Have you ever wondered if AI is truly your business's friend or foe?
In this episode, I talk about the role of AI in businesses and how to strategically navigate this cutting-edge technology and how it's impacting different industries and offering a practical guide for integrating it into your business model.
Topics We Discussed:
🧭 The AI Revolution - Understanding the current AI landscape and its implications for businesses
💡Building a Successful AI Strategy - Strategies for educating your workforce on AI and its potential.
🎯 Harnessing AI for Business Growth - The importance of reevaluating the concept of diminishing returns in the context of AI.
🔄 Implementing AI in Your Business - Using AI to scale up and enhance business operations.
👥 Nurturing Human Relationships - The significance of human connections in the AI age.
If you've been thinking how AI fits into your business strategy, this episode is your golden ticket to clarity and action.
About Leveraging AI
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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. Today is a different episode than my regular episodes. And the reason it's different is because of the fact I got approached by multiple people who listen to the podcast, who love the content, who ask me for some more basic introduction episodes, so they have a better initial understanding of the concepts and the basics of AI before they start diving into the details. And since that request came from multiple listeners, I'm obviously willing to do that. So if you are an advanced user of artificial intelligence in your business, maybe this is not the episode for you. If you know a little bit and want to have more basic understanding of how it works, how to apply it in business, how to build strategy around it and start using it in your business, this is still a great episode. And if you know very little, this is gonna be an awesome episode for you
As in most episodes, at the end of this episode, I will share some interesting news about what happened in this past week in the AI world. And now let's begin our intro to AI.Isar Meitis:
So let's begin. I will start by saying that you have been using ai, whether you know it or not for a while now, because many tools and applications that we use regularly use AI behind the scenes. When you talk to Alexa or Siri, that's what actually is running. It is an AI algorithm that replies on their behalf when you listen to songs on Spotify and it suggests the next song, that's an AI function. When you get recommendations for products on Amazon or for movies on Netflix, that's an AI function. When you have navigation on whatever navigation platform you're using, and you get suggestions on where to turn and how to turn. There's an AI algorithm defining all of that, and so on and so forth. So you've been using AI and we as the human race, have been using AI and been using it regularly, daily for a while now. And you've also been using tools in your work probably that has AI infused to them. If you've been using tools like script for editing videos and audio, if you've even been using simple web apps like Remove.Bg, that removes backgrounds for pictures, that's an AI tool. So you have been using AI tools both on your personal life and your business life for a while, so why the hell is suddenly everybody going crazy about AI this AI that AI gen, what the hell happened? What happened was on November of last year, OpenAI which is a company that's been around for a while has released one of its models called ChatGPT to the world in a simple chat interface, and that started a wildfire of ai. And the reason for that, they brought in a mature, very capable model in an interface that was an extremely easy to use user interface. It's basically one line and you can chat back and forth with it. And it made advanced AI language capabilities accessible to basically anyone very quickly. And that started the snowball rolling. But what is ChatGPT? ChatGPT is what's called a large language model or an L L M, and what it basically means, think about somebody who's an expert on everything that doesn't forget any information it sees. That is amazing at analyzing data and using it to predict data. That has read every paper, every article, every podcast, every Slack channel it had access to and so on, that was created and is available to the public in the last 20 years. So it's extremely capable in understanding. Language and concepts and translating that into predictions, what it actually does, which is really incredible when you think about the outcome, thinking how simple it is. It literally just guesses the next word in the sentence, so it doesn't really know stuff. It just uses a huge amount of historical data on multiple topics to create a sentence word by word, and then the second sentence and then paragraphs, and then really long segments as long as you want on specific topics based on historical information and based on the information that you give it and the requests that you provided. And those requests are called prompts. So you can prompt it to give you answers on specific topics in specific lengths in specific formats playing specific roles as an expert on whatever topic you want. And that's why it caught like wildfire because suddenly you, as a random person in business or your personal life, has access to the accumulated data in the internet to ask its specific questions on specific topics and getting answers in specific formats in seconds without paying anybody. And that's magical, and that's why it started rolling very quickly once ChatGPT was introduced. What is a large language model? What is even AI and how the whole thing works, and how is it different than traditional software programming? The main difference is software programming is deterministic You tell the computer what to do, and the computer does it. In artificial intelligence or machine learning, you actually do not tell the computer anything on what to do. It learns just like a human learns. So you give it a large amount of data on specific topics, and then you start asking it questions based on that data, and then you provide it feedback on how it's doing with its answers, And then you follow the cycle again and again, giving it