Key Arguments

• He critiques the simplistic Augustinian and Turing picture that language is taught by ostension alone—merely pointing at an object and pronouncing a word—by noting Wittgenstein’s argument that a child could not know whether 'brown' picks out color, size, shape, kind of object, or proper name.
• If the child already had language, we could say we are pointing out the color, but with a prelinguistic child we face the bootstrapping problem of how language learning can even begin.
• Wittgenstein’s solution, which Dreyfus endorses, is that the child must be involved in a "form of life" sharing some goals and interests with the teacher, so that the ongoing activity helps delimit possible referents and roles of the words used.
• From this, Dreyfus asks what, in this richer, practice‑embedded sense, can be taught to a machine and highlights A. L. Samuel’s objection that machines cannot be intelligent because they can only do what they are instructed to do.
• He notes that Minsky dismisses Samuel’s point by saying we can be surprised by our machines, but Samuel clearly knows this and must instead mean that machines require being given their programs in a way that is categorically different from how children are taught.
• Dreyfus reports that Michael Scriven counters Samuel by claiming that new machine strategies are 'put into' computers by designers in exactly the same metaphorical sense as everything we put into children, but he rejects this assimilation as bullying and inaccurate.
• He emphasizes that data and programs are 'put into' machines in an entirely different way than language is taught to children: linguistic meaning cannot be precisely defined and must be disambiguated and assimilated through a shared context.
• Quoting Wittgenstein, Dreyfus underscores that real learning is not acquisition of a technique defined by explicit rules but the learning of correct judgments; there are rules, but they 'do not form a system' and only experienced people can apply them correctly, unlike algorithmic calculation rules.
• He concludes that the ability to 'grasp the point in a particular context' constitutes true learning, which is why children, who can make this leap, are capable of genuinely surprising us in ways that programmed machines are not.

Source Quotes

What is involved in learning a language is much more complicated and more mysterious than the sort of conditioned reflex involved in learning to associate nonsense syllables. To teach someone the meaning of a new word, we can sometimes point at the object which the Word names. Augustine, in his Confessions, and Turing, in his article on machine intelligence, assume that this is the way we teach language to children. But Wittgenstein points out that if we simply point at a table, for example, and say "brown," a child will not know if brown is the color, the size, or the shape of the table, the kind of object, or the proper name of the object.

Augustine, in his Confessions, and Turing, in his article on machine intelligence, assume that this is the way we teach language to children. But Wittgenstein points out that if we simply point at a table, for example, and say "brown," a child will not know if brown is the color, the size, or the shape of the table, the kind of object, or the proper name of the object. If the child already uses language, we can say that we are pointing out the color; but if he doesn't already use language, how do we ever get off the ground?

If the child already uses language, we can say that we are pointing out the color; but if he doesn't already use language, how do we ever get off the ground? Wittgenstein suggests that the child must be engaged in a "form of life" in which he shares at least some of the goals and interests of the teacher, so that the activity at hand helps to delimit the possible reference of the words used. What, then, can be taught to a machine?

Scriven argues that new strategies are "'put into' the computer by the designer . . . in exactly the same metaphorical sense that we put into our children everything they come up with in their later life."47 Still, Samuel should not let himself be bullied by the philosophers any more than by his colleagues. Data are indeed put into a machine but in an entirely different way than children are taught. We have just seen that when language is taught it is not, and, as we shall see in Chapter 6, cannot be, precisely defined.

Data are indeed put into a machine but in an entirely different way than children are taught. We have just seen that when language is taught it is not, and, as we shall see in Chapter 6, cannot be, precisely defined. Our attempts to teach meaning must be disambiguated and assimilated in terms of a shared context. Learning as opposed to memorization and repetition requires this sort of judgment.

This is what learning and teaching are like here. . . . What one acquires here is not a technique; one learns correct judgements. There are also rules, but they do not form a system, and only experienced people can apply them right. Unlike calculation rules.48* It is this ability to grasp the point in a particular context which is true learning; since children can and must make this leap, they can and do surprise us and come up with something genuinely new. The foregoing considerations concerning the essential role of context awareness and ambiguity tolerance in the use of a natural language should suggest why, after the success of the mechanical dictionary, progress has come to a halt in the translating field.

There are also rules, but they do not form a system, and only experienced people can apply them right. Unlike calculation rules.48* It is this ability to grasp the point in a particular context which is true learning; since children can and must make this leap, they can and do surprise us and come up with something genuinely new. The foregoing considerations concerning the essential role of context awareness and ambiguity tolerance in the use of a natural language should suggest why, after the success of the mechanical dictionary, progress has come to a halt in the translating field.

Key Concepts

• To teach someone the meaning of a new word, we can sometimes point at the object which the Word names. Augustine, in his Confessions, and Turing, in his article on machine intelligence, assume that this is the way we teach language to children.
• But Wittgenstein points out that if we simply point at a table, for example, and say "brown," a child will not know if brown is the color, the size, or the shape of the table, the kind of object, or the proper name of the object.
• Wittgenstein suggests that the child must be engaged in a "form of life" in which he shares at least some of the goals and interests of the teacher, so that the activity at hand helps to delimit the possible reference of the words used.
• Data are indeed put into a machine but in an entirely different way than children are taught.
• We have just seen that when language is taught it is not, and, as we shall see in Chapter 6, cannot be, precisely defined. Our attempts to teach meaning must be disambiguated and assimilated in terms of a shared context.
• What one acquires here is not a technique; one learns correct judgements. There are also rules, but they do not form a system, and only experienced people can apply them right. Unlike calculation rules.48*
• It is this ability to grasp the point in a particular context which is true learning; since children can and must make this leap, they can and do surprise us and come up with something genuinely new.

Context

Following his critique of EPAM, Dreyfus deepens his account of language learning using Wittgenstein’s philosophy to distinguish human teaching and judgment from machine programming, responding indirectly to debates between Samuel, Minsky, and Scriven about whether machines truly 'do only what they are told.'