Leveraging AI

27 | Beyond the Hype: Generative AI's True Potential vs. Bubble Talk for Business Leaders, with Jeffrey Funk, PHD and Globally Renowned Tech Consultant

August 29, 2023 Isar Meitis and Dr. Jeffrey Funk Season 1 Episode 27
Leveraging AI
27 | Beyond the Hype: Generative AI's True Potential vs. Bubble Talk for Business Leaders, with Jeffrey Funk, PHD and Globally Renowned Tech Consultant
Show Notes Transcript Chapter Markers

Is Generative AI Really a Game-Changer or Just Hype? Get the Inside Scoop from 40-Year Tech Industry Veteran Jeffrey Funk

On this episode, Jeffrey Funk has seen it all when it comes to tech trends and hype cycles. Jeffrey Funk, a 40-year technology consultant to governments and companies, provides an insider's skeptical take on whether generative AI will truly transform businesses and the economy. 

Jeffrey argues that while generative AI like ChatGPT has mind-blowing potential, we need to look beyond the hype and ask hard questions. Is it really improving processes, productivity and the bottom line for companies? Or mainly benefiting individual users?

Topics we discussed:
🔥 The constant rise and fall of hyped technologies - from VR to blockchain to AI. Why healthy skepticism is key.
🔥 Real-life business uses vs. individual play: where generative AI is actually boosting productivity now.
🔥 Criteria for spotting technologies with staying power and economic impact.
🔥 Why proprietary organizational data is the secret weapon for AI competitive advantage.
🔥 Mapping your core processes first before implanting AI. Understanding how roles intersect. 
🔥 The industries already feeling major impacts from AI - both good and bad.

Jeffrey Funk shares his unique framework for how businesses should evaluate and selectively implement generative AI, including focusing on proprietary data and core processes. He reveals the industries already feeling major impacts from AI. And he discusses which jobs and roles may be most at risk in the near future as individuals harness exponential productivity gains. 

If you want the sobering truth about real-world AI adoption from someone who's seen countless tech bubbles burst, don't miss this pragmatic conversation. Jeffrey may shatter some of your AI euphoria, but he'll also show you how to tap its power prudently.

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Isar Meitis:

Hello and welcome to Leveraging ai. This is Isar Metis, your host. As you know, I am a serious enthusiast of ai and most of my guests, and I would say all of my guests until today were just like me, meaning very enthusiastic about AI and its potential impact on the world. When I started seeing some of the content by Jeffrey Funk, who is a technology expert and has been for years, both as a professor in academia as well as a consultant, he is somewhat skeptical about the immediate impact of AI on the actual economy and the business world, hence, I thought bringing a different voice, somebody who is more skeptical, who has a different view on where AI is going, would be very interesting for me and hopefully for you as well. In this episode, we're going to explore whether generative AI is another bubble like the Metaverse or blockchain, and these are just recent examples, or does it have real merit and actual impact on the business world? And if it does where are the actual places that it's gonna provide business value and efficiencies that can impact businesses in the economy? Like every week at the end of the episode, I'm gonna share some exciting news that happened this week, and a lot have happened this week, and I find myself saying that almost every week, but really a lot of things happen this week. So stay tuned for that at the end of the episode. And now let's discuss whether generative AI is a bubble and where can it actually have real impact on businesses. Hello and welcome to Leveraging ai, the podcast that shares practical, ethical ways to leverage AI to improve efficiency, grow your business, and advance your career. This is Isar Meitis, your host, and I've got an exciting conversation for you today. Our guest today, Jeffrey Funk, is a technology expert. He has been consulting to government bodies and companies on how to implement new technologies in the past many years, I'm not gonna say how many, because I don't wanna date him. If he wants to share, he will share, but he's been doing this on the highest level for a very long time. He's obviously also an expert on AI recently, because that's the recent craze and that's gonna be the topic of our conversation. How much of it as a business person, You need to consider as a craze and how much of it actually has substance that you need to pay attention to understand how to leverage, and that can really help you grow your business versus, okay, it's another topic just like a few others in the past few years that are gonna disappear in the next two years. So Jeffrey, I'm really excited and humbled to have you of the show. We're doing this. Jeffrey is in Singapore. I am in an Airbnb on the outskirts of the old city of Jerusalem, so it's a very exciting international conversation. I'm really glad and happy to have you as a guest. Welcome to Leveraging ai.

