Artificial narrow intelligence (ANI) is a type of artificial intelligence (AI) that tackles a specific subset of tasks. ANI is often considered a “weak” form of AI. It pulls information from a particular data set, and its programming is limited to performing a single task, such as playing chess or crawling web pages for raw data. ANIs, like other AI systems, can perform tasks in real-time despite not having any other functions outside their initial programming.
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ANI is one of the most common applications of AI technology that we use in our daily lives. ANI systems are present in many forms, such as Google Translate and Siri. For some, these applications are not weak because they are capable of sophisticated interactions. These applications, however, are considered weak because they can’t match human intelligence, since ANI is not sentient, or conscious. That doesn’t mean these systems don’t provide several benefits.
What Are the Benefits of Using ANI?
Improving Customer Engagement
For most online businesses, customer engagement plays a significant role in expanding their market bases. Those that improve their engagement gain much in terms of sales. But what do ANIs have to do with this scenario?
Chatbots make use of ANI to effectively and accurately answer repetitive queries without getting frustrated and bored as people would. That makes it easier for customers to get the answers they want in real-time.
Using ANI-powered chatbots unburdens customer-facing departments in that these not only keep customers engaged but also allow them to focus on other tasks that AI systems can’t do.
An organization’s success also relies on employee productivity. And since ANI systems can process data quicker than humans, they can be crucial in improving productivity and efficiency. ANI applications can be programmed to crawl web pages and databases to come up with results within seconds.
An example of such an application is IBM’s Watson, which allows healthcare service providers to harness the power of AI to craft evidence-based decisions, making it easier to provide the best care possible for patients. Watson uses ANI to crawl both structured and unstructured patient data then collates it in a format that doctors can quickly analyze to come up with a personalized care plan so the patient can achieve optimal recovery.
Boosting User Experience
While most people are aware of automation and so expect chatbots to give out canned answers to questions, ANI systems can do more than just that. They can be programmed to provide the best user experience (UX) possible so long as they get the right context of user requirements.
An example would be assistants like Siri. When you ask it where the nearest bank is in your area, it will crawl the Web and come up with a list. Again, ANI systems can do more than that. They can be programmed to provide you with the same list but give more relevant information by stating exactly how far each location is from where the user is at, what modes of transportation are available, and even a map.
What Are the Types of Artificial Narrow Intelligence?
The ANI that commonly comes into play in our daily lives fall under two categories—symbolic AI and machine learning (ML).
Symbolic Artificial Intelligence
Also known as good, old-fashioned AI (GOFAI), symbolic AI used to be the primary focus of research in the area. This type of ANI requires programmers to explicitly define the rules they expect the intelligent system to follow. As such, they are best used in applications that have clear-cut steps to follow and predictable outcomes. While symbolic AI is no longer widely used today, it paved the way for most rule-based systems.
ML, on the other hand, is a type of ANI that creates intelligent systems through examples. Developers first build a model and train it by presenting many different instances. The algorithm uses ML processes to derive mathematical representations by predicting outcomes and classifying tasks based on the examples.
For example, developers can train a program to analyze millions of credit card transactions and determine legitimate and fraudulent transactions. Over time, the model can predict if a particular transaction should be approved or not.
The benefits of ANI highlight that though it is a so-called “weak AI system,” it still serves as a useful building block for creating more intelligent AI applications.