The global market for Affective
Computing is projected to reach US$131 billion by 2025, driven by growing
awareness over the real importance of emotion in advancing the capability of artificial
intelligence (AI) technology. Given that human decision making processes is a
complex combination of cognitive functions and emotional, also known as affective
functions, machines will need to understand and respond to human emotions in
order to function effectively, efficiently and safely alongside humans. Emotional
intelligence is therefore the next logical step in the evolution of AI based
machines in order to help humans fully realize the many benefits of the
technology. Few the factors which indicate affective computing as ripe for
commercial growth include rapid proliferation of AI and the continuous quest
for emotionally responsive AI for a more natural interaction between humans and
machines; need for emotion-detection and emotion intelligence becomes more
important than ever as AI robots and assistants take over more and more of
human functions in education, industry, healthcare, and day to day living. Read More…
The Global Market for Big Data Technologies and Services is Projected to Reach $60 Billion by 2022
GIA launches comprehensive analysis of industry segments, trends, growth drivers, market share, size and demand forecasts on the global Big Data market. The global market for Big Data Technologies and Services is projected to reach $60 billion by 2022 , driven by soaring digital data volumes in organizations and the resulting need to turn this data into valuable insights for enhancing operational efficiency, tapping new opportunities and gaining competitive edge. Defined as a natural result of mankind’s obsession with information technology and digitalization, “Big Data” refers to extremely large sets of structured, semi-structured and unstructured data of different types, including text, audio or video, generated from diverse data sources that has the potential to be mined for required information. While data supply associated with big data ecosystem has always been large and voluminous in most organizations, the ability to use these large datasets and convert them into meaningf
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