Read about the top 5 trends in which Semantic Technology enables enterprises to make sense of their data and fine-tune their offerings to customers.
Digitalization in our lives has become so fast and widespread that today’s advances in technology and computer science have been called the Fourth Industrial Revolution or Industry 4.0.
The digital revolution today is a lot like the transformation that the Industrial Revolution or the launch of the smartphone brought to enterprises and to people.Big data and data analytics are today’s textiles. Machines capable of processing huge amounts of data and making sense of all that data are today’s steam engines. Click To Tweet
These days, organizations need to make fast data-driven decisions if they want to stay ahead of competitors. Enterprises need advanced applicable high-tech solutions that help them stand out from the crowd.
Creative minds and the unstoppable advance in technology team up to give enterprises increasingly inventive solutions to solve their data challenges. Technology is evolving and as we are going into 2018, here is Ontotext’s list of the top 5 semantic technology trends to keep an eye on in the New Year.
Artificial Intelligence (AI) is no doubt the busiest buzzword in technology these days. People often associate it with the Terminator movies or the Westworld TV series, but AI is often used as an umbrella term for various smart technologies that help organizations make faster and more informed business decisions and improve in-house data and knowledge discovery.
Knowledge discovery becomes easier, faster and more precise with the use of Knowledge Graphs that have gained popularity in recent years and are bound to continue influencing the way organizations discover and deliver data, information and insights.
Thanks to semantic technology and graph database technology – Ontotext’s core specializations – data integration and knowledge discovery are based on meaning and relationships linked in ways close to the human thinking. Machines are able to distinguish between people and places in sentences such as “Paris Hilton stayed at the Hilton hotel in Paris,” because each concept in the Semantic Web has its own unique identifier and is interconnected with concepts that explain the links – the relationships – with other concepts.
Knowledge graphs are capable of combining billions of facts from the Linked Open Data cloud and from proprietary data sets to describe relationships and discover implicit links between various datasets.
Why it is important: Knowledge graphs in big enterprises can solve internal information challenges such as preserving their global information, making sense of it and eventually gaining insights so to control it. This knowledge will be increasingly governed by “intelligent agents”, rather than individuals.
This is the next trend we’ve identified as worth following in 2018. Machine learning (ML) is a branch of AI that provides machines with the ability to automatically learn, improve and adapt from experience, without explicit programming to perform specific tasks. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
Machine learning algorithms ‘teach’ machines how to identify patterns in data, how to build models of the world and how to predict behavior.
Why it is important: Machine learning is the most developed field in the AI domain, disrupting technologies ranging from consumer electronics and web services to big enterprise information services. ML-enabled devices can offer various benefits to consumers such as smarter homes or more efficient personal assistants (Remember the movie “Her”?)
Machine learning algorithms, pattern recognition and natural language processing (NLP) are then used in another technology trend on which we will be keeping a close eye in 2018:
As the name itself suggests, cognitive computing aims to simulate the human perception and thought processes in a machine. Machine learning models and automated reasoning help cognitive computing systems to mine non-stop for knowledge from the data they receive.
According to a November 2017 report by Research and Markets, the cognitive computing market is expected to grow at a compound annual growth rate (CAGR) of 24.45% by 2022, to reach US$15.183 billion by then, compared to US$5.086 billion in 2017. The largest portion of the revenues in cognitive computing will be coming from NLP, according to Research and Markets.
Why it is important: The ultimate goal of cognitive computing is to have a machine that can adapt to processing data and information to the point of predicting problems or behavior and create models of the best possible solutions. So cognitive computing can boost the technological disruption of major industries such as Banking, Financial Services and Insurance (BFSI), healthcare and defense.
In the closely-interrelated subfields of AI, there is a type of technology that is a subset of Machine learning:
This approach uses networks capable of unsupervised learning through processing of unstructured or unlabeled data. In 2016, MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) developed a deep-learning algorithm that, when given a still image, can generate a short video that predicts what will happen next in a scene.
Why it is important: Even though deep learning is not yet much developed as a field, future versions of such algorithms could have various applications, including self-driving cars and improved security tactics.
The deep learning branch of machine learning is using the fifth trend we will closely watch in 2018:
The artificial neural networks are modeled similarly to the biological neural networks in humans and enable machines to learn from observational data. Deep learning and neural networks provide solutions for speech and image recognition, for example.
In December 2017, a team of scientists and engineers from universities and medical centers in the Netherlands published an article in The Journal of the American Medical Association (JAMA) that investigates the accuracy of deep learning algorithms compared to the diagnoses of pathologists in detecting metastases in breast cancer patients.
In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow, the researchers said, noting that evaluation in clinical setting will be needed to see if this approach could be of clinical use.
Why it is important: As neural networks are capable of recognizing huge and complex sets of data, they can recognize patterns and predict outcomes in ways that have been previously unfeasible for experts.
Researchers, scientists and engineers continue to explore how AI, machine learning, cognitive computing, deep learning and neural networks can improve our lives and the ways organizations deal with the heaps of new data created every second.
In August 2017, Tractica, a market intelligence firm that focuses on human interaction with technology, estimated that the annual revenues from implementing AI software is set to grow to US$59.8 billion by 2025 from US$1.4 billion in 2016, with use cases and implementations across virtually all industries.
AI as we have seen it in the movies or read in sci-fi books may be decades away, but the business uses of semantic technology and the various branches of artificial intelligence such as machine learning are already changing the way organizations gain insights from data or improve in-house operations.
MIT’s Erik Brynjolfsson and Andrew McAfee wrote in a July 2017 article in the Harvard Business Review:
Over the next decade, AI won’t replace managers, but managers who use AI will replace those who don’t.
Welcome to the future.
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Our Technology Solutions are based on various machine learning algorithms.