Mind and Machine - An Outline

The project draws an extensive parallel between computer technology, specifically computer programming languages and the open-ended aspect of culture that includes language but also the wider methods, especially those aspects of technology that are used to explain or communicate culture. Humans interact with computers in many other ways, including paying for them and using them to "make money" (computers become actors in the "real economy" with huge impact on the "economy", which is right-wing word for "society").You can't entirely ignore the broad issue of "automation", which is perhaps the topic of most interest to the average reader. Seen from this angle, Part 1 (below) is a primer on the nature of automation, especially "computerization". Most primers on computers ignore the way computers actually "work" in the world and focus on aspects that most people do not need to know (such as CPU's, disks, RAM etc.)

This project drills down to the history of computation before "computers". Many of the problems we blame on computers are thousands of years old.

I'd like to make some approximate distinction between language and art as means of communicating culture, but there is an overlap and a lot of cross-talk, which should provide material for a chapter. These are not distinct capabilities that you see in discussions of "bicameral" brain (left and right brain). In fact, this project may be seen as documenting the complex relationship between "logic" and "art".

"Intelligence" is embedded in the language and only appears in the individual through a process of assimilation. The parallel to this in the machine world is virtualization, where some part of the class library is loaded into a specific hardware platform for a specific purpose. This "purpose" is ultimately a bridge between the machine and the mind - a "User Interface" in some cases or in others, simply the purpose that a human designer has "baked in" to the program. An important topic is therefore to describe how the machine "mind" maps to the human mind in specific situations. Interesting special cases can be described as a specific human interacts with a specific device, such as a smart phone.

The mapping of the human mind, especially "purposes" to machine design is a particularly interesting facet of the discussion. Russel's ideas are relevant, where he includes the goals of society as a whole along with ideas like "uncertainty" in his ideal AI implementation. A machine can't "learn" unless it knows what it doesn't know - a tricky proposition even for a human.

Part 1: From machine language to AI

• My summer as a computer
• Logic as the free-floating computer. The abacus. Logic as both human and computer language.
• Clay tablets - the first external memory. Written language first used to count and only gradually to "account" - slow acceptance of modern idea of money.
• From machine language to COBOL - surely we can just "talk" to the computer
• Structured programming and the Object model - the role of symmetry, elegance and aesthetics
• Virtual machines and the Cloud - Java. Software floats free from hardware platform.
• The shifting role of the computer - from the math department to sexting
• The emerging role of the computer as generic master of logic games, especially the logic game of money
• Rising anxiety 

Part 2: Models of Mind

• Descartes creates the "mind body problem" by a quaint machine analogy
• Dennett struggles to "explain" the mind as "kinda" like a computer
• Hofstadter and Jaynes hint at how the mind actually works 
• The "cloud" paradigm calls for a total re-think of what we mean by a "computer" and hints that a similar re-definition of "mind" is called for
• Common buried assumptions in language and computer applications
• Models of value. The rocky road to our current assumptions about money and the value of life itself.
• Models of human decision-making
• The bureaucracy as a machine (Dragon Theory)

Part 3: The User Interface

• Primitive user interfaces - switches, paper tape ...
• The smart phone as an example of a 2017 "User Interface". Who is the "user"?
• Who is the "designer"?
• How computers and people are embedded in the logic of money
• How are humans and machines embedded in the same culture, especially when it comes to economics and connectivity?
• Considering the total role of the computer and the individual, in what sense does a computer replace a human being?

Affordances, biology, physics and problems of interfacing logic with biology. A broader definition of the "user" of the machine, including the designer, the "end user", the end user's employer, the designer's boss, the marketing system that connects them all together and so on. Like the individual, the machine is embedded and assimilated into a specific culture with countless points of contact, many of which are deeply buried in long-forgotten assumptions and arbitrary decisions. Modern computers are part of the Internet of "hosts" which has been specifically used to visualize the corresponding network of human connections - the "social network". They are also embedded in the "economy" in a symbiotic relationship with humans - sometimes replacing humans in that role. Possible amusing anecdote about my job as a "computer" in an oil company circa 1967. Focus on the concept of "work" and how automation can "take jobs" from "real people". "jobs", "work" and "money" are all models of the real world where ruthless "logic" competes with art and "quality of life". Money is a language of its own and computers "speak" that language very well.

