All right, we've got a lot of stuff to talk about. So it's technically in one more minute, but I think I'm going to get started. Hi, everybody. My name is Van Lindberg. Just to give you a little bit of background, I've been around open source and coding for 25 years. I've been a lawyer for 20 and doing both of those. And so when I come at this, I come at this both from the perspective of someone who has been doing technology a long time as well as doing the law. So let's start by talking about a few of the terms. This is the AI and data track, but this is a legal foundations presentation. So I expect that I've got probably two distinct groups here. I've got probably AI devs or data scientists who want to learn a little bit about the law. And I've got lawyers who probably want to learn a little bit about AI. And I'm surely going to disappoint both of you. But this talk is intermediate because I am going to try and go into a reasonable amount of depth, and that's one reason why I got started a minute early. Both about the tech side and the data side. For anyone who is an AI expert, I've tried to be as complete and correct as possible while creating a description of AI that is suitable for lawyers. And for anyone who's a copyright expert, I'm sure I'm going to be talking about cases that you already know, issues that you're already familiar with, but it's really in the coming together of the both that this becomes interesting. Perhaps at some point AI law will become a well-developed area. Right now it is the Wild West. It changes almost every week, literally, both on the legal side and the technical side. And both sides of those are essential to really understanding what, for getting things right, understanding how to apply the law to these various AI questions. Unfortunately, most legal analyses in this area are either incomplete or inaccurate in the way in which they describe the technical underpinnings of what's going on. And so they come to unsteady or incorrect analogies and analyses. And I see this in law review articles. I see this in lawsuits. I see this all over the place. Also, I want to specify what I'm talking about when I say AI in this presentation. I'm specifically referring to machine learning or ML and more specifically I'm talking about generative ML. That's what you all want to hear about anyway, but it's important to understand that I'm not talking about expert systems or regression or even inference. Those are all fun to play with, but I'm really going to be focusing on the questions that come up with generative ML. I'll also largely be referring only to advances in ML that have happened over the past five years and really only over the past 18 months to two years. Machine learning as a technique is not new. The foundations were laid in the 1960s and working systems were created in the 1980s. What has really changed in the past essentially 18 months is scale and quality. Data scientists have been able to harness the increasing capacity of computers and the explosion of digital content to make sure that to create programs that can rival humans in their output and it's only because of those, because of that jump in capability that we're talking about this anyway. So also for anyone who's interested, if you're really interested in the long version, this is only 40 minutes long and a lot of what I'm covering here, some different, some different, but a lot of it is covered in this upcoming article. What I've got here is a pre-print version and if anyone wants to send me feedback over the next week, I've got one week to finalize it. So, after all that presentation, let's begin. We're going to talk today a lot about models, how we get them, what they are, how they're used. I find that understanding models is really at the core of the problem in terms of people's understanding. When they say the word model, and I'm really talking to the lawyers in the audience, they effectively mean a magic black box that confirms all of my pre-existing point of view. But if we really understand how models work, then we can actually start to apply the correct legal analysis to the facts. So, before diving into the mechanics of machine learning training and models, there's an analogy that I think is helpful in developing a mental model of what, of how machine learning training works. So, imagine a newly hired art inspector that has the job of examining every painting in the Louvre. The inspector has no background or experience in art and he has no preconceived ideas of what's good, what's bad, or what even is significant about any particular painting, what makes a Picasso a Picasso, for example. Without any guidance, the art inspector studies each painting by measuring everything about it, everything. The number of brushstrokes, the paint thickness, the average space between the brushstrokes, the size of the painting, where they happen to sign their name, how many bumps there are in the name. Anything that you can think of that can be measured, he measures it. And the inspector measures these aspects that even seem bizarrely random or unimportant, such as the number of consonants in the artist's name or the relationship between colors that are six inches apart. He just does everything. Nothing is left untouched. And then he records it all in his database. Now, as the inspector studies each painting, he gets a little bored, and he decides to make his job more interesting by starting to play a game. He decides to make each one of these measurements a guessing game. Before he makes a measurement, he tries to predict what the answer will be.  Well, it's a Rembrandt from the middle third of the career, I'd say 87 or, and then he checks the measurement and he sees whether he's right or he's wrong, how close he was. And as he starts, his answers are usually wrong, but as he goes through and studies more and more of these paintings, he becomes more and more correct. And so after studying thousands and thousands or millions of these paintings, this is fictional art inspector, he becomes the world's most foremost authority on validating paintings. He's regularly asked his opinion on who wrote this, who did this? Which, who created painting and the other things about it. And what he does is instead of taking the knowledge about it to predict the measurements, he goes sort of in reverse and takes the measurements in order to predict the other information that is going back. He uses the measurements to predict the painter. So training and machine learning model is very similar to this process of the art inspector. In both cases, the basic steps are the same. You receive an example, you predict the relationship between the various elements of the example, you check the result, you adjust to improve your capability of predicting, and then you repeat, then you repeat over and over again. These commonalities apply to both the mechanical process performed by the computer. These also apply to the mechanical process performed by the computer during the training. But there are some individual points that we should identify. The art inspector analogy is good at showing how ML models start with a clean slate. They don't know anything. In fact, they start with being filled with random data. All the associations that are included in ML models are learned by observation. The inspectors measurement of small random details is also closer to the fixed process that occurs during ML training. But one significant difference is that the inspector recorded all of his particular measurements in his database. The model doesn't do that. It actually records the changing probabilities associated with the various inputs. So in order to build a model, a data scientist begins by describing a logical structure for processing inputs to create outputs. Now each part of the training process corresponds to a different part of this structure. I've got an exemplary structure. There are all sorts of structures. This is just a stylized example. But they usually share three general structures. The input layer, the hidden layers, and the output layer. And each part of these correspond to essentially a different part of the process. The input layer is where the data is provided to the model. This is similar to just the art inspector looking at the painting, making the measurements, doing things like that. But unlike humans who can take in a whole painting at a time, the computer just gets individual pieces of data. They just get individual numbers. The goal of the input layer is just to provide a uniform representation that can be worked on by the other parts of the machine learning process. It's just, from the machine's point of view, it's just a vector of numbers, just a list. Now the hidden layers of the neural network are where the majority of the processing occurs in an ML application. These layers are called hidden because you typically don't look at them directly. They're intermediate processing layers between the input that you put stuff in and the output where you take stuff out. There consists of a dense series of interconnected nodes. Again, these are logical nodes, not necessarily physical ones. And each one of them has an associated probability or weight that it will take one of its inputs, that it will pass it on, where it will pass it on, if it changes it, et cetera. Similar to the art inspector, scientists actually really have no idea what ends up being significant and when it starts investigating a lot of these various, when it starts being trained. It used to be that data scientists tried to specify specific features. It was called feature engineering. It was a big deal for a long time. A lot of that was because we had smaller computers and that made it so it was more efficient to identify specific things to look at. However, isolating the right features was error-prone, took a lot of time and didn't honestly work super well in many cases because you didn't know what was going to be important. So the current trend is just to send it all into the neural network and let the network figure out what's important. So the output is the result of all of the predictions that happen as it progresses through the hidden layers. And it is essentially the final guess that corresponds to whatever this architecture. However, like the input layers, it's just a number. What we do is we essentially apply the same sort of process that we use to take a work and put it into the input layers in order to interpret whatever that number means in order to get it out. And then what happens during training is that you have a known answer. And so you compare the answer that you predicted with a known answer and then depending on how close it is or whatever, you actually adjust all of the various weights in order to make it so that you're more likely to get the right answer the next time. Because this worked backward from the output layer through the hidden layers, this is called back propagation. Then repeat a billion times. So then eventually you're satisfied and you stop training. And at that point, using the ML model is basically like training it, except there's no check and there's no adjust. You just use the output directly. For the difference, by the way, between inference and generative ML is also highlighted here. For inference, basically you take just that one output and you run with it. That's the final thing. Frequently that's actually going to be a question that you're asking about the input that you're looking at. Going to be metadata. For generative AI, you actually take the output and you essentially do things recursively. You apply it to the input as part of the next input and go over and over again. And the result that you get out is the series of outputs that are stuck together. So what is a model? In computer science terms, the interconnected network of weighted nodes is equivalent to a program, a very complicated program that does something. Training is the process of iteratively evolving the neural network's weights, their probabilities, so that it can emulate running this desired computer program. And so the model is the combination of the neural network design and the weights. It is a set of numbers and equations that essentially encode the statistical probabilities about everything that it observed.  