Data Science: Back to the Future I (About Beginnings)

Data Science Series

I've attended again, after several years, a webcast on performance improvement in SQL Server with Claudio Silva, “Writing T-SQL code for the engine, not for you”. The session was great and I really enjoyed it! I recommend it to any data(base) professional, even if some of the scenarios presented should be known already.

It's strange to see the same topics from 20-25 years ago reappearing over and over again despite the advancements made in the area of database engines. Each version of SQL Server brought something new in what concerns the performance, though without some good experience and understanding of the basic optimization and troubleshooting techniques there's little overall improvement for the average data professional in terms of writing and tuning queries!

Especially with the boom of Data Science topics, the volume of material on SQL increased considerably and many discover how easy is to write queries, even if the start might be challenging for some. Writing a query is easy indeed, though writing a performant query requires besides the language itself also some knowledge about the database engine and the various techniques used for troubleshooting and optimization. It's not about knowing in advance what the engine will do - the engine will often surprise you - but about knowing what techniques work, in what cases, which are their advantages and disadvantages, respectively on how they might impact the processing.

Making a parable with writing literature, it's not enough to speak a language; one needs more for becoming a writer, and there are so many levels of mastery! However, in database world even if creativity is welcomed, its role is considerable diminished by the constraints existing in the database engine, the problems to be solved, the time and the resources available. More important, one needs to understand some of the rules and know how to use the building blocks to solve problems and build reliable solutions.

The learning process for newbies focuses mainly on the language itself, while the exposure to complexity is kept to a minimum. For some learners the problems start when writing queries based on multiple tables -  what joins to use, in what order, how to structure the queries, what database objects to use for encapsulating the code, etc. Even if there are some guidelines and best practices, the learner must walk the path and experiment alone or in an organized setup.

In university courses the focus is on operators algebras, algorithms, on general database technologies and architectures without much hand on experience. All is too theoretical and abstract, which is acceptable for research purposes,  but not for the contact with the real world out there! Probably some labs offer exposure to real life scenarios, though what to cover first in the few hours scheduled for them?

This was the state of art when I started to learn SQL a quarter century ago, and besides the current tendency of cutting corners, the increased confidence from doing some tests, and the eagerness of shouting one’s shaking knowledge and more or less orthodox ideas on the various social networks, nothing seems to have changed! Something did change – the increased complexity of the problems to solve, and, considering the recent technological advances, one can afford now an AI learn buddy to write some code for us based on the information provided in the prompt.

This opens opportunities for learning and growth. AI can be used in the learning process by providing additional curricula for learners to dive deeper in some topics. Moreover, it can help us in time to address the challenges of the ever-increase complexity of the problems.

Data Management: Data Literacy (Part I: A Second Language)

At the Gartner Data & Analytics Summit that took place in 2018 in Grapevine, Texas, it was reiterated the importance of data literacy for taking advantage of the emergence of data analytics, artificial intelligence (AI) and machine learning (ML) technologies. Gartner expected then that by 2020, 80% of organizations will initiate deliberate competency development in the field of data literacy [1] – or how they put it – learning to ‘speak data’ as a ‘second language’.

Data literacy is typically defined as the ability to read, work with, analyze, and argue with data. Sure, these form the blocks of data literacy, though what I’m missing from this definition is the ability to understand the data, even if understanding should be the outcome of reading, and the ability to put data into the context of business problems, even if the analyzes of data could involve this later aspect too.

Understanding has several aspects: understanding the data structures available within an organization, understanding the problems with data (including quality, governance, privacy and security), respectively understanding how the data are linked to the business processes. These aspects go beyond the simple ability included in the above definition, which from my perspective doesn’t include the particularities of an organization (data structure, data quality and processes) – the business component. This is reflected in one of the problems often met in the BI/data analytics industry – the solutions developed by the various service providers don’t reflect organizations’ needs, one of the causes being the inability to understand the business on segments or holistically.  

Putting data into context means being able to use the respective data in answering stringent business problems. A business problem needs to be first correctly defined and this requires a deep understanding of the business. Then one needs to identify the data that could help finding the answers to the problem, respectively of building one or more models that would allow elaborating further theories and performing further simulations. This is an ongoing process in which the models built are further enhanced, when possible, or replaced by better ones.

Probably the comparison with a second language is only partially true. One can learn a second language and argue in the respective language, though it doesn’t mean that the argumentations will be correct or constructive as long the person can’t do the same in the native language. Moreover, one can have such abilities in the native or a secondary language, but not be able do the same in what concerns the data, as different skillsets are involved. This aspect can make quite a difference in a business scenario. One must be able also to philosophize, think critically, as well to understand the forms of communication and their rules in respect to data.

To philosophize means being able to understand the causality and further relations existing within the business and think critically about them. Being able to communicate means more than being able to argue – it means being able to use effectively the communication tools – communication channels, as well the methods of representing data, information and knowledge. In extremis one might even go beyond the basic statistical tools, stepping thus in what statistical literacy is about. In fact, the difference between the two types of literacy became thinner, the difference residing in the accent put on their specific aspects.

These are the areas which probably many professionals lack. Data literacy should be the aim, however this takes time and is a continuous iterative process that can take years to reach maturity. It’s important for organizations to start addressing these aspects, progress in small increments and learn from the experience accumulated.

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References:
[1] Gartner (2018) How data and analytics leaders learn to master information as a second language, by Christy Pettey (link) 

Data Science: Big Data (Just the Quotes)

"If we gather more and more data and establish more and more associations, however, we will not finally find that we know something. We will simply end up having more and more data and larger sets of correlations." (Kenneth N Waltz, "Theory of International Politics Source: Theory of International Politics", 1979)

“There are those who try to generalize, synthesize, and build models, and there are those who believe nothing and constantly call for more data. The tension between these two groups is a healthy one; science develops mainly because of the model builders, yet they need the second group to keep them honest.” (Andrew Miall, “Principles of Sedimentary Basin Analysis”, 1984)

"Big Data is data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or doesn’t fit the strictures of your database architectures. To gain value from this data, you must choose an alternative way to process it." (Edd Wilder-James, "What is big data?", 2012) [source]

"The secret to getting the most from Big Data isn’t found in huge server farms or massive parallel computing or in-memory algorithms. Instead, it’s in the almighty pencil." (Matt Ariker, "The One Tool You Need To Make Big Data Work: The Pencil", 2012)

"Big data is the most disruptive force this industry has seen since the introduction of the relational database." (Jeffrey Needham, "Disruptive Possibilities: How Big Data Changes Everything", 2013)

"No subjective metric can escape strategic gaming [...] The possibility of mischief is bottomless. Fighting ratings is fruitless, as they satisfy a very human need. If one scheme is beaten down, another will take its place and wear its flaws. Big Data just deepens the danger. The more complex the rating formulas, the more numerous the opportunities there are to dress up the numbers. The larger the data sets, the harder it is to audit them." (Kaiser Fung, "Numbersense: How To Use Big Data To Your Advantage", 2013)

"There is convincing evidence that data-driven decision-making and big data technologies substantially improve business performance. Data science supports data-driven decision-making - and sometimes conducts such decision-making automatically - and depends upon technologies for 'big data' storage and engineering, but its principles are separate." (Foster Provost & Tom Fawcett, "Data Science for Business", 2013)

"Our needs going forward will be best served by how we make use of not just this data but all data. We live in an era of Big Data. The world has seen an explosion of information in the past decades, so much so that people and institutions now struggle to keep pace. In fact, one of the reasons for the attachment to the simplicity of our indicators may be an inverse reaction to the sheer and bewildering volume of information most of us are bombarded by on a daily basis. […] The lesson for a world of Big Data is that in an environment with excessive information, people may gravitate toward answers that simplify reality rather than embrace the sheer complexity of it." (Zachary Karabell, "The Leading Indicators: A short history of the numbers that rule our world", 2014)

"The other buzzword that epitomizes a bias toward substitution is 'big data'. Today’s companies have an insatiable appetite for data, mistakenly believing that more data always creates more value. But big data is usually dumb data. Computers can find patterns that elude humans, but they don’t know how to compare patterns from different sources or how to interpret complex behaviors. Actionable insights can only come from a human analyst (or the kind of generalized artificial intelligence that exists only in science fiction)." (Peter Thiel & Blake Masters, "Zero to One: Notes on Startups, or How to Build the Future", 2014)

"We have let ourselves become enchanted by big data only because we exoticize technology. We’re impressed with small feats accomplished by computers alone, but we ignore big achievements from complementarity because the human contribution makes them less uncanny. Watson, Deep Blue, and ever-better machine learning algorithms are cool. But the most valuable companies in the future won’t ask what problems can be solved with computers alone. Instead, they’ll ask: how can computers help humans solve hard problems?" (Peter Thiel & Blake Masters, "Zero to One: Notes on Startups, or How to Build the Future", 2014)

"As business leaders we need to understand that lack of data is not the issue. Most businesses have more than enough data to use constructively; we just don't know how to use it. The reality is that most businesses are already data rich, but insight poor." (Bernard Marr, Big Data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance, 2015)

"Big data is based on the feedback economy where the Internet of Things places sensors on more and more equipment. More and more data is being generated as medical records are digitized, more stores have loyalty cards to track consumer purchases, and people are wearing health-tracking devices. Generally, big data is more about looking at behavior, rather than monitoring transactions, which is the domain of traditional relational databases. As the cost of storage is dropping, companies track more and more data to look for patterns and build predictive models." (Neil Dunlop, "Big Data", 2015)

"Big Data often seems like a meaningless buzz phrase to older database professionals who have been experiencing exponential growth in database volumes since time immemorial. There has never been a moment in the history of database management systems when the increasing volume of data has not been remarkable." (Guy Harrison, "Next Generation Databases: NoSQL, NewSQL, and Big Data", 2015)

