There is, alas, no such thing as a free lunch. This simple and obvious truth is invariably forgotten whenever irrational exuberance teams up with digital technology in the latest quest to change the world. A case in point was the bitcoin frenzy, where one could apparently become insanely rich by mining for the elusive coins. All you needed was to get a computer to solve a complicated mathematical puzzle and lo! you could earn one bitcoin, which at the height of the frenzy was worth $19,783.06. All you had to do was buy a mining kit (or three) from Amazon, plug it in and become part of the crypto future.

The only problem was that mining became progressively more difficult the closer we got to the maximum number of bitcoins set by the scheme and so more and more computing power was required. Which meant that increasing amounts of electrical power were needed to drive the kit. Exactly how much is difficult to calculate, but one estimate published in July by the Judge Business School at the University of Cambridge suggested that the global bitcoin network was then consuming more than seven gigwatts of electricity. Over a year, thats equal to around 64 terawatt-hours (TWh), which is 8 TWh more than Switzerland uses annually. So each of those magical virtual coins turns out to have a heavy environmental footprint.

At the moment, much of the tech world is caught up in a new bout of irrational exuberance. This time, its about machine learning, another one of those magical technologies that change the world, in this case by transforming data (often obtained by spying on humans) into depending on whom you talk to information, knowledge and/or massive revenues.

As is customary in these frenzies, some inconvenient truths are overlooked, for example, warnings by leaders in the field such as Ali Rahimi and James Mickens that the technology bears some resemblances to an older speciality called alchemy. But thats par for the course: when youve embarked on changing the world (and making a fortune in the process), why let pedantic reservations get in the way?

Recently, though, a newer fly has arrived in the machine-learning ointment. In a way, its the bitcoin problem redux. OpenAI, the San Francisco-based AI research lab, has been trying to track the amount of computing power required for machine learning ever since the field could be said to have started in 1959. What its found is that the history divides into two eras. From the earliest days to 2012, the amount of computing power required by the technology doubled every two years in other words, it tracked Moores law of growth in processor power. But from 2012 onwards, the curve rockets upwards: the computing power required for todays most-vaunted machine-learning systems has been doubling every 3.4 months.

When youve embarked on changing the world, why let pedantic reservations get in the way?

This hasnt been noticed because the outfits paying the bills are huge tech companies. But the planet will notice, because the correspondingly enormous growth in electricity consumption has environmental consequences.

To put that in context, researchers at Nvidia, the company that makes the specialised GPU processors now used in most machine-learning systems, came up with a massive natural-language model that was 24 times bigger than its predecessor and yet was only 34% better at its learning task. But heres the really interesting bit. Training the final model took 512 V100 GPUs running continuously for 9.2 days. Given the power requirements per card, wrote one expert, a back of the envelope estimate put the amount of energy used to train this model at over 3x the yearly energy consumption of the average American.

You dont have to be Einstein to realise that machine learning cant continue on its present path, especially given the industrys frenetic assurances that tech giants are heading for an AI everywhere future. Brute-force cloud computing wont achieve that goal. Of course smarter algorithms will make machine learning more resource-efficient (and perhaps also less environmentally damaging).

Companies will learn to make trade-offs between accuracy and computational efficiency, though that will have unintended, and antisocial, consequences too. And, in the end, if machine learning is going to be deployed at a global scale, most of the computation will have to be done in users hands, ie in their smartphones.

This is not as far-fetched as it sounds. The new iPhone 11, for example, includes Apples A13 chip, which incorporates a unit running the kind of neural network software behind recent advances in natural language processing language and interpreting images. No doubt other manufacturers have equivalent kit.

In preparation for the great day of AI Everywhere, I just asked Siri: Is there such a thing as a free lunch? She replied: I can help you find a restaurant if you turn on location services. Clearly, the news that there is no such thing hasnt yet reached Silicon Valley. Theyll get it eventually, though, when Palo Alto is underwater.

Capital ideaThe Museum of Neoliberalism has just opened in Lewisham, London. Its a wonderful project and website my only complaint is that neoliberalism isnt dead yet.

Who needs humans?This Marketing Blog Does Not Exist is a blog entirely created by AI. Could you tell the difference between it and a human-created one? Not sure I could.

