AI adoption in the enterprise

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Welcome to another episode of the Practical AI Podcast. I'm your host, Chris Benson. I am the Chief AI Strategist at Lockheed Martin. Today I am recording live from the O'Reilly AI Conference in New York City. The date is Wednesday, April 17, when we're doing the recording and I have the great privilege today of having a conversation with Ben Lorica. And did I get your last name pronounced correctly?

That's perfect, Chris.

Okay. And Ben is the Chief Data Scientist for O'Reilly Media. He is also the program chair for the Strata Conference and this AI Conference and it's a pleasure to have you on the show.

Thanks for having me.

So really hoping to cover a bunch of different topics and stuff, but I noticed I wanted to start out with that. I noticed that you had put out a publication through O'Reilly called AI Adoption in the Enterprise. It's an ebook that I know that our listeners can go and download and we'll put a link to that in the show notes and I was just wanting to see if you could kind of give us a little overview of what you're covering and maybe do a couple of deep dives enough to at least tease everyone on what you've hid in the book.

So I think at a high level we wanted to understand the state of adoption of AI, which for the most part these days refers to machine learning technologies. And so the first thing we did was we tried to get people to kind of self describe their level of maturity. So at a high level, so we, we consider people with mature practice to be companies that have certain number of years of having models in production. And then on the lower end we have companies who are just at the evaluation and exploring stage. So if you take these two buckets, mature and exploring. So a couple of interesting things that jump out. One is level of investments, planned level investment. The people, the organizations with mature practice plan to invest a substantial amount of money compared to the ones who are still in the exploratory stage.

Do you think that's mainly just because they're still kind of convincing themselves proving out the technology and its usefulness?

Yeah, I think so. But so before I dive into that, for us, that seems to indicate that maybe the gap between the leaders and the laggards may widen a little more. So far as machine learning, as far as what are some of the key bottlenecks that the respondents cited? Again, there's a distinction between those in the exploratory stage and those with mature practice. The ones in the exploratory stage cited problems identifying the right use cases and company culture. So convincing people to invest in AI technologies. Sure, the ones who consider themselves more mature cite lack of data. So they may have an idea, but they haven't quite collected the right data to execute on that project. And lack of skilled people.

Gotcha. So are you saying that even the more mature ones are struggling with lack of data at this point point in the survey?

I think that or just the immature company is always. So once you start down the machine learning and AI path, you probably start generating ideas and use cases because you gain more confidence. For many of these use cases, you may not have the right data yet. You have to start generating the data and then execute.

So could you, could you kind of just taking that a little farther, could you kind of describe maybe what a typical on the forefront, those who are kind of leading the way and are making the investments, what some of those may look like based on what the survey results showed in terms of did you go into use cases in IT at all or just talking about whether or not they were making the investment?

So we didn't have them describe their use cases, but I think at a high level, the more advanced companies are probably using more Deep learning these days. Right. So I think that companies who have existing machine learning products or applications have been evaluating how deep learning can either augment or replace their existing systems. Sure. So this applies not to the traditional areas that you associate deep learning with, like computer vision or speech or even text, but problems that involve structured data like recommender systems and time series forecasting. So I think companies are beginning to examine whether or not deep learning can play a role in improving those systems. And then the really bleeding edge companies, I think, are beginning to examine machine learning against live data. And now you're starting to enter the world of reinforcement learning. So I don't know if you were in the keynotes this morning, but Tony Jabara from Netflix talked about their work in adding contextual bandits to the recommender systems. So now you're beginning to enter the world of reinforcement learning, which I think is super interesting. At this conference we had a tutorial on an open source project from UC Berkeley called Ray from Rice Lab. And it's a distributed computing framework that you can use for a lot of things because it's got a certain amount of flexibility. So people. So the Rice Lab team have written some libraries on top of it, including one for hyper parameter tuning. They have a library on top of it called Modin, which for people who are listening who are familiar with pandas in the Python world. So Modin is basically Pandas on Ray, which means it runs faster on your laptop and it automatically scales a cluster if you needed to. All you need is to add one line of code. And then it turns out the most popular library on top of Ray is rllib, which is the reinforcement learning library. And so what's nice about this is now reinforcement learning becomes a library that you can just use if you're a developer, you don't need to write your own algorithms. So the experts can use Ray to write algorithms. And in fact, what they've designed Ray so that both users and reinforcement learning researchers can find utility in it. So it's a great new project to pay attention to.

