AI News, Machine Learning FAQ

Machine Learning FAQ

Artificial Intelligence (AI) started as a subfield of computer science with the focus on solving tasks that humans can but computers can’t do (for instance, image recognition).

Now, there’s also deep learning, which in turn is a subfield of machine learning, referring to a particular subset of models that are particularly good at certain tasks such as image recognition and natural language processing.

Or in short, machine learning (and deep learning) definitely helps to develop “AI,” however, AI doesn’t necessarily have to be developed using machine learning – although, machine learning makes “AI” much more convenient ;).

Machine Learning FAQ

Artificial Intelligence (AI) started as a subfield of computer science with the focus on solving tasks that humans can but computers can’t do (for instance, image recognition).

Now, there’s also deep learning, which in turn is a subfield of machine learning, referring to a particular subset of models that are particularly good at certain tasks such as image recognition and natural language processing.

Or in short, machine learning (and deep learning) definitely helps to develop “AI,” however, AI doesn’t necessarily have to be developed using machine learning – although, machine learning makes “AI” much more convenient ;).

Machine learning

Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to 'learn' (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.[1] The name machine learning was coined in 1959 by Arthur Samuel.[2] Evolved from the study of pattern recognition and computational learning theory in artificial intelligence,[3] machine learning explores the study and construction of algorithms that can learn from and make predictions on data[4] – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,[5]:2 through building a model from sample inputs.

Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: 'A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.'[13] This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms.

Machine learning tasks are typically classified into two broad categories, depending on whether there is a learning 'signal' or 'feedback' available to a learning system: Another categorization of machine learning tasks arises when one considers the desired output of a machine-learned system:[5]:3 Among other categories of machine learning problems, learning to learn learns its own inductive bias based on previous experience.

Developmental learning, elaborated for robot learning, generates its own sequences (also called curriculum) of learning situations to cumulatively acquire repertoires of novel skills through autonomous self-exploration and social interaction with human teachers and using guidance mechanisms such as active learning, maturation, motor synergies, and imitation.

Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation.[17]:488 By 1980, expert systems had come to dominate AI, and statistics was out of favor.[18] Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming, but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval.[17]:708–710;

Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).

Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge.

Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics.[20] He also suggested the term data science as a placeholder to call the overall field.[20] Leo Breiman distinguished two statistical modelling paradigms: data model and algorithmic model,[21] wherein 'algorithmic model' means more or less the machine learning algorithms like Random forest.

Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into (high-dimensional) vectors.[27] Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features.

In machine learning, genetic algorithms found some uses in the 1980s and 1990s.[31][32] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[33] Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply, knowledge.

They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.[35] Applications for machine learning include: In 2006, the online movie company Netflix held the first 'Netflix Prize' competition to find a program to better predict user preferences and improve the accuracy on its existing Cinematch movie recommendation algorithm by at least 10%.

A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[41] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ('everything is a recommendation') and they changed their recommendation engine accordingly.[42] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of Machine Learning to predict the financial crisis.

[43] In 2012, co-founder of Sun Microsystems Vinod Khosla predicted that 80% of medical doctors jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[44] In 2014, it has been reported that a machine learning algorithm has been applied in Art History to study fine art paintings, and that it may have revealed previously unrecognized influences between artists.[45] Although machine learning has been very transformative in some fields, effective machine learning is difficult because finding patterns is hard and often not enough training data are available;

as a result, machine-learning programs often fail to deliver.[46][47] Classification machine learning models can be validated by accuracy estimation techniques like the Holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set.

Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[50] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[51][52] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning.

There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these 'greed' biases are addressed.[54] Software suites containing a variety of machine learning algorithms include the following :

How do I compare between AI and Machine Learning?

Artifical Intellicence (AI) started as a subfield of computer science with the focus on solving tasks that humans can but computers can't do (for instance, image recognition).

One would be to look at all of these images and come-up with a set of (nested) if-this-than-that rules to say which image is displayed in a particular image (for instance, by looking at the relative locations of pixels).

