How Are Big Data and Artificial Intelligence Related?

C O N T E N T S:


  • Why has Big Data brought attention to AI?(More…)
  • As the amounts of data from different sources (computers, smartphones, IoT, embedded systems) continue to grow, it will become more and more accessible for Businesses to start adopting solutions that run on Artificial Intelligence.(More…)
  • Artificial intelligence is a form of computing that allows machines to perform cognitive functions, such as acting or reacting to input, similar to the way humans do.(More…)
  • Machine learning is the term used for the current cutting-edge in artificial intelligence algorithms – computer software designed to become increasingly efficient at processing data as it “learns” in much the same way as humans do.(More…)
  • Chao discusses with Futuretech how her journey as a data scientist led her to Lucidworks, what’s happening in deep learning, and how big data and AI have changed over the years.(More…)
  • This edition of ITCC TOE provides a snapshot of the emerging ICT led innovations in artificial intelligence, machine learning, data analytics, and blockchain.(More…)
  • On June 6, 2018, Davis Wright Tremaine will host the Cloud, Big Data & AI Conference at our Seattle office.(More…)
  • While machine learning algorithms have been around for decades, they’ve attained new popularity as artificial intelligence (AI) has grown in prominence.(More…)
  • The learning is supervised because you’re telling the algorithm the correct answer (the label) as it is exposed to many examples using big data.(More…)


  • That’s because AI needs data to build its intelligence, particularly machine learning.(More…)


How Are Big Data and Artificial Intelligence Related?
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Why has Big Data brought attention to AI? The answer is simply that Artificial Intelligence can deal with large and complex data sets in ways that traditional data processing-or humans-cannot. [1] Over the years we?ve been hearing a lot of discussions around two of the most popular Business trends, commonly known as Big Data and Artificial Intelligence. [2] It’s safe to say there is no Artificial Intelligence without Big Data. [3]

Introduction The term Data Science or its many variants – such as Analytics, Big Data, Artificial Intelligence, Robotics, Machine Learning, Deep Learning, all used interchangeably – are much used (and abused) buzz-words that are used to analyze, explain and predict a whole range of events and happening. [4] DWT’s annual exploration of cutting-edge legal, business, and technology issues in cloud, big data, and artificial intelligence will include a panel on artificial intelligence, machine learning and autonomous systems. [5] Helping digital marketing to also evolve, unsupervised learning in artificial intelligence has increased because of our big data necessities. [6] The country’s government is already discussing about the development of a crime prevention system using artificial intelligence and big data, as reported by The Japan Times. [7]

Artificial intelligence, big data analytics and deep learning are converging on health care in a big way, information technology experts insist. [8] Jun 22 – 2018 – Patterson: With the rise of social media, big data, and artificial intelligence, it’s clear that our personal information is in the hands of just a few very large technology firms. [9]

As the amounts of data from different sources (computers, smartphones, IoT, embedded systems) continue to grow, it will become more and more accessible for Businesses to start adopting solutions that run on Artificial Intelligence. [2] Our Big Data and Artificial Intelligence: Extracting Business Value course is a user-friendly, non-technical, highly interactive and engaging introduction to the topics of Big Data, Machine Learning, and Artificial Intelligence (AI). [10] March 21, 2018 artificial intelligence, Big Data, Customer Experience, Data Analytics, Digital Transformation, Internet of Things, Internet of Things, Iot, machine learning no. [11] Implementing big data, artificial intelligence, and machine learning to healthcare services will bring both solutions and problems. [12] March 19, 2018 artificial intelligence, Big Data, Data Analytics, Data Science no. [11] Big Data and Artificial Intelligence can help hospitals and doctors improve the complete experience of providing healthcare services along with controlling the cost of running several tasks. [12] Big data and artificial intelligence have the power to improve a patient’s life and healthcare journey. [12]

Artificial intelligence is a form of computing that allows machines to perform cognitive functions, such as acting or reacting to input, similar to the way humans do. [3] IAGON uses a Smart Computing Grid based on advanced Artificial Intelligence components that include more than 100 Machine Learning algorithms, methods and techniques that integrate to form their AI-Tracker system, which is the “brain” behind their Smart Computing Grid. [2]

With Big Data to feed these processors, machine learning algorithms can learn how to reproduce a certain behavior, including collecting the data to in turn speed up the machine. [3] Big Data can provide the data needed to train the learning algorithms. [3] “The data you start with is Big Data, but to train the model, that data needs to be structured and integrated well enough that machines are able to reliably identify useful patterns in the data,” he said. [3] Although they are very different, AI and Big Data still do work well together. [3] AI is a way to navigate and gather insights in the world of Big Data. [1] A survey about Big Data and AI by NewVantage Partners of c-level executives found 97.2% of executives stated that their companies are investing in, building, or launching Big Data and AI initiatives. [3] Once you’re logged in, you can witness Big Data being analyzed with AI in some of the sample repositories. [1] 76.5% of executives feel AI and Big Data are becoming closely interconnected and that the greater availability of data is empowering AI and cognitive initiatives within their organizations. [3] Let’s now look at some of the AI technologies employed with Big Data. [1] Big Data analytics finds patterns through sequential analysis, sometimes of cold data, or data that is not freshly gathered. [3] This is a wonderful fit for Big Data because as more historical data is fed to a Bayes algorithm, the more accurate its predictive results become. [1] In Big Data sets there can be structured data, such as transactional data in a relational database, and less structured or unstructured data, such as images, email data, sensor data, and so on. [3] Hadoop, the basic framework for Big Data analysis, is a batch process originally designed to run at night during low server utilization. [3] Big Data hoovers up massive amounts of data and the wheat has to be separated from the chafe first before anything can be done with it. [3]