No, that sounds great.

Also, one thing I should add is that actually Ant Finance, which is the largest unicorn company in the world, it's a financial services in China. I don't know the exact valuation, but I think it's over 100 billion.

Wow.

Big. Yeah. And so they're using Ray in production in multiple use cases. One of them is real time personalization recommendation.

Okay, well, you talked also about some of the ones that we're just exploring still. And I know in this book that you guys put out. You also talk about, you know, some of the things that are holding back adoption of AI. Can you kind of talk a little bit about that? Because actually, you know, I know that we have a lot of listeners and a lot, you know, some of them have been doing it for a while, but we also have listeners who are trying. One of the reasons they listen is to try to kind of figure their way into the space. What have you found on things that are holding back adoption of AI?

So, as I said, the people in the valuation stage, site problems identifying the right use cases, which to me points to one thing, Chris, that I think sometimes we undervalue, which is the need to educate not just your developers and engineers about machine learning, but your organization. Right. So your managers and decision makers think about when we started talking about big data and how data can drive decision making. Well, you had to educate your workforce about how to make decisions using data. So I think the same thing with machine learning and AI. There's a certain amount of education that needs to be done so that your organization is aware about what's possible, what are the limitations and what are the requirements for the technologies that we have today day. And then the second main bottleneck they cite is related to this as well, which is basically just convincing the rest of the company. So company culture about investing in AI. Sure. And so one of the things that we found is that the companies that seem to have taken initial steps and succeeded in terms of using machine learning and AI, they tended to build on existing analytics infrastructure, just kind of iteratively moving it on. Yeah. So you have data that you were using for something else. You maybe start using it for machine learning and AI, layer a bit of machine learning on top of your decision making.

So doing that iteratively in that way is probably kind of one of those success factors and that incentive, instead of starting something entirely new to take an existing team.

Yeah. You might tell yourself, oh, this computer vision is cool. Let's do a project in computer vision. But then now you have to gather the data, develop kind of the expertise on how to store that data. So maybe you're better off starting with things you're already familiar with. And the rest of organization already appreciates whatever KPIs you have. Sure. So maybe improve those KPIs by layering in this new technology.

I know, speaking from personal experience in industry, getting the data that you need. I have one without naming the company, it was a company that I was working for, and the CTO of the company said we have all the data you could ever want. The thing that I found in reality was it wasn't the right data for doing the kinds of projects that we wanted. And then a lot of other companies simply don't have the data pipelining at all in position. Any thoughts toward what companies can do in terms of getting that kind of prerequisite work done so that they can get to productive machine learning?

So one of the things that I've been trying to socialize and evangelize is that if you want to build an organization where you can have a sustainable machine learning practice, you can't ignore some of these foundational technologies that you described. So things that you might find they're not. What does this have to do with AI like data integration and etl, data governance, data lineage, and then that's the data aspect of what you need to do. But then it turns out machine learning, people are realizing, requires some special tools for machine learning development. One of the most popular open source projects over the last year is a project called ML Flow out of Databricks, which, full disclosure, I'm an advisor to. Okay, it's a 10 month old project and it's basically a project which has three components and you can use any of the components. But it's meant to facilitate ML development. Within 10 months, over 200 companies are using it.

Oh, that's fantastic.

They have contributors from over 40 companies. Then what they're finding is one of the. The most popular component of MLFlow is the component that helps you track and manage machine learning experiments. So then there's the whole tooling for helping you develop machine learning. But I think as if you look ahead, if you use more and more machine learning and machine learning becomes more and more important to your company, the models themselves will become kind of assets that you have to manage. Just like you have data and data assets. And a chief data officer or data governance data catalog will have to have tools for model governance, model operations. Right? So monitoring, tracking alerts, dashboards for different Personas. Right. So business users may have a dashboard for tracking models. Data engineers may have their own dashboards, the data scientists may have their own dashboard, but also just a catalog listing all of the models, their state, who built them, all these things.

