[0:00] Artificial intelligence is everywhere right 
now. In your phone, in your car, even writing  
[0:05] emails for you. You may be wondering if there 
are actually any limits to what AI can do. I've  
[0:11] heard many people over the last few decades 
confidently assert AI can do certain things,  
[0:16] but it's never going to be able to do, and 
then you fill in the blank. Guess what most  
[0:20] of those predictions have in common? They were 
wrong. The past few years have shown exponential  
[0:25] growth in AI capabilities, bringing it from the 
research lab to everyday life. And it's doing  
[0:30] most of those things that so many thought 
it never would or even could do. Of course,  
[0:35] many limitations still exist, but my advice would 
be this. Don't bet against AI, unless of course  
[0:42] you want to be wrong. In this video, we're going 
to start with a look at what knowledge really is,  
[0:47] how it differs from data and information, and 
this will help set the context. Then we'll take  
[0:52] a look at what have been considered to be the 
limits of AI and see which ones of those things  
[0:58] have actually been accomplished and what's still 
left to do. Then we'll conclude with some ideas  
[1:02] about the role of AI and humans and where each one 
excels with the hope of learning how to use this  
[1:09] amazing technology to our best advantage. Let's 
start off with looking at the relationship that  
[1:15] exists among data, information, knowledge, and 
wisdom. and we'll use this pyramid to spell it  
[1:23] out. So, we'll start with data. Okay, this is just 
basically raw facts. If I give you data that looks  
[1:30] like this, I say 10 six uh 42 and 8. Okay, that's 
raw facts. So, what you don't know really what to  
[1:39] do with that, but that's data for you. Okay, now 
if I add some context to this data, now we have  
[1:47] information. So this is where we sort of processed 
it a little more and now I'm going to tell you  
[1:53] that this data actually represents the ages of 
people in a room. So now we have more context.  
[2:01] This has more meaning to us. Now if I take that 
and say okay but let's apply some interpretation  
[2:08] to the information that we just had. Now we end up 
with knowledge. Now knowledge tells us yet more.  
[2:16] So for instance in this case we might say okay 
I've observed that most of the people in this  
[2:22] room are under the age of 21. So now we've done 
yet more processing with this. And now finally the  
[2:30] last piece of this is applied knowledge. Applied 
knowledge now gives us wisdom. and wisdom might  
[2:38] look at this all of this information, all of this 
data, all of this knowledge and say, you know  
[2:44] what, we've got these people in a room. Let's 
do something like uh do age appropriate games  
[2:50] to keep them occupied. So, uh the 42-year-old 
probably won't mind too much playing a game that  
[2:57] a 10-year-old and a and an 8-year-old would play, 
but you know, they they can go along with that  
[3:01] for a little while. So this is an example very 
trivial example but you can see what I've done  
[3:06] here data information knowledge and wisdom each 
one of these adds more context more interpretation  
[3:14] and all of these then lead to the ultimate of 
wisdom. So another way to look at this pyramid  
[3:20] is data. Well, that's a database. For instance, 
you know, we can store a lot of stuff in there,  
[3:25] but that's all it is, just a collection of raw 
facts. Information, okay, we have an application  
[3:30] running on a computer. That's now information 
technology. That's why we call it that. We've  
[3:35] added context to all of that data. Knowledge, this 
is where AI really starts to come in. Now, we're  
[3:41] adding more interpretation to the information 
that we've just processed. But here is where we're  
[3:46] still trying to get. And that's wisdom. Back when 
I was an undergrad riding my dinosaur to class and  
[3:52] studying AI in its earliest days, there were a lot 
of things that people said, "These are the limits  
[3:57] of AI. Maybe one day we'll have a system that's 
able to do these, but they won't be anywhere,  
[4:02] maybe even in our lifetimes." For instance, one of 
the things that was talked about was the ability  
[4:07] to reason. We needed a system. If we really 
consider it intelligence, then then reasoning  
[4:12] is a part of that. So the ability to figure out 
and do problem solving, complex problem solving,  
[4:19] uh this was beyond our capability uh certainly in 
those days. But since then we've come out with a  
[4:26] computer that can play chess. IBM in 1997 came 
out with a computer called Deep Blue that played  
[4:33] Gary Kasparov, the best chess player in the world, 
a grandmaster. That's a lot of reasoning. That's  
[4:38] a lot of problem solving. People thought you'd 
never have a computer that would be able to beat  
[4:42] a grandmaster. Again, that's already happened. So, 
what seemed to be a limitation wasn't. Another one  
[4:49] that was really difficult for a long time was 
natural language processing. Uh, human language  
[4:56] has a lot of nuance, a lot of idioms, things 
where we say things that we don't mean literally.  
