As of now, humans have conquered the lowest caliber of AI — ANI — in many ways, and it’s everywhere:
MORE IDEAS FROM THE ARTICLE
Most experts agree that there are three categories, or calibers, of AI development:
In 2013, Vincent C. Müller and Nick Bostrom conducted a survey that asked hundreds of AI expert the following:
So the median participant thinks it’s more likely than not that we’ll have AGI 25 years from now.
n it comes to developing supersmart AI, we’re creating something that will probably change everything, but in totally uncharted territory, and we have no idea what will happen when we get th
If we conquer nanotechnology, the next step will be the ability to manipulate individual atoms, which are only one order of magnitude smaller.
Nanotechnology is an idea that comes up in almost everything you read about the future of AI. It’s the technology that works at the nano scale — from 1 to 100 nanometers. A nanometer is a millionth of a millimeter.
AI wouldn’t see ‘human-level intelligence’ as some important milestone — it’s only a relevant marker from our point of view — and wouldn’t have any reason to ‘stop’ at our level.
And given the advantages over us that even human intelligence-equivalent AGI would have, it’s pretty obvious that it would only hit human intelligence for a brief instant before racing onwards to the realm of superior-to-human intelligence.
Very broadly, AI can be divided into two: narrow AI and general AI.
1. Narrow AI systems handle singular or limited tasks. Also referred to as weak AI sometimes, such systems have applications in email spam filtering, recommendation systems, and autonomous vehicles.
2. On the other hand, general AI or strong AI, refers to a machine’s capability to think and function as a human. It denotes the ability to distinctly recognise other intelligent entities’ needs, emotions, and thoughts.
What is Artificial Intelligence? Who knows. It’ s an ever-moving target to define what is or isn’t AI. So, I’d like to dive into a science that’s a little more concrete — Computational Intelligence (CI). CI is a three-branched set of theories along with their design and applications. They are more mathematically rigorous and can separate you from the pack by adding to your Data Science toolbox. You may be familiar with these branches — — Neural Networks , Evolutionary Computation , and Fuzzy Systems . Diving into CI, we can talk about sophisticated algorithms that solve more complex problem
Artificial intelligence (AI) has been around since the 1950s. The original pioneers dreamed of a computer that could perform tasks like humans, such as playing chess or translating languages. But the plans didn't come to fruition, and AI soon fell out of favour.
AI technology continued to improve exponentially over the next few decades. Many organisations now embrace AI as a core element of their business.
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