Jeff Funk:

Thank you. We live in the age of bubbles from driverless vehicles and, IBM Watson to delivery drones to nuclear fusion, to solid state batteries, to V two Ls. There's a long list of technologies that have been hyped. Bubbles have happened, and then they've popped. We've all been watching that over the last few years, and so AI has been part of this. I mentioned, two of em, IBM Watson and self-driving vehicles, and now we have another one, right? We have a new one, Generative Ai. And a lot of people are excited about Generative ai and one of the reasons they're excited about it is because it's the fastest growing technology of all times. So at least until this week when threads grew faster than ChatGPT. So we need to be careful, right? We need to be careful on the metrics we use to conclude that a technology's important, clearly, that the fastest growing technology of all time that, that, that's really not very useful. We need something better. And so that's what, we're gonna talk about,

Isar Meitis:

some today. Yeah. That's fantastic. I think there is, Like you said, there's a lot of, the metaverse thing, like everybody was talking Metaverse, metaverse for two years. Yeah. And insane budget was thrown into this. And I'll be fair, I know a lot of people who are techie like me, who are geeks like me, and I know, I don't know a single person who actually uses the metaverse regularly. To me, by the way, that's the biggest difference between at least the metaverse and generative ai. I know a lot of people, or if I'll be more specific, almost everybody I know uses generative AI daily. So let's really talk about what are parameters that we do need to look at as good predictions or good set of parameters that we can look at to say, okay, this is something that's really here to stay. We should figure out a way, as a business, as a society, as a community, whatever it is to actually learn how to use this.

Jeff Funk:

Yeah, so I look for a real application that's being done. Okay, so think about personal computers. The first applications for them were, spreadsheets and word processing. Think about e-commerce while there were books and music so there were first applications where you could, where people were using them. They were fighting benefits to people. And you could see that there was a future to this. Yes. And one of the reasons there was you thought there was a future, is that because there was also something going on behind the scenes, some underlying technology was being improved. So in the case of personal computers, you knew that they were gonna get better because Moore's law was making these processors faster. In the case of e-commerce, you had faster bandwidth. And those are very well documented. The data was real right there. You could see that these were growing very quickly. So I look for that type of stuff. I look for that type of stuff in every technology. okay,

Isar Meitis:

so if we look today on Generative Ai, there are many real applications. Like I said, I almost everybody I know uses it almost daily right now. What buckets would you put it in, as in, in your world when you view this? I'm like, okay, this is a real bucket that you really should pay attention to as a real life application

Jeff Funk:

of generating ai. Yeah. when I said being used, I meant successfully improving productivity. Okay, so think again about word processing software and spreadsheet software. So there was a process by which people prepared documents that involves secretaries and other people, and so this process got faster. The secretary, at first, the secretaries were typing up. They, you'd give'em something written and they would type it, but they could now edit it with the word processing software And then there were people who had to do quantitative analysis and they used spreadsheets. so there was a process, that was being improved. You could see the same thing with e-commerce, because there was a process. You go shopping, you go to the mall, you look around. now you don't have to do that. Now, you can find it online. In addition, books, the first application were very important because a lot of us, I remember when I was a young man going to bookstores, looking for books. Oh, you don't have the book. Oh, you'd go around all these bookstores and you'd look and look. Suddenly, you didn't have to do that anymore. Suddenly these weird books, because a lot of people do, a lot of people like these, these unusual books. the long tail was one term for it. Same thing with music. There's unusual music. People like to buy unusual music that they don't want to go to 15, music stores before they find the music they want to. Do it much faster. So there has to be some kind of process of being improved. And so my problem with ChatGPT is that a lot of the discussion is about individual productivity. It's about, oh, I can send more emails. there's a process there. What about the guy who has to read your emails? Yeah. Oh, you're able to send a lot more now. Oh, okay. I wonder about all those people have to read them. so there's a process there. And the same with coding, because you make some code, you don't care that only 1% of the time it doesn't work, but somebody else cares. Somebody else has to fix it. So that whole process I. It has to be thought about. You have to think about, are these processes being improved? And maybe they are because a lot of companies aren't gonna tell everybody. They're gonna hide it. They're gonna figure out how to do something. But I have a feeling that a lot of the conversations that we're hearing are mostly about individuals saying, I can do something faster. And it's not about companies saying we can develop new products faster, because of ChatGPT. And the reason why I think that is because a lot of companies are not allowing their employees to use this. Cuz they know that mistakes occur and that causes problems for other people in the process. So we wanna focus on the process and understand is the process being improved.