All the right notesTheres a lovely post about Handel by Ellen T Harris on the Bank of Englands blog, Bank Underground. The German composer was a shrewd investor, but it was The Messiah that made him rich.

See more here:

Can the planet really afford the exorbitant power demands of machine learning? - The Guardian

A joint press release from Bristol Myers Squibb and Pfizer has highlighted the findings of an artificial intelligence (AI)-based machine learning (ML) technique that has been shown in a test database to exhibit greater predictive performance than other currently available risk prediction models for atrial fibrillation (AF). The data from the UK study were published in PLoS ONE.

The study found that the algorithms, developed using routine patient records, have the potential to enrich the patient population for targeted screening. According to the joint statement, the next stage is to test the algorithm in routine clinical practice and quantify its impact in terms of the number of AF cases identified, and the associated potential cost savings in the earlier detection of AF.

Current methods for AF detection, such as opportunistic pulse checking in those >65 years and in the over age group, mean that around 100 people are screened to identify one person with AF. The study found that adopting the AI algorithm could reduce this number to one in nine. It tested whether AI was more accurate than existing risk prediction models, using the health records of nearly three million people.

Commenting in the press release, Mark ONeill (St Thomas Hospital and Kings College, London, UK), one of the study authors, says: This AI technique represents quite an astonishing leap in precision. The implications are huge, especially because ML can be so easily and affordably used in routine clinical practice with the potential to transform the diagnosis of AF. If we can find and treat people living unwittingly with AF, we can do a much better job of preventing complications like stroke and heart disease.

The press release states that the ML algorithm is potentially more precise than routine practice because it not only looks for risk factors, but also how they change, and can spot complex relationships between risk predictors, that cannot be readily identified by humans, such as subtle changes in blood pressure prior to diagnosis or frequency of GP visits.

In 2007, Pfizer and Bristol-Myers Squibb entered into a global alliance to commercialise the oral anticoagulant apixaban.

Originally posted here:

AI-based ML algorithms could increase detection of undiagnosed AF - Cardiac Rhythm News

This class is offered as CS7641 at Georgia Tech where it is a part of the Online Masters Degree (OMS). Taking this course here will not earn credit towards the OMS degree.

Machine Learning is a graduate-level course covering the area of Artificial Intelligence concerned with computer programs that modify and improve their performance through experiences.

The first part of the course covers Supervised Learning, a machine learning task that makes it possible for your phone to recognize your voice, your email to filter spam, and for computers to learn a bunch of other cool stuff.

In part two, you will learn about Unsupervised Learning. Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? Such answers can be found in this section!

Finally, can we program machines to learn like humans? This Reinforcement Learning section will teach you the algorithms for designing self-learning agents like us!

Read the rest here:

Machine Learning | Udacity

Introducing: Machine Learning in R

Machine learning is a branch in computer science that studies the design of algorithms that can learn. Typical machine learning tasks are concept learning, function learning or predictive modeling, clustering and finding predictive patterns. These tasks are learned through available data that were observed through experiences or instructions, for example. Machine learning hopes that including the experience into its tasks will eventually improve the learning. The ultimate goal is to improve the learning in such a way that it becomes automatic, so that humans like ourselves dont need to interfere any more.

This small tutorial is meant to introduce you to the basics of machine learning in R: more specifically, it will show you how to use R to work with the well-known machine learning algorithm called KNN or k-nearest neighbors.

If youre interested in following a course, consider checking out our Introduction to Machine Learning with R or DataCamps Unsupervised Learning in R course!

The KNN or k-nearest neighbors algorithm is one of the simplest machine learning algorithms and is an example of instance-based learning, where new data are classified based on stored, labeled instances.

More specifically, the distance between the stored data and the new instance is calculated by means of some kind of a similarity measure. This similarity measure is typically expressed by a distance measure such as the Euclidean distance, cosine similarity or the Manhattan distance.

In other words, the similarity to the data that was already in the system is calculated for any new data point that you input into the system.

Then, you use this similarity value to perform predictive modeling. Predictive modeling is either classification, assigning a label or a class to the new instance, or regression, assigning a value to the new instance. Whether you classify or assign a value to the new instance depends of course on your how you compose your model with KNN.

The k-nearest neighbor algorithm adds to this basic algorithm that after the distance of the new point to all stored data points has been calculated, the distance values are sorted and the k-nearest neighbors are determined. The labels of these neighbors are gathered and a majority vote or weighted vote is used for classification or regression purposes.

