IEEE Technology and Society Magazine - September 2023 - 102

Opinion
insights into soil conditions, weather patterns, and
crop health. For example, using sensors and drones
to collect data on soil moisture levels, temperature,
and nutrient content, farmers can leverage DCAI to
predict the optimal time to plant, fertilize, and irrigate
their crops. They can also use AI-powered predictive
models to anticipate weather patterns and
adjust their farming practices accordingly, reducing
the risk of crop failure due to extreme weather conditions.
In addition to improving crop yields and sustainability,
DCAI can also help farmers reduce costs
by optimizing the use of resources such as water and
fertilizer, and by enabling more precise harvesting
and processing of crops. The application of DCAI in
agriculture has the potential to improve food security,
reduce waste, and promote sustainable farming
practices, all while increasing profitability for
farmers.
Fraud detection and prevention
DCAI is a powerful tool that can be used to prevent
fraud across various industries by analyzing
large amounts of transaction data to detect patterns
of fraudulent activities. AI can identify abnormal
behavior, relationships between individuals and
organizations, and patterns of fraud using ML algorithms,
which can help prevent future fraud. AI can
also monitor transactions in real time, assess overall
risk, and trigger alerts to prevent fraudulent activity.
By applying DCAI to fraud detection and prevention,
organizations can detect and prevent fraud more
effectively and efficiently, protecting themselves
and their customers from the harmful effects of
fraud [22].
Opportunities for new ventures and
innovation
New ventures and innovations in DC technology
have been rapidly growing in recent years. They are
leveraging data to develop innovative products and
services that are transforming industries and disrupting
traditional business models [26], [27]. Some of
these are focused on developing advanced analytics
solutions to help businesses analyze large data sets
and extract insights to make data-driven decisions.
AI-based young enterprises are leveraging ML and
other advanced techniques to develop intelligent
applications that can automate tasks, make predictions,
and provide insights. Internet-of-Things
(IoT)-oriented startups are developing connected
102
devices that collect and transmit data, allowing businesses
to monitor and optimize their operations.
Blockchain-focused startups are developing decentralized
applications that use distributed ledgers to
store and manage data securely. These applications
have the potential to disrupt traditional industries
such as finance, supply chain management, and
healthcare. Cloud computing is another area where
startups are developing infrastructure and platforms
that enable businesses to store, process, and
analyze data in the cloud [9], [28]. DC technology
startups are transforming industries by providing
new insights, increasing efficiency, and enabling
new business models. As data continue to play an
increasingly important role in business and society,
these young enterprises are well-positioned to continue
driving innovation and growth.
Opportunities in academic research
DCAI education and academic research involve
the study of techniques, methods, and algorithms
for analyzing, processing, and making predictions
based on large data sets. It is a multidisciplinary field
that draws upon knowledge from computer science,
statistics, mathematics, and engineering [29], [30],
[31]. In terms of education, DCAI programs typically
cover topics such as data mining, ML, deep learning,
NLP, and big data analytics. Students also learn about
the various programming languages and tools commonly
used in the field, such as Python, R, TensorFlow,
PyTorch, and open source tools in DCAI [32],
[33], [34] like Albumentations-Image augmentation,
Amazon SageMaker Debugger, AutoAugment
learning augmentation policies from data, Bootleg-
self-supervision for named entity disambiguation at
the tail, CleanLab-clean data set labels, DataPrep-
prepare your data with a few lines of code, Deequ-
unit tests for data, HoloClean: an ML system for data
enrichment, Knodle-knowledge-supervised deep
learning framework, Meerkat-handling data for
ML models, mltrace-a python package to make ML
models observable, nlpaug-augment NLP data sets,
Picket: guarding against corrupted data in tabular
data, Raha & Baran-configuration free error detection
tools, skweak-weak supervision for NLP, etc.
Many universities around the world offer degree
programs in DCAI, such as bachelor's and master's
degrees in data science or AI, as well as PhD programs
in related fields. There are also many online
courses like MIT Introduction to DCAI and massive
IEEE Technology and Society Magazine
IEEE Technology and Society Magazine - September 2023

Table of Contents for the Digital Edition of IEEE Technology and Society Magazine - September 2023