Isar Meitis:

I absolutely love what you're saying, and I want to piggyback on two specific things you said. The first is about company implementation or adoption of this technology is very different than an adoption of an invi, an individual, and I do this for a living. I help companies implement this in-house and it goes back to, okay, it's a business strategy. It's a new business tool, just like any other business strategy, like just any other business tool in history. And there are a lot of moving parts. You cannot just say, oh, I can allow people to do this. So yeah, that's topic number one, which I think is extremely important. Like you gotta understand if you wanna bring this into your business, and from my perspective, you have to, because if you do figure this out, if you do figure out how to do this, you will be more efficient by a big spread from what you were before. And I don't wanna call numbers, I dunno if that's 20% or 50%, but you've gotta be more efficient. Yeah. Meaning your bottom line is gonna be higher. You'll be able to run faster than your competition, cheaper than your competition, whatever the case may be. You become more competitive and you can make more money or grow market share. Yeah. so there's merit to that. The other side that you mentioned that is very important, there are risks and some companies still do not understand the risk and they're like, yeah, this is awesome guys. Go use this. Yeah. And then stuff breaks either within the organization or big embarrassments outside because it delivers something that doesn't work. Yeah. there's multiple examples already, like that. So I like both these components. What do you think is the right way to do this? If I'm in business today, first of all, what cues do I need to look for to say, okay, this is ready. You said, okay, maybe writing code is not ready. Yeah. But should I start working with this in a sandbox, should I like, what are the recommendations that you make to companies on how to, a, evaluate, and B, figure out what are the right building blocks to actually start using right now? Yeah.

Jeff Funk:

Well, you wanna experiment on a small scale, try things and see what happens. But at the same time, you wanna think about your processes. You wanna think about, how can this impact on the process? What are our processes and how do we expect, different tasks interact? And as you said, what are the risks? if we get Make a software mistake here, what, how, what does that impact? Do people die or are people just inconvenienced? So in some cases the, it's just a small inconvenience, whereas in others it's a big outcome. So you have to decide on that. You have to decide what those risks are, and, but you do definitely want to experiment. You want to try things. Okay, so we're

Isar Meitis:

saying step number one is figure out the benefit versus risk for different scenarios in the company. Step number two is yes, absolutely. Where the risk is low or can be contained one way or another. Yeah. Or the benefit is really high and you have a way to mitigate the risk. Go and experiment. Can you give me specific examples of companies that you've seen doing this successfully? On which areas or how they built these sandboxes to make this happen?

Jeff Funk:

Well in ai, I don't know them. I'm looking for them. But, I don't see. What I see happening is that, a lot of individuals are going ahead. I imagine there are companies going ahead, but as we talked about earlier, I think that they're probably not telling everything they're not shouting out their success stories, or at least they're not gonna shout out details because this is the proprietary advantage. If they figure out how to do something well, it's gonna help a lot. And they may not, Saying things even though they have some success stories.

Isar Meitis:

Yeah. I think one of the examples that I've seen, and I've interviewed the CEO of a rapid software development company, they help, mostly startups get their product out the door faster, cheaper. They're a development house and they're doing a lot of this stuff for their own software, so they're still not using it. For their clients. But then when they're developing in-house tools, that's a lower risk because it's their tools. Worst case scenario, they can still work the way they worked before. Oh yeah. But that allows them to experiment in a sandbox, but on a real project, it's not okay, go play with this. Tell me what you think. It creates a product that the rest of the company can use. So that's just one example of how people can use that. You mentioned proprietary data. Have you seen companies or reading about companies and how they've using their proprietary data to get benefits from this new tool, new set of tools? Yeah.

