# The History of AI and Chatbots w/ Dr. Richard Wallace (TECH011)

## Метаданные

- **Канал:** Preston Pysh
- **YouTube:** https://www.youtube.com/watch?v=ENW3PL50yPw

## Содержание

### [0:00](https://www.youtube.com/watch?v=ENW3PL50yPw) Segment 1 (00:00 - 05:00)

(00:00) What it's really showing us is that people are more like robots than we would like to think we are. Because it's not that the robot's becoming more like a human, it's that it's revealing to us how robotic we are. And you know, back in the early days of working on Alice, uh, I came to realize that most people most of the time are saying things that they themselves have said before or that they've heard other people say before. (00:32) And even when they're, you know, writing, they're basically synthesizing thoughts and ideas that are not necessarily original. And all of these chatbots work because language is predictable. And you know, predictable means robotic. So where I want to start is I'm just super curious how people kind of fall into, you know, their field of expertise. (00:58) And when I look at what you accomplished very early on back in the 1990s, I'm curious what drove you or motivated you to be paying attention to chat bots and the Turing test and all of that at such an early phase because I think for most of the listeners, they know that all this stuff has really come to fruition in the last five or 10 years. It's gone on everybody's radar. (01:23) But you were doing this literally decades before anybody was even aware of these ideas of chat bots and whatnot. So what was your initial motivation to get into something to get into stuff? That's absolutely right. You know, I like to say that nobody knew what artificial intelligence was until a couple of years ago. Yeah. And now I'll be sitting in a restaurant somewhere and I'll hear a conversation at the table next to me and they're talking about AI. (01:51) Well, anyway, there are several threads that came together that inspired me to work on the chatbot house. Uh, and I'll just pull on a couple of those threads here. One is that in 1990 I read an article in the New York Times about the first Loner Prize contest. Now, the Logner Prize was an annual touring test, an annual contest based on a touring test funded by a rather eccentric philanthropist, Hugh Loner. (02:21) And the story with the very first contest was that none of the programs competing came close to passing the touring test. They were all just terrible chatbots. But Loer awarded a bronze medal every year to the chatbot that was ranked highest by the judges in terms of being the most human. 10. (02:47) That first year of the bot that won was simply based on the old Eliza psychiatrist program uh which if you're familiar with that was a very primitive chatbot developed by Joseph Weisenbomb in 1966 and it you know had very few responses but had some clever tricks to it. It could sort of match keywords in the input and it had canned responses associated with those keywords. (03:12) It could invert prepositions. So, you know, if I said, "I came here to talk to you," then it would repeat back, "You came here to talk to me. " So, it did that sort of pronoun swapping trick. But when I was in graduate school in the 1980s, the Saliviza program was basically considered kind of a dead end or at best kind of a hoax AI. (03:39) And not only that, the inventor, Joseph Weisenbomb, ended up pulling the plug on it because he thought it was too dangerous. He thought that people were reading too much into it than was actually there. Pete was a psychiatrist program, so people were trusting it with their, you know, their personal issues and problems. (03:58) Uh they were surprised to find out that Wise and Bomb could read all the transcripts of their conversations. Wow. And so he wrote a whole book after that computer power and human reason where he criticized the whole field of AI in his Eliza program in particular. You know it's really hard to imagine this now that someone would come up with a new AI application that's very engaging and popular. People are using it and then they would say, "Oh no, this is too dangerous. (04:26) We have to put the genie back in the bottle. " I think most people would now run out and try to find venture capital to start a company to commercialize. True. Very true. But isn't this fascinating that the thing that he discovered very early on in the '9s, I mean, he started playing around with this in the 60s, which is mindblowing to me, but what he found in the '9s was that there was a huge concern with privacy and what people were putting into these discussions, which is now a, you know, a major talking point with AI. And it (05:02) doesn't seem that, you know