[5:01] And sometimes you're supposed to interpret it 
literally, sometimes it is figurative speech.  
[5:06] Uh for instance, as I've given examples before, if 
we say it's raining cats and dogs, we know that it  
[5:10] doesn't mean that there are small animals falling 
out of the sky. That's an idiom. So we have if a  
[5:16] system is going to really be intelligent in 
the way that we are. It needs to be able to  
[5:20] understand those things. It needs to be able 
to understand things like humor and understand  
[5:24] when you're cracking a joke and when you're not. 
Well, sometimes people can't tell that either,  
[5:28] and sometimes it's because it's a bad dad joke. 
But be that as it may, in general, we're able to  
[5:34] tell the difference between what is humor and what 
is not. And we've actually made some advancements  
[5:39] here. In 1965, there came about a first the first 
of what really is the modern chat bots, but this  
[5:47] was not using modern technology called Eliza. And 
it was able to have conversations with you. Now,  
[5:53] it wasn't very great conversations, but it would 
ask you questions and and answer questions.  
[5:59] how are you feeling today? How does that make you 
feel? Uh this kind of thing almost like you feel  
[6:04] like you're talking to one of these very passive 
psychologists. Uh but IBM advanced this a lot in  
[6:11] 2011 when we came out with Watson which played 
Jeopardy the uh TV game show and was able to win  
[6:18] and beat champions at that because Jeopardy is 
full of natural language and play on words, puns  
[6:24] and things like that. You can't program all of 
those into the system and have it know those. It  
[6:29] really has to understand the meanings behind 
those things in order to do it. And in fact,  
[6:34] as I say, we've already accomplished that. And 
look at today's modern chat bots. They're able  
[6:39] to understand a lot of this nuance and they're 
able to take the instructions you give it in  
[6:44] natural language and understand what you mean in 
a surprising way. In fact, I think that's maybe  
[6:50] one of the most remarkable aspects of generative 
AI technology is that it's able to do that for  
[6:55] the first time. We feel like a computer really 
understands us. It's able to infer what we're  
[7:01] asking for. In some cases, even anticipate the 
next thing that we need, just like a person  
[7:05] would. We consider that to be intelligent. 
How about creativity? The ability to create.  
[7:10] I remember hearing a lot of people say, you know, 
computers can't really create information. Well,  
[7:16] they actually do. Uh, we've got where with 
generative AI, we can create art. We can create  
[7:22] new works of music. And you can say, well, but 
those are really just mashups of existing. Well,  
[7:27] guess what? When people compose a new song or draw 
a new picture, we're influenced by the things that  
[7:34] we've heard as well. Listen to all the top musical 
artists that you know, and they'll tell you, "Oh,  
[7:39] yeah. Here are my musical influences." So, those 
things all went into the back of their heads and  
[7:43] influenced the way that they create. So, we are 
creating new things and they are variations on  
[7:48] the old. But that doesn't mean just because a 
computer did it, it wasn't creative because in  
[7:53] fact it is. They're coming up with new ideas and 
will continue to do that. We base our learning and  
[7:58] our creativity on certain things that have 
been done in the past and so does AI. Now,  
[8:04] here's another one. Real time perception. 
Things like robots. Well, that was the stuff  
[8:12] of science fiction at one point, but we have them 
today. And you might not think of it as a robot,  
[8:16] but a self-driving car is one of those where it's 
having to in real time perceive its environment,  
[8:22] see what's going on, anticipate where the next 
car is going to move, and where it's going to  
[8:28] be at a specific point in time and do all of 
those calculations in real time, and make real  
[8:34] uh decisions about that. Robots are having to do 
the same thing in order to navigate around a room.  
[8:39] So all of these things that basically we used to 
consider to be limits of AI, I'm going to say,  
[8:46] you know what, we've done all of those. Now, let's 
take a look at some other areas where we've made  
[8:51] progress, but I don't know if we would say, you 
know, it's sort of mission accomplished yet. And  
[8:55] one of those would be uh the area of you've 
heard of an IQ, how about an EQ, an emotional  
[9:02] intelligence uh and an index for that? Well, these 
systems are able to simulate that. And honestly,  
[9:10] I feel like some people are just able to 
simulate emotional intelligence as well,  
[9:13] but that's a whole other subject. But an EQ in 
a system, you can see in the modern chat bots  
[9:19] the ability for them to understand your moods 
and the way that you're expressing yourself.  