Jeff Funk:

Yeah. I think that, it's that most of the benefits are gonna come from training your, your system on proprietary data, not on public data. a lot of people have talked about this. It's, most software is not available publicly. It's just not available publicly. It's available. it's in our company, Because, for security and a whole bunch of reasons, it's not available publicly. So a lot of these systems that have trained, been trained on publicly available data. Probably aren't gonna work that well. They're probably, it's probably only certain applications that, this, this generative AI can be used for. we talked earlier about, one of the examples that I have been. associated with for 40 years, which is product development. So my earlier years, I, one of the big issues in the eighties was how to develop products faster. What kind of product development method you used. And at that time, Japan was the leader, by the way. I spent a lot of time in Japan. I spent from. I first went there in 85 and I lived there for about 13 years. Wow. To finally leave to 2007, because I thought what they were doing was really great. It turned out that it was only in certain areas. They were really great. but anyways, they were able to develop automobiles much faster than other companies. And so there was a number of major, studies that documented this. And in those studies, they found that Japanese companies were very good at developing, doing lesson learned analyses, where they looked at what they had done well, good or bad. So they developed all this data on this process. So when I think about proprietary data, I think of somebody has a development process, somebody has documented the process, somebody has written about lessons learned and other things about the process. So there's all this data that the system could be trained on, right? There's a lot of these proprietary processes that are in companies, manufacturing processes, product development processes, all kinds of, there's order fulfillment processes. and I think that companies, are gonna have to define those process better, get back to it. We used to call it process re-engineering is a really big deal back 30 years ago. It's gotta been forgotten. I think that too many people don't even know what processes they're in. They're obsessed by what they do individually. And oh, I can send more emails and oh, I can do this more faster and that faster. And they don't understand that, somebody's got to read those emails or some process, that we need to improve. and so I think this is where companies have to spend a lot of time defining their processes and improving those processes, training their systems on these processes.

Isar Meitis:

I love that. I'm gonna, again, piggyback on two points that I think are critical. One is to really gain. From generative AI and AI in general, by the way, it's not just generative ai. The key is having a lot of data and if you have a lot of proprietary data, then obviously you can do stuff that nobody else can do because nobody has access to that data. So companies who will understand how to leverage the data that they've collected through the years. And it like, like you said, it could be customer data, process data, marketing data, sales data, like whatever data you have, if you have documented that, and at least some of it is documented by definition. if you've sent. I dunno, 1500 proposals in the last three years. guess what? You have data that nobody else has because you've sent proposals. Some were successful, some were not successful. What size of company you send this to, what products were in the Yeah, you have all that data, even if it's not in a proper database, you have the data so you can take it and put it in a place that a system can now read through and analyze. Yeah, so one thing yeah. Is really understanding that your proprietary data is huge. The other. Is documenting processes. Yeah. Cause at the end of the day, what AI knows how to do really well is to take segments of a process. And do them very effectively if you give it the right connections. But for that, you need to understand what the process is, which components in the process AI can do well, data analysis, data collection, stuff like that. And then say, okay, this little component, I'm gonna, I allow AI to do. Put somebody at the end of it to see that it's actually doing the right thing. And then you can maybe do this and I see you have a comment on that, but I think these are two critical points on implementation of AI in companies. Yeah.

Jeff Funk:

Yeah. and the proposal process is also very important, not just to do it. More with higher productivity, so you can automate more to do it faster. A lot of these processes involve doing things faster. So you're trying to understand which tasks are sequential and which tasks you can do concurrently. Because if you just do everything sequentially, it takes a long time. Yeah. So you want to figure out how to do it concurrently. And so this was the one of the most interesting outcomes from the, The study that compared the Japanese, the Europeans, and the Americans, terms of product development. The Japanese were doing tons of things concurrently that the Americans swore could not be done concurrently, but they figured it out. And that requires companies to really define their process as well. And it's quite likely that generative AI can help firms do that if you have good data on those processes to do things, more things concurrently.

Isar Meitis:

In your eyes, when, and maybe in what industries generative AI making significant impact that companies has to pay attention to? Because in the next, and you can name the number, 18 months, 24 months, three years, there's gonna be significant

Jeff Funk:

