Pedro Góes KeyNote Speech on Artificial Intelligence {AI} at MPI Georgia Luncheon. 

Posted on January 19, 2024

Introduction: 

Pedro Góes, a renowned figure in the tech industry and CEO of InEvent, recently enlightened esteemed guests at the MPI Georgia Luncheon with a keynote speech centered on “Optimizing Your Events with Artificial Intelligence {AI} and Human Creativity.” Syncing with his reputation for groundbreaking insights and inspiring discussions, Góes delved deep into the multifaceted world of Artificial Intelligence {AI}, bridging technology and human intelligence in ways unheard of. This blog post offers an exclusive look into his thought-provoking keynote, capturing its essence for those who couldn’t be there in person. Prepare to embark on a journey through AI’s fascinating landscape, guided by one of the event tech industry’s most influential voices.

“Good morning or afternoon. Let me just open these notes so we can have a fantastic presentation today. Back in 1939, we had a person who put together a team of cryptologists, and these cryptologists had written a combination of different machines that they used to decipher some of humanity’s most common problems back then. These problems were very tricky because they allowed the enemies to communicate without us knowing who was behind their messages. This was a big issue then because they could not prevent attacks or even understand what they should do to learn that. This machine combined a few things to help break a code. It was a complex machine that used mechanical words to change and simulate the enemy machine and potentially test different settings to decipher the message behind its received communication. This machine, by that time, was unbreakable. 

They believed it was unbreakable. There was no way for you to know what was behind their message, and the enemy believed it would be completely secure, so they did for many years. But then this British Mathematician and computer scientist saw the potential to build something that would change humanity, and this machine was called ‘’THE BOMBE.’’ It was used to decipher the enigma machine. The scientist Alan Turing assembled this machine and helped the Allies win the Second World War. This was a significant step and one of the very first steps in the history of computing. Some say that this was the very first attempt at building Artificial Intelligence {AI}. This was the first moment we saw that somebody could use AI for an excellent purpose. This machine (THE BOMBE) allowed you to automate certain things by trying multiple times to achieve the message that was behind the German communication lines, and this was used later to lay out most of the foundations that we see on computers today, so if you look at all the artifacts that Elaine was explaining earlier today, you’re probably going to find some of the inspirations that were used to create all of the devices that we have today with us.

So today, a few years later, like 80 years later, Artificial Intelligence {AI} is a huge market. Last year alone, some of the AI companies we had, like ChatGPT, today boast over 180 million users, so it went from just a few people using that in a small room to millions of people using it for daily communications on their computer devices and iPhones. Today, 65% of companies and CEOs believe they are going to use Artificial Intelligence {AI} in their business plan for this year, for the coming years, or in the next five years, and 97% believe they will positively impact their work, the way they’re producing business and developing services for their clients. So this is a big thing that we’ll probably see more and more. There is no way that we will go back to the way we were doing things 10/15 years ago because AI will be commonplace everywhere we go. 

Since the early days of AI, the process of breaking war machine codes has evolved a lot. Today, one of the things about AI is that it’s not just going to be repeated information from a code-breaking process. Instead of attempting all the thousands, millions, and billions of solutions available behind the code, AI today learns from the behavior of past examples. So when you get a sample of something the machine learned about, it will take that behavior/information that it already knows and uses that information forward. This capability with machine learning allowed computer codes to improve exponentially because you do not have just one key data point. Still, millions of data points and billions of pieces of information are stored up there, so the computers mesh together and create something really interesting. 

We can use so many applications with Artificial Intelligence {AI}, some of which I will share with you today. One of those are machine learning processes. What machine learning processes do is that they combine the data that they already know with new things. An example of a machine-learning process is this: You take a million pictures (it can be pictures of cats, horses, or dogs and you put all of these pictures into a computer, and then you tell the computer to figure out what the new thing is for me, and you feed it a picture of a giraffe. Then you ask it what this is. So the computer will figure out and tell you that the information you fed is what AI can do for us today. 

Then, there is something deeper. There is machine learning that goes beyond that, and that’s going to be ‘’Neural Networks’’. Neural networks can take the knowledge without you categorizing it and transform it into something new. It will take that same information and figure out how many legs the cat has and how many heads a centipede has. It will automatically figure out this information by accessing thousands and millions of photos. It will come up with real things just by using its own analysis. It’s really unprecedented that a machine can learn everything, and without us telling it what it is, it can just come up and provide insight into that information.