Part 3: Essential Analogies

• Logic and value
• The brain as a super fast "value computer"
• The Cloud and the Brain
• Is a neuron "like" a bit?
• What is it to "know" something?
• What is "attention"?
• Can value and beauty be computed?
• The problem of accountability

Part 4: Computing Value

We allow machines to make such judgments all the time with the effect that we can forget the assumptions and escape accountability for our errors. For centuries, philosophers have attempted to reduce ethics and value to logic. We must not pretend that this problem has been solved. Ethical and value judgments must be left to human beings, whatever the shortcomings of brains may be. The brain justifies such judgments using language (pseudo-logic) which is prone to rhetorical tricks, selective memory and hidden assumptions "baked in" to our way of talking about the world.

• Does automation tend to boil away value? 
• Does it make sense to sell life itself, one hour at a time? Is this perhaps the root "computation"? If a laborer costs $15 per hour, 100 cost $1,500. How much would 1,000 Paul McCartney's cost? Are we buying millions of cheap robot Pauls?
• Technology itself challenges human values and turns people into machines
• Can current technology reverse the long term trend and allow machines to be machines and people to be people?
• What are the fundamental challenges that face us as we "automate" human choices to maximize value? Whose value? 
• The ultimate role of the machine is to "disappear" and become an extension of human will. Optimizing "value" is the core issue.

This line of thought is continued here

Gary Kasparov and Stuart Russel on Machine Intelligence

This is a great talk by Kasparov, the chess master who "lost" to the Deep Blue computer.

Stuart Russel has more to say on the subject.

Stuart Russell is a professor (and formerly chair) of Electrical Engineering and Computer Sciences at University of California at Berkeley. His book Artificial Intelligence: A Modern Approach (with Peter Norvig) is the standard text in AI; it has been translated into 13 languages and is used in more than 1,300 universities in 118 countries. His research covers a wide range of topics in artificial intelligence including machine learning, probabilistic reasoning, knowledge representation, planning, real-time decision making, multitarget tracking, computer vision, computational physiology, global seismic monitoring and philosophical foundations.

He also works for the United Nations, developing a new global seismic monitoring system for the nuclear-test-ban treaty.

Intelligent machines (or even dumb ones) are a model of the human mind, or, more exactly, a model of certain capabilities of the human mind. Russel's talk begins with the notion that "soon", intelligent machines will have read everything humans have ever written and understood it all. It would be hard to find a clearer statement of what computers are and what their capabilities might ultimately be. I'd say that the computer models human language - ultimately all human language, along with the complex structure of metaphor that underlies language. The question is, could a machine understand language? Or, to put it another way, what is it to understand?

In "Surfaces and Essences", Hofstader points out that our linguistic categories that underly language are ultimately rather arbitrary and based on situations encountered in human life. This would seem to be a problem for a machine that can obviously never encounter anything like human experience. If this problem is to be dealt with, perhaps Jaynes provides a clue as to the kind of "experience" that the computer must use as the basis for metaphorical "boot strapping". This would include things like the human perspective of being in a specific place at a specific time, along with a "narrative" about what is going on there and why. 

The biggest challenge for a computer will be to model the non-linguistic part of the human mind, including recognition of objects and general "situations" -- in other words, the context of the computer's actions. It seems like a stretch for us to model this in a computer, which is more and more "cloud based" (no location) and (with Russel's assumption) "aware" of all human history. How is this moment different? What is the "situation"?

Perhaps the trickiest thing to "model" (hinted at by Russel) is that the human experience always involves lack of knowledge and massive uncertainty. What would the computer need to "forget" in order to "understand" a human "situation"?