### [5:00](https://www.youtube.com/watch?v=ENW3PL50yPw&t=300s) Segment 2 (05:00 - 10:00)

and I know I'm generalizing here, but it doesn't seem like the population really cares too much or even thinks about these issues that caused him to shut down his entire effort behind this. I don't know. (05:22) I find that really fascinating that he discovered this what, four decades before it became like or three decades before it became, you know, something that the rest of the world should be very concerned about. Well, he really discovered it in the 1960s. Wow. When he first created the program, the other ironic thing about Eliza was that up until very recently, I would say, well, let's say 20 years ago, Eliza was by far the most widely distributed, popular, and well-known AI application. (05:50) Yeah. If you knew anything about AI up until maybe the year 2000, then you would know about Eliza. M I'm curious when you read this I think you said New York Times article in 1990. Did you ever think that you would be the winner of this Lobner Prize a decade later? Well, that planted a seed in my mind and I didn't really do anything about it for about 5 years. (06:14) Okay. Um so another thread that led to the development of Alice or the inspiration for Alice was around that time in the early 90s. So it was the end of the cold war and so there was decreased amount of government funding available for AI and robotics research compared to the 1980s and so a number of us in the robotics field I was working robotics at the time got interested in the idea of minimalism robot minimalism and basically that was the idea that we could build robots with very simple inexpensive sensors and actuators commodity micro processors and (06:56) as a result of that you could actually get more lifelike behavior out of these robots than you could with approaches people had tried in the past with much larger computers and so forth. One of the interesting inventions that came out of that period was the Roomba. (07:16) So if you think of the Roomba rolling around and you know bumping into things and changing its direction, it's all basically just a stimulus response application. So we call that stateless. So it's sensing something and then taking an action based on what it's sensing by changing direction for example. Yeah. So that whole approach of minimalism you know was also on my mind at the time and that kind of dovetailed with the very simple approach of the Eliza program which was also kind of a stimulus response. (07:48) You know it was so simple that it could respond very quickly. It didn't have to go and do a lot of computations to come up with the responses. What was your inspiration for thinking that simplicity was going to lead you to better results? Was there something in your life or some something that you were reading at the time? What drove you to that intuition? Well, like I said, we were working on the minimalist philosophy of robotics and at that time I was working on the development of a robot eye. (08:20) And by that I mean a visual sensor that's based on the architecture of the human eye. So that the human eye differs from a TV camera in the sense that the TV camera is basically a square grid of square pixels. But the human eye is more like concentric rings of pixels with higher and higher resolution towards the center. We call that a log map. (08:45) And so we had developed a sensor that had that log map pixel organization. In order to use a camera like that effectively, you have to be able to point it. So we developed a little motor, a high-speed pointing motor based on a direct drive design. And that motor could point the camera, the eye camera in pan and tilt directions very quickly. (09:11) And again, it was a very simple kind of actuator, simple sensors, and it could move very quickly, could move actually faster than the human eye. You sort of see this thing whipping around and looking at different things, and it was very lifelike. So, just for the audience to understand, so in 2000, I believe 2001 and 2004, Dr. (09:37) Wallace won the Lobner prize which is this Turing test with his Alice protocol or chatbot that he had created and I guess for me what was the major insight that you think that you had back then? You talk about this idea of simplicity, but what would you say was the major insight that you had to outperform everybody else that was competing on what is I mean for anybody listening that the most complex challenging

