Comparing the PC revolution in the 1990s with the AI revolution in 2020s

In the history of computing lots of revolutionary decades were available, in the 1950s the first mainframe computers were build and in the 1970s the first Minicomputer like the Dec PDP-11 were available. The shared similarity was, that a new sort of technology was build / invented which has replaced former technology.

The last great computer revolution took place in the early 1990s. From 1988-1994 the IBM PC has improved drastically. Until 1988 a PC was a very expensive office machine with a text mode graphics card, but in 1993, the typical 486SX PC was great for gaming, for writing texts and for getting access to the Internet. The amount of software for the Intel/Windows PC has increased dramatically, because many millions of private and commercial PCs were sold and used on a daily basis.

In the 2020s there was another big revolution available which was the AI revolution. This AI revolution was initiated by chatgpt and has evolved into a full-scale introduction of Artificial Intelligence which goes beyond of simple chatbots. The difference to the early 1990s revolution is, that its much harder to describe the AI revolution in detail.

In the early 1990s, the transition could be isolated to the availability of new computer hardware/software and computer-networks. That means, there was a cheap Intel 486SX processor available, there was a new MS Windows 3.11 operating system programmed and new computer networks like the Internet and Compuserve were there. in contrast, the current AI revolution doesn't belong to classical categories of computing.

From a hardware perspective, the former Moores law seems to be dead. That means, the existing 64bit CPUs are no longer improving anymore, and software remains also stable. A modern Windows 11 operating system looks the same or even worse than the former version which was Windows 10 or Windows 7. So the development can't be called a revolution but a stagnation. At the same time, the AI revolution is a reality, but it doesn't belong to the computer industry.

To explain this contradiction we have to describe what Artificial Intelligence is about. Its not a certain sort of software and it doesn't belong to computer hardware. So it doesn't take place in the reality but its a virtual revolution. Let us go a step backward and describe what the situation was for the computer revolution in the early 1990s.

This revolution was a bottom up movement initiated by the people. Lots of computer users have bought a PC including software packages at this time, and new computer stores were created world wide. In addition, book publishers have discovered the subject and endless amount of computer related books were sold about the internal working of Winword 6.0, about the Turbo Pascal programming language and about the Win 3.11 API.

In contrast, the AI movement since the 2020 is working with a top down approach. There are no stores available in which AI technology is sold to the masses, also there are no computer expos available like CeBIT or Comdex. And even books about AI are not available. The most famous AI book is "Russel/Norvig: AIMA" which

quote "... is considered the standard text in the field of AI" [1]

but in this AI bible, no section about Large language models is available. The only chapter which explains the subject on the surface is section 24 "Deep Learning for NLP" [2].

On the other hand, there is a dedicated place in which AI is visible. In the Gutenberg Galaxy, and especially in the conference papers after the year 2020, the AI revolution is visible everywhere. Lots of new algorithms, datasets, and applications for large language models are presented to the reader in over 1 million papers. The only concern is, that the Gutenberg galaxy and especially the published conference paper are ignored by the public. The total amount of readers is very small.

The surprising situation is, that the AI revolution isn't mirrored in the mainstream book market, and not in computer journals, there are no AI related expos available and even large scale tech companies doesn't know how Large language models are working. In combination, this is a unusual situation because in the former 1990s computer revolution, the situation was the opposite.

The working thesis is, that the AI revolution was initiated top down with its roots in the academic papers since the 2020s. Its a virtual revolution which is bypassing existing economical or technological structures.

sources:

- [1] https://en.wikipedia.org/wiki/Artificial_Intelligence:_A_Modern_Approach

- [2] AIMA book, 4th edition for U.S., 2020, https://aima.cs.berkeley.edu/

np hard problems as terra incognita

Computer science is an academic discipline with a long tradition. Its core elements are programming languages and algorithms. .Endless amount of source code was written in multiple programming languages, there are even esoteric languages available like Intercal and Befunge which are examples for computer art. Unfortunately, computer science consists of a seldom described weakness which are np hard problem.

An np hard problem is a mathematical challenge which can't be solved with existing programming languages nor algorithms. That means, modern languages like C++ in combination with modern 64bit operating systems like Linux isn't powerful enough to determine the shortest route for 15 cities or find the shortest sequence for solving the rubik's cube puzzle. What makes the situation more complicated is, that even after adding more RAM to a computer, np hard problems remain unsolvable.