[9:24] So there is some level of awareness in terms of 
the way that you're describing things. I mean,  
[9:28] we have the stories about people who felt an 
emotional relationship to a chatbot. Well,  
[9:34] some people feel emotional relationship to their 
shoe, but that's a whole other thing. The fact  
[9:39] that these systems can talk to us and understand 
at least give the appearance of understanding  
[9:45] moods and things like that is certainly in the 
area of okay, I it looks like we're doing this  
[9:51] at least in some cases. Now, another area that's a 
limitation though that we still have is this area  
[9:57] of hallucinations. Hallucinations are a difficult 
problem. and they're a a byproduct of generative  
[10:03] AI where the system basically confidently asserts 
something that just isn't true. So it's trying to  
[10:10] predict what the right answer would be and many 
many times it's right. It's shockingly right.  
[10:15] But when it's wrong, it is shockingly wrong in 
these cases. Now we've got technologies that  
[10:20] are making hallucinations less and less likely. 
Uh things like retrieval, augmented generation  
[10:26] uh helps with this where we feed additional 
information to give more context so that the  
[10:31] model doesn't just use its own imagination to come 
up with answers. Uh things like mixture of experts  
[10:37] helps as well where we have different models 
used for different areas. Chaining of models.  
[10:42] Uh so there are things that we can do in order 
to reduce the hallucination problem and we're  
[10:49] doing that. So this is one of those I wouldn't say 
uh is a solved problem but we can certainly see  
[10:55] that we're moving into it. So this one's somewhat 
solved. Okay. So, those are the things that we've  
[11:01] kind of already done or are still working on 
and maybe be able to see an end in sight. Let's  
[11:06] move those out of the way. And now, let's take a 
look at the future. In other words, what are the  
[11:11] current limits? What are the problems that we're 
still having to to work on these days? Well, one  
[11:16] of the limits of AI is a thing called artificial 
general intelligence. Right now, we see AIs that  
[11:22] are super smart in a specific area, in a specific 
knowledge area. Now again with some of these chat  
[11:28] bots that we have today, they seem to know a lot 
about pretty much everything, but they also have  
[11:33] limitations. For instance, they don't do real-time 
perception. Uh they can't tie their own shoes,  
[11:38] for instance. So artificial general intelligence 
would be something that was as smart as a person  
[11:43] doing all the things that we consider to be 
intelligent and at least on par with what a  
[11:48] person would do across all the different domains. 
That's something that we haven't really fully  
[11:52] achieved in a single system yet. The next level 
beyond that would be artificial super intelligence  
[11:58] where we have something that is better than 
humans in every domain and that's the right  
[12:03] now again the stuff of science fiction. Not saying 
that we won't do it but we haven't really done it  
[12:08] yet. Another problem that's still to be solved is 
with sustainability. So right now we have systems  
[12:16] that can do amazing stuff but boy do they suck 
up the gas. They take up all the electricity.  
[12:22] They need lots of cooling. They're very expensive 
to run. This is not something that's going to be  
[12:27] able to scale if we just keep throwing more and 
more processors at this situation. Uh that's not  
[12:32] going to work. We're going to end up using all the 
electricity that's on the planet just in order to  
[12:37] uh to to run some of these queries. So, we're 
going to have to be able to make better, smarter  
[12:42] decisions with sustainability. Use models that are 
the right size, not just the biggest model, but  
[12:47] the right size model. In some cases, a small model 
might be more efficient and do a better job and  
[12:52] might even hallucinate less if we've got the right 
use case. So, this is still work that we're that  
[12:58] we're doing that is not yet, I would say, a solved 
problem, but there's a lot of things we can do  
[13:02] about it. Another one that is really the area that 
is is science fiction today is self-awareness. So,  
[13:10] is a system self-aware? Does it know it exists? 
Does it have consciousness? Well, I don't really  
[13:17] know the answer to that. This is really not a 
computer science question. This is a philosophy  
[13:22] question. So, I'm not going to try to deal with 
that one here because I'm not even sure how the  
[13:27] answer would be. But another thing that gets us 
back into this area though is understanding. So,  
[13:33] a system can spit out a lot of things, but 
it actually understand what it's saying. Um,  
[13:39] does it really know what the meaning of the 
things are? Seems like it's done a lot of that,  
[13:44] but there's always the question of is this really 
just simulating? Is it simulating thought? Well,  
[13:50] I don't know. I'll tell you there's a lot 
of people I've talked to and I think they  
[13:53] may be only simulating thought and simulating 
intelligence. So, again, it's a little hard to to  
[13:59] draw the line clearly, but uh this seems to be an 
limitation where the AI maybe doesn't understand  
[14:06] the biggest broadest context that we'd like it 
to understand. Uh judgment. So remember when I  
[14:12] was talking about data, information, knowledge 
and wisdom. Well, this is that last one. This is  
[14:18] the business of wisdom and judgment. And in this 
case, is the system able to make good judgments?  