### [10:00](https://www.youtube.com/watch?v=ENW3PL50yPw&t=600s) Segment 3 (10:00 - 15:00)

problem you could ever try to go after, right? Like what was what would you say was your keen insight that you had that allowed you to do this? Well, it was basically the idea that I (10:10) could build on the Eliza program. So the Eliza program had about 200 rules, 200, you know, stimulus response rules. And you could think of that as a pattern and a response. My idea was to build kind of a super Eliza where instead of 200 rules, you had thousands and thousands of rules. Yeah. (10:34) And in fact, I, you know, I by the time I was entering those contests, I got Alice up to about 50,000 patterns and responses. Wow. Amazing. So Richard, one of the things that I found really fascinating about you back at this time was that you came up with this artificial intelligence markup language. You effectively, for all intents and purposes, and correct me if I'm mischaracterizing this, you had to come up with your own language in order to kind of build efficiency into how this chatbot was working, which is, you know, as a person who's not very good with languages, I'm much more of a math person. I'm reading this and I'm (11:10) thinking this is mind-blowing. So talk to us about this and what was this insight that you had to come up with the AIML uh artificial intelligence markup language at the time that you did this. Well, a IML is based on XML and XML was very popular at the time. One thing that appealed to me about XML for the purpose of writing chatbots was that I always say XML has an implicit print statement. (11:40) when you write the responses, you don't have to put in an expression that says print blah blah something, you know, between the parentheses because the XML already just provides the text inside the markup. So the response is just And then the basic unit of knowledge in a IML I call the category, which is like the rules I was talking about a second ago. (12:08) So the category consists of a pattern that matches some natural language input and then a response called the template. Reason it's called a template is because it's not exactly the answer, but it's a template for the answer that can be populated with various other things. (12:27) And then there was also a recursive element to it where the response could actually simplify the input into a kind of simpler input. So the example of that is um I want you to tell me who you are right now. So you can reduce that by removing the right now. So I want you to tell me who you are and then you can remove the I want you. (12:52) So it reduces to just tell me who you are and then that reduces to who are you. So there was that recursive element built into the responses as well. So in general, you're just you were taking language and you were making it way more efficient, but like where do you even start with something like that? I mean, you literally have to go through there's just so many different variations of language. And I think of the complexity of this. (13:16) I wouldn't even know where to begin to start writing something that makes it more efficient like Yeah. So how did you think about solving that problem? Well, it all goes back to the conversation logs. So, just like why isn't, you know, I could read the transcripts of conversations people were having. By the way, this would have never worked without the internet, without the worldwide web. Yeah. (13:40) Because with the worldwide web, I could start to accumulate conversations from a very large audience of people. And by looking at the transcripts of those conversations, I could basically program responses to the things people were saying. Later on, I realized that there was a kind of zip distribution over the things people were saying. (14:07) So, you know, there's kind of the most common thing people say, which is hello, and then, you know, who are you and you know, how are you and I like something. So you can create the responses in order of how frequently people say particular things. My question for you is so you write these thousands of rules and you're also working on a way to compress or make the English language uh more efficient. (14:36) What did you fundamentally learn through the experience of writing these thousands of rules and rules of thumb of compression? Because when I think about it, like we look at these LLMs and machines are doing all of this really hard and complex work, but I would imagine what you were doing there in the '9s and early 2000s was exactly what all these LLMs are doing today, but you were doing it manually.

### [15:00](https://www.youtube.com/watch?v=ENW3PL50yPw&t=900s) Segment 4 (15:00 - 20:00)

(15:03) And so I guess it's almost I hear these people that say, "Well, we have no idea what's behind these ones and zeros in all these LLMs. " Which I guess is a true statement, right? But if we but if a human was going to maybe be able to understand what it is that it's doing, I think you would be one of the very few people on the planet that could maybe help us understand what that is because you did this manually for so many years. Right. Well, there's so many things wrapped up in that question. (15:35) So, there's always been a kind of tension in the history of artificial intelligence between, let's say, supervised learning and unsupervised learning. So, what I was doing was what we call supervised learning because I was playing the role of a teacher or, you know, a guide. So whenever I added a new response, it was manually added as you're saying driven by a particular input that I saw in the conversation logs. (16:07) And so the way that I'm teaching the robot is by acting as its teacher basically and saying, you know, when you see this, you should say that. Yeah. You know, that's in contrast to unsupervised learning, which is what these LLMs are doing. They're basically, you know, trying to accumulate a lot of inputs and correspond and find the neural network weights that match it to particular outputs. (16:31) And so with that technique, you can get phenomenal results obviously, but as you're saying, it's difficult to know how the LLM came up with particular uh responses. Whereas in the supervised learning case where it's all a symbolic process, it's very easy to trace back through the you know the logic of the program and see where the you know what caused a particular response to be generated. (16:57) And I always say that people who do supervised learning approaches spend all of their time doing creative writing which is what I was doing with the Alicebot. But people who do unsupervised learning spend all of their time deleting crap from the database. Yeah. And that's sort of what's going on with the LLMs now is, you know, they're having to put a lot of work into filtering to make sure they don't say anything inappropriate or offensive or political. And, you know, that ends up being a lot of manual work as well. (17:32) Yeah. And I guess my understanding is that everybody that's on the cutting edge of AI today, like that's the holy grail for them is to get the human out of the loop and for it to be completely AI generated and filtered and just like there's no humans there. As a person who deeply understands this and the way that you frame that is this back and forth and there's consequences to one side and the other. (18:02) Is there a moment where you think that they will be able to get away from complete removing the human out of the loop and it progressing in a way that's actually beneficial? Or do you think that the more that they lean into removing the human out of the loop that they actually are setting themselves up for a systemic failure because it's going to spiral into this AI slop, if you will, or it's creating and generating content in a direction that's so fast and so extreme that they get away from human filtering altogether and it just kind of turns into this almost like a runaway virus, if you will. Is that how (18:38) you kind of see this that it needs to be balanced or is it even possible for it to go in that direction without humans? Well, it's so hard to predict the future of hey, I you know, I would have never expected this whole LM development to come along in the first place. Yeah. (18:58) But, you know, I always think of a child learning language and there are big differences here between a child learning language and an LLM. Yeah. You know, a kid doesn't have to scan the whole internet to learn how to speak a language. In fact, they're pretty good at, you know, what we call oneshot learning. (19:16) You know, if you say to a kid, "This is a dog," then they can instantly recognize every dog in the world as a dog. Yeah. What also comes into play here is the supervised unsupervised learning dichotomy, which is, you know, if you are a kid and you have a good teacher and good parents, you'll learn to speak very well. (19:37) But if you're a kid who has to pick up language on the street without any supervision, then your language learning won't be nearly as good. And so the LLM is more like the kid out on the street um learning language without any supervision. And that's why they learned so much inappropriate and offensive material and so on. (19:57) What did the winds teach you back in the day when you were winning this about how humans judge intelligence?

