Artificial intelligence (AI) is a field that enables computers and robots to carry out real activities, whereas applied AI takes AI out of the lab and into the real world. By enhancing software programs and utilizing powerful machine learning (ML), applied AI offers extremely high levels of accuracy and continuous adaptation for almost unlimited use cases. Business models and industry processes are being contextualized by applied AI, which is also enhancing how humans interact with everything around us.
As businesses and industries continue to invest in applied AI, it’s important to address the key benefits, technologies, use cases, trends, and challenges of this rapidly advancing technology.
With the help of quick, iterative processing, advanced algorithms, and a huge amount of data, applied AI enables software to automatically learn from patterns and features in the data. High integration, improved accuracy, and the quick flow of data along with efficient analytical frameworks are instrumental in its effective results.
According to the Fu Foundation School of Engineering and Applied Science, “machine learning is a pathway to AI.” This branch of AI applies learning to make ever-better decisions by using algorithms to automatically discover patterns and learn insights from data.
Use cases: Fraud detection and prevention (banking and finance industry), image recognition, speech recognition, schedule optimization, etc.
|Natural language processing
Machines can now comprehend human language with the help of an AI field called natural language processing (NLP). Its objective is to create computer programs that can comprehend text and carry out automatic tasks like topic classification, translation, and spell-checking.Use cases: Speech recognition, part-of-speech tagging, word sense disambiguation, named entity recognition, co-reference resolution, sentiment analysis, natural language generation, etc.
|Deep reinforcement learning
According to viso.ai, deep reinforcement learning is the combination of reinforcement learning with deep learning techniques and is used to solve challenging sequential decision-making problems.
Use cases: Robot control, self-driving cars, push notifications, fast video loading, etc.
According to IBM, computer vision is a subfield of ML. AI research in the area of computer vision teaches computers to extract and decipher information from picture and video data.
Use cases: Image classification, object detection, object tracking, content-based image retrieval, satellite drought analysis, etc.
Target is known to use data from clicks and API hits on its websites to forecast user demand, better understand its consumers' buying interests, and deliver targeted, individualized marketing campaigns to people based on these findings.
According to AI Data Analytics Network, Starbucks uses data from its various sources to enable personalized product recommendations and predictive inventory management. It also equips espresso machines with sensors that record and analyze each shot that is made, combined with predictive analytics to identify possible machine repair and tuning opportunities.
AI Data Analytics Network also cites Facebook using AI to accomplish all kinds of intriguing things, from detecting hate speech to assisting advertisers in more effective customer targeting and personalized marketing messaging.
Netscribes explains how this leading telecom brand uses AI to digitize customer experiences with a vision to upsell and cross-sell new products. According to studies, omnichannel cloud communication – the technology that aggregates video, voice, messaging, etc., into one platform – combined with AI technologies can cut expenses by up to 20%.
Although applied AI is on the rise and multiple use cases are popping up, there are certain risks attached to it. The debate around these is expected to set the path for technology in the future.
Applied AI has immense potential to transform industries and society by solving real-world problems and improving efficiency. However, its successful implementation requires a proactive approach to addressing its ethical, regulatory, and technological implications. Businesses that embrace applied AI must work closely with stakeholders to identify their needs and design more effective solutions around them.