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Tuesday, May 28, 2024

Why AI’s Success Depends on Making It More Explainable and Conversational

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Ciro Donalek
Ciro Donalek
CTO and Co-founder, Virtualitics

How Intelligent Exploration, Explainable AI (XAI), and Large Language Models (LLMs) are revolutionizing data analysis, making it more accessible and user-friendly. Learn how these technologies address the barriers to widespread AI adoption and enhance trust in AI solutions.

The consumerization of generative AI has opened up new ways for enhanced human-machine collaboration and groundbreaking discoveries. With its power to create new content starting from a prompt, such as images, texts, audio, video, or even other types of data, generative AI has the potential to revolutionize data analysis in many diverse fields.

However, despite being lauded as one of the most beneficial technological advancements in history, AI still hasn’t been widely adopted by organizations. There are three main reasons for this:

  1. Resources: One of the primary barriers to AI adoption is the scarcity of expert users. Data scientists are increasingly in demand as organizations seek meaningful and actionable insights from the millions of bytes of data they produce daily. However, the data science talent pool is small and even when they get hired, they’re simply juggling too many competing projects to focus on AI adoption.
  2. Trust: While some still fear the doomsday potential of AI, what’s hurting trust in AI is confidence in its results. Adding to that distrust is a lack of knowledge of the proposed solutions and their implications, even at a high level. Hallucinations, for example, occur when generative AI bots return made-up information, but this issue usually stems from an incomplete query or inaccurate dataset. Building up trust in the technology is challenging without the right data science talent to train the model and help encourage AI adoption.
  3. Usability: Many existing AI tools are challenging to use, typically requiring specialized knowledge and expertise in data science. Increased adoption requires user-friendly tools analysts and other non-data science experts can leverage to understand complex, multidimensional, and heterogeneous data fully.

Democratizing AI with Intelligent Exploration

The barriers to widespread AI adoption ultimately stem from the scarcity of data science experts. Waiting to hire for these skills risks companies being left behind in the race to analyze enterprise data for business-changing insights. Fortunately, these problems can be addressed through Intelligent Exploration, Explainable AI (XAI), and Large Language Models (LLMs). 

An Intelligent Exploration platform leverages XAI, Generative AI, and rich visualizations to guide users through the analysis of complex datasets. To be successful, these platforms must be low-code or no-code environments that allow users to log in and immediately begin exploring for insights. Furthermore, the platform should incorporate generative AI technology to:

  • Use embedded AI routines to generate multidimensional visualizations based on available data and contextual information.
  • Deliver key insights in natural language augmented with compelling, AI-generated visualizations.
  • Use LLMs to suggest the next steps in the analysis based on user prompts. These prompts can be specific (“I want to understand what drives sales in summer”) or more open-ended (“Tell me something interesting about my data”).

Intelligent Exploration leads teams to findings that can be clearly understood, prioritized, and acted upon.

Enabling a Conversational Approach to Data Analysis

Two keys to democratizing complex data analysis with Intelligent Exploration are using XAI and LLMs. 

To be effective, XAI has to strike the right balance between model interpretability and accuracy. We mustn’t compromise accuracy while focusing on context-aware explanations, which entails designing explanations that consider the specific context of the analysis conducted. 

Additionally, XAI systems must generate explanations suitable for different audiences that don’t require a data expert to interpret them. A few years ago, advancements in Natural Language Processing (NLP) introduced the capability to ask queries using natural language. Still, these systems faced limitations due to their restricted vocabulary and ad hoc syntax.

LLMs overcome these limitations by presenting results as a narrative, featuring simple language and relevant charts that are generated automatically.

The AI-Friendly Future

Intelligent Exploration enables organizations to take advantage of their data without waiting to hire more data scientists. It also plays a critical role in augmenting trust in AI solutions and fostering a more widespread, fair, and ethical use of AI. This increased trust will contribute to more responsible deployment and wider acceptance of AI solutions across various domains, positively impacting society.

By enhancing resource allocation, providing transparent and interpretable AI solutions, and developing user-friendly tools, Intelligent Exploration platforms pave the way for wider AI adoption and for all organizations to reap the benefits of these transformative technologies. 

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