It's basically just a big matrix of numbers. It just looks like that, or something like that. It doesn't contain any of the copies of the inputs, even in compressed form. In fact, it's hard to say frequently, although there was, as of last week, an effort to do this using GPT-4. Very interesting, I haven't studied it yet though. What any single probability associated with this means. We can identify some of them sometimes, but there are, when we're talking about these models, we're talking about 768 million different parameters, or four, six billion different parameters, or 135 billion different parameters. For anyone who's familiar with Bayes reasoning, Bayes theorem, it's a way of taking the information you know and making an educated guess of what's gonna come out. This is like a very complicated Bayesian prediction with 160 billion different parameters. What we do know is that through all of this, it has a very complex and nuanced statistical picture of what we humans would think of as knowledge. It thinks it has encoded things about what makes a story a story as opposed to an article, or what makes an 80s pop song, an 80s pop song, or, and it has embedded so much of this stuff that implicit that the models can seem quite intelligent. They are not, they are not creative, they are just the statistical amalgam of all the probabilities of things that it has observed. So, now that I have gone and probably bored half the audience, now I'm gonna bore the other half. Why do I spend 20 minutes or 15 minutes talking about the process of training a model? It has everything to do with correctly understanding the law and understanding how we're going to apply the law to this. Because, as I said, if you think about the model and you think about these machine learning applications as something that reinforces your preexisting conception, it's a magic box, it just does the thing, then you are going to start, everyone who is, who wants to get paid for using their stuff is gonna say, well, this is this thing that what makes me wanna get paid. For those who want to use it, they're gonna say, well, this is a thing that it's completely separate that makes me wanna use it. It doesn't, you're just projecting it as an actually analysis. So, let's look at where are we going to find legal issues? And really, these are going to be the big four areas that you are going to find legal issues. There are many others, but I'm gonna put them into these four categories. We'll start with IP and we'll go through these as far as we can and then we can take some questions. By the way, I'm gonna talk about some generalities. I'm a lawyer, I'm not your lawyer, et cetera, et cetera, et cetera. Now, let's look at intellectual property. This is the thing that is going to come up most often and it's probably gonna be the most interest to the people here. So, when you're looking at applying IP law to machine learning, the most important question you have to ask first is what part of machine learning are you trying to look at? Because training is different from inference, is different from generation. And what about memorization, which we'll get to in a second? So, let's start for a moment by looking at the training process and the legality of training. This is one of those places where some of, depends on your priors, depending on what you already think, but there are gonna be people who, artists, writers, musicians, they're saying, look, you're using my stuff without my stuff, if you would have nothing, I wanna get paid. And that's a very reasonable and emotionally correct sort of way of looking at this. Because they, to a certain extent, they are providing this value. However, this is one of those places where the technicalities of the ML process and the law intersect to actually give a different answer. So when you're looking at the ML training process, what are you doing? You are taking measurements about the works. You're not copying the work itself. You're not copying anything about the work. You are making measurements about the work. You're recording a series of facts that already exist in the world. And one of the important, one of the important cases in Feist, this has been a doctrine for a long time, but it was most recently discussed in a Supreme Court case called Feist, where they argued about the copyrightability of a phone book. And they said, look, facts are out there. Facts cannot be copyrighted. A compilation of facts possibly can be, but you have to have some sort of human input into it. You have to have human creativity. And to a certain extent, they said, well, we arranged it in alphabetical order. And they say, no, no, it can't just be a wrote order. It can't be something that's obvious. It has to show some sort of creteteries, some sort of human intelligence. And a good example is to think about is between the distinction between something that might be copyrightable and not, is the top 10 songs of last year is just a fact. By whatever record, however you would measure the top 10, they just are, they were the top 10. However, if you said the top 10 best songs of last year where you were saying, oh, I'm going, this one was number five actually in this, but it was a hip hop song. I think it should be number one because it was the top ranked hip hop song. And then number eight was EDM. I don't like EDM, that drops off the list. That shows some sort of creativity that comes from the human, the human inside the human. And so that is the copyrightable element. Now let's look at what are the arguments that they would have associated with being able to copyright this series of numbers. Well, first of all, as I said, they're facts. There's not going to be something that's going to be inherent in the facts that's copyrightable. It's going to have to be something about the compilation. But number one, they are not choosing which facts that they are going to say. They set a number of parameters and it is actually, remember how I said, the current trend is just to dump everything into the model and have the model figure out what is relevant? That means that the human is not the one deciding what's going into these various parameters. It's the model. And what's more is it's happening, wrote over exactly the same for every single thing that it sees. It is not the human who even designed the model architecture or who's performing the training who's making any of these decisions. It is the statistical probabilities that are being changed over time. So then you could say, well, I chose the particular things that are going to be part of this input. Now there is a possibility that you could argue, for example, if you chose things that had a particular value, a particular aesthetic and you wanted to only train with that, perhaps there's an argument there. But in general, that's going to work against the grain of how machine learning works because the more inputs that you have, the better your model, the more complex and more nuanced the latent space is, the better your outputs. And so you actually, most of what's happening is we're having these large linked models these foundation models where it's basically used train on the whole internet. You're not choosing, picking and choosing in a significant way.  So that's what we're training on. So let's talk about inference. I'm going to spend about 30 seconds on inference. Like I said, inference is usually metadata. It's a question about one of the inputs. Is this, is this, what song is this? Can you identify it? What style is this? Who wrote this? Et cetera. That becomes not really legally, no one cares about that legally. But let's talk about generation. The thing about generation is that it's going to, most of the time, create a new, in fact, almost all the time it's going to create a new work. It is designed, what happens is as you go through, we actually have a parameter associated with the weights it's generally called temperature that basically says you have a certain probability of not picking the highest probability thing. And the ML application doesn't have creativity. It has randomness. And so what's happening is that you just pull different places, slightly different from the, slightly different. And we have learned to interpret those things as being creative, but it is not, it is simply the application of randomness to this iterative picking situation. Now, I know everyone over here thinking, but haven't I heard about some of these ML models that will spit out the same thing that they saw? I mean, they're just trying to wash the copyright through, et cetera, et cetera. That is, I refer to that as memorization. That's what gets people excited. Memorization is essentially a training error. If the training continues beyond the level at which the guesses stop improving, the probabilities associated with a specific input can get pinned to a specific output. So it still does not actually store the work inside it, but instead, if you over-train it in this particular way, you can actually store sort of the instructions for recreating it, usually in a lossy way. So while the model will not contain the copies, it could nevertheless reproduce them if prompted to do so. But over-training and memorization is not the purpose of machine learning. In fact, it's a type of failure that scientists are trying to avoid. The desired outcome is to encode enough probabilities that can respond effectively to new types of inputs, and there are new types of inputs and create new types of outputs. If you had something that memorized too much, it'd be like a student that memorizes, and that memorizes all the entire textbook, but can't do any problems that they haven't seen before. It's brittle, it doesn't work. One interesting implication for copyright purposes is that this means that number one, the larger, the bigger the model, the better. Number two, the less duplication in the input, the better, because both of those things are going to lead to, the first one leads to a more complex representation. The second means that you're going to have less chance of over-training. Now, let's talk about data and privacy. Trade secrets. Now one of the interesting things about trade secrets is that they're only valuable if they're a secret. The definition of trade secret according to the Defend Trade Secrets Act is that it is a piece of information that holds its value by virtue of being secret. It doesn't have to be copyrightable, it can be anything that holds values as a result of being secret. One of the interesting things about machine learning though is that if you number one, a lot of, if you give someone your model, you've just disclosed your trade secret, it's there. But what's more is if you allow people to interact with your model, you are also leaking that trade secret. It's not a trade secret. Because you can use the existing model as an oracle to recover the weights and train your own model. So it becomes, maybe it's a trade secret for a while, but there are ways in which if you find a trade secret without essentially doing anything wrong without breaking the law, then it's not a trade secret anymore and you can use it. So trade secrets are very, very difficult to keep. It's going to be very difficult to keep in a model. Let's talk about confidentiality for a moment. There's sort of the standard concept, there's the standard thing that people are worried about ChatGPT in particular. And there have been circumstances where peoples, where we've seen trade secrets, trade secrets from one company using ChatGPT actually be leaked to another company. How did this happen? The answer is that it's actually in open AI's terms of service where they say anyone who's using the API has essentially a dedicated instance. Anybody using the web based version, we are going to retain and use your inputs as further training material. So if you are worried about putting your trade secrets or your employees or whatever into this, be aware that if you are using the web version, you are disclosing your trade secrets to open AI for possible partial reconstruction as part of a later iteration of ChatGPT or one of their other products. This is just because sometimes a very specific prompt will only have a small number of examples and you may be able to get that sort of leakage because if you've something that is unique enough, well, if you ask it, prompt the model correctly, it may generate it. This is also tied to this idea of PII. PII stands for Personally Identifiable Information. This is what the GDPR and similar things are about. Interesting thing about most machine learning is they don't care about the names. They don't care about the names. In fact, it just becomes