change. the industry that's probably receiving the biggest impact right now is media, because there's all this fake news out there and there's fake news sites and things like this. there's, there's an article in the Wall Street Journal yesterday that talks about this, talks about all these, news sites that are, it's all fake news and, there's YouTube videos on how to develop fake news so that you can, get Google ads get Google ad revenue. And so of course Google doesn't like that and a lot of people don't like that. so this kind of bad thing that's occurring, software, I think. Is in terms of a positive impact, it's gonna be the first. but I still think that's gonna take a long time because it takes a while for companies to work out their processes and, some of'em will say they've done it, but it's probably on something not their core part of their business. They're experimenting on something that, it's not the core part of their business and that makes sense, right? Like you, the example you presented earlier there, is something that, doesn't have the bad impact that. looking at, adding generative AI to another aspect of the business might have. I oftentimes wonder when we'll see an impact on generative ai, on the productivity numbers for Israel or America for anyone, because, people have been talking about this for a long time. So Eric, Ben Joelson said this in two, this 2012 book with, Andrew McAfee, that this was gonna be a big productivity miracle. And and there were people who wrote articles saying, we're still waiting for the miracle. And so the 2000 tens had the lowest, had the lowest improvement of productivity ever in America, ever. And the last five quarters have been negative. Now, a lot of this is from the, Covid working from home Covid. Yeah. Working from home. And now, there's been all this change in industries and things, manufacturing facilities closed down, all kinds of things. Yeah. But still, I think that a lot of people who talk about these new technologies fail to set themselves in the right context, which is productivity. Growth has been so slow. Therefore it's highly unlikely we'll see an acceleration. Yeah, we might see some kind of improvement, but even if we see some, if it goes up to 1% a year, that's pretty good. 2% a year. and I look at, all these technologies that we're supposed to diffuse and didn't. Some of'em I mentioned earlier and, you mentioned the, the Metaverse and Crypto and, Web3. There's a whole bunch of these. and you look at the numbers cuz I've looked at the numbers for these things. They're not very big for these new technologies. Even AI is nott the biggest, like 60 billion this year, even though people projecting that it was supposed to be 15 trillion by 2030. So it's nothing 40 billion. And yet, if you look at previous decades and look at technologies like PCs and, mobile phones and e-commerce grew much faster. they, they had much bigger impact, much earlier than the latest technologies. So when we think about what's gonna happen to these technologies, we need to keep in mind that well, Things haven't been happening as fast as we thought they would. So probably this one also won't happen as fast. We don't know, right? All this is probability. Everything. It's all about probability. It's all about we want to make, take actions that have the highest probability of success. It's not that all this isn't gonna happen or never gonna happen, what actions will ha have the highest probability? And so you made the comment of Carl that, we talked about experiments and things like that, so we do experiments so that we're ready so that if things do happen, if things do seem to be taking off, then we can be there with them, but we don't wanna bet everything on this technology and then risk everything. Okay. Awesome.

Isar Meitis:

so I think what you're saying, and I'm, I wanna try to sum it up, is that there's, don't go into this because of the craze, because there'd been multiple crazies before that either didn't pan out at all or just pan out much slower than everybody else thought. yeah. Number two is figure out the overall company process, meaning, Where does that fit? Which blocks that is replaced or enhanced or whatever. And then experiment,

Jeff Funk:

because it's multiple processes, of course, it's multiple processes. It's not one process. Of course. Yeah, of

Isar Meitis:

course. Okay, go ahead. Yeah, we mentioned multiple before. whether it's your Yeah. Sales process, whether it's even simple processes, like internal processes of how decisions are made or how Yeah. Budget, the handoff happens between marketing and sales, or whatever the case may be. Yeah. and then figure out how to. Experiment with your internal data as much as possible. Yeah. Cause that will give you benefits that nobody else has. Yeah. To gain some benefit that you didn't have before while still keeping the risk reasonable. Yeah. Is that a

Jeff Funk:

good summary? No, I agree. I agree.

Isar Meitis:

I also think another thing that you mentioned that is a big deal that I think a lot of people are projecting wrongly, and I've never done that. I've never drew the line like you have. There's a big difference between personal productivity that generative AI can do magic to. Right now. Yeah. Like even without improving to company productivity, which is something completely different because it's a lot more complex, a lot more people, a lot more processes and a lot more risk. Yeah. And so I think what a lot of people are doing today, myself included, is taking the benefits in personal productivity. Yeah. And trying to apply the one-to-one to business productivity. Yeah. Which is not the same thing.

Jeff Funk:

Yeah, no, I agree. That's a good thing.

