17 December 2020
Los Angeles, CA


A lot of progress has been made over the years with the advancement of artificial intelligence technology in real-world applications. With the rise of this technology solving complex problems, we are also seeing industries adopt, adapt, and evolve through automation and digital transformation. But in order for humans to make better decisions, we need to be able to work with technology to gain actionable insights, solve problems, and drive value.
Conventional machine learning and deep learning have evolved, but in high-risk industries like healthcare, energy, and finance, there is a need for a more intelligent, more explainable AI. The core foundations of conventional AI are found in machine learning, deep learning, and neural networks. These approaches, on their own, give us the “what” but not the “why” humans need in order to gain more actionable insights for making better decisions. Cognitive AI bridges the gap between solely conventional and symbolic AI techniques to deliver a more intelligent solution known as Cognitive AI. This advanced technology is designed to mimic human cognition by incorporating conventional AI, machine learning, deep learning, and the cognitive capabilities of symbolic AI.
What is Cognitive AI and Why is it Important?
Cognitive AI is a hybrid of AI technologies like conventional numeric AI and symbolic AI. Cognitive computing technology is important because it combines structured data with natural language processing to derive meaning from human languages. By combining these two types of AI technologies, we are better able to identify complex problems before they happen and recommend solutions in high-risk industries such as manufacturing, healthcare, energy, and natural resource operations.
The ability to derive meaning from Cognitive AI is important because it empowers humans to interact in real-time and makes their jobs more functional by improving communications across multiple team silos, reducing risks, and evolving operations to become more proactive.
The Role of Explainable AI in Industrial Process Automation
For AI systems to be trusted by humans during the critical decision-making process in high-risk situations they need to understand the “why” – not just the “what.” Cognitive AI goes beyond simply providing system alerts; the technology also provides operators with drilled-down reasoning traces through audit trails that detail evidence, probability, and risk. This capability turns on the light in an otherwise black box of information. By taking out the mystery of solely conventional methods, explainable AI provides stakeholders at every level the critical details they need to trust their artificial intelligence solutions.
Cognitive AI is Smarter Artificial Intelligence
Cognitive AI technology moves beyond conventional AI toward a more human-like ability to perceive, understand, correlate, learn, teach, reason, and solve problems faster than other AI solutions. Cognitive systems utilize a unique hybrid procedure, combining the best of conventional numeric AI approaches and advanced symbolic AI techniques to deliver reasoning and intelligence that emulates human intuition.
Pioneering AI Technology with an Advanced Approach
Using a hybrid approach to incorporate numeric AI foundations and symbolic AI enables systems to empower operators, who work alongside the technology, to make faster and more confident decisions that boost their company’s bottom line. Being proactive and pivoting quickly to avoid disruption ensures business continuity and creates cost savings by minimizing system failures.
Cognitive AI solutions delivering transparent audit trails that explain the reasoning behind their recommendations and show all evidence, risk, confidence, and uncertainty are designed to be understood by people and interpretable by machines. These advanced systems are built to think like industry experts and produce operational efficiencies at scale, resulting in new revenue and increased profits.