noise. And remember how I said when you put things into the model, it's just a bunch of numbers. And so you could actually have a hash of people's names and put it through and it will be equally as useful to you as putting in their actual name. And so one of the things that's going to be a big deal, well, it was a big deal five years ago, it's going to be an even bigger deal now, is going to be both in terms of data prominence, but cleaning your data from any PII before it starts going into the training process. Because when you do that, if you do that, you're not ever going to be able to put, you're not ever going to put it into your model and it can't ever come out. Here's the last little bits. Liability. Stuff goes wrong. Who is responsible? If you, there's sort of an interesting dichotomy here in that the terms of service for essentially every single one of these says anything that comes out of it is your problem, buddy. They will not cover you. That's what they say. You have to agree to your terms, et cetera. But that's if you're taking their output and doing something with it. What about situations where you have something like, people always like to think about self-driving cars? Who's responsible? Turns out that this is a very difficult question and it is giving the insurance industry fits already because product liability is something called strict liability. If you, if it breaks, you're responsible. If it causes harm, you're responsible. There's no one who's made a bad judgment, just if it broke your fault. Whereas if a human is doing these sorts of things, the human is allowed to exercise judgment. There's a reasonable person rule. So if you've got essentially an AI, is it going to be strict liability or is it going to have some sort of reasonable person rule? To make this more specific, let's say you've got a self-driving car, is going, something happens, it makes a decision that a reasonable human being would make and someone gets harmed. Who's responsible? I can tell you that the personal injury attorneys will tell you that it's the car manufacturers because that's where the big bucks are. But we don't know. When, for people who are looking here though, you're probably going to be thinking about co-pilot, chat GPT, mid-journey, et cetera. Who is responsible for that output? In general, the person who's going to be able, who's going to create and prompt that output and then use it is going to be the person who's going to be responsible. And the reason why is first of all, not only the terms of service, but the ML application is a tool. It is just like Photoshop or just like Word. It's a very complex tool. It can do a lot of things. It's got a lot of automation, but it's just a tool. The creativity and the judgment that comes, that goes into it and adjusts what comes out, comes from the human. One interesting place where this comes out is in the area of something called character copyright. Character copyright is a little corner of copyright law that says some fictional characters especially, comic book characters especially, especially, have such a well-defined personality, a well-defined look that you can't, that even if you create a new image, or a new image with that one, you're still necessarily going to be copying, for example, Superman's S, or Tony Stark's Iron Man costume. And so if you prompt, this can also come up in not as much in code, but even in textual works, but it's easy to see in pictures. If you prompt it to create something that is effectively copyright or takes a copyrighted character, you can be, that is an infringement, you've just made it, it's no different than if you used Photoshop, or Word, or what have you to create a copy. You used a tool, you created a copy, you're responsible. So be careful. The thing that gets tricky is really who is, what about inadvertent copying? This is going to be unlikely for something like the image generation, and maybe a little bit, chat GPT and especially co-pilot are going to be ones where people, especially because of the duplication and the way it was trained, is going to be places where you can prompt it in a certain way and it will spit out one of its memorized outputs. This happens maybe between 0.03% and 1% of the time in these commercial models, which isn't a whole lot, but you can make it do it. As a concrete thing, if you're going to be using one of these things, especially for code, I recommend snippet scanning. Used to be that this was only for copied and pasted code. That's essentially what it might be doing behind your back. So just something to look at. The last thing before I stop and allow any questions is also be aware of the terms of service in general. One really interesting piece about OpenAI's API terms of service is that they say that you cannot use any part and their API to create or participate in the creation of any model, not service, any model that might compete with OpenAI. Now when you think about it, that's an extremely broad provision. That doesn't talk about creating a commercial service. And if you are going to displace them, that competes with what they're doing. So be very, very careful. Google has on some of their beta barred, they have a similar, they have a similar thing. They say, we can use any of your input to improve our model, but you can't use ours to improve yours. That's gonna be how it is though. So just be aware that these terms of service restrictions, the thing that is going to bind people most when it comes to the law in AI is gonna be contracts. Because a contract is just an agreement, it can be enforced. And so be aware of the agreements that you get into, because that's going to, for the foreseeable future, I think govern the law around AI. Now, I'll stop, ask questions. I think we've got a mic, so that people on the live stream can hear. Wonderful presentation, Van. My name's Alana Whitaker, came over from the US and this is a particular topic that I find fascinating because we are having these conversations. When it comes to more of the stable diffusion models, like Lenza, where you're feeding it your images, you're feeding it data and then it can replicate, almost like say we fed it 1,500 images of Scarlett Johansson and then we output another new image of Scarlett Johansson. What legal precedence does she as a public figure have to sue to protect herself? That's difficult because you can't use her, there's name, image and likeness, essentially rights of publicity. You can't use that stuff in order to suggest that Scarlett Johansson is the source of whatever you're doing. On the other hand, Scarlett Johansson is also a public figure, you can take a picture of Scarlett Johansson, you can print it in the tabloids, you can create new pictures of Scarlett Johansson and there's not a lot that she can do about it. Was really have to be defamation or defamation, right of publicity, things like that. Okay, I guess, and then my follow-up question, I think how this conversation came more to layperson conversations recently, some different artists, they've been generating artificial songs and it didn't generate from an artist. And the style of. So is it the same thing with that? Style is not copyrightable, period, end of sentence. So in order to have a copyright, in order to have a copyright interest, you have to have an identifiable part of an identifiable work that has been copied. So it's not enough to copy, do something in the style of somebody. You have to actually be able to see a specific copied piece. So you had mentioned in the training corpus that the assumption is that you've got a large body of data that's not being curated, you're just throwing a ton of data into the model, letting it figure things out and then set its own weights. If I were to curate a data set that's specific to cats, right, I have something very specific, there's a lot of human involvement that goes into choosing what goes into the corpus, does that change the calculation at all? Possibly. I'm not 100% sure, but something like that I think would have a chance of being able to be copyrightable as a specific compilation. That said, I think it would be a hard argument. So you argue that taking the measurements of a work is not copying the work. Must you also have in the first place in training the permission to read, view, or listen to the work, and is that something that can be restricted, say in a software license or just copyright law, not even permit you to restrict the measurements? Okay, this is going to be jurisdiction-specific. In many jurisdictions, you're going to have something called TDM allowances basically that is explicitly outside the scope of copyright. In the United States, that would be covered by fair use. That's basically been litigated. The most prevalent cases there are the Google, books cases, authors Guild v. Hathie Tress and authors Guild v. Google. If you remember, that was about Google scanning all these various books and creating index. If you think about it, actually, and there were also, there's the perfect 10 case, there's a bunch of others. All this was when they created the internet and they started creating these search engines, they had all the same arguments. Hey, the value that you get in your search engine is from the fact that you indexed our stuff. We want some of that. Well, it's the same argument. They said, you know what, a search engine is completely separate from, it's a separate type of work. It is not a market substitute for the original work. It is transformative, it doesn't, it's transformative. We allow people to read, there's a generalized right to read anyway, even if that involves an incidental copy. And then there's a technical argument that I'm not going to get into, that this is actually one of the few places that 17 USC 117 applies to, that just copies of digital works that are owned by a person. Hey, awesome, Doc, I really appreciate some of the real cases you're mentioning in all this. I'm digging us. I was wondering about the OpenAI API thing that you were mentioning, how they specifically say you can't train models on top of it, compete with us. And I know a bunch of different other people are trying to talk about it. I have two questions on that. One is like, is that enforceable just because you say something in your... As a contract. As a contract, but I mean like, now that they release a public product that is as wireless to our GPT and the entire internet is, awash with like text generated by OpenAI. And in fact, you can see reviews on site saying, I mean, like how can they possibly claim in going to let's say two years down in the future, that just because we flooded the internet with OpenAI text, somehow all of the internet is now belong to us. I don't think that they can say that. Number one, they explicitly grant you any copyright that exists. There's an argument about whether it does or not. But whatever it is, they grant it to you as, they grant it to you. They just have restriction on the use of the API in the model. But they don't have restrictions on the use of the text. Can I just very quickly, can you talk about the stability AI and like other diffusion models, like them being sued and like what... The trademark argument is the best one. Okay. Everything else is not good. By the way, this is probably the last one and I'll talk to anybody outside. So I heard you say in your talk that the outputs generated by this could be infringing on copyright. Yep. I didn't quite hear you say whether outputs that you generate with these things can be copyrightable. Now your answer there just kind of sounded like you were saying like, it's a little unclear if that is the case or not. And I'm specifically thinking in terms of when the work being generated is code and can we then put it under a certain software license that requires us to have copyright on it. So the official way that the Copyright Office is saying right now is that you have to disclaim anything that was generated by the machine and that it is not copyrightable. It is just out there. You can only claim copyright on the specific edits or changes that you might have made to the output code. I believe very strongly that the Copyright Office is wrong and it's going to actually go to more of a model similar to photography where the AI tool is just a tool and they're going to recognize even the modicum of input that was put in by the human as supporting at least a thin copyright. But that's not there yet. All right, thanks all.