[14:26] Maybe ethical judgments. Can it determine what 
is right and what's wrong? Again, can people do  
[14:32] that? Some people have a real hard problem with 
those kind of judgments. So, it's hard for us  
[14:37] to program a system that will if we can't figure 
out what those rules would be. But we we certainly  
[14:42] know that right now these are limitations that 
the systems have. How about in terms of judging  
[14:47] something that's just very subjective like the 
quality of something, maybe music? You know,  
[14:52] what I think is really great music, you may 
not think. So, you know, you say, "Well, Jeff,  
[14:56] you have no judgment at all." Uh, but I have a 
different view of that. But these systems are they  
[15:02] able they're able to generate music and they're 
able to throw away stuff that is just absolute  
[15:06] gibberish but can they tell what is going to be a 
hit and what's not going to be for instance in the  
[15:11] music area. So there's a lot of of work here in 
this space so that it's able to do some of those  
[15:16] qualitative judgments as well. How about this one 
common sense? And I'm going to really put that one  
[15:24] in uh in quotes because air quotes because um I 
mean is it really all that common? It seems like  
[15:31] again we have limitations with people. So we can't 
really expect a system to be able to perfectly do  
[15:37] what we consider to be common sense because we all 
might have a different idea about that. Certainly  
[15:42] there are some things that we know and the systems 
ought to be able to understand that but today  
[15:48] there are some certainly some limitations to 
that. How about in terms of goal setting? Well,  
[15:53] some people would say that with today's agentic 
AI that a system can in fact set its own goals and  
[16:00] go off and accomplish those things. And what I'm 
going to make a distinction here is that we have  
[16:05] micro goals. These are sort of the small things 
that we need to do if I give you a a larger task  
[16:11] and the macro goals. So the larger task, this 
is what needs to be done. This is how I go about  
[16:18] doing it. And right now today's agents are able 
to do these kind of micro goals, the goals within  
[16:23] the larger objective, but the big goal, why would 
we do this in the first place? That's maybe still  
[16:29] uh without uh beyond its reach at the moment. And 
then sensation, how about this? Does this does a  
[16:36] AI system really sense things? Does it understand 
what's happening? What how things feel? How things  
[16:42] taste? Um that sort of stuff. The things that 
are of the senses. Well, we're building robots  
[16:46] that are able to certainly see and hear. Can 
they taste? In some cases, maybe to an extent,  
[16:52] but there's a lot of other things that go into uh 
these kinds of sensations that we haven't put all  
[16:58] together in one system. And then here's the really 
big one, I think, and that's deep emotions. Is a  
[17:05] system really able to feel the same way that we 
do? Is it able to experience joy? Is it able to  
[17:12] experience sadness, loss, uh, accomplishment? Does 
it really get what all that's about? And again,  
[17:19] I know some people who don't really do all 
that particularly well. So, so this is one  
[17:24] of the things that is difficult to put into a 
system and we can simulate it today, but is it  
[17:31] really feeling these kinds of things? So, I would 
suggest to you these are some of the things that  
[17:35] to one degree or another are limitations with 
today's AI. Now, what is the role for humans  
[17:41] and for AI? How do we work together? How do we 
make sure this is a tool that works for us? Well,  
[17:47] people really should be over here doing this kind 
of stuff. Answering the what question. What is it  
[17:53] we want to do? That's the overall macrolevel goal, 
the objective and answering the question why.  
[18:00] What's the purpose of this? Is there meaning in 
what we're doing? What's the ultimate thing that  
[18:05] we're trying to accomplish? And without purpose, 
all of this is just meaningless work. So people  
[18:11] are still far better at that kind of thing. And we 
should be the ones controlling this tool that way.  
[18:17] Over here on this side, once we've told the system 
what needs to be done, AI can many cases with an  
[18:23] agent figure out the how and go off and perform 
it, actually do it. Agents are able to automate a  
[18:30] lot of things much faster than a person could. and 
they can do it in an optimized way, but they need  
[18:36] to know what to do in the first place. We need 
to know why. So, if you look at a history of AI,  
[18:41] it felt like for the longest time we were making 
very little progress and then all of a sudden it  
[18:47] just took off. And we're at this this inflection 
point where the developments and where all of this  
[18:53] is going to go, no one really knows. But what I 
can say this for sure, we can look at a history  
[19:00] of milestones that we've accomplished already. 
And we can look at lots of future research, things  
[19:06] that still need to be done, which is actually 
very exciting. If you're someone that enjoys  
[19:10] it and the possibilities of problem solving, then 
we're going to be able to do a lot more work and  
[19:16] ultimately we're going to get end up with systems 
that do things we didn't even imagine yet.  
[19:22] So my advice to you if you start looking at 
the limitations of AI today I would say don't  
[19:28] become preoccupied with those because the people 
who have and have asserted that AI will never do  
[19:33] this that or the other thing have generally been 
wrong. My advice to you, don't bet against AI.