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Thus far, humanity has only developed weak AI systems.

1. Consumers use weak AI whenever they run a search on Google or Bing. They speak with weak AI whenever they ask Alexa, Bixby, Cortana, Google Assistant, or Siri a questio.
2. Lawyers are using weak AI for a wide variety of applications too.
3. Even AlphaGo, the AI system that famously conquered the complicated game of Go in 2016, is classified as weak AI because it can only play the game—it cannot, for example, understand the written game rules and then create a Go-playing computer program.
4. Despite the “weakness” of AI systems like AlphaGo, weak AI can perform a wide variety of human tasks. Companies use weak AI for applications ranging from price setting to self-driving cars and loan approval to warehouse robot operation. Researchers have used weak AI to identify breast cancer and the outbreak of infectious viruses like the “Wuhan Virus.

AI systems are classified as “strong” or “weak” based on the degree to which they can perform human abilities:

That distinction helps delineate the scope of existing AI systems’ capabilities, although those capabilities fall across a spectrum rather than into distinct categories.AI is truly “strong” when it can at least “successfully perform any intellectual task that a human being can,” if not also have the ability to experience consciousness and truly understand concepts like language. That kind of AI, also known as artificial general intelligence, does not exist yet.Moving down the capabilities spectrum, AI is “weaker” or “narrower” the fewer problems it solves

The ability to learn and understand, and to apply this knowledge to achieve a goal or complete a task

artificial intelligence is simply intelligence exhibited by machines. It describes when a machine can learn from information (data) and then use that knowledge to do something . Put simply, AI is the use of “technology to automate tasks that ‘normally require human intelligence.’”(2)Such tasks include the human abilities of reasoning, analyzing, generalizing, problem-solving, and iterative learning