A great video today we got about the 2023 national bonus list and then also we're adding the payout schedule which a lot of you may have never seen which will tell you when you get paid and how much and kind of detail those installments. Hello again I'm Andre Acosta I'm a recruiter for the California Air National Guard if you have any questions specific to the California Air National Guard feel free to reach me direct on my work cell you can text or call that phone number is<PHONE_NUMBER> and of course I want to send a big thank you out to Air and Revision as usual thank you for allowing us to to be on this platform and put this information out so people can get paid and make the best decisions for them. First we're going to talk about how do you get a bonus in the Air National Guard so an enlistee needs to qualify via ASVAB that's that entrance exam and then also that physical which is a medical examination they need to qualify for that job with that bonus attached to it they need to also enlist for six-year to commit to a six-year contract and then for prior service members they need to have never had a previous bonus and be under 16 years time in service. Second we're going to talk about how much the 2023 national bonuses are for a non-prior service again that's people brand new to the Air National Guard they're $15,000 and for prior service that somebody you know either coming out of active duty or transferring from another component or branch but having already served if they're staying in that career field that already has a bonus or it's a transferable field like they're coming from a different branch but we need that specific job it's a $20,000 bonus that's a good chunk of change right there right and if they're cross trading meaning prior service who wants to pick a brand new job it's a $15,000 bonus. Third what are the bonus career fields so right now we're going to put the bonus list up that's for 2023 and I've also highlighted the jobs that are specifically applicable to the California Air National Guard but this is the national bonus list meaning it's you know going to be applicable to a lot of these jobs are applicable across the country and lastly we want to show you guys the payout schedule which is going to identify when you get paid and how much in those installments so for example for a non-prior service member meaning brand new who picks a bonus career field they would actually get five payouts the first initial payout would be after they graduate from tech school that'd be for $3,000 and then from their first to their fourth anniversary they would get a payout each of $3,000 totaling $15,000 over that time frame or if they're prior service and they're staying in their career field and it's a bonus career field they get four payouts $5,000 each it start on their second anniversary and go till their fifth anniversary so I hope that was valuable I hope this helps you make the best decision for you to get some extra money in your pocket and you know consider the Air National Guard if you're thinking about the California Air National Guard again feel free to reach out to me direct on my work site you can call you can text my phone number<PHONE_NUMBER> thanks let's see you next time


Hey everyone, thanks for tuning into this webinar hosted by Product School. My name is Anurag and I was most recently working in product at Google. Today's session is going to focus on the topic of productivity. I'm low-key obsessed with this topic. To me, it's a lifelong journey to improve one's personal productivity and the effectiveness that results from it. This topic underpins every product manager's day-to-day activities. It's true regardless of the sector of space you operate in, the size of your organization or the seniority with which you operate within the organization. It's a foundational philosophy, so to speak, and I hope to share some of my learnings from over the years. Here's a quick preview of what we are going to be covering today. We'll start with taking a look at how productivity can be defined and the key challenges faced by a PM. We'll then move on to looking at some key productivity principles and the underlying psychological factors at play. The last aspect we'll cover is to look at a few examples of productivity systems and techniques that you can use in your current role. I'll then wrap up with some resources for those of you who might be interested to take further into this topic. Before we proceed, a couple of things to call out here. One, we will not be looking at specific tools and their usage in the context of productivity. And two, we will try to focus this session on personal productivity and not team productivity, which is a standalone topic in itself, and we might not have the time to go over that aspect. A couple of other caveats. One is your context is different from mine, right? Everything I talk about are strategies and approaches that have worked for me that I've experimented with over the years and that I've found to be effective. However, these aren't meant to be prescriptive. They serve as primers and I hope that you use them as thought starters when considering your own personal productivity system. Second, the content that I plan to cover in the next couple of slides is relevant for all functions, not necessarily just for product managers. Well, the challenges that I highlighted in the next slide are specific to PMs. The techniques that we look at can be applied by all functions at any level. So just an important thing to note here. Now with that out of the way, let's dig in. So what is productivity and why does it matter? So we often operate in a time-pressed culture where there never seems to be enough time, right? We see and feel it around us every day. The world rewards accomplishments and business. And there's real value in that. We are pushed and driven towards achievement, towards creation, towards action. And there's this common assumption that productivity means purely getting more things done each day, but that's not entirely accurate, right? So here's my perspective, right? And I've tried to put it in a formula to try and capture the essence of what I'm trying to call out here is that productivity is a measure of consistently getting the important things done, regardless of what you're working on. Only a few things are truly significant, right, when you think about it. And there are two aspects to this. There's efficiency and then there's effectiveness. And simple efficiency is one's ability to achieve an intended result, but with the least waste of time, energy, attention, resources, right? Well, effectiveness, on the other hand, is the capacity to yield a superior result that delivers more value or achieves a better outcome towards a goal that you're working on, right? So this is good, sure. But then there's also potential downside to this and that's burnout. And so I appreciate the concept of using awareness, observation, and experimentation to boost productivity. But then over time I've realized that it's okay to not be as efficient as a machine, right? And we can still find our own ways to get things done. So why is this important for PMs? And what are the top challenges that PMs face? And here's how I like to describe it and this is something that captures this pretty well. Is that a product manager's day can be described as being a productivity quicksand, right? The choice of what you choose to do next becomes extremely important. Think about it. On any given day, you have to choose between a broad spectrum of responsibilities, depending on the projects you're working on and depending on the stage of the product lifecycle that they are going through, right? You could be working on tasks that are strategic in nature. You could be looking at customer discovery related tasks. You could be handling a lot of XFN, engagement in any given day. You might be doing some data heavy work or you might just be advocating for your products, your team within the organization trying to get more headcount, buy in from leadership, so on and so forth, right? And then there's the height task volume, right?  And there might be times where you're double booked or triple booked. You're being bombarded with emails, with notifications, with questions.  You might be figuring out which features to prioritize, evaluating trade-offs between different solution options, trying to balance stakeholders' needs. And then you have all this information that's sort of coming your way. And then you have to keep context switching. And the moment you think that you have a grip over things is when a crisis hits, right? Maybe a service goes down. Maybe there is a PR issue that comes up, or maybe your product feature just isn't working as intended, right? There's like P3 escalation, high priority escalation that comes in from leadership.  So the point that I'm trying to make here is that you've got to be adaptable to a dynamic work environment to drive impact and to be able to make progress consistently, right? And given the multitude of activities and decisions that a PM has to undertake, right, a personal productivity system becomes extremely crucial for managing this workload and reducing stress at the end of the day, right? And so let's take a look at some of the principles and the psychological factors that play here. So we have a lot of ground to cover here. And I put together an architecture diagram, as you can see on the slide, where we have, on the lowest level, the psychological factors at play. A level higher are the productivity principles and concepts. And then at the very top are the productivity systems and techniques that are shaped by these principles and underlying psychological factors, right? And before we delve into the specific productivity systems and techniques, I believe it's going to be extremely helpful to view productivity through the lens of these psychological factors and the principles that stem from them. So we'll follow this flow, right? From bottom to top, we'll start with looking at the foundational psychological factors, we'll then move to the principles, and then finally we'll discuss the systems and tactics that derive from these, right? So let's start with the foundational layer that underpins everything else, the psychological factors. Now these relate to the mental and emotional state of a person. Come to think of it. And I'll quickly voice over what's shown on the slide, the blue boxes from left to right, right? And then I'll also, on a high level, call out strategies that you can use to mitigate these. The idea here is to understand that these can be used to your advantage, right? So as to enhance your productivity and not let them hinder your productivity, right? So first up, we have mental overload. So judging too much information can lead to cognitive overload, which in turn hinders productivity, right? To mitigate this, you can prioritize tasks, delegate when possible, and use tools to manage and organize information, thereby preventing cognitive overload. Next, we have perfectionism. And this is a challenge that many of us face from time to time, I'm sure. And striving for excellence is