Tag: AI Infrastructure

Explore how AI innovation and sustainable data center management intersect, focusing on energy-efficient strategies to balance performance and environmental impact.

With all that’s being said about the growth in demand for AI, it’s no surprise that the topics of powering all that AI infrastructure and eking out every ounce of efficiency from these multi-million-dollar deployments are hot on the minds of those running the systems.  Each data center, be it a complete facility or a floor or room in a multi-use facility, has a power budget.  The question is how to get the most out of that power budget?

Balancing AI Innovation with Sustainability

Optimizing Data Management: Rapidly growing datasets that are surpassing the Petabyte scale equal rapidly growing opportunities to find efficiencies in handling the data.  Tried and true data reduction techniques such as deduplication and compression can significantly decrease computational load, storage footprint and energy usage – if they are performed efficiently. Technologies like SSDs with computational storage capabilities enhance data compression and accelerate processing, reducing overall energy consumption. Data preparation, through curation and pruning help in several ways – (1) reducing the data transferred across the networks, (2) reducing total data set sizes, (3) distributing part of the processing tasks and the heat that goes with them, and (4) reducing GPU cycles spent on data organization​.

Leveraging Energy-Efficient Hardware: Utilizing domain-specific compute resources instead of relying on the traditional general-purpose CPUs.  Domain-specific processors are optimized for a specific set of functions (such as storage, memory, or networking functions) and may utilize a combination of right-sized processor cores (as enabled by Arm with their portfolio of processor cores, known for their reduced power consumption and higher efficiency, which can be integrated into system-on-chip components), hardware state machines (such as compression/decompression engines), and specialty IP blocks. Even within GPUs, there are various classes of GPUs, each optimized for specific functions. Those optimized for AI tasks, such as NVIDIA’s A100 Tensor Core GPUs, enhance performance for AI/ML while maintaining energy efficiency.

Adopting Green Data Center Practices: Investing in energy-efficient data center infrastructure, such as advanced cooling systems and renewable energy sources, can mitigate the environmental impact. Data centers consume up to 50 times more energy per floor space than conventional office buildings, making efficiency improvements critical.  Leveraging cloud-based solutions can enhance resource utilization and scalability, reducing the physical footprint and associated energy consumption of data centers.

To Know More, Read Full Article @ https://ai-techpark.com/balancing-brains-and-brawn/

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Whether you’re choosing a restaurant or deciding where to live, data lets you make better decisions in your everyday life. If you want to buy a new TV, for example, you might spend hours looking up ratings, reading expert reviews, scouring blogs and social media, researching the warranties and return policies of different stores and brands, and learning about different types of technologies. Ultimately, the decision you make is a reflection of the data you have. And if you don’t have the data—or if your data is bad—you probably won’t make the best possible choice.

In the workplace, a lack of quality data can lead to disastrous results. The darker side of AI is filled with bias, hallucinations, and untrustworthy results—often driven by poor-quality data.

The reality is that data fuels AI, so if we want to improve AI, we need to start with data. AI doesn’t have emotion. It takes whatever data you feed it and uses it to provide results. One recent Enterprise Strategy Group research report noted, “Data is food for AI, and what’s true for humans is also true for AI: You are what you eat. Or, in this case, the better the data, the better the AI.”

But AI doesn’t know if its models are fed good or bad data— which is why it’s crucial to focus on improving the data quality to get the best results from AI for IT use cases.

Quality is the leading challenge identified by business stakeholders

When asked about the obstacles their organization has faced while implementing AI, 31% of business stakeholders involved with AI infrastructure purchases had a clear #1 answer: the lack of quality data. In fact, data quality ranked as a higher concern than costs, data privacy, and other challenges.

Why does data quality matter so much? Consider OpenAI’s GPT 4, which scored in the 92nd percentile and above on three medical exams, which failed two of the three tests. GPT 4 is trained on larger and more recent datasets, which makes a substantial difference.

An AI fueled by poor-quality data isn’t accurate or trustworthy. Garbage in, garbage out, as the saying goes. And if you can’t trust your AI, how can you expect your IT team to use it to complement and simplify their efforts?

The many downsides of using poor-quality data to train IT-related AI models

As you dig deeper into the trust issue, it’s important to understand that many employees are inherently wary of AI, as with any new technology. In this case, however, the reluctance is often justified.

Anyone who spends five minutes playing around with a generative AI tool (and asking it to explain its answers) will likely see that hallucinations and bias in AI are commonplace. This is one reason why the top challenges of implementing AI include difficulty validating results and employee hesitancy to trust recommendations.

While price isn’t typically the primary concern regarding data, there is still a significant price cost to training and fine-tuning AI on poor-quality data. The computational resources needed for modern AI aren’t cheap, as any CIO will tell you. If you’re using valuable server time to crunch low-quality data, you’re wasting your budget on building an untrustworthy AI. So starting with well-structured data is imperative.

To Know More, Read Full Article @ https://ai-techpark.com/data-quality-fuels-ai/ 

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