### [20:00](https://www.youtube.com/watch?v=ENW3PL50yPw&t=1200s) Segment 5 (20:00 - 25:00)

You know, I can say the same thing about LLM's now that I said about my chatbot back then, which is that people say, well, these chatbots are becoming more and more like humans. And I have a different opinion about that, which is that what it's really showing us is that people are more like robots than we would like to think we are. Because it's not that the robot's becoming more like a human. (20:26) It's that it's revealing to us how robotic we are. And you know, back in the early days of working on Alice, uh, I came to realize that most people most of the time are saying things that they themselves have said before or that they've heard other people say before. And even when they're, you know, writing, they're basically synthesizing thoughts and ideas that are not necessarily original. (20:55) And all of these chatbots work because language is predictable. And you know, predictable means robotic. So, um, I always say that, you know, if we were all William Shakespeare's uttering an original line of poetry with every sentence we spoke, then, you know, these chaplines would never work because they're based on language being predictable and not original in every utterance. Yeah. (21:19) Is it fair to say that you would suggest that humans judge intelligence by their flow or by this response of like most people are looking at that and they're saying, "Oh, that's intelligence. " But then you're looking at it and you're saying that's not intelligence. Just it's just repetition. I think that's kind of what you're getting at. Yeah. (21:39) Not really repetition, but robotic predictable. Yeah. You know, it's interesting. I just read something uh it was like last week that Google and I think Google came out with this many months ago, but for them to do this long-term learning where it has much more of a memory, it's highly based on whether something's novel or not relative to its index of everything that it's been trained on. (22:04) And that's and when it sees this novel thing that it wasn't predicting or expecting to come next that it then stores that in its long-term memory or it's and I apologize for the terminology here uh Dr. Wallace but it flags it as something that is worthy of being remembered because it's novel and so different and outside of what it would have predicted to be the next thing. (22:27) It's interesting that it's in keeping with Claude Shannon's information theory and how it's all aligned. I'm curious if you have any opinions on that in particular and whether you think that has a key component to intelligence or how new things are discovered and knowledge in general. (22:52) That really gets to the heart of what I think the difference is between humans and robots, which is that like I said, I think most people most of the time are acting like robots. They're just acting in kind of a stimulus response fashion. Just as an aside, I always used to say that most human conversation is stateless, meaning that what I'm saying to you right now only depends on the question that you just asked me. (23:16) And we could forget the whole history of our conversation up to this point. You know, one of the pieces of evidence for that is, you know, if you can imagine yourself having a casual conversation with someone at a party, say, and then you say, um, oh, where did you go to college? And they say, oh, I went to Harvard. I already told you that. (23:38) Because you kind of forgot that, you know, you would argue you had already talked about college earlier in the conversation. Yeah. you're just responding to the most recent thing you heard and most recent input. But what really gets to the difference between humans and robots is even though most people most of the time are speaking in this kind of reactive behaviorist way, it is possible for people to have original thoughts and be creative. (24:07) And you know, it's almost like a muscle that you need to exercise in order to build it up. If you want to break out of that robotic mold, then you have to put some effort into trying to be creative and original with your thoughts and thinking and ideas. Do you think that the touring test actually measures intelligence or is it something else entirely? I'm so happy you asked me about the touring test. (24:33) So most people understand the touring test as a sort of game where there's three players. You have a person who's, you know, called the interrogator or the judge and then they're communicating through a teletype, a texton medium, uh, you know, much like texting on your phone, but without any audiovisisual, just typing. (24:56) And then the two entities that the judge is talking to, one is a human and one is a machine. So then the judge has to decide which

