In the field of artificial intelligence (AI), there is an enduring trend to confuse definitions and AI-related terminology. Terms like cognitive computing, data science, machine learning and advanced analytics are often used a lot, though rarely do these terms define AI.
Having a basic understanding of AI-related terminology is important as AI is reaching a tipping point, creating a societal shift towards an AI-based landscape, largely defined by smart, AI-related technologies. While today’s version of AI is primarily focused on specific intelligence, the necessary ingredients are coming together to develop the field of AI into a future concept of general intelligence.
Influential discoveries are emerging, and with the volume of big data ever-increased, and drastically lowered technology costs, AI is being transformed to better augment flexibility and agility in business applications and processes, providing users and businesses alike accelerated access to particularly useful data-driven, cloud-based applications.
The generally accepted definition of AI as “the theory and development of computer systems able to perform tasks normally requiring human intelligence” can be further broken down into its respective sub-disciplines.
1. Machine learning
While machine learning comprises of AI, utilizing and involving its technology across all of its applications, machine learning itself is not artificial intelligence. Machine learning is decision tree or version space learning, requiring AI to employ big data to learn from experience and past case data to develop its “intelligence.”
2. Neural networks and deep learning
While there are certain distinctions between neural networks and deep learning, both terms are often used synonymously.
Neural networks is a key technique of machine learning algorithms. Neural networks are modeled to superficially mimic how the brain works.
Deep learning is another branch of machine learning, using complex algorithms to perform abstract tasks for solving particular problems.
When discussing neural networks, you may encounter certain terms: feed forward and recurrent (or feedback or interactive). Both configurations use complex algorithms to look for patterns, using a set of input values to produce an output value to learn these patterns and make forecasts.
3. Natural language processing
Written language is an important channel to disseminate information. This knowledge can be disseminated through particular mediums, such as symbol systems, spoken language, written text speech, and other semiotic systems. Siri or Cortana, along with specific chatbots (KLM Messenger Bot et al.), manipulate data to interpret the sentiment behind natural language dialogue.
4. Data Science and other AI-related terms
Data science is an interdisciplinary field to data mining, utilizing computer and information science as well as statistics to produce insight from either structured or unstructured data. Though machine learning is the most recognizable facet of data science, data science encompasses many other important techniques and methods.
While a relatively recent concept, the goal of data science is to make accurate predictions without the need for human interaction. It employs and is attributable to a diverse range of tools, methods and techniques to handle big data. AI applications require data science to develop proper, usable models.
As AI technologies continue to evolve, there is a real need for data scientists to optimize required parameters and to deploy the most suitable algorithms to use, ensuring interpreted results culled from big data support business performance.
Advanced analytics is another AI-related term that refers to tools and techniques that can autonomously gain insights from data. Often, the lines between data science and advanced analytics can get blurry, though, at their core, they have significant differences.
Traditional analytical tools cannot effectively parse unwieldy amounts of data, so as its name suggests, advanced analytics is a more ‘advanced’ technique capable of supplementing data science with modern exploratory data visualization.
While advanced analytics is related and similar to data science, the best way to understand the seemingly nuanced differences between data science and advanced analytics is that advanced analytics is more specific and concentrated.
To differentiate data science from advanced analytics, it can help to look at data science as the house that supports or automates the analysis of data, and advanced analytics is a room in that house, using human-centric processes to take advantage of the variety of tools present to address specific challenges.
Data science can be viewed as an activity (applying data science) and its algorithms and techniques are commonly applied to industries like internet searches, search recommendations and digital advertising.
Advanced analytics is used in the context of a product or service (advanced analytics platform); it is often employed in industries like energy management, healthcare, gaming and oil and gas software applications.
What does AI mean?
As machine learning is interwoven in other disciplines of AI, so are many AI-related terms. Data science and advanced analytics have weighted interconnections between them and AI.
Each interdisciplinary and AI-related term has purposeful use in various business applications and processes. AI and its various technological elements transform cutting-edge AI technology into customizable, scalable applications.
The manifestation of AI and its prevalent nature as it weaves its way across multiple industries and many computer systems today demonstrates real business value. With many industries providing high levels of investment for the development and growth of AI, AI is establishing itself as an integral aspect of business performance that will only become further evident for the foreseeable future.