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To extend that a little bit. I've also seen that people, not only are they, are they not necessarily ready for that as they're trying to get an operation up and running, running, but they'll also not have really thought their way through. How do you get the model back into a software stack and usable out there in a product service? You know, whatever your target environment is that you're going to get that model into and what's the process? And so not only is there a whole aggregate the data you need, get the right data, get it into the right form so that you can use it for training, but then afterwards, when you have a model that presumably you may be iterating on, having that feedback loop that not only places the model out there into the target, but also is pulling it back in. Any advice on how people should be thinking about actually productizing their model, putting it into production?

Yeah, that's an interesting question. Because traditionally data scientists have been somewhat not involved with deploying these models and these analytic products into production. And in fact, a couple of years ago we started noticing in the Bay Area a new Job role with the title machine learning engineer. This role sits somewhere in between data science and data engineering and data ops. So the focus of this machine learning engineer is to productionize ML models. That means that they're stronger on the software engineering side and data engineering side, but they have enough data science knowledge to build some of the more routine models. We started hearing about this role a couple of years ago. And about a month ago, before our Strata Data conference in San Francisco, I threw up a Twitter poll because I've been hearing that data scientists were rebranding themselves somewhat into a machine learning engineer, because a machine learning engineer, anecdotally, I think, is higher compensated. So the poll question was clear and simple, which was, if two years ago you were describing yourself as a data scientist or using the title data scientist, what are you using to. And so I found over a third said they're now using the job title machine learning engineer. So now it might be the case that some of the data scientists have upskilled their software engineering skills and become machine learning engineers, but there might also be a cohort of them who have rebranded themselves. Now, the other thing too is that the tools for going from a model that's a prototype to production, there are startups and companies trying to build tools to blur that distinction so that data scientists who are working on an internal data science platform where they can collaborate, can take those models and deploy them into production systems. But traditionally the production systems are run and managed by a different team. And I mean, data scientists don't normally wear pagers and get paged when something goes wrong.

That's true.

So I think that. So whenever someone tells me that you don't need to make this distinction, I always ask them, so do your data scientists.

That's pretty funny, actually. Yeah. So you've kind of gone into talking about how roles are changing. And so I'm going to ask you a little bit about skills and skill gaps, and I actually want to lead in a little bit. One of the things that I have noticed in recent years as I've been part of organizations that have started to turn that corner and try to set up their own AI operations and make those happen, was that in a lot of cases, the data scientists that were already there had no experience or real understanding about deep learning as they were trying to ramp up, and that it was certainly a distinct skill set from things that they had done in the past. Speaking towards that, as well as the general set of skills that it takes to make all this stuff happen in this space, what are you seeing what are the gaps? How are people managing that?

So first off, the job title data scientist has kind of become confusing to some people. So I will not name these companies, but they're in the Bay Area. One is in social media, one is in ride sharing. So I'm not going to name them. But inside many companies, the term data scientists increasingly refers to two types of people. One is a business analyst, business analytics, mostly does SQL type of work. And then the actual data scientist who does machine learning. But to us on the outside, when we see Ben is from Company X, he's a data scientist. So I've complained to my friends, I said it's confusing for us on the outside because if you guys have really two different types of Personas, then you should give them two different titles. But I think the fact that data scientists is a hot title, so then they have to kind of incentivize their employees.

Yeah. One of the things, it's interesting, I think when people talk about the need for skills in this area and that they need more people to do it, I certainly sympathize with that. But I've also, I've come to where I perspective, where I'll disagree with people who say there's not enough data scientists in the world because I think that's fragmenting. I think this kind of catch all position called data scientists, that was once one thing and now that we're moving into the space is becoming a number of different specific roles that people are taking on in the future.

