Publication Date
The 7 types of artificial intelligence
These days, journalists and specialists are flooding the media with articles and information about artificial intelligence and its growing influence, and businesses are feeling the need to explore and adopt these rapidly emerging technologies.
The explosion in fields of application for artificial intelligence has transformed how organizations can approach their decision-making and how they optimize their operations. However, it’s also important to avoid improvising because there are many potential pitfalls.
This article will demystify artificial intelligence and provide a clear approach for corporate managers who want to get in on the revolution. There are multiple types of artificial intelligence, and as you read, you’ll see that several concepts are already familiar to you.
Generative AI
The generative artificial intelligence model has been getting the most media attention. It’s the method used by ChatGPT, among others, with an approach based on creating new data from an existing set using probability models. These algorithms can produce original content by applying their learnings of structures and models in the training environment.
Predictive AI
Predictive artificial intelligence uses statistical models and historical data to predict future trends. Take sales forecasts, for example. It can analyze past transaction data to generate precise forecasts. Applied to retail, this type of algorithm can predict seasonal demands, allowing a company to better adjust their inventory to meet consumer needs.
Discriminative AI
Discriminative artificial intelligence uses models to identify unusual patterns related to activities. For example, a discriminative algorithm used to detect fraud in financial transactions could analyze unusual spending behaviour in credit card usage models to alert you to potentially fraudulent activities.
Descriptive AI
Descriptive artificial intelligence analyzes existing data to provide detailed information about past performances. Used extensively in business intelligence, it uses algorithms to produce reports and dashboards to analyze monthly sales, market trends, and product performance, to better understand a company’s strengths and weaknesses, for example.
Prescriptive AI
Prescriptive artificial intelligence recommends specific actions to achieve defined goals. For example, a prescriptive algorithm applied to a grocery store can recommend price adjustments in real time based on demand, stock level, and competitor behaviour, while maintaining the objective of maximizing profits and being competitive.
Reinforcement Learning AI
Reinforcement learning is a type of artificial intelligence wherein a virtual agent learns to make decisions by interacting with their environment. Among other applications, this model is applied to chatbots that can refine their purchasing recommendations as consumers specify their requirements and preferences. They are intelligent virtual assistants that learn and improve by themselves over time.
Computer Vision AI
Computer vision AI is designed to enable machines to understand and interpret visual data via images or videos. In the case of facial recognition, the algorithm analyzes a person’s unique facial features to establish and authenticate their identity. Often used to unlock smartphones, this AI technique is also used frequently in two-factor authentication to open applications.
Before you begin: determine your data maturity
Over their lifecycle, data progress through various levels of development in an organization. To assess their ability to leverage this data with artificial intelligence, a company needs to determine the data’s maturity level.
Initially, there is raw data, which is unprocessed information extracted directly from various sources. This data is often complex and requires cleaning to eliminate errors and redundancies, which will ensure quality and enable reliable analysis. Once the data is clean, the information can be used to generate standard reports that provide information about the company’s performance.
For more in-depth analysis and to answer specific questions, you can generate ad-hoc reports using precise data. There are also online analytical processing (OLAP) reports that allow you to explore the data multi-dimensionally for even more complex analysis.
Lastly, with the emergence of self-service business intelligence tools, users can now explore and analyze data autonomously, for quick and customized decision-making.
Data assessment is crucial to prepare a company to use AI in day-to-day operations. The different data maturity levels reflect a progression from raw to refined information, which is essential if a company wants to take full advantage of its corporate data and make informed strategic decisions.
Data quantity, quality, availability and protection
Adequate data volume is necessary to form strong and reliable models, but data quality is just as important. To avoid bias, the data must be accurate and complete. Data availability, which ensures the accessibility of the information, is critical, as is data protection, especially confidentiality. It’s also important to monitor and analyze the historic development of the data to identify trends. Various tools exist, such as Microsoft Purview, to help you govern your data. Feel free to contact our experts to help you understand your responsibilities in terms of security.
Remember:
Our experts can help you find the best strategy and approach to assess your company’s current level of data maturity and evaluate your capacity to begin harnessing the power of artificial intelligence.