"Dimensionality reduction is essential for coping with big data - like the data coming in through your senses every second. A picture may be worth a thousand words, but it’s also a million times more costly to process and remember. [...] A common complaint about big data is that the more data you have, the easier it is to find spurious patterns in it. This may be true if the data is just a huge set of disconnected entities, but if they’re interrelated, the picture changes." (Pedro Domingos, "The Master Algorithm", 2015)

"Science’s predictions are more trustworthy, but they are limited to what we can systematically observe and tractably model. Big data and machine learning greatly expand that scope. Some everyday things can be predicted by the unaided mind, from catching a ball to carrying on a conversation. Some things, try as we might, are just unpredictable. For the vast middle ground between the two, there’s machine learning." (Pedro Domingos, "The Master Algorithm", 2015)

"The human side of analytics is the biggest challenge to implementing big data." (Paul Gibbons, "The Science of Successful Organizational Change", 2015)

"To make progress, every field of science needs to have data commensurate with the complexity of the phenomena it studies. [...] With big data and machine learning, you can understand much more complex phenomena than before. In most fields, scientists have traditionally used only very limited kinds of models, like linear regression, where the curve you fit to the data is always a straight line. Unfortunately, most phenomena in the world are nonlinear. [...] Machine learning opens up a vast new world of nonlinear models." (Pedro Domingos, "The Master Algorithm", 2015)

"Underfitting is when a model doesn’t take into account enough information to accurately model real life. For example, if we observed only two points on an exponential curve, we would probably assert that there is a linear relationship there. But there may not be a pattern, because there are only two points to reference. [...] It seems that the best way to mitigate underfitting a model is to give it more information, but this actually can be a problem as well. More data can mean more noise and more problems. Using too much data and too complex of a model will yield something that works for that particular data set and nothing else." (Matthew Kirk, "Thoughtful Machine Learning", 2015)

"We are moving slowly into an era where Big Data is the starting point, not the end." (Pearl Zhu, "Digital Master: Debunk the Myths of Enterprise Digital Maturity", 2015)

"A popular misconception holds that the era of Big Data means the end of a need for sampling. In fact, the proliferation of data of varying quality and relevance reinforces the need for sampling as a tool to work efficiently with a variety of data, and minimize bias. Even in a Big Data project, predictive models are typically developed and piloted with samples." (Peter C Bruce & Andrew G Bruce, "Statistics for Data Scientists: 50 Essential Concepts", 2016)

"Big data is, in a nutshell, large amounts of data that can be gathered up and analyzed to determine whether any patterns emerge and to make better decisions." (Daniel Covington, Analytics: Data Science, Data Analysis and Predictive Analytics for Business, 2016)

"Big Data processes codify the past. They do not invent the future. Doing that requires moral imagination, and that’s something only humans can provide. We have to explicitly embed better values into our algorithms, creating Big Data models that follow our ethical lead. Sometimes that will mean putting fairness ahead of profit." (Cathy O'Neil, "Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy", 2016)

"While Big Data, when managed wisely, can provide important insights, many of them will be disruptive. After all, it aims to find patterns that are invisible to human eyes. The challenge for data scientists is to understand the ecosystems they are wading into and to present not just the problems but also their possible solutions." (Cathy O'Neil, "Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy", 2016)

"Big Data allows us to meaningfully zoom in on small segments of a dataset to gain new insights on who we are." (Seth Stephens-Davidowitz, "Everybody Lies: What the Internet Can Tell Us About Who We Really Are", 2017)

"Effects without an understanding of the causes behind them, on the other hand, are just bunches of data points floating in the ether, offering nothing useful by themselves. Big Data is information, equivalent to the patterns of light that fall onto the eye. Big Data is like the history of stimuli that our eyes have responded to. And as we discussed earlier, stimuli are themselves meaningless because they could mean anything. The same is true for Big Data, unless something transformative is brought to all those data sets… understanding." (Beau Lotto, "Deviate: The Science of Seeing Differently", 2017)

"The term [Big Data] simply refers to sets of data so immense that they require new methods of mathematical analysis, and numerous servers. Big Data - and, more accurately, the capacity to collect it - has changed the way companies conduct business and governments look at problems, since the belief wildly trumpeted in the media is that this vast repository of information will yield deep insights that were previously out of reach." (Beau Lotto, "Deviate: The Science of Seeing Differently", 2017)

"There are other problems with Big Data. In any large data set, there are bound to be inconsistencies, misclassifications, missing data - in other words, errors, blunders, and possibly lies. These problems with individual items occur in any data set, but they are often hidden in a large mass of numbers even when these numbers are generated out of computer interactions." (David S Salsburg, "Errors, Blunders, and Lies: How to Tell the Difference", 2017)

"Just as they did thirty years ago, machine learning programs (including those with deep neural networks) operate almost entirely in an associational mode. They are driven by a stream of observations to which they attempt to fit a function, in much the same way that a statistician tries to fit a line to a collection of points. Deep neural networks have added many more layers to the complexity of the fitted function, but raw data still drives the fitting process. They continue to improve in accuracy as more data are fitted, but they do not benefit from the 'super-evolutionary speedup'."  (Judea Pearl & Dana Mackenzie, "The Book of Why: The new science of cause and effect", 2018)

"One of the biggest myths is the belief that data science is an autonomous process that we can let loose on our data to find the answers to our problems. In reality, data science requires skilled human oversight throughout the different stages of the process. [...] The second big myth of data science is that every data science project needs big data and needs to use deep learning. In general, having more data helps, but having the right data is the more important requirement. [...] A third data science myth is that modern data science software is easy to use, and so data science is easy to do. [...] The last myth about data science [...] is the belief that data science pays for itself quickly. The truth of this belief depends on the context of the organization. Adopting data science can require significant investment in terms of developing data infrastructure and hiring staff with data science expertise. Furthermore, data science will not give positive results on every project." (John D Kelleher & Brendan Tierney, "Data Science", 2018)

"Apart from the technical challenge of working with the data itself, visualization in big data is different because showing the individual observations is just not an option. But visualization is essential here: for analysis to work well, we have to be assured that patterns and errors in the data have been spotted and understood. That is only possible by visualization with big data, because nobody can look over the data in a table or spreadsheet." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"With the growing availability of massive data sets and user-friendly analysis software, it might be thought that there is less need for training in statistical methods. This would be naïve in the extreme. Far from freeing us from the need for statistical skills, bigger data and the rise in the number and complexity of scientific studies makes it even more difficult to draw appropriate conclusions. More data means that we need to be even more aware of what the evidence is actually worth." (David Spiegelhalter, "The Art of Statistics: Learning from Data", 2019)

"Big data is revolutionizing the world around us, and it is easy to feel alienated by tales of computers handing down decisions made in ways we don’t understand. I think we’re right to be concerned. Modern data analytics can produce some miraculous results, but big data is often less trustworthy than small data. Small data can typically be scrutinized; big data tends to be locked away in the vaults of Silicon Valley. The simple statistical tools used to analyze small datasets are usually easy to check; pattern-recognizing algorithms can all too easily be mysterious and commercially sensitive black boxes." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"Making big data work is harder than it seems. Statisticians have spent the past two hundred years figuring out what traps lie in wait when we try to understand the world through data. The data are bigger, faster, and cheaper these days, but we must not pretend that the traps have all been made safe. They have not." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"Many people have strong intuitions about whether they would rather have a vital decision about them made by algorithms or humans. Some people are touchingly impressed by the capabilities of the algorithms; others have far too much faith in human judgment. The truth is that sometimes the algorithms will do better than the humans, and sometimes they won’t. If we want to avoid the problems and unlock the promise of big data, we’re going to need to assess the performance of the algorithms on a case-by-case basis. All too often, this is much harder than it should be. […] So the problem is not the algorithms, or the big datasets. The problem is a lack of scrutiny, transparency, and debate." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"The problem is the hype, the notion that something magical will emerge if only we can accumulate data on a large enough scale. We just need to be reminded: Big data is not better; it’s just bigger. And it certainly doesn’t speak for itself." (Carl T Bergstrom & Jevin D West, "Calling Bullshit: The Art of Skepticism in a Data-Driven World", 2020)

"[...] the focus on Big Data AI seems to be an excuse to put forth a number of vague and hand-waving theories, where the actual details and the ultimate success of neuroscience is handed over to quasi- mythological claims about the powers of large datasets and inductive computation. Where humans fail to illuminate a complicated domain with testable theory, machine learning and big data supposedly can step in and render traditional concerns about finding robust theories. This seems to be the logic of Data Brain efforts today. (Erik J Larson, "The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do", 2021)

"Visualizations can remove the background noise from enormous sets of data so that only the most important points stand out to the intended audience. This is particularly important in the era of big data. The more data there is, the more chance for noise and outliers to interfere with the core concepts of the data set." (Kate Strachnyi, "ColorWise: A Data Storyteller’s Guide to the Intentional Use of Color", 2023)

"Visualisation is fundamentally limited by the number of pixels you can pump to a screen. If you have big data, you have way more data than pixels, so you have to summarise your data. Statistics gives you lots of really good tools for this." (Hadley Wickham)

Data Science: Deep Learning (Just the Quotes)

"Despite the enormous success of deep learning, relatively little is understood theoretically about why these techniques are so successful at feature learning and compression." (Pankaj Mehta & David J Schwab, "An exact mapping between the Variational Renormalization Group and Deep Learning", 2014)

"Deep learning is about using a stacked hierarchy of feature detectors. [...] we use pattern detectors and we build them into networks that are arranged in hundreds of layers and then we adjust the links between these layers, usually using some kind of gradient descent." (Joscha Bach, "Joscha: Computational Meta-Psychology", 2015)

