Modern day Cognitive Computing date back to the late 19th century, with the work of mathematician George Boole and his book The Laws of Thought, and the propositions of Charles Babbage on creating what he termed an "analytical engine." The term Artificial Intelligence (AI) was coined by the late John McCarthy in 1955 (revised in 2007), when he defined AI as "the science and engineering of making intelligent machines." Artificial intelligence has been a far-flung goal of computing since the conception of the computer, but we may be getting closer than ever with new cognitive computing models. While computers have been faster at calculations and processing than humans for decades, they haven't been able to accomplish tasks that humans take for granted as simple, like understanding natural language, or recognizing unique objects in an image. The study of AI really began to excel during the 1980s when funding increased considerably over previous decades to develop new technologies into Machine Learning and AI. Then on May 11, 1997 the world's imagination was captivated when IBM's Deep Blue beat Garry Kasparov, the current world chess champion.

We present the reinterpreted and reimplemented Copycat project, an architecture solving letter analogy domain problems. To support a flexible implementation change and rigor testing process, we propose a implementation method in DrRacket by using functional abstraction, naming system, initialization, and structural reference. Finally, benefits and limitations are analyzed for cognitive architectures along the lines of Copycat.

In the complex, diverse insurance industry, it can be hard to reconcile theory and practice. Adapting to new processes, systems, and strategies is always challenging. However, with the arrival of new opportunities driven insurtech, cultural transformation will go smoother. Insurance companies that are considering how to plug into the insurtech landscape should understand the various models within the innovation ecosystem. Carriers have to weigh their options carefully before choosing between incubators and accelerators, or venture capital and partnerships, when creating their best internal and external teams.

Between AI-powered chatbots and gadget-based voice assistants, cognitive computing capabilities have captured the public imagination. But consumer products are just the tip of the iceberg. Under the surface, cognitive computing adoption in the business world is growing rapidly. In fact, IDC predicts that global spending on cognitive systems will grow to more than $31 billion by 2019. And within the next two years alone, half of all consumers are expected to interact with cognitive technology on a regular basis, even if they don't realize it.

Artificial Intelligence (AI) has long pursued models, theories, and techniques to imbue machines with human-like general intelligence. Yet even the currently predominant data-driven approaches in AI seem to be lacking humans' unique ability to solve wide ranges of problems. This situation begs the question of the existence of principles that underlie general problem-solving capabilities. We approach this question through the mathematical formulation of analogies across different problems and solutions. We focus in particular on problems that could be represented as tree-like structures. Most importantly, we adopt a category-theoretic approach in formalising tree problems as categories, and in proving the existence of equivalences across apparently unrelated problem domains. We prove the existence of a functor between the category of tree problems and the category of solutions. We also provide a weaker version of the functor by quantifying equivalences of problem categories using a metric on tree problems.

My colleagues and I had the privilege of attending Tata Consultancy Services' (TCS) Analyst Day event held in Boston on September 7, 2018. There were several interesting and informative presentations covering the concepts of Business 4.0 and MachineFirst Philosophy and how TCS is leveraging its unparalleled domain expertise and its deep and vast portfolio of services and solutions to provide its customers with exponential value to create abundance through mass customization, leveraging ecosystems to help its customers embrace and manage risk while maximizing business outcomes using technologies and concepts such as Cloud, Intelligence, Automated and Agile. As one of the ARC Analysts focused on upstream and midstream oil & gas it was great to learn more about how TCS is leveraging'cognitive automation' through its solutions such as Ignio, an product that provides some very powerful horsepower through its self-learning capability, empowered by machine learning and artificial intelligence (AI), that can move customers from predictive maintenance to prescriptive maintenance, thereby extending the life (and availability) of an asset such as a pump or compressor and also optimizing that asset's performance and the process for which it is being utilized. MachineFirst Philosophy creates exponential value by empowering enterprise agility through reimagination and reinvention of a company's DNA. Harrick Vin, Vice President & Global Head of Ignio, explained that he envisions cognitive computing platforms such as Ignio, the Intelligent Machine", as being technology that is being augmented by people and one that is capable of learning over time.

The history of science and technology is often delineated by paradigm shifts. A paradigm shift is a fundamental change in how we view the world and our relationship to it. The big paradigm shifts are sometimes even referred to as an "age" or a "revolution". The Space Age is a perfect example. The middle of the 20th Century saw not only an incredible increase in public awareness of space and space travel, but many of the industrial and technical advances that we now take for granted were byproducts of the Space Age.

An enterprise's response to existing or emerging products depends upon its approach towards the two terms of machine thinking – artificial intelligence (AI) and cognitive computing. Is your enterprise an early adopter when it comes to leveraging technology to automate enterprise processes, configure chatbots, embed sensors or improve customer experience by drawing insights? Does your company stay on a constant lookout for fresh data sources coming via ever-increasing interactions or does it prefer to observe these changes from the sidelines, not sure of whether to adopt them or not? Three years ago, the C-suite was engaged in a fierce battle to bring disruptive technologies and innovations to the market. But today on an average, the C-suite is less inclined to view competition from outside the industry as a threat and is focusing extensively on searching for innovation externally among partners.

Tamr – Founded in 2013 by Vertica's founding CEO Andy Palmer and Turing Award winner and MIT professor Michael Stonebraker, Tamr provides a patented software platform for enterprise-scale data unification that combines ML with human expertise--fusing the power of ML with the experts' knowledge of their own data to automate the rapid unification of data silos at scale.

And a pinnacle of success in the context of technological advancements is the field of artificial intelligence, also called AI. In current times, AI is utilized immensely in a variety of sectors such as health, e-commerce, retail, automotive, defense and security, chemical plants, packaging, construction, and BFSI. This indicates that AI has found immense potential in myriad applications, thus giving rise to the global artificial intelligence and cognitive computing market. Rising disposable incomes and changing lifestyles have primarily caused an increase in the demand for enhanced systems to exist in various walks of life. Such a high demand is propelling the market to experience extensive growth too. A phenomenal progress in the development of computers as well as internet facilities has primarily been responsible for improving different functions of systems, wherein a computer is highly needed.