The Rise of GenAI: Challenges and Opportunities in the Age of Artificial Intelligence

AllCloud Blog:
Cloud Insights and Innovation

Artificial intelligence (AI) is already an essential component of our daily lives, powering a wide range of applications. However, the arrival of Generative AI (GenAI) has sparked both excitement and anxiety about its potential impact.

Will GenAI eventually replace humans? Will it result in widespread job loss?

These questions remain unanswered, even for AI professionals. Instead of fixating on the unknowns, it is more helpful to understand how GenAI will affect our current work processes, and adjust accordingly.

Generative AI: Who are the players?

As GenAI evolves at a rapid pace, distinct actors are emerging, shaping the market. On the one hand, there are the providers, who have the skill and resources in deep machine learning (ML) to create GenAI models, also known as foundation models.

On the other hand, there are the consumers of Generative AI, those companies seeking to incorporate the new tech into their applications or services, without necessarily possessing ML expertise internally, among their own teams.

The Fine-tuners: Bringing the players together

Bridging the gap between providers and consumers of GenAI is not straightforward. While the foundation models developed by providers are impressive, they still exhibit limitations in certain high-value domain-specific tasks: typically, they hallucinate and perpetuate data biases. This necessitates the involvement of a third actor: the “fine-tuners.”

Fine-tuners are companies that possess significant ML engineering experience and can integrate GenAI into consumer apps, overcoming the final obstacles of efficient deployment of GenAI.

Effective GenAI: How it can be done

How will these fine-tuners accomplish the task? Not in the way you might expect. Interestingly, the key lies in bolstering methodologies and approaches that are not GenAI specific, but were previously under-utilized in traditional AI. Let’s take a look:

Monitoring and evaluating model accuracy will become more crucial than ever before. While monitoring is already common, it is often treated as an afterthought, sometimes designed after models are deployed. Going forward, ML engineers must prioritize monitoring from the start of an ML project.

Furthermore, fairness and bias evaluations, which have remained a largely unaddressed concern, will necessitate substantial industry attention. The buzz around GenAI may even prompt legislators to take action, leading to a surge in the fairness evaluation market, similar to how GDPR boosted the data privacy market.

GenAI will shift the focus back to data, rendering “data-centric AI” the new norm. The importance of the model diminishes as foundation models improve through fine-tuning and prompting, emphasizing the significance of quality data. Consequently, having the right cloud infrastructure becomes vital in this context.

Cloud-based solutions already facilitate the monitoring and evaluation of models at scale. Moreover, the concepts of data lakes, lakehouses, or data meshes have propelled the data analytics field forward, thanks to the flexibility, security, and scalability capabilities offered by the cloud. Hence, transitioning from model-centric to data-centric AI will be considerably easier in the cloud environment.

Bearing these two points in mind…

AllCloud’s deep expertise and understanding in building modern cloud data architectures and deploying AI applications means it is ideally positioned to bridge the gap between consumers and providers of GenAI.

The advent of GenAI presents a paradigm shift in the AI landscape. While uncertainties and concerns persist, focusing on the areas within our control is paramount. Understanding the potential effects of GenAI on existing processes and responding quickly is critical for individuals and companies alike. The collaboration between providers, consumers, and fine-tuners will shape the market, enabling effective integration and utilization of GenAI.

By prioritizing accurate monitoring, fairness evaluation, and data-centric approaches, we can navigate the challenges and capitalize on the opportunities offered by GenAI in this new era of artificial intelligence.

Are you prepared to evaluate the Generative AI approach for your organization?
Take the first step using AllCloud’s Generative AI & ML Readiness Assessment

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Jonathan Chemama

AI Tech Lead

Read more posts by Jonathan Chemama