Isar Meitis:

So I gotta ask you a follow-up question because of what I, now I think I understood what you believe. Do you see risks to specific, jobs, like specific kind of titles, professions. Skills that are at risk in the two year timeframe that people need to start paying attention

Jeff Funk:

to? if you look at employment in different professions, I did this a few years ago, back about five years ago. I was very optimistic about AI. There was another form of generative AI that people talked about for engineering, where it helped you generate new engineering designs. And so I was very optimistic. So I studied this impact of AI on accounting, law, all of these, and I, first thing I did is I looked at how many graduates were they increasing each year, and how many people, how many jobs, how many people got jobs? I didn't look at the percentage, but, and there was no problem. There was the number of lawyers getting jobs, kept going up, everyone kept going up. Lawyers, doctors, engineers, accountants. The one that didn't was journalists. So journalists have been very badly. Im, impacted by, the end of subscription newspapers and things and all this free information on the internet so they don't get paid so well. So there isn't so many jobs. So a lot of, journalists have now become like content creators and content managers and, trying to get more likes or, views for their, articles or for articles in general. So that's one of them that hasn't done very well, and I have a feeling that's probably going to. Be impacted even more. so we mentioned all the, the disinformation and stuff and how there's all these sites, fake news sites and things. Probably a lot of journalists, they're getting, they're getting jobs there and so you're gonna be critical. Why are you doing this bad stuff? But it's like they don't have a job, they take it cuz they're everything else. Yeah.

Isar Meitis:

I would extend that by the way, to anybody who's creating media, like anybody, even in companies, like if you're creating images or videos or blog posts, like I think these jobs are probably at a higher risk, not because AI can replace humans right now, but one person can do a lot more. Can do more, yeah. Than he could do a year ago, which means one person can do the job of three, meaning now you don't need three people.

Jeff Funk:

yeah. Yeah. I think the same thing also extends to, TV and movies. So apparently, a lot of movies, are not so original, there's like all these Marvel movies that build off the past Marvel movies. There's, I'm trying to think of the one with, one with all the car crashes, there's eight of them. Oh,

Isar Meitis:

yeah. Fast

Jeff Funk:

and Furious. Fast and Furious. There's eight of'em. So there's a lot of these movies that build from past movies. Yeah. And You're using a lot of old footage, you're modifying it somehow. and so generative, I can help you do that. There's a lot of TV series where you're telling almost the same story. You got the same characters. They're doing kind of the same thing over and over again. every episode. So again, generative, I can help them do that better. now, if you're trying to do something original, then it's hard. And so one, one of this, this article I mentioned about Wall Street Journal, about all the fake news. They talked about how some of these science fiction websites were getting all of this AI generated stories, and they just said they were terrible. They were just, they were just completely uninteresting. they started off saying the world was gonna end, and then it suddenly ended without any explanation that was, they said they were getting all these stories. So for the really, creative stories. They're really creative movies. They're really creative books, right? It's gonna be very hard to do. It's gonna be very hard to do with generative ai. It's gonna, those won't, but for ones that aren't creative, I, for the low end ones, you're gonna get, people are gonna use generative ai, but it's gonna be somebody who's good. It's not gonna be a low level person. It's be somebody who's really able to think about all of these things in some detail and the trade offs and, where can I just use the past? where can I just generated value? where don't I want to, where do I have to think about it myself and think about a good storyline? so yeah, that will happen there too,

Isar Meitis:

which is aligned with everything else you said. So the same thing with happen with, people who write code. The same thing's gonna happen with people who create sales proposals. The same thing. Yeah. if you'll find the places where a good person can leverage this technology to do Yeah. More, better, faster. Then these are the places where, going back to your concept that step in the process can be enhanced to do more, which means now it puts jobs at risk because one person can do the job of several people in the same level, or even a better level of efficiency. Yeah. Yeah.

Jeff Funk:

Yeah. interesting. The other profession is hard to say, right? There was the legal profession is one that I've gathered all the state on and, there was recently a story about this New York lawyer who used, he did use generative idea to create these, I forget what you call it, past cases to

Isar Meitis:

yeah. Reference cases in the court.

Jeff Funk:

Yeah. And, Most of'em were wrong. And so that really hurt him. where I see, this working, AI working is for very low level cases. Things like, traffic tickets and things. And I think it would be very useful if governments implemented. a very fast, some very simple way to take care of these low level cases so you don't have to judge doing it. It's, so these low level where the risk is small, it doesn't involve somebody dying. It's just, parking tickets and red lights and things like this.

Isar Meitis:

yeah, I agree. I think a lot of, and in addition, I think in the legal world, a lot of legwork of research. and especially going back to what you said, if you think about today, going back to this guy with the case, with the airline, where he presented five or six cases as references that didn't exist. Once going back to proprietary data, once the US government, there's, the research platform that they use today, most lawyers is LexisNexis. It's like a government Yeah. Huge database that they can research, once there's AI implemented on top of that. Yeah. Then again, one paralegal can do research significantly faster than they can do today. You will need a lot less paralegals, but that requires a few steps along the way of, yeah. Approved real data that you can now query Yeah. in a, not a high level of certainty, but complete certainty that it's real and that it's, and then you can use it, which then will drive a lot of efficiency in the legal world as well. I agree with. Yeah.