Machine learning is the term used for the current cutting-edge in artificial intelligence algorithms – computer software designed to become increasingly efficient at processing data as it “learns” in much the same way as humans do. [13] Wagle’s use of artificial intelligence (AI) with analytics is not unusual. [14] If you use Twitter to track trends in Artificial Intelligence, Machine Learning and Analytics you should follow Amr Awadalla (@awadallah) and Mike Olson (@mikeolson). [15] The increasing prevalence of sensors in machinery, vehicles, production plants, and other hard equipment spaces means physical equipment can be digitized and be monitored by artificial intelligence, a topic we’ve covered before in machine learning applications in industry. [16] Artificial Intelligence (AI) continues to become a focus for many enterprises and these organizations are increasing realizing how important it is to have the right people and skills in place. [15] Superior relevancy with machine learning, and artificial intelligence. [17] TechEmergence conducts direct interviews and consensus analysis with leading experts in machine learning and artificial intelligence. [16] Thanks for staying in touch we’re glad to keep you ahead of the curve on the applications and implications of artificial intelligence. [16]

Chao discusses with Futuretech how her journey as a data scientist led her to Lucidworks, what’s happening in deep learning, and how big data and AI have changed over the years. [17] Although there have been solutions for big data, unstructured data has typically gone untapped because it was more difficult to access prior to AI. The combination of ML and less expensive cloud compute power means that some types of dark data are also within reach. [14] Machine learning is also the right tool to recognize and catalog unstructured data, such as documents, images and video, as well as dark data, information that you’ve never accessed (probably because it’s an amorphous part of your big data). [14] This has been achieved by building advanced analytics systems utilizing big data and machine learning on top of the huge amount of data collated and published by the company in its 140-year history. [13] Big data has turned out to be a key ingredient in turning machine learning from an abstract technology into a potentially invaluable tool of insi. [16] Now that large data systems are common and AI/ML is being used to leverage these big data sets, the use of the term is dropping off. [15] In most every case where Big Data is being usefully applied today, it is associated with analytics. [15] NCI and DOE also plan to support population-based cancer monitoring programs with advanced big data analytics and natural language processing. [18] Here at LinkedIn and at Forbes I regularly write about management, technology and Big Data. [13] Pay particular attention to our Big Data Companies section. [15] We will continue to track the latest developments in both concepts of Big Data and infrastructure for making it work. [15] Several converging factors have made JDACS4C possible, including the rapid increase of big data that is now available to the scientific community, as well as the long-term collaborative projects from DOE that have enhanced computational capabilities across multiple disciplines. [18]

This edition of ITCC TOE provides a snapshot of the emerging ICT led innovations in artificial intelligence, machine learning, data analytics, and blockchain. [19] According to Computerworld, “In fact, the artificial intelligence boom is as much about the availability of massive data sets as it is about intelligent software. [20]

As the financial industry increasingly realizes the impact of faster analytical insights on overall business strategy, artificial intelligence (AI) techniques like machine learning are permeating nearly every industry. [21] And, t his past June, a panel of global experts convened by the World Economic Forum (WEF) named Artificial Intelligence, – Open AI Ecosystems in particular – as one of its Top Ten Emerging Technologies for 2016 because of its potential to fundamentally change the way markets, business and governments work. [22] Last month I attended AI and the Future of Work, a conference hosted by MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and its Initiative on the Digital Economy (IDE). [22] “Artificial intelligence is getting ready for business, but are businesses ready for AI?,” asks McKinsey in a recently published report – Artificial Intelligence: the Next Digital Frontier. [22] How about artificial intelligence? Beyond its use by leading edge technology companies, w e?re still in the early stages of AI deployment. [22] I made some predictions about Artificial intelligence back in January, and I wasn?t alone in stating that AI is likely to become the next transformational technology on the horizon for marketers, and the sales teams we support. [20] Artificial Intelligence (AI) is already advancing so quickly that computers can learn specific tasks without being programmed to do so, and many experts are predicting it will become integral in marketing and marketing automation systems. [20] Two years ago, Stanford University launched the One Hundred Year Study of AI (AI100), “to study and anticipate how the effects of artificial intelligence will ripple through every aspect of how people work, live and play.” [22] These questions were addressed in Artificial Intelligence and Life in 2030, a report that was recently published by Stanford University’s One Hundred Year Study of AI (AI100). [22] The first such Study Panel recently published Artificial Intelligence and Life in 2030, a report that examined the likely impact of AI on a typical North American city by the year 2030. [22] On December 20, the White House released Artificial Intelligence, Automation, and the Economy, a new report that investigates how AI will likely transform job markets over time, and recommends policy responses to address AI’s impact on the U.S. economy. [22] With greater use of sensors and artificial intelligence to gather and process information, robots are capable of a greater number of tasks. [23] Instead of just scratching the surface, artificial intelligence will be able to go beyond customers? surface level of information and give many more insights into potential prospects. [6] Artificial intelligence can make decisions based on collected personal information with important consequences for privacy. [23] After many years of promise and hype, artificial intelligence is now being applied to activities that not long ago were viewed as the exclusive domain of humans. [22] Although you can relax and stop worrying about Skynet overtaking your marketing campaigns, it is expected that artificial intelligence will take a more human shape as it advances. [6] Instead of mimicking human thinking, artificial intelligence of tomorrow will mimic the functions of the brain’s biological neurons. [6] Millions of people are already getting used to interacting with artificial intelligence through speech recognition. [6] AI100 was launched in December, 2014 “to study and anticipate how the effects of artificial intelligence will ripple through every aspect of how people work, live and play.” [22] Artificial intelligence and robotics are also being tested outside the laboratory. [23] Artificial intelligence has the power to cross out all the automated and boring tasks, so you can focus on being creative. [6] Instead of cashiers, the store employs an array of cameras and artificial intelligence to track customer purchases and charge their Amazon accounts. [23] Although it’s barely been 60 years since the term artificial intelligence has been integrated into our pop culture, artificial intelligence in marketing has become a whole different beast. [6]

Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems that perform tasks which ordinarily require human intelligence. [24] There are many practical applications for GAN. GANs might prove to be an important step toward inventing a form of general AI, artificial intelligence that can mimic human behavior and make decisions and perform functions without having a lot of data. [25] The next frontier is what some experts call “strong AI”, so that artificial intelligence not only extracts patterns in data but can also be creative. [26] Another study is using artificial intelligence to try and monitor multiple high-risk patients, and this is done by asking each patient numerous questions based on data acquired from live doctor to patient interactions. [27] Sony’s new chief executive has positioned data and artificial intelligence at the centre of its survival strategy, warning that the likes of Amazon and Google pose an existential threat to the Japanese technology and entertainment group. [26]

On June 6, 2018, Davis Wright Tremaine will host the Cloud, Big Data & AI Conference at our Seattle office. [5] Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as MA activity, people movements, funding news, financial results, industry alignments, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz. [21] More specifically, the survey asked them about their progress in embracing five major digital trends: big data and advanced analytics, digital engagement of customers, digital engagement of employees and external partners, automation, and digital innovation. [22] We have to learn how to deal with the very messy world of big data, and how to best apply our learning to make good decisions and predictions. [22] “The AI-based system would employ a “deep learning? algorithm that allows the computer to teach itself by analyzing big data. [7] Big data needs translate to how leads are generated; customer profiles and data analysis are done much more accurately and quickly. [6] In this special guest feature, David Friend, co-founder CEO of Wasabi Technologies, takes a look at the big data and cloud storage technology stack as it relates to the finance industry. [21] Big data and the cloud serve to provide important competitive advantage to this important industry. [21] An IDC white paper recently estimated the big data global market at $136 billion per year in 2016. [20]

Business Intelligence, big data, data science, and data analytics provide companies with illuminating insights. [25] You will leverage knowledge of advanced big data analytics, machine learning and AI, and other algorithms in the cybersecurity domain. [28] Big data and AI predictive analytics can continuously monitor and alert users of abnormalities, and before the outset of more major medical problems. [26]

Marketers need the reports generated by big data to find new business, optimize campaigns and help companies make a profit, and each business has its own needs and its own way of making the most of big data technology. [25] Big data technology, cross-functional integration and social media adoption are powerful and effective ways to reduce costs, discover new opportunities and launch new services and products. [25]

Regardless of industry or size, organizations that wish to remain competitive in the age of big data need to efficiently develop and implement data science capabilities or risk being left behind. [24] Thanks to big data and machine learning, any company can now create more transparent and trustworthy systems we will all benefit from. [26] Experfy Insights provides cutting-edge perspectives on Big Data and analytics. [25] Big Data has been the talk of the technological and business world for a while now. [25] Jun 26 – 2018 – Big data is essentially revolutionizing the business world in every category. [9] These algorithms have only become feasible in the age of big data, as they require massive amounts of training data. [29]

