Artificial intelligence (AI) is everywhere. It is beyond commercialized. This momentous trend is largely driven by deep neural networks or deep learning. This interesting topic came to us from The Next Web in their article, “What are neural-symbolic AI methods and why will they dominate 2020?”
Around since the 1960s, deep neutral networks are fueled by the combination of internet-scale datasets and distributed graphics processing unit (GPU) farms. However, AI is much richer than just this. Symbolic reasoning algorithms such as artificial logic systems may soon find themselves in the spotlight.
When it comes to image recognition and machine translation, deep neural nets have gone above and beyond. Unfortunately, for many other complex applications, traditional deep learning approaches cannot match the ability of hybrid architecture systems that additionally leverages other AI techniques. Symbolic AI is powerful at manipulating and modeling abstractions, but deals poorly with massive empirical data streams.
Deep neural nets have done amazing things over the last few years, bringing applied AI to a whole new level. The next phase has potential to do even more.
Melody K. Smith
Sponsored by Access Innovations, the world leader in thesaurus, ontology, and taxonomy creation and metadata application.
