Explore the vital link between AI and sustainability as we discuss strategies for eco-conscious AI deployment with Dr. Eng Lim Goh, Senior vice president of Data & AI at Hewlett Packard Enterprise

In this episode of Unpacked, Arvind introduces Dr. Eng Lim Goh, SVP of Data and AI at Hewlett Packard Enterprises, to discuss the topic of sustainable AI. They agree on the importance of being conscious of the planet’s well-being while charting business growth and profitability. 

Dr. Goh explains the need for corporations to consider sustainability due to their net-zero goals, the conscious younger generation of employees, and the economic implications of power consumption in supercomputing. He shares his experience of working on the International Space Station and how he realized the importance of a sustainable approach to technology. 

So for e-waste, what’s better than, what’s more sustainable than e-waste is to recycle. But in fact, what’s more sustainable than recycle is reuse.

– Dr. Eng Lim Goh

Similarly, he suggests that businesses should consider long-term goals while investing in AI and related technologies, adding that it is important to measure the impact of such efforts quantitatively. He also talks about the importance of collaboration between businesses, governments, and academia to achieve sustainable progress. The conversation then moves on to the topic of energy consumption in AI, and Dr. Goh explains how the power consumption of large models has been a challenge in the supercomputing industry. He suggests that businesses should consider using more efficient hardware and software to reduce energy consumption and how they can approach this. He also mentions the importance of using renewable energy sources to power data centers. 

The conversation concludes with Dr. Goh’s vision for the future of AI and sustainability. Dr. Goh emphasizes the need for businesses to consider the long-term impact of their actions and to invest in sustainable technologies. He believes that AI can play a crucial role in achieving sustainability goals and that it is important for businesses to collaborate and share knowledge to achieve sustainable progress. 

I shouldn’t say pick the right size model. Pick the right model.

– Dr. Eng Lim Goh

Overall, the conversation highlights the need for businesses to consider sustainability while investing in AI and related technologies. It emphasizes the importance of transparency, collaboration, and measurement in achieving sustainable progress.

Production Team
Arvind Ravishunkar, Ankit Pandey, Chandan Jha

“Developing GenAI Strategy” with guest Omid Bakhshandeh, AI Scientist with a PhD in Artificial Intelligence, discusses how organizations can foray into adoption of GenAI.

Whether you are the company's CEO or leading a business unit, if you're asking yourself the question, should I develop an AI strategy? That's the wrong question because today, we know that if you don't have an AI strategy, the odds of you being successful in the next couple of years will diminish. So, the right question is, what is my AI strategy, and how fast can I deploy this strategy? To answer this question, large language models are at the heart of every company's AI strategy. In a previous episode with Professor Anum Datta, we unpacked LLMs and explored what LLMs are. In this episode, that conversation was taken to the next level, and we discussed the key things you need to know about LLMs that'll help you develop your company's AI strategy.

Looking at the current landscape of Large Language Models (LLMs), these LLMs capture vast amounts of knowledge and serve as repositories of knowledge that have given rise to foundational models. With this concept, there's no need to initiate the training of an LLM from the ground up. Instead, existing LLMs available in the market, which have already encapsulated knowledge, can be harnessed and seamlessly integrated into applications. It is beneficial for companies in most cases to follow this strategy. The inherent trade-off pertains to the risk of foregoing the utilization of established LLMs, which could result in a delay in promptly reaching the market.

On the contrary, some companies, characterized by their possession of significant volumes of unique and customized data, may contemplate the development of proprietary foundational models and specific LLMs. This strategic manoeuvre facilitates the integration of such models into their respective industries and provides avenues for potential monetization opportunities.

The key for leaders is to pay close attention to the potential use cases, data, and the support system available when building the AI strategy.

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Arvind Ravishunkar, Ankit Pandey, Chandan Jha

"Exploring what should organization considering when choosing to adopt LLMs" with guest Nick Brady, Senior Program Manager at Microsoft Azure Open AI Service

AI has been at the forefront of transformation for more than a decade now. Still, the Open AI launch of chat GPT in November 2022 will be noted as a historical moment – the scale of which even Open AI did not expect – in the history of technological innovations. Most people don't realize or fully appreciate the magnitude of the shift that we're in. Now, we're able to directly express to a machine a problem that we need to have solved; equipping these technologies with the right reasoning engines and the right connectivity could bring the biggest technology leapfrog not just for enterprises but even in everyday lives.

The onset of leapfrog does bring out a few questions for enterprises looking to adopt GenAI as a part of their strategy, operations and way ahead, like:

What use cases are best suited to adopt the models?

While most customers are looking for how this could reduce business costs in their organizations, the true value is when it is used to maximize business value productivity and downstream that could lead to employee satisfaction and customer satisfaction. Any place where there's language – programming or natural language – is a good use case for generative AI, and that probably would be the most profound shift. So, if you have language, if you have a document, if you have big data where you're trying to sort of synthesize, understand what that content and what the content is, generative AI models can do this ad nauseam without any delay.

The most common metric used across the world to describe LLMs is the number of parameters; in the case of GPT 3, it is trained on 175 billion parameters, but what does this mean?

Parameter size refers to essentially the number of values that the model can change independently as it learns from data and stores all information in the vast associative ray of memory as its model weights. What's perhaps more important for these models, and it speaks to more of their capability, is their vocabulary size.

How does one decide and evaluate which would be the best-suited model for the selected use cases?

The best practice really is to start with the most powerful and advanced language model like GPT 4.0 to test, if it's even possible, with your use case. Post confirming the possibility of use case trickle down to simpler models to find its efficacy and efficiency. If the simpler model can probably achieve 90% of the way, with just a little bit of prompt engineering, then you could optimize for costs.

Organizations would have to define what quality means to them. It could be the model's output, its core response, or performance in terms of latency, where the quality of the output may not be as important as how quickly we can respond back to the user.

The key for leaders is to pay close attention to the potential use cases, test them with the best model and then optimize the model to balance the cost, efficacy and efficiency factors.

Production Team
Arvind Ravishunkar, Ankit Pandey, Chandan Jha