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Neural Networks: An Essential Beginners Guide to Artificial Neural Networks and Their Role in Machine Learning and Artificial Intelligence

There is a lot of coding and math behind neural networks, but the listener is presumed to have no prior knowledge or interest in either, so the concepts are broken down and elaborated on as such.  Each chapter is made as standalone as possible to allow the listener to skip back and forth without getting lost, with the glossary at the very end serving as a handy summary.  So if you want to learn about Neural Networks without having to go through heavy textbooks, listen to this audiobook now!

Big data

'Big data' is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.

Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate.[2]

Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source.

Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set.

Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet searches, fintech, urban informatics, and business informatics.

Data sets grow rapidly, in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.[10][11]

data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time.[21]

data requires a set of techniques and technologies with new forms of integration to reveal insights from datasets that are diverse, complex, and of a massive scale.[24]

2016 definition states that 'Big data represents the information assets characterized by such a high volume, velocity and variety to require specific technology and analytical methods for its transformation into value'.[25]

Similarly, Kaplan and Haenlein define big data as 'data sets characterized by huge amounts (volume) of frequently updated data (velocity) in various formats, such as numeric, textual, or images/videos (variety).'[26]

2018 definition states 'Big data is where parallel computing tools are needed to handle data', and notes, 'This represents a distinct and clearly defined change in the computer science used, via parallel programming theories, and losses of some

Data analysts working in ECL are not required to define data schemas up front and can rather focus on the particular problem at hand, reshaping data in the best possible manner as they develop the solution.

CERN and other physics experiments have collected big data sets for many decades, usually analyzed via high-performance computing (supercomputers) rather than the commodity map-reduce architectures usually meant by the current 'big data' movement.

The methodology addresses handling big data in terms of useful permutations of data sources, complexity in interrelationships, and difficulty in deleting (or modifying) individual records.[49]

The data lake allows an organization to shift its focus from centralized control to a shared model to respond to the changing dynamics of information management.

preferring direct-attached storage (DAS) in its various forms from solid state drive (SSD) to high capacity SATA disk buried inside parallel processing nodes.

Between 1990 and 2005, more than 1 billion people worldwide entered the middle class, which means more people became more literate, which in turn led to information growth.

The world's effective capacity to exchange information through telecommunication networks was 281 petabytes in 1986, 471 petabytes in 1993, 2.2 exabytes in 2000, 65 exabytes in 2007[12]

While many vendors offer off-the-shelf solutions for big data, experts recommend the development of in-house solutions custom-tailored to solve the company's problem at hand if the company has sufficient technical capabilities.[67]

Data analysis often requires multiple parts of government (central and local) to work in collaboration and create new and innovative processes to deliver the desired outcome.

Research on the effective usage of information and communication technologies for development (also known as ICT4D) suggests that big data technology can make important contributions but also present unique challenges to International development.[69][70]

Advancements in big data analysis offer cost-effective opportunities to improve decision-making in critical development areas such as health care, employment, economic productivity, crime, security, and natural disaster and resource management.[71][72][73]

However, longstanding challenges for developing regions such as inadequate technological infrastructure and economic and human resource scarcity exacerbate existing concerns with big data such as privacy, imperfect methodology, and interoperability issues.[71]

Predictive manufacturing as an applicable approach toward near-zero downtime and transparency requires vast amount of data and advanced prediction tools for a systematic process of data into useful information.[75]

A conceptual framework of predictive manufacturing begins with data acquisition where different type of sensory data is available to acquire such as acoustics, vibration, pressure, current, voltage and controller data.

Big data analytics has helped healthcare improve by providing personalized medicine and prescriptive analytics, clinical risk intervention and predictive analytics, waste and care variability reduction, automated external and internal reporting of patient data, standardized medical terms and patient registries and fragmented point solutions.[78][79][80][81]

This includes electronic health record data, imaging data, patient generated data, sensor data, and other forms of difficult to process data.