### [25:00](https://www.youtube.com/watch?v=ENW3PL50yPw&t=1500s) Segment 6 (25:00 - 30:00)

one is the human and which one is the machine. And if they misidentify the uh machine as the human, then it's said to pass the touring test. (25:17) But you see, this has a big problem as a scientific experiment because it's not really clear how often the interrogator has to misidentify the human, you know, is it 50% of the time, 75% of the time, 100% of the time, what does that even mean? But it's the robot is more human than a human. So um in Turing's 1950 paper computing machinery and intelligence he actually describes two different versions of the test or the game and earlier in the paper he described something called the imitation game which as far as I understand was a was based on a real parlor game that people played in (26:00) Victorian England. And in this game again there are three players the judge or the interrogator and the other two players are a man and a woman. And let's just set aside the you know the gender issues and the context of 19 writing in 1950 here. So there's a man and a woman sequestered away in the Victorian England case in different rooms and then the judge is sending them handwritten questions back and forth and the judge's job is to decide which one is the man and which one is the woman. Now furthermore, Turring stipulated that (26:37) the woman should always tell the truth and the man should always lie. So now if you ask the man are you a woman he would say yes because he has to lie. Okay. And then you know the judge's job is to try to figure out which one is the man and which one is the woman. (27:00) Now if you replace the line man in that scenario with a machine. Okay. Let's say you replace the man with a very crude chatbot like Eliza or even Alice. Yeah. then the judge could identify the woman correctly 100% of the time. Okay? Because it's clear that only one of the players is a human at all and that has to be the woman. (27:24) So now as a scientific experiment, we can say let's run this experiment with, you know, 100 judges and 100 men and 100 women. I don't know exactly how many are needed for statistical, you know, accuracy, but you know, let's just say we did a random sample where we collected the results of the game for a large number of players, then you could measure a certain percentage of the time that the judge would identify the woman correctly. (27:51) And you know, let's say that's 70% of the time. Now, if you replace the line man with a computer, and the computer is a very good AI that can actually play the role of the line man, then you should get closer and closer to that actual 70% measurement. So, that's actually a better scientific experiment than the touring test. (28:17) The loner contest was really based on the original standard touring test. Okay. Yeah. You know, the rules change from year to year depending on, you know, who is hosting the contest. But Loner's rule was basically if 50% of the judges, usually there were four judges of two out of four judges, misidentified the robot as a person, then he would award the silver medal for passing the touring test. That's so cool. And it was never awarded, by the way. It was never awarded. Interesting. (28:47) Yeah. If you could get in a time machine right now and go back to your days, call it 2000 when you had, you know, done this, what would be the thing that you would whisper to yourself as a hint as to how to improve the chatbot that you had back then? I would probably tell myself, don't even do this. Why? Because you know how hard it is. No. (29:17) No. because there was no money to be made from chat bots for you know until very recently. Um before that you know very early the loner contest was always the domain of you know hobbyists and amateur programmers. There were a few you know academic entries but no big companies ever got involved in it. (29:43) In the 2000s, I organized a number of chatbot conferences, you know, international chatbot conferences, and we'd have a hard time getting 25 people to attend. Oh, really? Okay. Yeah. So, you know, after many years of really struggling with this and trying to figure out how to make a living with chatbots, um, and I did

