Artificial Intelligence

Cognitive Computing Transformation

A technical and architectural overview

Most of us in the IT industry have most likely heard, read about, or already started working with cognitive systems for various practical use cases. Would you like to have a quick taste? Then, keep reading! I simplified this complex topic as much as possible in this article.

The purpose of this article is to briefly introduce to you what cognitive computing entails, its current progress in the industry, the value proposition for its necessity, and my personal observations and thoughts on trends and future plans.

Cognitive computing comprises a combination of emerging technologies, processes, and approaches in cross-disciplines: cognitive and computer science. It is being built on long-term research and development work in Artificial Intelligence (AI).

Cognitive computing, as a strong extension of AI, is the simulation of our thought processes in computerized models.

From a scientific discipline perspective, in a nutshell, cognitive computing emerged by combining related attributes of cognitive science dealing with natural intelligence and computer science dealing with artificial intelligence.

Many systems in the industry and research settings can display knowledgeable behavior. These behavioral elements are the simulation of human thought processes using computerized models.

The value of cognitive computing can be realized by three major capabilities in computer science:

a) Self-learning systems using pattern recognition

b) Data mining and analytics

c) Natural Language Processing

These three key points can enable the creation of autonomous computer systems that can solve business, scientific, academic, and other problems with minimal input or intervention from human beings.

I ask two critical questions while assessing the merits of cognitive transformation:

1. What is the significance of this era needing new solution approaches?

2. What made it a new milestone and steppingstone in our technological transformation?

These two broad questions pop up three key considerations in my analysis:

1. We are stuck in our technological progress!

2. Emerging technologies in computer and cognitive science can offer new capabilities to extend our current capabilities to produce novel solutions.

3. Computers finally started mimicking human thinking to some limited extent.

Whilst there are some skeptics, some of us are about to reach the consensus that cognitive computing can be a real game-changer for our productivity and effectiveness in addressing the growing and sticky world problems.

Transformative leaders keep emphasizing that we need new approaches and more productive ways in agility to meet our growing demands on this aging planet.

The prime premise is articulated as “today’s problems cannot be solved with the technologies of the past”. This emphasis resonated with computer and cognitive scientists; hence a strong thought leadership prevailed globally.

The ultimate goal of cognitive systems is the capability to solve complex and time-consuming problems without human intervention.

This statement has tremendous implications for our technological development and, consequentially, economies. We attempt to bring the technology to such a state that it is almost autonomous in solving our difficult problems faster and more efficiently than we perform as human beings.

From emerging technologies perspective, the key contributions to cognitive computing have been in developing research in deep machine learning, neural networks, big data analytics, data mining, predictive analytics, smart embedded objects, mobility, and natural language processing.

We have been dealing with these technologies for decades however now the fusion of these technologies in an integrated manner has started creating synergistic outcomes for the progress and rapid growth of cognitive computing.

Even though there is a massive debate going on in various forums, in my humble opinion, artificial intelligence and cognitive computing are two different fields from developmental and scientific discipline perspectives. However, they are closely and tightly interrelated.

Whilst the focus in cognitive computing is that even though computers learn by themselves, the process is still managed and controlled by human beings.

By looking from the same perspective, AI literature envisages that the augmented intelligence of machines can surpass human capability hence they can be autonomous.

This premise has been a controversial topic and AI progress rightly or wrongly created fear in society.

We need to learn more about the implications of emerging technologies, increase our knowledge experimentally across disciplines, and gain new experiences in mass collaboration to deal with this fear and leverage the augmented intelligence of technological offerings.

These comparative observations in my interactions with the thought leader collaborators in the industry and academia created an interesting sentiment that I wanted to share here.

Whilst AI pessimistically scares people of losing control over the machines, cognitive computing is displaying an optimistic acceptance of its use without dominating and controlling our lives.

To simplify this view metaphorically, AI is like an aggressive boss entering our lives as a fearful controller, whereas cognitive is considered like a docile personal assistant who facilitates the process, corrects our errors gently, improves our tasks, solves our problems faster, and does our jobs and lives easier.

From a technical point of view, cognitive systems acquire knowledge from the vast amount of data turned into processed information using various analytical techniques such as descriptive, prescriptive, predictive, and semantics.

Big data analytics has been the key enabler of generating information and turning it into knowledge.

The cognitive systems transform content into context using confidence-weighted responses and supportive pieces of evidence. For example, the machine learning algorithms in the systems refine the way they use patterns with multiple repeat loops.

This specific way of processing data can enable guessing new problems and hence can generate new solution models. This approach is characterized as deep learning, which contributes to the functional requirements of cognitive systems.

So far, AI, as a growing discipline in computer science, has achieved many intelligence marks in various fragmented forms. For example, there is a growing body of knowledge in expert systems, neural networks, virtual reality, and robotics.

The challenge has been integrating these fragmented pieces for coherent capability and value propositions. Based on this premise, cogent speculative ideas prevailed that a cognitive approach must leverage AI technologies and integrate them for productive outcomes and value propositions.

From a historical point of view, computers demonstrated fast calculations and data processing. However, they struggled to understand natural language or image recognition.

With the introduction of AI, they started having these new capabilities. With integrated capabilities introduced by cognitive computing, computers can now learn in a way mimicking human learning.

From the industry point of view, as you may notice from the media, there is a tremendous focus on cognitive computing. One of the recent remarkable developments in cognitive computing was achieved with the implementation of IBM’s Watson.

