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With growing amounts of computational power, machine learning and deep learning are increasingly making their way into numerous sectors. They are widely used for recognizing objects, translating speech in real-time, determining potential outcomes, understanding consumer habits, making personalized recommendations, and a lot more. In fact, consumer market leaders such as Amazon, Netflix, Apple, Google, and Microsoft are all backed up by these technologies.
Still, some questions remain uncertain. What do these two entail? And should companies invest in machine learning development to benefit their businesses? We provide the answers to these questions in our article.
What is Machine Learning?
Machine learning combines the principles of computer science and statistics. It focuses on solving real-world problems with neural networks designed to mimic our decision-making. Machine learning engineers create statistical models used for making predictions and identifying patterns in data. Its main objective is to build algorithms that can receive input data and use statistics for prediction without being explicitly programmed.
Machine learning uses algorithms to analyze data, learn from that data, and make informed decisions based on what it has learned. It is employed in a range of computing tasks where designing and programming explicit algorithms with excellent performance are difficult or infeasible. Thus, with the help of machine learning, software applications can become more accurate in predicting outcomes, automatically recognizing unknown patterns, obtaining deep insights, and creating high performing predictive models from data.
How companies leverage machine learning
On the whole, machine learning enables companies to optimize their processes and increase customer satisfaction. It has a lot of applications ranging from price prediction and service automation to detection of fraudulent credit card transactions or network intruders. Among the most powerful machine learning applications in a business setting are:
- Fraud detection
According to ACFE, businesses lose on average 5% of revenues each year due to fraud. Machine learning algorithms use pattern recognition and other techniques to spot anomalies, exceptions, and outliers by building models based on historical transactions, social network information, and other external data sources. So machine learning development enables companies to detect and prevent fraudulent transactions in real-time.
For instance, banks can utilize historical transaction data for building algorithms that recognize fraudulent behavior and discover suspicious patterns of payments and transfers between networks of individuals with overlapping corporate connections. This kind of algorithm-based security applies to a wide range of scenarios, including cybersecurity and tax evasion.
- Predictive maintenance in IoT
IoT solutions with built-in sensors are widely used for equipment maintenance. They gather data about everyday objects, such as fuel gauges and tires, and share it across a network. With the help of machine learning, IoT systems can analyze various data patterns such as temperature and humidity to predict performance and further outcomes.
For instance, Caterpillar utilizes IoT and machine learning to uncover patterns in equipment and device data. The company identified that fuel meter readings were correlated with the amount of power used by on-board refrigerated containers. Caterpillar used that data to optimize operating parameters by modifying generator output. This resulted in impressive cost savings (of more than $30 per hour or $650,000 over a year).
- Service personalization
Machine learning and AI enable companies to provide high-quality customer care by combining historical customer service data, natural language processing, and machine learning algorithms that continuously learn from business interactions. Thus customers can ask questions and get immediate answers generated by the algorithms.
So customer service representatives can get involved only in exceptional cases, while the algorithms can take over not only routine tasks but also more advanced communication. In fact, around 44% of U.S. consumers have stated that they prefer chatbots to humans regarding customer service efficiency and this is just the beginning of the AI era.
What is Deep Learning?
In practical terms, deep learning is a subfield of machine learning which conducts supervised or unsupervised learning from data, using multiple internal layers of nonlinear processing units. While traditional machine learning algorithms are linear in nature, deep learning algorithms are stacked by increasing complexity. Software programs use the deep learning approach to apply the knowledge gained from the preceding layer of hierarchy.
With the help of DL, precise details are cognitively spotted in the automated learning process and later applied for accurate decision making. Deep learning can accelerate the problem-solving process for certain types of complex computer issues, most notably in the computer vision and natural language processing (NLP) fields. Deep learning also involves mathematical modeling where some of the components can be adjusted to better predict the final outcome.
On the whole, a DL model is designed to continually analyze data with the human-like logic structure. To achieve this, deep learning engineers use a layered structure of algorithms called an artificial neural network (ANN), which is inspired by the biological neural network of the human brain. This makes for machine intelligence that’s far more capable than standard machine learning models.
While both machine learning and deep learning fall under the broad category of AI, deep learning is most commonly behind human-like artificial intelligence software.
How deep learning is creating value for companies
Global brands adopt deep learning to recognize speech or gestures, analyze documents and pictures connected to a large database, translate speech in real time, build recommendation systems, and more. For instance, Google, Apple, and Microsoft utilize deep learning in their voice and image recognition algorithms, while Netflix and Amazon use it for analyzing and influencing consumer decision making. However, deep learning has a number of other revolutionary use cases such as:
- Pattern recognition
Deep learning is widely used for recognizing patterns, which allows companies to monitor and process a multitude of things. For instance, PayPal utilizes deep learning via an open-source predictive analytics platform H2O to prevent fraudulent payment transactions or purchases.
Another example is Enlitic, which uses deep learning to process X-rays, MRIs, and other medical images to help doctors diagnose and treat various diseases. The company uses deep learning algorithms that can discover the subtle patterns which characterize disease profiles. Deep learning networks can also provide rich insights in terms of early-stage disease detection, treatment planning, and monitoring.
- Drug discovery and medical treatment
Modern researchers use deep learning for drug discovery by combining data from a variety of sources. It helps them predict novel candidate biomolecules for several disease targets (most notably treatments for the Ebola virus, etc.) or discover new kinds of medicine.
For instance, Bay Labs – a healthcare startup applies deep learning to medical imaging to help in the diagnosis and management of heart disease. With the help of deep learning, Bay Labs has potential to dramatically impact the leading cause of death, cardiovascular disease by improving access, value, and quality of medical imaging.
In addition, an AI startup Atomwise uses deep learning algorithms to solve the problem of drug discovery. Deep learning networks help to discover new medicines, as well as explore the possibility of repurposing known and tested drugs for use against new diseases.
- Cybersecurity
Deep learning in cybersecurity allows the companies to reduce risks and expenses associated with failing to detect a threat. It deals efficiently with malware, malicious URL, and malicious code detection. Its self-taught algorithms recognize user activities which might put valuable data at risk and act to isolate them. In fact, deep learning can automate intrusion detection with impressive accuracy.
Afterword
Machine learning and especially deep learning are revolutionizing many industries today including fintech, healthcare, transportation, edtech, etc. They show great potential in terms of developing autonomous self-taught machine learning applications, which can be used for detecting fraud, forecasting results, attracting new customers, and much more. However, there is a shortage of machine learning engineers on the world market, and companies need to develop effective strategies for resolving this challenge.