While machine learning algorithms have been around for decades, they’ve attained new popularity as artificial intelligence (AI) has grown in prominence. [29] The company, which is already a member of the Partnership on Artificial Intelligence including dozens of tech firms committed to AI principles, had faced criticism for the contract with the Pentagon on Project Maven, which uses machine learning and engineering talent to distinguish people and objects in drone videos. [26] With social media sites overtaking TV as a source for news for young people and news organisations increasingly reliant on social media platforms for generating distribution, major publishers now use artificial intelligence (AI) technology to post stories more effectively and generate higher volumes of traffic. [27] Artificial intelligence ( AI, also machine intelligence, MI ) is intelligence demonstrated by machines, in contrast to the natural intelligence ( NI ) displayed by humans and animals. [27] Gopnik, Alison, “Making AI More Human: Artificial intelligence has staged a revival by starting to incorporate what we know about how children learn”, Scientific American, vol. 316, no. 6 (June 2017), pp.60-65. [27] SAN FRANCISCO: Microsoft announced on Wednesday that it has signed an agreement to acquire Bonsai, an artificial intelligence (AI) startup based in San Francisco, to boost its AI and machine learning capabilities. [26] Jun 26 – 2018 – Artificial Intelligence (AI) and Machine Learning (ML) have already started making inroads into various industries. [9] According to Bloomberg’s Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI within Google increased from a “sporadic usage” in 2012 to more than 2,700 projects. [27] Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect. [27] The Carnegie Mellon School of Computer Science (SCS) has launched the first undergraduate degree in artificial intelligence (AI) in the United States. [26] Artificial Intelligence (AI) has become part of the business landscape. [26] Thanks to Artificial Intelligence! We are currently in the era where AI revolution is rewriting the power of technology. [26] Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an ” AI winter “), followed by new approaches, success and renewed funding. [27] Unlike previous waves of automation, many middle-class jobs may be eliminated by artificial intelligence; The Economist states that “the worry that AI could do to white-collar jobs what steam power did to blue-collar ones during the Industrial Revolution” is “worth taking seriously”. [27] As part of its Industrial Strategy unveiled last November, the government identified artificial intelligence (AI) as one of its four ‘Grand Challenges’ facing the UK. [26] What Is AI? – An introduction to artificial intelligence by John McCarthy –a co-founder of the field, and the person who coined the term. [27] What do artificial intelligence (AI), invention, and social good have in common? While on the surface they serve very different purposes, at their core, they all require you to do one thing in order to be successful at them: think differently. [25] The report, AI in the UK: ready, willing and able?, concludes that the “UK is in a strong position to be among the world leaders in the development of artificial intelligence during the 21st century”. [26] The opinion of experts within the field of artificial intelligence is mixed, with sizable fractions both concerned and unconcerned by risk from eventual superhumanly-capable AI. [27] According to a new press release, “Today at the 2018 International Supercomputing Conference in Frankfurt, Germany, global supercomputer leader Cray Inc. announced it is accelerating and simplifying its customers’ path to value from artificial intelligence (AI). [30] Mary Shelley’s Frankenstein considers a key issue in the ethics of artificial intelligence : if a machine can be created that has intelligence, could it also feel ? If it can feel, does it have the same rights as a human? The idea also appears in modern science fiction, such as the film A.I.: Artificial Intelligence, in which humanoid machines have the ability to feel emotions. [27] The implications of a constructed machine exhibiting artificial intelligence have been a persistent theme in science fiction since the twentieth century. [27] Chalmers argued that a machine, one more advanced than we have today, could become conscious, but Koch disagreed, based on the current state of neuroscience and artificial intelligence technology. [26] Onlookers commonly discount the behavior of an artificial intelligence program by arguing that it is not “real” intelligence after all; thus “real” intelligence is whatever intelligent behavior people can do that machines still cannot. [27] One high-profile example is that DeepMind in the 2010s developed a “generalized artificial intelligence” that could learn many diverse Atari games on its own, and later developed a variant of the system which succeeds at sequential learning. [27] Mr Pichai set out seven principles for Google’s application of artificial intelligence, or advanced computing that can simulate intelligent human behaviour. [26] Throughout the novel, Dick portrays the idea that human subjectivity is altered by technology created with artificial intelligence. [27] IBM has created its own artificial intelligence computer, the IBM Watson, which has beaten human intelligence (at some levels). [27] The ability for a computer to not only persuasively compete in a debate against a live person, but to actually win the argument, is only likely to feed into fears expressed by Tesla and SpaceX CEO Elon Musk and the late cosmologist Stephen Hawking that artificial intelligence could spell doom for human civilization. [26] Other counterarguments revolve around humans being either intrinsically or convergently valuable from the perspective of an artificial intelligence. [27] The development of full artificial intelligence could spell the end of the human race. [27] Once humans develop artificial intelligence, it will take off on its own and redesign itself at an ever-increasing rate. [27] Technological singularity is when accelerating progress in technologies will cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and control, thus radically changing or even ending civilization. [27] McCorduck 2004, pp.100-101, who writes that there are “two major branches of artificial intelligence: one aimed at producing intelligent behavior regardless of how it was accomplished, and the other aimed at modeling intelligent processes found in nature, particularly human ones.” [27] “AlphaGo beats human Go champ in milestone for artificial intelligence”. [27] While we may be decades away from interacting with intelligent robots, artificial intelligence and machine learning has already found its way into our routines. [26] Abbie Celniker, a partner Third Rock Ventures, says so far, her venture capital firm hasn?t invested directly in artificial intelligence or machine learning companies. [8] Using artificial intelligence to pick out inconsistencies and unusual patterns has quickly become standard for insurance companies, whether they?re looking for sophisticated rings of fraudsters rigging auto accidents or just individuals embellishing how much their damaged property was worth. [26] Several technology firms have already agreed to the general principles of using artificial intelligence for good, but Google appeared to offer a more precise set of standards. [26] Google announced Thursday it would not use artificial intelligence for weapons or to “cause or directly facilitate injury to people,” as it unveiled a set of principles for these technologies. [26] Banks use artificial intelligence systems today to organize operations, maintain book-keeping, invest in stocks, and manage properties. [27] Widespread use of artificial intelligence could have unintended consequences that are dangerous or undesirable. [27] Note that they use the term “computational intelligence” as a synonym for artificial intelligence. [27] One of his main strategies is to get more app developers to use artificial intelligence tools such as recognizing objects in front of an iPhone’s camera. [26] She suggests that the answer won?t be piecing together algorithms, as we often do to solve complex problems with artificial intelligence. [26] This approach to the philosophical problems associated with artificial intelligence forms the basis of the Turing test. [27] Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th ed.). [27] “Some philosophical problems from the standpoint of artificial intelligence”. [27] Machine learning is one example of artificial intelligence in practice. [26] Deep learning has transformed many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing and others. [27] “You can’t just be a computer scientist working in isolation on artificial intelligence if you want to change biology and medicine,” he says. [8] In video games, artificial intelligence is routinely used to generate dynamic purposeful behavior in non-player characters (NPCs). [27] “Hedge funds have long used artificial intelligence, with their short-term trades. [26] Musk also funds companies developing artificial intelligence such as Google DeepMind and Vicarious to “just keep an eye on what’s going on with artificial intelligence. [27] Nothing short of a concerted effort by the government, and the public and private sectors, will be enough if the UK is to be a world leader of artificial intelligence, argues Mike Rebeiro, head of digital and innovation at law firm Macfarlanes. [26] Beran gave the example of works of art generated by artificial intelligence. [26] Artificial intelligence is breaking into the healthcare industry by assisting doctors. [27] Marks & Spencer is turning to artificial intelligence to sharpen its appeal to customers through a “game-changing” partnership with Microsoft. [26] Wrench.AI (, recently announced in a press release that its proprietary artificial intelligence platform has officially launched and is now open to enterprise and client-driven organizations world-wide. [30] “The artificial intelligence program cannot learn by playing itself – at least, not to the extent that it did when teaching itself chess. [26] Thanks to improvements in artificial intelligence, Hill says, “we?re now able to learn that kind of causality — those kinds of cause-effect relationships — straight from real-world data.” [8] After a half-decade of quiet breakthroughs in artificial intelligence, 2015 has been a landmark year. [27] We haven?t found satisfactory definitions in the 70 years since artificial intelligence first emerged as an academic pursuit. [26]