Human inspection at the big data scale is impossible and there is a desperate need in health service for intelligent tools for accuracy and believability control and handling of information missed.[83]

Because one-size-fits-all analytical solutions are not desirable, business schools should prepare marketing managers to have wide knowledge on all the different techniques used in these subdomains to get a big picture and work effectively with analysts.

The industry appears to be moving away from the traditional approach of using specific media environments such as newspapers, magazines, or television shows and instead taps into consumers with technologies that reach targeted people at optimal times in optimal locations.

For example, publishing environments are increasingly tailoring messages (advertisements) and content (articles) to appeal to consumers that have been exclusively gleaned through various data-mining activities.[93]

Health insurance providers are collecting data on social 'determinants of health' such as food and TV consumption, marital status, clothing size and purchasing habits, from which they make predictions on health costs, in order to spot health issues in their clients.

defines the Internet of Things in this quote: “If we had computers that knew everything there was to know about things—using data they gathered without any help from us—we would be able to track and count everything, and greatly reduce waste, loss and cost.

By applying big data principles into the concepts of machine intelligence and deep computing, IT departments can predict potential issues and move to provide solutions before the problems even happen.[101]

In this time, ITOA businesses were also beginning to play a major role in systems management by offering platforms that brought individual data silos together and generated insights from the whole of the system rather than from isolated pockets of data.

Besides, using big data, race teams try to predict the time they will finish the race beforehand, based on simulations using data collected over the season.[142]

They focused on the security of big data and the orientation of the term towards the presence of different type of data in an encrypted form at cloud interface by providing the raw definitions and real time examples within the technology.

Moreover, they proposed an approach for identifying the encoding technique to advance towards an expedited search over encrypted text leading to the security enhancements in big data.[148]

The SDAV Institute aims to bring together the expertise of six national laboratories and seven universities to develop new tools to help scientists manage and visualize data on the Department's supercomputers.

The British government announced in March 2014 the founding of the Alan Turing Institute, named after the computer pioneer and code-breaker, which will focus on new ways to collect and analyse large data sets.[158]

In May 2013, IMS Center held an industry advisory board meeting focusing on big data where presenters from various industrial companies discussed their concerns, issues and future goals in big data environment.

used Google Trends data to demonstrate that Internet users from countries with a higher per capita gross domestic product (GDP) are more likely to search for information about the future than information about the past.

The authors of the study examined Google queries logs made by ratio of the volume of searches for the coming year ('2011') to the volume of searches for the previous year ('2009'), which they call the 'future orientation index'.[165]

Eugene Stanley introduced a method to identify online precursors for stock market moves, using trading strategies based on search volume data provided by Google Trends.[166]

An important research question that can be asked about big data sets is whether you need to look at the full data to draw certain conclusions about the properties of the data or is a sample good enough.

Even as companies invest eight- and nine-figure sums to derive insight from information streaming in from suppliers and customers, less than 40% of employees have sufficiently mature processes and skills to do so.

As a response to this critique Alemany Oliver and Vayre suggest to use 'abductive reasoning as a first step in the research process in order to bring context to consumers' digital traces and make new theories emerge'.[183] Additionally,

Agent-based models are increasingly getting better in predicting the outcome of social complexities of even unknown future scenarios through computer simulations that are based on a collection of mutually interdependent algorithms.[184][185]

Finally, use of multivariate methods that probe for the latent structure of the data, such as factor analysis and cluster analysis, have proven useful as analytic approaches that go well beyond the bi-variate approaches (cross-tabs) typically employed with smaller data sets.

new postulate is accepted now in biosciences: the information provided by the data in huge volumes (omics) without prior hypothesis is complementary and sometimes necessary to conventional approaches based on experimentation.[187][188]

Large data sets have been analyzed by computing machines for well over a century, including the US census analytics performed by IBM's punch card machines which computed statistics including means and variances of populations across the whole continent.

However science experiments have tended to analyze their data using specialized custom-built high performance computing (supercomputing) clusters and grids, rather than clouds of cheap commodity computers as in the current commercial wave, implying a difference in both culture and technology stack.