### [30:00](https://www.youtube.com/watch?v=ENW3PL50yPw&t=1800s) Segment 7 (30:00 - 35:00)

co-found a company called Pandora Bots, which is based on attempting to commercialize the AIL bots, but you know, after a while in the early teens, I should say, I just decided to get out of the field completely and I went to work in (30:19) healthcare. Yeah. Um, but then in the past five or six years, I've gotten back into AI as it's become more lucrative, I should say. It seems like in 2017, Google came out with this paper. It was called attention is all you need. And this seemed to be a very seinal breakthrough in how to for all intents and purposes do what you were doing in a very manual way and let machines do it way faster and with way more horsepower and more data. Right. (30:53) I'm curious when this paper came out, did you read it when it first came out and were you kind of aware of this or did it kind of hop on your radar a couple years after when we started seeing the breakthroughs? Yeah, I was really not paying attention to it at the time. (31:12) Like I said, I was working in healthcare and I don't think the LLM industry really came to my attention until, you know, we started hearing about GPT. Mhm. Do you think that paper was kind of like a really important seinal piece of work for people to kind of understand how to start doing this in a mechanical machine kind of way? Yeah. And obviously that was a breakthrough. Yeah. Wow. (31:36) And so in your own words, what would you say? I mean, we know attention is a big piece of it, but I think for somebody that just kind of hears that label, it's like, okay, what does that mean? If you were going to try to explain to somebody in a very simple way like what is that paper saying that has enabled you know machine learning to do what it does. (32:01) I'm reminded of the work we talked about earlier which was the robot eye in the early 90s um because that was also an attentionbased mechanism. M. So I described how in order to make use of that log map arrangement of pixels where there's high resolution towards the center, you have to be able to point the camera so that the high resolution can be aimed at something interesting. (32:24) Well, how do you know it's interesting? It's by if you see something in the periphery, you know, for example, movement, then you'd want to move your eye towards the thing that you're seeing in the periphery and place the attention on that. (32:45) So attention has to do with focusing your highest resolution sensor sensory capability on whatever seems most interesting in a scene. I think there's an analog for that in the LM version of attention as well. You know, they're sort of swinging in the direction of where the gaze of the robot is looking depending on what they see in the periphery. (33:09) And so, okay, so this is super I love this example because it's very physical and you can kind of make sense of it very simply because it's dealing with vision. And so when you're changing your attention and you're able to zoom in because you have the capacity to zoom in on something, how are you filtering or knowing what's novel in that broader sight picture in order to know to adjust the focus to that thing? What gives us that capacity to know, oh well, I'm looking at you and now I'm focusing on the tree back behind you and I'm zooming in on that and I'm putting my attention there. What would be that insight in order to say, "Oh, that's different. That's something I (33:49) need to dial in on or pay more attention to. " Yeah. A long time ago, a guy called Hans Morave, who's very interesting. We should talk about him some more. He came up with an attention mechanism called an interest operator. And this is for computer vision again. (34:08) Okay? And it's basically that things in your visual field that have high variance, you know, a high ratio of dark to light are more interesting than other things. So that would typically be edges like the edges of the tree you just described or corners of things or just you know any sort of bright spot against a dark background or vice versa. (34:36) And then recognizing those in the periphery of your visual field would cause you to move the center of your visual field towards whatever the interest operator is highlighting. Fascinating. Okay, here's an odd question for you. Do you think your uh real subject of study ended up being humans rather than machines? Oh, well, you know, I'm a computer programmer, so I was always more interested in the machine side of it. (35:03) I think I did learn a lot about human conversation from monitoring those conversation