more and more feedback. It learns just like a human would what's correct, what's incorrect? What's a good answer, what's a bad answer? And that's how it learns the different aspects. This process is called training a model, and these models comes in differents modals, meaning they can do different things. Large language models are like the name suggests, they're language modes, but there's also modes that can write code that can create or analyze images that can create or analyze music that can create or manipulate video. And so on, and there's even multimodality, meaning there are different tools out there that can do more than one of those things. Then eventually we will get into what's called AGI or artificial general Intelligence, which will be a AI entity that will be able to do everything that a human does at a human level or above, rather than doing it at a human level or above on specific different aspects like it happens today. Today we already have all these stand-alone ones, and there's a few multimodal tools. We still don't have agi, probably every company out there that is working in this field, that's their holy grail, and that's what they're working towards. And there's a big debate on when that's gonna happen. Is it gonna happen in a year, in five years, or maybe never? It depends who you ask. And these answers are coming from people who are way smarter than me. So the biggest expert in the industry that have been developing this for years, have different opinions on when we will reach AGI or Super Intelligence, which even above AGI level. So what can you do? With these AI tools that can help you in either your personal or your business life. So let's take some of them one by one and give a few examples and we'll start with large language models because they're probably the most commonly used right now. So what can the model do? First of all, you need to understand that the model understands the language, meaning things like text to speech when you speak to your phone and it writes on your behalf is, Language model of an AI natural language processing. So when you speak and it actually understands what you're saying, like Siri or Google or, Alexa, all of these use natural language processing in order to understand what you're saying. But because it can do it in so many different languages, it also can translate from one language to the other and I'm not talking about just translating literally word to word because that sometimes doesn't make any sense in the other language. But actually understanding the language, understanding the context, understanding what you're trying to say, and saying the right sentence in the other language. So translation is a big benefit of that. But it also knows how to analyze large amount of text data and produce text data based on the data that it has consumed and was trained on. So what can you do with them in your work? You can write anything. It can help you write detailed emails or short emails. It can help you write Slack messages on specific topics that you need to update the team. It can help you write PowerPoint presentations, social media, post full blog post, et cetera. So basically anything you need to write it can help you write. And the more data you give it and the more access you give it to information within your business, it will be able to do a better and better job in representing the way you want to write like your voice, your tone, the company's brand guidelines, et cetera. But in addition to writing, it can also help you analyze data. Now, for a very long time, we had multiple tools who are very good at analyzing quantitative data. Anything from Excel, Google Sheets, to dedicated tools on specific tasks in accounting or sales, et cetera. But now for the first time, we have a tool that can analyze qualitative data such as customer reviews, such as self-reported attribution such as sentiments on social media mentions such as the effectiveness of sales calls. How is our brand represented across the communication, all these kind of things. It can analyze because it understands the language, it's very good at qualifying text. And going back to what I said before, recordings can be translated into text and then the text can be analyzed with the other tools. So it doesn't matter how the language is available, whether it's in video, audio, or text, you can convert it to text and then do the analysis and then get very detailed analysis that was almost impossible to do before large amounts of data because you had to have humans in the loop doing it. It's obviously also very good in analyzing quantitative data better than we had before. So analyzing large quantities of data, whether financial performance, sales velocity any other numerical information we have, aI can be very helpful in analyzing and making and getting into relevant, actionable conclusions. What else can you do with AI language related tools? So creating chat bots. Creating internal chat bots that can help people within the company understand what's going on, get answers on anything we need, instead of going to Sheila from accounting or to Dave from sales because they're the experts on something. You can take the organizational knowledge, train a bot on that, and make it available to anybody in the company to get immediate answers, accurate answers on multiple topics. You can use similar chat bots for external communications, whether for customer service, for sales, for marketing, for answering questions that people have on the website without having to actually communicate with the employees of the company using the same concepts of training, the model on information you want to give it. What tools can you use to do that? First of all, they're the core language models themselves, so ChatGPT from OpenAI, Bard from Google, Claude from Anthropic are probably the biggest names you can think of. But there are a lot of other large language models. Some are closed, some are open source that you can use for many different tasks. But since those tools all have APIs, many companies created tools that are built on top of the underlying large language model layer. And so you have the large language model layer provided by the companies I just mentioned, and then you have an