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What is machine learning?

WHAT IS MACHINE LEARNING?







MACHINE LEARNING IS A SUBSET OF ARTIFICIAL INTELLIGENCE (AI). IT IS FOCUSED ON
TEACHING COMPUTERS TO LEARN FROM DATA AND TO IMPROVE WITH EXPERIENCE – INSTEAD
OF BEING EXPLICITLY PROGRAMMED TO DO SO. IN MACHINE LEARNING, ALGORITHMS ARE
TRAINED TO FIND PATTERNS AND CORRELATIONS IN LARGE DATA SETS AND TO MAKE THE
BEST DECISIONS AND PREDICTIONS BASED ON THAT ANALYSIS. MACHINE LEARNING
APPLICATIONS IMPROVE WITH USE AND BECOME MORE ACCURATE THE MORE DATA THEY HAVE
ACCESS TO. APPLICATIONS OF MACHINE LEARNING ARE ALL AROUND US –IN OUR HOMES, OUR
SHOPPING CARTS, OUR ENTERTAINMENT MEDIA, AND OUR HEALTHCARE.



Machine learning explained



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HOW IS MACHINE LEARNING RELATED TO AI?

Machine learning – and its components of deep learning and neural networks – all
fit as concentric subsets of AI. AI processes data to make decisions and
predictions. Machine learning algorithms allow AI to not only process that data,
but to use it to learn and get smarter, without needing any additional
programming. Artificial intelligence is the parent of all the machine learning
subsets beneath it. Within the first subset is machine learning; within that is
deep learning, and then neural networks within that.



Diagram of the relationship between AI and machine learning






WHAT IS A NEURAL NETWORK?

 

An artificial neural network (ANN) is modeled on the neurons in a biological
brain. Artificial neurons are called nodes and are clustered together in
multiple layers, operating in parallel. When an artificial neuron receives a
numerical signal, it processes it and signals the other neurons connected to it.
As in a human brain, neural reinforcement results in improved pattern
recognition, expertise, and overall learning.

 


WHAT IS DEEP LEARNING?

 

This kind of machine learning is called “deep” because it includes many layers
of the neural network and massive volumes of complex and disparate data. To
achieve deep learning, the system engages with multiple layers in the network,
extracting increasingly higher-level outputs. For example, a deep learning
system that is processing nature images and looking for Gloriosa daisies will –
at the first layer – recognise a plant. As it moves through the neural layers,
it will then identify a flower, then a daisy, and finally a Gloriosa daisy.
Examples of deep learning applications include speech recognition, image
classification, and pharmaceutical analysis.





HOW DOES MACHINE LEARNING WORK?

Machine learning is comprised of different types of machine learning models,
using various algorithmic techniques. Depending upon the nature of the data and
the desired outcome, one of four learning models can be used: supervised,
unsupervised, semi-supervised, or reinforcement. Within each of those models,
one or more algorithmic techniques may be applied – relative to the data sets in
use and the intended results. Machine learning algorithms are basically designed
to classify things, find patterns, predict outcomes, and make informed
decisions. Algorithms can be used one at a time or combined to achieve the best
possible accuracy when complex and more unpredictable data is involved. 



How the machine learning process works






WHAT IS SUPERVISED LEARNING?

 

Supervised learning is the first of four machine learning models. In supervised
learning algorithms, the machine is taught by example. Supervised learning
models consist of “input” and “output” data pairs, where the output is labeled
with the desired value. For example, let’s say the goal is for the machine to
tell the difference between daisies and pansies. One binary input data pair
includes both an image of a daisy and an image of a pansy. The desired outcome
for that particular pair is to pick the daisy, so it will be pre-identified as
the correct outcome.

 

By way of an algorithm, the system compiles all of this training data over time
and begins to determine correlative similarities, differences, and other points
of logic – until it can predict the answers for daisy-or-pansy questions all by
itself. It is the equivalent of giving a child a set of problems with an answer
key, then asking them to show their work and explain their logic. Supervised
learning models are used in many of the applications we interact with every day,
such as recommendation engines for products and traffic analysis apps like Waze,
which predict the fastest route at different times of day.

 


WHAT IS UNSUPERVISED LEARNING?

 

Unsupervised learning is the second of the four machine learning models. In
unsupervised learning models, there is no answer key. The machine studies the
input data – much of which is unlabeled and unstructured – and begins to
identify patterns and correlations, using all the relevant, accessible data. In
many ways, unsupervised learning is modeled on how humans observe the world. We
use intuition and experience to group things together. As we experience more and
more examples of something, our ability to categorize and identify it becomes
increasingly accurate. For machines, “experience” is defined by the amount of
data that is input and made available. Common examples of unsupervised learning
applications include facial recognition, gene sequence analysis, market
research, and cybersecurity.

 


WHAT IS SEMI-SUPERVISED LEARNING?

 

Semi-supervised learning is the third of four machine learning models. In a
perfect world, all data would be structured and labeled before being input into
a system. But since that is obviously not feasible, semi-supervised learning
becomes a workable solution when vast amounts of raw, unstructured data are
present. This model consists of inputting small amounts of labeled data to
augment unlabeled data sets. Essentially, the labeled data acts to give a
running start to the system and can considerably improve learning speed and
accuracy. A semi-supervised learning algorithm instructs the machine to analyse
the labeled data for correlative properties that could be applied to the
unlabeled data.

 

As explored in depth in this MIT Press research paper, there are, however, risks
associated with this model, where flaws in the labeled data get learned and
replicated by the system. Companies that most successfully use semi-supervised
learning ensure that best practice protocols are in place. Semi-supervised
learning is used in speech and linguistic analysis, complex medical research
such as protein categorization, and high-level fraud detection.

 


WHAT IS REINFORCEMENT LEARNING?