He is a winner of the SIGKDD Innovation Award, the highest honour in data science, and a Fellow of the Association for the Advancement of Artificial Intelligence. [31] What the steam engine and its descendants did for muscle power, the Internet of Things (IoT), computers, digital technology, artificial intelligence (AI) and machine learning (ML) will do for brainpower. [32] With the ongoing developments in the Artificial Intelligence (AI) realm, all these possibilities might not appear far-fetched after all. [33] Last month, the European Commission (Commission) announced plans to bolster the future of artificial intelligence (AI) across the bloc. [34] The impressive advances in artificial intelligence and machine learning over the past decade are supported by supervised deep learning : training ML algorithms to perform narrow, single-domain tasks. [32] The challenge is people have not developed the level of trust in artificial intelligence and machine learning that they have in other technologies that automate tasks. [32] Artificial intelligence (AI)-based algorithms, due to their growing sophistication, have proven to be viable alternatives to traditional credit risk assessment models. [35]

The learning is supervised because you’re telling the algorithm the correct answer (the label) as it is exposed to many examples using big data. [32] Putting big data and AI to work for the healthcare industry can bring in remarkable revolutions. [12] With big data and AI every aspect of this industry can be made more responsive, intensive, quick, transparent, and secure. [12]’s portfolio of solutions is powered by Neuralytics, the AI engine that combines big data, predictive analytics, and AI. Yet, this AI is tame: It comes pretrained for the sales environment and literally delivers value out of the box. [36] Without any doubt, Big Data and AI technologies are quite powerful and have matured to be used in healthcare domain for multiple of purposes. [12] Alibaba Cloud has developed ET Brain, a suite of AI and Big Data technologies for different industrial and social applications. [33] They can be harnessed with AI and Big data in exciting ways. [12] It’s really difficult map-out the exact concept of Big Data and AI in healthcare. [12] ODBMS.ORG is designed to meet the fast-growing need for resources focusing on Big Data, Data Science, Analytical Data Platforms, Scalable Cloud platforms, NewSQL databases, NoSQL datastores, In-Memory Databases, and new approaches to concurrency control. [31] June 12, 2018 Analytics, Big Data, big data analytics, Data Analytics, Data Science no. [11] March 11, 2018 Big Data, Customer Experience, Data Science, Digital Transformation, Internet of Things, Internet of Things, Iot no. [11] Sofia is a digital marketing expert in Rapidsoft Technologies which is a leading IT consulting company providing full range it services including, IoT app development, Blockchain development, and big data app development solutions. [12] There are limitless benefits of employing big data for healthcare operations and using machine learning to streamline several manually performed processes. [12] The 2018 Thales Data Threat Report (DTR) has great information on Big Data use and security. [37] We surveyed more than 1,200 senior security executives from around the world, and virtually all (99%) report they plan to use Big Data this year. [37] Implementing big data and analytics doesn?t mean hospitals have to start things from the scratch. [12] The main reason for this breakthrough in technology is advancements in Big Data. [11] The DTR also gives us some insight into what these security executives are doing to secure their Big Data. [37] The Big Data platform doesn?t care what kind of data you put into it. [37]


