What is machine learning and how can it be used to improve the industry?

Applications such as search engines, voice assistants, e-commerce sites, “smart” cameras, facial recognition for unlocking the smartphone are now part of our daily lives. Many do not know, however, that these applications make use of artificial intelligence.
Artificial intelligence is used both as a foundation without which the system would not be feasible, and as a support to provide a service as addressed and customized as possible to the user.

This makes us reflect on how artificial intelligence is an already heavily used tool, even if it is currently for the most part confined to the major players in the market.
However, it is expected that in the years to come, AI will become increasingly widespread even on medium-small businesses, with a growing impact on all types of industries and activities.
In fact, we can say that AI will very often become a feature that is already expected to be integrated into products.

Machine learning is a discipline of artificial intelligence through which many forms of AI can be achieved.
Machine learning collects and uses a multitude of methods such as artificial neural networks, adaptive algorithms, processing images, data mining, and others, to create systems capable of learning autonomously and progressively.
In fact, machine learning can be considered a variant to traditional programming, in which it is predisposed in a machine the ability to learn from data (experience) independently, without being explicitly programmed to do it, to then be able to reuse what has been learned about a certain task.

Types of machine learning

Machine learning is usually divided into three different types, plus others of lesser importance (which are often a hybrid of the three most important).
Each typology differs from the other on the basis of the form of data used for training and on the basis of the task to be performed.

Supervised learning

In this type of machine learning the models are trained starting from labeled data, i.e. data for which the desired output signals are known.
What the model learns are a series of patterns and correlations between the data and the associated label, which it will reuse as a basis for making decisions on future data.
In the case of supervised learning, machine learning problems are divided into two main classes: Regression and Classification.

Regression

An example of a regression problem may be to estimate the cost of a home, described by a series of variables, such as the surface, the number of rooms, etc…
In the training data, for each home, in addition to the descriptive variables, the label (the expected result) is known, which in our case is the price of the house.
In regression, the label is always a continuous variable. What we try to do in these problems is to find, through a more or less complex continuous function, a relationship between descriptive variables and labels.

The figure shows, for example, the use of two functions of different complexity, capable of learning a certain trend of the curve, which will then be used to produce estimates on future data.

Classification

In the classification, labels are unordered discrete values that can be considered as belonging to a group of a class.
Using an automatic learning algorithm with supervision, we will be able to separate the two classes (by identifying decision-making boundary) and associate the data, based on their values, to different categories, with a certain confidence value.
Classification can be binary or multiclass.

An example of classification could be to determine if, given an image, it is a cat or dog image.
The predictive variables in this case could be the shape of the ears and the size of the nose (features that come extracted through appropriate image processing techniques), while the label simply specifies if it is of a dog or cat.

Unsupervised Learning

In unsupervised learning, unlike supervised learning, we have unlabeled data. In these cases, therefore, we cannot count on a known variable relating to the result.
These techniques try to observe the data structure and to extrapolate from this meaningful information.
Most unsupervised tasks fall into the Clustering problem class.

Clustering

Clustering is an exploratory technique that allows you to aggregate within groups (called clusters) data for which we have no previous knowledge of belonging to groups. We will therefore have large datasets, where the data inside them have similar features to each other.
Within each individual group (or cluster) we will find those data that have many similar characteristics to each other.
Clustering is an excellent technique that allows us to find relationships between data, and to carry out the so-called exploratory data analysis. For example, clustering allows vendors to identify customer clusters based on their profiles to improve marketing activity (market segmentation).

A clustering problem might be to group documents from a corpus into sets of documents they deal with the same topic.
The documents could be represented in space in the form of word vectors, and then grouped together in based on some form of similarity, which is able to relate documents containing words and expressions to each other common.

Reinforcement Learning

The third type of machine learning is Reinforcement Learning. The goal of this type of learning is to build a system (agent) that through interactions with the environment improves its performance.
In order to improve the functionality of the system, reinforcements, or reward signals, are introduced.
This reinforcement is not given by the labels or the correct truth values, but is a measurement of the quality of the actions taken by the system. For this reason it cannot be assimilated to supervised learning.

Reinforcement learning is widely used for robot navigation training. In the figure is shown the example of the return negative or positive rewards when the robot sucks them.

Deep Learning

Deep Learning is a large family of methods for machine learning based on artificial neural networks. Artificial neural networks are computational models based on a simplification of biological neural networks.

As can be seen in Figure 6, they can be made up of many nodes, or neurons, and multiple layers of neurons. Theoretically more the number of neurons and layers increases, more the network is able to represent more complex non-linear functions.
We are talking about computational models theorized as early as the 1970s, but for which at that time it was not possessed still enough computing power to use and apply them.
Today that computing and storage capacities have increased dramatically, they are back in fashion, and are used in many contexts of artificial intelligence.
If used in the form of nets with many layers, we speak precisely of deep learning, because we use very deep networks.
The ability to be able to formulate very complex non-linear functions across these networks has meant that they now come widely used in a series of very advanced tasks related above all to “natural perception”, such as computer vision and natural language processing.