One possible explanation why computer science is ignoring np hard problems might be, that its useless to invest ressources into problems, which can't be solved. If 100 researchers in the past have independent from each other tried to solve the Traveling salesman problem, and all of them have failed, it doesn't make much sense if additional researchers are trying to solve the same problem. .np hard problems can be compared to the perpetuum mobile in mechanical engineering which is also described as a dead end.

On the other hand there is a discipline available which is devoted to np hard problems. This discipline is called Artificial intelligence and its main problem is to solve all these unsolvable problems like rubik's cube, path planning and motion planning. Its important the difference between computer science which is only about polynomial problems vs. Artificial intelligence which is only about np hard problems. Both disciplines are trying to achieve different goals and they are operating with opposite tools.

Computer science vs artificial intelligence

AI was created as part of classical computer science, but both disciplines are operating with different assumptions. Computer science is a well defined engineering discipline which is successfully solving real world problems. For example a mathematician has a need for an electronic calculator and the machine is build and programmed by computer scientists. Or a statistician likes to store data in a table and computer scientists are programming a database which can solve this task. The result is, that doing computer science results mostly into successful projects which are evolving over the years, in the sense that todays computer have more memory and can sum up numbers faster.

In contrast, it remains unclear what the goal of Artificial intelligence is. The missing goal is a structural problem and the only way to solve it is to define goals by itself. That means, the AI community has to find objectives including the solution by itself. There is no higher instance available which has a need for robot hand or vision system, but these projects have to be formulated from scratch.

Let me give some example for self created goals only available within AI research: line following robot, robocup rescue, Tetris playing AI software, chess AI, scene recognition in motion capture recording.

All these problems have only a sense within the AI community. In the reality, nobody has a need for a line following robot or a Tetris playing AI. The only reason why these projects are researched is because of the hope for new knowledge what thinking is about. Especially in the past, successful AI research was always connected to a certain project, for example to build a machine which can play chess. A certain problem allows to benchmark an algorithm. Either the computer is able to do so or not.

In contrast, additional computer science has no need for artificial constructed problems. Computer science has no lack in existing well defined problems, but the missing resource is how to solve all these problems with better hardware and improved algorithms. AI is working the opposite way. There are endless amount of hardware and software but a lack of well defined problems.

Its a bit paradox, but AI engineers have a certain understanding about their tools. A tool isn't a computer e.g. a Unix workstation, but a tool is a problem formulation, e.g. the 15 puzzle problem or the traveling salesman problem is a tool. These tools are helping to guide the discussion into a certain direction. For example, the 15 puzzle problem is a great choice for introducing heuristics, which is not available in classical search algorithms.

The second paradox situation is, that AI isn't evolving in terms of better hardware or more intelligent software, but AI is evolving in terms of well formulated problems. In the year 2025 there are more such problems available than 20 years ago. A very recent problem is Visual question answering which was first described in 2015. The VQA challenge has evolved from similar challanges which are text based question answering which was available since 2000s. Its likely, that in 20 years from now, more advanced robotics problems are available unknown today.

How to identify Artificial Intelligence technology?

Computer science in the past was operating with certain tools which were developed once and reused frequently. These tools were located in hardware, software and algorithms and are similar to cooking recipe a blueprint how to build technology from scratch. For example, a 32bit microchip is designed with a hardware description language like VHDL, while the Unix operating system is written in the programming language C. If someone has access to the VHDL file or the c source code he can create a copy of the technology because he has a better understanding how the system is working.

A naive assumption is to translate this model towards the Artificial Intelligence domain, which is a subpart of computer science. AI can be located in hardware, software or algorithms, at least under the assumption of the computer science bias. So its natural to ask for dedicated AI hardware and AI software, in the hope to get access to advanced robotics technology.

Unfortunately, AI works quite different from the computer science paradigm. Even if dedicated AI tools were developed, for example AI related FPGA chips and AI related software libraries, there is something missing to build a robot. This something else is unknown and because of inability to locate the magic ingredient, AI wasn't realized over decades. It was unknown how exactly an AI machine or tool has to look like so it was impossible to find such tools.

With more advanced knowledge about the AI subject its possible to describe with more details what the recipe is for creating robots. The missing ingredient is a multimodal dataset. That is a .csv file which contains of motion capture data in combination with natural language annotation like “jump”, “Walk”, “stand up”. Such a dataset can be used as the core element and a robot control system can be developed around this dataset.