application layer developed by other companies, helping solve very specific issues on very specific use cases such as writing assistants, seo, assistant meeting, transcription and summary, et cetera, et cetera. Like almost anything you can imagine that has to do with understanding and analyzing language. There's dedicated tools today that are using an underlying level of a large language model from one provider another, and in some cases more than one. So these are a lot of examples on the language modality. Now let's talk about image. AI with images can do one of three things. It can generate images based on a prompt out of thin air, meaning you can describe to it exactly what you want in the image, what type of image you want. Do you want a Renaissance painting? Do you want a photo realistic image? Do you want a cartoon, like whatever style you want it, it will be able to do it, and you just tell it what you want to have in the image and it will create it for you in seconds. What does that mean for your business? First of all, it means you should stop paying for any stock photo membership that you have right now, and you can start generating photos on the fly. It will be faster than searching through the stock photos. It will be exactly the image you need. It will be unique because it's a one-off. It was created specifically for you, and it's either free in some tools or almost free on some of the other tools. So you can create any image for any need, whether it's creating emails, whether it's PowerPoint presentations for sales or for your next speaking gig, whether it is for newsletters or blog posts, social media, any image you need in any format, in any style, in any resolution you can create with these tools in seconds, and it's absolutely amazing. What tools can you use for that? The one that I use most, because I find that right now it provides the best results is mid journey. It actually does cost money, and the only way to use it right now is through a discord server, which is not the most user friendly thing to do, but it's, as far as I'm concerned right now, the best in getting you exactly what you need in the best resolution, in the most accuracy. Next one on the list is probably, stable diffusion from stability AI, and the third one is Dall-e from OpenAI. The same company that developed ChatGPT. But again, there are multiple others that you can use. The last two that I mentioned are completely free. So if you're using stable diffusion in its various forms and shapes through different websites, or if you're using Dall-e, it's completely free to use. That's for image generation, but it also knows how to analyze images. You can actually upload images to an AI platform and it can tell you what is in the image or what's gonna happen next, or whatever question you want to ask it When it comes to image processing. Now some of the more advanced tools allow you to edit images based on understanding what's in the image, analyzing the background and the ability to make really crazy changes to existing photos. change the pose of an animal or a person that you actually took a picture off. So you have a static image and you can make the person in the image turn their head or a lion roar. While it wasn't when you took the original picture, just by dragging and dropping. And because it understands what a person is and how that person looks like, and it can guess how it's gonna look like when that person is looking to the left or to the right and have the lighting perfect and so on. So you can manipulate images, you can also add things to images without knowing Photoshop. So, Adobe, recently introduced Adobe Firefly. That allows you to write what you want to add to an image, circle the area where you wanna add it, and it will add it in a very realistic lighting, et cetera, that it would look as if it's a part of the photo. In the same way, you can remove things from images, like people that stood in the way when you took a picture of a famous monument you were visiting, you can take those people out of the picture and it will fill out the missing pieces. The last thing that it knows how to do it knows how to expand images. So if you have an image of something and you want the AI to make stuff around it, that would look realistic as if it was a part of the original image taken. It knows how to do that as well. So there's multiple ways to create images, analyze images, and manipulate images. All that can be used for any kind of business task that you can imagine from ad creation to social post and everything I mentioned before. The next big domino that's probably gonna happen that's gonna have a huge impact on businesses and the way we produce different kinds of content is video. There's already initial tests that are working that you can play with that allows you to write a prompt and create a short video from it out of thin air without a camera, without editing, without anything. It's still not amazing, but it's moving in that direction. The other aspect of this is obviously deep fakes, is the ability to manipulate a video to look like somebody else, to sound like somebody else, or. To have a completely realistic view of a situation that doesn't really happen. That's obviously extremely scary on one hand, but if you think about the creative possibilities for a business, they are literally endless because something that used to cost tens of thousands or hundreds of thousands of dollars and take months. Can now take minutes with one single person in a home computer. So the ability to create stuff that is professional grade, just based on the fact you're really creative, becomes really incredible. And at the reach of anybody that has good creative skills. now that you have a basic understanding of what is artificial intelligence And large language models and differents and what they can do and how can it work in your business? How do you approach this? How do you really get started? What are the things you need to do? A checklist as a business leader or somebody who aspires to be in business or just as somebody in the organization that wants to step up and saying, I'm gonna help my company understand how to implement ai. I'm gonna start with some concepts before we dive into the checklist. And