It is practically used in industry as a support system, for example by doctors. It helps collate the deep knowledge around a patient’s condition, and history, and links them to established journal articles, medical best practices, and diagnostic tools, by analyzing these factors in an integrated manner, it rapidly provides informed advice to health professionals.

Other leading technology organizations such as Google, Microsoft, Samsung, Amazon, and Apple made a considerable amount of progress with cognitive computing.

For example, most of us are familiar with and have been interacting with Siri, Cortana, Alexa, and Google Assistance using natural language day by day.

There can be limitless use cases for cognitive computing. Some areas which are leveraging the capabilities of cognitive computing are cybersecurity, insurance, governance, education, climate, financial, manufacturing, and medical solutions.

The future of cognitive computing can be outlined in three keywords discovery, engagement, and decision. In the near future, cognitive systems are expected to be able to simulate how the brain truly works.

Imminently, they can help us understand complex issues that we couldn’t grasp before, manage complex risks with informed decisions, and gain insight for solving our most difficult problems.

These can be achieved with cross-discipline studies and tight integration among multiple disciplines toward fundamental shared goals and objectives of humanity.

From a practical point of view, cognitive computing also interrelates with Cloud Computing and uses the Cloud service model efficiently as its enabling hosting infrastructure. It leverages Internet technologies and contributes to the further development of IoT (Internet of Things).

In the near future, we can see the creation of a new integrated platform of Cognitive, Cloud, IoT, and Mobility, integrated with Analytics and Big Data ubiquitously in the workplace, homes, schools, shopping centers, banks, entertainment venues, and everywhere else that we can imagine.

As a technologist and cognitive scientist, this is a topic close to my heart, and with a strong interest in growing my research, hence I serve as an advocate of the topic by creating awareness in all walks of my life.

I believe that it is time to embrace cognitive computing not only for its business value but also for the potential benefits that it can offer to our society, as we have been yearning and fantasizing about it as depicted in science fiction books and movies for many decades.

Thank you for reading my perspectives.

About the Author

I am a technologist, postdoctoral researcher, author of several books, editor, and digital marketing strategist with four decades of industry experience.

I write articles on Medium, NewsBreak, and Vocal Media. On Medium, I established ILLUMINATION, ILLUMINATION-Curated, ILLUMINATION’ S MIRROR, ILLUMINATION Book Chapters, Technology Hits, SYNERGY, and Readers Hope publications supporting 19,000+ writers on Medium. You can join my publications requesting access here. You may subscribe to my account to be notified when I post on Medium. I share my health and well-being stories in my publication, Euphoria.

If you are a new reader and find this article valuable, you might check my holistic health and well-being stories reflecting my reviews, observations, and decades of sensible experiments.

Sample Health Improvement Articles for New Readers

I write about various hormones and neurotransmitters such as dopamine, serotonin, oxytocin, GABA, acetylcholine, norepinephrine, adrenaline, glutamate, and histamine.

One of my goals as a writer is to raise awareness about the causes and risk factors of prevalent diseases that can lead to suffering and death for a large portion of the population.

To raise awareness about health issues, I have written several articles that present my holistic health findings from research, personal observations, and unique experiences. Below are links to these articles for easy access.

Metabolic Syndrome, Type II Diabetes, Fatty Liver Disease, Heart Disease, Strokes, Obesity, Liver Cancer, Autoimmune Disorders, Homocysteine, Lungs Health, Pancreas Health, Kidneys Health, NCDs, Infectious Diseases, Brain Health, Dementia, Depression, Brain Atrophy, Neonatal Disorders, Skin Health, Dental Health, Bone Health, Leaky Gut, Leaky Brain, Brain Fog, Chronic Inflammation, Insulin Resistance, Elevated Cortisol, Leptin Resistance, Anabolic Resistance, Cholesterol, High Triglycerides, Metabolic Disorders, Gastrointestinal Disorders, Thyroid Disorders, Anemia, cardiac output, and major disorders.

I also wrote about valuable nutrients. Here are the links for easy access:

Lutein/Zeaxanthin, Phosphatidylserine, Boron, Urolithin, taurine, citrulline malate, biotin, lithium orotate, alpha-lipoic acid, n-acetyl-cysteine, acetyl-l-carnitine, CoQ10, PQQ, NADH, TMG, creatine, choline, digestive enzymes, magnesium, zinc, hydrolyzed collagen, nootropics, pure nicotine, activated charcoal, Vitamin B12, Vitamin B1, Vitamin D, Vitamin K2, Omega-3 Fatty Acids, N-Acetyl L-Tyrosine, Cod Liver Oil, and other nutrients to improve metabolism and mental health.

Disclaimer: My posts do not include professional or health advice. I document my reviews, observations, experience, and perspectives only to provide information and create awareness.

I publish my lifestyle, health, and well-being stories on EUPHORIA. My focus is on metabolic, cellular, mitochondrial, and mental health. Here is my collection of Insightful Life Lessons from Personal Stories.

If you enjoy writing and storytelling, you can join Medium, NewsBreak, and Vocal as a creator to find your voice, reach out to a broad audience, and monetize your content.

You may also check my blog posts about my articles and other writers’ articles contributing to my Medium publications. I share them on my website, digitalmehmet.com. Here is my professional bio. You can contact me via weblink.

As a writer, blogger, content developer, and reader, you might join Medium, Vocal Media, NewsBreak, Medium Writing Superstars, Writing Paychecks, WordPress, and Thinkers360 with my referral links. These affiliate links will not cost you extra to join the services.

You might join my six publications on Medium as a writer by sending a request via this link. 124K+ writers contribute to my publications. You might find more information about my professional background.