beneficial, sure, but excessive perfectionism also sometimes referred to as premature optimization can lead to an excessive amount of time spent on tasks and a fear of failure. So to mitigate this, it's important to understand that not everything has to be perfect, right? Focus on doing your best, and it's perfectly fine to aim for good enough to start with. And you can always come back and refine it further, right? But it's okay to start with good enough. Next, we have procrastination. And quite simply put, procrastination is the act of delaying tasks or actions. Now, the reasons for this could range from a fear of failure to perfectionism or simply a lack of motivation or energy, right? It often hampers task completion. It leads to rushed work, and it increases stress. Now, some solutions include using time management techniques, which we'll talk about in a moment, and then breaking tasks into manageable chunks so as to eliminate distractions. The next psychological factor is willpower. Also, in a sense, your mindset, right? Willpower is the ability to resist short-term distractions in order to meet long-term goals. It is like a muscle that when overused can become exhausted. And what this leads to is, again, reduced productivity, poor decision-making, and a lack of impulse control. So, one can prioritize important tasks for early in the day, ideally take regular breaks, and develop habits to reduce the need for willpower, right? So, it all boils down to good habits. Next up is stress and anxiety. Now, these terms generally refer to the mental and emotional strain that a person feels resulting from demanding circumstances. They can often impact focus and can lead to burnout in the long term, right? Techniques for managing stress and anxiety include mindfulness practices and just ensuring a good work-life balance. And there are a variety of ways to do that.  And this is a very critical factor, right, when you think about it. This condition occurs when a person doesn't get enough sleep, obviously. And it can lead to cognitive effects like reduced focus, slower reaction times, and more changes. A simple solution, no surprises, is to establish a regular sleep schedule and ensure a suitable sleep environment. And we'll look at a few factors that we can implement in the slides that follow. Now, moving up the diagram a level higher are the productivity principles in the yellow sticky notes. Let's take a look at those in the next few slides. All right, so the next few slides, we'll take a look at the different principles at play in the context of productivity. And they are mostly visual, so I'll just voice over the concept that you see here and give you a high level sense of what they mean and how they impact productivity. So first up, we have some principles in the context of decision-making. The first one is decision fatigue, right? As the name suggests, this refers to the declining quality of decisions that a person is able to make, either after a long session of decision-making or after having made multiple decisions through a day. To apply this principle, one could ensure that the most critical decision-making tasks are scheduled for when they are fresh and alert. Minimalism, the concept of focusing on what's truly important and eliminating the rest can be applied to productivity as well. And you can do this by reducing unnecessary tasks and distractions. This approach to decision-making emphasizes clarity and it also helps with prioritization. Next up is opportunity cost and this is one of my favorites. Though it's originally an economic principle, opportunity cost is definitely relevant in the context of productivity as well. It states that there is a cost that is potential benefits lost associated with choosing to do one task over the other. And so recognizing that the time spent on one task is time taken away from another can help you make more informed decisions as to how to allocate your time. Next, we have the parental principle and this is a very popular principle. Every person out there and the grandma knows this, right? And what this principle proposes is that 80% of your results for a particular endeavor come from 20% of efforts. It helps identify high-impact activities to focus on for higher productivity, right? And then lastly, we have the 40s principle. Again, a very common principle in that it's known by various names like the Eisenhower matrix or some other two-by-two decision matrices where the labels used for the quadrants might differ but the underlying concept is the same, right? And the basic idea is that for each task that you're evaluating, you should decide to either do it, either defer it, delegate it to someone who's better equipped to handle it or just drop it, right? And then obviously there are factors that you might consider it based off of in terms of either urgency or importance or the combination of these two, right? All right, moving on to principles related to time. The first one out here is high leverage or standardization. This, as the name suggests, is a principle that involves focusing on tasks that produce outsized results with relatively minimal inputs. And we'll talk about this a little more in a slide that follows, but it's a really useful concept. The next one is the law of diminishing returns. Now, this is another economic concept, but it's applicable in the realm of productivity. And what it states is that there is a point beyond which spending more time or effort on a task doesn't lead to proportional increases in output. So for example, you could be writing a draft for a PRD and make significant progress in the first few hours, right? But eventually you might notice that additional hours of work don't result in substantial improvements, either possibly due to fatigue or loss of focus, which is fine. Understanding this principle can help you optimize your time and effort in a way where you recognize when to stop working and maybe take a break or switch over to a different task, right? But it's important to know when to stop. Next up, we have batching. And this is simply the grouping of similar tasks together in order to reduce the transition time switching between these and to improve focus, right? And the last one is Parkinson's law, another common principle out there. And it's based on the idea that your work expands to fill out the time available for its completion. What this means is if you give yourself less time to do something, right? If you create an artificial deadline, you would execute that task with a little more urgency, which is great, right? So try doing that the next time you have a lot of time to get something done, but ideally you want to get done quicker so that you can refine it over the next, you know, the remaining set of days. Now let's take a look at executive functions which are basic cognitive processes that include attention control, working memory and cognitive flexibility. The first one is goal setting pretty straightforward. And this is just the process of identifying what you want to achieve and how do you establish measurable objectives for those goals. Next is the task goal alignment, right? It's important that what you choose to do aligns with your overall set of goals, right? You want to be steering in the right direction and not just steering. Next up we have GED, which stands for getting things done. Now this is a method that was popularized by David Allen and it involves capturing, clarifying, organizing and engaging with tasks with the use of a trusted note-taking system. And we'll take a look at this a little more in a slide that follows, but it's a super popular principle. So it's good to know how to use this. And the last one is the single handling principle. With this principle, the goal is to handle each piece of work that comes your way, especially correspondence like email, only once. What that means is you decide what action to take and it could be either choosing to reply, archive or make a task out of it and do it immediately rather than revisiting the same task multiple times. And finally, let's examine the principles related to cognition. And these include your processes like thinking, reasoning and remembering. Note that a lot of these concepts are interrelated, but it's good to know them individually so that you know how they relate to each other. The first one is the Zegarnik effect. I'm not sure if I'm saying that right, but what this principle effectively means is, and this is backed by science, is that people have the tendency to remember uncompleted tasks better than completed ones. And this can lead to mental suffering and stress and reduce cognitive ability for a given task. So what this means is that it serves as a reminder to always finish what you start or at least make a plan for finishing it so that it's not in this weird limbo state lingering in your head, right? The next one is attention residue. Now attention residue reflects the persistence of cognitive activity about let's say a task A, even when you're working on task B and have stopped working on task A. So in other words, multitasking and context switching, which a lot of us do, right? It impairs focus and productivity. So it's best to avoid task switching as much as possible because it's not efficient. And the last one is deep work. Deep work is basically a state of peak concentration when you're working on a difficult task that allows you to produce high quality work. And you can foster this by blocking time on your calendar to carry out tasks that require a deep work state, that require your best work. Now the last set of principles, we have convergent and divergent thinking, which is another one of my favorites where it talks about how there are like two different ways we can approach a problem, right? So divergent thinking involves more creativity and it relates to generating multiple potential solutions for a problem statement that you're working on. On the other hand, convergent thinking focuses on finding one well-defined solution to a problem. Now understanding these two approaches can help you determine the best method for a particular task, right? And sometimes it also matters as to which environment you are problem solving in. So what I mean by that is if you're brainstorming design solutions for a particular product specification, you know, use case, that's divergent thinking, right? And you might want to be in a different environment for that versus say, prioritizing a solution from a list of available options, right? Converging on to something that you would want the team to move ahead with, right? The next one is flow state or focus. Now the flow state is a mental state in which a person performing an activity is fully immersed in a feeling of energized focus and involvement. And a lot of us refer to this as being in this zone, right? It's the same thing. And what's important here is you want to identify the conditions that get you into the state to maximize productivity, right? And why is that important? Because if you know what conditions lead to that state, you might be able to replicate those conditions when you want to slip into the state faster, right? So it's