Right, right, right. And then to answer your question about deep learning, right. So when I first started for focusing on deep learning in 2013, so there weren't the open source libraries we have today that are well documented with easy to use examples that you can get started with. So it was mostly confined to a few research groups. You literally had to apprentice with one of these research groups because a lot of the knowledge was passed through oral tradition. So these days, of course we have good libraries like TensorFlow and PyTorch and Big DL and a bunch of other libraries that have documentation. And the researchers in the academic and industry labs tend to publish their papers and have code that you can start to play with. So there's some notion of a running start.

I was just going to say, since I can say that for you, you have media companies like O'Reilly Media that are putting out great material on this to learn by.

Right, exactly. So then the question is, is that material enough for companies? I think that to the extent that the pre built models pre Built architectures and pre trained models apply to their domains. Yes. So for example, if what you need is a speech to text tool, maybe many of the existing off the shelf or cloud services will be enough. But take data type like text. Last year was a big, big year for natural language models and research. But if you drill down, many of the models were published with code. Some of the models are even pre trained so you can use them, but then they may not be quite tuned to your domain. So for example, if you're in healthcare and you want to use one of these pre trained models, well, even within healthcare and medicine, different areas of specialization have different, very different lingo and shortcuts for how they communicate with each other, right? Yes, that's true. So you might still need staff who are capable of doing some of this tuning and retraining and things like this.

And how does domain knowledge about that, since that's kind of what we're getting into at this point, how do you overcome that? My next question was going to be how are organizations using AI and all that? But you're kind of pointing out another one. You know, we've talked about several of the challenges on making all this stuff happen, and that is one is being able to marry your domain knowledge in very specific areas to the people, the teams of people in the data science space, whatever your team is calling those that are doing this, ML engineers. How do you make that domain knowledge transfer happen in an efficient way that keeps the business pushing forward?

So this is part of the kind of the evolution of how some of these software systems are going to be built. If machine learning will play a role moving forward in many of these systems, then a lot of software development may start resembling ML development, which means gather data, train a model, evaluate the results, and then repeat, rinse and repeat. But then that might also mean consulting with domain experts who know what data might be useful. And actually, honestly, in many cases, data is not perfectly cleaned in the beginning. You have to kind of clean the data and prepare it. And again, that's where the domain experts might be helpful to you. So then one trend that I am seeing is that in the case of data preparation and data cleaning, companies are starting to use tools that actually use machine learning. Because you have a set of domain experts, they can label a few examples and then maybe a system will automatically go through the rest of the data and try to extract similar examples. Yes, I think that domain knowledge at least is going to be essential. To the extent that we're not talking about general intelligence here, we're talking about very narrow and, and tuned systems that can help companies automate many, many very specific workflows. So another example I like to cite is robotic process automation. I don't know if you're familiar with this.

I am, yes.

With this term. Right. So I think it's a mistake to think that robotic process automation is something that will be directed from above. Right. Because it's, I think it's got to be a ground from the ground up.

I think it's terribly name field though.

Yeah, yeah, yeah.

It's very confusing for most people to.

Understand that because it's those workers who are in the front lines who know which stance are repetitive. Absolutely. And if you explain to them enough about at a high level what the current technologies are capable of doing, they can help you identify which of these workflows lend themselves to automation or partial automation because maybe it won't be full automation.

So one of the things that I'm kind of extracting from your explanation is there's this kind of democratization of the technology as it's becoming widespread and finding many, many use cases even within a given organization. But you're seeing it all over, I think, is it fair to say, say that this field, and I'm kind of talking about deep learning as it's finding more and more use cases, it's going to become somewhat synonymous with software development in the sense that as you have ML engineers become part of that team as a standard thing. It's no longer the cool new hotness that you're doing, but it's just an everyday thing down the road that you're really going to have. Neural computing is really kind of the future versus the deterministic. I have all my if then and case statements of the past that neural computing will be part of many, many software stacks out there.