In other words, the higher the score for a certain data point that was already stored, the more likely that the new instance will receive the same classification as that of the neighbor. In the case of regression, the value that will be assigned to the new data point is the mean of its k nearest neighbors.

Machine learning usually starts from observed data. You can take your own data set or browse through other sources to find one.

This tutorial uses the Iris data set, which is very well-known in the area of machine learning. This dataset is built into R, so you can take a look at this dataset by typing the following into your console:

eyJsYW5ndWFnZSI6InIiLCJzYW1wbGUiOiJpcmlzIn0=

If you want to download the data set instead of using the one that is built into R, you can go to the UC Irvine Machine Learning Repository and look up the Iris data set.

Tip: dont only check out the data folder of the Iris data set, but also take a look at the data description page!

Then, use the following command to load in the data:

eyJsYW5ndWFnZSI6InIiLCJzYW1wbGUiOiIjIFJlYWQgaW4gYGlyaXNgIGRhdGFcbmlyaXMgPC0gcmVhZC5jc3YodXJsKFwiaHR0cDovL2FyY2hpdmUuaWNzLnVjaS5lZHUvbWwvbWFjaGluZS1sZWFybmluZy1kYXRhYmFzZXMvaXJpcy9pcmlzLmRhdGFcIiksIFxuICAgICAgICAgICAgICAgICBoZWFkZXIgPSBGQUxTRSkgXG5cbiMgUHJpbnQgZmlyc3QgbGluZXNcbmhlYWQoaXJpcylcblxuIyBBZGQgY29sdW1uIG5hbWVzXG5uYW1lcyhpcmlzKSA8LSBjKFwiU2VwYWwuTGVuZ3RoXCIsIFwiU2VwYWwuV2lkdGhcIiwgXCJQZXRhbC5MZW5ndGhcIiwgXCJQZXRhbC5XaWR0aFwiLCBcIlNwZWNpZXNcIilcblxuIyBDaGVjayB0aGUgcmVzdWx0XG5pcmlzIn0=

The command reads the .csv or Comma Separated Value file from the website. The header argument has been put to FALSE, which means that the Iris data set from this source does not give you the attribute names of the data.

Instead of the attribute names, you might see strange column names such as V1 or V2 when you inspect the iris attribute with a function such as head(). Those are set at random.

To simplify working with the data set, it is a good idea to make the column names yourself: you can do this through the function names(), which gets or sets the names of an object. Concatenate the names of the attributes as you would like them to appear. In the code chunk above, youll have listed Sepal.Length, Sepal.Width, Petal.Length, Petal.Width and Species.

Once again, these names dont come out of the blue: take a look at the description of the data set that is linked above; Youll normally see all these names listed.

Now that you have loaded the Iris data set into RStudio, you should try to get a thorough understanding of what your data is about. Just looking or reading about your data is certainly not enough to get started!

You need to get your hands dirty, explore and visualize your data set and even gather some more domain knowledge if you feel the data is way over your head.

Probably youll already have the domain knowledge that you need, but just as a reminder, all flowers contain a sepal and a petal. The sepal encloses the petals and is typically green and leaf-like, while the petals are typically colored leaves. For the iris flowers, this is just a little bit different, as you can see in the following picture:

First, you can already try to get an idea of your data by making some graphs, such as histograms or boxplots. In this case, however, scatter plots can give you a great idea of what youre dealing with: it can be interesting to see how much one variable is affected by another.

In other words, you want to see if there is any correlation between two variables.

You can make scatterplots with the ggvis package, for example.

Note that you first need to load the ggvis package:

You see that there is a high correlation between the sepal length and the sepal width of the Setosa iris flowers, while the correlation is somewhat less high for the Virginica and Versicolor flowers: the data points are more spread out over the graph and dont form a cluster like you can see in the case of the Setosa flowers.

The scatter plot that maps the petal length and the petal width tells a similar story:

You see that this graph indicates a positive correlation between the petal length and the petal width for all different species that are included into the Iris data set. Of course, you probably need to test this hypothesis a bit further if you want to be really sure of this:

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

You see that when you combined all three species, the correlation was a bit stronger than it is when you look at the different species separately: the overall correlation is 0.96, while for Versicolor this is 0.79. Setosa and Virginica, on the other hand, have correlations of petal length and width at 0.31 and 0.32 when you round up the numbers.