That’s because AI needs data to build its intelligence, particularly machine learning. [3] In the past, AI didn’t work well because of slow processors and small data sets. [3] Data used in AI and ML is already “cleaned,” with extraneous, duplicate and unnecessary data already removed. [3] They also can be used to discover previously unknown data patterns with a process called unsupervised learning. [1] Pattern recognition systems are taught with training data, and this process is called supervised learning. [1]

As its name implies, pattern recognition is used to detect patterns and regularities in data and is a form of machine learning. [1] Rule-based systems can be used to extract, store, and manipulate knowledge from humans for the purpose of interpreting data in useful ways. [1]

It learns through trial and error, and that requires massive amounts of data to teach the AI. [3] The more data an AI app has, the more accurate the outcome it can achieve. [3] Because AI systems get smarter as more data is given them, they are well-suited for identifying anomalies over time. [1]

“Big data is a term for that are so large or complex that traditional data processing application software is inadequate to deal with them.” [1] Recently with the advent of “Big Data,” it’s been getting more attention. [1]

An AI-enabled machine is designed to analyze and interpret data and then solve the problem or address the issue based on those interpretations. [3] Unlike anomaly detection, which screens potential anomalies based on a single type of data, pattern recognition can discover previously unknown patterns in several pieces of data and take into consideration the patterns (or relationships) among the data. [1]

There are two types of data learning: the initial training, which is a sort of priming the pump, and routinely gathered data. [3] Your self-driving car never stops gathering data, and it keeps learning and honing its processes. [3] IAGON’s AI-Tracker is a dynamic learning system that continuously analyzes past and current data streams that reflect the availability of storage space and processing capacities of miners. [2]

In some situations, it may be possible for a human being to analyze large amounts of data, but it proves exhausting over time. [1] Consider a company that has a human expert capable of analyzing data for a specific objective. [1] Even with hundreds of humans tasked with analyzing possible fraud conditions, the sheer volume of data simply overwhelms human decision-making capabilities. [1]

Today, we have everything we need; the fast processors, the input devices, the network, and the massive amounts of data sets. [3] Because today we finally have a cost efficient way to collect, store, compute and transfer massive amounts of data in a way that wasn?t possible before. [2] They continue to take in new data and adjust their actions along the way as the data changes. [3]

Machine learning learns from collected data and keeps collecting. [3] Using Bayes, historical data of dissatisfied customers can be collected and used to predict customers likely to be lost in the future. [1] With graph theory, insights into relationships between data can be easily obtained. [1] It defines very large sets of data, but also data that can be extremely varied. [3] Then how about traditional data processing systems? The problem is that they are algorithmic — bound to follow the same logic over and over. [1] In this situation, people can’t possibly process or analyze more than a tiny fraction of this volume of data, second-by-second, to prevent or halt a crime. [1]

The big leap has been the advent of massively parallel processors, particularly GPUs, which are massive parallel processing units with thousands of cores, vs. the dozens in a CPU. This has greatly sped up the existing AI algorithms and has now made them viable. [3]

Without using machine learning we would need millions of lines of code and very complex rules to achieve AI. Machine learning involves training an algorithm with huge amounts of data so that it can learn about the data and its patterns. [4] Data is key to unlocking the potential, and the aviation industry must leverage AI. So, while both the business case and context of AI in the aviation industry is set, we need to discuss the use cases being implemented currently. [38] Machine learning and, to a lesser extent, deep learning are the branches of AI that are being harnessed for data analytics work. [14] Real-world AI data projects, based primarily on machine learning, are impressive and largely successful. [14] Humongous volumes of data will be in use as the aviation industry embraces AI, and that will give rise to data confidentiality risks. [38] Companies can now use machines algorithms to identify trends and insights in vast reams of data and make faster decisions that potentially position them to be competitive in real-time. [16] This gives companies using Domo a way to pull data from Salesforce, Square, Facebook, Shopify, and many other applications that they use to gain insight on their customers, sales, or product inventory. [16] Siemens says MindSphere, developed with SAP, companies that use MindSphere get a box that connects to their machines and collects data to show how the machines are operating. [16] That makes it possible to access the data in real-time for use with applications and analytics built on top of the HANA platform for faster decision making. [16] Accessing unstructured and dark data has been the impetus for many data analytics breakthroughs for organizations using machine learning in recent years. [14] Machine learning (ML) works by categorizing data, a basic building block of data analytics, leading to a sort of natural synergy between the two. [14] Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. [4] In an interview strictly for this article, Nicholson stated that deep learning, a subset of machine learning, is in many cases hitting an accuracy of 96% in interpreting data. [16]