"The power of deep learning models comes from their ability to classify or predict nonlinear data using a modest number of parallel nonlinear steps4. A deep learning model learns the input data features hierarchy all the way from raw data input to the actual classification of the data. Each layer extracts features from the output of the previous layer." (N D Lewis, "Deep Learning Made Easy with R: A Gentle Introduction for Data Science", 2016)

"Although deep learning systems share some similarities with machine learning systems, certain characteristics make them sufficiently distinct. For example, conventional machine learning systems tend to be simpler and have fewer options for training. DL systems are noticeably more sophisticated; they each have a set of training algorithms, along with several parameters regarding the systems’ architecture. This is one of the reasons we consider them a distinct framework in data science." (Yunus E Bulut & Zacharias Voulgaris, "AI for Data Science: Artificial Intelligence Frameworks and Functionality for Deep Learning, Optimization, and Beyond", 2018)

"Deep learning broadly describes the large family of neural network architectures that contain multiple, interacting hidden layers." (Benjamin Bengfort et al, Applied Text Analysis with Python, 2018)

"Deep learning has instead given us machines with truly impressive abilities but no intelligence. The difference is profound and lies in the absence of a model of reality." (Judea Pearl, "The Book of Why: The New Science of Cause and Effect", 2018)

"DL systems also tend to be more autonomous than their machine counterparts. To some extent, DL systems can do their own feature engineering. More conventional systems tend to require more fine-tuning of the feature-set, and sometimes require dimensionality reduction to provide any decent results. In addition, the generalization of conventional ML systems when provided with additional data generally don’t improve as much as DL systems. This is also one of the key characteristics that makes DL systems a preferable option when big data is involved." (Yunus E Bulut & Zacharias Voulgaris, "AI for Data Science: Artificial Intelligence Frameworks and Functionality for Deep Learning, Optimization, and Beyond", 2018)

"[…] deep learning has succeeded primarily by showing that certain questions or tasks we thought were difficult are in fact not. It has not addressed the truly difficult questions that continue to prevent us from achieving humanlike AI." (Judea Pearl & Dana Mackenzie, "The Book of Why: The new science of cause and effect", 2018)

"In essence, deep learning models are just chains of functions, which means that many deep learning libraries tend to have a functional or verbose, declarative style." (Benjamin Bengfort et al, Applied Text Analysis with Python, 2018)

"The second big myth of data science is that every data science project needs big data and needs to use deep learning. In general, having more data helps, but having the right data is the more important requirement" (John D Kelleher & Brendan Tierney, "Data Science", 2018)

"People who assume that extensions of modern machine learning methods like deep learning will somehow 'train up', or learn to be intelligent like humans, do not understand the fundamental limitations that are already known. Admitting the necessity of supplying a bias to learning systems is tantamount to Turing’s observing that insights about mathematics must be supplied by human minds from outside formal methods, since machine learning bias is determined, prior to learning, by human designers." (Erik J Larson, "The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do", 2021)

Data Science: Artificial Intelligence (Just the Quotes)

"There is no security against the ultimate development of mechanical consciousness, in the fact of machines possessing little consciousness now. A mollusc has not much consciousness. Reflect upon the extraordinary advance which machines have made during the last few hundred years, and note how slowly the animal and vegetable kingdoms are advancing. The more highly organized machines are creatures not so much of yesterday, as of the last five minutes, so to speak, in comparison with past time." (Samuel Butler, "Erewhon: Or, Over the Range", 1872)

"In other words then, if a machine is expected to be infallible, it cannot also be intelligent. There are several theorems which say almost exactly that. But these theorems say nothing about how much intelligence may be displayed if a machine makes no pretense at infallibility." (Alan M Turing, 1946)

"A computer would deserve to be called intelligent if it could deceive a human into believing that it was human." (Alan Turing, "Computing Machinery and Intelligence", 1950)

"The original question, 'Can machines think?:, I believe too meaningless to deserve discussion. Nevertheless I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted." (Alan M Turing, 1950) 

"The view that machines cannot give rise to surprises is due, I believe, to a fallacy to which philosophers and mathematicians are particularly subject. This is the assumption that as soon as a fact is presented to a mind all consequences of that fact spring into the mind simultaneously with it. It is a very useful assumption under many circumstances, but one too easily forgets that it is false. A natural consequence of doing so is that one then assumes that there is no virtue in the mere working out of consequences from data and general principles." (Alan Turing, "Computing Machinery and Intelligence", Mind Vol. 59, 1950)

"The following are some aspects of the artificial intelligence problem: […] If a machine can do a job, then an automatic calculator can be programmed to simulate the machine. […] It may be speculated that a large part of human thought consists of manipulating words according to rules of reasoning and rules of conjecture. From this point of view, forming a generalization consists of admitting a new word and some rules whereby sentences containing it imply and are implied by others. This idea has never been very precisely formulated nor have examples been worked out. […] How can a set of (hypothetical) neurons be arranged so as to form concepts. […] to get a measure of the efficiency of a calculation it is necessary to have on hand a method of measuring the complexity of calculating devices which in turn can be done. […] Probably a truly intelligent machine will carry out activities which may best be described as self-improvement. […] A number of types of 'abstraction' can be distinctly defined and several others less distinctly. […] the difference between creative thinking and unimaginative competent thinking lies in the injection of a some randomness. The randomness must be guided by intuition to be efficient." (John McCarthy et al, "A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence", 1955)

"We shall therefore say that a program has common sense if it automatically deduces for itself a sufficient wide class of immediate consequences of anything it is told and what it already knows. [...] Our ultimate objective is to make programs that learn from their experience as effectively as humans do." (John McCarthy, "Programs with Common Sense", 1958)

"Although it sounds implausible, it might turn out that above a certain level of complexity, a machine ceased to be predictable, even in principle, and started doing things on its own account, or, to use a very revealing phrase, it might begin to have a mind of its own." (John R Lucas, "Minds, Machines and Gödel", 1959)

"When intelligent machines are constructed, we should not be surprised to find them as confused and as stubborn as men in their convictions about mind-matter, consciousness, free will, and the like." (Marvin Minsky, "Matter, Mind, and Models", Proceedings of the International Federation of Information Processing Congress Vol. 1 (49), 1965)

"Artificial intelligence is the science of making machines do things that would require intelligence if done by men." (Marvin Minsky, 1968)

"There are now machines in the world that think, that learn and create. Moreover, their ability to do these things is going to increase rapidly until - in the visible future - the range of problems they can handle will be coextensive with the range to which the human mind has been applied." (Allen Newell & Herbert A Simon, "Human problem solving", 1976)

"Intelligence has two parts, which we shall call the epistemological and the heuristic. The epistemological part is the representation of the world in such a form that the solution of problems follows from the facts expressed in the representation. The heuristic part is the mechanism that on the basis of the information solves the problem and decides what to do." (John McCarthy & Patrick J Hayes, "Some Philosophical Problems from the Standpoint of Artificial Intelligence", Machine Intelligence 4, 1969)

"It is essential to realize that a computer is not a mere 'number cruncher', or supercalculating arithmetic machine, although this is how computers are commonly regarded by people having no familiarity with artificial intelligence. Computers do not crunch numbers; they manipulate symbols. [...] Digital computers originally developed with mathematical problems in mind, are in fact general purpose symbol manipulating machines." (Margaret A Boden, "Minds and mechanisms", 1981)

"The basic idea of cognitive science is that intelligent beings are semantic engines - in other words, automatic formal systems with interpretations under which they consistently make sense. We can now see why this includes psychology and artificial intelligence on a more or less equal footing: people and intelligent computers (if and when there are any) turn out to be merely different manifestations of the same underlying phenomenon. Moreover, with universal hardware, any semantic engine can in principle be formally imitated by a computer if only the right program can be found." (John Haugeland, "Semantic Engines: An introduction to mind design", 1981)

"The digital-computer field defined computers as machines that manipulated numbers. The great thing was, adherents said, that everything could be encoded into numbers, even instructions. In contrast, scientists in AI [artificial intelligence] saw computers as machines that manipulated symbols. The great thing was, they said, that everything could be encoded into symbols, even numbers." (Allen Newell, "Intellectual Issues in the History of Artificial Intelligence", 1983)

"Artificial intelligence is based on the assumption that the mind can be described as some kind of formal system manipulating symbols that stand for things in the world. Thus it doesn't matter what the brain is made of, or what it uses for tokens in the great game of thinking. Using an equivalent set of tokens and rules, we can do thinking with a digital computer, just as we can play chess using cups, salt and pepper shakers, knives, forks, and spoons. Using the right software, one system (the mind) can be mapped onto the other (the computer)." (George Johnson, Machinery of the Mind: Inside the New Science of Artificial Intelligence, 1986)

"Cybernetics is simultaneously the most important science of the age and the least recognized and understood. It is neither robotics nor freezing dead people. It is not limited to computer applications and it has as much to say about human interactions as it does about machine intelligence. Today’s cybernetics is at the root of major revolutions in biology, artificial intelligence, neural modeling, psychology, education, and mathematics. At last there is a unifying framework that suspends long-held differences between science and art, and between external reality and internal belief." (Paul Pangaro, "New Order From Old: The Rise of Second-Order Cybernetics and Its Implications for Machine Intelligence", 1988)

