs across sourcing locations in India, Brazil, China, Turkey, Italy, Spain, and Mexico. Shah realizes dreams aren't reached without hard work and robust development of rural areas. This helps people from having to move to overcrowded cities to make a living. He established the Shah Family Global Innovation Lab in Engineering, providing grants to sustainable technology businesses in developing countries. The lab will be a place of learning, exploration, and problem-solving with international impact. For his remarkable success in modernizing the natural stone industry by establishing MS International Inc as the first nationwide distributor in the United States for residential and commercial usage, and developing diverse supply chains across several continents as well as introducing RFID, imaging, and data science into the hard-surfacing product sector, the College of Engineering is proud to present the Distinguished Engineering Alumnus Award to Benoher R. Shah. To learn more about how Purdue influenced these honorees, please click on their video link below. Boiler up and hail Purdue.


Okay, today we're going to be solving the hidden message I posted a couple days ago on July 2nd and again, I'm trying to make these progressively harder and Luckily there are a handful of people who made it through this secret message at the time of recording this So let's go ahead and look at the original video and it's a video of me talking Maybe hear that in the background there are some modem tones And I am talking about dial-up modems and I make a hint towards modem speeds of 56k Which I even said in the video it was a hint so at this point you're going to want to you know Download the video and figure out how to decode the modem tones So if you don't know how to do that you can always use Google and I'll type in Linux Decode modem tones and the first thing that comes up is a program called mini modem Which should be in your repository? so use your package manager to install that and it allows you to generate and And decode Modem tones there's lots of protocols if that's the right word out there for different types of tones one I've used in the past called psk 31 The reason I went with mini modem over or something like psk 31 or the other ones is just because this is a nice Shell option all the applications I know I know three or maybe four that can do other tones like the psk 31 But they're all gooey's and that's just overkill for what I'm trying to do I want to just pipe my message in to generate it and then hand it a file and have it decode it so mini modem is what I went with and That's how you figure that out another way you might have figured it out Although I don't anybody knows this is if you look at the source code for that YouTube video There's a section called keywords, which is what's I have the tags I put for that video and the tag I put for that video was mini modem I don't know where else you can see the tags for a video But right there and I'll tell you that might come in handy in the future knowing that Because I might put other information there again little tips and stuff for some videos But mini modems the program we're going to use Let's go to our shell here and the first thing we need to do again is download the YouTube video So I'm going to use YouTube DL and pass it the URL for that video It's only a 20 second video should so it should download pretty fast now at this point We know we want an audio file because that's what Mini modem is going to take and I'm just going to take the video and create a wave file out the audio of it So I'm going to say ffmpeg. I'm going to give it the name of our video file And I'll just call the output m.wave now we have a wave file now I can say mini modem and We're going to do dash dash rx for receive when I'm generating it. I used dash dash tx dash dash which is transmit and our T our x is received and The part in the video I talked about 56k modems that through some people off some people were putting 56,000 I want you to put there was 56 We're going to do dash f for file and give it our file input, but this will not work when I hit enter It tells us that your stream must be one channel not to our video file is a Stereo and this program only takes mono not a problem at all. We run the same ffm Ffmpeg command, but I'm going to add dash ac for audio channel and do one and that will give us a mono file It will ask me if I want to override the last one I'm just going to say yes, and now we have a new wave file and we can run that same command again it takes that audio file and These lines of the pounds you can ignore the standard output is just this year Which is a song by me and it just tells you to go there and comment So that is how you decode that now 56k is or 56 is just one of the rates Which is rather slow. I actually again time this out So it'd be a 20 second video approximately for this message, but the same message could have been transmitted in Probably about a second by upping that number letting you know that because that may happen in the future Well, I'm trying to think of anything else. I wanted to say about this, but that's pretty much it I'll post another hidden message video in a couple of days. Oh, I do want to give credit to the people who made it again I'm posting this I'm recording this video a little earlier than I'm posting it, but at the time of recording it Well, let's go to the video where they were supposed to go We'll pause that and So these are the people that made it there and they have explained they all pretty much the same thing Oh another thing I wanted to say is you might have been confused in the video. I'm talking and you have the modem sounds Some people thought that maybe one was in one channel and one was in the other But no, you can talk over these audible type channels And as long as you're not making too much noise or a staticky noise You it should decode of course I test these before I make them public. So I knew that it worked So no, you didn't have to remove the my voice at