Machine learning applications in industry

Machine learning and the set of techniques mentioned, can be very useful in industry in order to optimize and improve the production and sales process.

Some possible applications are:

• Predictive maintenance: predict failures and anomalies on machinery with the aim of managing efficiently maintenance , with an important return in terms of cost reduction
• Sales forecast: predict future sales levels in order to optimize the production process
• Product quality control (Quality 4.0): check the product quality by detecting production defects
• Forecast of energy consumption: forecast future energy consumption, with advantages both for those who have to request it and for those who it is an energy supplier

Applications in predictive maintenance

Below is an example of applying Supervised Learning algorithms and techniques for the use case of predictive maintenance.
The data used for the training are sensor data, coming from real machines, collected through the SMC IoT Experience platform. The goal is to create systems capable of:

• Predict how quickly a failure could occur on the machine (Regression)
• Predict if a failure could occur within a certain time window (Binary classification)

In trying to achieve all this, machine learning comes to our aid, thanks to the use of algorithms and models that are able to learn the correlations between the sensor data and and failures that occurred on the machinery.

Construction of the dataset

To build the dataset on which to train the algorithms, it was necessary to acquire the data relating to the historical faults that occurred on the machinery, as well as the telemetry history in the period of time preceding the considered failures.

Once all the data was acquired, an in-depth feature engineering activity was carried out, i.e. an activity in which the data they have undergone filtering, processing and aggregation; this activity, every time you face a machine learning task, is fundamental to be able to put the data in the best shape to be able to efficiently train what is the chosen algorithm.

The data was subsequently labeled, so that a supervised training approach can be used.
To do this we have correlated two different values to each telemetric record:

• A continuous scale value, for the regression task, which specifies the time remaining to the fault at the time that the telemetry record was recorded
• A binary value, for the binary classification task, which indicates whether the telemetry record has been recorded within a certain time window since the fault

At the end of the labeling, a data set consisting of about 170 telemetry sequences preceding failures occurred on the same machine, is resulted, where each telemetry sequence has an amplitude between 250 and 300 minutes

Finally, the dataset was divided into:

• Training set (90% of the total, used for training)
• Test set (10% of the total, used to evaluate the performance of the trained model)

Trainings and evaluations

Some candidate algorithms for the task have been trained and tested. Some of these are:

• Light Gradient Boost Machine Classifier
• Extra Trees Regressor
• Random Forest Classifier
• Long Short-Term Memory Networks

After each training, it is essential to evaluate the performance of the resulting model. This is to compare both different algorithms between them, both different training configurations of the same algorithm, and identify the tool that work better on that specific task.
To carry out these evaluations, a series of metrics and graphs are used, which show how well the model is in question is able to perform the task for which he was trained.
In the case in question we can see, for example, the results of the evaluation carried out on the forecasting task if a fault could occur within a certain time window.
In particular, the results refer to the training of a “Light Gradient Boost Machine Classifier” with time window set at 30 minutes.

The “holdout score” column contains the values for metrics that present the model’s performance across the test set.
We have a total accuracy of 98.6%, with values of Precision and Recall also very high, which show satisfactory performance for the model in question.
For a more detailed view we also see the confusion matrix, which consists of a matrix that allows you to visualize false positives and false negatives, putting the real labels in relation with the values foreseen by the model..

From the confusion matrix it is found that:

• In 92.7% of cases, if we are close to a possible failure, the system is able to correctly predict it, while in 7.3% of cases is unable to report it
• 98.2% of the times that a possible imminent failure is reported, this will actually happen, while in 1.8% of the cases it will be a false alarm

Conclusions

The application techniques seen represent a possible action strategy to achieve, through machine learning, systems that support predictive maintenance.
For example, a system capable of predicting whether a failure could occur within 30 minutes could be used for install a traffic light on a machine that will signal, if the system foresees it, possible imminent faults.
This represents only one example of how machine learning and more generally artificial intelligence can help what is the production and decision-making process within the industrial environment.
In line with the transformation towards the so-called Industry 4.0, machine learning will play a role in the coming years fundamental for the growth and the ability of industries to keep up with the times.

Machine learning (ML) is an application of artificial intelligence where computer programs use algorithms to find patterns in data. They can do so without being specifically programmed to, with no dependence on humans. In today’s world, machine learning algorithms are behind almost every artificial intelligence (AI) technological advancement and application that is in the market.

