Articles
Artificial Intelligence - AI
by Awesome POWER Duplication VIRTUAL Employment INDUSTRY
NEW AI-INDUSTRY REVIEWS 2022
Business interest in artificial intelligence (AI) and machine learning (ML) has soared over the past few years. Surveys show corporate investment in AI increased by 40 per cent between 2019 and 2020. In the financial sector, AI is used to refine the guidance provided by chatbots and robo-advisors, and to detect fraud patterns and make decisions on customer creditworthiness.
In retail, AI is used to provide customized consumer recommendations, manage supply chain logistics, and streamline store operations.AI potential has attracted government attention as a key generator of digital transformation. In May, the Biden administration launched AI.gov as the central information source for the National Artificial Intelligence Initiative established in 2020.AI Regulations are Rapidly EmergingHowever, the use of AI comes with significant concerns, particularly in areas of ML which rely on enormous stores of data.
Current events have highlighted ethical concerns associated with AI, spurring governmental regulatory and enforcement action. Capping a recent wave of new AI guidelines and standards, the EU recently published a proposed EU Regulation on Artificial Intelligence, intended to address AI risks to fundamental rights and safety. In the absence of specific federal legislation for AI and data protection,
the US Federal Trade Commission recently finalized a settlement ordering a company to destroy algorithms and AI/ML models derived from privacy violations found in the misuse of consumer biometrics. As consumers begin to question the levels of surveillance in their home devices, their usage patterns being monitored, and the logic applied to automated decisions affecting their lives, operating on a build first, question later basis comes with increasing risk.Data Risks and Vulnerabilities in AI DevelopmentIn ML, the availability of vast quantities of data has constituted the basis for innovation. Data is used to train algorithms to apply desirable inferred labels to new input data. Understanding this reality highlights certain data risks in AI/ML, particularly the following examples:Poor data quality.
Considering the large quantity of data used to enable AI, it can be difficult to maintain sufficient data quality. The risk associated with poor data quality includes problems in already-labelled data being used as training data—for example, it may contain unconscious biases or fail to be a truly representative sample. In addition, data may be inaccessible, inconsistent, disorganized, and lack proper controls for the creation and addition of metadata.Privacy and other compliance concerns in the use of personal data. Training and input data used in AI technology often involves personal data, whether in the consumer or employment context. Processing this data requires close attention to privacy requirements, particularly where personal data is used in
algorithms
to make certain assumptions about individuals.Where Information Governance Can InterveneFundamentally, ML requires the availability of accessible, trusted, secure and reliable data, which is already a key goal of information governance. Implementing an IG program can help an organization to:Improve data quality. The report on AI data quality of the EU Agency for Fundamental Rights expounded on the meaning of quality data in the context of creating quality
algorithms,
which includes ensuring that data is accurate, representative, complete, and not outdated. Businesses can save considerable time and expense in the laborious data cleaning and preparation process by strategically setting policies and protocols on data collection, storage, taxonomy, and sufficient metadata.Facilitate data accessibility. An IG program should aim to break down the information silos between business functions. Creating consistency in structures, terminology and
protocols,
as well as mapping organizational data flows, should encourage greater collaboration and information sharing between functions. Better decisions can then be made on additional data types that an organization can harvest.Achieve AI transparency and establish audit trails. As more governments look to implement audit requirements for AI algorithms, IG programs can respond by adjusting existing IG policy considerations to reflect AI system concerns. IG programs that inventory and map information creation and usage can establish and ensure data lineage. In
addition,
keeping technical documentation and system logs provides the ability to trace the AI systems function for both regulatory compliance and performance-tracking purposes.Comply with privacy regulations. AI systems using personal data must be in compliance with applicable privacy requirements. An IG program may set controls for processing sensitive personal data, such as tagging the data to ensure appropriate access, use and retention.
Summary: In the financial sector, AI is used to refine the guidance provided by chatbots and robo-advisors, and to detect fraud patterns and make decisions on customer creditworthiness. In retail, AI is used to provide customized consumer recommendations, manage supply chain logistics, and streamline store operations. Creating consistency in structures, terminology and protocols, as well as mapping organizational data flows, should encourage greater collaboration and information sharing between functions. Additional
This article," was first published on "
Source of Article:
Sponsor Ads
About Awesome POWER Duplication 
VIRTUAL Employment INDUSTRY
476 connections, 35 recommendations, 2,451 honor points.Joined APSense since, July 6th, 2014, From Books-Poetry-Writing, United States.
Created on Jul 21st 2021 12:10. Viewed 210 times.
Comments
|
Marketing Trends
  Digital IntelligenceWikipedia says: Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by humans or animals. Leading AI textbooks define the field as the study of "intelligent agents": any system that perceives its environment and takes actions that maximize its chance of achieving its goals.[a] Some popular accounts use the term "artificial intelligence" to describe machines that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving".[b] AI applications include advanced web search engines, recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (such as Siri or Alexa), self-driving cars (e.g. Tesla), and competing at the highest level in strategic game systems (such as chess and Go), As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect. For instance, optical character recognition is frequently excluded from things considered to be AI having become a routine technology. 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. AI research has tried and discarded many different approaches during its lifetime, including simulating the brain, modeling human problem solving, formal logic, large databases of knowledge and imitating animal behavior. In the first decades of the 21st century, highly mathematical statistical machine learning has dominated the field, and this technique has proved highly successful, helping to solve many challenging problems throughout industry and academia. The various sub-fields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects. General intelligence (the ability to solve an arbitrary problem) is among the field's long-term goals. To solve these problems, AI researchers use versions of search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, probability and economics. AI also draws upon computer science, psychology, linguistics, philosophy, and many other fields. The field was founded on the assumption that human intelligence "can be so precisely described that a machine can be made to simulate it". This raises philosophical arguments about the mind and the ethics of creating artificial beings endowed with human-like intelligence. These issues have been explored by myth, fiction and philosophy since antiquity Jul 22nd 2021 17:45 |
Tags Links
patterns make decisionsai customized consumer retail recommendationsfinancial sectorcollaboration and information sharingai chatbots and robo advisorsai make decisions on customer creditworthinessmapping organizational data patterns
More Articles
7 Things Every Home Business
Clickbank Traffic Secrets You Need To Know
'' START DOING THIS TODAY!!!
What Methods Do You Use To Increase Site Visitors?
WHAT Would YOU RATHER HAVE ENDLESS MONEY OR LOVE?
Similar Articles
What are the Components of Artificial Intelligence? How is Artificial Intelligence used?
Business Intelligence Applications That Use Artificial Intelligence
What are Artificial Neural Network in Artificial Intelligence?
Artificial intelligence
What everything you need to know about Artificial Intelligence?
Jul 21st 2021 14:06