Machine Learning in Data Analytics

Machines do not run in order to enable men to live, but we resign ourselves to feeding men in order that they may serve the machines.

It’s possible to imagine a machine that could scoop up material – rocks from the Moon or rocks from asteroids – process them inside and produce just about any product: washing machines or teacups or automobiles or starships. Once such a machine exists it could gather sunlight and materials that it’s sitting on, and produce on call whatever product anybody wants to name, as long as somebody knows how to make it and those instructions can be given to the machine.

Although it is not, abstractedly speaking, of importance to know who first made a most valuable experiment, or to what individual the community is indebted for the invention of the most useful machine, yet the sense of mankind has in this, as in several other things, been in direct opposition to frigid reasoning; and we are pleased with a recollection of benefits, and with rendering honour to the memory of those who bestowed them. Were public benefactors to be allowed to pass away like hewers of wood and drawers of water, without commemoration, genius and enterprise would be deprived of their most coveted distinction, and after-times would lose incentives to that emulation which urges us to cherish and practise what has been worthy of commendation or imitation in our forefathers; and to make their works, which may have served for a light and been useful to the age in which they lived, a guide and a spur to ourselves