“AI is a computer system that can perform tasks that normally require human intelligence… Many of these artificial intelligence systems are powered by machine learning, some of them are powered by deep learning, and some of them are powered by really boring things like rules.”
Other AI classifications
There are three ways to classify artificial intelligence, based on its capabilities. Rather poland mobile phone number list than types of AI, these are stages through which AI can evolve – and only one of them is actually possible at the moment.
⦁ Narrow AI: Sometimes called “weak AI,” this type of AI operates within a limited context and is a simulation of human intelligence. Narrow AI typically focuses on performing a single task extremely well, and while these machines may appear intelligent, they operate under far more constraints and limitations than even the most basic human intelligence.
⦁ Artificial general intelligence (AGI): AGI, sometimes called “strong AI,” is the kind of AI we see in movies—like the robots in Westworld or the character Data from Star Trek: The Next Generation. AGI is a machine with general intelligence and, just like a human, can apply that intelligence to solve any problem.
⦁ Superintelligence: This will likely be the pinnacle of AI evolution. Superintelligent AI will not only be able to replicate the complex emotion and intelligence of humans, but surpass it in every way. This could mean making judgments and decisions on its own, or even forming its own ideology.
Narrow examples of AI
Narrow AI, or weak AI as it’s often called, is all around us and is easily the most successful realization of AI to date. It has limited functions that can help automate specific tasks.
Because of this focus, narrow AI has seen several breakthroughs over the past decade that have had “significant societal benefits and contributed to the nation’s economic vitality,” according to a 2016 report released by the Obama administration.
Examples of artificial intelligence: narrow ai
⦁ Siri, Alexa, and other smart assistants
⦁ Self-driving cars
⦁ Google Search
⦁ Conversational bots
⦁ Email spam filters
⦁ Netflix recommendations
Machine Learning and Deep Learning
Much of narrow AI is fueled by advances in ML and deep learning. Understanding the difference between AI, ML, and deep learning can be confusing. Venture capitalist Frank Chen provides a good overview of how to distinguish between them, noting:
“Artificial intelligence is a set of algorithms and intelligence to try to mimic human intelligence. Machine learning is one of them, and deep learning is one of those machine learning techniques.”
Simply put, an ML algorithm is fed data by a computer and uses statistical techniques to help it “learn” how to progressively improve at a task, without necessarily having been specifically programmed for that task. Instead, ML algorithms use historical data as input to predict new output values. To this end, ML consists of supervised learning (where the expected output for the input is known thanks to labeled datasets) and unsupervised learning (where the expected outputs are unknown due to the use of unlabeled datasets).
Machine learning is everywhere in everyday life. Google Maps uses location data from smartphones, as well as user-reported data about things like construction and car accidents, to monitor the ebb and flow of traffic and assess the quickest route. Personal assistants like Siri, Alexa, and Cortana can set reminders, search for information online, and control the lights in people’s homes, all with the help of ML algorithms that collect information, learn a user’s preferences, and improve their experience based on previous interactions with users. Even Snapchat filters use ML algorithms to track users’ facial activity.
Meanwhile, deep learning is a type of ML that runs inputs through a neural network architecture inspired by biology. Neural networks contain a series of hidden layers through which data is processed, allowing the machine to delve deeper into its learning, making connections and weighting inputs to achieve the best results.
Self-driving cars are a recognizable example of deep learning, as they use deep neural networks to detect objects around them, determine their distance from other cars, identify traffic signs, and more. Sensors and wearable devices used in the healthcare industry also apply deep learning to assess a patient’s health condition, including their blood sugar levels, blood pressure, and heart rate. They can also derive patterns from a patient’s past medical data and use it to anticipate any future health conditions.