the first concept is that we're moving from a very long era, hundreds of years of trying to improve efficiencies of processes to an era where we can just get the outcome without having to worry about the process. And I'll give you a few examples. Example, number one, customer service, multiple tools and capabilities and processes and best practices. Are being continuously updated in order to have better customer service. How can we as answer faster? How can the first answer be the right answer, et cetera, et cetera. All these kind of things. Entire companies are built around building these tools that will help other companies provide better customer service. But the goal is not providing more efficient customer service. The goal is having happy customers that can get a answer in a very short amount of time, and it's still one of the biggest frustrations for people dealing with companies and the bigger the companies they're dealing with, usually the worst the customer service is. But what if we can train an AI on all the knowledge that a customer service person has to know and also give you tools to take all the actions that they need, upgrade them, downgrade them, send them the bill, revoke their rights, add rights to them. Anything that a customer service extremely capable agent can do, give that to the ai. Then you have the outcome. You have happy customers that get immediate results because they don't have to wait. And they don't have to talk to three different people and they don't have to re-explain themselves, and they don't have to go through a phone IVR or through 20 different forms. They just go in, they type or speak what they actually need, and they will get an answer, most likely, the right answer immediately. So you're going and you're circumventing the process. You're not trying to make the process more efficient anymore, you're just going straight to the outcome. Another example, you. Want to rank higher on Google, so you had to do SEO keyword research. You had to understand what's the competitive landscape looks like. You had to start defining different topics you need to do. You had to then create outlines that will be aligned with those topics that you need to cover. You have to design the website accordingly. Then you have to actually write the blog post, optimize them for SEO, and then deploy them. That's a very long process that takes a lot of people with a lot of expertise and takes a lot of time. And now you can just tell it. Here's my competition. Tell me what keywords they're ranking for. I wanna rank higher than them. Write as many articles as you need and set them up in the right places, in the right format, in the right website, and you get it. Now what does that mean long term for SEO? I don't have a clue. I think what I just mentioned is an amazing short-term opportunity that I think will go away once a big amount of company will start doing that. In addition, what's happening for SEO is that because chatbots like Bard and Bing and ChatGPT are going to probably replace traditional search because of the same reason we used to try to make search better and better. So Google leading this charge are the best in giving you the best search results for what you're looking for. But then you still have to go through multiple links and try to figure out the bits and pieces of information from each link that actually answers your question versus. The outcome is I just want the answer, and if the chat bot can give it to me, I don't need to click any links. So the whole concept of SEO will erode either quickly or slowly, but in the next few years, it will either be very different than it is today or disappeared completely. So the first concept is really understanding that we're moving from. Improving process efficiencies to moving to this is the outcome. Can I get the outcome without going through the process? I'll touch on a few additional concepts once I go through the checklist, but the first thing on checklist, and is the most important thing is education. you have to commit to continuous education on the topic of ai. Why am I saying that? I'm saying that because the AI world is moving so fast, there's really amazing news, amazing new tools, new capabilities that are coming out daily and whatever you learn right now may be obsolete and Middle Ages like. A month from now. So do you have to do this every single day? No. But do you have to keep yourself continuously educated on what's happening in this field so you can at least not be left far behind? Absolutely. Yes. So that's the number one recommendation. How do you do that? find the right podcasts like leveraging AI or any other podcast. There's multiple other podcasts out there that will educate you on that. Read books, read articles, follow the right people on social media that share tips and tricks and strategies around ai. Keep educating yourself on this topic so you can stay, if not ahead of the curve, at least on the curve and not behind it. The next step is you do not want to take this path alone. Why? Because you have your expertise and your capabilities, but you also have a job to perform, and you're limited with your understanding, and you're limited with your area of expertise. So the right thing to do within every business today is to start a aI Innovation Committee. And the people on the committee needs to be people who, a, are quote unquote geeks and will actually enjoy doing this. B people who come from different departments because they will represent different needs and different mindsets and different approaches while looking through the AI lens on different processes within the company. those people need. Time on their calendar to do AI related things, and we'll talk in a minute on what they should do. And they need access to the right resources, whether it's a sandbox to play with, so they don't mess up anything that is currently company infrastructure or budget to have access to tools they want to test. So they need resources in order to do their work correctly. What does this committee needs to do? First thing is they're in charge of educating themselves and the company all the time. That means, Learning about different tools. It means experimenting with different tools for different use