2022 Trends in Artificial Intelligence and Machine Learning: Reasoning Meets Learning - insideBIGDATA

For most organizations, the bifurcation of Artificial Intelligence has been as stark as it's been simplistic. AI was either machine learning or rules-based approaches (the former of which outnumbered the latter), supervised or unsupervised learning, computer vision or natural language technologies. Due to a number of developments in the past year around ModelOps, composite AI, and neuro-symbolic AI, there's currently a growing awareness throughout the enterprise that AI--and its ROI--not only involves each of the foresaid dimensions, but does so optimally when they operate in conjunction with each other to pare the costs, difficulty, and time they otherwise require. CTO Marco Varone, "There are situations where you can get better results combining the different approaches; there are situations where you can use both and it's not too different, and there are situations where it's better with one approach." By incorporating the full AI spectrum into their toolkits, organizations can not only deploy the most appropriate method for their cognitive computing tasks, but also exploit surrounding areas of opportunity like intellectual property for machine learning models, cloud or Internet of Things use cases, and explainable AI. "The future is what we call a hybrid or composite approach where you use all the techniques that are available and you put them together in a way that the end user or data scientist trying to solve a specific problem can take different techniques and decide to use the ones giving the best results," Varone predicted.