In This Article, We Examine The Interesting Topic Of Machine Learning And Its Applications In Various Scientific And Technological Fields.

Machine learning (ML) is a form of artificial intelligence (AI) that allows software programs to be more accurate in predicting outcomes without being specifically programmed to do so.

Machine learning algorithms use historical data as input to predict new output values. Recommendation engines are a typical case for machine learning; Because they offer popular applications such as fraud detection, spam filtering, threat detection, malware, business process automation, and predictive maintenance.

Why is machine learning meaningful?

One of the most important reasons for machine learning is that it provides companies with an insight into customer behavior trends and business operating patterns and supports the development of new products. Many of today’s leading companies, such as Facebook, Google, and Uber, use machine learning as a central part of their operations. This technology has become an important competitive differentiator for many companies.

What are the different types of machine learning?

The type of algorithm that algorithmic data scientists choose to use depends on the kind of data they are trying to predict. Classical machine learning is often classified based on the algorithm learning method in more accurate prediction. This field has four basic approaches: supervised, unsupervised, semi-supervised, and reinforcement learning.

Supervised learning: In this type of machine learning, data scientists provide algorithms with labeled training data and define the variables they intend to evaluate with the algorithm for correlation. The input and output of the algorithm are specified in this method.

Unsupervised learning: This machine learning type involves algorithms trained on unlabeled data. The algorithm searches through the data set for any meaningful relationships. The data on which the algorithms are introduced and the predictions or recommendations they produce are predetermined.

Semi-supervised learning: This approach to machine learning includes a combination of the previous two types. Scientists can feed an algorithm labeled chiefly with training data, But this model can explore the data freely and independently and develop its understanding of the data set.

Reinforcement Learning: Data scientists typically use reinforcement learning to train a machine to complete a multi-step process with specific rules. Scientists program an algorithm to complete a task and give it positive or negative cues to learn how to complete the job, But in most cases, the algorithm itself decides what steps to take along the way.

How does supervised machine learning work?

Supervised machine learning requires a data scientist to train the algorithm with labeled inputs and arbitrary outputs. Supervised learning algorithms seem to be suitable for the following tasks:

• Binary classification: dividing data into two categories.
• Multi-class sort: choosing between more than two types of answers.
• Regression modeling: prediction of continuous values.
• Ensembling: Combining the predictions of several machine learning models to produce an accurate forecast.

How does unsupervised machine learning work?

Most types of deep learning, including neural networks, are unsupervised algorithms. Unsupervised machine learning algorithms do not need to label the data. They sift through unlabeled data to look for patterns that can be used to group data points into subsets. Unsupervised learning algorithms are suitable for the following tasks:

• Clustering: dividing the data set into groups based on similarity.
• Anomaly detection: Identifying unusual data points in a data set.
• Correlation Mining: Identifying items in a data set that often occur together.
• Dimensionality reduction: reducing the number of variables in a data set.

How does semi-supervised machine learning work?

Data scientists with semi-supervised learning provide an algorithm with a small amount of labeled training data. In this way, the algorithm learns the dimensions of the data set and can then apply them to new, unlabeled data.

Semi-supervised learning falls between the performance of supervised learning and unsupervised learning. The performance of algorithms usually improves when trained on labeled data sets; labeling data can be time-consuming and costly. Some of the fields in which semi-supervised knowledge is used are:

• Machine translation: training algorithms to translate language based on data from less than a complete dictionary of words.
• Fraud detection is when you only have a few positive samples.
• Data Labeling: Algorithms trained on small data sets can learn to apply data labels to larger data sets automatically.

How does reinforcement machine learning work?

Reinforcement learning works by programming an algorithm with a specific goal and a set of rules to achieve that goal. This method makes the algorithm look for more practical actions to achieve the goal and prevent activities that take it away from the plans. Data scientists also program the algorithm based on earning positive rewards and avoiding punishments. Reinforcement learning is often used in the following cases:

• Robotics: Robots can learn to perform tasks in the real world using reinforcement machine learning techniques.
• Video game gameplay: Reinforcement learning is used to train robots to play several video games.
• Resource management: Given limited resources and a defined goal, reinforcement learning can help companies plan resource allocation methods.