"The cybernetics phase of cognitive science produced an amazing array of concrete results, in addition to its long-term (often underground) influence: the use of mathematical logic to understand the operation of the nervous system; the invention of information processing machines (as digital computers), thus laying the basis for artificial intelligence; the establishment of the metadiscipline of system theory, which has had an imprint in many branches of science, such as engineering (systems analysis, control theory), biology (regulatory physiology, ecology), social sciences (family therapy, structural anthropology, management, urban studies), and economics (game theory); information theory as a statistical theory of signal and communication channels; the first examples of self-organizing systems. This list is impressive: we tend to consider many of these notions and tools an integrative part of our life […]" (Francisco Varela, "The Embodied Mind", 1991)

"The deep paradox uncovered by AI research: the only way to deal efficiently with very complex problems is to move away from pure logic. [...] Most of the time, reaching the right decision requires little reasoning.[...] Expert systems are, thus, not about reasoning: they are about knowing. [...] Reasoning takes time, so we try to do it as seldom as possible. Instead we store the results of our reasoning for later reference." (Daniel Crevier, "The Tree of Knowledge", 1993)

"The insight at the root of artificial intelligence was that these 'bits' (manipulated by computers) could just as well stand as symbols for concepts that the machine would combine by the strict rules of logic or the looser associations of psychology." (Daniel Crevier, "AI: The tumultuous history of the search for artificial intelligence", 1993)

"Artificial intelligence comprises methods, tools, and systems for solving problems that normally require the intelligence of humans. The term intelligence is always defined as the ability to learn effectively, to react adaptively, to make proper decisions, to communicate in language or images in a sophisticated way, and to understand." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"But intelligence is not just a matter of acting or behaving intelligently. Behavior is a manifestation of intelligence, but not the central characteristic or primary definition of being intelligent. A moment's reflection proves this: You can be intelligent just lying in the dark, thinking and understanding. Ignoring what goes on in your head and focusing instead on behavior has been a large impediment to understanding intelligence and building intelligent machines." (Jeff Hawkins, "On Intelligence", 2004)

"The brain and its cognitive mental processes are the biological foundation for creating metaphors about the world and oneself. Artificial intelligence, human beings’ attempt to transcend their biology, tries to enter into these scenarios to learn how they function. But there is another metaphor of the world that has its own particular landscapes, inhabitants, and laws. The brain provides the organic structure that is necessary for generating the mind, which in turn is considered a process that results from brain activity." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"From a historical viewpoint, computationalism is a sophisticated version of behaviorism, for it only interpolates the computer program between stimulus and response, and does not regard novel programs as brain creations. [...] The root of computationalism is of course the actual similarity between brains and computers, and correspondingly between natural and artificial intelligence. The two are indeed similar because the artifacts in question have been designed to perform analogs of certain brain functions. And the computationalist program is an example of the strategy of treating similars as identicals." (Mario Bunge, "Matter and Mind: A Philosophical Inquiry", 2010)

"Artificial intelligence is a concept that obscures accountability. Our problem is not machines acting like humans - it's humans acting like machines." (John Twelve Hawks, "Spark", 2014)

"AI failed (at least relative to the hype it had generated), and it’s partly out of embarrassment on behalf of their discipline that the term 'artificial intelligence' is rarely used in computer science circles (although it’s coming back into favor, just without the over-hyping). We are as far away from mimicking human intelligence as we have ever been, partly because the human brain is fantastically more complicated than a mere logic engine." (Field Cady, "The Data Science Handbook", 2017)

"AI ever allows us to truly understand ourselves, it will not be because these algorithms captured the mechanical essence of the human mind. It will be because they liberated us to forget about optimizations and to instead focus on what truly makes us human: loving and being loved." (Kai-Fu Lee, "AI Superpowers: China, Silicon Valley, and the New World Order", 2018)

"Artificial intelligence is defined as the branch of science and technology that is concerned with the study of software and hardware to provide machines the ability to learn insights from data and the environment, and the ability to adapt in changing situations with high precision, accuracy and speed." (Amit Ray, "Compassionate Artificial Intelligence", 2018)

"Artificial Intelligence is not just learning patterns from data, but understanding human emotions and its evolution from its depth and not just fulfilling the surface level human requirements, but sensitivity towards human pain, happiness, mistakes, sufferings and well-being of the society are the parts of the evolving new AI systems." (Amit Ray, "Compassionate Artificial Intelligence", 2018)

"Artificial intelligence is the elucidation of the human learning process, the quantification of the human thinking process, the explication of human behavior, and the understanding of what makes intelligence possible." (Kai-Fu Lee, "AI Superpowers: China, Silicon Valley, and the New World Order", 2018) 

"AI won‘t be fool proof in the future since it will only as good as the data and information that we give it to learn. It could be the case that simple elementary tricks could fool the AI algorithm and it may serve a complete waste of output as a result." (Zoltan Andrejkovics, "Together: AI and Human. On the Same Side", 2019)

"It is the field of artificial intelligence in which the population is in the form of agents which search in a parallel fashion with multiple initialization points. The swarm intelligence-based algorithms mimic the physical and natural processes for mathematical modeling of the optimization algorithm. They have the properties of information interchange and non-centralized control structure." (Sajad A Rather & P Shanthi Bala, "Analysis of Gravitation-Based Optimization Algorithms for Clustering and Classification", 2020)

"A significant factor missing from any form of artificial intelligence is the inability of machines to learn based on real life experience. Diversity of life experience is the single most powerful characteristic of being human and enhances how we think, how we learn, our ideas and our ability to innovate. Machines exist in a homogeneous ecosystem, which is ok for solving known challenges, however even Artificial General Intelligence will never challenge humanity in being able to acquire the knowledge, creativity and foresight needed to meet the challenges of the unknown." (Tom Golway, 2021)

"AI is intended to create systems for making probabilistic decisions, similar to the way humans make decisions. […] Today’s AI is not very able to generalize. Instead, it is effective for specific, well-defined tasks. It struggles with ambiguity and mostly lacks transfer learning that humans take for granted. For AI to make humanlike decisions that are more situationally appropriate, it needs to incorporate context." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)

"In an era of machine learning, where data is likely to be used to train AI, getting quality and governance under control is a business imperative. Failing to govern data surfaces problems late, often at the point closest to users (for example, by giving harmful guidance), and hinders explainability (garbage data in, machine-learned garbage out)." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)

"Many AI systems employ heuristic decision making, which uses a strategy to find the most likely correct decision to avoid the high cost (time) of processing lots of information. We can think of those heuristics as shortcuts or rules of thumb that we would use to make fast decisions." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)

"We think of context as the network surrounding a data point of interest that is relevant to a specific AI system. […] AI benefits greatly from context to enable probabilistic decision making for real-time answers, handle adjacent scenarios for broader applicability, and be maximally relevant to a given situation. But all systems, including AI, are only as good as their inputs." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)

"Every machine has artificial intelligence. And the more advanced a machine gets, the more advanced artificial intelligence gets as well. But, a machine cannot feel what it is doing. It only follows instructions - our instructions - instructions of the humans. So, artificial intelligence will not destroy the world. Our irresponsibility will destroy the world." (Abhijit Naskar)

More quotes on "Artificial Intelligence" at the-web-of-knowledge.blogspot.com.

Data Science: Convolutional Neural Network (Definitions)

"A multi layer neural network similar to artificial neural networks only differs in its architecture and mainly built to recognize visual patterns from image pixels." (Nishu Garg et al, "An Insight Into Deep Learning Architectures, Latent Query Features", 2018)

"In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that has successfully been applied to analyzing visual imagery. CNNs use a variation of multilayer perceptrons designed to require minimal preprocessing. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on their shared-weights architecture and translation invariance characteristics." (V E Jayanthi, "Automatic Detection of Tumor and Bleed in Magnetic Resonance Brain Images", 2018)

"A special type of feed-forward neural network optimized for image data processing. The key features of CNN architecture include sharing weights, using pooling layers, implementing deep structures with multiple hidden layers." (Lyudmila N. Tuzova et al, "Teeth and Landmarks Detection and Classification Based on Deep Neural Networks", 2019)

"A type of artificial neural networks, which uses a set of filters with tunable (learnable) parameters to extract local features from the input data." (Sergei Savin & Aleksei Ivakhnenko, "Enhanced Footsteps Generation Method for Walking Robots Based on Convolutional Neural Networks", 2019) 

"A convolutional neural network (CNN) is a type of artificial neural network used in image recognition and processing that is specifically designed to process pixel data by means of learnable filters." (Loris Nanni et al, "Digital Recognition of Breast Cancer Using TakhisisNet: An Innovative Multi-Head Convolutional Neural Network for Classifying Breast Ultrasonic Images", 2020)

"A convolutional neural network (CNN) is a type of artificial neural network used in image recognition and processing that is specifically designed to process pixel data. CNNs are powerful image processing, artificial intelligence (AI) that use deep learning to perform both generative and descriptive tasks, often using machine vision that includes image and video recognition, along with recommender systems and natural language processing (NLP)." (Mohammad F Hashmi et al, "Subjective and Objective Assessment for Variation of Plant Nitrogen Content to Air Pollutants Using Machine Intelligence", 2020)

"A neural network with a convolutional layer which does the mathematical operation of convolution in addition to the other layers of deep neural network." (S Kayalvizhi & D Thenmozhi, "Deep Learning Approach for Extracting Catch Phrases from Legal Documents", 2020)

"A special type of neural networks used popularly to analyze photography and imagery." (Murad Al Shibli, "Hybrid Artificially Intelligent Multi-Layer Blockchain and Bitcoin Cryptology", 2020)

"In deep learning, a convolutional neural network is a class of deep neural networks, most commonly applied to analyzing visual imagery. CNNs use a variation of multilayer perceptrons designed to require minimal preprocessing." (R Murugan, "Implementation of Deep Learning Neural Network for Retinal Images", 2020)