This description for an AI tool looks a bit uncommon because it doesn't fit to existing categories in computer science. 'Its not a hardware description, it is not an algorithm and its not the source code for a computer program. But a multi modal dataset is simply a sensor recording, similar to a temperature log file from a weather station. The usefulness will become visible only on a second look. A multimodal dataset creates a machine learning problem. The question is which sort of neural network can learn the data and how to interpolate the missing data. The attempt to answer these questions results into a robotics project.

In contrast to a numerical dataset, a multimodal dataset consists of natural language annotation in the natural language of English. These annotation transform a robot control problem into a text adventure. The textual layer is the birds eye perspective towards a problem. Every robot control problem can be interpreted as a text adventure which consists of nouns, verbs and adjectives. Natural language is the best tool to describe the reality. Let me give an example:

A kitchen robot will perceive objects like table, plate, apple and bread. Also a kitchen robot can do actions like open, close, grasp and transfer. The natural language including its vocabulary is used to identify the parts of the reality. The robot cointrol system has to memorize the same words, otherwise the human to machine interaction will fail.

In classical computer science there is no need to utilize natural language. A pocket calculator and even advanced workstation computers are working fine without knowing any vocabulary. Words from the reality like “apple” “bread” and so forth are never included in the VHDL hardware description and they aren't stored in computer programs. Sometimes these words are available as commentary, but they are not important for running the software itself. In other words, classical computers are working fine without any interaction with a human.

In contrast, a robot control system operates with the opposite paradigm. A robot is mostly a user-interface which is using the same lexicon as a human. Its impossible to build a kitchen robot without 100 and more kitchen words.

Walking downstairs

 

Isometric traffic junction with map key

 

The guess naming game

 

Its a language game for researching grounded language. The user enters a word like “green” and the computer has to highlight all the objects in the GUI which fits to the word. These game rules allow to benchmark if a certain visual dictionary was learned correctly because the computer has to map the entered textual word to the shown graphical representation on the screen.

The main advantage of the guess naming game is, that it can be realized in under 300 lines of code in Python. It explains on an example what the symbol grounding problem is about.

AI as search for a problem

 In the history of AI development it was mostly unclear, what exactly AI is about. In the 1980s a common definition was, that AI has to do with search in the problem space. But this definition fits only to small subsection of AI which is state space search in games. And search algorithms like A* can't be applied to more complex problems.

A possible improved definition is, that AI has to do with search for the problems. This definition shifts the focus away from algorithm centric solving of existing challenges towards an explorative search for new problems.

Example problem might be the 8 puzzle problem, chess puzzles, path finding in a maze, or the VQA problem (visual question answering). The assumption is, that more advanced AI techniques can be unlocked by inventing more advanced puzzles first.

Let me given an example. Suppose the self selected problem to solve is the 8 queens problem. Under such a constraint, the following debate consists of a mathematical problem description, and a comparison of different algorithms which are mostly back tracking algorithms. The decision to solve the 8 queen problem results into a certain bias.

By selecting other problems the discussion space gets modified. Especially by selecting language games like Visual question answering, the debate gets modified drastically. Instead of describe only search based algorithms, new topics have to be discussed like vocabulary definition or grounded language. These new subjects are not available in classic problems like the 8 queen problem.

So we can say, that no hard subjects within computer science like hardware, software, algorithms or programming language are creating the discourse space for Artificial Intelligence, but the debate gets influenced by the preference for certain AI problems. The decision for or against certain problems affects the discovery of possible problem solving techniques. If the attempt is to solve only the 15 puzzle problem, a certain sort of computer program is the result. But if the task is to solve problems with grounded language like the instruction following problem, more advanced algorithms are needed.

The main question which has to answered by the AI community is, what are the advantages and disadvantages of a certain problem category. The traveling salesman problem was popular in the 1980s because it can be described easily and can be converted into an mathematical algorithm. The main disadvantage of this problem is, that even the algorithm has found the shortest route, the same algorithm can't control a real robot because robotics has to be described with different problems.

The holy grail is perhaps an AI problem, which can be described easily but fits to a variety of real world robotics problems. If it matches to the reality, the algorithms used for solving the problem can be adapted to real world scenarios.