Who uses machine learning, and in what fields is it used?

Today, machine learning is used in a wide range of fields. Perhaps one of the most famous examples of this technology is the recommendation engine that manages the Facebook social network news feed.

The world’s social media giant is using machine learning to personalize how it presents its news feed to each member. If Facebook users keep reading posts from a particular group, the platform’s recommendation engine will serve more content in their news feed.

Additionally, Facebook’s machine learning-based engine in the background is trying to reinforce known patterns in users’ online behavior. If a member of this social network changes his ways and cannot read the posts of his favorite group in the coming weeks, his news feed will be adjusted according to the recent changes.

 In addition to recommendation engines, there are other applications for machine learning that we will mention below:

• Customer relationship management: CRM software can use machine learning models to analyze emails and prompt sales team members to respond to emails that matter most. More advanced systems can even recommend potentially effective responses to the sales team.
• Business intelligence: Business intelligence (BI) and analytics vendors use machine learning in their software to identify critical data points, patterns, and anomalies.
• Human Resource Information Systems: HRIS operating systems can use machine learning models to filter applications and identify the best candidates for a position.
• Self-driving cars: Machine learning algorithms can make it possible for semi-autonomous cars to recognize visible objects and announce them to the driver.
• Virtual Assistants: Intelligent assistants typically combine supervised and unsupervised machine learning models to interpret natural speech.

What are the advantages and disadvantages of machine learning?

Machine learning is used in various fields, from predicting customer behavior to building operating systems for self-driving cars. When it comes to the benefits of this technology, it can help companies understand their customers on a deeper level. Machine learning algorithms can learn associations and help teams tailor product development and marketing initiatives to customer demand by collecting customer data and relating it to behaviors over time.

Some companies use machine learning as their business models’ primary driver. For example, Uber uses machine learning-based algorithms to match drivers with passengers. Google also uses this technology to display ads in its search results.

Machine learning, like all technologies, also has disadvantages: first, using this technology can be expensive. Machine learning projects are usually managed by data scientists, whose salaries are high. These projects also require software infrastructure that can be prohibitively expensive for companies.

In addition, there is a problem of bias in machine learning. Algorithms trained on datasets that exclude specific populations or contain errors can lead to inaccurate models of the world that fail at best and are discriminatory at worst. When a company bases its core business processes on biased models, it may face legal and credit damage.

How to choose the suitable machine learning model

Choosing a suitable machine learning model to solve a problem can be time-consuming if not considered strategically.

• Stage 1 . Align the problem with potential data inputs. This step requires the help of data scientists and experts who deeply understand the problem.
• Stage 2. Collect and format data and label it as needed. Data scientists, with the help of developers, usually lead this phase.
• Step 3 . Choose and test the algorithm to see how well it performs. Data scientists usually do this step.
• Step 4 . Continue fine-tuning the outputs until an acceptable level of accuracy is reached. Data scientists typically perform this step with feedback from experts who have a deep understanding of the problem.

The importance of machine learning is that humans can interpret

There are some industries where data scientists should use simple machine learning models; Because businesses need to explain how each decision was made. Describing how a particular machine learning model works can be challenging as that model becomes more complex. This is especially true in banking and insurance areas with a heavy compliance burden.

Sophisticated models can provide accurate predictions, But it can be tough to explain their decision-making method and output determination to a non-expert.

What is the future of machine learning?

While machine learning algorithms have existed for decades, their popularity has increased due to the growth of artificial intelligence. This is especially true for deep learning models, powering today’s most advanced AI-based applications.

Machine learning platforms are considered one of the most competitive areas of enterprise technology. Large companies such as Amazon, Google, Microsoft, IBM, etc., are registering customers for platform services that cover a range of machine learning activities such as data collection, preparation, classification, model building, and training. And they cover the program’s deployment; they compete with each other.