all. This is true with a lot of these tones So what's great about these things is a little more information here Dial up modems for those of you remember the noise and it's actually the tones It's like your modem was basically like an audio card listening to that and decoding them similar things again I said like PSK 31 There's a lot of other formats out there that are used in ham radio for sending messages So you can chat with people that way But another example is if you pick up any of your credit cards or any type of swipe card with a magnet strip I've mentioned in previous videos those magnet strips in the back are the Basically the same exact thing as a cassette tape and actually if you pull the cassette tape head the reader out of a cassette tape player You can actually read the magnet stripe on cards like that and you'll hear tones And then you just need software to decode them you can actually find software out there That will decode cards like that. So that's a little more information on there. I hope you enjoyed this video I hope you learned something. I hope you look forward to the next in video again I'm having fun making these. I hope you guys are having fun trying to solve them and You know it takes a little bit, you know of figuring out But once you know what you need to do, it's usually only a couple of steps I don't do anything. We're it's gonna take you hours to decode something You just need to figure it out But look for those hidden messages in the videos and as always I thank you for watching and I hope that you have a great day


Welcome back everyone here live in Palo Alto, California, and John Furrier, Dave Vellante in our studios for the VAS Presents Build Beyond. We are here with the CEO, Rene Halleck, co-founder and CEO, great to have you. Thank you. Congratulations on your success since 2019, VAS data, doing all the hard work, announcing the data platform. Really impressive, big idea. It is. This is actually an idea that we had a long time ago, but it was too early to reveal it to the world, and so we wanted to do it in phases. We had just had the keynote announced with all the experts on really kind of breaking down kind of the story. There's a lot going on here in the platform. What was the inspiration? What was the vision? I think the inspiration was always artificial intelligence. Even before we started building the storage system many years ago, it was all about how do we give fast access to a lot of information to enable these new algorithms and these new applications. And this is one more step that we make in that direction. So, everybody now saying, oh yeah, we saw this coming, and I kind of have, you have a little more credibility because what you've announced is you can't just invent that overnight. But yeah, two questions. One is, you launched the company formally in 2019, but I look at what you've built and all the functions and the services and I'm like there's no way they could build that in three or four years. So you obviously started before that.  And you didn't know that it was gonna be the AI heard around the world and chat GPT, you didn't know that. So what did you know that gave you confidence to basically give up everything and start this journey? We didn't have confidence. We didn't know that it would work. We didn't know that this path was the right one, but we thought that if it was possible to do the things that we presented in this keynote, it was worth a big bet. And I remember we didn't start the company in 2019, we launched the first product then. We started working on this beginning of 2016. And if you remember at that time, the most AI could do was find cats on YouTube videos. It was a big leap to think we can build something that will enable what we have today, let alone what we see in the future. We did it because we thought it was important and we took inspiration from others that came before us. I remember seeing a video with a gentleman by the name of Danny Hillis. He built the original thinking machine. And he said, we wanted a name that we would never outgrow. And so we wanted a mission that we would never outgrow and we're on that mission. Okay, so thinking machines, learning systems. How is your vision of AI different than what we're seeing today? Which is probably a lot of pattern matching, maybe a lot of statistics. Can you explain for the audience sort of the difference in terms of how you see really the potential of AI versus what we're seeing and touching today? Yeah, today I think we're, well, first of all, a lot further along than I thought we would be in 2023. And definitely a lot further along than we were when we started, but it's still not real intelligence. You can tell that today's systems are parroting back information from the internet to us in a very natural form and allowing us to ask questions in a natural form, but they're not generating new ideas. They're not creating new things just yet. And we hope, we think that that's coming and that they can help us solve the hard problems. Yeah, I mean the interaction, obviously it's amazing, but you're right, you can basically game the system and tell it to tell you something and you can control it in a way. And in a way that's good, but it's not as much sort of independent machine thought as you would expect. And so it's interesting you say you're further along. When you say we're further along, you mean the industry is further along. Yes. And so you would expect that, do you expect that pace of change to continue to accelerate? I do, I do. As more people see that something is possible, they try it and they innovate on top of it and that acceleration happens just from that. And when you listen to Ilya from OpenAI talk about, it was like an epiphany. And they underestimated the importance of scale. And then once they figured out how to program the GPUs and they'd be able to ingest all that data, the scale and vision became groundbreaking. What do you see as those sort of groundbreaking milestones ahead that will enable these types of