awareness and then being able to implement that day in and day out. For example, for me, it involves wearing noise-canceling headphones and being in a space with minimal distractions. It could vary for you, right? The last concept here is the principle of neuroplasticity. And this basically refers to the brain's ability to continue growing and rewiring in response to new experiences. So it affects productivity by shaping our ability to learn and adapt. And it improves efficiency and effectiveness over time. And the way this plays out is in the concept of habit formation, right? Habits play a significant role in how they shape our actions, our behaviors, and ultimately the level of productivity that stem from those behaviors, right? So this is a really important principle to be aware of. So we have covered the key psychological factors at play and the principles relevant to personal productivity. Now let's explore some examples of actual productivity systems and techniques that can be derived from these concepts. Let's delve into these in the next few slides. But before that, a quick recap of the architecture diagram that we saw earlier, right? So going from the bottom to the top, we had, we took a look at these psychological factors, right? Which could either enhance or hinder your productivity system. And then you have a set of productivity principles that also help shape your system. So it's good to keep this in the back of your mind as we talk about actual systems, techniques in the next few slides. All right, so here's a mind map of the different productivity systems and techniques that I've experimented with over the years. And I've tried to bucket these in a few subcategories, right? These are not meant to be exhausted. And most of you are likely familiar with a lot of these concepts in some shape or form. And so it's hopefully a good summarization of those key concepts in the context of personal productivity. A quick call out for the terms used here before we dig into this. We use the term productivity system and then the productivity technique, right? And they're often used interchangeably, but they differ in terms of the scope and application, right? And this is an important distinction to make no doubt. A productivity system is a holistic approach to managing your work and life, right? And it typically encapsulates a set of principles, habits and routines that guide how you organize your time and your tasks. It's more of a strategy or philosophy that you adopt and it helps give you structure as you process and work through your set of tasks and all the information that's coming your way, right? And the specific tools here don't matter, like I mentioned earlier, right? The tools that you choose to use are completely subjective, but it's useful to understand the underlying concepts here that make up the system, right? And then you could use whatever tool fits into that system. For example, in the GTD system, the Getting Things Done system, you could use something like Google Tasks for your task management, right? Or someone else could choose to use an app like To Do Wish, right? Which is another popular app out in the market or some other app, right? It doesn't really matter. What matters is that you understand what concepts are at play and how you can best leverage them to be more productive. Now, a productivity technique, on the other hand, is more specific, right? It's more tactical. And they're obviously smaller in scope than systems, and they can be used within the framework of different systems, right? And what I mean by this is, you could have a technique called batching, which is relevant both in the execution sense, right? Under the GTD system, where you could choose to do a similar set of tasks together, right? In the same time block. And it's also relevant in the context of time management, right? Where you might want to batch and time block for a certain set of tasks that are similar in nature. So they're pretty flexible in that sense. Now you might ask, how are they related, right? So just to sort of cap it off, the productivity systems and techniques are related in that techniques can be used as components of a system that you choose to implement, right? So that's the distinction that I hope you take away from this slide. Now, before we move on, there's another call out here around chat GPT, right? It's an intentionally not depicted chat GPT or like generative AI tools here. Do I think it'll make life easier for PMs? Sure, 100%. It can definitely help with, you know, reducing the net task completion time for a given task based on what you're trying to do, right? You could be looking at doing some initial research, getting started with the participants, getting some proofreading done, et cetera. But at the end of the day, it's just a tool, right? And my intent here is to focus more on the underlying principles and the systems that can be derived from these principles and concept. So the focus is less on the tools, the specific tools, and that's why you don't see chat GPT here. Now let's move on to looking at some techniques in the subcategories mentioned in red here. And I won't be talking about everything that's mentioned here in the interest of time, but I would like to give a high level sense of a few of these, you know, within each subcategory and how you can implement these in your specific use cases and contexts. All right, first up, we have techniques related to time management. And I want to start with talking about the process of time auditing, right? Which is almost like a prerequisite. And the reason this is important is because we usually tend to underestimate the time we would take that we think we would take to complete a task, right? And so it's important to be able to benchmark how much time previous similar tasks took to be able to project better for future tasks, right? And here's what you can do periodically to be able to do that. So you start with logging all your calendar events and activities for a month. Or you can start for two weeks, right? The look back window doesn't matter. The idea is to be able to log these events over a decent amount of time.  Like Toggle, Google Calendar has this nifty feature called time insights. Use whatever, it doesn't matter. And then you bucketize these events and activities into categories or labels that make sense to you, right? And then you analyze those buckets and assess which ones can be pruned or dropped, right? And you might want to ask yourself questions like, are there certain meetings that you can skip? Are there certain tasks that perhaps might be better delegated to someone who's better positioned to handle those, so on and so forth, right? You get the gist. And the idea here is to be able to use those findings then to better estimate the time you would take to complete a similar task whenever they feature on your to-do list, right? So this is a periodic activity, but I think it's super valuable to do this from time to time. Next up is meeting hygiene. And here's how I think about meeting, right? So it has three elements. Any given meeting has three elements. So you have your agenda. You have the people participating in the meeting and then you have the time spent in that meeting. And the first thing that I assess wherever a meeting shows up on my calendar is whether that meeting can be an email instead, right? But then there is a framework that I fall back on and it's called the POP framework where the idea is to ask a bunch of questions with respect to the purpose, the outcome that we want to drive and the plan that might stem from the meeting discussion, right? And that helps drive clarity as to whether it's a good use of time and on. Another thing that you could do to have better meeting hygiene is, if your company culture allows for it, is to have a dedicated no meetings day, right? And it's usually Fridays from what I've seen in some of the big tech companies. And you can use that no meeting day to just focus on deep work and get to tasks that you haven't been able to over the week, right? All right, so next up we have calendar hygiene, right? So there's techniques like time blocking, time boxing and ad hoc buffers. And they're pretty straightforward, but the idea is to be able to block time on your calendar. DS stands for do not share you for deep work. And then you also want to be boxing or like time boxing for specific tasks on your calendar. And the way you do this is being able to estimate how much time you would take for a particular task. And that's where the time audit process comes into play. And you would stop when the timer is done. And that helps provide a certain sense of urgency to the task at hand. And then the ad hoc buffer is basically a concept where the idea is to recognize that ad hoc requests could show up and it's an inevitable part of your role, right? So you account for these every day and have some buffer in your calendar to be able to squeeze in in case something comes up last minute and you have to move things around, right? So don't just keep your calendar jump back. The next technique is time batching. Again, straightforward, you just group similar tasks together, right? One thing that I used to try and do was to have all my one-on-one meetings with my cross-functional partners in the second half of any given day, right? So that's one way to ensure that you minimize your tasks switching costs from a mental bandwidth standpoint. Then there's the Pomodoro technique, which is again, one of my favorites. And the concept is pretty simple, right? It's basically you allocate two R blocks on your calendar for deep work, right? And the way you utilize this two R block is you have four individual sessions that are each 25 minutes long, right? So you have a 25-minute session to start with, then you take a break for five minutes, then you have the second session, so on and so forth. And after four sessions, you would take a longer break, right? And then maybe have another two R block to get done with four more sessions if your calendar allows for it. But this is a super useful technique and I've been using it for almost a decade now. So do give it a shot if you haven't. The last two techniques that might be useful here are what I simply think of as the calendar is the source of truth, right? Sure, you have your ever-growing task list and it's a good way to capture everything that you need to get done, right? But the idea here is to make sure that it features on your calendar at some point, right? So whatever shows up in the calendar is more likely to get done. And so you want to make sure that you have the practice of moving things from your task management app or like your task management system to the calendar and just have a better chance of completing it faster. And the last one is what I refer to as comms hub handling, right? So I think of a PN as a comms hub, right? Because you have all this information coming your way, all these questions coming your way, and then you have like any other profession, right? You have all this emails and things and whatnot. So the best way that I've found to deal with these, and again, this is not full