You must have heard about machine learning as it has become a buzzword. Machine learning is an innovative method of analyzing data that has the capability to automate analytical model building. It is a field of computer science and an important branch of artificial intelligence. Machine learning is based on the revolutionary idea that computer systems could learn from data, just like humans. As a result, they can identify patterns and make informed decisions without resorting to much human intervention.
Machine learning is now a keyword in the world of technology. This is because it represents a huge improvement in how modern computers can learn and adapt.
The term machine learning was first used by Arthur Samuel in 1959. Machine learning has emerged from the discipline of computational learning theory and study of pattern recognition in artificial intelligence. It explores in detail the study and development of computer algorithms that could not only learn from data but also make useful predictions on the basis of data. These algorithms can overcome strictly static or rigid program instructions and make data-driven decisions and predictions by building complex models from sample inputs.
Machine Learning Models
The iterative nature of machine learning has become vital because when models are exposed to new sets of data, they can independently adapt. They are able to learn from prior computations in order to produce repeatable, reliable and consistent decisions and results. Although machine learning isn’t a new science by any stretch of the imagination, it has gained plenty of fresh momentum in the recent past.
This is why machine learning can now be employed in a wide variety of computing tasks, especially where programming and designing explicit computer algorithms with great performance is either difficult or cost-prohibitive. Some examples of machine learning applications are detection of network intruders, email filtering, detection of malicious insiders attempting to perpetrate a data breach, learning to rank, optical character recognition, and computer vision.
We will discuss some of the simplest ways to make the best use of machine learning. A majority of industries that work with huge volumes of data have gradually recognized the importance of machine learning. By gleaning valuable insights from the data, usually, in real time, companies are able to gain competitive advantage and work more effectively and efficiently.
Machine learning provides a variety of techniques, methods, and tools to help solve diagnostic and prognostic issues in different medical domains across the globe. Machine learning can be used to analyze and study the significance of various clinical parameters along with their diverse combinations for prognosis.
For example, it is used to predict progression of disease, extract medical information for therapy planning, outcomes research and support, and overall patient management. Also, machine learning can be used for detailed data analysis, like detecting regularities and anomalies in data by suitably dealing with imperfect data and interpreting continuous data that is used in intensive care units and for smart alarming systems that result in efficient and effective monitoring.
Experts argue that the implementation of machine learning methods and tools could integrate computer-based systems in our healthcare environment and provide unique opportunities to enhance and promote the work of medical professionals and experts, which can ultimately improve the overall quality and efficiency of medical care.
One of the most important facets of medical diagnosis is to establish the presence of a disease and its accurate identification on a timely basis. There is a different category assigned to each disease and a category for those cases where no disease is identified. As you can imagine, this generates loads of data. In this case, machine learning can considerably improve the accuracy and quality of medical diagnosis through the analysis of patient data.
Some of the measurements in these applications are often the results of specific medical tests (such as blood pressure, blood and urine tests, and temperature) and medical diagnostics (like medical images). This includes the absence or presence and intensity of different medical symptoms along with basic physical information regarding the patient (such as sex, age, and weight). Doctors can narrow down the disease affecting the patient on the basis of the outcomes of the measurements.
Virtual Personal Assistants
Alexa, Siri, and Google Now are a few of the most popular examples of virtual assistants. As their names suggest, they help us find information, when asked over voice. You can use them to fetch all types of information. All you have to do is activate your virtual personal assistant and ask, for example, “What is my today’s schedule?” or “What is the weather in Germany or London”, or other similar questions.
To answer your questions, the assistant searches for the relevant information, or recalls all your related queries, or sends commands to other resources (such as other apps on your smartphone) in order to collect information. You could even instruct your virtual assistant for specific tasks such as “Remind me to collect the loan application form day after tomorrow” or “Set an alarm for 7 AM the next morning”.
Keep in mind that machine learning plays a vital role in the functioning of these virtual personal assistants. This is because they gather and refine information with the help of machine learning depending on your prior involvement with them. After that this data is used to render suggestions and results that are customized to your preferences.