AI systems generally have the ability to plan, learn, reason, problem solve, perceive, move and even manipulate. Machine learning is one of the many approaches being used in AI systems. Others include evolutionary computation, and expert systems.

Machine learning is a part of many things that we do every day. Think about where machine learning systems might influence your life:

• Recommendation systems on your favorite streaming services like Netflix or Spotify are run by machine learning.
• Search engines use machine learning to clarify and optimize your search results.
• Social media channels recommend friends, groups and videos to watch.
• If you have a modern fridge, often these learn when you use it the most and cool it in anticipation of dinner time.
• GPS anticipates what parts of your route will have heavy traffic and re-routes you using machine learning algorithms.
• Voice-based assistants like Alexa and Siri use machine learning to operate.

Each of these platforms amass data from the everyday choices you make. It learns about you, and from the information gained it makes predictions about what you will watch next, what time you’ll make dinner, or where you might travel or buy.

All of this data powers the machine learning algorithms, which then will help a brand anticipate what you may want to do or buy next. Not only that, but your likes and dislikes are combined with other data points from millions of other people, allowing companies to create accurate and highly effective suggestion lists.

AI is poised to scale newer heights using machine learning applications.

Applications of Machine Learning

The applications of machine learning are vast. Here is a look at how it is being used in key areas that are integral to everyday human life.

Machine learning in education

When applied in the field of education, machine learning can help teachers examine the type of lessons students can consume. They can evaluate how students are managing with lessons taught – how much they are able to grasp, what are the common topics that the students tend to struggle with, and what’s too easy. This helps teachers to better plan lessons, and identify students who may be falling behind, allowing far more effective interactions and interventions.

Machine learning in search engines

When you type a search term into Google, it’s frustrating when the results that come up aren’t what you’re looking for. Machine learning has been an integral part of search engine optimization for a long time now. It is constantly helping search engines show more relevant results to searches. It has also helped power voice-based search services, image searches and several other search related features.

Machine learning in digital marketing

Personalization is the key to modern digital marketing campaigns and machine learning has been integral in achieving this. With data based on consumer interactions, machine learning has helped companies personalize their approaches to potential customers, focusing the right messaging at the right time. From personalized emails, to cross or upselling based on recent purchases, machine learning has helped businesses leverage their data on consumer behavior.

Machine learning in health care

Machine learning has been extensively applied in the medical field. Diagnosis using medical imaging is an important example where machine learning works with diagnostic tools. Machine learning views the medical images, identifies areas that are unusual or abnormal, doing so without any bias that a medical professional may have.

Machine learning is also being used to help doctors in treating unique cases of specific illnesses by providing them with suggestions on treatment protocols based on information gathered from other cases. For instance, a library of macrophages can be trawled in hours by machines that identify likely efficacious phages to treat strains of antibiotic resistant bacteria.

The application is also experimenting with how to convert pooled consumer data gathered from personal devices to provide medical professionals with suggestions and options on treatment. This is of course a sector that is constantly evolving.

The applications for machine learning are diverse and can be found in just about any field or kind of business. The benefits for commercial, government, and social ventures are immense.

Benefits of Machine Learning

Machine learning has incredibly wide ranging benefits across almost every facet of life. These are just some of the universal benefits of machine learning:

Predicting customer behavior

Analyses of consumer purchase patterns helps give companies insight into the way forward for product and service lines. These patterns can be as precise as why a customer may opt for one product over another, the influences of pricing, season, brand loyalty and more on these decisions. Such data-oriented findings are made much faster with machine learning and speed is the key to smarter decision-making.

Sustained accuracy in data entry

The most boring of human tasks is that of data entry. The chances of an error are high with such repetitive tasks. These errors can prove costly to a company on several levels. Machine learning ensures that data entry is completed quickly, with precision, leaving no room for error. It also takes mundane tasks away from employees allowing them to concentrate on more challenging and business beneficial jobs.

Discovering leads in user experiences

Every business grows on the basis of new leads that convert to paying customers. Being able to stay at the top of your game is about evolving to meet the needs of the customer. Machine learning helps businesses by diving into customer journeys and providing insights into trends and anticipating needs. Research has shown that machine learning has made a difference to the upward growth trajectory of businesses by helping them to predict customer behaviors, find inefficiencies, etc.

Maintaining a competitive edge

Businesses are able to grow alongside the market when they have good business intelligence to fall back on. Machine learning has an important role to play here in providing businesses with insights on their unique selling points and its positive aspects in comparison to competing brands. Any new approach can be quickly hypothesized, tested based on available data and help businesses build a go-to-market plan quickly.

