A new vision for artificial intelligence is using smaller more relevant data sets for dynamic learning generating more effective outcomes and better predictions.
This model uses cognitive architecture, learns, transfers learning, and retains knowledge – enabling more valuable and compelling artificial intelligence applications. Our approach is more closely related to the brain’s actual structures and much more effective than “neural networks,” which is a catchy name but the similarity to the brain’s actual functioning is in name only. Real advancement in artificial intelligence must live in reality, not theoretical marketing.
This video discusses our perspective on the current state of artificial intelligence, the shortcomings of big data and trial and error approaches, and the most effective solution and its prospects. Smaller data sets, more relevant information, dynamic data, and algorithms will lead to more appropriate outcomes, better tools, and more effective applications, especially within Arcadia’s algorithmic trading.
Please click on the link below to see this video.