"A class of deep neural networks applied to image processing where some of the layers apply convolutions to input data." (Mário P Véstias, "Convolutional Neural Network", 2021)

"A convolution neural network is a kind of ANN used in image recognition and processing of image data." (M Srikanth Yadav & R Kalpana, "A Survey on Network Intrusion Detection Using Deep Generative Networks for Cyber-Physical Systems", 2021)

"A multi-layer neural network similar to artificial neural networks only differs in its architecture and mainly built to recognize visual patterns from image pixels." (Udit Singhania & B K Tripathy, "Text-Based Image Retrieval Using Deep Learning", 2021) 

"A type of deep learning algorithm commonly applied in analyzing image inputs." (Jinnie Shin et al, "Automated Essay Scoring Using Deep Learning Algorithms", 2021)

"It is a class of deep neural networks, most commonly applied to analyzing visual imagery." (Sercan Demirci et al, "Detection of Diabetic Retinopathy With Mobile Application Using Deep Learning", 2021)

"They are a class of deep neural networks that are generally used to analyze image data. They use convolution instead of simple matrix multiplication in a few layers of the network. They have shared weights architecture and have translation invariant characteristics." Vijayaraghavan Varadharajan & J Rian Leevinson, "Next Generation of Intelligent Cities: Case Studies from Europe", 2021) 

Data Science: Swarm Intelligence (Definitions)

"Swarm systems generate novelty for three reasons: (1) They are 'sensitive to initial conditions' - a scientific shorthand for saying that the size of the effect is not proportional to the size of the cause - so they can make a surprising mountain out of a molehill. (2) They hide countless novel possibilities in the exponential combinations of many interlinked individuals. (3) They don’t reckon individuals, so therefore individual variation and imperfection can be allowed. In swarm systems with heritability, individual variation and imperfection will lead to perpetual novelty, or what we call evolution." (Kevin Kelly, "Out of Control: The New Biology of Machines, Social Systems and the Economic World", 1995)

"Dumb parts, properly connected into a swarm, yield smart results." (Kevin Kelly, "New Rules for the New Economy", 1999)

"It is, however, fair to say that very few applications of swarm intelligence have been developed. One of the main reasons for this relative lack of success resides in the fact that swarm-intelligent systems are hard to 'program', because the paths to problem solving are not predefined but emergent in these systems and result from interactions among individuals and between individuals and their environment as much as from the behaviors of the individuals themselves. Therefore, using a swarm-intelligent system to solve a problem requires a thorough knowledge not only of what individual behaviors must be implemented but also of what interactions are needed to produce such or such global behavior." (Eric Bonabeau et al, "Swarm Intelligence: From Natural to Artificial Systems", 1999)

"Just what valuable insights do ants, bees, and other social insects hold? Consider termites. Individually, they have meager intelligence. And they work with no supervision. Yet collectively they build mounds that are engineering marvels, able to maintain ambient temperature and comfortable levels of oxygen and carbon dioxide even as the nest grows. Indeed, for social insects teamwork is largely self-organized, coordinated primarily through the interactions of individual colony members. Together they can solve difficult problems (like choosing the shortest route to a food source from myriad possible pathways) even though each interaction might be very simple (one ant merely following the trail left by another). The collective behavior that emerges from a group of social insects has been dubbed 'swarm intelligence'." (Eric Bonabeau & Christopher Meyer, Swarm Intelligence: A Whole New Way to Think About Business, Harvard Business Review, 2001)

"[…] swarm intelligence is becoming a valuable tool for optimizing the operations of various businesses. Whether similar gains will be made in helping companies better organize themselves and develop more effective strategies remains to be seen. At the very least, though, the field provides a fresh new framework for solving such problems, and it questions the wisdom of certain assumptions regarding the need for employee supervision through command-and-control management. In the future, some companies could build their entire businesses from the ground up using the principles of swarm intelligence, integrating the approach throughout their operations, organization, and strategy. The result: the ultimate self-organizing enterprise that could adapt quickly - and instinctively - to fast-changing markets." (Eric Bonabeau & Christopher Meyer, "Swarm Intelligence: A Whole New Way to Think About Business", Harvard Business Review, 2001)

"Swarm Intelligence can be defined more precisely as: Any attempt to design algorithms or distributed problem-solving methods inspired by the collective behavior of the social insect colonies or other animal societies. The main properties of such systems are flexibility, robustness, decentralization and self-organization." (Ajith Abraham et al, "Swarm Intelligence in Data Mining", 2006)

"Swarm intelligence can be effective when applied to highly complicated problems with many nonlinear factors, although it is often less effective than the genetic algorithm approach discussed later in this chapter. Swarm intelligence is related to swarm optimization […]. As with swarm intelligence, there is some evidence that at least some of the time swarm optimization can produce solutions that are more robust than genetic algorithms. Robustness here is defined as a solution’s resistance to performance degradation when the underlying variables are changed." (Michael J North & Charles M Macal, "Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation", 2007)

[swarm intelligence] "Refers to a class of algorithms inspired by the collective behaviour of insect swarms, ant colonies, the flocking behaviour of some bird species, or the herding behaviour of some mammals, such that the behaviour of the whole can be considered as exhibiting a rudimentary form of 'intelligence'." (John Fulcher, "Intelligent Information Systems", 2009)

"The property of a system whereby the collective behaviors of unsophisticated agents interacting locally with their environment cause coherent functional global patterns to emerge." (M L Gavrilova, "Adaptive Algorithms for Intelligent Geometric Computing", 2009) 

[swarm intelligence] "Is a discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization. In particular, SI focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment." (Elina Pacini et al, "Schedulers Based on Ant Colony Optimization for Parameter Sweep Experiments in Distributed Environments", 2013). 

"Swarm intelligence (SI) is a branch of computational intelligence that discusses the collective behavior emerging within self-organizing societies of agents. SI was inspired by the observation of the collective behavior in societies in nature such as the movement of birds and fish. The collective behavior of such ecosystems, and their artificial counterpart of SI, is not encoded within the set of rules that determines the movement of each isolated agent, but it emerges through the interaction of multiple agents." (Maximos A Kaliakatsos-Papakostas et al, "Intelligent Music Composition", 2013)

"Collective intelligence of societies of biological (social animals) or artificial (robots, computer agents) individuals. In artificial intelligence, it gave rise to a computational paradigm based on decentralisation, self-organisation, local interactions, and collective emergent behaviours." (D T Pham & M Castellani, "The Bees Algorithm as a Biologically Inspired Optimisation Method", 2015)

"It is the field of artificial intelligence in which the population is in the form of agents which search in a parallel fashion with multiple initialization points. The swarm intelligence-based algorithms mimic the physical and natural processes for mathematical modeling of the optimization algorithm. They have the properties of information interchange and non-centralized control structure." (Sajad A Rather & P Shanthi Bala, "Analysis of Gravitation-Based Optimization Algorithms for Clustering and Classification", 2020)

"It [swarm intelligence] is the discipline dealing with natural and artificial systems consisting of many individuals who coordinate through decentralized monitoring and self-organization." (Mehmet A Cifci, "Optimizing WSNs for CPS Using Machine Learning Techniques", 2021)

Resources:
More quotes on "Swarm Intelligence" at the-web-of-knowledge.blogspot.com.

Data Science: Genetic Algorithms (Definitions)

"A method for solving optimization problems using parallel search, based on the biological paradigm of natural selection and 'survival of the fittest'." (Joseph P Bigus, "Data Mining with Neural Networks: Solving Business Problems from Application Development to Decision Support", 1996)

"Algorithms for solving complex combinatorial and organizational problems with many variants, by employing analogy with nature's evolution. The general steps a genetic algorithm cycles through are: generate a new population (crossover) starting at the beginning with initial one; select the best individuals; mutate, if necessary; repeat the same until a satisfactory solution is found according to a goodness (fitness) function." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"The type of algorithm that locates optimal binary strings by processing an initially random population of strings using artificial mutation, crossover, and selection operators, in an analogy with the process of natural selection." (David E Goldberg, "Genetic Algorithms", 1989)

"A technique for estimating computer models (e.g., Machine Learning) based on methods adapted from the field of genetics in biology. To use this technique, one encodes possible model behaviors into a 'genes'. After each generation, the current models are rated and allowed to mate and breed based on their fitness. In the process of mating, the genes are exchanged, and crossovers and mutations can occur. The current population is discarded and its offspring forms the next generation." (William J Raynor Jr., "The International Dictionary of Artificial Intelligence", 1999)

"Genetic algorithms are problem-solving techniques that solve problems by evolving solutions as nature does, rather than by looking for solutions in a more principled way. Genetic algorithms, sometimes hybridized with other optimization algorithms, are the best optimization algorithms available across a wide range of problem types." (Guido Deboeck & Teuvo Kohonen (Eds), "Visual Explorations in Finance with Self-Organizing Maps" 2nd Ed., 2000)

"learning principle, in which learning results are foully from generations of solutions by crossing and eliminating their members. An improved behavior usually ensues from selective stochastic replacements in subsets of system parameters." (Teuvo Kohonen, "Self-Organizing Maps 3rd Ed.", 2001)

"A genetic algorithm is a search method used in computational intelligence to find true or approximate solutions to optimization and search problems." (Omar F El-Gayar et al, "Current Issues and Future Trends of Clinical Decision Support Systems", 2008)

"A method of evolutionary computation for problem solving. There are states also called sequences and a set of possibility final states. Methods of mutation are used on genetic sequences to achieve better sequences." (Attila Benko & Cecília S Lányi, "History of Artificial Intelligence", 2009) 

"Genetic algorithms are derivative free, stochastic optimization methods based on the concepts of natural selection and evolutionary processes." (Yorgos Goletsis et al, Bankruptcy Prediction through Artificial Intelligence, 2009)