As machine learning becomes increasingly essential to business operations and artificial intelligence becomes more widely used in enterprise settings, the machine learning platform war will intensify. Ongoing research in deep machine learning and artificial intelligence is increasingly focused on developing more profound applications. Today’s AI models require extensive training to produce an algorithm that is highly optimized to perform a specific task. Of course, some researchers are considering ways to increase the flexibility of models. They are also looking for techniques that allow machines to take advantage of the context learned from one task to perform different functions in the future.

How has machine learning evolved?

In the following, we briefly review the evolution of machine learning technology.

• 1642: Pascal invented the mechanical machine to perform addition, subtraction, multiplication, and division operations.

• 1679: Gottfried Wilhelm Leibniz invented the binary code system.

• 1834: Charles Babbage conceived the idea of ​​a general-purpose machine that could be programmed with punched cards.

• 1842: Ada Lovelace describes a sequence of operations to solve mathematical problems using Charles Babbage’s punch card theory machine, becoming the world’s first programmer.

• 1847: George Boole created Boolean logic, A form of algebra in which all values ​​can be reduced to True or False binary values.

• 1936: Alan Turing, a famous English logician, and cryptographer, proposed a universal machine to decode and execute instructions. His published proof is the foundation of modern computer science.

• 1952: Arthur Samuel developed a program to help the IBM computer perform better by playing more checkers games.

• 1959: Medline is the first artificial neural network to be applied to a real problem: echo cancellation from telephone lines.

• 1985: Terry Sejnowski and Charles Rosenberg’s artificial neural network taught itself how to pronounce 20,000 words correctly in a week.

• 1997: IBM’s Deep Blue defeated chess grandmaster Garry Kasparov.

• 1999: A CAD prototype intelligent workstation analyzed 22,000 mammograms and detected cancer 52 percent more accurately than radiologists.

• 2006: Computer scientist Jeffrey Hinton coined the term deep learning to describe neural network research.

• 2012: An unsupervised neural network developed by Google learned to recognize options in YouTube videos with 74.8% accuracy.

• 2014: A chatbot passed the Turing test by convincing 33% of human judges that it was a Ukrainian teenager named Eugene Gustman.

• 2014: Google’s AlphaGo defeated the human champion at Go, the world’s most challenging board game.

• 2016: LipNet, DeepMind’s artificial intelligence system, identifies lip-reading words in video with 93.4% accuracy.

• 2019: Amazon has 70% of the virtual assistant market share in the United States.

Machine learning in medicine

The ever-increasing number of machine learning applications in healthcare allows us to glimpse a future in which data, analytics, and innovation combine to help vast numbers of patients. Without these people knowing about this.

It will soon become common to find machine learning-based applications that interact with real-time patient data from various healthcare systems in different countries, resulting in new treatment options that were previously unavailable.

Among the most important applications of machine learning in medicine the following can be mentioned:

1. Identification and diagnosis of diseases

One of the most important applications of machine learning in medicine is identifying and diagnosing diseases that would be difficult to diagnose without this technology. This can include any disease, From early cancers to genetic disorders. IBM Watson Genomics is a prime example of this machine learning-based program that can help diagnose diseases quickly by integrating cognitive computing and genome-based tumor sequencing.

2. Drug discovery and production

One of the primary clinical applications of machine learning lies in the early stages of the drug discovery process. It also includes research and development technologies such as next-generation sequencing and precision medicine that can help find alternative ways to treat multifactorial diseases.

Machine learning techniques, including unsupervised methods, can identify patterns in data without providing predictions. The Hanover project, developed by Microsoft, uses machine learning-based technologies for several initiatives, including developing artificial intelligence-based technology for cancer treatment and personalizing AML (acute myelogenous leukemia) drug combinations.

3. Medical imaging diagnosis

Machine learning and deep learning are both responsible for advancing computer vision technology. It works on Microsoft’s InnerEye initiative on image recognition tools for analyzing photos. As machine learning becomes more accessible and its capabilities increase, we expect more data sources from medical images in various domains to become part of the AI-based diagnosis process.

