5 ms·

I hope this question doesnt come off as too new naive but due to the amount of links on the front page about ML - what is so fascinating about ML?? Why is there

I hope this question doesnt come off as too new naive but due to the amount of links on the front page about ML - what is so fascinating about ML?? Why is there not the same level of interest/links on topics such as cryptology, graphics, circuits, comp architecture ?

deleted 15y ago
[deleted]
kaybe 15y ago
I find the potential fascinating. ML allows computers to solve problems without a human and become more powerful tools that can eventually act on their own. The extent to which this is possible is not clear yet, but is potentially very far-reaching and can be very empowering for human-kind, in all areas of our existence. The other topics you mentioned are interesting as well, of course, but their potential impact is much lower (although computer architecture can border on ML). (edit typo)
deleted 15y ago
[deleted]
law 15y ago
There's this enormous focus on 'web scale' technologies. This focus necessarily invokes visualizing and making sense of terabytes and eventually even petabytes of data; conventional approaches would take thousands or millions of man hours to accomplish the same level of analysis that computers can perform in hours or days. Tom Mitchell's definition of machine learning algorithms as those that improve their performance at some task with experience is precisely the way in which humans go about learning what's necessary to perform the same tasks that formerly took thousands or millions of hours. For highly dimensional problems, such as text classification (i.e., spam detection) or image classification (i.e., facial detection), it's almost impossible to hard code an algorithm to accomplish its goal without using machine learning. It's much easier to use a binary spam/not spam or face/not face labeling system that, given the attributes of the example, can learn which attributes beget that specific label. In other words, it's much easier for a learning system to determine what variables are important in the ultimate classification than trying to model the "true" function that gives rise to the labeling.
- tapertaper 15y ago
  Great comment. Probably also worth speculating on why this is happening NOW. Why is this breaking out of CS departments in 2011 and not 2002? The datasets are new. Bandwidth? Storage capacity? Computing power? All of the above?
  - law 15y ago
    Actually, this has been actively researched since ICs started gaining widespread usage in the 1970s! Even before that there were plenty of journal papers produced that deal with the basics of ML and AI. It wasn't until the 1990s that computers started becoming reasonably priced and more accessible to researchers and hobbyists that we began seeing an exponential growth in the amount of research output. In many way, one could argue that the proliferation and development of AI has very much followed Moore's law, since these are extremely complex and costly calculations. Bandwidth increases have certainly increased the availability of data sets (Google has its entire ngrams data set fully available, and it's multiple terabytes in size), but storage capacity (hard disk, RAM, and CPU cache) and computing power have really formed the bottle neck. It's not just storage capacity, either: I/O read/write times are also immensely important. It's all just a huge balancing act right now.
zeratul 15y ago
There was a comment here from SandB0x saying that ML has great potential for startups. ML algorithms have many practical applications and avenues that are commercializable.
gms 15y ago
Because web companies are starting to use ML nowadays, and most of HN's users are associated with the web-company crowd.

deleted 15y ago

[deleted]

kaybe 15y ago

I find the potential fascinating. ML allows computers to solve problems without a human and become more powerful tools that can eventually act on their own. The extent to which this is possible is not clear yet, but is potentially very far-reaching and can be very empowering for human-kind, in all areas of our existence. The other topics you mentioned are interesting as well, of course, but their potential impact is much lower (although computer architecture can border on ML). (edit typo)

deleted 15y ago

[deleted]

law 15y ago

There's this enormous focus on 'web scale' technologies. This focus necessarily invokes visualizing and making sense of terabytes and eventually even petabytes of data; conventional approaches would take thousands or millions of man hours to accomplish the same level of analysis that computers can perform in hours or days. Tom Mitchell's definition of machine learning algorithms as those that improve their performance at some task with experience is precisely the way in which humans go about learning what's necessary to perform the same tasks that formerly took thousands or millions of hours. For highly dimensional problems, such as text classification (i.e., spam detection) or image classification (i.e., facial detection), it's almost impossible to hard code an algorithm to accomplish its goal without using machine learning. It's much easier to use a binary spam/not spam or face/not face labeling system that, given the attributes of the example, can learn which attributes beget that specific label. In other words, it's much easier for a learning system to determine what variables are important in the ultimate classification than trying to model the "true" function that gives rise to the labeling.

tapertaper 15y ago
Great comment. Probably also worth speculating on why this is happening NOW. Why is this breaking out of CS departments in 2011 and not 2002? The datasets are new. Bandwidth? Storage capacity? Computing power? All of the above?
- law 15y ago
  Actually, this has been actively researched since ICs started gaining widespread usage in the 1970s! Even before that there were plenty of journal papers produced that deal with the basics of ML and AI. It wasn't until the 1990s that computers started becoming reasonably priced and more accessible to researchers and hobbyists that we began seeing an exponential growth in the amount of research output. In many way, one could argue that the proliferation and development of AI has very much followed Moore's law, since these are extremely complex and costly calculations. Bandwidth increases have certainly increased the availability of data sets (Google has its entire ngrams data set fully available, and it's multiple terabytes in size), but storage capacity (hard disk, RAM, and CPU cache) and computing power have really formed the bottle neck. It's not just storage capacity, either: I/O read/write times are also immensely important. It's all just a huge balancing act right now.

zeratul 15y ago

There was a comment here from SandB0x saying that ML has great potential for startups. ML algorithms have many practical applications and avenues that are commercializable.

gms 15y ago

Because web companies are starting to use ML nowadays, and most of HN's users are associated with the web-company crowd.