"Genetic Algorithms (GAs) are algorithms that use operations found in natural genetics to guide their way through a search space and are increasingly being used in the field of optimisation. The robust nature and simple mechanics of genetic algorithms make them inviting tools for search learning and optimization. Genetic algorithms are based on computational models of fundamental evolutionary processes such as selection, recombination and mutation." (Masoud Mohammadian, Supervised Learning of Fuzzy Logic Systems, 2009)

"The algorithms that are modelled on the natural process of evolution. These algorithms employ methods such as crossover, mutation and natural selection and provide the best possible solutions after analyzing a group of sub-optimal solutions which are provided as inputs." (Prayag Narula, "Evolutionary Computing Approach for Ad-Hoc Networks", 2009)

"The type of algorithm that locates optimal binary strings by processing an initially random population of strings using artificial mutation, crossover, and selection operators, in an analogy with the process of natural selection." (Robert Nisbet et al, "Handbook of statistical analysis and data mining applications", 2009)

"These algorithms mimic the process of natural evolution and perform explorative search. The main component of this method is chromosomes that represent solutions to the problem. It uses selection, crossover, and mutation to obtain chromosomes of highest quality." (Indranil Bose, "Data Mining in Tourism", 2009)

"Search algorithms used in machine learning which involve iteratively generating new candidate solutions by combining two high scoring earlier (or parent) solutions in a search for a better solution." (Radian Belu, "Artificial Intelligence Techniques for Solar Energy and Photovoltaic Applications", 2013)

"Genetic algorithms (GAs) is a stochastic search methodology belonging to the larger family of artificial intelligence procedures and evolutionary algorithms (EA). They are used to generate useful solutions to optimization and search problems mimicking Darwinian evolution." (Niccolò Gordini, "Genetic Algorithms for Small Enterprises Default Prediction: Empirical Evidence from Italy", 2014)

"Genetic algorithms are based on the biological theory of evolution. This type of algorithms is useful for searching and optimization." (Ivan Idris, "Python Data Analysis", 2014)

"A Stochastic optimization algorithms based on the principles of natural evolution." (Harish Garg, "A Hybrid GA-GSA Algorithm for Optimizing the Performance of an Industrial System by Utilizing Uncertain Data", 2015)

"It is a stochastic but not random method of search used for optimization or learning. Genetic algorithm is basically a search technique that simulates biological evolution during optimization process." (Salim Lahmir, "Prediction of International Stock Markets Based on Hybrid Intelligent Systems", 2016)

"Machine learning algorithms inspired by genetic processes, for example, an evolution where classifiers with the greatest accuracy are trained further." (David Natingga, "Data Science Algorithms in a Week" 2nd Ed., 2018)

Data Science: Deep Learning (Definitions)

"Deep learning is an area of machine learning that emerged from the intersection of neural networks, artificial intelligence, graphical modeling, optimization, pattern recognition and signal processing." (N D Lewis, "Deep Learning Made Easy with R: A Gentle Introduction for Data Science", 2016)

"Methods that are used to train models with several levels of abstraction from the raw input to the output. For example, in visual recognition, the lowest level is an image composed of pixels. In layers as we go up, a deep learner combines them to form strokes and edges of different orientations, which can then be combined to detect longer lines, arcs, corners, and junctions, which in turn can be combined to form rectangles, circles, and so on. The units of each layer may be thought of as a set of primitives at a different level of abstraction." (Ethem Alpaydın, "Machine learning : the new AI", 2016)

"A branch of machine learning to whose architectures belong deep ANNs. The term “deep” denotes the application of multiple layers with a complex structure." (Iva Mihaylova, "Applications of Artificial Neural Networks in Economics and Finance", 2018)

"A deep-learning model is a neural network that has multiple (more than two) layers of hidden units (or neurons). Deep networks are deep in terms of the number of layers of neurons in the network. Today many deep networks have tens to hundreds of layers. The power of deep-learning models comes from the ability of the neurons in the later layers to learn useful attributes derived from attributes that were themselves learned by the neurons in the earlier layers." (John D Kelleher & Brendan Tierney, "Data science", 2018)

"Also known as deep structured learning or hierarchical learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms." (Soraya Sedkaoui, "Big Data Analytics for Entrepreneurial Success", 2018)

"Deep learning broadly describes the large family of neural network architectures that contain multiple, interacting hidden layers." (Benjamin Bengfort et al, "Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning", 2018)

"It is a part of machine learning approach used for learning data representations." (Dharmendra S Rajput et al, "Investigation on Deep Learning Approach for Big Data: Applications and Challenges", 2018)

"The ability of a neural network to improve its learning process." (David Natingga, "Data Science Algorithms in a Week" 2nd Ed., 2018)

"A learning algorithm using a number of layers for extracting and learning feature hierarchies before providing an output for any input." (Tanu Wadhera & Deepti Kakkar, "Eye Tracker: An Assistive Tool in Diagnosis of Autism Spectrum Disorder", 2019)

"a machine-learning technique that extends standard artificial neural network models to many layers representing different levels of abstraction, say going from individual pixels of an image through to recognition of objects." (David Spiegelhalter, "The Art of Statistics: Learning from Data", 2019)

"A part of a broader family of machine learning methods based on learning data representations." (Nil Goksel & Aras Bozkurt, "Artificial Intelligence in Education: Current Insights and Future Perspectives", 2019)

"A recent method of machine learning based on neural networks with more than one hidden layer." (Samih M Jammoul et al, "Open Source Software Usage in Education and Research: Network Traffic Analysis as an Example", 2019)

"A subbranch of machine learning which inspires from the artificial neural network. It has eliminated the need to design handcrafted features as in deep learning features are automatically learned by the model from the data." (Aman Kamboj et al, "Ear Localizer: A Deep-Learning-Based Ear Localization Model for Side Face Images in the Wild", 2019)

"It is class of one machine learning algorithms that can be supervised, unsupervised, or semi-supervised. It uses multiple layers of processing units for feature extraction and transformation." (Siddhartha Kumar Arjaria & Abhishek S Rathore, "Heart Disease Diagnosis: A Machine Learning Approach", 2019)

"Is the complex, unsupervised processing of unstructured data in order to create patterns used in decision making, patterns that are analogous to those of the human brain." (Samia H Rizk, "Risk-Benefit Evaluation in Clinical Research Practice", 2019)

"The ability for machines to autonomously mimic human thought patterns through artificial neural networks composed of cascading layers of information." (Kirti R Bhatele et al, "The Role of Artificial Intelligence in Cyber Security", 2019)

"The method for solving problems that have more probabilistic calculations based on artificial neural networks." (Tolga Ensari et al, "Overview of Machine Learning Approaches for Wireless Communication", 2019)

"A category of machine learning methods which is inspired by the artificial neural networks" (Shouvik Chakraborty & Kalyani Mali, "An Overview of Biomedical Image Analysis From the Deep Learning Perspective", 2020)

"A sub-field of machine learning which is based on the algorithms and layers of artificial networks." (S Kayalvizhi & D Thenmozhi, "Deep Learning Approach for Extracting Catch Phrases from Legal Documents", 2020)

"A type of machine learning based on artificial neural networks. It can be supervised, unsupervised, or semi-supervised, and it uses an artificial neural network with multiple layers between the input and output layers." (Timofei Bogomolov et al, "Identifying Patterns in Fresh Produce Purchases: The Application of Machine Learning Techniques", 2020)

"An extension of machine learning approach, which uses neural network." (Neha Garg & Kamlesh Sharma, "Machine Learning in Text Analysis", 2020)

"Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised." (R Murugan, "Implementation of Deep Learning Neural Network for Retinal Images", 2020)

 "Deep learning is a collection of algorithms used in machine learning, used to model high-level abstractions in data through the use of model architectures, which are composed of multiple nonlinear transformations. It is part of a broad family of methods used for machine learning that are based on learning representations of data." (Edward T Chen, "Deep Learning and Sustainable Telemedicine", 2020)

"Deep learning is a collection of neural-network techniques that generally use multiple layers." (Alex Thomas, "Natural Language Processing with Spark NLP", 2020)

"Deep learning is a kind of machine learning technique with automatic image interpretation and feature learning facility. The different deep learning algorithms are convolutional neural network (CNN), deep neural network (DNN), recurrent neural network (RNN), genetic adversarial networks (GAN), etc." (Rajandeep Kaur & Rajneesh Rani, "Comparative Study on ASD Identification Using Machine and Deep Learning", 2020)

"Deep learning is a subset of machine learning that models high-level abstractions in data by means of network architectures, which are composed of multiple nonlinear transformations." (Loris Nanni et al, "Digital Recognition of Breast Cancer Using TakhisisNet: An Innovative Multi-Head Convolutional Neural Network for Classifying Breast Ultrasonic Images", 2020)

"In contradistinction to surface or superficial learning, deep learning is inextricably associated with long-term retention of pertinent and solid knowledge, based on a thorough and critical understanding of the object of study, be it curricular content or not." (Leonor M Martínez-Serrano, "The Pedagogical Potential of Design Thinking for CLIL Teaching: Creativity, Critical Thinking, and Deep Learning", 2020)

"Is a group of methods that allow multilayer computing models to work with data that has an abstraction hierarchy." (Heorhii Kuchuk et al, "Application of Deep Learning in the Processing of the Aerospace System's Multispectral Images", 2020)

"It is a part of machine learning intended for learning form large amounts of data, as in the case of experience-based learning. It can be considered that feature engineering in deep learning-based models is partly left to the machine. In the case of artificial neural networks, deep neural networks are expected to have various layers within architectures for solving complex problems with higher accuracy compared to traditional machine learning. Moreover, high performance automatic results are expected without human intervention." (Ana Gavrovska & Andreja Samčović, "Intelligent Automation Using Machine and Deep Learning in Cybersecurity of Industrial IoT", 2020)