learning systems? So everything that we did from day one was centered around scale, around performance at scale, resilience at scale, ease of use at scale. Our entire architecture was meant to break that scale versus everything else trade off that has existed in storage and data systems forever. And that is the reason we did that. We saw that with more information, you turn from a quantitative advantage to a qualitative advantage and new abilities emerge as you give these systems more data. And so we built this for not just exabyte scale but beyond that. And today, four and a half years after we launched the company and have a lot of exabytes on the platform, we're seeing that that scale is not just theoretical, it actually works. You guys have the databases in there. You got the data store, front structure, database for structured. The data engine and the data space is interesting. And I think those are unique things that jump out at me. When did you kind of know you had it, had the right answer here? Because I think this is one of those things where you got the right answer for AI generation deep learning. But now the operationalizing is going to be there. So first of all, great customers, Pixar, large scale, but they're agile. They're actually making product improvements. Their applications is the movie and they're doing more. We heard that and we saw the science side of it. The producers on the creative side is increasing. So we're seeing now this next gen application with large scale data. When did you know you had this answer right? When did it kick in? I don't know that we have the answer right today, but we're definitely I think in the right direction. And what we do, our process is a very simple one. We talk to our customers and we ask them, what do you see, what do you need? They help us see around corners because they know where their challenges are. And what we try to do is listen and aggregate. And when we hear the same problem over and over and over again, we ask ourselves, how can we leverage our unique architecture to solve it in a better way than it was solved before? And this data space idea came from a lot of media and entertainment customers initially where they have teams all around the world and they have production and post production and editing and they need that collaboration across very, very large data sets and they had a challenge to solve. And when we figured out how to do it, we came with that to a lot of other industries and we realized everybody wants a single global namespace across cloud and edge and device. We have autonomous driving car companies. They want the cars to be part of that namespace. We have genome sequencing companies. They want the sequencer to be part of the namespace. In effect, it gives computers access to the natural world, direct access unfiltered. And I think that's a big piece in the puzzle of having them generate ideas. Multiple cars, part of that namespace because the Byzantine fault tolerance is a problem in fully autonomous vehicles, right?  That's right. And you're envisioning doing that obviously and it has to be real time. Yes, everything needs to be real time. There's no more batch processing. That's a big part of our value proposition. You don't need to compromise on time or on amount of data anymore when you have a platform like this. I love how you guys mentioned the data developer because Dave and I have been talking about it. The first time I've seen anyone use that term in their presentation, so awesome. What do you mean by that? Because we've been seeing this trend. We've been trying to, we just call it data developer because it's what jumps out at us where the developers who were writing code want to use data in their application and enable their applications on their own to work with data, which is kind of obvious on one hand but not obvious on the other. So where do you see this data developer coming from? Who is it? Can you just share your vision on the data developer? Yes, I think all developers will be data developers relatively soon. The paradigm is flipped on its head. We used to have applications and they were at the center and they were writing to storage systems and reading from storage systems and from databases. Now we have data and data flows in and data needs to be manipulated and cleaned and authenticated and encrypted and all of these functions on data as it flows into a system means that the whole development paradigm needs to change and be data centric and we're seeing that happen in these new workloads that we discussed. I think it'll happen across all the workloads. You know, it's interesting, Dave. We've been saying also on theCUBE that cloud is horizontally scalable. Your distributed namespace is a distributed system that's scalable. So you get horizontal scalability but vertical specialization in domains, you mentioned industries are all affected. They are their own linguistics, their own data, their own jargon, that's proprietary. So how do you see that horizontal scalability and vertical specialization? Cause that's where the AI lives and that derives but also they need all that other data. That causes a compliance challenge and all kinds of data management issues. What do you let free and how do you manage it? How do you build that in? Can you share your vision on how you guys achieve that horizontal scalability and that vertical specialism? Yeah, so I think you have two layers of horizontal. The first one is the hardware layer that's underneath us. We partner with companies like NVIDIA to leverage the latest and greatest of what they have to offer. And then you need that software infrastructure layer that makes it easy for the various verticals to focus on their applications and that's where we come in. We think that it's a very similar pipeline that you see across these verticals. We can't tell the difference between a genome and an image but from an underlying infrastructure perspective, you have the same tools and you need the same tools in order to make those