What is Weak Artificial Intelligence? Understanding the Basics. Discover what weak artificial intelligence is, how it works, and its benefits.
When a machine or computer program is considered intelligent, it is generally a question of weak artificial intelligence. It can perform a predefined task by reasoning, in a way, like the human brain.
Artificial intelligence is being talked about in almost all areas. It presents itself as a computer solution to a problem that previously only a human could have solved. The question is how smart a machine is. To answer this, we bring you to discover what weak artificial intelligence is.
weak artificial intelligence
Weak artificial intelligence, sometimes called narrow artificial intelligence, is a computer program designed to solve a given problem. “Intelligent” is an adjective relating to the cognitive abilities of a human (to understand, to reason, and to learn). Then the term “artificial” is used because it is a manufactured program. The qualification of “weak” or “narrow” refers to the application scope of AI. In other words, the weak AI is programmed for a predefined task.
It brings us back, among other things, to the definition of artificial intelligence. This branch of computing aims to mimic the way the brain works. It solves problems that require some reasoning. Furthermore, thanks to machine learning, intelligent systems can learn and improve themselves without human intervention. Therefore, given a task to solve, they can process the information, calculate, and produce a result. It can be a predictive analysis, classification task, image and voice recognition, etc.
Weak AI and strong AI
It is clear that if there is weak artificial intelligence, there is also strong artificial intelligence. Indeed, AI is divided into broad categories: weak or narrow AI, which we have already defined previously, and strong AI, also known as general AI.
This second is a theoretical form of artificial intelligence that denotes a fully cognitive system. In other words, strong AI could solve multiple tasks. She could, among other things, apply her intelligence to any problem, as an average human could. But she might even surpass the latter with increased power and speed. Finally, a strong artificial intelligence would develop a consciousness, a mind, emotions, and a body.
Such a form of AI has yet to exist. No system called intelligence currently exists that has yet reached this level of cognition. Certainly, artificial intelligence, even weak, is sometimes faster than a human in solving a task. It is one of its greatest advantages. It makes it possible to process large amounts of data quickly, which would require months or years of work for a human. However, it needs to meet the description of general artificial intelligence given by the experts.
Examples of Weak Artificial Intelligence
So remember that all AI applications that exist today belong to the category of weak artificial intelligence. Given the number of fields and application sectors of these technologies, it is impossible to make a complete list. However, they can broadly be categorized as follows:
Conversational AI
One of the most widespread weak artificial intelligence technologies is conversational AI. Simply put, it’s a computer program that uses human language to chat. It can be a voice or text conversation. On the other hand, there are still different types of conversational AI, like chatbots and virtual assistants. AI chatbots are mostly used by businesses to automate customer services and support. For their part, virtual assistants like Siri and Alexa are intended for more personal use.
Automatic recognition systems
In terms of AI, automatic recognition represents another highly exploited area. On the one hand, image recognition and classification occupy an important place in several applications. We can, for example, cite medical diagnoses. Similarly, automatic text recognition facilitates document classification tasks such as EMRs, administrative documents, or CVs.
But weak artificial intelligence also stands behind the authentication of people. We are discussing biometric identification techniques such as facial recognition, fingerprint analysis, or voice recognition. Note that automatic text recognition and speech also affect conversational AI.
Predictive analysis
Predictive analytics is a data science technique that analyzes historical or behavioral data to predict future outcomes. As discussed earlier, weak AI offers the power and speed to process large datasets. Therefore, it can improve predictive analysis for more accurate and detailed results. It is, for example, useful in predictive maintenance or forecasting future sales.
Recommender systems
In marketing, the predictive analysis also forms the basis of recommendation systems. AI uses information extracted from data to predict a customer’s preferences. It then makes recommendations based on these elements. This type of system can be seen in social media platforms such as Facebook, Instagram, or TikTok.
A recommendation algorithm is based on user activities to suggest content likely to interest them. Similarly, commercial businesses can recommend products based on their previous purchases to their customers.
Search engines
Weak artificial intelligence is also present in search engines. It allows the system to understand the meaning of queries and to rank the results according to their relevance. But it also intervenes in different ways to control the results’ quality and prevent abuses such as over-optimization and spam detection.
Autonomous machines
When talking about autonomous machines, we inevitably think of robots. But autonomous vehicles are also devices that operate without human intervention. Autonomous technologies are taking up more and more space in today’s technological world. And they rely heavily on weak artificial intelligence to perform their tasks without supervision.
More specifically, it is, above all, computer vision that gives these devices the ability to be autonomous. Robots use sensors and cameras to perceive their environment and its objects. As a result, they can avoid obstacles and grab and interact with objects. The same goes for autonomous vehicles.
The advantages of weak artificial intelligence
A weak AI system is optimized to perform tasks more accurately and faster than a human. In other words, he can be better than humans up to a point. This ability makes her especially useful in tasks that require quick decision-making. It is, for example, the case in the field of health, when AI helps doctors identify a patient’s disease and determine the treatment to be administered.
We also talked about predictive analytics and what it means for marketing and maintenance. This second point is particularly important since all the activities of a company depend on it. Thus, weak artificial intelligence can help estimate the lifespan of the equipment used to anticipate when it could fail. In other words, predictive maintenance makes it possible to set up ready-to-use solution systems if necessary.
The limits of weak artificial intelligence
All of the examples of weak artificial intelligence that we presented earlier have one thing in common: they are limited to specific tasks. Indeed, although AI systems may be better than humans in their field, they cannot solve other problems. For example, an AI model trained to recognize a face cannot translate text into another language.
Aside from the limits of what weak AI can do, its effectiveness depends, above all, on infrastructure and data. Therefore, AI could cause more damage if there is a system failure. Similarly, it may produce erroneous results if fed with inconsistent, incomplete, or outdated data. Take the case of chatbots. They are trained on a set of data to respond to user queries. They can’t provide specific answers if they don’t have enough information. One way to remedy this would be to do predictive maintenance and guarantee the quality of the data.
Finally, another concern with weak artificial intelligence is its inability to explain its results, especially if these are incorrect. But this problem is also surmountable by implementing an eXplainable AI process.
Theoretically, in a future where AI manages to become widespread, all these limits could eventually be pushed back. But for now, researchers must content themselves with finding quick ways to address these challenges.