proof, but the way that it has worked for the most part is you allocate specific time slots in your calendar to process these comms type tasks, right? Be it emails, things, drafting a comms document, whatever the case might be, right? The way I used to do it was I would have a 30 to 35 minute or like a 45 minute slot depending on the volumes that I've seen come in, but I would have a slot at the start of the day, right? And then a slot at the end of the day. And this sort of helps sandwich anything that I might have missed throughout the day while working on some other stuff or while standing between meetings. All right, next up we have energy and mind management, right? And this is an interesting set of techniques as well. The first one is what I refer to as energy peaks. Now you want to identify the times of the day when your energy peaks occur, right? And you want to guard them fiercely. For example, I would try to not have any meetings in the mornings between eight to 10 a.m. and would use that time instead for tasks requiring deep work, a deep work state of mind, right? And there's some science-backed reasons that play here, the body, every human body has something called the circadian rhythm, which you might be familiar with, which is pertinent to your sleep cycle. And then you have something called the ultradian rhythms, which is more of a 90-minute cycle that keeps repeating. So you have these 90-minute cycles where your energy peaks and then it dips, right? So you want to make sure and you want to try and recognize what those, like when those peaks occur for you personally and you want to make sure that you align that with the task that is the most important for a given day, right? Next up, you have morning routines and rituals. This again is pretty straightforward, right? The idea is to have a morning routine that gets you into a peak state of productivity. To give you an example, for me, that means getting a good dose of natural light, the first thing in the morning, right? I don't look at my phone. I try to get as much sunlight as possible, the first 10 to 15 minutes after waking up. And then I also take some cold showers and there's a lot of science-backed reasons there which you can find on the internet. And then have an exercise and breakfast schedule, maybe do some meditation, et cetera. Again, I'm not saying you have to do all of these, but figure out something that you can sort of incorporate into your morning schedule and that signals to the brain that, hey, it's time to get productive, right? It's time to get working. The next one is task energy alignment. And we spoke about this when we looked at energy peaks. I'm not going to go into it again. And after that, we have attention management, right? And this, again, borrows from the concept of attention residue that we looked at in the principles slides. And the idea is just to not try and multitask. You want to avoid constant context switching if you can help it. And I know it's not easy in a PM's day-to-day, but you want to be conscious about it and try to do it as much as possible. And then you also want to schedule time for deep work so that your attention is purely on a particular task at hand. One explicit way that I have found useful sometimes is to use anti-distraction apps, right? On the device that you're using, be it your phone or your laptop.  Somewhat like blinders on a horse, right? And the next technique that is useful here are effectively taking mini breaks, right? And I call it the 3M concept where the mini break could range from five to 10 minutes. It's not prescriptive. You can take as long as a break as you can and that your schedule allows for. The idea is to either listen to some music, get some movement, you can walk around if you're working from home, you could do some stretches at your desk, or you could maybe get in a quick meditation session, right? Figure out what works best for you, but these are some of the options.  Now, mental models are thinking tools that help with problem solving and decision making and all that good stuff. And they let you do that in a way where it doesn't lead to decision fatigue, right? And I've spoken about this extensively in a previous session. So feel free to look that up on YouTube. But the idea here is to have a toolbox of your own set of mental models that you can continue going back to and sort of execute faster on your decisions whilst maintaining that decision quality that you care about. All right, let's look at a set of techniques for prioritization now. First up, we have the 4Ds technique. We touched upon this earlier, so I'll not dig deeper into this. But the idea is to be able to classify any given task depending on the urgency and importance. The next concept is this high leverage, right? It's a simple question. It's something that I keep coming back to and it's something that I use every time I'm trying to figure out what task I want to do next, right? The idea here is to identify and work on only those tasks that yield the highest return, right? On the time and energy that you spend. So strategic work, for instance, is usually high leverage, right? Versus say something that's more execution focused, which, while it's important, right? It's not necessarily high leverage, it's more of an optimization and it's something that you do day in and day out. So be able to recognize and distinguish between what is high leverage versus not, because that's where you want to be spending most of your time on. The related concept here is the concept of standardization, right, which basically is the idea that you can make life easier and create shared understanding with templates or documentation. And a simple heuristic that I've used over the years is, you know, if I or my team gets asked the same question multiple times, and I usually have a threshold of three, right? I would just create a document and capture the answer to those questions or to that question. It just makes life easier for when those questions might come up in the future, right? So try doing that if you haven't done that. Next up, we have acceptance criteria, which is a simple technique to avoid perfectionism. Now the idea here is simple, right? You just, before you start working on a task, you just define an acceptable state of task completion and then you don't go overboard, right? The idea is to not have everything be perfect from the get go, but just to get to a state where you can then come back to it and define it over time. Maybe get feedback from your stakeholders, get their inputs back to that end, so on and so forth. So it's really useful in that context. And then we have task goal alignment where it's just a simple question, right? If you choose to do a task, ask yourself, is this the right thing to do right now? And if the answer is yes, you also want to be able to answer why, right? And ideally it maps to a set of priorities that you and your team are working on. And the last one on here is something that's really important, right? Is the art of saying no and doing that often? Now I would encourage that you practice the art of saying no out loud for different situations, right? Because knowing what to say and being able to say it in an actual situation that requires it are two completely different things. And I've seen this firsthand where, you know, I knew what exactly I wanted to say, right? But then it didn't flow as smoothly as I would have liked it to, right? So it all boils down to practicing it like any other skill. And I've put a few scripts that you could use as examples on the slide, but the idea is to practice that skill deliberately, right? Now let's take a look at principles related to execution, right, or getting things done. The first one is, and I think I alluded to this in the principle slide, but the first one is just to have a note-taking system which you trust, right?  And the key underlying idea here, and it doesn't matter how you have your note-taking system laid out, right? But the key idea here is to use your brain to think and only think and not to store things, right? So as long as you can incorporate that idea, you're golden. And another important thing to note here is that while this is mostly in the context of having a digital note-taking system, I've seen folks, myself included, where we also have note-taking systems offline, right? And one example is something that I put on the slide where, I have a waterproof notebook and a pencil in the shower because sometimes you just get these ideas, right? For a problem that you might be deliberating on and you want to capture those ideas, let's just forget them, right? So that's another thing that you could potentially try. Eat the frog. This is a common concept out there as well, right? And what it states is basically you identify one to two challenging, high-impact tasks, right? A previous day. And these are your frogs. And then you complete those tasks, the first thing in the morning, right?  And ideally, you want to align it with your energy piece like we saw earlier. You know, this concept has other names out there. There's this concept called the concept of MIT, the most important task, but it's all at the end of the day, the same thing, right? Just identify what's the most important one or two things you have to get done and get that out of your way first thing in the morning. The next concept is something that I love, right? It's called the shitty first draft. I read about this in a book related to writing a couple of years ago, and then I also heard about this from Shreyas Doshi, who is very well-known in the PM world, so I'm not going to dig deeper into that. But the idea here is to mainly approach work with a focus on the process of creation rather than the end result and how perfect that end result might be, right? So just get started with that draft PRD that you have to make or something else that you need to do, just get started. And then you can obviously allocate separate, delegated time to edit it, right? But it's always helpful to A, get started, and B, decouple the writing task from the editing task, right? Because they sometimes employ different parts of the brain. Next up, you have triaging or the one-touch rule, right? And the goal of the rule is simple. You basically want to handle each task or piece of work coming your way only once. And ideally, you would do it in a way where it gets funneled into your note-taking system, or you deal with it right then and there, right? But you don't want to be looking at it again and again because then you're just wasting cognitive bandwidth there. The two-minute rule is, as the name suggests, right? If a task takes less than two minutes, just do it. And it applies to emails, it applies to pings. It's just easy to get it done with if it's a small task. The next technique that I've used sometimes is the four-criteria action model. And the way to think about this is, you know, whenever you're deciding what you want to do in any given moment, you want to factor in a couple of things, right? You want to think about the context that you're operating in. You want to think about how much time you have left, right? Maybe it's end