Tip: are you curious about ggvis, graphs or histograms in particular? Check out our histogram tutorial and/or ggvis course.

After a general visualized overview of the data, you can also view the data set by entering

eyJsYW5ndWFnZSI6InIiLCJzYW1wbGUiOiIjIFJldHVybiBhbGwgYGlyaXNgIGRhdGFcbmlyaXNcblxuIyBSZXR1cm4gZmlyc3QgNSBsaW5lcyBvZiBgaXJpc2BcbmhlYWQoaXJpcylcblxuIyBSZXR1cm4gc3RydWN0dXJlIG9mIGBpcmlzYFxuc3RyKGlyaXMpIn0=

However, as you will see from the result of this command, this really isnt the best way to inspect your data set thoroughly: the data set takes up a lot of space in the console, which will impede you from forming a clear idea about your data. It is therefore a better idea to inspect the data set by executing head(iris) or str(iris).

Note that the last command will help you to clearly distinguish the data type num and the three levels of the Species attribute, which is a factor. This is very convenient, since many R machine learning classifiers require that the target feature is coded as a factor.

Remember that factor variables represent categorical variables in R. They can thus take on a limited number of different values.

A quick look at the Species attribute through tells you that the division of the species of flowers is 50-50-50. On the other hand, if you want to check the percentual division of the Species attribute, you can ask for a table of proportions:

eyJsYW5ndWFnZSI6InIiLCJzYW1wbGUiOiIjIERpdmlzaW9uIG9mIGBTcGVjaWVzYFxudGFibGUoaXJpcyRTcGVjaWVzKSBcblxuIyBQZXJjZW50dWFsIGRpdmlzaW9uIG9mIGBTcGVjaWVzYFxucm91bmQocHJvcC50YWJsZSh0YWJsZShpcmlzJFNwZWNpZXMpKSAqIDEwMCwgZGlnaXRzID0gMSkifQ==

Note that the round argument rounds the values of the first argument, prop.table(table(iris$Species))*100 to the specified number of digits, which is one digit after the decimal point. You can easily adjust this by changing the value of the digits argument.

Lets not remain on this high-level overview of the data! R gives you the opportunity to go more in-depth with the summary() function. This will give you the minimum value, first quantile, median, mean, third quantile and maximum value of the data set Iris for numeric data types. For the class variable, the count of factors will be returned:

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

As you can see, the c() function is added to the original command: the columns petal width and sepal width are concatenated and a summary is then asked of just these two columns of the Iris data set.

After you have acquired a good understanding of your data, you have to decide on the use cases that would be relevant for your data set. In other words, you think about what your data set might teach you or what you think you can learn from your data. From there on, you can think about what kind of algorithms you would be able to apply to your data set in order to get the results that you think you can obtain.

Tip: keep in mind that the more familiar you are with your data, the easier it will be to assess the use cases for your specific data set. The same also holds for finding the appropriate machine algorithm.

For this tutorial, the Iris data set will be used for classification, which is an example of predictive modeling. The last attribute of the data set, Species, will be the target variable or the variable that you want to predict in this example.

Note that you can also take one of the numerical classes as the target variable if you want to use KNN to do regression.

Many of the algorithms used in machine learning are not incorporated by default into R. You will most probably need to download the packages that you want to use when you want to get started with machine learning.

Tip: got an idea of which learning algorithm you may use, but not of which package you want or need? You can find a pretty complete overview of all the packages that are used in R right here.

To illustrate the KNN algorithm, this tutorial works with the package class:

eyJsYW5ndWFnZSI6InIiLCJzYW1wbGUiOiJsaWJyYXJ5KC4uLi4uKSIsInNvbHV0aW9uIjoibGlicmFyeShjbGFzcykiLCJzY3QiOiJ0ZXN0X2Z1bmN0aW9uKFwibGlicmFyeVwiLCBhcmdzPVwicGFja2FnZVwiKVxuc3VjY2Vzc19tc2coXCJBd2Vzb21lIGpvYiFcIikifQ==

If you dont have this package yet, you can quickly and easily do so by typing the following line of code:

Remember the nerd tip: if youre not sure if you have this package, you can run the following command to find out!