Another really cool one, and we’re going to see some examples of that, is something called an ‘’NLP’’ which is known as ‘’Natural Language Processing’’. What it does is that it has the ability to learn about the voices of humans. Then, we can actually use that communication to reproduce voice and create communication standards moving forward. One of these, for example, enables us to understand what the person is saying, allows us to transcribe speech so we can get texts, and we can use that to translate this outwards and use, for example, Google Maps when we know the machine is talking to us. More recently, it allows us to take input and have conversations live. So we can have another person speak to us in a different language, where you can say something, and the other person can listen to this in a different language and have a real-time conversation using NLP. 

Another example of using Artificial Intelligence {AI} is when you get something that complex and try to break it down into smaller concepts. I can try to give you an example: you can tell the machine, can you tell me how to make a payment? That for the machine is a complex phrase, but the machine is able to get that information, compare it to similar phrases that it has in its database, and understand that this is the same thing. So if it finds a phrase like “How do I transfer money” for the machine, this is the same thing. It means the same thing as how to make a payment and transfer money. The machine can understand that today by using this deep network of communication and how to put that together using the neural network, which is really interesting. For example, things like Baxter Road understand that it is an address. Also, if you tell the machine that Baxter is a person’s name, they understand that this is not an address anymore. So all these things are inventions of AI, and what they can do are really unprecedented.

I can give another example of how AI evolved over the years and became really powerful. 1950, for example, one of the first AI programs was called “Logic Theorist.” This was after World War 2 had ended, and it was really interesting at the time because that was the first time that we had a progress that was actually thinking and not just trying a lot of things to come up with an example of computer usage. So this was put together by 2 scientists, “Newell and Simon.” They were both based in the UK and America. They created this program at the time to see how to decipher mathematical theories so they could actually prove basic things like why 1 + 1 = 2. They could actually resolve this in the mathematical logic by using this machine effectively. It was actually funny because, then, they did a presentation for the society in the UK. After doing that and showing the machine (they had the machine running using AI, which was deciphering the algorithms by itself), they told them that there was nobody beyond Newell. Simon himself believed that there was any long-range significance to what they were doing, which is funny because they had AI in the 50s, 70 years ago. 

Still, they didn’t understand the machine’s purpose because they thought of a mathematical theorem, and the machine has no use case, so it’s probably not something we can use. They actually had this published as a paper, and one of the co-authors of the paper was not a person but the computer itself. This was interesting and ironic at the time, and they didn’t want to publish the paper. So, this highlights the difference in what we will see moving forward. New technologies, in the beginning, are not easy to adapt; they can be really early before time and take a lot of time to adapt later, but this is going to show us that in every interaction that we’ve had, they actually had some type of improvement just like we’ve seen previously in those examples 70 years ago.

So here we have two examples of how Artificial Intelligence {AI} shapes our world today. 

We will now see a few examples of things we have put together so you can see how we’re using these tools. The first one is huge companies (which are becoming increasingly common) like McDonald’s and other corporations, such as Coca-Cola. They’re using this Artificial Intelligence {AI} technology to move things forward in every type of environment, whether it’s in retail or event; we will see some examples of these events, but it’s everywhere. For example, when you go to the McDonald’s checkout and select your product, they can automatically recommend new products based on your check-out experience. When you go to Amazon, they can predict new products that you should buy 3 months from now, just because you have been purchasing those for the last 2/3 months. So these logics are being applied at these companies, but now it’s our time to apply these in our daily jobs because AI will be for everyone, and we can take the leap forward.

Another example is this video that we have on the right. This is a type of video that’s being used more and more by companies such as SONY Pictures. They use this to run scripts and automatically generate them, and based on this, they automatically come up with the scripts that work or not based on previous successful classics. So today, before you record the movie, you upload the final script to the machine, and it will tell you if it’s missing emotion in a row in the script. They can find emotions and predict how the viewer will feel at the theaters when they’re watching the movie just by uploading the script into the machine. So, these things are already happening today, and companies like this are already using AI in their daily activities to put things together. So next time you’re watching a movie in the theater, you have to think, did someone write that, or is it the machine tricking me to cry? That’s something we should think about. 

