Artificial Narrow Intelligence (ANI) or Weak AIs are systems that have pre-installed, specific instructions to handle a particular function or problem on a machine such as a Smartphone, computer, or self-driving autonomous vehicle. Although these machines can learn patterns and predictions, this “learning” isn’t the same as humans learning with new experiences and memories. Instead, AI develops behaviors via processing training data.
What Is Artificial Narrow Intelligence (ANI)?
The development and improvement of Artificial Intelligence or AI has changed (and is still evolving) the technological landscape. Even if you’re aware or not, we use these AIs in our daily internet lives.
When somebody says “AI,” most people often think about machines or robots with sentience and intelligence much like a human being. However, there are two major AI classifications that not many people have come to know. This article discusses Artificial Narrow Intelligence (ANI) and the things that it can do.
Artificial Narrow Intelligence (ANI) or Weak AI are machines set to operate within a specific set of instructions or area. They can only do functions and instructions that are pre-programmed to them. ANI doesn’t have intelligence capable of thinking out of the box as a human would. Artificial Narrow Intelligence is often used for automating tedious tasks such as uploading vast quantities of data through one of its branches, Machine Learning.
Artificial Intelligence Trends
Artificial Intelligence or AI made significant trends that affected the internet and technology direction in the past few decades. In fact, if you use your Smartphone or your computer regularly, it’s likely that you’re also using different kinds of AIs. Siri and Google Assistant, as we have discussed in many articles and also our last article, “Smartphone AI: What are Some Applications of Applied Artificial Intelligence?”. Both AIs are made to help users use their Smartphones and devices in a more seamless and hassle-free way.
Currently, the future of AI learning is still very much focused on the direction of machine learning. Whether AI impresses you or not, there’s no doubt that you’ll see much more of it in time. But without a doubt, the current state of AI development is still a fledgling compared to its projected potential.
According to a study by Nicolas Miailhe and Cyrus Hodes, Artificial Intelligence’s definition is still unclear. This anomaly, while not only frustrating but is partly because no one has completely defined what “intelligence” really is because humans have not figured that out either. The human brain and intelligence is still a vast frontier of uncharted territory.
As of now, there are multiple different types of Artificial Intelligence, depending on who is describing the definition or the name. Two are Artificial Narrow Intelligence (ANI) and Artificial General Intelligence (AGI). We work to simplify and explain to the average person what Artificial Narrow Intelligence is and how it is used in everyday situations.
Types Of Artificial Intelligence
Most machine learning practitioners usually categorize Artificial Intelligence by its response characteristics. All of these AI classifications are under the Artificial Narrow Intelligence classification or ANI.
According to Arend Hintze, Professor for Artificial Intelligence, Department of Complex Dynamical Systems and Microdata of Michigan State University and his article on Govtech, there are four types of AIs:
- Reactive Machines
- Limited Memory
- The Theory of Mind
Artificial Narrow Intelligence (ANI)
Artificial Narrow Intelligence, which is also called Weak AI, is an Artificial Intelligence type that can only operate within the confines of pre-programmed scenarios. A Weak AI will become less and less accurate if it operates beyond the conditions in which it is programmed to work.
Although these AIs can recognize patterns and “transfer” information to other artificial intelligence systems for better results, these functions are still within their predetermined capacities. All the existing AIs at this moment are considered Artificial Narrow Intelligence.
Smartphones are a classic example of the use of Artificial Narrow Intelligence (ANI) in everyday life. For instance, this may be how you found this article on your phone or tablet on Google Search. Google Search is currently an ANI. Writing a text on your iPhone and having suggested words or text come up or doing a bank transaction on your Samsung Galaxy is another example.
Other cloud services and platforms like Facebook use AIs in some capacity, face recognition for tagged photo recommendations and friend suggestions. Whether it is Facial Recognition for security or adjusting the incoming picture on the camera of your Smartphone, these areas are examples of Artificial Narrow Intelligence.
But Artificial Narrow Intelligence is not restricted to your Smartphone. Self-driving cars, commercial airlines AI autopilot, sensor-operated cleaning devices, and even AI toys (like Boxer and Sphero Robots) are physical machines that use machine learning.
Artificial General Intelligence (AGI)
Meanwhile, Artificial General Intelligence, or strong AI, is a theoretical form of an AI that can think and function at the human level. Instead of being limited to a set of pre-programmed scenarios, it can think outside of the box and create answers or produce results using self-consciousness.
IBM defines Strong AI as a machine that has intelligence equal to humans. It can learn, identify, and solve problems, and plan, as a human would. As Strong AI progresses, evolves, and eventually passes the Turing Test (The current benchmark for AI), it would first be compared to primitive mammals, then a human child, finally an intelligent adult and beyond. It can learn via experiences and interactions with other objects and stimuli.
As of now, there is no example of a strong AI yet. No one has created a machine that is that advanced and intricate so far. The AI industry itself is a very young industry, although its current lifespan spans decades of research and work.
What Can Artificial Narrow Intelligence Do?
Without data, a weak AI cannot learn a new behavior or predict results. By supplying training data, these machines learn what kind of behavior is “good” and “bad.” ANI is taught to repeat “good behaviors” and avoid “bad behaviors,” depending on the defined parameters. Improving the pre-programmed algorithm can result in better pattern-finding function and prediction making.