After exploring your data and preparing your workspace, you can finally focus back on the task ahead: making a machine learning model. However, before you can do this, its important to also prepare your data. The following section will outline two ways in which you can do this: by normalizing your data (if necessary) and by splitting your data in training and testing sets.

As a part of your data preparation, you might need to normalize your data so that its consistent. For this introductory tutorial, just remember that normalization makes it easier for the KNN algorithm to learn. There are two types of normalization:

So when do you need to normalize your dataset?

In short: when you suspect that the data is not consistent.

You can easily see this when you go through the results of the summary() function. Look at the minimum and maximum values of all the (numerical) attributes. If you see that one attribute has a wide range of values, you will need to normalize your dataset, because this means that the distance will be dominated by this feature.

For example, if your dataset has just two attributes, X and Y, and X has values that range from 1 to 1000, while Y has values that only go from 1 to 100, then Ys influence on the distance function will usually be overpowered by Xs influence.

When you normalize, you actually adjust the range of all features, so that distances between variables with larger ranges will not be over-emphasised.

Tip: go back to the result of summary(iris) and try to figure out if normalization is necessary.

The Iris data set doesnt need to be normalized: the Sepal.Length attribute has values that go from 4.3 to 7.9 and Sepal.Width contains values from 2 to 4.4, while Petal.Lengths values range from 1 to 6.9 and Petal.Width goes from 0.1 to 2.5. All values of all attributes are contained within the range of 0.1 and 7.9, which you can consider acceptable.

Nevertheless, its still a good idea to study normalization and its effect, especially if youre new to machine learning. You can perform feature normalization, for example, by first making your own normalize() function.

You can then use this argument in another command, where you put the results of the normalization in a data frame through as.data.frame() after the function lapply() returns a list of the same length as the data set that you give in. Each element of that list is the result of the application of the normalize argument to the data set that served as input:

Test this in the DataCamp Light chunk below!

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

For the Iris dataset, you would have applied the normalize argument on the four numerical attributes of the Iris data set (Sepal.Length, Sepal.Width, Petal.Length, Petal.Width) and put the results in a data frame.

Tip: to more thoroughly illustrate the effect of normalization on the data set, compare the following result to the summary of the Iris data set that was given in step two.

In order to assess your models performance later, you will need to divide the data set into two parts: a training set and a test set.

The first is used to train the system, while the second is used to evaluate the learned or trained system. In practice, the division of your data set into a test and a training sets is disjoint: the most common splitting choice is to take 2/3 of your original data set as the training set, while the 1/3 that remains will compose the test set.

One last look on the data set teaches you that if you performed the division of both sets on the data set as is, you would get a training class with all species of Setosa and Versicolor, but none of Virginica. The model would therefore classify all unknown instances as either Setosa or Versicolor, as it would not be aware of the presence of a third species of flowers in the data.

In short, you would get incorrect predictions for the test set.

You thus need to make sure that all three classes of species are present in the training model. Whats more, the amount of instances of all three species needs to be more or less equal so that you do not favour one or the other class in your predictions.

To make your training and test sets, you first set a seed. This is a number of Rs random number generator. The major advantage of setting a seed is that you can get the same sequence of random numbers whenever you supply the same seed in the random number generator.

Then, you want to make sure that your Iris data set is shuffled and that you have an equal amount of each species in your training and test sets.

You use the sample() function to take a sample with a size that is set as the number of rows of the Iris data set, or 150. You sample with replacement: you choose from a vector of 2 elements and assign either 1 or 2 to the 150 rows of the Iris data set. The assignment of the elements is subject to probability weights of 0.67 and 0.33.

Note that the replace argument is set to TRUE: this means that you assign a 1 or a 2 to a certain row and then reset the vector of 2 to its original state. This means that, for the next rows in your data set, you can either assign a 1 or a 2, each time again. The probability of choosing a 1 or a 2 should not be proportional to the weights amongst the remaining items, so you specify probability weights. Note also that, even though you dont see it in the DataCamp Light chunk, the seed has still been set to 1234.

Remember that you want your training set to be 2/3 of your original data set: that is why you assign 1 with a probability of 0.67 and the 2s with a probability of 0.33 to the 150 sample rows.