There are more than 400 native software connectors that let Domo collect data from third-party apps, which can be used to offer insights and give context to business intelligence. [16] Most business intelligence (BI) systems still require structured data. [14]

Increasingly, every aspect of business is becoming data driven, and putting the right tools and systems in place to convert the vast amount of knowledge into power – actionable insights – is key to successful digital transformation. [13] The reasons we have so much data is the continued drive to improve business outcomes in industry, the need to pursue scientific discovery in academia, and the need to improve government support to citizens and the mission of government. [15] The first thing they need to do is to develop analytics that will help them develop and process accurate data. [38] A recent study fielded by Capgemini shows that, of nearly 1,000 AI-using enterprises, almost 80 percent have used it for data analytics and report gaining valuable insights as a result. [14] Television broadcaster Univision offered up a testimonial about the way it uses Domo to give more visibility to its own data, which is then used to unify and focus targeted campaigns. [16] There is a broad variety of use cases because almost everything touches business data. [14] The goal was to use customer and policy data to help the team drive more growth. [16] If your sales staff uses company smartphones or tablets in the field to record purchase orders, data from those transactions can be analyzed and understood by HANA to spot trends and irregularities. [16]

In today’s world where data is everywhere the term “Big Data” may be losing some of its usefulness. [15] “Start by looking at your data, work out what problems you?ve got to solve, and work out what data you need to solve those problems. [13] We need new ways of doing more analysis and gaining more understanding, and of course we need smart data solutions. [15] We need new concepts and technologies to deal with these massive amounts of new data. [15] The need to properly manage data isn’t exactly a new challenge for airlines. [38]

Once these features are rolled out, expected in late spring 2017, the platform is supposed to issue new alerts and notifications for significant changes, such as the detection of anomalies or new patterns in data (similar to approaches used in cyber security already). [16] For instance, Domo users who are merchants can extract data from their Shopify point-of-sale and e-commerce software, which is used to manage online stores. [16] That includes taking data from pipeline assets and external sources to manage safety and how resources are used. [16]

If a factory manager has an application installed on their computer to monitor the equipment on an assembly line, data from a slowdown in production could be collected and processed through HANA. The gathered results can be queried to determine if a new course of action is needed, such as a service inspection of the equipment. [16] That information gives the data center services provider a chance to improve its bookings accuracy by improving service and planning for likely occupancy rates to come. [14] It replicates and ingests structured data, such as sales transactions or customer information, from relational databases, apps, and other sources. [16] One incident has already come to light, when it was revealed that Emirates, a leading airline, leaked customer data to third parties without authorization. [38]

It’s estimated that the total amount of data in the world doubles every two years. [13] It can collect and process petabytes of data per hour from millions of sources, enabling applications to be written that take action on or because of that streaming data. [15] Oil and gas, aviation, and other industries, for example, have been using General Electric’s Predix operating system, which powers industrial apps to process the historic performance data of equipment. [16]

Organizing data collection and testing an algorithm with this data for accuracy over the first few months are is where many businesses get stuck. [16] Quickly create bespoke data applications for web and mobile. [17] As these capabilities and technologies become more accessible, large data sets that today require weeks and months to analyze will instead only take hours or days to evaluate. [18] Amazon Web Services launched AWS Kinesis in 2013 as a fully managed service for real-time processing of streaming data at scale. [15] SAP says that HANA performs differently from comparable platforms by storing replicated data in RAM rather than on disk. [16]

The term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”. [4] “We are shifting our mindset away from treating analytics as after-the-fact reports toward embedding that intelligence in the business process itself,” he said. [14] As AI has gained momentum, prominent application providers have gone beyond creating traditional software to developing more holistic platforms and solutions that better automate business intelligence and analytics processes. [16] Based on our past interviews withs executives and investors in the field, we predict that business intelligence applications will be one of the fastest growing areas for leveraging AI technology over the next five to 10 years. [16] What was once the realm of science fiction, AI in business intelligence is evolving into everyday business as we know it. [16]

It’s not a simple process for companies to incorporate machine learning into their existing business intelligence systems, though Skymind CEO and past TechEmergence podcast guest Chris Nicholson advises that it doesn’t have to be daunting. [16] The anticipated benefits of using machine learning platforms for business intelligence include infrastructure cost reductions and operational efficiency. [16]

Avanade is a joint venture between Microsoft and Accenture that leverages the Cortana Intelligence Suite and other solutions for predictive analytics and data-based insights. [16]

This sort of information is known as unstructured data – because it doesn?t fit neatly into the rows and columns that traditional computer analytics software needs to be able to process data. [13] “What machine learning does for us is unlock the ability to start processing this unstructured data, and start to derive insights from it. [13]

Instead of trying to explicitly program intelligence, this new approach was based on feeding lots and lots of data to the machine, and then letting the algorithms discover patterns and extract insights from all that data. [22] New data means an added layer of information with subsequent increase of intelligence and learning from mistakes. [6]