Integration across heterogeneous data resources—some that might be considered big data and others not—presents formidable logistical as well as analytical challenges, but many researchers argue that such integrations are likely to represent the most promising new frontiers in science.[198] In

the authors title big data a part of mythology: 'large data sets offer a higher form of intelligence and knowledge [...], with the aura of truth, objectivity, and accuracy'.

(Ramsey theory) or existence of non included factors so the hope, of early experimenters to make large databases of numbers 'speak for themselves' and revolutionize scientific method, is questioned.[203]

the other hand, big data may also introduce new problems, such as the multiple comparisons problem: simultaneously testing a large set of hypotheses is likely to produce many false results that mistakenly appear significant. Ioannidis

Controlling Skynet: Regulatory Risks in Artificial Intelligence and Big Data

These regulators have focused, to date, on questions regarding process transparency, error correction, privacy concerns, and internalized biases, even as they see promise in AI and big data’s ability to reduce lending risk and/or open credit markets to previously underserved populations.

At the same time, the GAO has issued two reports (in March 2018 and December 2018) promoting or recommending interagency coordination on flexible regulatory standards for nascent financial technology (“Fintech”) business models (including through “regulatory sandboxes”) and the use of alternative data in underwriting processes.

Various state Attorneys General, for example, have joined the discussion by opposing revisions to the CFPB’s policy on no-action letters due, in part, to concern over the role machine learning could play in replacing certain forms of human interaction in overseeing underwriting questions such as “what data is relevant to a creditworthiness evaluation and how each piece of data should be weighted.”

In addition, the New York Department of Financial Services (“NYDFS”) has moved perhaps as far as any regulator—albeit in the context of life insurance, rather than banking or consumer finance—by issuing two guiding principles on the use of alternative data in life insurance underwriting: (i) that insurers must independently confirm that the data sources do not collect or use prohibited criteria;

and a study by the Federal Deposit Insurance Corporation (“FDIC”) noted that one in five financial institutions cited profitability as a major obstacle to serving underbanked consumers, but that new technologies may enable consumers whose traditional accounts are closed for profitability issues to continue to have access to financial services.

As financial institutions increase their use of AI in marketing, underwriting, and account management activities, decision-making that is removed from—or at least less comprehensively controlled by—human interaction raises the risk of discrimination in fact patterns that courts and regulators have not previously addressed.

With respect to federal consumer financial laws, ECOA prohibits a person from discriminating against an applicant on a prohibited basis regarding any aspect of a credit transaction or from making statements that would discourage on a prohibited basis a reasonable person from making or pursuing a credit application.

While such laws frequently protect similar classes as federal fair lending requirements do, some states add protected classes such as military servicemembers, or expressly protect consumers on the basis of sexual orientation in a manner that may only be implied by federal fair lending requirements.

At a November 2018 Fintech conference on the benefits of AI, for example, Lael Brainard, a member of the FRB, noted that firms view artificial intelligence as having superior pattern recognition ability, potential cost efficiencies, greater accuracy in processing, better predictive power, and improved capacity to accommodate large and unstructured data sets, but cautioned that AI presents fair lending and consumer protection risks because “algorithms and models reflect the goals and perspectives of those who develop them as well as the data that trains them and, as a result, artificial intelligence tools can reflect or ‘learn’ the biases of the society in which they were created.”

Brainard cited the example of an AI hiring tool trained with a data set of resumes of past successful hires that subsequently developed a bias against female applicants because the data set that was used predominantly consisted of resumes from male applicants.

In a white paper, “Opportunities and Challenges in Online Marketplace Lending,” the Treasury Department recognized this same risk, noting that data-driven algorithms present potential risk of disparate impact in credit outcomes and fair lending violations, particularly as applicants do not have the opportunity to check and correct data points used in the credit assessment process.

Some of the lenders surveyed tested their credit models for accuracy, and all discussed testing to control for fair lending risk.” Even in the absence of discriminatory intent or outcomes, AI may complicate compliance with technical aspects of federal and state fair lending requirements.

