Machine learning and deep learning are increasingly making their way into consumer-related industries. They are widely used for various tasks:
- Recognizing objects;
- Translating speech in real-time;
- Determining potential outcomes;
- Understanding consumer habits;
- Making personalized recommendations and a lot more.
In fact, leaders in consumer industries such as Amazon, Netflix, Apple, Google, and Microsoft extensively use these technologies.
What do these mysterious technologies entail? Should smaller companies invest in machine learning and deep learning? N-iX engineers try to answers these questions.
What is Machine Learning?
Machine learning combines the principles of computer science and statistics. It focuses on solving real-world problems by mimicking our decision-making logic.
Machine learning engineers create statistical models that make predictions and identify patterns in data. Its main objective is to build algorithms that can receive input data and use statistics to predict the outcomes.
Machine learning algorithms work this way:
- They analyze benchmark data sets;
- Learn from that data;
- Make informed decisions based on what they’ve learned.
Software applications powered by machine learning accurately predict outcomes, recognize patterns in highly unstructured datasets, and create high performing predictive models from data.
How companies leverage machine learning?
Machine learning has many real-life applications like task automation, financial security, cybersecurity, and more. Here are some of the most promising machine learning applications:
Machine learning enables companies to detect and prevent fraudulent transactions in real-time.
According to ACFE, businesses lose around 5% of their revenues each year due to fraudulent transactions. Machine learning algorithms analyze data like historical transactions, social network information, and other data to recognize patterns and spot anomalies.
For instance, banks can utilize historical transaction data for building algorithms that recognize fraudulent behavior and discover suspicious patterns of payments. 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 the network. With the help of machine learning, IoT systems can track various data 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 a year).
Machine learning and AI enable companies to provide high-quality customer care. An AI-powered system can combine 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.
AI development is still quite expensive, but it’s getting cheaper every year. In fact, small businesses can already afford simple AI implementations for customer support. For instance, take a look at some of the best AI development companies in Europe and what they have on offer.
What is Deep Learning?
Deep learning uses multiple layers of nonlinear processing units to analyze highly unstructured data and predict outcomes. While traditional machine learning algorithms are linear in nature, deep learning algorithms are stacked by increasing complexity.
Deep learning can accelerate the problem-solving of complex computer issues, most notably in the computer vision and natural language processing (NLP).
A deep learning uses human-like logic. To achieve this, engineers use a layered structure of algorithms called an artificial neural network (ANN). Artificial neural networks are similar to neural network of the human brain.
How deep learning is creating value for companies
Here are some use cases of deep learning:
- Speech or gesture recognition.
- Analysis of documents and pictures.
- Speech translation real time.
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 other great use case.
Deep learning is widely used for recognizing patterns, which allows companies to monitor and process different types of data. 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 discover subtle patterns that correspond to 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 diagnose heart diseases. Deep learning helps Bay Labs diagnose cardiovascular diseases 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.
Deep learning can reduce risks and expenses of cyber threat. It deals efficiently with malware, malicious URLs and spots suspicious behavior. Its self-taught algorithms recognize user activities that can put valuable data at risk and act to isolate the threats. In fact, deep learning can automate intrusion detection with impressive accuracy.
Machine learning and deep learning are set to revolutionize the whole industries and companies of all sizes need to take advantage of that technology. Surely, there is a shortage of machine learning engineers on the world market, and companies need to develop effective strategies for resolving this challenge (like this one).