Artificial Intelligence: 2020

Artificial Intelligence (AI) technology and its applications have been in the development since 1930 and continue to evolve as of today. The year 2010 was a milestone point for AI as there were a few key developments such as deep machine learning and advanced graphic processing units.  Decades from now, the year 2015 will be recognized as a key year for AI as classical algorithms and new technologies have become available that will form the growth engine of tomorrow.

AI algorithms in Data Mining, Machine Perception, Pattern Recognition, Intelligent Decision Support Systems, and Natural Language Processing are the core driver for AI in various industrial applications.

Presently, AI is becoming a part of many modern technology applications including robotics, Big Data, analytics and statistical modeling, machine learning, game playing, natural language processing, speech recognition, automated reasoning, expert systems, and many more areas.

Significantly faster processing than humans, inferences and connections among non-obvious elements, and connected intelligence across multiple domains are some of the key benefits for various industry verticals and specialty areas such as Marketing and Business Decision Making, Workplace Automation, Predictive Analysis and Forecasting, and Fraud Detection and Classification.

An emerging new set of disruptive technologies and platforms such as virtualization, software defined data centers and computing, cloud based services and platforms will accelerate AI development and deployment and bring capabilities to the next level with new applications and significantly improved functionality.

In opportunities, AI sees a lot of prospects in building cognitive computing systems such as smart machines, robotics, and virtual reality. There are continued and increasing investments in cognitive computing by market leaders such as IBM, Microsoft, Apple, Google, and HP.

Industry specific applications will be the driving opportunities in both machine learning and cognitive systems. Companies that produce large amounts of multi-variable data are the target customers for AI.

Big data and IoT bring a large level of personalized data with key inputs such as buyer behavior, user experience in certain commodities, utilization of resources and utilities, health monitoring and other such issues. This is a big field opening for personal virtual assistance and personal humanoid robots that can help assist and monitor people, patients, and children. Personal assistance will be a big opportunity in AI through 2022.

Major companies in Internet-related services, products, and cloud-based services are integrating AI into their products and services to improve their performance, manage SLAs (Service Level Agreements) and to increase business efficiency through understanding buyer behavior and predicting future business prospects.

The telecom industry, and the carrier services industry and mobile manufacturers, in particular, are great beneficiaries of AI in their operations. The telecom sector generates huge amounts of data on a real-time basis and it also has a huge amount of historical data.  AI can help telecom companies to analyze historical data and predict future trends in consumer behavior, customer switching trends and help in developing personalized programs and advertisement campaigns and offers for the individual customer based on historical data usage. This is very necessary for telecom companies to retain and develop business in the highly competitive market.  AI will help telecom carriers in operations support systems and business support systems (OSS/BSS) processing and analysis.

Pharmaceuticals and healthcare, in general, has a great pool of information regarding disease history, research, metadata studies, clinical research, product research and molecule research. Other data includes patient history, images and scans, diagnosis reports and patient history. Retrieval, use, and application of data manually to diagnose and predict appropriate treatment is a time-consuming job. Ai makes this time-consuming job easy by reducing the time for processing data and images by several times over manual time required for similar actions. This is helping medical and healthcare facilities to predict pattern and forecast treatment in faster pace saving on time and examining more patients a day than before. Clinical data mining and studying historical data are helping in rapid and faster research in the medical field and AI systems can help in predicting future cause or probable side effects of drugs. Image recognition and interpretation, maintaining e-Health records, providing telemedicine and mobile health programs (M-Health), pervasive and intelligent support through Intelligent Decision Support Systems in Critical Health Care are some of the upcoming benefits of AI in healthcare.

Financial services are one of the key beneficiaries of AI and its applications such as fraud detection and classification and predictive analysis and forecast. The inclusion of AI has helped financial services to process fraud cases faster than manual analysis and has helped in predictions and forecast for stock markets as well as other financial products such as insurance claims estimations. AI in wealth management is most commonly related to automated, algorithm-based portfolio management called the Robo-advisors. Recently, Charles Schwab a wealth management company has launched Robo-advising service Schwab Intelligent Portfolios. Bridgewater, the world’s largest hedge-fund manager uses AI and has created a team under the supervision of IBM Watson veteran David Ferrucci. ANZ Global Wealth is using IBM’s Watson Engagement Advisor since 2013. AI is becoming part of core business strategy of financial advisory and wealth management companies who want to race ahead of competitors using high-performance technologies at lower costs.

AI is playing a key role in manufacturing and heavy industries mainly in production and transportation of materials. In heavy industries, AI is used to increase productivity, quality, and energy efficiency. Mechanical use of robot arms and transport vehicles is not new to manufacturing and heavy engineering. However, use of machine intelligence and a capacity to take own decisions by machines is new field AI brings to manufacturing and heavy industry.

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