Adverse action notices must contain either a statement of specific reasons for the action taken or a disclosure of the applicant’s right to a statement of specific reasons taken within 30 days if the statement is requested within 60 days of the creditor’s notification.

Financial institutions using less transparent AI systems may find it difficult to populate an appropriate list of reasons for adverse action and those with more transparent AI systems may find themselves responding to consumer inquiries or complaints about credit decisions made on seemingly irrelevant data points over which an AI happened to find a correlation with default rates or other material considerations.

(FCRA also requires users of consumer reports to issue adverse action notices that include specific disclosures regarding numeric credit scores when such scores are used in deciding to take adverse action.) FCRA: When is “Big Data” a “Consumer Report?” Big data also presents risks under FCRA, and such risks are amplified if AI-driven underwriting systems have access to alternative data sources without the establishment of proper controls restricting the use of particular data elements.

Except as expressly exempted, a “consumer report” under FCRA is “the communication of any information by a consumer reporting agency bearing on a consumer’s creditworthiness, credit standing, credit capacity, character, general reputation, personal characteristics, or mode of living which is used or expected to be used or collected in whole or in part for determining a consumer’s eligibility for credit, employment purposes, or any other purposes enumerated in the statute.”

(The term “consumer reporting agency” somewhat circularly includes most parties who provide “consumer reports” on a for profit or a cooperative non-provider basis, so the fact that a data source does not consider itself to be a “consumer reporting agency” is not necessarily relevant to a financial institution’s obligations when using alternative data.)

Entities that use AI algorithms for credit decisions may have difficulty providing information required in FCRA adverse action notices (such as the specific source of the consumer report and the factors affecting any credit scoring model used in underwriting credit) when it is unclear what data points comprise of the consumer report.

A consumer reporting agency is subject to specific legal obligations, such as obtaining certain certifications from users of consumer reports, ensuring the accuracy of consumer information, investigating consumer disputes of inaccurate information, and filtering out certain items that cannot be reported.

If the data used reflects on FCRA-regulated characteristics (e.g., the consumer’s creditworthiness, credit standing, reputation, etc.) such that its use in credit underwriting renders the information a “consumer report,” the false representation to the data source may be a false certification to a consumer reporting agency for the purpose of obtaining a consumer report.

For example, the FTC and FDIC have pursued an enforcement action against a provider of credit cards to consumers with poor credit histories for alleged violations, including a UDAP prohibition for failing to disclose to consumers that certain purchases that triggered the company’s risk algorithm could reduce the consumer’s credit limit.

As black box AI systems become more prevalent, and such systems may train themselves to use novel algorithms and approaches to underwriting and account management, financial institutions may want to consider the need for broader disclaimers regarding the factors that may impact credit decisions and/or the processes that may develop new approaches to creditworthiness analysis altogether.

Finally, beyond direct concerns as to violations of law and control of risk by financial institutions themselves, regulators have expressed interest in limiting the risk that financial institutions expose themselves and/or consumers through partnerships with vendors who may rely on AI or big data processes.

More concretely, NYDFS has taken the position that an insurer “may not rely on the proprietary nature of a third-party vendor’s algorithmic process to justify the lack of specificity related to an adverse underwriting action,” and that expectation to understand a vendor’s AI models could also apply to the context of credit underwriting.

For example, the FDIC guidance discusses risks that may be associated with third-party lending arrangements, as well as its expectation that financial institutions implement a process for evaluating and monitoring vendor relationships that include risk assessment, due diligence, contract structuring and review, and oversight.

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Artificial intelligence is putting new teeth on the old saw that cheaters never prosper.

New companies and new research are applying the cutting edge technology in at least three different ways to combat cheating — on homework, on the job hunt and even on one’s diet.

In California, a new company called Crosschq is using machine learning and data analytics to help employers with the job reference process.

The technology is meant to help companies avoid bad hires and compare how job candidates present themselves with how their references see them.

In Pennsylvania, Drexel University researchers are developing an app that can predict when dieters are likely to lapse on their eating regimen, based on the time of day, the user’s emotions — even the temperature of their skin and heart rate.