I would say machine learning because as you know, right now, of course deep learning is very successful in many, many areas. That affect companies like computer vision, speech recognition, mission and text. But if you follow the history of machine learning, there are things that go in and out of fashion. Although right now we're not seeing anything coming close to challenging deep learning in a variety of tasks.

Right, but that's probably.

I agree with. That's not a given. I think the workflow will probably remain the same. To the extent that machine learning is part of, of software development, that workflow will be the same.

It's becoming a part of everyday life that companies are using in production for all these areas.

Yeah. And so actually one of the things that we aspire for in this conference is to kind of. So we have, in this conference, the AI conference, the O'Reilly AI conference, we have a business summit. So we have content for decision makers and managers so they know what other people are doing. So case studies, but also give them high level overviews of important topics through executive briefings. But we also have content for developers who are not data experts who just want to build things. But then also we want to show people the bleeding edge. So we have researchers and machine learning experts. So one other area I think that I've been trying to emphasize is this notion that when it comes to machine learning, I think companies are coming to realize that it's not a simple trying to optimize some business metric or some statistical metric, right? So there's other important considerations which over the last year I've been giving these, I've been trying to give thoughts around this notion of managing risk and I've been collecting a bunch of these other considerations like fairness and bias, privacy and security, safety and reliability, explainability. If you take any one of these considerations and risks and you imagine yourself as a company that has begun to use more and more machine learning, then you start realizing, oh, I really need the foundational technologies. For example, you look at security, your machine learning model gets attacked by an adversary or starts behaving weirdly well. Now you got to retrace back. So I need tools that will allow me to go all the way back and audit maybe now we're talking about data governance, data lineage, right? So where did this data come from and things like this. So a lot of these foundational technologies are not just important because you want to do more and more machine learning, but it also will allow you to manage risks that come with having a lot of machine learning. Foreign.

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So referencing back to the ebook, I was noticing that you had some sections on building block technologies and data types. And I was noticing within that you Kind of list, kind of the respondents, what they were using and some of them, you know, like supervised learning was right there at the top and deep learning and then it kind of shows the usage of each. I noticed down here that reinforcement learning was still fairly low on the list and yet we're talking about about it so much, you know, out there in conferences and talks. You know, we've really seen an enormous interest over the last year or so, maybe two years in reinforcement learning. Do you see deep learning and reinforcement learning kind of together going forward? Do you think we'll see that rise up on the list or what other. I'll leave it a little bit more open ended. What other technologies are you seeing in the future as likely use cases?

So I think that, I think that I would say reinforcement learning right now, as I mentioned earlier, the tools are improving and becoming more accessible. So that might let companies play around with it some more. And then I think over the next six to 12 months we're going to hear companies share what they've done. And so that's always inspiring because it's one thing to hear the reinforcement learning is being used for AlphaGo or for a self driving car, but if you hear an enterprise in your own industry using it. So that may cause you to take pause and try to understand, okay, so how did they do it, what did they use and can we do it ourselves? I think we need companies to start talking about how they use reinforcement learning and we're going to begin to see that. So as I mentioned earlier, Ray already has use cases in production. They're going to start the team from Berkeley Rice Lab is going to start trying to convince some of these users to write blog posts and some of them are enterprise users. So that might inspire people to jump in. But I think in the short term more companies are probably going to start playing around with deep learning because that seems to be at a point where you can really relate. If you are in a specific industry, chances are there's a company who's using it that you can really relate to.

So it sounds like you kind of mentioned that you went back into tools and having those use cases that companies are doing out there. So would it be fair to say that one of the reasons for deep learning that maybe things like reinforcement learning need and probably will get in the near future are having a set of tools out there that are accessible and easy to use so they can start experimenting along with that, probably data sets that are applicable to that.