Jeff Funk:

a lot of this has already been approved because, being able to do computer searches on LexisNexis, in the old movies, lawyer movies, the lawyer was in the library reading through all these books. They don't have to do that anymore. It's much easier now. So the AI comes on top of that. yeah, for sure. Yeah. But to get back to the process thing, I think that you really want. Just have the process for the whole case. That's why some of these simple cases, you can have the whole case be done with ai. So you wanna understand the process that you're using AI to try to improve, to try to get a faster outcome, not just lower cost, but a faster outcome for the, for everybody involved. I agree.

Isar Meitis:

I think in the legal system, speed is definitely something that we're missing dramatically. Yeah. probably all over the world, but definitely in the us Yeah. I can say the same thing about Israel. a lot of cases that should take a day, take two years just because Yeah. Cause nobody's really available in attentive and there's bigger things that needs to happen. And I agree with you. Yeah. Like small, simple cases could be solved maybe without humans in the loop at all sometime in the near future. Yeah. Awesome. Jeffrey. This was a really fascinating conversation. I think there's a lot to take. I think the, what I said before, understanding the difference between personal and company productivity, understanding that you have to look at the broader process when you start in implementing this understanding what are the low hanging fruits and low risks that you can actually start playing with this. Like you said, do encourage the usage of this because it will make a difference. And it will make more difference if in specific places, but nobody knows exactly where and when. And if you figure this out for your company, you have a big advantage. So yeah, really great conversation. I appreciate you taking the time and sharing with us. if people wanna follow you, work with you, read more of what you share, what's the best way to do that?

Jeff Funk:

you can follow me on LinkedIn. So I'm on LinkedIn or Jeffrey Funk. and then my email is JeffreyLeeFunk@gmail.com. So that's a simple one. It is.

Isar Meitis:

