The modern world is driven by machines. All of those machines, including computers, lack intelligence on their own, but with each passing day, Artificial Intelligence (AI) brings them closer to ruling the world. AI is slowly inching us toward a reality in which computers will work as a truly intelligent agent, taking into account many aspects that currently only the human mind can consider.

 

AI already has a strong foothold in social networking, mobile solutions and apps, and technology giants are investing considerable resources into AI research and solutions. The emerging IT world is exploring new horizons in the field of AI in which new research on infinite boundary solutions are carried out in pattern recognition, neural networks, fuzzy systems, expert systems, swarm intelligence and other areas.

 

Genetic Algorithms (GA) are one research approach in which AI searches heuristic results and mimics the process of natural evolution. Inspired by nature (e.g. inheritance, mutation, selection, and crossover), GA are used to generate meaningful solutions for optimization and search-related problems. Genetic Algorithms have already proven useful in bioinformatics, phylogenetics, computational science, engineering, chemistry, economics, mathematics, manufacturing and many more areas. They have even delivered solutions to combat “fake news.”

 

As fake news inundates people through various social-media sources, GA-based machine learning can effectively filter out information based on authenticity and reliability. This filtering is achieved by ranking data in terms of its validity, thus filtering out fake news due to its lower rank in matching.

 

 

In the example above, a news feed uses GA to filter-out fake news from users’ COVID-19 headlines. This solution can be incredibly helpful as people search for reliable information in times of crisis, but Genetic Algorithms also have day-to-day uses as well.

 

Google uses GA in several instances. Advanced deep learning techniques, for instance, power intelligent suggestions when users begin their search. For example, when people type “deep,” Google may suggest phrases like “Deep Learning,” “Deep Diving,” or “Deep Forest” based on various parameters. Google also uses deep-learning technology to cut the speech-recognition error rate in its latest Android OS-based App tool. The company’s Gmail inbox also leverages the technology to handle incoming emails more easily. One of the best solutions has long been Smart Reply, which uses Google’s machine-learning (ML) intelligence to scan an email and suggest three possible (and very short) answers. Google says more than 10 percent of replies in the mobile app already use Smart Reply, with the ML-based solution using deep learning to continually refine its reply options based on usage.

 

Facebook’s Language Technology Group is strengthening Messenger with voice-to-text input, and the social-media company has begun testing a framework that can transcribe speech to text and then let users command it to send the message. Facebook’s mobile-app work has been bolstered since its acquisition of Parse, enabling the company to dramatically increase the pace with which it develops apps (from 60,000 apps in 2013 to 500,000 apps today). Apple’s Siri is part of the smart-app trend as well, enabling users to transcribe text when small wearable screens (e.g. Apple Watch) are simply too small to efficiently type text.

 

Various AI techniques can predict news reliability by training a model based on metadata and search phrases, but many traditional AI methods and algorithms are time-consuming and require considerable training time to improve the AI’s knowledge base and accuracy. Genetic Algorithms are better in situations in which its advanced ML methods can reduce training times and improve recognition rates while accelerating adoption for new detection models such as ranking news from multiple language feeds, extracting text from images or text-to-speech recognition methods.

 

As GA become an AI driver, organizations would be wise to consider its applicability for their own business and IT operations. After all, Genetic Algorithms can be a powerful tool well beyond identifying fake news.

Dr. Magesh Kasthuri

Dr. Magesh Kasthuri

Principal Consultant, Cloud Applications and Platforms

@WiproDigital

Dr. Magesh Kasthuri is a Distinguished Member of Technical Staff and Principal Consultant of Cloud Applications and Platforms at Wipro Digital. He has completed his Ph.D. in Deep Learning and Genetic Algorithms and published more than 40 papers in Blockchain, Cloud Technology, DevOps Solutions, and Digital Transformation Architecture. He has patents in technology areas including Cloud, DevOps, Machine Learning and Blockchain. He is an avid blogger on LinkedIn, writing technical blogs on a daily basis with the hashtag #shorticle. He can be reached at magesh.kasthuri@wipro.com

What you’ve read here? Tip of the iceberg. Are you ready to be part of the excitement?