Do you understand the main types of AI, how they work, and where they add value? Let's break down machine learning, deep learning, natural language processing, computer vision, and explainable AI
Artificial intelligence (AI) is redefining the enterprise's notions about extracting insight from data. Indeed, the vast majority of technology executives (91 percent) and 84 percent of the general public believe that AI is the next technology revolution, according to Edelman's 2019 Artificial Intelligence (AI) Survey. PwC has predicted that AI could contribute $15.7 trillion to the global economy by 2030.
Understanding the types of AI, how they work, and where they might add value is critical.
AI, in short, is a pretty big deal. However, it's not a monolithic entity: There are multiple flavors of cognitive capabilities. Understanding the various types of AI, how they work, and where they might add value to the business is critical for both IT and line-of-business leaders.
Five important kinds of AI:
ML is perhaps the most relevant subset of AI to the average enterprise today. As explained in the Executive's guide to real-world AI, our recent research report conducted by Harvard Business Review Analytic Services, ML is a mature technology that has been around for years.
ML is a branch of AI that empowers computers to self-learn from data and apply that learning without human intervention. When facing a situation in which a solution is hidden in a large data set, machine learning is a go-to. ML excels at processing that data, extracting patterns from it in a fraction of the time a human would take, and producing otherwise inaccessible insight, says Ingo Mierswa, founder and president of the data science platform RapidMiner.
ML use cases
ML powers risk analysis, fraud detection, and portfolio management in financial services; GPS-based predictions in travel; and targeted marketing campaigns, to list a few examples.
ML learning can get better at completing tasks over time-based on the labeled data it ingests, explains ISG director of cognitive automation and innovation Wayne Butterfield, or it can power the creation of predictive models to improve a plethora of business-critical tasks.
An explainer article by AI software company Pathmind offers a useful analogy: Think of a set of Russian dolls nested within each other. Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart.
Deep learning uses so-called neural networks, which learn from processing the labeled data supplied during training, and uses this answer key to learn what characteristics of the input are needed to construct the correct output, according to one explanation provided by deep AI. Once a sufficient number of examples have been processed, the neural network can begin to process new, unseen inputs and successfully return accurate results.
Deep learning use cases
Deep learning powers product and content recommendations for Amazon and Netflix. It works behind the scenes of Google's voice- and image-recognition algorithms. Its capacity to analyze very large amounts of high-dimensional data makes deep learning ideally suited for supercharging preventive maintenance systems, as McKinsey pointed out in its Notes from the AI frontier: Applications and value of deep learning: Layering in additional data, such as audio and image data, from other sensors including relatively cheap ones such as microphones and cameras - neural networks can enhance and possibly replace more traditional methods. AI's ability to predict failures and allow planned interventions can be used to reduce downtime and operating costs while improving production yield.
- Natural language processing (NLP)
NLP itself has a number of subsets, including natural language understanding (NLU), which refers to machine reading comprehension, and natural language generation (NLG), which can transform data into human words.
But, says ISG's Butterfield, the premise is the same: Understand the language and sew something on the back of that understanding.
NLP has roots in linguistics, where it emerged to enable computers to literally process natural language, explains Anil Vijayan, vice president at Everest Group. Over the course of time, it evolved from rule-based to machine-learning infused approaches, thus overlapping with AI, Vijayan says.
NLP might employ both ML learning and deep learning methodologies in combination with computational linguistics in order to effectively ingest and process unstructured speech and text datasets, says JP Baritugo, director at business transformation and outsourcing consultancy Pace Harmon.
NLP use cases
Natural language processing makes it possible for computers to extract keywords and phrases, understand the intent of language, translate that to another language, or generate a response. The enterprise literally runs through communication, either the written word or spoken conversation, says Butterfield. The ability to analyze this information and either find intent or insight will be absolutely critical to the enterprise of the future.