Another example that we have is this commercial for “Synthetic Summer.” What’s really cool about the video (it looks nice, and it’s a beverage, I believe) is that there are no humans in it. There is no background. All of it is 100% machine-generated, so this is the way that they’re using this technology today. This was built with the early version. Some weird things sometimes happen in the video. Still, the point is that this specific commercial was built with something called Gen 2, which was a data set of about 240 million images and about 6.4 million video clips. So, that’s using a combination of previous content that people have categorized. For example, the beverage that they have in their hands. A human went to another random video on the internet, and they categorized it by saying this is a video with a beverage in it. They have categorized over 6.4 million video clips, and there is a database available to generate this. 

So what the AI can do is it can mash the videos and images together and use these 240 million images, combine them, and put this on a video, and a human can go there and edit them as a single thing, and that’s how you have videos like this. That video on the left (synthetic summer) was built and is an excerpt of different things and mashing them up together. The video on the right is more interesting, I believe because this video was built. This video was completely built by a computer without human input. A prompt like “Come up with a video about a cat playing with something” automatically came up with something by using all the script fed to it, and all the information and images you’re seeing right now were generated. So it’s really fantastic what’s going on and also a bit scary because how will we differentiate between something real and something that’s not in the future? With computers today, this is something that you can do at home. You can go home and open one of these algorithms and generate a video like this just by using the knowledge that is available today. So, in the future, there will be many challenges to validate the video because commercials are built like this every day. It’s the future, right?

Then, we have other examples from other industries, which are really cool, by the way. We have this being applied everywhere today, from supply management to customer service and marketing analysis. 

Then, we have a couple of examples of Automation. This is something that we can apply in our daily jobs. In previous examples, it was interesting to see the impact of what’s coming (it’s already coming today). Still, I wanted to share a few examples of today that we can use in our daily jobs (customer service, sales and marketing, and event planning). These services sometimes take mundane activities and automate them for us, and it’s not hard to use them. Some of their tools are really affordable and cost you like $10 to $20 a month, and you can get them from a website, where some of them, for example, are going to be customer service. In the past, we had to go through prompts like “What do you want to talk about,” “I want to talk to sales,” and “Price 1, price 2, price 3.” This is long gone. This is not going to be the future.

In the future, we’re going to have someone talking to you, that’s a machine, and you’re going to tell them what you want, and you’re going to feel like it’s a person talking to you, but it’s not going to be a person, it’s going to be a bot. You’re going to have a conversation, and if the machine doesn’t know what’s going on at a certain point in the conversation, they could say that they’ll transfer it to their supervisor. And then this will be the first person you’re talking to. This is happening already. There are technologies that can be implemented in customer service that talk to a human and are available via phone and text.

The same thing can happen to sales and marketing. Today, you can automate communication and things like the spam you’re getting on LinkedIn. That sometimes is not a person at all. That could be someone who’s spamming you to get things out there, but the content of the communications received, and the landing pages written out there are also generated by machines. You can just go somewhere and have the machine write the content for you, and you can even perceive the difference between AI-generated and human-generated content. Sales and marketing could really benefit from this.

Also, we have things like Human Relations (HR), which are taking this a leap forward. For example, a use case where you can have a performance review done automatically by bots, and you don’t really feel like that’s a bot talking to you or reviewing your performance. They can also come up with recommendations. Even if your manager doesn’t know, the bot knows what to improve. It’s actually scary that some companies are able to do that. The use case that most of us already identify is e-commerce in retail. This is available out there all the time. We can mention McDonald’s, which is doing this all the time. Amazon is also doing the same thing for us, and then there are companies that enable us to build our own shops. So you can take your products and input them on these online stores, for example, your own website where you sell tickets or other technologies, and that will automatically recommend which product you should buy. It will remind the customer if they need to buy a refill, which will be done automatically for just $20. Companies like Shopify are doing things like that. 

Then, I have examples here of how learning and development can benefit, like making the videos you learn from when you join a company. 

As you know by now, these are not humans. These are built by machines, and the voices are also built by machine learning. So what’s really interesting here is that in the past, you would watch a video like this and you would say, “This is not human,” but if you look at this video now, you have to pay very good attention to figure out that maybe that’s not a human. So that’s the level of things that we’re going to see. 