### [35:00](https://www.youtube.com/watch?v=ENW3PL50yPw&t=2100s) Segment 8 (35:00 - 40:00)

logs. And yeah, the reason I asked this question is, you know, in kind of research and preparation for the interview, it seemed to me that you have this opinion, I suspect, and correct me if I'm saying any of this wrong, but it seems like you were not convinced that any of these chat bots were actually saying anything intelligent. (35:31) it was this canned response that was coming back and then the reaction that humans had was like, "Wow, this thing is real and there's like something behind it. " And so I guess that's the impetus for the question is because you were I suspect you were fascinated at the response of people and how duped I guess they were by interacting with some of these chat bots. So I guess that's more of the impetus to the question. (35:55) And would you agree with everything that I just said? Bitcoin mining has a reputation for being impersonal, risky, and full of hidden fees. But one company is flipping that script, and it's Abundant Mines. Abundant Mines was founded by Bo and Christine Marie Turner. (36:16) Two Bitcoiners who lost over a half a million dollars to mining providers that overpromised and underdelivered. Instead of walking away, they built the company they wish had existed when they first started mining. With Abundant Minds, clients actually own their machines. And in Oregon, they come with no sales tax. There's one flat monthly fee for hosting, no surprise repair invoices, and if a rig ever goes down, their system redirects hash power so your earnings don't miss a beat. (36:41) But what really stands out is how personal the experience is. Every client gets direct support, ongoing education, and guidance from a real human being who lives and breathes this mission, so you never have to be left guessing. In addition, through 100% bonus depreciation, mining can offer major tax advantages that you don't get by just buying Bitcoin. (37:03) Their clients describe it like acquiring Bitcoin for half price when factoring in the returns and the write offs. They've put together a thoughtful gift just for listeners of this show that could potentially save you thousands of dollars. There's no pressure, just something to help you think through if mining is actually right for you. So, if this is something you're curious about and you want to learn more, you can check it out at abundantminds. com/preston. (37:24) That's abundantminds. com/preston. You know, most people don't realize this, but almost 60% of the average American homeowner's net worth is tied up in their home. Home prices are up more than 75% since co. That looks great on paper, but most of the wealth is locked in your home and not diversified. (37:47) If you let your equity sit there, you could miss out on better growth opportunities elsewhere. Now, there's a way to put your home equity to work in Bitcoin, thanks to Horizon. Horizon helps homeowners buy Bitcoin with their home equity without taking on a loan or adding monthly payments. Here's how it works. (38:06) You unlock tax-free cash today to stack Bitcoin by selling a small slice of your home's future value. You stay in your home as usual while Bitcoin does the work in the background, and that's it. Later on, when you sell or refinance, Horizon's providers take an agreed upon share of your home's future value. And that's the trade. (38:25) And what really sets Horizon apart is that there's no term limits and no risk of forced liquidation. You keep 100% of your Bitcoin upside, and you custody the Bitcoin however you want. With money printing inflating home prices to their highest level ever, it may be wise to take some gains off the table. If you want to diversify into Bitcoin, an asset with a proven record of beating inflation, Horizon may be the product for you. Head to joinhorizon. (38:51) com to see if you qualify and see your home's Bitcoin potential in just 2 minutes. Their team of experts will work with you oneon-one from start to finish and help you unlock your home equity to purchase Bitcoin. Transform your home equity into Bitcoin today with Horizon. I used to categorize the users or the clients I called them into three categories A, B, and C. Okay? So, A clients are abusive. (39:19) Okay? So, they're going to say, you know, very inappropriate things to the chatbot, and you see those on the conversation logs. Although you always have to wonder if someone is saying I hate you or I love you even is that what they're what they really have in mind or are they just you know trying to get a response out of the robot and see what testing the limits testing the limits. Exactly. Yeah. (39:45) And then the next category B are just average users. So those category B people were the ones who could suspend their disbelief and they would be very engaged with the bot and have you know very long conversations come back and continue their conversations and so on. (40:06) And so you know that would be the

### [40:00](https://www.youtube.com/watch?v=ENW3PL50yPw&t=2400s) Segment 9 (40:00 - 45:00)

group that you know as you're saying would be kind of reading more into the pimps of the bot that was than was actually there because they're engaged with it on an emotional level. And then the last category I call the critics which are people who know something about computer programming and AI and they just think this thing is terrible and you know they walk away after a few interactions. Yeah. (40:29) Well, I'm curious to hear your thoughts on where we're at now and where you see some of this going next. Um, you know, you have some really smart people in this space that have demonstrated their knowledge through the things that they've built. And I think, you know, if we back up the tape 3 years ago, many of them were very suspect as to whether AGI could ever be possible today. (40:56) And I have a hard time knowing if this is them trying to get more capital or they actually believe that we're on the cusp of AGI. I don't know which one of those two it is, but I'm just curious to hear your general thoughts on where you see us today and like what the next five years might bring. (41:20) Are we going to see as exciting of a next five years as we've seen in the past five years? Kind of just give us your one over the world on it. Well, I definitely think it'll be exciting. The term AGI seems a little strange to me because it's what we've always called AI. Mhm. You know, AI has always been a goal that's just out of reach and, you know, we have an imagination of what it is based on seeing science fiction movies and that sort of thing. (41:43) You know, HAL and R2-D2 and all those examples give us um a template for what we'd like to see in an AI. And so, you know, it seems kind of odd that they've come up with a new term AGI to kind of move the goalpost even further. But I'm very skeptical about that. I you know a very simple answer to this question which a lot of people I know would not agree with is that God gave human beings a soul but machines don't get a soul. (42:15) So you know in the sense that human beings have freedom of thought and self-reflection and creativity I don't think those things will be reproduced in a computer anytime soon. Mhm. Yeah. And I think I'm with you 100% on what you just said. And I know uh there's a lot of people that want to argue these ideas and we're not here to do that, but I'm with you 100%. (42:44) I think that there is something very special and unique about just any living being, not just humans. I think any living being has this special connection from, you know, a higher source. And I don't think that we're necessarily going to see, you know, these humanoid robots have whatever that is. And I have no idea how to define that. (43:03) But I do think that some of these humanoid robots, call it five or 10 years from now, are going to do things. And it goes back to some of your earlier comments about these chat bots and how people were just like, "Oh my god, I feel like I'm talking to a real person. This feels real. " And I think that some of these humanoid robots are going to feel like real humans to a lot of people. (43:21) But that doesn't mean that it's the same thing as us. I think we're we are something very hard to define, very different. But um oh my goodness, Richard, I really enjoyed this conversation. Anything else that you want that you think is super important on this particular topic that you see right now or kind of going into the future that you think is worthy of highlighting or that the audience should know? Yeah. Well, the company I work for right now, France. Okay. It's actually a very old AI company. (43:51) um founded in 1985 and France started out as a company selling lisp compilers but then by the end of the 1990s very few people were paying money for software you know because there's so much free language software available so they pivoted to graph database technology and without getting into too much detail about what that is now that we have the LLMs we are taking an approach called neuros symbolic computation. (44:26) In the history of AI, he talked about supervised versus unsupervised learning. Another dichotomy in AI is between uh symbolic and neural approaches. So symbolic approaches are things like theorem proving programs or the early chatbots that we were talking about based on rules where basically you're manipulating symbols or you can also think of a chess playing program you know which is very mechanical and manipulating symbols and searching through the space of moves. (44:59) And so the symbolic approach is in