"Is a subset of AI and machine learning that uses multi-layered artificial neural networks to learn from data that is unstructured or unlabeled." (Lejla Banjanović-Mehmedović & Fahrudin Mehmedović, "Intelligent Manufacturing Systems Driven by Artificial Intelligence in Industry 4.0", 2020)

"This method is also called as hierarchical learning or deep structured learning. It is one of the machine learning method that is based on learning methods like supervised, semi-supervised or unsupervised. The only difference between deep learning and other machine learning algorithm is that deep learning method uses big data as input." (Anumeera Balamurali & Balamurali Ananthanarayanan,"Develop a Neural Model to Score Bigram of Words Using Bag-of-Words Model for Sentiment Analysis", 2020)

"A form of machine learning which uses multi-layered architectures to automatically learn complex representations of the input data. Deep models deliver state-of-the-art results across many fields, e.g. computer vision and NLP." (Vincent Karas & Björn W Schuller, "Deep Learning for Sentiment Analysis: An Overview and Perspectives", 2021)

"A sub branch of Artificial intelligence in which we built the DL model and we don’t need to specify any feature to the learning model . In case of DL the model will classify the data based on the input data." (Ajay Sharma, "Smart Agriculture Services Using Deep Learning, Big Data, and IoT", 2021)

"A sub-set of machine learning in artificial intelligence (AI) with network capabilities supporting learning unsupervised from unstructured data." (Mark Schofield, "Gamification Tools to Facilitate Student Learning Engagement in Higher Education: A Burden or Blessing?", 2021)

"A subarea of machine learning, which adopts a deeper and more complex neural structure to reach state-of-the-art accuracy in a given problem. Commonly applied in machine learning areas, such as classification and prediction." (Jinnie Shin et al, "Automated Essay Scoring Using Deep Learning Algorithms", 2021)

"A subset of a broader family of machine learning methods that makes use of multiple layers to extract data from raw input in order to learn its features." (R Karthik et al, "Performance Analysis of GAN Architecture for Effective Facial Expression Synthesis", 2021)

"An artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making." (Wissam Abbass et al, "Internet of Things Application for Intelligent Cities: Security Risk Assessment Challenges", 2021)

"Another term for unsupervised learning that includes reinforcement learning in which the machine responds to reaching goals given input data and constraints. Deep learning deals with multiple layers simulating neural networks with ability to process immense amount of data." (Sujata Ramnarayan, "Marketing and Artificial Intelligence: Personalization at Scale", 2021)

"Application of multi neuron, multi-layer neural networks to perform learning tasks." (Revathi Rajendran et al, "Convergence of AI, ML, and DL for Enabling Smart Intelligence: Artificial Intelligence, Machine Learning, Deep Learning, Internet of Things", 2021)

 "Deep learning approach is a subfield of the machine learning technique. The concepts of deep learning influenced by neuron and brain structure based on ANN (Artificial Neural Network)." (Sayani Ghosal & Amita Jain, "Research Journey of Hate Content Detection From Cyberspace", 2021)

"Deep learning is a compilation of algorithms used in machine learning, and used to model high-level abstractions in data through the use of model architectures." (M Srikanth Yadav & R Kalpana, "A Survey on Network Intrusion Detection Using Deep Generative Networks for Cyber-Physical Systems", 2021)

"Deep learning is a subfield of machine learning that uses artificial neural networks to predict, classify, and generate data." (Usama A Khan & Josephine M Namayanja, "Reevaluating Factor Models: Feature Extraction of the Factor Zoo", 2021)

"Deep leaning is a subset of machine learning to solve complex problems/datasets." (R Suganya et al, "A Literature Review on Thyroid Hormonal Problems in Women Using Data Science and Analytics: Healthcare Applications", 2021)

"Deep learning is a type of machine learning that can process a wider range of data resources, requires less data preprocessing by humans, and can often produce more accurate results than traditional machine-learning approaches. In deep learning, interconnected layers of software-based calculators known as 'neurons' form a neural network. The network can ingest vast amounts of input data and process them through multiple layers that learn increasingly complex features of the data at each layer. The network can then make a determination about the data, learn if its determination is correct, and use what it has learned to make determinations about new data. For example, once it learns what an object looks like, it can recognize the object in a new image." (Bistra K Vassileva, "Artificial Intelligence: Concepts and Notions", 2021)

"Deep learning refers to artificial neural networks that mimic the workings of the human brain in the formation of patterns used in data processing and decision-making. Deep learning is a subset of machine learning. They are artificial intelligence networks capable of learning from unstructured or unlabeled data." (Atakan Gerger, "Technologies for Connected Government Implementation: Success Factors and Best Practices", 2021)

"It is a machine learning method using multiple layers of nonlinear processing units to extract features from data." (Sercan Demirci et al, "Detection of Diabetic Retinopathy With Mobile Application Using Deep Learning", 2021)

"It is a subarea of machine learning, where the models are built using multiple layers of artificial neural networks for learning useful patterns from raw data." (Gunjan Ansari et al, "Natural Language Processing in Online Reviews", 2021)

"It is an artificial intelligence technology that imitates the role of the human brain in data processing and the development of decision-making patterns." (Mehmet A Cifci, "Optimizing WSNs for CPS Using Machine Learning Techniques", 2021)

"One part of the broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised." (Jan Bosch et al, "Engineering AI Systems: A Research Agenda", 2021)

"Part of Machine Learning, where methods of higher complexity are used for training data representation." (Andrej Zgank et al, "Embodied Conversation: A Personalized Conversational HCI Interface for Ambient Intelligence", 2021)

"Sub-domain in the field of machine learning that deals with the use of algorithms inspired by human brain cells to solve complex real-world problems." (Shatakshi Singhet al, "A Survey on Intelligence Tools for Data Analytics", 2021)

"This is also a subset of AI where unstructured data is processed using layers of neural networks to identify, predict and detect patterns. Deep learning is used when there is a large amount of unlabeled data and problem is too complex to be solved using machine learning algorithms. Deep learning algorithms are used in computer vision and facial recognition systems." (Vijayaraghavan Varadharajan & Akanksha Rajendra Singh, "Building Intelligent Cities: Concepts, Principles, and Technologies", 2021)

"A rapidly evolving machine learning technique used to build, train, and test neural networks that probabilistically predict outcomes and/or classify unstructured data." (Forrester)

"Deep Learning is a subset of machine learning concerned with large amounts of data with algorithms that have been inspired by the structure and function of the human brain, which is why deep learning models are often referred to as deep neural networks. It is is a part of a broader family of machine learning methods based on learning data representations, as opposed to traditional task-specific algorithms." (Databricks) [source]

"Deep Learning refers to complex multi-layer neural nets.  They are especially suitable for image and voice recognition, and for unsupervised tasks with complex, unstructured data." (Statistics.com)

"is a machine learning methodology where a system learns the patterns in data by automatically learning a hierarchical layer of features. " (Accenture)

Data Science: Data Mining (Definitions)

"The non-trivial extraction of implicit, previously unknown, and potentially useful information from data" (Frawley et al., "Knowledge discovery in databases: An overview", 1991)

"Data mining is the efficient discovery of valuable, nonobvious information from a large collection of data." (Joseph P Bigus,"Data Mining with Neural Networks: Solving business problems from application development to decision support", 1996)

"Data mining is the process of examining large amounts of aggregated data. The objective of data mining is to either predict what may happen based on trends or patterns in the data or to discover interesting correlations in the data." (Microsoft Corporation, "Microsoft SQL Server 7.0 Data Warehouse Training Kit", 2000)

"A data-driven approach to analysis and prediction by applying sophisticated techniques and algorithms to discover knowledge." (Paulraj Ponniah, "Data Warehousing Fundamentals", 2001)

"A class of undirected queries, often against the most atomic data, that seek to find unexpected patterns in the data. The most valuable results from data mining are clustering, classifying, estimating, predicting, and finding things that occur together. There are many kinds of tools that play a role in data mining. The principal tools include decision trees, neural networks, memory- and cased-based reasoning tools, visualization tools, genetic algorithms, fuzzy logic, and classical statistics. Generally, data mining is a client of the data warehouse." (Ralph Kimball & Margy Ross, "The Data Warehouse Toolkit" 2nd Ed., 2002)

"The discovery of information hidden within data." (William A Giovinazzo, "Internet-Enabled Business Intelligence", 2002)

"the process of extracting valid, authentic, and actionable information from large databases." (Seth Paul et al. "Preparing and Mining Data with Microsoft SQL Server 2000 and Analysis", 2002)

"Advanced analysis or data mining is the analysis of detailed data to detect patterns, behaviors, and relationships in data that were previously only partially known or at times totally unknown." (Margaret Y Chu, "Blissful Data", 2004)

"Analysis of detail data to discover relationships, patterns, or associations between values." (Margaret Y Chu, "Blissful Data ", 2004)

"An information extraction activity whose goal is to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques, and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results." (Sharon Allen & Evan Terry, "Beginning Relational Data Modeling" 2nd Ed., 2005)

"the process of analyzing large amounts of data in search of previously undiscovered business patterns." (William H Inmon, "Building the Data Warehouse", 2005)

"A type of advanced analysis used to determine certain patterns within data. Data mining is most often associated with predictive analysis based on historical detail, and the generation of models for further analysis and query." (Jill Dyché & Evan Levy, "Customer Data Integration", 2006)