Before we go to the next slide, something happened to my brother around November last year (2023) that brought to me the potential of AI in a bad way. So we received a call from his bank, and it was his branch manager talking to him, saying that they needed to wire someone to wire some money to someone else. My brother was going to make some investments, and she told him that this was an opportunity that could not pass. Now, note that the call was from his bank, and the voice sounded like his bank manager. Now, he logged into the app, and a link was sent to him, which he clicked, and he got a code that he shared with her on the phone. 

Eventually, he found out that it wasn’t her, not his bank manager. In the past, when a scam like this was perpetrated, you’ll notice some discrepancies in the information the scammer would share with you. It could be the voice or the codes you’re asked to send. These scammers will get better and better at getting information from us right now. Now, we need to be very careful and figure out if that’s the actual bank app that you’re actually using. A brother, at the time, lost a substantial amount of money that he had to recover the next day because he figured out that something was wrong. AI can be used for both good and also for the bad.

Then, as we go into event planning, I have a few combinations that were used for the best practices. So, when you’re using Artificial Intelligence {AI}, I recommend always trying it. Use your time to experience it properly and just try new ideas. There are many things that we could do as event planners. 

We have things like Audience Analysis, who I should invite to the next event, and where they are. Just go to ChatGPT and ask it, “I am going to host an event for the MPI chapter at this day and time. Who should I invite?” “Can you put together a list of people?” It’s not going to give you a list as good as you would yourself, but that will provide new insight that sometimes we don’t have and allow us to learn from what the machine is saying and improve that as well. 

The same thing happens with budgeting and financial planning. We have seen some good examples of taking information from past/historical events and saying, “Predict my budget for the next year. Here are my budget reports for my last 3 events.” Just upload the spreadsheet on the system (the AI has an option to upload that), and you have incredible results. I tried that the other day, and it was quite accurate compared to what we usually have in our manual calculations of spending on annual tradeshows. This is a good example of what you can try for budget and financial spending.

Then, for marketing, we have Promotions. We have implemented this on our own software at InEvent through our ChatGPT Integration, which allows you to type in something like “Make marketing communications for me for this event in Panama,” and it comes up with all the mail cadences that you need to send that you need to send. For example, send an email, send a reminder early on, invite people, and tell them what will happen in this event. Then, we have event planning tools that you can automate as well. You can ask an AI to do something like “List all the tasks that have to do with my event.” You’ll get a response like “Email these pictures, make sure they are going to arrive on time, sponsors insurance, hardware.” You can automate all these tasks and review them by the end using some of these tools for task management.

Then we come to Registration. Registration is changing a lot; we have a lot of AI-recommended tickets and products you can buy. It can also predict when you can sell better. Do they sell better on a Monday or Friday evening? You don’t know, but you can let AI figure that out for you. You can just plug and play all of these for registration as well.

Then, we also have this for virtual reality experiences, which we are improving greatly. We are seeing a lot of really good use cases from here. For example, you can have the host of a virtual event be one of these bots that we’ve discussed today. 

Facial Recognition is another very good use case we’ve seen in the past year. So, moving forward on events, especially on larger events that need people to check in, we don’t need to get queues anymore. We can end this once and for all. You’re just going to go directly into the check-in process. There will be a machine that’ll take your photo, and you’ll be able to check in instantly.

As we wrap up our discussion, we are stepping into an age where technology is transforming our experiences and making them more convenient and efficient. Whether it’s a mass event or a virtual reality experience, our innovations aim to redefine the landscape. No more queues, no more waiting – just seamless integration with advanced technology. We’re working tirelessly to ensure that this future is not a distant dream but an immediate reality. Let’s embrace this change and look forward to a world teeming with endless possibilities. Thank you for joining us today and investing your time in what promises to be an exciting journey ahead.”

Conclusion.

Thank you for reading. Be sure to share this article with your fellow event professionals and event planners, who will find the information shared here helpful. 

About InEvent 

InEvent is a leading all-in-one event management platform that simplifies the planning and execution of virtual and hybrid events. With comprehensive features, including registration, event marketing, attendee management, and more, InEvent has one of the most robust platforms in the market in terms of stability, branding, and engagement. Since its establishment in 2013, InEvent has garnered numerous awards, serving as tangible evidence of its transformative impact on the event planning industry. InEvent continues to transform the event planning industry through strategic partnerships and groundbreaking technology.

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