Philip Morrison on challenges with AI

Philip Morrison who was a top-notch physicist and polymath with an incredible knowledge of things beyond his immediate field was also a speed reader who reviewed hundreds of books on a stunning range of topics. In one of his essays from an essay collection he held forth on what he thought were the significant challenges with machine intelligence. It strikes me that many of these are still valid (italics mine).

“First, a machine simulating the human mind can have no simple optimization game it wants to play, no single function to maximize in its decision making, because one urge to optimize counts for little until it is surrounded by many conditions. A whole set of vectors must be optimized at once. And under some circumstances, they will conflict, and the machine that simulates life will have the whole problem of the conflicting motive, which we know well in ourselves and in all our literature.

Second, probably less essential, the machine will likely require a multisensory kind of input and output in dealing with the world. It is not utterly essential, because we know a few heroic people, say, Helen Keller-who managed with a very modest cross-sensory connection nevertheless to depict the world in some fashion. It was very difficult, for it is the cross-linking of different senses which counts. Even in astronomy, if something is “seen” by radio and by optics, one begins to know what it is. If you do not “see” it in more than one way, you are not very clear what it in fact is.

Third, people have to be active. I do not think a merely passive machine, which simply reads the program it is given, or hears the input, or receives a memory file, can possibly be enough to simulate the human mind. It must try experiments like those we constantly try in childhood unthinkingly, but instructed by built-in mechanisms. It must try to arrange the world in different fashions.

Fourth, I do not think it can be individual. It must be social in nature. It must accumulate the work–the languages, if you will- of other machines with wide experience. While human beings might be regarded collectively as general-purpose devices, individually they do not impress me much that way at all. Every day I meet people who know things I could not possibly know and can do things I could not possibly do, not because we are from differing species, not because we have different machine natures, but because we have been programmed differently by a variety of experiences as well as by individual genetic legacies. I strongly suspect that this phenomenon will reappear in machines that specialize, and then share experiences with one another. A mathematical theorem of Turing tells us that there is an equivalence in that one machine’s talents can be transformed mathematically to another’s. This gives us a kind of guarantee of unity in the world, but there is a wide difference between that unity, and a choice among possible domains of activity. I suspect that machines will have that choice, too. The absence of a general-purpose mind in humans reflects the importance of history and of development. Machines, if they are to simulate this behavior- or as I prefer to say, share it–must grow inwardly diversified, and outwardly sociable.

Fifth, it must have a history as a species, an evolution. It cannot be born like Athena, from the head full-blown. It will have an archaeological and probably a sequential development from its ancestors. This appears possible. Here is one of computer science’s slogans, influenced by the early rise of molecular microbiology: A tape, a machine whose instructions are encoded on the tape, and a copying machine. The three describe together a self-reproducing structure. This is a liberating slogan; it was meant to solve a problem in logic, and I think it did, for all but the professional logicians. The problem is one of the infinite regress which looms when a machine becomes competent enough to reproduce itself. Must it then be more complicated than itself? Nonsense soon follows. A very long

instruction tape and a complex but finite machine that works on those instructions is the solution to the logical problem.”