Awesome. Thank you so much. This was absolutely great. Okay, thank you..Before we dive into this week's AI news, I wanna share some exciting news of my own. We have performed several different courses For business people, helping them prepare and understand how to infuse AI into their work and into their business strategy these courses have been immensely successful and they were done in closed groups so far, and we're going to open them to the public. These courses are led by me. I'm a four-time c e o with two exits behind me, and I'm the one teaching these courses. And they deal both with practical implementation within businesses as well as how to build strategy and business processes around them. If you're interested in learning more about this, just follow me on LinkedIn. There's gonna be more and more news about this on my feed on how to find these courses and how to sign up to them. And now let's dive to some really interesting news from this past week in the AI world that can impact your business. First piece of news that I wanna share because it's probably the most relevant to everybody. Is that Anthropic, the company behind Claude-2, which is now one of my top favorite tools, and I use it practically every day. another LLM, like ChatGPT, is now exploring a paid model. So, so far Claude-2 has been completely free and open to anybody who wanted to use it, and they've just performed a very large survey across their users, around the globe in order to explore what would they be willing to pay for the service and what. Ways are they using Claude-2 and other tools like it? And the price point that they've been checking is actually$50 a month, which is significantly higher than other tools like ChatGPT, which charges$20 a month for their service. My personal take on this is they're obviously going to charge monies. That's just a way for them to pay for the huge expenses that it takes to develop and then to run these models. which prices are going to land on? I'm not exactly sure. My gut tells me it's not going to be$50. It's probably gonna be very close to what everybody else is charging, which is around 20 bucks a month. Based on my experience so far, it will be worth the$20 if that's what it's gonna be. I'm going to pay that because again, I see great value from using it every single day. I think in the bigger picture, over time, you will have to choose a bunch of tools and pick them instead of paying for every tool out there, because otherwise it's gonna get expensive. But that being said, paying 20 bucks a month times 6, 7, 8 tools that do incredible. Things and create amazing efficiency in a business is still not a significant amount of money. Another big release from a big player in the industry is meta just released Code Lama. Meta the company behind Facebook has been releasing a lot of great open source AI platforms out there that generate text, that do translation and languages and creating audio and this week they've released Code Lama, which is a code generator and debugger that knows how to do that in Python and C plus plus and Java and P H P and TypeScript, and C# and Bash So it's. A very capable coder, and they've done something very interesting. They've released variations of this that are additionally trained on specific languages to make them even better the one that got the most attention so far is a version that's optimized for Python, which a gives a lot of people the capability to create data and AI related code, but in addition, it provides an alternative to using Open AI's code interpreter, which by itself knows how to generate Python code. So several different reasons why they focused on that. My take on this is, this is not new. We've seen companies like GitHub release copilot and Amazon release code, whisper and stable diffusion, releasing stable code and so on. So we'll see more and more of those specialized large language models that do specific things in general. And definitely we're gonna see more and more stuff with generating code. If you think about code, it's a very specific, very strict. Language, and hence large language models can become really good, very fast at generating high quality code. We're not there yet, but I think within the next few months, and especially at the rate that things are moving right now with some of the biggest players, training models specifically for that, we're gonna be in a situation Where more and more code is gonna be generated by these large language models instead of human developers. Another interesting large player from the open source world who released something interesting this week is a group of researchers on hugging Face released something they called IDEFICS, and while they're potentially very good at developing generative AI language models, they're not very good at picking names. But this thing is basically spelled I D E F I C S. What it is, it's a multimodal conversational AI model with visual language, meaning it knows how to do text and images extremely well, which by itself is somewhat unique. There are not a lot of. Models out there today that do this very well. But the interesting thing about it, it's that it's performing as good and sometimes better than its parallels like ChatGPT and so on while it's being open sourced. And while it's been only trained on publicly available data, which is definitely not the way the other big players like Google and OpenAI and Anthropic have trained their model, this is an interesting and refreshing new direction where open source researchers join forces together. Release something that anybody can use that is extremely powerful, that is multimodal and that doesn't deal with IP and ethical issues of where does the training data come from? Now we spoke about meta and we spoke about open source, and now let's talk about a few engineers who left meta. Some former meta researchers have developed an AI language model for biology, it's Using concepts from large language models in order to analyze and potentially edit proteins and to create a database that contains today over 700 million possible three D protein structures, which can be components in developing new drugs to cure diseases to deal with pollution, to deal with either generation or decomposing of different chemicals and so on. Personal opinion on this is that we're gonna see more and more of those meaning people who are going to leave some of the big labs with a very specific idea that maybe the labs were not interested in developing, but those specific individuals with the passion and now the knowledge to do these things. We'll spin off a new company. We'll take away some IP that they can now use and develop a new company. We'll get the funding for this and dive into something very specific, very niche, different than what the parent company was doing and develop those things. I think we're gonna see more and more of that globally across every different domain you can imagine. That last piece of news I wanna share with you this week is relating to the education world. In a recent study released on scientific reports, Researchers found that ChatGPT currently outperforms college students in multiple courses, meaning taking the same tests that college students are taking in computer science, politics, engineering, psychology, et cetera. Nine out of 32 courses that were tested, ChatGPT. Outperformed the human participants. The, actually, the only two courses which consistently human participants outperformed ChatGPT are mathematics and economics, which are actually surprising to me, but that's what the study found. Another interesting piece of information that this study found is they've interviewed both educators and students Across multiple countries, Brazil, India, Japan, the US, and the uk, and they found out that 74% of students indicated that they would use Chachi piti in their work. Educators dramatically underestimated that number. Moreover, 70% of educators said that if they would know that their students is using ChatGPT or other large language models in their work, they would consider that plagiarism. So the education world currently is definitely not moving in the right direction when it comes to adopting ai. Instead of understanding the amazing opportunity that it represents, they're more or less ignoring the problem, which I'm sure will come back as a very serious backlash. Because this genie is out of the bottle and there's no way of putting it back, and the education system will have to figure out ways in order to teach people how to use that, because that's the role of the education system is to prepare people for the world of tomorrow, which includes AI Instead of looking for ways to prevent students from using these tools, figure out ways on how to make them an integral part of the learning process and the execution process of everything that they're teaching. And that will have to happen from early ages and all the way to advanced degrees. If you found this podcast interesting, please consider subscribing to this podcast. Share it with people you think would find value in it. Rate and review it on your podcasting platform and please connect with me on LinkedIn. If you have any questions or suggestions for topics that we should cover, please let me know and until next time, play with ai. Try different things, share it with different people, share it with me, and have an amazing week.

LAI-POD-Jeffrey Funk