Yeah yeah. Welcome to pocket guide ai uh artificial intelligence for executives. My name is on capitol man. Ceo of Goldman consulting and I'm your host for today and with me are my great global panelists. All experts in their own field. Thank you and thank you for having me. Um My name is Samir A and I'm based in Saudi Arabia and I had research at an economic consultancy Maxwell stamp. I am sorry to uh I am a I'm an enterprise architect at uh spin master. We are a company in Toronto Canada. Thank you and scare. Hi this is Nicole. Nicole right now from Germany I'm project management consultant I. T. Project management consultant at Cinema Small I. T. Project management consultancy. Happy to be here today. Hi everyone. This Manjit from Perth Western Australia. I've been working in data analytics for quite some time now. Recently joined Bernard.

So as a manager for debt engineering and architecture. Thank you. Hi everyone I'm mr kobayashi from Tokyo Japan. I'm project manager are now aim to create a guidebook called air. Project. I'm so exciting to participate apartment thank you. Hi my name is Nick remote Germany. Nurnberg Justin it's house I'm of Germany nice to be here. Good thank you very much for coming. So let's let's start talking about what is artificial intelligence and I mean we can all agree that the word artificial uh is relatively easy to define. Its something not natural but what actually is intelligence. Um A lot of people When when they coined that term in 19 in the 1950s artificial intelligence. Um nobody really knew what they actually meant when they said artificial intelligence because the word intelligence itself is like such a buzzword.

Nobody really uh you know, there's not one right definition for intelligence. So that's why we want to go back a little bit in time and see how people started to think about intelligence and what intelligence meant for them and how intelligence is tested today. Because if we look at intelligence tests, then we know how they clearly define intelligence. So it all started um roughly around in the 19th century with a guy called Paul Broca. And uh he was a man with a very big head and that is actually important for him because paul Volcker uh he tried to identify at that time um Intelligence by measuring the size of the skull. And uh he was living between 1824 to 1880 and at that time a lot of not so many things were known about the brain and they made analogies to muscles for example, and they say, okay if I have a bigger muscle then, you know, I'm stronger.

So they, you know, from that kind of perspective, it made sense for them to try to use that method. But paul Broca uh he was a scientist. So he started to measure things and this was one of his theories and he tried to prove or disprove this theory, but well he didn't really go anywhere with that as we learned later. But this was the beginning. Um this was the beginning of the intelligence research and everybody has an area in his brain, in your brain which is called the Broca area. Not so many people know that, but he's uh he was very famous at the time and he was also the discoverer of the limbic or emotional system. And uh so so he opened the skulls, he looked at the brain, but you know also at that time if you if you if you get a sense of it, also electricity was not really that known, you know, what is electricity doing our in our body when you think about Frankenstein's monster, you know, they used electricity and they were fascinated by, you know, putting electricity on muscles and the muscles are moving.

So a lot of things were beginning to uh at the beginning of understanding. But then uh we go a little bit further in time. And there was a guy called Free Beanie and you see it at that time, the beginning of intelligence research was in France. And this uh free beanie, he was living from 1815 7 to 1911. And in 1889 the first French psychological research laboratory was founded and he was the director of this research laboratory. And for him, living in this enlightenment face of french history. He tried to use experimental methods to decipher the world of psychological wonders and for that time it was very important to use experimental methods because the catholic church still had a big hold on on everything when it came to research, especially to the brain.

And that's why, you know, they had the slogan that they said, you have to go in so small steps that faith can't fit in between these two steps. So that's why they tried these experimental methods. But then in 1904, it was after, you know, a few years after they had lost the big war guns Germany. But then the first world war was at the beginning, you know, was at the horizon, you know, it's still at 10 years, but it was at the horizon. And then the french ministry approached Alfred Beanie and his research laboratory and they asked him to develop the first test for the french Ministry specifically to discriminate between, you know, mentally challenged and normal kids. So it started off in the educational area, but then it also Grew into the let's say military complex. But then this test, which he developed together with a young doctor called Theodore Simon.

This became a huge hit. And this test was known as the semen beneath test. And you might think like, yeah, it's 1904. Um But this test um then was transformed a little bit in 1916 by by research at Stanford University uh term in and he revised it and this Seymour Benitez became the stanford Binet test. And this stanford Binet test is still used today in its fifth revision, but it's still used today to determine what is intelligence For us here in Germany. The big name was good of him to and he was living from 1920 to 1989. He was fighting actually for the Germans in the Second World War, he was captured and uh but then he was brought to America and as a prisoner of war, he was allowed to study psychology in Berkeley.