So with reinforcement learning actually usefully involves a simulation Environment, Yeah, that's true. It's more of a agent interacting with an environment and you have a reward function and you're trying to learn a policy, which is basically what to do given the certain settings of the environment. It's a combination of tools. You have Ray, which I mentioned earlier, which will give you the rl, which will allow you to learn the policy. But you need to have a simulation environment in order to play around or the ability to simulate data. Right. But I think the main thing is that what motivates companies is seeing their peers use something and seeing how much reward. So the reward function of the, of their peers. Right. So if you see, if you're in financial services and you hear Ant Finance has used Ray to do real time recommendations of users interacting and learning from live data, then you might be more motivated. Right. As opposed to hearing about it being used for AlphaGo.

Gotcha. I want to actually also go back to something else you mentioned earlier. You were talking about risk in general and there are different variations. There's bias, there's the ethical and moral considerations of how you're using data.

There's security and adversaries and privacy.

Absolutely. The concept of AI safety where how you use it in benevolent ways or.

Not just benevolent ways. So AI safety also refers to safe systems. So if you have AI in mission critical situations, we don't have them yet. And I probably won't ride a plane yet that completely relies just on deep learning and not control theory. But you can imagine deploying these systems in environments where they can kill people.

Sure, yeah.

You need error bars and robust and really the same types of discipline that a lot of these fields of engineering have had to go through and learn.

That's true. And for what it's worth, for the record, the FAA doesn't actually allow. They will not certify a neural network at this point because of the black box component. So that's one of the big challenges working for a company that's aeronautical at Lockheed Martin. That's certainly a big challenge is being able to, to say, to pass all the certifications well enough both for ourselves and for FAA requirements.

So actually the notion of explainability is interesting because people think about it in terms of the need to understand what the black box is doing from a variety of angles. So one is think about it from the end user's perspective. If the end user has some level of understanding about what the system is doing, they're more likely to use it, probably more comfortable. But also maybe explainability might help you if Things go wrong and you have to audit the system and go back and understand how you can improve it and things like this. There's a bunch of startups working on explainability for, for machine learning and deep learning. I've always tried to get them to think about it more ambitiously, which is don't aim your tools at the model builders and the data engineers and machine learning engineers and data scientists in the back end and helping them understand how their model is working and therefore help them improve their model, which is great, but make yourself have a role for the end user. Right. So if I have a machine learning product or product in the enterprise that I want more people in the company to use, maybe there's an explainability widget that allows them to get more comfortable so that they end up using the system more.

Gotcha. And as we kind of wind up here, I wanted to ask kind of a very pie in the sky, open ended question and just kind of where you have this opportunity to talk to so many different companies and get perspectives. I'd love it if you would just kind of share where you think, how you think this field is going to evolve over the relatively near future, over the next few years. I'm not asking for long term prediction, but where do you think we're going as a kind of in an overview, as a summary, how do you see this field of evolving?

So I think so on the research side, so the researchers will continue to publish at a furious pace. Right. So. And a lot of that research is now in the open, usually with code, using open source libraries, so people can take advantage of that research. On the enterprise side, I think companies will continue to struggle if they don't understand the limitations of these technologies and understand how they work and how these models get built. So I think there's a certain level of education that needs to take place across the company, not just the technologists. Then I think as companies become more comfortable with machine learning and AI, they'll realize they need to build some of these foundational pieces. But also the industry needs to help companies by building better tools for machine learning development, model governance, model operations, and frankly automation as well. I don't. It's no surprise that one of the areas in technology where you're seeing a lot of automation is in data science and data engineering itself. Right. Because these are the people who understand this technology and what are they going to do? They're going to automate the things that they can automate, right?

Absolutely.

So to the extent that you're studying data science today, don't be surprised if by the time you graduate, some of the things you studied have been automated.

Yes, I would agree with that. I think that process is accelerating too. Well, thank you very much. It's been a great conversation with you. I know you have been as I've watched you. You've been very busy through this conference as the program chair. So thanks for taking a few minutes to talk with me about this. And if viewers want to reach, sorry, listeners want to reach out to you, how can they access you? Are you out on social media already?

Yeah. So my Twitter handle is impossible to remember. It's IGData. And then if you want to reach me on email, I have an impossibly hard to remember email address. Data scientistmail.com that's those are two fantastic handles there.

So. Well, thank you very much and I'll let you get back to the conference.

