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Artificial intelligence (AI) is a field that often attracts hyperbole and misinformation fueled by a general lack of understanding. Popular media hasn’t done much to help the public image of AI, with stories of machines capable of thinking for themselves, stealing people’s jobs and threatening humanity.
In reality, AI isn’t close to having the cognitive capacity to surpass human intelligence. However, advances in the field are facilitating innovation and extending human capabilities. For example, AI and machine learning project AlphaFold is transforming understanding of how the human body produces protein, which will drive the creation of new drugs to fight diseases.
Advances in Artificial Intelligence
AlphaFold is just one concept that’s benefiting from AI. Critical advances in the field include:
Generalized AI
Generalized AI, or artificial general intelligence, refers to a machine’s ability to perform tasks that humans can. Typically, a computer system can learn how to perform one task well but can’t apply the same learnings to other functions in the same way that humans can. Advancing this field will be crucial to furthering machines’ ability to understand language and human interaction.
Natural Language Processing
With natural language processing (NLP), machines understand and gain meaning from human language. NLP handles simple conversational tasks by transforming freeform text into a standardized structure, which enables it to power AI tools like chatbots.
Natural Language Understanding
Gaining a solid grasp of generalized AI is a critical step toward fully understanding language. Machines are currently very good at understanding customer intent in phone calls, but they’ve yet to excel in truly understanding the process of human conversation. We still don’t completely know how and why human language works in the way it does, which means we can’t develop a theory to apply to machines.
Natural language understanding (NLU) is a subset of NLP. It helps machines interpret spoken and written communication and determine the intent behind human interaction. It uses machine learning to recognize elements of speech, such as intent, language patterns, sentiment and syntax. Enhancing this field will enable AI-powered tools like chatbots and virtual assistants to better understand consumers’ meaning and purpose.
Reinforcement Learning
Reinforcement learning is a generalized AI and machine learning concept that rewards positive behavior and punishes negative behavior. The process involves a machine experimenting and learning from its mistakes, then rectifying its actions to gain the maximum reward.
Examples include DeepMind’s AlphaGo machine, which used reinforcement learning to fully understand the board game Go and beat the human world champion. It has also been used to successfully solve a Rubik’s Cube with a robotic arm and advance autonomous driving systems.
What to Expect from AI in the Near Future
Various industries are leveraging AI to drive change. From improving patient care to making production floors safer, here are examples of AI advancements to watch out for.
Defense and Military
The U.S. Army is utilizing AI to enhance the technology development of unmanned aircraft and wearable devices. A collaboration with the University of Maryland will combine computer science, engineering and robotics to increase battlefield and search-and-rescue operation capabilities.
Healthcare
AI is already massively impacting the healthcare industry, enabling medical experts to provide more insightful diagnoses and treatment plans. It’s also improving the effectiveness of medical devices, such as computed tomography (CT) and magnetic resonance imaging (MRI) scanners, which will enhance critical medical practices like neurology.
Manufacturing
Advances in AI, machine learning and 3D printing are helping improve production processes in sectors like aerospace, defense and manufacturing. AI can analyze massive volumes of data from sensors and use machine learning algorithms to detect anomalies, predict and prevent failures and even automate the self-healing of machines across the production environment.
Real Estate
AI tools are helping real estate agents close deals faster and more efficiently. Real estate firms are already using AI to simplify and automate time-consuming processes, accurately estimate a home’s market value and compare homes.
Uniphore’s Contribution to AI
Uniphore’s AI-powered tools are already capable of identifying customer intent on phone calls and understanding the language used in articles. This insight helps us analyze and predict accents, behavior and emotions, which, in turn, strengthens customer adoption and improves interactive voice response (IVR) systems.
But the future of AI will be reliant on generalized AI to improve machines’ understanding of the human communication process. We are looking beyond speech recognition and computer vision to advance this ideal and have recently acquired Spanish startup Emotion Research Labs, which uses facial emotion and eye-tracking technology to understand human intent. This will help us analyze and understand voice and video conversations in real time.
Our global ecosystem of innovation that includes startups and research centers is also allowing us to establish and develop new conversational AI capabilities. Partnerships with organizations like the Idiap Research Institute, the Indian Institute of Technology Madras and the World Economic Forum’s Global Innovators Community are helping us develop cutting-edge algorithms and insights into language understanding and visual AI.
Enhancing AI Capabilities
Popular media’s portrayal of all-powerful AI will remain a fantasy for the considerable future. But technological advances in the field will play a key role in training machines to understand language and human communication.
As a result, AI will continue to advance human capabilities in critical industries like healthcare, defense and manufacturing. Discover how Uniphore sees the future of contact centers through AI by downloading our ebook.