So in 1944 he started to study psychology in Berkeley after the war. He came back and studied in the second oldest university in Germany, no, that actually the second oldest psychological research facility in the world and gutting. And there he also wrote his PhD thesis actually he and I was studying in the same building. So I came several years after him but you know, they always told us that story, that's why I bring up his name and he wrote his PhD thesis to develop the test. And this test was called the STD test. And and this PhD thesis was first sold in 1953 by a company called Whoever and whoever is nowadays the biggest testing company in the world. And this I str test which are stands for revised uh is still used today and here we are Goldblum, we're using the str also to determine intelligence for employees in the company.

So if we would look at this I str test and if we see how they define intelligence then we could get a good look at what intelligence actually means. And in 1955 that's why I brought in the str test. There are many more tests. Not many more, but there's a handful of other tests which are used today. But it's nicely that you know this test came out in 1953 and in 1955 the guy John McCarthy, he coined that term artificial intelligence. And uh and he also was the man who organized the famous Dartmouth conference and and some uh 1956. So in that conference started a I. As a field and everybody, you know many of you we did the M. I. T. Course together. So you have all had to learn that to get your credits and uh and then this, you know, and uh Minsky, he also later joined McCarthy at M. I. T. In 1959. But the thing is that you know when he coined the term artificial intelligence, that was at the time where also I'm Tower developed his uh intelligence tests.

So we can imagine that these definitions of intelligence are pretty close together. So what we're seeing here the sTR test um for for our listeners, I just brought our panelists, the picture of exemplary um intelligence test from the I. S. T. R. And what we're seeing here, we are measuring with these candidates on several scales to solve verbal numerical and figural problems. For example we tell them you know um there's uh there's a line of numbers 12357 and then tell us the next number or uh you know you have words and then you have to match them or you have 55 Cubes in a row and you have to say Okay how should the 6th 1 look? So these all are you know some kind of problem we are solving and we are still solving them. That if you look at the stanford Benitez it's pretty pretty similar you know and that's why these two tests are still around because the str and the uh stanford Benitez there have a high correlation between them but all of them what they're doing.

They're solving verbal numerical and figural problems. And these three areas then are shown as interpret ble scales for verbal intelligence, numerical intelligence. In federal intelligence we add one more dimension to that and that is our ability or human ability to store and decipher memory uh information from our memory. So that you know layman's terms would be memory. You know you have ultra short short and long term memory and the better the memory that is also part of your intelligence or what we are or what they were looking for back then for the general factor Because you know math and data science had a very very early encounter with intelligence because they did a lot of factor analysis to find the general factor of intelligence.

You can imagine 400 years scientists they're beating their heads you know it's really verbal numerical and figural problems. Are they the basis of intelligence or not? Mhm. And then what we add there together to this let's say fluid global factor. You know the that's what makes our intelligence, we add something it's called crystallized intelligence or crystallized factor. And that crystallized factor is knowledge. How do you use your let's say software and hardware to learn? And you can imagine that this crystallized factor has a lot to do with education for us humans as education you know machines learn differently you know but how well do you learn, how well do you use the capacity you have to make sense of your surrounding And the 4th area which we are using and the SDR is logic?

Yeah. How well is your logical thinking? And there we differentiate between inborn logic and learned logical behavior. But the focus here is solving problems you know that is one of the key things for for intelligence and because a lot of people especially the ones who are not good in math um they all thought that a guy called Howard Gardner, he's a psychologist and in Harvard and uh he proposed a theory that there's more to intelligence than just verbal numerical and figural um and memory and logic and he introduced the theory of multiple intelligences. you know, he had for example, visual spatial intelligence for fighter plane pilots or linguistic verbal intelligence or logical mathematical intelligence.

You know, this is these are the three which were also using, but then he said, okay, there must be something musical, rhythmical intelligence or bodily kinesthetic like Ronaldo when he plays soccer um or interpersonal, you know, the social touch we have or the interpersonal. How well are you able to self reflect? You know, how well are you able to perceive your thoughts, your feelings, your bodily bodily functions and to make sense out of them? You know, for example, somebody who would be um easily aggravated would not have a high level of interpersonal um uh intelligent. And then he also had one, it's called naturalistic naturalistic means that uh that you're good uh in the nature with the nature, for example, farming, gardening, uh working in a zoo or working with animals. That will be the naturalistic and existential means that he's um that a person is spiritually well uh well off, you know, that they are able to meditate well and find the connection to God what to Godly Godly entity.