developers successful. How do you see your ecosystem evolving? Obviously you've got, you mentioned technology partners like NVIDIA, we were down in HPE in April so you've got to go to Market Partner there and they're OBMing your file system, et cetera. How do you see your ecosystem evolving? As we progress, what we see more and more is clouds and not just the top three clouds that everybody knows and talks about, we see more and more specialized AI clouds and we see the big clouds spilling over sometimes into these more regional clouds in order to provide services to their customers faster and we are enabling those clouds. The software infrastructure layer that they are building their clouds on top. So for example, you could see a financial services industry or a company saying, I'm going to build my cloud, same thing in healthcare, same thing in the industry 4.0, name and industry, energy, they're all going to have their own clouds is essentially what you're saying. They're probably going to be running a lot of their stuff in the public cloud. They're probably going to be thinking about oil rigs. It is the edge, right? Space is another awesome edge. That's right. We have that systems in all of those places today on ships and cars and airplanes out in outer space. This lightweight instance allows us to span the globe. So you're an enabler for those clouds and you'll partner with other enablers of those clouds and those industry clouds will be consumers of your technology. That's correct. So where the customer store all the data? Where is it on drives? Is it in a data centers in the cloud? We heard multi-cloud is in there. Take me through the vision of how this gets deployed. Where is everything set? It depends on the customer. Some customers prefer to have everything very close to the chest. Other customers are more open and want the ability to run on top of multiple public clouds and collaborate with other organizations through that. We're agnostic. Wherever that data is, we will manage it and we will make sure that it is accessible to the applications that need it. I noticed you guys are very standard space. I heard open a lot in the messaging there. How important is that to you guys? Very. I think if we were to try to change the way everybody works, it won't work. We need to make it easy and simple and that is why we support standard interfaces. From the early days, we supported NFS and SMB and S3. Very, very standard on the outside. Very unique on the inside. Same will continue going forward. Right, and I got to ask you a question since you have such great customers and the video was awesome and impressive. Scale jumps out at me. And the benefits at scale, you see when you're there, you really, it's hard to predict what they could be. What do you see from your customers that are at that scale with data, that they're learning, that they're positioned for, what comes out of that value? What's the value that comes out of that? We saw Pixar was a great example. They could reuse and reuse the characters and then even if it gets bigger and bigger, they still could flex on the cluster. They enable them. What are some of the benefits you see that companies get at scale when they're managing data with your system? So it allows them to start to analyze natural information rather than just store it and have people able to access it or analyze numbers and rows and columns of a database like they did before. Now they have full access to everything and GPU access to everything which allows computers to generate insight from information that just a few years ago, it was unthinkable. And I think that scale, if you look at our system, it's three to four orders of magnitude bigger than any other system out there. And it's because of the type of data that we're storing and our customers are now generating insight from it. They're improving their business because they have access to more information. So don't hate me for asking this question but if you had 100 points to allocate toward luck and good, how much is luck and how much is good? Well, I think we work really hard to build our luck but we're definitely lucky to be in the space that we are at the time that we are. I'll say. And we're very fortunate. The AI is a great tailwind and it's a great time. People are looking to store in their data for many, many reasons, so congratulations. In the final 30 seconds we have left to explain to the audience who are curious what is then this data platform about? Build Beyond, we're talking about the data platform, not storage. That's right. It's a system, it's an operating system, it's a platform. What is the VAS data platform? So you see from our name VAS data that we've been thinking about this forever. Storage was the first piece of the puzzle. I think there's a triangle here around storing unstructured data, making sense of it and making it into a database where you can query and understand and then running functions on all of this information, which is the data engine and all of that across geographies will allow things that aren't possible today and that's what we're building. One more quick one because I think I got it wrong. You said the fastest growing in a keynote infrastructure company. That's correct. We've been growing since we started selling this faster than any other company that we know of in history, in this space and beyond that, we've been growing in a very efficient manner. We're not burning through cash in order to fuel that growth. We've been generating cash for the last few years and I think that too is a unique way of breaking that trade off. Well certainly people looking to manage their data is a great time to be in this business and the great platform. Thanks for coming on theCUBE. And great to see you and thanks for coming on and sharing your vision. Thank you. Okay, we'll be right back. Jeff Danworth coming up next co-founder of Vast on theCUBE here live in Palo Alto for the Vast Presents Build Beyond. I'm Jeff with Dave Vellante. We'll be right back.