By Steve Wallo, CTO
Data-intensive workloads in the real life
Artificial intelligence (AI) and machine learning (ML) have already captured the attention of the world. The thrilling possibilities of technological revolution lurking beyond a close horizon have proven to amass people from every industry and walk of life alike-those ready to increase productivity and cultivate innovation, efficiency, and competitiveness within almost any and every vertical. Tools from ChatGTP to Amazon Bedrock are making it easier to experiment with analytics and Generative AI, and we see industry leaders activating their analytics and AI journeys. For instance, the Chicago Transit Authority (CTA) recently announced it was partnering with Google on AI-powered ‘Chat with CTA’ bot to ease rider experiences and CocaCola used GenAI to create a new advertisement for their trademark product in record time. Seems the possibilities are endless. But… what can go wrong? Unfortunately, a lot.
The highly popularized adage “with great power comes great responsibility” really rings true in the realm of analytics, AI, and ML. As we see the power of AI being harnessed in countless industries, we also see an uptick of faulty AI outputs… or as we like to call them, hallucinations. Without accurate, diverse, and up-to-date data being fed into the AI engine (as we warmly refer to the as the AI beast), the output will be erroneous. And when organizations act based on bad insights-they can take a major hit when misguided decisions are made. Here is where the more responsibility part comes into play-it is crucial to implement safeguards within those models to prevent AI hallucinations. These can serve as foundation guidelines to maintain the integrity of data inputs-obtaining the right amount, diversity, and freshness thereof-as well as defining risk tolerances, thereby ensuring the outputs stay within acceptable boundaries and accuracy. That’s no small charge on its own and before you even consider how to get ahold of that data, you need to understand what data you need to get ahold of.
So many data options…
That’s where retrieval-augmented generation (RAG) comes into play, providing organizations a framework for how to optimize the output of a large language model (LLM) with more contextualized and up-to-date data-as well as where to get it. To guarantee the fastest possible data ingest into AI/ML models for the most accurate outputs, organizations must first fix their access to widespread (pun intended) data.
…In so many places… in so little time…
The concept of democratized data is nothing new but the actual execution of it isn’t quite there. Yet, this will be a mission-critical capability for those leaders seeking successful implementation of AI, ML, and Gen AI. Data creation is booming from all over-outside the data center, at the edge, in the cloud. Points of data consumption, whether a uniquely skilled data scientist working from home, scattered branch offices, or a centralized yet hybrid infrastructure, are equally as dispersed. Getting any dataset-regardless of its level of perishability, diversity, sensitivity-from point of production to point of consumption can be challenging, considering geographic distance, software and hardware interoperability, networks. Throw in the scale and speed of data access necessitated by today’s data-intensive workloads… and we got ourselves a roadblock. How do we get around that?
…A perfect use for high-performance remote data access technology
To effectively and correctly feed the AI beast, we need both a guideline to ensure the AI model has the right composition of data to improve large language model (LLM) outputs, as well as an ability to enable timely, ubiquitous access to data when it’s needed. The future of tech and the major focus for the next few years, will be just that-solving for data curation to optimize AI/ML model output.
This can be done today by technologies that allow organizations to move data at outrageous speeds and seamlessly integrate with organizations’ existing infrastructure for ubiquitous data management. AI and ML innovations need an accelerated data solution that fuels these initiatives with the fastest data movement and access. This kind of technologies will be at the forefront of accurate and near real-time data curation.
The rapid expansion of disperse and diverse data creation is a wealth of opportunity-prospectively through the pairing with analytics, AI, and ML-to improve the way we live, work, and play. Yet, there will be a turning point for organizations to address the holes (or, in this case, hallucinations) of that expansion. Safeguarding the future of AI means elevating the composition of your data inputs for accurate outputs. The overall success of an organization’s AI journeys starts and ends with the (right) data. Originally posted on VM Blog
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