And then a few years later he added one more like the ability to teach, but although it was very very famous and a lot of people liked it and he wrote a lot of books about it. It could never be proven that these multiple intelligences um you know that they actually are their own factors. Uh as we talked about this general factor, you know when when I when I take it to effect analysis with stanford Binet test or with the I str we see that they load on the same factors. But when I try to identify autonomous factors like for example bodily kinesthetic intelligence, we could never find that. Yeah. And one more thing um you know I'm a big office fan, you know the office, the american version of the Office and this Howard Gardner was born in Scranton pennsylvania. So if anyone knows Diana Mifflin in the office, you know, they would think, oh yeah, so he chose not to work in a paper company but so this is how it gotten.

But what this Howard Gardner was actually known for is his own definition or let's say, a very popular definition of a cycle of intelligence and it's a very short one. And as we saw in the str he also focuses on problem solving. And he says that intelligence the ability to solve problems or to create products that are valued with one or more cultural settings. So if we adapt that definition of intelligence, because today we are talking about what is artificial intelligence, you know, we would say that artificial intelligence is a non natural system because it's not natural with the ability to solve problems or to create products that are valued within one or more cultural settings and but then there's one thing if you think about this artificial intelligence is a it's a system with ability to solve problems or to create products.

If you think of Excel, you know words Excel, it could also be considered as an Ai because Excel also has the ability to solve problems which are valued within our society. You know, it's like so many people use Excel and to make a distinction here between Excel and True Ai uh we will have to add one more component to that and that is learning No. So in comparison to a normal software program, artificial intelligence has the ability to change and to become better. The more data is fed, you know, like like as humans, you know as you saw in the str learning with a crystallized factor is a huge component of intelligence. And so we have to add this crystal light. What for human is crystallized function, but for artificial intelligence is this learning capability. So and it means it has to be adapted adaptable. And as Darvin put it, it has to fit, you know, a lot of people make a mistake when when Darwin says survival of the fittest, it doesn't mean you know the one who can run uh the fastest or to be fit but it just means that it has the ability to fit to certain circumstances that is the ability to change.

Right? So uh if we add this learning component to our definition would mean that artificial intelligence is a non natural adaptable system that has the ability to solve problems or to create products that are valued within one or more cultural settings. And I like but I don't really I'm not a big fan of the multiple intelligence from howard you got now but I really like what he said about value because a lot of companies when they start A. I. And we talked about a few other things sessions you know that they just do A. I. For the purpose of AI but it has to create value and so we can either create it because we're business podcast here. You know we focus on the business so it has to create value either for the customer mean better service or value for the employee or value for the business itself. So for example increase the revenue if it it increases value for the employee.

You know they could just work faster or better or have have a better work experience. So in summary that means that I AI system has or a system which wants to call itself a I has to increase either the value of the customer, the employer or the business. And if they are not they are not considered a. I. System. And I know this is a very let's say aggressive statement or you know it's an edgy statement but we want we want to use that statement and companies or goblin. We're using that definition. And companies because we want to put pressure on the company so that they first think about what they can do with a I. Uh and that they define a true business case before they start their first AI project. So there you have it it's the definition for artificial intelligence. It's a non natural adaptable system that has the ability to solve problems or create products that are valued within one or more cultural or business settings.

Thank you. And scar. It's very enlightening to go through the how the intelligence actually came about in the history of intelligence. But I have 11 question for the entire panelists is the word artificial intelligence really fitting um to uh because it is at the end of the name, it all these theories are created by human, whether you look at mathematics, you know, whether you look at algebra vectors, uh they're all made uh you know, all these theories are created to solve certain problems. Right? So at the end of the day we're using uh these human created theories uh through through a machine. Uh and and you know using data and coming up to some sort of a conclusion right at the end of the day, so this is OK.

I understand it is artificial but at the end of the day it is the ability to solve problems which which is originally solved by a human. Right? So at the end of the day is artificial truth, You know, a definition to answer sata if artificial intelligence is really artificial, I wouldn't say completely artificial because it is um intelligence of humans um you know, input in machines that learn and that gives an outcome. So it's blended, I would say Yeah, I mean this is this is what I mean, obviously there are many theories, Right? So there's also talk about augmented intelligence, right? So um you know, it's something that is used to enhance human human ability to make decisions, Right? So you're enhancing um you know, this is you are actually, I mean the whole concept of the artificial intelligence is only gonna be um be um you know, the the whole point of the article intelligence is not to eliminate the humans, right or not to replace humans, but it is it is to actually enhance the enhanced the ability for humans to make decisions by looking at, you know, these bottlenecks or black holes of where they cannot see what is happening.