And in Denmark, University of Copenhagen professors say they can spot cheating on an academic essay with up to 90% accuracy.

The results add to the growing amount of technology that pinpoints plagiarism in schoolwork.

These are a few of the ways algorithms, analytics and machine learning are pervading the lives of consumers and workers.

It powers robo-advisers like Betterment and Wealth, it it assists in medical diagnoses, and it aids school systems when they sort through students’ ranked preferences for charter schools.

“Algorithms help manage information and can help reveal insights not immediately apparent to humans,” West said.

Don’t miss: More Americans believe it’s OK to cheat on your taxes, according to IRS poll Artificial intelligence won’t cure the human weakness to fudge facts and cut corners — and the technology itself isn’t foolproof.

“No algorithm is perfect,” he said, noting that its conclusions depended deeply on the data it received in the first place.

“You have to make sure the conclusion reached by AI actually is true in fact.” One example of that issue: facial recognition algorithms have had trouble recognizing darker skin tones and women’s faces, in part because the algorithms are trained with images of lighter-skinned male faces.

Mike Fitzsimmons, Crosschq’s co-founder and CEO, was partly inspired to start the business by bad hires he’d made in the past, he told MarketWatch.

“We believe there is so much bias in the old way of doing this,” he said, noting how job candidates can find friends and past colleagues who will overhype the candidate.

The program has candidates rate themselves on various factors like attention to detail and self motivation, and also has their references rate the candidate on the same things.

The technology then compares the ratings, and triangulates the results with the job skills the employer values.

“It’s when you start to see inconsistencies, that’s when the flags go up,” he said, adding that the program is meant to control “the ability of the candidate to game the system.” The company was founded last year and tested its product until formally launching last week.

Yoni Lateiner, NerdWallet’s head of talent, said the technology “provides consistency across our reference checks and uncovers surprisingly candid insights about our candidates and new hires.” Fitzsimmons noted the Crosschq technology wasn’t passing judgment on whether to hire a candidate.

See also: 3 golden rules to turbo-charge your job hunt Artificial intelligence is coming into the hiring process in other ways.

A recent survey from the large employment law firm Littler Mendelson said 37% of polled companies were using artificial intelligence.

Eight percent said they used it to analyze applicant body language, tone and facial expressions during interviews.

Approximately 45 million Americans diet each year, but many don’t lose weight because they backslide, said Drexel University psychology professor Evan Forman.

While there are plenty of apps telling users the foods they should be eating and the activities they should be doing, that only goes so far, said Forman, who is director of the school’s Center for Weight, Eating and Lifestyle Science.

Harnessing user data, the app learns when diet lapses are statistically likely and then warns users right before the next one could happen.

Though users had to manually input data in early trials — like telling the program if they felt stressed — Forman said the end goal is to make OnTrack as automated as possible.

For example, participants using new versions of OnTrack are incorporating data from sensors including FitBits

Forman used OnTrack himself to try breaking his post-dinner habit of snacking on Trader Joe’s tortilla chips.

Late last month, Danish researchers unveiled a program that they say can determine with 90% accuracy whether a high school research paper was written by the student handing in the assignment, or someone else.

The program scrutinizes writing style and word choice, and then sees how the paper measures up against the student’s past work.

Don’t miss: College counselors: Wealthy parents regularly ask about illegal ways to get their kids into college There are already established companies using artificial intelligence to spot bogus schoolwork, such as Turnitin.

They said it could be used to spot forged documents during police work, and it could also be applied to social media.

are grappling with misinformation from internet trolls, automated bots and fake accounts.

They hope to determine whether it’s a genuine user, a chatbot or an imposter behind a tweet.

Last fall, Twitter CEO Jack Dorsey said even though his company uses machine learning to find fake accounts, advanced technology can’t catch all of them.

“Where it becomes a lot trickier is where automation is actually scripting our website to look like a human actor.

So as far as we can label and we can identify these automations we can label them — and I think that is useful context.” A Twitter spokeswoman declined to comment.

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