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The terms Artificial Intelligence (AI) and Cognitive Computing (CC) are often used interchangeably but there the approaches and objectives of each differ. The two topics are closely aligned; while they are not mutually exclusive, both have distinctive purposes and applications due to their practical, industrial, and commercial appeal as well as their respective challenges amongst academia, engineering, and research communities. This topic is explored further in my book, “Understanding the Role of AI and its Future Social Impact”. Before we get into too much detail, let’s start by defining each of these terms. We will then explore what they have in common, their differences and typical uses. AI has been studied for decades and, despite threatening to disrupt everything human, remains one of the least understood subjects in computer science. We use it every day without even noticing it. Google Maps applies it to provide directions. Gmail applies it to locate spam. Spotify, Netflix and others apply intelligent customer service via automatic response systems. As the popularity of AI grows, there remains a misunderstanding of the technical jargon that comes with it. In simple terms, AI is an umbrella term that includes a diverse array of sub-topics such as machine learning, natural language processing, expert systems, computer vision, computer speech, planning and robotics. In the words the person who coined the term artificial intelligence, John McCarthy, AI is “the science and engineering of making intelligent machines”. In layman’s terms, AI is an understanding that is achieved by machines that interpret, mine and learn from external data in ways that the machine functionally imitates the cognitive processes of a human. These processes include learning from constantly changing data, reasoning to make sense of the data and related self-correction mechanisms. Human intelligence is rooted in sensing the environment, learning from it and processing its information. Thus, AI includes (1) A simulation of human senses: sight, hearing, smell, taste and touch (2) A simulation of learning and processing: deep learning, ML, etc. (3) Simulations of human responses: robotics AI applications includes problem-solving, game playing, natural language processing (NLP), speech recognition, image processing, automatic programming and robotics. Cognitive computing refers to the development of computer systems based on mimicking human brains. It is a science that was developed to train computers to think by analysing, interpreting, reasoning and learning without constant human involvement. Cognitive computing represents the third era of computing. In the first era (19th century) Charles Babbage, the ‘father of the computer’, introduced the concept of a programmable machine. Used for navigational calculation, his computer tabulated polynomial functions. The second era (1950s) resulted in digital programming computers and ushered an era of modern computing and programmable systems. Cognitive computing utilises deep-learning algorithms and big-data analytics to provide insights. The “brain” of a cognitive system is a neural network the fundamental concept behind deep learning. A neural network is a system of hardware and software that mimics the central nervous system of humans to estimate functions that depend on a huge amount of unknown or learned inputs. By the 1980s, two trends affected the way experts and researchers began to unpack ‘the black box’ of the neural approaches to studying, thinking and learning. This was the advent of computing and cognitive sciences. Thus, cognitive computing refers to (1) Understanding and simulating reasoning (2) Understanding and simulating human behaviour (3) Using CC systems, we can make better human decisions at work. Applications include speech recognition, sentiment analysis, face detection, risk assessment and fraud detection. Now that we've provided some definitions, let's move on and describe the differences and similarities of AI and Cognitive computing. First the differences. This includes (1) Augmentation AI augments human thinking to solve complex problems. It focuses on accurately reflecting reality and providing accurate results. Cognitive computing tries to replicate how humans solve problems, whereas AI seeks to create new ways to solve problems potentially better than humans. (2) Mimicry Cognitive computing focuses on mimicking human behaviour and reasoning to solve complex problems. AI is not intended to mimic human thoughts and processes but are instead to solve problems using the best possible algorithms. (3) Decision-making Cognitive computing is not responsible for making the decisions of humans. They simply provide intelligent information for humans to use to make better decisions. AI is responsible for making decisions on their own while minimising the role of humans. How about similarities? Well, this includes (1) Technologies The technologies behind Cognitive computing are similar to those behind AI, including ML, deep learning, natural language processing, neural networks, and so son. In the real world, applications for Cognitive computing are often different than those for AI. (2) Industrial Use AI is important for service-oriented industries, such as healthcare, manufacturing and customer service. Cognitive computing is important in analysis intensive industries, such as finance, marketing, government and healthcare. (3) Human decision-making People do not fear Cognitive computing because it simply supplements human decision-making. However, people DO fear AI systems will displace human decision-making when used in conjunction with Cognitive computing. Think about it. The middle-man is now humans, who still make the decisions. Do we need to cut out the middle-man and replace him/her with AI to facilitate optimal decision making? I'd like to switch focus now and share a few observations. Calling Cognitive computing a form AI is not wrong, but it misses a fundamental distinction that is important to understand. When we talk about AI, we are most often talking about an incredibly sophisticated algorithm that includes some form of complex decision tree. This is how autonomous vehicles work: they take a starting point and a destination as input and navigate between the two points through a mind-bogglingly long sequence of ‘if-then-else’ statements. AI enables computers to do intelligent things. The possible applications for AI are quite extensive and already are fully embedded into our daily routines. For example, AI and fully autonomous vehicles are an inseparable part of the future. ‘AI’ watches countless hours of driving footage for training and is assigned variables that enable them to identify lanes, other cars and pedestrians and then to provide decision results nearly instantly. Cognitive computing, while a handy marketing term, helps solve problems by augmenting human intelligence and decision making, not by replacing it. Several AI fundamentals are included, such as machine learning, neural networks, natural language processing, contextual awareness and sentiment analysis, to augment problem-solving that humans constantly need. This is why IBM defines Cognitive computing as ‘systems that learn at scale, reason with purpose and interact with humans naturally’. The main driver and common thread across the topics of AI and Cognitive computing is ‘data’. Without these technologies, there is not much we can do with data. Hence a renewed push in areas of advanced analytics, giving rise to solutions that improve predictability in areas where silos exist, decision making via visualised dashboards that draw upon real-time and historical data made possible via the improved handling of unstructured data. We're coming to the end of this episode. Let's wrap this up! To summarise, AI empowers computer systems to be smart (and perhaps smarter than humans). Conversely, Cognitive computing includes individual technologies that perform specific tasks that facilitate and augment human intelligence. When the benefits of both AI and Cognitive computing are combined within a single system, operating from the same sets of data and the same real-time variables, they have the potential to enrich humans, society, and our world. Although countries such as China may be ‘leading the race’ in many areas related to AI, the question of combining emerging technologies with Cognitive computing and AI is one that, if done ethically with social good as the focus, could lead to many societal benefits that empower individuals, communities, institutions, businesses and governments throughout the world while driving competition, research and development. It is undeniable that Covid-19 has transformed the lives of humans everywhere. We have been forced to quickly adapt to these ‘new norms’ due to the pandemic. On a positive note, we have all learnt valuable life lessons and become more resilient. Let’s work together to create a digitally-driven civil society underpinned by socially minded technology. That concludes this episode. Thank you for listening. Until the next time. Be safe, well and keep listening .. and learning!