Google 'Rethinking Everything' Around Machine Learning (itworld.com)

itwbennett writes: Sundar Pichai took part in his first earnings call Thursday when Google's parent company Alphabet reported its quarterly results, and 'in between discussing the numbers he revealed how important Google thinks machine learning is to its future,' writes James Niccolai. 'Machine learning is a core, transformative way by which we're rethinking everything we're doing,' Pichai said. 'We're thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. We're in the early days, but you'll see us in a systematic way think about how we can apply machine learning to all these areas.'

Comments

Uhhhh

[Aighearach]

Yeah, but there is nothing there to tell us wtf he's actually talking about.

Re:Uhhhh

[phantomfive]

Eh, he started his career as a product manager, and moved up the corporate ladder on the management side.

Let's be honest: he has no clue what he's talking about either. :)

Re:Uhhhh

[Okian Warrior]

Yeah, but there is nothing there to tell us wtf he's actually talking about.

Don't worry, in this field *no one* knows what they are talking about.

Machine learning is a part of AI, neither of which have good definitions. In textbooks you find things like "AI is the study of machines that think" and similar tautologies.

What is the definition of "machine learning"?

If you check a configuration box in Mozilla, the machine has "learned" your preference for something. Is that machine learning?

If you tell Siri "Siri, call me David", and Siri then addresses you by that name, is *that* machine learning?

If you tell a coder "this is a bubble sort", then the coder can check the "bubble sort" definition against the code, and see if it corresponds. The coder can then say yes or no, and if not, can identify attributes that the code is missing and how to bring it into compliance with the definition.

For "Machine learning"... not so much.

Since the definition isn't clear, the field of AI and machine learning are free to incorporate all sorts of ideas and theories and algorithms under the umbrella term "machine learning". This is good because you can get grant proposals passed by using the term, but it's bad because you can extend the definition to include things that seem to have nothing to do with the common-sense definition.

There are schools of thought, of course. Lots of people will try to clarify the issue by saying "I think...". Post replies here if you know what machine learning actually is, in a way that can be used to classify existing algorithms in a concrete manner.

But there's no real consensus; and as a result, research in machine learning runs all over the conceptual map.

Re:Uhhhh

[martas]

Not sure where you're getting your information, but there is absolutely consensus on what machine learning is. Machine learning is statistics, except with less emphasis on theoretical justification and more emphasis on computational problems. That's it. The philosophical questions you raise seem to be about the general concept of "learning" in the typical human notion of the word, but machine learning is a specific term referring to a specific field of study. It is not the same as "learning as performed by a machine", because very few of the people who actually work in machine learning have any interest in discussing the metaphysical nature of learning except perhaps over a round of drinks. We prefer to spend our time minimizing squared errors, parallelizing descent algorithms, and factoring matrices, not questioning whether a hypothetical book containing translations of all possible sentences can be said to truly know a language or not.