Thank you.

All right, thank you for tuning into this episode of Practically I. If you enjoyed this show, do us a favor. Go on itunes and give us a rating. Go in your podcast app and favorite it. If you are on Twitter or social network, share a link with a friend. Whatever you got to do. Share the show with a friend if you enjoyed it and bandwidth for Changelog is provided by Fastly. Learn more@fastly.com and we catch our errors before our users do here at Changelog because of Rollbar. Check them out every robot.com changelog and we're hosted on Linode Cloud Servers and at linode.com changelog. Check them out. Support this show this episode is hosted by Daniel Whitenack and Chris Benson. The music is by Breakmaster Cylinder and you can find more shows just like this@changelow.com when you go there, pop in your email address. Get our weekly email keeping you up to date with the news and podcasts for developers in your inbox every single week. Thanks for tuning in. We'll see you next week. Congratulations. You've listened all the way to the end of the show and guess what? Got a little surprise for you. Here's a preview of Brain Science, our upcoming podcast coming out very soon. The easiest way to subscribe is to subscribe to our Master feed@the changelog.com Master get all of our podcasts in one single feed feed, plus some extras that only hit the Master feed, including Brain Science. Brain Science is a podcast for the curious. We're exploring the inner workings of the human brain so we can understand things like behavior change, habit formation, mental health, and this thing we call the Human Condition. It's hosted by myself, Adam Stokoviak, and Muriel Reese, a doctor in clinical psychology. It's brain science applied not just how does the brain work, but how do we apply what we know about the brain to better our lives? Here we go. As humans, one of the things that separate us from any other animal out there is the fact that we have language, we have words, and we have super powerful words that truly change how we feel and how we make other people feel. If the words we say have so much potential to influence ourselves and the world around us, how do we begin to understand the power of words?

So words really are the thing that separates us from all other animals because. Right. Sharks, bats, dogs, lizards, they don't talk. And this is really critical when it comes to managing our moods and our feelings. One of the things that I sort of talk about, or even I mentioned earlier, about the way in which we file things in our mind according to feelings. This is exactly how we differentiate it too. Thinking about an example, like with professional athletes, they. You might say that they get anxious, like before a race or before, you know, a run or a dive. But using that word, it's not really a threat, right? But their brain would be like, oh, I'm nervous and now I start this whole sequence of events in my body. Whereas if I just change the word to like I'm anticipating or I'm excited, it creates a different sort of rollout of emotions as well as physiological responses. I mean, I'm anxious about going to Disneyland is not usually what we say, right?

I'm excited.

Exactly, exactly. So it then puts a lid on or files things differently in our mind, which then changes how we feel about it. So in my field in psychology, I would say we always say name it to tame it. The better I can name different feelings, the more I can tame whatever emotion that is. And so then I'm not really stuck living in this sort of mammal and reptile lane where I'm always just flipping my lid. I'm reactive, I'm angry, or I'm sad, but rather I can go, I recognize this is how I'm feeling. Or like I'm afraid of some other threat, like losing my job. And I can go, you know what, here's the words I can use to talk to myself about that fear so that I'm not just stuck feeling afraid of a possible threat which has never occurred.

Yet.

You use this concept too to say customized thinking. I'm not sure I fully understand what you mean by customized thinking. What do you mean by that?

Well, because we are human. We do have the power of choice, which is super powerful. Like, nobody has to tell you how you need to think or how you need to feel, right? And, like, your version of success might be very different than mine, which is going to impact my desert, my choices, and the direction I'm headed. And so when you think about customized, right? I mean, you can customize a car, you can customize your order at a restaurant. Like, it really is tailored specifically to you and going, how do I want to think and how do I want to feel? One example I consider is I want to always. I want every day of the week to feel like I do on the weekend. Because to me, the weekend feels great. I'm with my family. I'm not sort of running things with such a tight timeline. And there's just a different sort of ethereal vibe to the weekend.

Right.

And I think, why does that only have to exist on the weekend? Yeah, I want that every day.