### [45:00](https://www.youtube.com/watch?v=ENW3PL50yPw&t=2700s) Segment 10 (45:00 - 48:00)

contrast to this neural learning approach. And now we're basically trying to find the best of both worlds. So one example of that is um in the medical field you can make predictions about how likely someone is to be well their mortality, how likely they're going to be readmitted to the hospital after being discharged within 30 days, how likely are they to be readmitted or how likely they are to have a stroke and various other things. But the medical field has developed these symbolic (45:39) techniques for making those predictions. And so in the case of stroke from aphib, there's a test called Chadvasque. And it basically takes into account criteria like you know your age and gender, whether you've had u congestive heart failure, a history of hypertension, and various other factors like that. (46:04) And when you plug in those values, it produces a number which can then be used to, you know, estimate the likelihood of you having a stroke. And now you could also do that with a neural network, a recursive neural network where you basically train it by feeding in the patient data, the diagnostic data and the medical history and so on and then just look at whether they had a stroke or not. (46:28) So you can train this neural network to take a new patient data and you know give some prediction about whether they're going to have a stroke. And then the third way of doing that is to use an LLM. You know you can just simply upload the entire patient chart to the LLM and say how likely is this person to have a stroke. (46:46) And so what we've been doing is sort of combining those three approaches together. you know, we've got the symbolic estimate. We've got the neural estimate and we've got the LLM estimate. So, you could, you know, you could potentially display all three of those and then it's up to the clinician to make a judgment. (47:06) Or you could even put them all back into a different LLM and ask the all which one of these measurements is best, predictions is best. So, it's an effort to combine the best of the symbolic approaches with these newer neural approaches. Wow. All right. Well, I am just so thrilled to be able to talk to somebody who's been in this space for decades. (47:26) It's miraculous to see what's happening and I can only imagine where we're going to be in 5 years from now. But, Dr. Richard Wallace, thank you so much for making time and coming on the show and imparting all of this knowledge that you have. We really appreciate it. (47:46) Well, I'm glad people want to talk to me about it after a long time of people not being very interested. Well, there's a lot of people interested now, let me tell you that. But thank you again for making time. Okay. My pleasure. It was great talking with you as well. I believe it will happen. But my big question is when? I think it's really important to be prepared for the reality. (48:04) There's a lot of people say, "Hey, this makes sense. I want this. We should have this soon. " But remember, there's a lot of cases where people have talked about that in the past. Fusion energy, nuclear fusion, sort of the technology is pretty obvious, but you have to contain this plasma. That seems like a technical issue. We can figure that out. (48:21) 50 years later, we're still working on it. So, there are problems that are extremely difficult and they take much longer than anyone expect. And it seems like robotics is like that. I want to be a voice to say, hey, it might not happen. And let's just think about that and be a little bit realistic because I know how a lot of people are thinking that it's inevitable.

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*Источник: https://ekstraktznaniy.ru/video/44866*