You can then use the sample that is stored in the variable ind to define your training and test sets:

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Note that, in addition to the 2/3 and 1/3 proportions specified above, you dont take into account all attributes to form the training and test sets. Specifically, you only take Sepal.Length, Sepal.Width, Petal.Length and Petal.Width. This is because you actually want to predict the fifth attribute, Species: it is your target variable. However, you do want to include it into the KNN algorithm, otherwise there will never be any prediction for it.

You therefore need to store the class labels in factor vectors and divide them over the training and test sets:

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

After all these preparation steps, you have made sure that all your known (training) data is stored. No actual model or learning was performed up until this moment. Now, you want to find the k nearest neighbors of your training set.

An easy way to do these two steps is by using the knn() function, which uses the Euclidian distance measure in order to find the k-nearest neighbours to your new, unknown instance. Here, the k parameter is one that you set yourself.

As mentioned before, new instances are classified by looking at the majority vote or weighted vote. In case of classification, the data point with the highest score wins the battle and the unknown instance receives the label of that winning data point. If there is an equal amount of winners, the classification happens randomly.

Note: the k parameter is often an odd number to avoid ties in the voting scores.

To build your classifier, you need to take the knn() function and simply add some arguments to it, just like in this example:

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You store into iris_pred the knn() function that takes as arguments the training set, the test set, the train labels and the amount of neighbours you want to find with this algorithm. The result of this function is a factor vector with the predicted classes for each row of the test data.

Note that you dont want to insert the test labels: these will be used to see if your model is good at predicting the actual classes of your instances!

You see that when you inspect the the result, iris_pred, youll get back the factor vector with the predicted classes for each row of the test data.

An essential next step in machine learning is the evaluation of your models performance. In other words, you want to analyze the degree of correctness of the models predictions.

For a more abstract view, you can just compare the results of iris_pred to the test labels that you had defined earlier:

Go here to read the rest:

Machine Learning in R for beginners (article) - DataCamp

What is a machine learning ?

Essentially, it is a method of teaching computers to make and improve predictions or behaviors based on some data. What is this "data"? Well, that depends entirely on the problem. It could be readings from a robot's sensors as it learns to walk, or the correct output of a program for certain input.

Another way to think about machine learning is that it is "pattern recognition" - the act of teaching a program to react to or recognize patterns.

What does machine learning code do ?

Depends on the type of machine learning you're talking about. Machine learning is a huge field, with hundreds of different algorithms for solving myriad different problems - see Wikipedia for more information; specifically, look under Algorithm Types.

When we say machine learns, does it modify the code of itself or it modifies history (Data Base) which will contain the experience of code for given set of inputs ?

Once again, it depends.

One example of code actually being modified is Genetic Programming, where you essentially evolve a program to complete a task (of course, the program doesn't modify itself - but it does modify another computer program).

Neural networks, on the other hand, modify their parameters automatically in response to prepared stimuli and expected response. This allows them to produce many behaviors (theoretically, they can produce any behavior because they can approximate any function to an arbitrary precision, given enough time).

I should note that your use of the term "database" implies that machine learning algorithms work by "remembering" information, events, or experiences. This is not necessarily (or even often!) the case.

Neural networks, which I already mentioned, only keep the current "state" of the approximation, which is updated as learning occurs. Rather than remembering what happened and how to react to it, neural networks build a sort of "model" of their "world." The model tells them how to react to certain inputs, even if the inputs are something that it has never seen before.

This last ability - the ability to react to inputs that have never been seen before - is one of the core tenets of many machine learning algorithms. Imagine trying to teach a computer driver to navigate highways in traffic. Using your "database" metaphor, you would have to teach the computer exactly what to do in millions of possible situations. An effective machine learning algorithm would (hopefully!) be able to learn similarities between different states and react to them similarly.

The similarities between states can be anything - even things we might think of as "mundane" can really trip up a computer! For example, let's say that the computer driver learned that when a car in front of it slowed down, it had to slow down to. For a human, replacing the car with a motorcycle doesn't change anything - we recognize that the motorcycle is also a vehicle. For a machine learning algorithm, this can actually be surprisingly difficult! A database would have to store information separately about the case where a car is in front and where a motorcycle is in front. A machine learning algorithm, on the other hand, would "learn" from the car example and be able to generalize to the motorcycle example automatically.

Originally posted here:

definition - What is machine learning? - Stack Overflow