"Refers to the process of identifying nontrivial facts, patterns and relationships from large databases. The databases have often been put together for a different purpose from the data mining exercise." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"Data mining is the process of discovering implicit patterns in data stored in data warehouse and using those patterns for business advantage such as predicting future trends." (S. Sumathi & S. Esakkirajan, "Fundamentals of Relational Database Management Systems", 2007)

"Digging through data (usually in a data warehouse or data mart) to identify interesting patterns." (Rod Stephens, "Beginning Database Design Solutions", 2008)

"Intelligently analyzing data to extract hidden trends, patterns, and information. Commonly used by statisticians, data analysts and Management Information Systems communities." (Craig F Smith & H Peter Alesso, "Thinking on the Web: Berners-Lee, Gödel and Turing", 2008)

"The process of extracting valid, authentic, and actionable information from large databases." (Darril Gibson, "MCITP SQL Server 2005 Database Developer All-in-One Exam Guide", 2008)

"The process of retrieving relevant data to make intelligent decisions." (Robert D Schneider & Darril Gibson, "Microsoft SQL Server 2008 All-in-One Desk Reference For Dummies", 2008)

"A process that minimally has four stages: (1) data preparation that may involve 'data cleaning' and even 'data transformation', (2) initial exploration of the data, (3) model building or pattern identification, and (4) deployment, which means subjecting new data to the 'model' to predict outcomes of cases found in the new data." (Robert Nisbet et al, "Handbook of statistical analysis and data mining applications", 2009)

"Automatically searching large volumes of data for patterns or associations." (Mark Olive, "SHARE: A European Healthgrid Roadmap", 2009)

"The use of machine learning algorithms to find faint patterns of relationship between data elements in large, noisy, and messy data sets, which can lead to actions to increase benefit in some form (diagnosis, profit, detection, etc.)." (Robert Nisbet et al, "Handbook of statistical analysis and data mining applications", 2009)

"A data-driven approach to analysis and prediction by applying sophisticated techniques and algorithms to discover knowledge." (Paulraj Ponniah, "Data Warehousing Fundamentals for IT Professionals", 2010) 

"A way of extracting knowledge from a database by searching for correlations in the data and presenting promising hypotheses to the user for analysis and consideration." (Toby J Teorey, "Database Modeling and Design" 4th Ed., 2010)

"The process of using mathematical algorithms (usually implemented in computer software) to attempt to transform raw data into information that is not otherwise visible (for example, creating a query to forecast sales for the future based on sales from the past)." (Ken Withee, "Microsoft Business Intelligence For Dummies", 2010)

"A process that employs automated tools to analyze data in a data warehouse and other sources and to proactively identify possible relationships and anomalies." (Carlos Coronel et al, "Database Systems: Design, Implementation, and Management" 9th Ed., 2011)

"Process of analyzing data from different perspectives and summarizing it into useful information (e.g., information that can be used to increase revenue, cuts costs, or both)." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"The process of sifting through large amounts of data using pattern recognition, fuzzy logic, and other knowledge discovery statistical techniques to identify previously unknown, unsuspected, and potentially meaningful data content relationships and trends." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Data mining, a branch of computer science, is the process of extracting patterns from large data sets by combining statistical analysis and artificial intelligence with database management. Data mining is seen as an increasingly important tool by modern business to transform data into business intelligence giving an informational advantage." (T T Wong & Loretta K W Sze, "A Neuro-Fuzzy Partner Selection System for Business Social Networks", 2012)

"Field of analytics with structured data. The model inference process minimally has four stages: data preparation, involving data cleaning, transformation and selection; initial exploration of the data; model building or pattern identification; and deployment, putting new data through the model to obtain their predicted outcomes." (Gary Miner et al, "Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications", 2012)

"The process of identifying commercially useful patterns or relationships in databases or other computer repositories through the use of advanced statistical tools." (Microsoft, "SQL Server 2012 Glossary", 2012)

"The process of exploring and analyzing large amounts of data to find patterns." (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"An umbrella term for analytic techniques that facilitate fast pattern discovery and model building, particularly with large datasets." (Meta S Brown, "Data Mining For Dummies", 2014)

"Analysis of large quantities of data to find patterns such as groups of records, unusual records, and dependencies" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"The practice of analyzing big data using mathematical models to develop insights, usually including machine learning algorithms as opposed to statistical methods."(Brenda L Dietrich et al, "Analytics Across the Enterprise", 2014)

"Data mining is the analysis of data for relationships that have not previously been discovered." (Piyush K Shukla & Madhuvan Dixit, "Big Data: An Emerging Field of Data Engineering", Handbook of Research on Security Considerations in Cloud Computing, 2015)

"A methodology used by organizations to better understand their customers, products, markets, or any other phase of the business." (Adam Gordon, "Official (ISC)2 Guide to the CISSP CBK" 4th Ed., 2015)

"Extracting information from a database to zero in on certain facts or summarize a large amount of data." (Faithe Wempen, "Computing Fundamentals: Introduction to Computers", 2015)

"It refers to the process of identifying and extracting patterns in large data sets based on artificial intelligence, machine learning, and statistical techniques." (Hamid R Arabnia et al, "Application of Big Data for National Security", 2015)

"The process of exploring and analyzing large amounts of data to find patterns." (Judith S Hurwitz, "Cognitive Computing and Big Data Analytics", 2015)

"Term used to describe analyzing large amounts of data to find patterns, correlations, and similarities." (Brittany Bullard, "Style and Statistics", 2016)

"The process of extracting meaningful knowledge from large volumes of data contained in data warehouses." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

"A class of analytical applications that help users search for hidden patterns in a data set. Data mining is a process of analyzing large amounts of data to identify data–content relationships. Data mining is one tool used in decision support special studies. This process is also known as data surfing or knowledge discovery." (Daniel J Power & Ciara Heavin, "Decision Support, Analytics, and Business Intelligence" 3rd Ed., 2017)

"The process of collecting, searching through, and analyzing a large amount of data in a database to discover patterns or relationships." (Jonathan Ferrar et al, "The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance", 2017)

"Data mining involves finding meaningful patterns and deriving insights from large data sets. It is closely related to analytics. Data mining uses statistics, machine learning, and artificial intelligence techniques to derive meaningful patterns." (Amar Sahay, "Business Analytics" Vol. I, 2018)

"The analysis of the data held in data warehouses in order to produce new and useful information." (Shon Harris & Fernando Maymi, "CISSP All-in-One Exam Guide" 8th Ed., 2018)

"The process of collecting critical business information from a data source, correlating the information, and uncovering associations, patterns, and trends." (Sybase, "Open Server Server-Library/C Reference Manual", 2019)

"The process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems." (Dmitry Korzun et al, "Semantic Methods for Data Mining in Smart Spaces", 2019)

"A technique using software tools geared for the user who typically does not know exactly what he's searching for, but is looking for particular patterns or trends. Data mining is the process of sifting through large amounts of data to produce data content relationships. It can predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. This is also known as data surfing." (Information Management)

"An analytical process that attempts to find correlations or patterns in large data sets for the purpose of data or knowledge discovery." (NIST SP 800-53)

"Extracting previously unknown information from databases and using that data for important business decisions, in many cases helping to create new insights." (Solutions Review)

"is the process of collecting data, aggregating it according to type and sorting through it to identify patterns and predict future trends." (Accenture)

"the process of analyzing large batches of data to find patterns and instances of statistical significance. By utilizing software to look for patterns in large batches of data, businesses can learn more about their customers and develop more effective strategies for acquisition, as well as increase sales and decrease overall costs." (Insight Software)

"The process of identifying commercially useful patterns or relationships in databases or other computer repositories through the use of advanced statistical tools." (Microsoft)

"The process of pulling actionable insight out of a set of data and putting it to good use. This includes everything from cleaning and organizing the data; to analyzing it to find meaningful patterns and connections; to communicating those connections in a way that helps decision-makers improve their product or organization." (KDnuggets)

"Data mining is the process of analyzing hidden patterns of data according to different perspectives for categorization into useful information, which is collected and assembled in common areas, such as data warehouses, for efficient analysis, data mining algorithms, facilitating business decision making and other information requirements to ultimately cut costs and increase revenue. Data mining is also known as data discovery and knowledge discovery." (Techopedia)

"Data mining is an automated analytical method that lets companies extract usable information from massive sets of raw data. Data mining combines several branches of computer science and analytics, relying on intelligent methods to uncover patterns and insights in large sets of information." (Sisense) [source]

"Data mining is the process of analyzing data from different sources and summarizing it into relevant information that can be used to help increase revenue and decrease costs. Its primary purpose is to find correlations or patterns among dozens of fields in large databases." (Logi Analytics) [source]

"Data mining is the process of analyzing massive volumes of data to discover business intelligence that helps companies solve problems, mitigate risks, and seize new opportunities." (Talend) [source]

"Data Mining is the process of collecting data, aggregating it according to type and sorting through it to identify patterns and predict future trends." (Accenture)

"Data mining is the process of discovering meaningful correlations, patterns and trends by sifting through large amounts of data stored in repositories. Data mining employs pattern recognition technologies, as well as statistical and mathematical techniques." (Gartner)

"Data mining is the process of extracting relevant patterns, deviations and relationships within large data sets to predict outcomes and glean insights. Through it, companies convert big data into actionable information, relying upon statistical analysis, machine learning and computer science." (snowflake) [source]

"Data mining is the work of analyzing business information in order to discover patterns and create predictive models that can validate new business insights. […] Unlike data analytics, in which discovery goals are often not known or well defined at the outset, data mining efforts are usually driven by a specific absence of information that can’t be satisfied through standard data queries or reports. Data mining yields information from which predictive models can be derived and then tested, leading to a greater understanding of the marketplace." (Informatica) [source]