Weak AI still needs human intervention to write the coding and create the algorithms and provide training data. The better the data used, the more accurate the predictions become. Better predictions provide overall better decision making and results. If these two factors are successfully combined, the general behavior of an Artificial Narrow Intelligence is improved. This result is why ANI or weak AI is very efficient in doing repetitive and tedious jobs.
Understanding ANI And AGI (Weak AI vs. Strong AI)
The big difference between Artificial Narrow Intelligence (ANI) (or Weak AI) and Artificial General Intelligence (AGI) (or Strong AI) is how they learn. Weak AI uses the same predefined algorithms over and over again to improve accuracy. Artificial Narrow Intelligence cannot find and solve new problems outside of its predetermined job. Instead, it gets better and better in solving a single problem over time.
For example, a Chess-playing Artificial Intelligence with Machine Learning only gets better at playing chess once it faces better chess opponents. The data reviewed by the Artificial Intelligence from human players moves help a Chess AI predicts better moves that can bring down the King in fewer steps. This method is how all current Artificial Intelligence learns through Machine Learning and gets better.
Artificial General Intelligence or Strong AI, on the other hand, can be compared to teaching a child how to read and write. Once it can read and write, it can teach itself other skills needed to solve problems. It’s an imitation of how humans learn in real life.
What Industries Use Artificial Narrow Intelligence?
Some of the biggest benefactors of using ANI machines are data, finance, education, healthcare, and the automobile industry. Artificial Narrow Intelligence is used for processing large quantities of data, which is often created by customers or users. This data can provide insight into the company for what its customers need.
This principle is almost the same for finance, education, automobile, and health care. Finance needs data processing for budgeting and forecasts. Education benefits with insights on how certain subjects are instructed more efficiently. AI is used in health care to help patients identify symptoms without even sometimes meeting a physician. And last but not least, one of the automobile industry uses for AI is to learn how to teach vehicles to drive autonomously.
The Limitations Of ANI
ANI’s biggest strength is also its biggest weakness. It is efficient in sometimes doing complex but singular focus tasks because it only needs to concentrate on the pre-installed instructions. However, when it encounters a problem that doesn’t have a listed solution, it sometimes does not know what to do next. This limitation can cause accuracy problems and miscalculations.
Let’s continue the Chess AI example. If a new player made a move that the Chess AI hasn’t encountered before, this could make the AI miscalculate the whole situation and make a wrong move. It cannot think outside the proverbial box yet.
Another limitation that ANI machines have is training data. A machine will simply not get better if no one supplies its training input in sufficient quantities for it to truly learn. Continuing the Chess AI, for example, if no human or other machine plays with the ANI machine, the AI will not be able to acquire new move sets without sufficient training data.
And last but not least, some of ANI’s most significant limitations are human concerns. People have concerns that they will lose their jobs to Artificial Intelligence, robots, and automation. This is a realistic legitimate concern and is worth consideration.
The Changing And Metamorphosis Of Artificial Narrow Intelligence
As human beings learn and understand better the capabilities of Artificial Intelligence, Neural Networks, and Machine Learning, we see a Metamorphosis before our very eyes.
There is no better example of this change than the Google DeepMind project with Alpha Go and now AlpaZero. Teaching AlphaGo to play against itself opened the door to a new threshold of what Artificial Intelligence can do.
“We created AlphaGo, a computer program that combines advanced search tree with deep neural networks. These neural networks take a description of the Go board as an input and process it through a number of different network layers containing millions of neuron-like connections.
One neural network, the “policy network,” selects the next move to play. The other neural network, the “value network,” predicts the winner of the game. We introduced AlphaGo to numerous amateur games to help it develop an understanding of reasonable human play. Then we had it play against different versions of itself thousands of times, each time learning from its mistakes.
Over time, AlphaGo improved and became increasingly stronger and better at learning and decision-making. This process is known as reinforcement learning. AlphaGo went on to defeat Go world champions in different global arenas and arguably became the greatest Go player of all time.”Google DeepMind AlphaGo
We see unbelievable progress, of course, in Machine Learning and the progress of games like this with Google’s latest AlphaZero general-purpose learning systems Artificial Intelligence.
The Future Of ANI and AGI
Data gathering and processes are far exceeding expectations. The AlphaGo project was ten years ahead of its time. Continued machine learning will only continue to get better.
That is why many people have such high hopes for what the future of AI brings to the table. According to Forbes, ANI can make monitoring and refining business processes more manageable. Artificial Narrow Intelligence runs more devices of every kind, and the Internet of Things (IoT) permeates all aspects of our life.
Smartphone Mobile Technology, Nano miniaturization of hardware, and increased connectivity through 6G and beyond will all facilitate the growing progress towards Artificial General Intelligence.
These machines also affect creative industries, such as film making, music, and gaming. These changes brought forth by ANI devices are expected to affect the socio-economic landscapes of the future. Although things that exist today might not exist tomorrow, the reverse is also true.
ANI is creating new opportunities, new industries, and new challenges. In fact, it is safe to say that these effects are happening right now. And since there is no certainty on whether AGI devices can exist, no one can tell what kind of AI it will be and how it can affect future technology.