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Why discriminative AI will continue to dominate enterprise AI adoption in a world flooded with discussions on generative AI

Recently, McKinsey released an extensive research paper on the economic potential of generative AI. Despite its topic, this report includes a very loud message that enterprise AI adopters should note: “Traditional advanced-analytics and machine learning algorithms are highly effective at performing numerical and optimization tasks such as predictive modeling, and they continue to find new applications in a wide range of industries.”

What the report calls “traditional advanced-analytics,” I prefer to call “discriminative AI.” And in my opinion, there are three main reasons discriminative AI will continue to hold importance in traditional enterprises.

DISCRIMINATIVE AI VERSUS GENERATIVE AI

In enterprise artificial intelligence (enterprise AI) technologies, there are two main types of models: discriminative and generative. Discriminative models are used to classify or predict data, while generative models are used to create new data.

Today, discriminative AI models are more widely used in enterprise AI adoption. This is because they are better suited for tasks that require accurate classification or prediction, such as fraud detection, customer segmentation, and risk assessment. Generative AI models are still in their early stages of development and they are not yet as accurate or reliable as discriminative AI models for these types of tasks.

WHY DISCRIMINATIVE AI WILL CONTINUE TO DOMINATE ENTERPRISE AI ADOPTION

These three are the main reasons why discriminative AI will likely continue dominating enterprise AI adoption in the near future.

Accuracy And Reliability

Discriminative AI models are more accurate and reliable than generative AI models for tasks that require precise classification or prediction. This is because discriminative AI models are trained on labeled data, meaning they have been taught explicitly to distinguish between different data classes.

Generative AI models, on the other hand, are trained on unlabeled data, which means that they have to learn to differentiate between different classes of data on their own. This task is more challenging, leading to less accurate and reliable models.

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Overcoming generative AI integration challenges in businesses a necessity

As corporations globally embrace digital transformation, artificial intelligence (AI) is emerging as a significant pivot in this new era. This transformation impacts all sectors, with financial services, banking, and insurance companies leading the way. Generative artificial intelligence (AI) has emerged as a powerful tool with the potential to revolutionize businesses across various industries. From content generation to process automation, the integration of generative AI holds great promise. However, it also brings forth several challenges that must be addressed for successful implementation.

Data Privacy and Security Concerns:

The integration of generative AI often necessitates extensive training datasets, giving rise to data privacy and security concerns. To tackle these challenges, businesses should prioritize implementing robust data protection measures. This will include adopting encryption, access controls and conducting regular security audits to protect sensitive information. Collaborating with ethical AI developers and establishing transparent policies regarding data usage will further enhance customer trust and privacy protection.

Bias and Ethical Considerations:

Generative AI models acquire knowledge from their training data, which can perpetuate biases and discriminatory patterns in the generated output. To address this issue, businesses must meticulously consider data selection and preprocessing. Organizations can foster fairness and inclusivity by incorporating a diverse range of training data sources and actively striving to diminish bias. Furthermore, implementing bias detection algorithms and regular audits can assist in identifying and rectifying any unintended biases, thereby upholding ethical standards.

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Envisioning the AI Age: Interview with Yasuko Kosaihira, CEO of Findability Sciences’ Joint Venture with SoftBank Corp.

Findability Sciences Inc., a US-headquartered global provider of AI and big data solutions, established a Japan-based joint venture called Findability Sciences K.K. with SoftBank Corp. (TOKYO: 9434) in 2017. On April 1, 2023, Yasuko Kosaihira took the helm of Findability Sciences K.K. as its new CEO.

Why did she take this challenging new role and what are her thoughts on how AI could transform society? Kosaihira spoke about these topics in this interview.

From back office administration to leading a team of passionate AI engineers

-Tell us about your career so far.

I started off at a domestic consulting firm right out of university, and after about three years, I took a job at the former Tohmatsu Consulting, where I worked on M&A and corporate restructuring projects. Gradually, I developed an interest in working for an operating company, which led me to join Fast Retailing. There, I was involved in IT audits and operational audits of its overseas subsidiaries. Later, I joined SoftBank Corp., which was starting to focus on Robotic Process Automation (RPA) at the time, and I was assigned to the Enterprise Business Unit. As I worked on various tasks, I transitioned to a department that oversees investments and alliances, and most recently, I was involved in managing WeWork Japan.

–From RPA to AI, your career has evolved quite a bit.

Although I didn’t have a background in RPA or AI, I always had an interest in these areas since my auditing days. I heard about technologies that could simplify complex tasks. In auditing, we handled a lot of data, and a lot of the work involved manual checks and cross-referencing. However, there are limitations to the routine checking methods used in audits. I’ve always thought that AI might be the best way to take advantage of accumulated data to gain new business insights.

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