4. Personal medicine

Personalized treatments can be more effective by pairing individual health and predictive analytics and applying them to more research and better disease assessment. Currently, doctors are limited to choosing from a set of diagnoses or estimating the risk for the patient. Machine learning in medicine is on the way to significant advances, and for example, IBM Watson Oncology uses a patient’s medical records to help create multiple treatment options for him.

5. Behavior modification based on machine learning

Behavior modification is an integral part of preventive medicine. Since the rise of machine learning in healthcare, countless startups have emerged in cancer prevention and detection, patient treatment, and more. Somatix is ​​a B2B2C-based data analytics company whose machine learning-based program recognizes our gestures in our daily lives, enabling us to understand our unconscious behavior and apply the necessary changes.

6. Smart health records

Keeping up-to-date medical records is a comprehensive process, and technology has played its role in facilitating the data entry process. The truth is that even now, most processes take a long time to evolve. The primary part of machine learning in healthcare is facilitating strategies to save time, effort, and money.

Document classification methods using vector machines and OCR recognition techniques based on machine learning, such as Google Cloud Vision API and handwriting recognition technology based on Matlab machine learning, are slowly evolving. MIT is developing the next generation of intelligent health record-keeping methods that use machine learning technology to help diagnose, recommend clinical treatments, and more.

7. Clinical trials and research

Multipurpose machine learning has potential applications in clinical trials and research. As anyone in the pharmaceutical industry will tell you, clinical trials are expensive and time-consuming; in many cases, the process can even take years. Using machine learning-based predictive analytics to identify potential clinical trial candidates can help researchers map out various data points, such as previous medical visits, social media, and more.

Machine learning is also used to ensure real-time monitoring. Accessing data from trial participants, finding the best sample size for testing, and using the power of electronic records to reduce data-driven errors are critical applications of machine learning in this field.

8. Collecting crowdsourced data

Today, crowdsourcing is very common in the medical field and allows researchers and doctors to access large amounts of the information uploaded by individuals based on their consent. This live health data has enormous implications for the way medicine is understood.

Apple’s ResearchKit allows users to access interactive applications that use machine learning-based facial recognition to diagnose and treat Parkinson’s disease. IBM has also recently partnered with Medtronic to decode, aggregate, and make insulin and diabetes data available in real time based on the collected information.

9. Better radiotherapy

One of the most demanding applications of machine learning in healthcare is in the field of radiology. Medical image analysis has many discrete variables that can be generated at any particular moment. There are lesions, cancer foci, and many other things that cannot be easily modeled using complex equations. Since machine learning-based algorithms learn from various available examples, it becomes easier to identify and find variables.

One of the most popular applications of machine learning in medical image analysis is classifying objects, such as lesions, into categories, such as normal or abnormal, lesion or non-lesion, etc. Google’s DeepMind Health project is actively helping UCLH researchers develop algorithms to distinguish between healthy and cancerous tissue and improve radiation therapy.

10. Prevalence prediction

Today, artificial intelligence and machine learning technologies are also used to monitor and predict epidemics worldwide. Scientists now have access to vast amounts of data collected from satellites, real-time social media updates, information from websites, and more. Artificial neural networks help gather this information and predict everything from malaria outbreaks to severe chronic infectious diseases.

Forecasting these epidemics will be very useful, especially in third-world countries, Because the medical infrastructure and vital education systems in these countries are not in satisfactory condition. An example in this field is ProMED-mail, A web-based reporting platform that monitors evolving and emerging diseases and shares real-time reports on their outbreaks.

Machine learning in architecture

Of all the innovations that are transforming the business world, architectural construction can benefit the most from machine learning and artificial intelligence.

1. Machine learning in design automation

One of the essential advantages of machine learning for architects is its ability to perform repetitive tasks, typically challenging to automate. These tasks are time-consuming and repetitive, But they are complex enough that solving them requires human problem-solving capabilities. This feature makes such tasks too complicated for more straightforward tools like robotic process automation (RPA) to handle.

