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To qualify as Artificial intelligence (AI), a system must exhibit some level of learning and adapting. For this reason, decision-making systems, automation, and statistics are not AI. Artificial intelligence is broadly classified into two categories: artificial narrow intelligence (ANI) and artificial general intelligence (AGI). To date, AGI does not exist. The key challenge for creating a general AI is to adequately model the world with the entirety of knowledge, in a consistent and useful manner. That's a massive undertaking, to say the least. Most of what we know as AI today has narrow intelligence – where a particular system addresses a particular problem. Unlike human intelligence, such narrow AI intelligence is effective only in the area in which it has been trained: fraud detection, facial recognition, or social recommendations. What are the 5 things people confuse Artificial Intelligence with but it is not?
Late last month, AI, in the form of ChatGPT, broke free from the sci-fi speculations and research labs and onto the desktops and phones of the general public. It's what's known as a "generative AI" – suddenly, a cleverly worded prompt can produce an essay or put together a recipe and shopping list, or create a poem in the style of Elvis Presley.
They spit out coupons and offers based on your previous purchases. You see these recommendation engines at checkouts in drug stores and grocery stores. An algorithm predicts when you're likely to run out of a previous purchase (such as dog food) and spits out a "right on time" coupon to entice you to buy it again. It does not know why you buy what you bought, and it doesn't learn your preferences or behavior changes over time.
You wonder why you're seeing an ad on Facebook or elsewhere online for an item you just bought. This isn't AI, because the algorithms typically analyze your search queries rather than your purchases. It doesn't figure out why you're searching for that item; it only notes your interest and spits out ads that match.
While ML and natural language search are hot trends in this space, most chatbots are automated FAQ responses pulled from a company's knowledge base. In other words, the chatbot store is a preprogrammed answer for every common question. But it's merely a database lookup. Stray from the most-asked path and the chatbot goes mum.
This category is a bit tricky because a lot of AI/ML is used in today's vehicles, with more to come. But for now, the application that shows you whether your car needs air in its tires or an oil change isn't AI. It is the product of sophisticated algorithms that simply compare the current metrics with pre-established and programmed standards, e.g., oil should be changed after certain number of miles, and tires should contain x amount of air pressure.
Most Internet of Things items are ordinary things outfitted with sensors and connected to the Internet. If there is any AI/ML in use, it's usually in the cloud or on the database back end. Most typically, IoT applications collect and analyze user information, rather than to make a consumer IoT device work better. For example, a smart home security camera may use traditional algorithms for reporting. If it uses ML/AI at all, it's typically to analyze user behavior for sale to other data collectors or to analyze activities in the local area to produce a larger crime study for law enforcement or insurance companies' use.