 

Reinforcement learning is the fourth machine learning model. In supervised
learning, the machine is given the answer key and learns by finding correlations
among all the correct outcomes. The reinforcement learning model does not
include an answer key but, rather, inputs a set of allowable actions, rules, and
potential end states. When the desired goal of the algorithm is fixed or binary,
machines can learn by example. But in cases where the desired outcome is
mutable, the system must learn by experience and reward. In reinforcement
learning models, the “reward” is numerical and is programmed into the algorithm
as something the system seeks to collect.

 

In many ways, this model is analogous to teaching someone how to play chess.
Certainly, it would be impossible to try to show them every potential move.
Instead, you explain the rules and they build up their skill through practice.
Rewards come in the form of not only winning the game, but also acquiring the
opponent’s pieces. Applications of reinforcement learning include automated
price bidding for buyers of online advertising, computer game development, and
high-stakes stock market trading.





ENTERPRISE MACHINE LEARNING IN ACTION

Machine learning algorithms recognise patterns and correlations, which means
they are very good at analysing their own ROI. For companies that invest in
machine learning technologies, this feature allows for an almost immediate
assessment of operational impact. Below is just a small sample of some of the
growing areas of enterprise machine learning applications.



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 * Recommendation engines: From 2009 to 2017, the number of U.S. households
   subscribing to video streaming services rose by 450%. And a 2020 article in
   Forbes magazine reports a further spike in video streaming usage figures of
   up to 70%. Recommendation engines have applications across many retail and
   shopping platforms, but they are definitely coming into their own with
   streaming music and video­ services.
 * Dynamic marketing: Generating leads and ushering them through the sales
   funnel requires the ability to gather and analyse as much customer data as
   possible. Modern consumers generate an enormous amount of varied and
   unstructured data – from chat transcripts to image uploads. The use of
   machine learning applications helps marketers understand this data – and use
   it to deliver personalised marketing content and real-time engagement with
   customers and leads.
 * ERP and process automation: ERP databases contain broad and disparate data
   sets, which may include sales performance statistics, consumer reviews,
   market trend reports, and supply chain management records. Machine learning
   algorithms can be used to find correlations and patterns in such data. Those
   insights can then be used to inform virtually every area of the business,
   including optimising the workflows of Internet of Things (IoT) devices within
   the network or the best ways to automate repetitive or error-prone tasks.
 * Predictive maintenance: Modern supply chains and smart factories are
   increasingly making use of IoT devices and machines, as well as cloud
   connectivity across all their fleets and operations. Breakdowns and
   inefficiencies can result in enormous costs and disruptions. When maintenance
   and repair data is collected manually, it is almost impossible to predict
   potential problems – let alone automate processes to predict and prevent
   them. IoT gateway sensors can be fitted to even decades-old analog machines,
   delivering visibility and efficiency across the business.





MACHINE LEARNING CHALLENGES

In his book Spurious Correlations, data scientist and Harvard graduate Tyler
Vigan points out that “Not all correlations are indicative of an underlying
causal connection.” To illustrate this, he includes a chart showing an
apparently strong correlation between margarine consumption and the divorce rate
in the state of Maine. Of course, this chart is intended to make a humorous
point. However, on a more serious note, machine learning applications are
vulnerable to both human and algorithmic bias and error. And due to their
propensity to learn and adapt, errors and spurious correlations can quickly
propagate and pollute outcomes across the neural network.

 

An additional challenge comes from machine learning models, where the algorithm
and its output are so complex that they cannot be explained or understood by
humans. This is called a “black box” model and it puts companies at risk when
they find themselves unable to determine how and why an algorithm arrived at a
particular conclusion or decision.

 

Fortunately, as the complexity of data sets and machine learning algorithms
increases, so do the tools and resources available to manage risk. The best
companies are working to eliminate error and bias by establishing robust and
up-to-date AI governance guidelines and best practice protocols.




Making the most of machine learning




Follow in the footsteps of “fast learners” with these five lessons learned.




Explore the research


MACHINE LEARNING FAQS

What's the difference between AI and machine learning?

Machine learning is a subset of AI and cannot exist without it. AI uses and
processes data to make decisions and predictions – it is the brain of a
computer-based system and is the “intelligence” exhibited by machines. Machine
learning algorithms within the AI, as well as other AI-powered apps, allow the
system to not only process that data, but to use it to execute tasks, make
predictions, learn, and get smarter, without needing any additional programming.
They give the AI something goal-oriented to do with all that intelligence and
data.



Can machine learning be added to an existing system?

Yes, but it should be approached as a business-wide endeavor, not just an IT
upgrade. The companies that have the best results with digital transformation
projects take an unflinching assessment of their existing resources and skill
sets and ensure they have the right foundational systems in place before getting
started.



Data science versus machine learning

Relative to machine learning, data science is a subset; it focuses on statistics
and algorithms, uses regression and classification techniques, and interprets
and communicates results.  Machine learning focuses on programming, automation,
scaling, and incorporating and warehousing results.



Data minig versus neural networks

Machine learning looks at patterns and correlations; it learns from them and
optimises itself as it goes. Data mining is used as an information source for
machine learning. Data mining techniques employ complex algorithms themselves
and can help to provide better organised data sets for the machine learning
application to use.



Deep learning versus neural networks

The connected neurons with an artificial neural network are called nodes, which
are connected and clustered in layers. When a node receives a numerical signal,
it then signals other relevant neurons, which operate in parallel. Deep learning
uses the neural network and is “deep” because it uses very large volumes of data
and engages with multiple layers in the neural network simultaneously. 



Machine learning versus statistics

Machine learning is the amalgam of several learning models, techniques, and
technologies, which may include statistics. Statistics itself focuses on using
data to make predictions and create models for analysis.






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