And and that level of uh information is presented to them in a format that they can actually make a decision. So they are going to make a more uh more uh educated decision of of a situation. Right? So I feel like you know, augmented is kind of uh is more appropriate than artificial. But you know, again, at the end of the day, uh you know, uh I am I am really, I'm just really trying to uh create some sort of a debate here and know know what other people think, right? So uh so because when you say artificial, we're just thinking that we're talking about machines, but that intel intelligence has created uh like the serious human intelligence. So so I look at look ai as a as a human powered machine intelligence because machines are not learning by itself initially for the first time, also humans started teaching machines.

So uh so in my uh so when I think of a I think of human power machine intelligence, because when I when I read this in my brain it tells me that whatever Ai systems I'm going to deliver, I have to make sure that humans in the loop. If I just keep thinking about a I will just keep thinking about machines only, but magic, this is exactly what the uh european Ai Ethics Commission just said that they said, you know, they want to keep humans in the loop, they don't want to have humans out of the loop because then there's no oversight anymore and they're a little bit afraid if you don't have human oversight um and they have a lot of problems, but you know when we when we measure, you know, when companies come to us and say, okay, can you help us assess um if a person fits into our company, we measure I. Q. But you know, this is just one part. We also measure personality because as you know just think of somebody who is hi in verbal numerical integrative intelligence but they're low in um emotional stability.

You know you would call them crazy genius and sent them in an office far far away from the others. You know because they can't really collaborative and when when we you know it's uh such a also said is the augmentation of intelligence. So it means what we also saw in the multi course. You know we want to have some sort of collaboration between the machine and the human and the moment you have this method for the the entity of um of collaboration. Then you need uh you need personality because you know if your if you want to accept an artificial life form or a machine to be your colleague colleague, you know they have to they have to have certain social skills, they have to be able to be open for you um for new endeavors and new things to do. And if you know if a machine doesn't have a personality uh then it can always just be considered as a tool. And otherwise it's not a real collaboration.

You know if you really think about these things, it's like what do we want from this machine to do with us? Do we want them to be our friends? Do we want to be you know we're afraid of them that they are going to be our masters but we want them to be our friends. Maybe, you know, our servants, you know, but there's some sort of level of, let's see assumption we have in our head how we want to interact with this with these machines. And but in the moment when we talk about ai right now the area of robotics is not really um it's not really pronounced everywhere, right? I mean we have boston boston dynamics which have now they're small robots running around, we have drones flying around. But just imagine we have the ability for robots to have a face because humans always, you know, react to face is very very, very much and then they would be in our uh in our environment and then they immediately, you know, we immediately attached, you know, we don't, you know, we we transfer our human feelings to machines very, very easily.

You know, just look at kids when they play with their dogs or you know, or we call it animation, you know, we were animals, mean spirit. You know, we put spirit into something and we are very very very, very good at that as humans. So I'm very excited to see when that happens when they hit that level. Then the artificial intelligence also needs personality. Yes, correct. I still remember when I started a I for the first time in 2001 as part of my masters, it was not a very hot field and because of because the technology was not available, infrastructure such as cloud was not available. So I started those things theoretically but I didn't get a chance to proceed in that but uh as you were touching upon human in loop I think because ai itself um uh has like has so much potential to add value but at the same time If not govern properly can lead to the disasters and hence in 2007 when I was taking Microsoft Ai program, I when I started ethics and law uh it was I'd like that model uh much more than other models because they talked about so many scenarios where ai without proper governance around it had had created so much chaos so so I think as you mentioned in the last few years uh Principal has been designed to ensure that people who develop those applications as those applications and deliver those applications, they comply with those rules otherwise.

Um and just to ensure, ensure that those those systems are so they can explain themselves as to what is going on going on inside them and also as a as a citizen, as a responsible citizen, we also pay our they at most attention to those aspects of Yeah, I mean I mean very very, very valid points that you brought there. Again, I want to touch upon. One additional point. I know you don't have much time here but um you know um the one thing that I I feel there will be a lot of development in is uh is the the competition and the fair ethics uh of using artificial intelligence. Right? So a I should not be um should not be something that is only um only applicable to the big corporations. Right? So uh the governments have to step in and make sure that uh they they actually bring in healthy competition between you know, the smaller companies who do not have the the ability to actually uh spend on um spend on technology or spend on a i uh you know, workforce uh to actually create a strength of economy for them.

So, so I feel like there will be a lot of development in the space um uh in the future and manage it. You've actually nailed it really well. Uh So uh that part is still I feel like it is still open for a lot of lot of development. Right? So thank you for bringing that up. Perfect. Thank you very much everyone for this very lively discussion today, and I hope our listeners have a better understanding of what artificial intelligence or intelligence is. Thank you very much. Mhm.