Machine learning algorithms instantiate knowledge. It is perfectly possible for a system to have knowledge, but not to understand that knowledge.

Many philosophers hold the justified true belief. Knowledge that human beings have is not justified. Knowledge also need not be true, e.g. - Newtonian mechanics is false, but it is knowledge. And one of the reasons knowledge is imagined to be belief is that you need a person to justify it, but that's not true, so no belief is necessary.

Since nothing is left of the JTB theory of knowledge, that leaves the question of what separates knowledge from non-knowledge. Knowledge is information that solves some problem.

It's not necessary for anybody to know of the existence of a problem for a problem to be solved. For example, the human heart is a pump that can work continuously for decades without any human intervention or maintenance. Sparrows' wings help solve the problem of how to make small, light objects fly. The information on how to solve these problems is contained in the genes of the relevant organisms. The fact that nobody knows that information is irrelevant. Many slight variants on those structures would not solve the problems they solve. This close match between those structures and a particular problem requires an explanation. The explanation for this match has strong structural similarities to how human knowledge is created. Both human knowledge and biological knowledge is created by many rounds of variation and selection. The variation and selection take place in different media, and have some other relevant differences (see "The Beginning of Infinity" by David Deutsch, Chapters 15 and 16), but they both involve variation and selection.

Machine learning algorithms instantiate knowledge that is largely created by people. People decide what information to feed to the algorithms. People write the code that produces variations. People decide what sort of results count as success. People decide how the selection should work. The algorithms instantiate information about how to solve some problem in a form that we don't know how to read explicitly. But the algorithm may nevertheless solve some problem, facial recognition say, so people don't have to do it anymore. So the machine learning program instantiates some knowledge.

Machine learning algorithms instantiate knowledge. It is perfectly possible for a system to have knowledge, but not to understand that knowledge.

Many philosophers hold the justified true belief. Knowledge that human beings have is not justified. Knowledge also need not be true, e.g. - Newtonian mechanics is false, but it is knowledge. And one of the reasons knowledge is imagined to be belief is that you need a person to justify it, but that's not true, so no belief is necessary.

Since nothing is left of the JTB theory of knowledge, that leaves the question of what separates knowledge from non-knowledge. Knowledge is information that solves some problem.

It's not necessary for anybody to know of the existence of a problem for a problem to be solved. For example, the human heart is a pump that can work continuously for decades without any human intervention or maintenance. Sparrows' wings help solve the problem of how to make small, light objects fly. The information on how to solve these problems is contained in the genes of the relevant organisms. The fact that nobody knows that information is irrelevant. Many slight variants on those structures would not solve the problems they solve. This close match between those structures and a particular problem requires an explanation. The explanation for this match has strong structural similarities to how human knowledge is created. Both human knowledge and biological knowledge is created by many rounds of variation and selection. The variation and selection take place in different media, and have some other relevant differences (see "The Beginning of Infinity" by David Deutsch, Chapters 15 and 16), but they both involve variation and selection.

Machine learning algorithms instantiate knowledge that is largely created by people. People decide what information to feed to the algorithms. People write the code that produces variations. People decide what sort of results count as success. People decide how the selection should work. The algorithms instantiate information about how to solve some problem in a form that we don't know how to read explicitly. But the algorithm may nevertheless solve some problem, facial recognition say, so people don't have to do it anymore. So the machine learning program instantiates some knowledge.