However, artificial intelligence and machine learning can use existing data to automate these complex tasks in architecture. These tools enable design strategies to use existing data to generate entirely new architectural information. One of the recent examples of machine learning to automate tasks is Finch’s artificial intelligence algorithm, A design feasibility tool that automatically generates spatial configurations according to predefined parameters.

2. Machine learning in sustainable design and operations

Sustainability has quickly emerged as one of the most critical topics in architecture. The Observatory estimates that around 40% of the total global carbon emissions can be attributed to the construction of buildings. Building structures that require less energy to operate and maintain allow the industry to reduce global carbon emissions significantly. The correct algorithm can help managers manage their facilities with better efficiency so that the amount of carbon emissions due to the construction process is reduced to the minimum possible.

3. Machine learning for generative design

Machine learning-based tools can enable the design of unique structures that may have been impossible or impractical to create with a conventional approach. These tools can take advantage of previous architectural projects to produce entirely new designs, and architects can use them to achieve design techniques they have never been able to discover.

Generative design is increasingly popular in architecture, engineering, and innovation. With this approach, artificial intelligence algorithms and machine learning models trained on large amounts of architectural information will create new designs from scratch.

Machine learning in psychology

Psychologists are increasingly looking to adopt powerful computational machine-learning techniques to predict real-world phenomena accurately. The current work of machine learning is introduced as a set of methods and tools that can be used in predictions.

1. Machine learning in testing psychological theories

Psychologists now have tools to detect patterns in data. When testing theories, such practices are examined for statistical significance to determine the influence of predictors on outcome variables. The remaining element of machine learning and predicting future data is vital to social scientists.

2. Increasing the accuracy of forecasts

Think a psychologist wants to know how much happiness can be predicted at the level of a city? We begin with predictive models familiar to most psychologists—linear and logistic regressions—in which outcome scores (i.e., city-wide happiness) are expressed as mathematical combinations.

The regression approach is to estimate the equation that minimizes the distance between the original data points and the values ​​predicted by linear regression. These models introduce the concepts of prediction accuracy and out-of-sample evaluation.

Machine learning in electrical engineering

Artificial intelligence and machine learning describe a wide range of systems built to mimic how the human mind makes decisions and solves problems. Electrical engineers have explored how to apply different types of machine learning to electrical and computer systems for decades. Following are some of the most important fields where machine learning can be used in electrical engineering:

1. Professional systems

They solve problems with an inference engine that draws from a knowledge base with information about a specialized domain, mainly in the form of if-then rules. These systems have been used since the 1970s and are less versatile than newer types of artificial intelligence, But in general, they help in better planning and maintenance.

2. Fuzzy logic control systems

Such systems make it possible to develop rules for how machines respond to inputs that present a set of possible conditions.

3. Perform tasks automatically

Machine learning includes various algorithms and statistical models that allow systems to find patterns, draw conclusions based on them, and learn new things. These models do the job without the need for specific instructions.

4. Artificial Neural Networks

Particular types of machine learning systems are made up of artificial synapses to mimic the structure and function of the brain. The network observes and learns as nodes pass data to each other and process the information as it passes through multiple layers.

5. Deep learning

Deep learning is a form of machine learning that is based on artificial neural networks. The Deep learning architectures can process increasingly abstract feature hierarchies and are particularly useful for speech and image recognition and natural language processing.

Frequently asked questions

What is meant by machine learning?

Machine learning is the ability of a system to learn to do something without planning and based on the data it receives. Machine learning focuses on developing computer programs that can access and use data to teach themselves.

What is deep learning, and how is it different from machine learning?

Deep learning is a sub-branch of representation learning, which is considered a machine learning sub-branch. To better express the difference between deep learning and machine learning, we must mention that deep learning is also known as deep neural networks. They form algorithms inspired by the working principles of the human brain, and then, using them, it learns to identify patterns in the data and Make a decision based on them.