a*****g
发帖数: 19398 | 1 Deep Learning Isn’t a Dangerous Magic Genie. It’s Just Math
Deep learning is rapidly ‘eating’ artificial intelligence. But let’s not
mistake this ascendant form of artificial intelligence for anything more
than it really is. The famous author Arthur C. Clarke wrote, “Any
sufficiently advanced technology is indistinguishable from magic.” And deep
learning is certainly an advanced technology—it can identify objects and
faces in photos, recognize spoken words, translate from one language to
another, and even beat the top humans at the ancient game of Go. But it’s
far from magic.
As companies like Google and Facebook and Microsoft continue to push this
technology into everyday online services—and the world continues to marvel
at AlphaGo, Google’s Go playing super-machine—the pundits often describe
deep learning as an imitation of the human brain. But it’s really just
simple math executed on an enormous scale.
In particular, deep learning is a class of algorithmic methods for ‘tuning
’ neural networks based on data. What does that mean? Well, a neural
network is a computer program, loosely inspired by the structure of the
brain, which consists of a large number of very simple interconnected
elements. Each element takes numeric inputs, and computes a simple function
(for example, a sum) over the inputs. The elements are far simpler than
neurons, and the number of elements and their interconnections is several
orders of magnitude smaller than the number of neurons and synapses in the
brain. Deep learning merely strengthens the connections in such networks.
Deep learning is a subfield of machine learning, which is a vibrant research
area in artificial intelligence, or AI. Abstractly, machine learning is an
approach to approximating functions based on a collection of data points.
For example, given the sequence “2, 4, 6,…” a machine might predict that
the 4th element of the sequence is 8, and that the 5th is 10, by
hypothesizing that the sequence is capturing the behavior of the function 2
times X, where X is the position of the element in the sequence. This
paradigm is quite general. It has been highly successful in applications
ranging from self-driving cars and speech recognition to anticipating
airfare fluctuations and much more.
In a sense, deep learning is not unique. Any machine learning system—deep
or not—consists of the following fundamental components:
Performance element: the component of the system that takes some action in
the world (e.g., making moves in the game of Go).
Target function: the function being learned (e.g., a mapping from board
positions to move choices in Go).
Training data: the set of labeled data points used to approximate the target
function (e.g., a set of Go board positions, each labeled with the move
chosen by a human expert in that position).
Data representation: each data point is typically represented as a vector of
pre-determined variables (e.g., the position of a piece on the Go board).
Learning algorithm: the algorithm that computes an approximation of the
target function based on the training data.
Hypothesis space: the space of possible functions the learning algorithm can
consider.
This architecture captures the full gamut of machine learning methods from
simple linear regression methods to complex deep-learning algorithms.
Technically, we are referring to supervised learning where each data point
is labeled, typically by humans. When the data isn’t labeled, we have
unsupervised learning or clustering, and that’s much harder to pull off.
When some of the data is labeled, we have semi-supervised learning.
Statisticians refer to estimating the value of an independent variable based
on dependent variables as regression.
It’s important to realize that the first five components of a machine
learning architecture are manually crafted inputs; the human programmer
constructs each of these elements, and they are outside of the control of
the learning program. In fact, the programmer typically analyzes the
behavior of the learning program, realizes that it is unsatisfactory, and
manually modifies one or more of these elements. This laborious process is
often repeated many times over the course of a year or more before the
desired performance level is achieved.
Helping Humans
We can see that that a learning program’s abilities are strictly curtailed
by this architecture. Specifically:
The program cannot modify any of the components of the architecture.
The program cannot modify itself.
The program cannot “learn” a function outside of its hypothesis space.
For this reason, a learning program such as AlphaGo cannot learn to play
chess or checkers without extensive human labor. Moreover, most programmers
are not able to successfully modify machine-learning systems without
substantial specialized training. Even highly trained data scientists
require substantial time and resources to build successful systems.
A learning program such as AlphaGo cannot learn to play chess or checkers
without extensive human labor.
The design and implementation of the AlphaGo system required more than 30
million training examples culled from the Internet, and years of effort by a
large team of researchers and engineers. In fact, merely improving AlphaGo
’s performance from defeating the European Go champion, Fan Hui, to
defeating Lee Sedol required several months of intensive work.
AlphaGo also utilized a class of machine-learning methods known as
reinforcement learning where the program learns to maximize a reward by
choosing actions, repeatedly, and observing the outcome. AlphaGo repeatedly
chose Go moves, and observed the outcome of the game. In reinforcement
learning, training data is not a pre-labeled input. Instead, the learning
program is provided with a “reward function” that assigns a reward to
different states of the world. While reinforcement learning methods acquire
their training data by taking actions, and observing rewards, the analysis
of machine learning in this article applies equally well to reinforcement
learning—such methods are still constrained by their target function, data
representation, and hypothesis space, among other things.
The Space of Possibilities
Evolution is often cited as an example of the unbridled power of learning to
produce remarkable results, but it is essential to understand the
distinction between the evolutionary process of natural selection and its
simulation in a computer program. Programs that attempt to simulate
evolutionary processes in a computer are called genetic algorithms, and have
not been particularly successful
Genetic algorithms modify a representation of the “organism,” and such
representations tend to be very large. For example, the human genome is
estimated to contain more than a billion bits of information. This means the
number of possible human DNA sequences is two to the power of a billion.
Exploring much of that space computationally is prohibitively expensive. Yet
, the topology of this space does not lend itself to algorithms that can
take “easy short cuts” to a solution. In contrast, the game of Go defines
a far smaller space of possibilities, and one that is far easier to explore
using machine learning methods.
When we can successfully define an objective function and reduce a real-
world task to an optimization problem, computer scientists, operations
researchers, and statisticians have a decades-long track record of solving
such problems (sooner or later). However, many problems require additional
analysis before they can even be represented to a machine in a form that it
can manipulate. For example, how do we write down the meaning of a single
sentence in a machine-understandable language? As Gerald Sussman put it, “
you can’t learn what you can’t represent.” In this case, the problem of
choosing an appropriate representation is far from being formulated
effectively, let alone solved.
Thus, deep learning (and machine learning in general) has proven to be a
powerful class of methods in AI, but current machine learning methods
require substantial human involvement to formulate a machine learning
problem and substantial skill and time to iteratively reformulate the
problem until it is solvable by a machine. Most important, the process is
narrowly circumscribed, providing the machine with a very limited degree of
autonomy; unlike people, AI does not beget autonomy
Machine learning is far from being a “genie” that is ready to spring from
a bottle and run amok. Rather, it is a step in a decades-long (or, perhaps,
centuries-long) research endeavor to understand intelligence and to
construct human-level AI. | m*****h
发帖数: 2292 | 2 Deep Learning都能算数学？？
jpg
not
deep
【在 a*****g 的大作中提到】
: Deep Learning Isn’t a Dangerous Magic Genie. It’s Just Math
: Deep learning is rapidly ‘eating’ artificial intelligence. But let’s not
: mistake this ascendant form of artificial intelligence for anything more
: than it really is. The famous author Arthur C. Clarke wrote, “Any
: sufficiently advanced technology is indistinguishable from magic.” And deep
: learning is certainly an advanced technology—it can identify objects and
: faces in photos, recognize spoken words, translate from one language to
: another, and even beat the top humans at the ancient game of Go. But it’s
: far from magic.
: As companies like Google and Facebook and Microsoft continue to push this
| i***h
发帖数: 12655 | 3 计算机本质不就是数学么
【在 m*****h 的大作中提到】
: Deep Learning都能算数学？？
:
: jpg
: not
: deep
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