Nowadays, virtual assistants are fully integrated into a number of platforms such as:
- Smartphones: Bixby on Samsung Galaxy S8
- Smart speakers: Google Home and Amazon Echo
- Mobile Apps: Google Allo
A lot of people are eager to predict what the bond and stock markets would do on any specific day — for obvious reasons. However, the algorithms that makeup machine learning with the help of accurate data are getting closer and more precise all the time. A majority of prestigious and leading trading firms use some of the best proprietary systems for predicting different trade patterns and executing trades at high volume and high speeds.
Most of these systems rely heavily on probabilities; however, even trades that have relatively low probabilities, at a sufficiently high speed or volume, could turn substantial profits for the companies. And humans simply cannot compete with fast and sophisticated machines, especially when it comes to analyzing and evaluating huge quantities of data or the immense speeds with which these machines could execute a trade.
This is why banks, financial institutions and other businesses in the financial sector leverage machine learning for two main purposes. The first is identifying key insights in data, and the second is preventing fraud. These insights could help identify investment opportunities for these institutions or help investors know exactly when to trade. In addition, data mining could identify clients that have unusually high-risk profiles or utilize cyber surveillance to identify warning signs associated with fraud.
Imagine just one person monitoring various video cameras at the same time! It is definitely a difficult job and is boring too. It is why the notion of training computer systems to perform this crucial job makes plenty of sense. Nowadays, video surveillance systems are powered using artificial intelligence and make it easier to detect crime.
This is because they are able to track unusual or strange behavior such as standing idle for a long time or taking naps on benches. These systems can provide alerts to human attendants, who can help avoid mishaps. These systems can increase detection of potential threats and reduce false alarms based on the detection and analysis of new anomalies. With the help of machine learning, these systems simultaneously analyze various moving figures such as people or cars in crowded areas.
And all this is done without the users specifying or feeding rules to find threats and abnormalities, which considerably lowers the need for human operators to monitor each scene. Also, keep in mind that when criminal or undesirable activities are reported, they play a part in improving the surveillance services.
Another important application of machine learning and AI is information extraction. Information extraction involves the extraction of structured information from volumes of unstructured data. Unstructured data could come from a variety of sources such as web pages, blogs, articles, business reports, minutes of meetings and e-mails. Relational databases are used to maintain the output generated by information extraction.
The output of the extraction process can be summarized in various forms like excel sheets or tables in a relational database. At present, extraction has gained immense importance in big data industry.
Detection of Fraud
Machine learning has come a long way when it comes to spotting potential instances of fraud across a number of different fields. For example, PayPal, an e-commerce company that facilitates online payments, is now using machine learning to combat money laundering. PayPal has advanced tools that can compare and analyze millions of financial transactions and accurately distinguish between fraudulent and legitimate transactions between sellers and buyers.
In finance, the term statistical arbitrage is used to describe automated financial trading strategies that are geared toward the short-term and usually involve a huge number of securities. When using these strategies, users try to implement specific trading algorithms for a group of similar securities. This is done on the basis of quantities and taking into account general economic variables, like interest rate and inflation rate, and historical correlations.
These measurements are usually complex in nature and could be cast as estimation or classification problems. Keep in mind that the underlying assumption, in most cases, is that security prices would move toward a historical average.
Machine learning methods and tools are applied in order to get an index arbitrage strategy. Mostly, traders employ support vector regression and linear regression to the security prices of exchange-traded funds as well as streams of diverse stocks. Through the use of principal component analysis in lowering the dimensions of feature space, traders can observe the issues and benefits in the application of support vector regression.
In order to produce trading signals, users model any residuals from the prior regression for the mean reverting process. In terms of classification of securities, the categories may be buying, selling, or doing nothing. On the other hand, when it comes to estimation users can predict the expected average return of every security over a given time frame.
Improve Customer Service
Machine learning and artificial intelligence could enhance the efficiency of customer service by better understanding customers and their preferences, issues and needs at a granular level. Machine learning can help whether customer support is provided by humans, or is virtual.
