Why No Code is the Future of AI
From ivory towers to everyone: No-code tools like Apteo are democratizing AI.
Since Dartmouth’s seminal paper on AI, the fundamental principles of AI haven’t changed a whole lot. In 1955, M.L. Minsky wrote:
A “machine may be ‘trained’ by a ‘trial and error’ process to acquire one of a range of input-output functions. Such a machine, when placed in an appropriate environment and given a criterior of ‘success’ or ‘failure’ can be trained to exhibit ‘goal-seeking’ behavior.”
AI Today is Still I/O Functions Driven By Trial and Error
In Andrew Ng’s recent AI for Everyone course, he points out that there’s been “almost no progress” in Artificial General Intelligence, but that incredible progress has made been made in “narrow intelligence” — input-output functions “that do one thing such as a smart speaker or a self-driving car.”
In fact, we could be “even thousands of years” away from an Artificial General Intelligence.
The systems we have today are similar to those described in 1955: Goal-seeking, trial and error I/O functions.