cases. It means developing processes when they find a tool that could actually be used within the company. Developing the processes on how to implement the tool within the business, and then training the people within the company on how to use the tool. They also become the go-to group when anybody has an AI related question and they need to develop a few very specific deliverables. One deliverable is. AI guidelines, what each person in the company should know that they should do or shouldn't do when it comes to using ai. Just recently, a lawyer arguing a case for an airline in a court in the US referenced six different cases in order to argue his case. That do not exist. So think about the embarrassment to A, the lawyer, and B, the law firm that he's representing when he presented cases that don't exist. And the reason they don't exist is because he used ChatGPT in order to do the research. And maybe the biggest problem that these large language models have is they hallucinate, they make stuff up, and the bad news is you have no clue. When they're factual and when they actually make stuff up, meaning you have to fact check them in these kind of scenarios. But he did not know that. And hence he used ChatGPT to do the research, went to court, and I'm sure again, he was extremely embarrassed when he found out that these cases don't actually exist. So creating guidelines for good and bad is extremely important, and that's something the committee has to define. Part of the people in the committee have to come from the company's leadership a because. It will involve making decisions and B, because that helps lead by example for the people in leadership to show the rest of the company that they are hands-on involved in the process. But the other reason to have leadership people within that process is because one of the most important things you have to do right now is to reevaluate your entire business strategy. Why do you do that? Because the world is changing extremely fast, faster than it has ever changed before, and the needs of clients and the capability to deliver those needs is changing very fast. You have to go and after you've educated yourself and have a decent understanding of what people can do today and what they might be able to do a year from now or two years from now is go and reevaluate who are gonna be your customers, what will they be willing to pay for in a year or two? And the flip side, what other opportunities within the scales and the expertise you have in your company are now open? That did not exist before. So let's take two quick examples. One for each side. And I'll take two very extreme examples. One example, there are multiple companies today who offer SEO writing services. They will go extinct very quickly. Why? Because the entire thing that they do can now be done by a machine faster and in many cases, probably better than these people can write. And yes, I know some people disagree with me and will say that AI writing is not as good as human writing. I think that's not true. I think it's how you use and leverage the AI tools in order to write, in order to get the results you want. But it's definitely way more efficient than letting humans write articles for you right now, assuming you are fact checking it in the end. As I mentioned before, same thing goes by the way for various types of consulting and training. if you are writing online courses today and that's your main income, Your income might be at serious risk because there is no doubt in my mind that the big platforms like Coursera will develop an AI model that can create courses on the fly, customized to the specific needs of each person. So instead of going and browsing through existing courses, you will say, I need a beginner's course on topic X. It's for X amount of people. That's their level of experience. That's what they know right now. The course has to be with videos. It needs tests within it. And we have six hours to take the course spread over six weeks with one hour a week, and you will create that course for you. It might even send a preliminary questionnaire to each and every one of the people, so it can customize it further to each individual needs. So if you're creating courses online, as your main source of income that may go away within the next year or two. The flip side of that, if you have a lot of data, like if you're a large consulting company, and you can take all that data and train models with that, you can now provide much more efficient, faster, more accurate consulting services than ever before. You can go after lower tiered clients that couldn't pay you before because now you can automate a lot of the processes. So there's. Amazing opportunities as well as big risks. And that's true for almost anything unless you're cutting grass or manufacturing something very specific. And even there, there's probably efficiencies to be made through supply chain management using ai, et cetera. when looking at building strategy, there's a few key points that are different than traditional strategy, and I'll mention them right now. The first thing is I see three different paths for business success in the AI era. Path. Number one is, as I mentioned, proprietary data. If you own proprietary data, and the more of it you own, the better you can train models. On that data and provide value that nobody else can provide, or very few companies who have access to this kind of data can provide. This way, you can stand out and create a blue ocean for yourself. That was not possible before. The second option is exactly the opposite side of the scale is you're in a highly competitive market and you are the best in implementing these AI tools to get better efficiencies. And hence you have better margins and can be more competitive with your pricing and win through efficient implementation and usage of AI tools versus your competition that may not be as good as that. the third path for success is human relationships. And that becomes amplified in the B2B world because so many companies will be able to do almost everything really well and vanilla like everybody else, because using those tools will allow everybody to do this relatively easily and without huge amount of resources. The human connections are gonna