A synthesis of these two solutions is neuro-symbolic AI: blending the pattern-recognition prowess of neural networks with the rule-based clarity of symbolic systems.

Artificial intelligence (AI) is rapidly transforming our understanding of human cognition by integrating insights from perception studies. Perception is the ...

Artificial Intelligence (AI) is often seen as the most important technology of our time. It is transforming industries, ...

For years, the race in artificial intelligence has been about scale. Bigger models, more GPUs, longer prompts. OpenAI, ...

Recently, Anhui Zhongji Star Electronic Technology Co., Ltd. submitted a patent application titled "Dynamic Control Method ...

Called symbolic AI, it uses rules pertaining to particular tasks, like rewriting lines of text, to solve larger problems. Symbolic AI can deftly tackle some problems that neural networks struggle ...

A hybrid approach to AI is powering Amazon’s Rufus shopping assistant and cutting-edge warehouse robots.

It’s also instructive that many early controversies still echo today, especially the symbolic AI vs. neural network debate or the question of how best to represent and manipulate knowledge.

Symbolic AI including generative AI and neural networks, turn words and meanings into mathematical vectors stored like coordinates in a vector database and trained to produce meaningful responses ...

Academic skepticism and business frustration have triggered past AI winters. This time, both factors are present. But there ...

Nosa Omoigui, CEO of Weave.AI, neuro-symbolic GenAI and intelligent agents that transform alpha decision making and risk analysis. As artificial intelligence (AI) accelerates across the financial ...

Binary Logic AI systems, regardless of their complexity, operate through symbolic computation. At the deepest level, every process within an AI—from decision trees to deep neural networks—is ...