Powering virtual assistants

Workplaces, big or small, are about increasing efficiency and making smart use of worker hours. Machine larning, when applied to automatic speech training, helps create smarter and more efficient virtual assistants, who can take down notes, develop minutes of meetings and maintain better records. All this reduces mundane paperwork that is essential but tiring to do. With better virtual assistants, precision is ensured and privacy regulations are well met.

Categorizations of Machine Learning Algorithms

Algorithms form the basis of machine learning’s entire structure and its growth. These algorithms can be divided into four main categories:

Supervised machine learning algorithms

Here, lessons learned earlier can be applied to new data with the help of labeled examples to predict future outcomes. This begins with the analysis of known training datasets. The learning algorithm creates an inferred function which will make predictions of possible outcomes. With the necessary amount of training, all new data inputs will be provided with targets.

Unsupervised machine learning algorithms

These are in contrast to supervised algorithms and come into play when the training information is not labeled or classified in any way. Unsupervised learning does not provide ‘correct’ outputs for new data. Instead these algorithms explore the data, draw inferences from datasets and reveal any hidden structures that might be in unlabeled data.

Semi-supervised machine learning algorithms

These algorithms follow the middle line between the first two types, because of the use of both labeled and unlabeled data for training. Typically, the amount of unlabelled data is larger than the amount of labelled data and the algorithm uses the labeled data to learn about the unlabelled data. Systems based on this constantly improve on the level of accuracy of learning.

Reinforcement machine learning algorithms

This is a learning method where interaction with the environment produces actions and uncovers errors and rewards. With this approach, machines and all software agents are able to determine the appropriate behavior within a specific context for the best performance possible.

The Challenges of Machine Learning

Despite all the leaps forward in technology, there are still a range of challenges that machine learning needs to overcome.

Networks still need huge amounts of working memory to store and process data. While some unsupervised learning techniques remove unneeded data, there is still a need for massive processing power. This can be partially resolved, with unsupervised learning algorithms stripping unneeded and excess data which cuts back on processing power needed. However, this is not enough for all scenarios.

Natural language processing is still a long way off from being a natural and accurate translation. Slang, accents and understanding of language are still huge challenges for machine learning. While the machine constantly has new data to listen to and learn from, it still needs a lot of training to resolve more obscure accents.

AI washing is when technology is labelled as artificial intelligence (or an intelligent computer), when it’s actually just machine learning or the same old algorithms they have always used. For many people, the distinction isn’t important, but it over-inflates technology expectations, undermines trust in technology and sets up both fields for backlash. Education of the general public and more understanding of AI and machine learning are needed.

Lack of video training is holding back the industry. Instead of relying on static images and a 2D world, video provides much richer datasets. Our world is dynamic, and our machines need to learn that. This is an emerging field of study.

Machines don’t think like humans. People use heuristics to make snap decisions. They use a broad field of attention to integrate a holistic understanding of a scene. But machine learning is still about granular data, which limits the current ways it can be effectively used. As machines learn more, this will resolve, but it’s unknown if they will ever truly think like humans or become “artificially intelligent.”

The Future of Machine Learning

As machine learning programs and data science techniques become more widely available, there are huge benefits for almost every facet of life.

• Fine-tuned personalization: Will empower businesses to anticipate and cater to customer needs.
• Better search engine experiences: Improved ranking of search engine results help both end users as well as admins in delivering pin-pointed results and insight.
• Evolution of data teams: Everyday data and IT team roles will evolve with improved machine learning, reducing the amount of time spent on manual programming. For instance, data scientists can spend less time cleansing the data as machine learning learns to do it effectively (through the use of AutoML).
• Rise of quantum computing: It may sound like something from a sci-fi film, but quantum algorithms do carry the potential to lead to multiple other innovations and it is something that will happen in the mid to long term.

Artificial intelligence and machine learning are poised to change the way the world does business, provides governance, and develops new technology. It will change the way application-development markets function in the future. Together these technologies have been accorded the importance given to electricity at the start of the industrial revolution. These two together herald a new era in information technology.

AutoML

AutoML is exciting new technology that means ordinary people can now run complex machine learning processes. In the past, data scientists have needed an in-depth understanding of statistics, data cleansing techniques, computer coding, algorithms and also access to powerful computers. This has meant that for most people, machine learning was out of reach.

New software being developed has changed machine learning. Online software programs take data uploaded by a user. The user identifies what kind of predictions they need, and the software chooses the correct algorithm to run, and produces a set of clear, concise and explainable results. While the predictions still require data to be accurate and labelled, there are also data cleansing techniques built into the software. They can assess outliers and missing information, often building strategies to manage the discrepancies as they go.

This is truly a window into the future for companies wanting the ability to make predictions and process data that don’t have the facilities or means to hire dedicated data scientists. For now, data scientists have mostly been taking advantage of AutoML’s data cleaning abilities which has saved a lot of time.