be a huge differentiator. So that's another path that I think every company should have done so far, but becomes way more important moving into the AI future. the last concept that I wanna mention when it comes to thinking on the strategy of using AI before we dive into the rest of the checklist is the understanding that the existing concept of diminishing returns may not apply anymore to different aspects of how your business runs. An example I used earlier is, let's say you're writing capability right now to create blog posts. You can create three a month or three a week, and now you can create 300 in a day. and I can give you multiple examples like that, but the ability to take something that was a significant bottleneck and had diminishing returns in order to scale, it can now scale infinitely. With almost zero marginal cost. And that concept of looking for things in your business, that AI allows you to scale dramatically with zero or almost zero marginal cost, leading you to limitless growth on those particular topics. If these things are major bottlenecks of your growth right now, they can go away, immediately. It's not a process. It's a almost a zero to one game that happens almost overnight once you figure out how to apply these tools to those specific bottlenecks. You have to have that thing in mind when you are analyzing your current and your future strategy. Now I want to go back to the checklist. We said continuous education, number one. We said AI innovation committee as number two, creating guidelines as one of the things the committee has to do, educating the company. You must continuously educate other people in the business. You can do this through talking about these things in town Hall. You can share this on Slack channels. You can define specific things as mandatory reading or mandatory listening, and then have people come in smaller groups in their departments, in whatever teams they're in, and share what they've learned from the things that was mandatory to read or listen. There are multiple ways on how to educate your business, but you have to have multiple people, preferably everybody in the business educated on AI and its capabilities and how it can be leveraged in order to run the business in a more efficient way. You need to define KPIs on ai, and yes, there's. Trailing indicators such as we now have more sales or our margins are better, but that will take a very long time. But what you can start with as KPIs is Lin leading indicators. How many people are using AI in the company right now? How many processes we have implemented AI into? How many tools have the committee evaluated even if we haven't implemented them, and so on. So these are great leading indicators to see that you're moving in the right direction for implementing AI across your company in a successful way. Another important aspect is ethical guidelines, because AI allows you to make stuff up. It can lead into doing things that are beyond the red lines of your company, yourself, your core values, et cetera. And that line is different for every individual and for every different company. But you have to define what's acceptable and not acceptable to do in your business using artificial intelligence. Next thing on the checklist Is encourage AI usage. How do you do that? share success stories. This person from that department has done this and was able to achieve that result. do this in company town hall. Invest a few minutes in sharing those kind of things. Show that you are doing it as a business leader. Again, lead by example. People will see you doing this. They will understand it's important to you, hence they're gonna follow your footsteps. Reward people that successfully implement AI capabilities and so on. There's multiple ways to do that, but the usage of AI has to be encouraged within. The guidelines that were defined by the committee, so nobody does anything stupid that actually hurts the business. Next topic is look for low hanging fruits for implementation. How do you look for low hanging fruits? AI is really good at very specific things. It's very good at doing repetitive tasks. It's very good at data analysis. It's very good at data prediction. So taking the existing data and extrapolating from that, and it's very good at generating content at scale. So any task that falls into any of those buckets will be a relevant thing that AI can help you make more efficient. If you have tasks that fall into more than one of these buckets. Then the benefits you will get from implementing AI for those tasks will be amplified by the fact it gets tick in several of those different boxes. So again, repetitive tasks, data analysis, data prediction, and content creation. So what you need to do, you need to look at all the tasks that the company's doing, and hopefully you have some kind of a task management tool like Trello or Jira or Monday or ClickUp or whatever other tool that you're using, or even just an Excel spreadsheet that has what tasks people are doing and. See which of those buckets they fall into, and that will help you prioritize the task, which will get you the most amount of benefit by leveraging different AI tools to solve or help within these tasks. Once you have them mapped and you have them prioritize, look for the right tools, give them to the committee, have the committee evaluate them, and once you pick up and define the right process, implement it across the business and measure the amount of time or efficiency that you're actually getting as an outcome of that implementation so you can learn for the next cycle. The last component has to do with what I mentioned before. How do you nurture human relationships within the business and outside of the business in order to create a moat that is not technological? Because technological moats will be very hard to create in this new era because it will be extremely easy to catch up to almost anything you can develop. So a quick summary of everything I shared with you today. The first thing is AI is here to stay. It's gonna have dramatic impact on society and on businesses and on individuals, and the best way to come ahead out of this revolution is to a continuously educate yourself, experiment with it. Find the right ethical ways to