According to the founder of a predictive analytics platform, customer effort has a direct correlation with lifetime value. This is why the period of time it usually takes a consumer to use a company’s product or resolve any problems typically have a direct relationship with the sum of money they spend on the product.
Machine learning has the capability to discern between those customers who are just starting to use a product against those who have more experience and know the product better; this can enable efficient customer support. On the other hand, it could also identify and proactively resolve customer problems as they occur. For instance, in case a customer is facing problems loading a specific product into his virtual shopping cart, then the intelligent system could offer instant assistance which is more relevant and useful compared to a generic pop-up that provides non-specific chat assistance.
You might be familiar with this application of machine learning if you use online services such as Amazon or Netflix. Machine learning algorithms are quite intelligent and can analyze user activity and then compare it with millions of other users in order to predict what you may like to binge watch or buy next. And these product and service recommendations are getting better all the time.
For example, they can recognize that you may buy specific products as gifts (and do not need them yourself) or there could be different family members with different television preferences.
Outsmarting Competing Litigators
Attorneys often need to comb through big volumes of data for building their cases. As an attorney, if you can separate signal from noise quickly and accurately, the more time you and your team will be able to spend on your litigation strategy. In the past, lawyers and their support staff had to manually review loads of documents, which could take weeks or even months.
However, now machine learning could expedite this process and unearth crucial details that humans might overlook. By using a combination of search analytics and machine learning, attorneys can identify patterns in language, indicating peoples’ behavior. At times, lawyers look for the best examples of a certain type of behavior, or a specific incident.
On other occasions, they have to establish a pattern of behavior in order to find as many instances of that behavior as possible. Machine learning can outperform humans in this respect as human often tend to search for familiar patterns, depending on their past experiences or other bias. On the other hand, machine learning could take a more objective look and help yield more accurate results, while expediting the process and lowering the costs.
This is probably one of the most popular uses of machine learning. Google, Bing and other search engines are continuously improving and enhancing what their search engine understands. Whenever you conduct a search on Google or Yahoo, the intelligent program watches and learns how you react to the search results.
For example, if you click on the top results and stay on the first web page, the program assumes you have found the information you were seeking and, thereby, your search was successful. On the other hand, if you click the second or third page of results, or use a different search string without clicking the current results, it is assumed that the search engine failed to serve up the specific results you were after — and these programs could learn from these mistakes to deliver better results in the future.
Fine-Tune Security Screenings
Concert attendees, airline passengers, and sports fans all have one thing in common: they are screened by guards and security systems. However, it is important to bear in mind that human screeners can often ignore certain items and aspects which machine learning can identify. Also, machine learning could easily and quickly adapt to a variety of seasonal changes that affect bag contents and bag types, or the particular requirements of a specific venue.
Venues use security in order to keep out guns, bombs, and other dangerous items. Some do not prefer selfie sticks as you may end up harming someone with it. When you have hundreds of thousands of people trying to make it inside a stadium, you can expect hundreds or even thousands of false alarms. However, with machine learning, you can reduce the false alarm rate and improve security at a variety of public events.
Natural Language Processing
Natural language processing is now being successfully used in a variety of exciting applications across many disciplines. Natural language and machine learning algorithms could be the new customer service agents, more quickly routing customers to the specific information they require.
For example, sentiment analysis could be used to figure out the opinion, feeling or belief associated with a statement, from very positive, to neutral, to very negative. In most cases, developers use algorithms for identifying the sentiment of a phrase or term in a specific sentence or utilize sentiment analysis to better analyze social media.
Kaspersky Lab reported in 2014 that it was detecting about 325,000 new malicious files on a daily basis. At this alarmingly high rate, humans and even some signature-based security measures and solutions just cannot keep up. This is why deep learning and machine learning are necessary.
According to the CTO of an intelligence company called Deep Instinct, almost all new malware files differ only less than two percent from previous malware. The machine learning algorithms at the company have no problem when it comes to handling mutations in the range of 2 to 10 percent. The company uses a huge core of millions of legitimate files, several million malicious files and malware that it mutated for training purposes.