Analytical AI and its Difference from Generative AI

Organizations that are new to AI risk overlooking an older, more established form of AI called “analytical AI.” This form of AI is far from obsolete and remains a vital resource for most companies. While some applications of AI use both analytical and generative AI, the two approaches to AI are largely separate.

The core difference between analytical AI and traditional data analytics lies in the types of technologies used to generate and access these insights.

Key Concepts and Features of Analytical AI

Analytical AI is a form of data analytics that leverages artificial intelligence – specifically advanced forms of machine learning – for business intelligence purposes. While meaningfully different from the traditional data analytics methods used by many organizations, analytical AI focuses on achieving the same goal: analyzing data sets to generate actionable insights and guide data-driven decisions.

Analytical AI uses advanced AI methods, such as natural language processing (NLP) and deep learning, to analyze large data sets, develop insights, and guide decisions in a dynamic way, responding directly to user interactions.

The core difference between analytical AI and traditional data analytics lies in the types of technology used to generate and access these insights. However, while these tools are impactful, they often provide a static view of the data for most users, relying heavily on statistical analysis to generate insights and requiring analysts to draw their own conclusions rather than relying on technology.

Key Features of Analytical AI

Descriptive Analytics: Descriptive analytics answers the question “What happened?”. This type of analytics is by far the most commonly used by customers, providing reports and analysis focused on past events.

Descriptive analytics is used to understand overall performance at an aggregate level and is by far the easiest way for a company to get started because the data is readily available to build reports and applications.

Diagnostic Analytics: Diagnostic analytics, like descriptive analytics, uses historical data to answer a question. But instead of focusing on the “what,” diagnostic analytics addresses the important question of why an event or anomaly occurred in the data. Diagnostic analytics tends to be more accessible and suitable for a wider range of use cases than machine learning/predictive analytics.

Predictive Analytics: Predictive analytics is a form of advanced analytics that determines what is likely to happen based on historical data using machine learning. Historical data comprises largely descriptive and diagnostic analytics that are used as the basis for building predictive analytics models.

Prescriptive Analytics: Prescriptive Analytics is the fourth and final pillar of modern analytics. Prescriptive Analytics deals with specific, prescriptive analytics. It is essentially a combination of descriptive, diagnostic, and predictive analytics to drive decision making. Existing situations or conditions and the consequences of a decision or event are applied to generate a guided decision or action for the user to take.

Generative AI focuses on creating new content by learning patterns from existing data. It uses deep learning techniques, such as generative adversarial networks (GANs) and transformational models, to generate text, images, music, etc. Generative AI has gained significant attention for its ability to generate human-like content and has applications in creative industries, content creation, and more. The main features of Generative AI are content creation, enhancing imagination and creativity, enhancing training data, and creating personalized impressions.

The main features of Gen AI are content creation, enhancing imagination and creativity, enhancing training data, and creating personalized impressions.

The Difference Between Analytical AI and Generative AI

There are many differences between analytical AI and generative AI, based on these differences, businesses/companies find ways to operate effectively through the use of AI. Differences between analytical AI and generative AI:

First, the purposes and capabilities are different. The main purpose of generative AI is to use deep learning neural network models to generate new content. As for analytical AI, it refers to statistical machine learning-based AI systems designed for specific tasks, such as classification, prediction, or decision-making based on structured data.

Second, the algorithms are different. In terms of algorithmic methods, generative AI often uses complex techniques such as turning sequential text inputs into coherent outputs, predicting the next word based on existing data context to generate content. Generative AI learns to understand patterns in data to generate new versions of that data. Analytical AI uses a range of simpler machine learning methods including supervised learning, unsupervised learning, and reinforcement learning.

Third, the difference in return on investment. Generative AI can make content creation profitable by offering lower costs than human content creation, as well as the potential to create unique and engaging content that attracts and retains customers. While generative AI offers many benefits, its economic value can be difficult to measure, and it costs users to train generative AI models.

For analytical AI it delivers better economic returns through predictive models that can help businesses forecast demand, optimize inventory management, identify market trends, and make data-driven decisions. This can lead to reduced costs, improved resource allocation, and increased revenue through better decision making.

Fourth, there is a difference in risk. Generative AI can produce convincing “deepfakes,” which can lead to misinformation, identity theft, and fraud. Additionally, these models can pose privacy risks if the training data contains sensitive information or is manipulated to produce unintended output.

Analytical AI training data also faces risks from cybersecurity breaches and being misused for malicious purposes, such as launching cyberattacks or spreading misinformation. Therefore, security measures are needed to mitigate these risks. Currently, analytical AI seems to be less risky than generative AI, so it has been used for a long time in many companies.

In summary, when deciding between analytical and generative AI, consider your specific requirements and goals. If the goal is to extract insights from data, make predictions, and optimize processes, analytical AI is the right choice. On the other hand, if you need to create new content, innovate, or personalize user experiences, generative AI is the ideal choice.

Generative AI tools are being used as chatbots, which are expected to replace not only internet searches but also customer service and sales calls.

Some recommendations

The use of analytical AI in diplomacy is essential because it has more criteria than any other AI technology to meet the requirements and tasks of the diplomatic sector. However, to be able to apply analytical AI in the sector, the following conditions must be met:

Firstly, it is necessary to build human resources with sufficient knowledge and experience in the AI ​​technology industry (including artificial intelligence and intelligence based on human intelligence).

Second, apply AI technology to industry services such as replying to emails, interacting directly with people through chatbot technology, typically the way the German Foreign Ministry used AI technology, called FACIL, to interact with citizens from 2021-2023 and processed 40,000 requests per month.

Third, building an infrastructure including a database system and a server system to be able to operate AI analysis to help predict and forecast the world's situation and events for the diplomatic sector. However, due to the increasing volume of data, a large enough server system is needed.

Fourth, the diplomatic sector needs to build its own analytical AI, which is most important to ensure security and ethical issues.

Source: https://baoquocte.vn/ai-phan-tich-va-su-khac-biet-voi-ai-tao-sinh-298800.html