Adonis Diaries

Posts Tagged ‘DMITRY YOUNG

NEURAL LEARNING AI: Machine learning the internet of things

Note: it is a propaganda piece, but it is okay if it helps learning

Six Introductory Terms & The Five Effects

Predictive Analytics Terminology

1. Predictive analytics

Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions.

In this definition, individuals is a broad term that can refer to people as well as other organizational elements. Most examples in this book involve predicting people, such as customers, debtors, applicants, employees, students, patients, donors, voters, taxpayers, potential suspects, and convicts.

Predictive analytics also applies to individual companies (e.g., for business-to-business), products, locations, restaurants, vehicles, ships, flights, deliveries, buildings, manholes, transactions, Facebook posts, movies, satellites, stocks, Jeopardy! questions, and much more.

Whatever the domain, PA renders predictions over scalable numbers of individuals.

2. Predictive Model

A mechanism that predicts a behavior of an individual, such as click, buy, lie, or die.

It takes characteristics (variables) of the individual as input and provides a predictive score as output. The higher the score, the more likely it is that the individual will exhibit the predicted behavior. (Basically, a mathematical equation)

3. Artificial Intelligence

Advanced machine capabilities that are by definition impossible to achieve since, once achieved, they have necessarily been trivialized (by way of being mechanized) and are therefore not impressive in the subjective sense of intelligence, so they no longer qualify. (Qualify for what?)

The word intelligence has no formal definition, so why use it in an engineering context? (Expressing the opinion of experts in the field domain in matter of what can go wrong?)

I still feel like IBM Watson seems truly intelligent when watching it play the TV quiz show Jeopardy!

This definition is not an excerpt from the book Predictive Analytics, but it does summarize one of my conclusions in the book chapter on Watson.

4. Uplift Model

A type of predictive model that predicts the influence on an individual’s behavior that results from applying one treatment over another. Synonyms include: differential response, impact, incremental impact, incremental lift, incremental response, net lift, net response, persuasion, true lift, or true response model.

The uplift score output by and uplift model answers the question: “How much more likely is this treatment to generate the desired outcome than the alternative treatment”?

For more information, see the article Personalization Is Back: How to Drive Influence by Crunching Numbers (which includes links for further reading at the end), Chapter 7 of Predictive Analytics, and, for more technical citations, the Notes corresponding to that chapter, which may be downloaded as a PDF.

5. Vast Search

The term that industry leader (and Chapter 1 predictive investor) John Elder coined for predictive modelings intrinsic automation of testing many predictor variables and the associated peril of stumbling across a correlation with the target variable that may be perceived as significant if considered in isolation without considering the search that was employed to unearth it.

But that in fact was only due to random perturbations.

Synonyms include: multiple comparisons trap, multiple hypothesis testing, researcher degrees of freedom, over-search (akin to over-fit), look-elsewhere effect, the garden of forking paths, fishing expedition, cherry-picking findings, data dredging, significance chasing, and p-hacking. (Domain of statistical analysis methods and tools)

For more information, see my article HBO Teaches You How to Avoid Bad Science.

Chapter 3 of the 2016 updated edition of my book, Predictive Analytics, and, for more technical citations, the Notes corresponding to that chapter, which may be downloaded as a PDF.

6. Automatic Suspect Discovery (ASD)

In law enforcement, the identification of previously unknown potential suspects by applying predictive analytics to flag and rank individuals according to their likelihood to be worthy of investigation, either because of their direct involvement in, or relationship to, criminal activities. (Biases related to racist behaviors, elite classes world view, and mental training?)

Further info: This topic is explored in a special sidebar on the NSA use of predictive analytics within the ethics and privacy-focused chapter 2 of Predictive Analytics. Also see my Newsweek op-ed on this topic.

ASD provides a novel means to unearth new suspects.

Using it, law enforcement can hunt scientifically, more effectively targeting its search by applying predictive analytics, the same state-of-the-art, data-driven technology behind fraud detection, financial credit scoring, spam filtering, and targeted marketing. (Application to target specific classes of people, and saying that science is supporting the actions)

ASD flags new persons of interest who may then be elevated to suspect by an ensuing investigation.

By the formal law enforcement definition of the word, an individual would not be classified as a suspect by a computer, only by a (trained enforcement agent)?

 16h16 hours ago (posted on Twitter)

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If Data Is The New Gold

NEW YORK.

SYSTEMS APPLICATIONS AND PRODUCTS SE (NYSE: SAP) today launched research findings showing that companies leaders believe that fragmented, siloed technology environments were stymieing their capability to make informed business decisions.

Nearly 74 %, of survey respondents said their own data landscape is so complex it limits agility, and 86 % said they were not getting the the majority of out of their data.

“We view consolidated and streamlined data as the next great enterprise asset, mentioned Greg McStravick, president, Database plus Data TIBCO® Management, SAP.

“Our study findings show that like natural energy resources, data resources are just beneath the surface, in places that are either inaccessible or invisible. If data is the new gold, then we aim to make data scientists the new gold miners. We support all business stakeholders tasked with taming and deriving value from their enterprise data to help make vital business decisions.€

“Data 2020: State of Big Data examined a wide variety of issues related to enterprise data and found a good enterprise data management landscape ready with opportunity, yet challenged to handle across job functions, departments plus geographies.

The results of the study point out complexities in data management which can be solved by analytics solutions that will add value and data researchers who can make sense of the staggering levels of information that enterprises collect.

Siloed, inaccessible and unhealthy data: 50 percent of respondents believe that information is inaccessible to a wide variety of company stakeholders, and 79 percent mentioned their company data needs more a checkup to make it healthy.

Analytics driving data-driven enterprises:

Analytics rated as the most important technology in the business, closely followed by the Internet associated with Things, machine learning and synthetic intelligence.

Data scientists on popular: While 79 percent of participants said that data scientists are important to make sure a company success, yet just 53 percent currently have data researchers employed.

In such a high-demand industry, 78 percent are concerned about a lack of data scientists and people using the right skills to successfully work together with data in the future.

In related information, SAP today announced the SYSTEMS APPLICATIONS AND PRODUCTS Data Hub solution to create worth across diverse data landscapes by means of data integration, data orchestration plus data governance, as well as by generating powerful data pipelines that can speed up positive business results.

“Data 2020: State of Big Data” had been supported by Regina Coroso Consulting and based on global survey comes from more than 500 IT decision manufacturers from enterprise-level companies. Countries selected include the United States, Brazil, the particular United Kingdom, Canada, Germany, France, Japan, China and Australia.

For more information on SAP Data Hub, visit http://www.sap.com/datahub. To learn more about the research, visit the SAP News Center. Follow SAP on Twitter at @sapnews.

Media Contacts:
Scott Malinowski, SYSTEMS APPLICATIONS AND PRODUCTS, +1 (617) 538 6297, scott.malinowski@sap.com, ET
Adam Novak, PAN Communications, +1 (617) 502-4300, sapplatform@pancomm.com, AINSI QUE

SAP and other SAP services and products mentioned herein as well as their particular logos are trademarks or signed up trademarks of SAP SE within Germany and other countries. Please observe http://www.sap.epx#trademark for additional trademark information {and|plu


adonis49

adonis49

adonis49

October 2021
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