of day. You want to think about your energy levels, right? Is it your energy peak period or is it low energy period? And you want to think about the priority of the task, right? So just another, it's like a good way of figuring out which task to work on and why. The last one on here is the personal user manual and I found this to be super effective. And it's a tool that you can use to improve your productivity and also help foster better collaboration with your colleagues, right? And it's, again, it's not static. You keep changing what's in there depending on, you know, how your preferences evolve and how your habits evolve. And just to give an example, here's an actual screenshot from the user manual that I had back at Google. And I think it's good for two reasons, right? It gives your colleagues a peak into your working style and the preferences that you operate with to help collaborate better. And it also forces you to stay disciplined and if required, access from time to time if a specific preference needs to be changed, right? So if you aren't doing this already, consider doing this and it's going to be super helpful. Next up, we have planning. Pretty straightforward, but I do want to call out that when I say planning, I'm not talking about the tasks pertaining to planning, right? For example, okay, our planning or road mapping exercises or whatnot, right? That's a type of task. What I'm referring to here is figuring out what you want to do and incorporating that as part of your upcoming schedules, right? So two things that you can do. One end of day planning where you plan your day the night before and you identify what are your top priorities. Using the techniques we looked at, then your time block as necessary and then you execute on those the next day. And then next you have end of week planning, which is both forward-looking and backward-looking, right? So forward-looking is when it's similar to daily planning, but you're doing it for the upcoming week at large, right? And ideally you want to do this on Fridays so that you don't go into the weekend with attention residue. And then retrospective is where you might want to reflect on the week's work, the tasks that you accomplished and if there are any learnings, capture those learnings. And also maybe look at any course corrections that may have been made and the impact to your priorities as a result of those. All right, and this brings us to the last slide of the techniques section, right? And these are what I think as a set of overlooked aspects for a lot of people, right? These are simple optimizations and they can greatly boost your productivity and are oftentimes backed by science, right? But a lot of people don't do these. So now that you know of these, please don't not do these, right? So I'll quickly go over these. You have workspace optimizations that you can make and then you have self-optimization techniques that you can leverage. In the workspace section, you have light regulation where the idea is to have the right kind of light depending on the time of the day, right? Because it helps regulate your circadian rhythm. It gets you into a peak state of productivity and all the good stuff that comes with it. Another thing that you could consider doing is using blue light blocking glasses after 5 or 6 p.m. in the evening, right? Because that then helps regulate your sleep schedule for the upcoming night. You could consider switching up locations for different types of work, right? Deep versus shallow work where, for instance, if you're working from home, you could have, you know, you could do some deep work from your home office desk, but you might want to respond to your emails from your colleagues. Next up, you have distraction dumping where the idea is to do whatever it takes, whatever works for you, and not get distracted, right? So noise-canceling headphones and distraction apps may be locking yourself in a cubicle sort of a setup. Whatever works for you. And then alertness levels, right? And this is an interesting concept and it's backed by science, where the finding is that, you know, where we look or where the island looks is directly related to the level of alertness that comes from it. So what this means is if you look above your island, your neutral island, you're likely to be more alert, versus say if you're lying down on your couch, you're likely to feel more sleepy or groggy, right? So the way to implement this concept is to consider using standing desks, right? It's good for your legs, it's good for your back. And at the same time, it helps you stay more alert. And at the same time, you could also consider having monitors that are elevated, right? So not necessarily at the same level as you or like your island, but slightly over your island, right? Where it would increase your level of alertness. And then in terms of self-optimization, you could look at consistent sleep schedules and then reducing blue light exposure in bed, right? Again, science-backed reasons for this, you can read up about these, but it does help in the long run. Now, in terms of techniques that you can use to enhance cognitive functioning, these simple hacks that you can maybe try out, right? So one that I use every day is to listen to binaural music or being, right? And it just helps me slip into a state of flow quicker. The other thing that I have started doing, you know, since remote work became a thing is taking power naps, right? If my schedule allows for it. And again, science-backed benefits, but the idea here is to take a nap for 10 to 20 minutes. And what that does is it resets your mind, you know, makes you fresh and effectively recharges your mental capacity, right? And then you can start working on your tasks for the remainder of your afternoon. And last up is the concept of dopamine detox, right? Dopamine, as some of you might know, is the chemical that the brain releases and makes you feel good, right? And it's usually triggered with activities like online shopping, scrolling through social media, watching TV, et cetera. So you want to recognize this, recognize these triggers, right? And make sure that you aren't spending a lot of time on these low leverage tasks, right? And this includes responding to pings or, you know, just scrolling through emails and trying to sort of close out your inbox, right?  And like I mentioned, you want to have dedicated slots for those, but you don't want to be working on those, you know, just jumping between tasks because that's going to give you the illusion of productivity. It's not ideal, right? And it's not efficient. So recognize those triggers and make sure that you're not giving it to those. All right. So with that, let's recap and take a look at everything that we've seen so far, right? So here's what I want you to take away from this slide. The best productivity system is the one that works for you, right? It's worth experimenting with different systems and combining elements from each to create a system that works for you specifically, but to spend time experimenting and figure out what works best for you based on your working style and your lifestyle. Again, like I said earlier, the specific tools do not matter as much as the underlying concepts that feed into those systems, right? So choose whatever tool you want, but that helps accomplish those, you know, those concepts there. Now, I encourage you to reflect on these ideas and I hope they might resonate with you. And I hope that, you know, it's helpful as you continue to evolve your productivity systems. As promised, here are a few resources for further reading if you're interested. And these are staples in the productivity world and super insightful. So if you have the time to take a look at these. And that's it for today. I realize we covered a lot of ground pretty fast. So please feel free to reach out if you would like more clarifications or if you want to discuss productivity in general. I would also love to learn about your key takeaways from today's session. So drop them in the comments section below and I'm happy to respond if there are any questions. And lastly, if you want to connect on LinkedIn, happy to and please feel free to reach out. Thank you. And I hope you found the session useful. Take care.


The magnitude and frequency of flooding has been increasing over the past few decades, with some studies suggesting this could be caused by climate change. However, these studies often lack a solid foundation, relying instead on qualitative reasoning or speculation. To address this issue, researchers have developed a hypothesis testing framework for flood trend attribution, consisting of three key elements, evidence of consistency, evidence of inconsistency, and a confidence statement. This framework provides a more robust methodology for determining the cause of observed changes in flood behavior. Additionally, it allows researchers to better understand the potential impacts of climate change on flooding. This article was authored by Y. Hun Deca, J. Delgado, S. Olman, and others.


What's up y'all? Dano here. Today, we're making white chicken chili. We're going to start out with six cups of chicken stock. About half a bushel of cilantro. Stems and off. Add that to our chicken stock. A juice of two limes. Squeeze the juice. Now dice you up one red onion. Onion in. Now time for one of the main ingredients. Dano's Chipotle. That's the maroon top. We're going to add a bunch of that in there. Now we're going to bring this to a boil and now let's chop up our chicken. Take each breast and cut it into about four smaller pieces. Now we're boiling. We'll simply just add our chicken. Let it boil for about 20 minutes. All right, it's been about 20 minutes. Our chicken's ready. Pull out the chicken and set it to the side and we're going to leave everything else right in here. You need two cans of green chilies. One can of fire roasted tomatoes. One can of navy beans and then one can of great northern beans. Now you need one block of cream cheese. Now we're simply going to bring this back to a boil. While that's heating back up, I'm going to pull apart the chicken. You could totally take a knife and dice this up, but I like the texture of the chicken when it's pulled apart. To really get the chili flavor in there, you got to get you some cumin. So we're going to add probably a tablespoon or two in there. You can add as much as you like. We're even going to add one more thing. A little bit of oregano. About that much will do. Keep stirring this till it starts to boil and all of that cream cheese, those lumps will all melt. Now the last thing you got to do is add the chicken. You might be asking yourself, why do you wait till the end to add the chicken? Because if you cook it while the chicken's in there, the chicken's going to just all fall apart. So we want it to keep its structure, so we add it last and let it sit in there for just a minute. Get you one tostada and we're going to crunch it up just like that.  It's time to get a bite.  If you want to try this recipe for yourself, make sure you get the Danos. Go to Walmart or check our store locator on danoseasoning.com. Yum, yum, get you some.