implement it in your personal life and in your business, and educate the people around you so it can have more brainstorming approach to this. Revalue your business strategy. Find fruits and keep running faster than your competition. That's it for this episode and I hope you found this helpful. And this is obviously just an introduction to AI in businesses. If you wanna learn more, just check out additional episodes of leveraging AI where me and my guests dive into specific use cases, specific strategic approaches that can help you in a much more practical way than this introduction. I wanna thank you so much for taking the time and listening to this. If you enjoy this episode. If this was helpful to you. Please share it with your friends or people that you think can benefit from this. please give us a five star and write a review on the platform you're on. That really helps us reach more people, which means you can help more people understand AI and help them implement it in their businesses. Thank
and now for some news from this week, maybe the biggest piece of news from this week, and that's something we've started seeing before and I guess we're gonna see more of, is a lawsuit has been placed against OpenAI, the creator of ChatGPT. It's another class action and it's a copyright lawsuit that claims that ChatGPT was trained on books without permissions from the authors. It was filed in a San Francisco federal court this past Wednesday, and it states that books and texts from books was copied by OpenAI without consent, without credit, and without compensation. Why is that important? It's important because a lot of these models, probably all the big ones, have used data. That they had no rights to use in order to train these models and the regulations that are coming out. And the first one that's out there is the one that was set by the European Union, clearly states that they will have to share where they got the data And if not, they can face some very serious fines. What does that mean? It means that these models will have to find a way to a share with at least the eu, but most likely later on the rest of the world, how they've trained their models, which could be very problematic for them because then they will have to start paying for all the data that they have used or to pay serious fines across the board. Now these companies are so big that I assume this will be settled one way or another, but in the meanwhile, it will put a burden on these companies and their ability to run forward as fast as they did so far. I still think they'll be able to somehow get away with it, with paying some kind of a fine. The problem with that is that the regulations will prevent new models from having access to the same level of data that these companies have attained in an illegal way, which is obviously extremely unfair. But if I have to guess, That will be the situation moving forward if we're already talking about regulations. There have been discussions with the Japanese government, this. Past few weeks, and they have announced that their regulations are not going to be as strict as the European Union regulations. They haven't exactly defined what does that mean. But what they're saying is that they wanna have looser regulations in order to drive innovation. And if you looked at what the Japanese government have been doing in the past, year or so, trying to drive growth and innovation in the country. You will see that they've been taking some very serious steps in that direction, which has dramatically increased their capacity to drive innovation, especially in the tech world. If you look at their stock market in the first six months of the year, you'll understand exactly what I mean. So huge innovation driven by the Japanese government and AI is gonna be. Probably one of the main frontiers for that. So I'm expecting for them to execute on exactly what they said they will, which means looser regulations than what we have seen in the European Union. Another really interesting regulation related AI news is that the US government is going to be preventing NVIDIA from exporting its chips that are the engine behind most of the world's AI innovation right now to China. Now they may start. Preventing the new chips from being exported to China as early as this month, meaning July of 2023. This obviously has a negative impact on China's ability to do it based on US chips. The flip side of that, it will just drive China to develop more of its own chips, and it may fuel some more fires with the current tension that China has with Taiwan already, which makes a lot of chips that are not made by Nvidia. So overall, this is a move by the US to try to prevent US technology to be used by the Chinese to build a new and more advanced AI tools. This. May be a short term fix that may cause a longer term backlash. That's obviously my personal opinion, but I don't think the US government can prevent China from having that technology and all it will do is will expedite their own internal development of similar capabilities. One last piece of news that is not directly related to AI but is related to the AI world and the big players in it. Google announced that its new quantum computer that it's been working on was able to make calculations in seconds that would take the best existing supercomputers 47 years. Now, that's a very extreme breakthrough in computing, and while it's not directly related to ai, if you think about the enormous amounts of data Required in order to get the next generation of AI models. And you combine that with unique breakthroughs in computing itself. You understand the direction this is all going. And when you think about Google, Google has probably the two largest AI labs, or they had the two largest AI labs in the world that merged into one. They definitely have more experience and most likely more. AI engineers than anybody else in the world, but they're definitely a big, big player. And the fact that they now also have maybe the strongest computer in the world, you understand the direction that this is all going. These are some of the big news that happened this past week in the AI world. I urge you to keep on learning, exploring, and playing with ai. And until next time, have an amazing week.