The necessary ingredients are finally coming together : lots and lots of data, with the volume of data pouring in expected to double every three years or so; advanced machine learning algorithms that extract insights and learn from all that data; drastically lowered technology costs for collecting, storing and analyzing these oceans of information; and access to an increasing variety of data-driven, cloud-based AI applications. [22] While AI initially uses data to respond to human inputs, it evolves independently to generate its own instructions through the process of machine learning–no human needed–unless errors arise. [20] The new AI paradigm enabled computers to acquire intelligent capabilities by ingesting and analyzing large amounts of data using powerful computers and sophisticated algorithms. [22] Much progress has been recently made in the ability to extract features from all the data we now have access to, as well as in machine learning algorithms that give computers the ability to learn by ingesting large amounts of data instead of being explicitly programmed. [22]

As much as we view AI may as the wave of the future, it needs access to a tremendous amount of data to “learn.? [20] Because AI relies so heavily on the accuracy and efficacy of monstrous amounts of data, experts in this field will become more and more important. [20] Nor have they integrated the large amounts of data, analytical tools and powerful AI systems now at our disposal into their decision making systems. [22] There are clearly other major technologies around us, – e.g., smartphones, IoT, analytics, AI, – but when you think about it, all of them rely on their connections to cloud-based data, applications, and services for much of their functionality. [22] AI will enable companies to integrate data and information seamlessly from all systems, making it easier for disparate marketing tools to work together. [20] Because of the expected importance of AI in major decision making for companies, improving the accuracy of data quality will be paramount for businesses globally. [20] Due to its ability to adapt and interpret patterns and data so quickly, AI predictions will be trusted as much as top-level C-suite executive recommendations. [20]

Beyond their use in operations, the information generated by these various applications was collected in data warehouses, and a variety of business intelligence tools were used to analyze the data and generate management reports. [22] Data processing was the term then used to describe the applications of IT to automate highly structured business processes, e.g., financial transactions, inventory management, airline reservations. [22]

RANKED SELECTED SOURCES(39 source documents arranged by frequency of occurrence in the above report)

1. (89) Artificial intelligence – Wikipedia

2. (82) News stories with latest developments how artificial intelligence will make a difference to business — AI Congress London

3. (53) Irving Wladawsky-Berger: Data Science and Big Data

4. (27) 6 Examples of AI in Business Intelligence Applications

5. (25) Big Data vs. Artificial Intelligence – Datamation

6. (24) Big Data and Analytics Blog – Experfy Insights

7. (23) What is machine learning (ML)? – Definition from

8. (20) Artificial Intelligence and Big Data: A Perfect Match – DZone AI

9. (19) Artificial Intelligence is Set to Explode. Is Your Data up to the Task?

10. (14) Artificial Intelligence: The Journey So Far and a Look Into the Future

11. (12) AI-powered data analytics: Inside this transformative trend | CIO

12. (12) The Business Impact and Technologies of Big Data

13. (11) How the Healthcare industry is employing Big data and Artificial intelligence

14. (10) The latest big data news and articles

15. (8) Artificial Intelligence And Big Data The Amazing Digital Transformation Of Elsevier From Publisher To Tech Company | LinkedIn

16. (8) What Is Data Science Definition | Learn Why Data Science Is Important

17. (8) Next Chapter For Biotech? Many Say ‘Convergence’ With Data Science | CommonHealth

18. (6) Views of AI, robots, and automation based on internet search data

19. (5) BIG DATA Ronald van Loons

20. (5) New Major Business Trends: Big Data and Artificial Intelligence

21. (5) MTS, Cybersecurity â , Big Data/Machine Learning/Artificial Intelligence | Bellevue, WA

22. (5) artificial intelligence Archives – DATAVERSITY

23. (5) The Role of Artificial Intelligence in the Aviation Industry

24. (5) AI, Machine Learning & Cognitive Computing Services | IBM

25. (4) Will artificial intelligence replace humans? – SAS Voices

26. (4) Three Key Strategies for Big Data Security – AI Expo Blog

27. (4) Artificial Intelligence vs Machine Learning vs Deep Learning DATASCIENCEGYAN

28. (3) Podcast: Data Science, Machine Learning, and Artificial Intelligence | Lucidworks

29. (3) DOE, NCI Developing AI, Big Data Tools to Advance Cancer Research

30. (3) Japan is planning to use artificial intelligence and big data for crime prevention

31. (3) Global Big Data Conference

32. (2) On Artificial Intelligence, Machine Learning, and Deep Learning. Interview with Pedro Domingos | ODBMS Industry Watch

33. (2) The Evolution of Robotics and Artificial Intelligence – Alibaba Cloud Community

34. (2) Explore AI Issues and Opportunities at DWT’s Cloud, Big Data & AI Conference | Artificial Intelligence Law Advisor

35. (1) Big Data and Artificial Intelligence: Extracting Business Value | Carey Business School

36. (1) Innovations in Artificial Intelligence, Machine Learning, Data A – – Tyler, Longview, Jacksonville |ETX News

37. (1) European Commission outlines plans to boost artificial intelligence | Technology Law Dispatch

38. (1) Cutting Edge Agriculture: How Artificial Intelligence, Satellites and Big Data are Transforming Farmers Access to Finance – NextBillion

39. (1) Practical AI: or why everything that says it is, isn?t | InfoWorld