Now, there's also deep learning, which in turn is a subfield of machine learning, referring to a particular subset of models that are particularly good at certain tasks such as image recognition and natural language processing.

A Tour of Machine Learning Algorithms

In this post, we take a tour of the most popular machine learning algorithms.

There are different ways an algorithm can model a problem based on its interaction with the experience or environment or whatever we want to call the input data.

There are only a few main learning styles or learning models that an algorithm can have and we’ll go through them here with a few examples of algorithms and problem types that they suit.

This taxonomy or way of organizing machine learning algorithms is useful because it forces you to think about the roles of the input data and the model preparation process and select one that is the most appropriate for your problem in order to get the best result.

Let’s take a look at three different learning styles in machine learning algorithms: Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.

hot topic at the moment is semi-supervised learning methods in areas such as image classification where there are large datasets with very few labeled examples.

The most popular regression algorithms are: Instance-based learning model is a decision problem with instances or examples of training data that are deemed important or required to the model.

Such methods typically build up a database of example data and compare new data to the database using a similarity measure in order to find the best match and make a prediction.

The most popular instance-based algorithms are: An extension made to another method (typically regression methods) that penalizes models based on their complexity, favoring simpler models that are also better at generalizing.

The most popular regularization algorithms are: Decision tree methods construct a model of decisions made based on actual values of attributes in the data.

All methods are concerned with using the inherent structures in the data to best organize the data into groups of maximum commonality.

The most popular clustering algorithms are: Association rule learning methods extract rules that best explain observed relationships between variables in data.

The most popular association rule learning algorithms are: Artificial Neural Networks are models that are inspired by the structure and/or function of biological neural networks.

They are a class of pattern matching that are commonly used for regression and classification problems but are really an enormous subfield comprised of hundreds of algorithms and variations for all manner of problem types.

The most popular artificial neural network algorithms are: Deep Learning methods are a modern update to Artificial Neural Networks that exploit abundant cheap computation.

They are concerned with building much larger and more complex neural networks and, as commented on above, many methods are concerned with semi-supervised learning problems where large datasets contain very little labeled data.

The most popular deep learning algorithms are: Like clustering methods, dimensionality reduction seek and exploit the inherent structure in the data, but in this case in an unsupervised manner or order to summarize or describe data using less information.

Ensemble methods are models composed of multiple weaker models that are independently trained and whose predictions are combined in some way to make the overall prediction.

Understanding the differences between AI, machine learning, and deep learning

With huge strides in AI—from advances in the driverless vehicle realm, to mastering games such as poker and Go, to automating customer service interactions—this advanced technology is poised to revolutionize businesses.

SEE: Inside Amazon's clickworker platform: How half a million people are being paid pennies to train AI (PDF download) (TechRepublic) AI is the broadest way to think about advanced, computer intelligence.

IBM's Deep Blue, which beat chess grand master Garry Kasparov at the game in 1996, or Google DeepMind's AlphaGo, which in 2016 beat Lee Sedol at Go, are examples of narrow AI—AI that is skilled at one specific task.

As Nick Bostrom describes it, this is 'an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills.'

ML systems can quickly apply knowledge and training from large data sets to excel at facial recognition, speech recognition, object recognition, translation, and many other tasks.

As mentioned above, in March 2016, a major AI victory was achieved when DeepMind's AlphaGo program beat world champion Lee Sedol in 4 out of 5 games of Go using deep learning.

The way the deep learning system worked was by combining 'Monte-Carlo tree search with deep neural networks that have been trained by supervised learning, from human expert games, and by reinforcement learning from games of self-play,' according to Google.

Text-based searches, fraud detection, spam detection, handwriting recognition, image search, speech recognition, Street View detection, and translation are all tasks that can be performed through deep learning.

CppCon 2017: Peter Goldsborough “A Tour of Deep Learning With C++”

— Presentation Slides, PDFs, Source Code and other presenter materials are available at: — Deep .

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