Why is that? I want that every day, too.

Well, and I think part of it is really our attitude and our expectations. I mean, there are legitimate threats all around us, but it doesn't help me do me or do my life any better if I am only focused on threats. So I want to practice changing the channel in my mind that says, hey, yeah, I see that potential job loss, but I also see I'm with my family right now, and right now, nobody can take sort of what I've been through and how I feel away from me. I'm in charge of how I feel. So I'm going to do things that actually contribute to feeling better.

So how do we apply this name entertainment idea to this model then? Because maybe if you name the week, the weekend, can you change how you feel about it? Because that's really what it's about. Like, how do we take, you know, the labels we apply things to things, the names we give things, the words we use, the choices. What I think we might call nuance. I'm not really sure how you. How you put that into play with the power of words, but the difference between, like you said before, being anxious or being excited, you know, fundamentally, it's almost the same feeling, but, you know, from a nuanced level, it's very different. Different. You know, it's one direction or the other of excitement, you know, negative excitement, potentially, or positive excitement. How do we apply that to customized thinking?

Well, I think that's a great way to say it, Adam. I really like that nuance, because what we're looking for. Even as I talk about the different brains, we want a symphony. I mean, I'm not going to fire the woodwind section because I don't like a violin, right? So I don't want to fire a certain part of my brain. Like, you're not really helpful. I don't need to see that. But what we need is a sense of congruence. And so, sure, not every day of the week can feel exactly like the weekend. So I'm not going to say this is how I feel, but I have to actually believe it for it to impact my mind, my brain, and my body in the way in which I desire it to. And so I might use the words like, I strive for every day to have a feeling that reminds me of exactly how I feel on the weekend so that I don't lose sight. That, like, every day really is a gift and I get to enjoy every day of my life to some degree. And so another example might be I'm living out in the Pacific Northwest. A lot of people have negative feelings about the weather. Imagine that. But so if someone were to say that they just need to learn to love it, that's going to create what we call cognitive dissonance. It doesn't fit. So it doesn't matter how much I'm like, oh, I do love the gray. I do love the clouds. It's not going to jive with me. And so it won't stick. So instead I can say, I love the way in which the rain creates the green. And in the summer when it is green, it is amazing.

This idea of learning to live with it, though get over is what it is. Like, there's so many phrases we use to say just that. Like, just learn to live with it. What is it called again?

Cognitive dissonance.

And what does that mean when you play it out?

It doesn't go together. So that if you're like, oh, just. Just do it. You just need to get over it. Like, that really isn't helpful either because your body's you a signal, and your brain is telling you, I don't like this sensation. I don't like how this feel. I mean, a lot of people will say, oh, I just hate the gray. And the gray is just overwhelming. And so we have to go, well, what's my emotional buy in? Like, what? What do I like? How does that even allow me to enjoy something else? And so I'm going to look at going, you know what I really like that I get to wear warm clothes or I really do love my coffee because it's for such a long time. It's gray and rainy. I want to be inside by a fire drinking my coffee, right? And so how can I look for going, you know what if I do these things I might not want to do, I do get some more of what I do want to do. And so it's really almost like a bartering system in your brain of saying if you do this thing you don't like, you get this thing you you do like or you know, I know you don't have to make yourself do this thing unless you can see a way in which it actually benefits you or speaks to you emotionally. Everything Adam really has to have this emotional buy in. And if there's no good emotion, no, really, the primary neurochemical in our brain is dopamine for feeling good. I don't get some hit of dopamine, my brain's gonna be like, it's not worth it and I'm not gonna do it, period.

That's a preview of Brain Science. If you love where we're going with this, send us an email to get on the list to be notified the very moment this show gets released. Email us@enreviews changelow.com in the subject line, put in all caps. Brain Science with a couple bangs. If you're really excited, you can also subscribe to our Master feed. To get all of our shows in one single feed, head to changelog.com master or search in your podcast app for changelove master. You'll find it. Subscribe get all of our shows and even those that only hit the master feed again. Changeloud.com master.

Sam.