Category Archives: AI

Whatever your feelings are about AI – It’s all great! It’s going to kill us all! You gotta take the bad with the good! It all depends! I’m not quite sure – yet! – most of us recognize that while AI is growing as a force, and will revolutionize entire industries, it does consume an awful lot of energy, leading to concerns about whether it is environmentally sustainable.

Consider these observations, based on the work of the International Energy Agency (IEA)

One of the areas with the fastest-growing demand for energy is the form of machine learning called generative AI, which requires a lot of energy for training and a lot of energy for producing answers to queries. Training a large language model like OpenAI’s GPT-3, for example, uses nearly 1,300 megawatt-hours (MWh) of electricity, the annual consumption of about 130 US homes. According to the IEA, a single Google search takes 0.3 watt-hours of electricity, while a ChatGPT request takes 2.9 watt-hours. (An incandescent light bulb draws an average of 60 watt-hours of juice.) If ChatGPT were integrated into the 9 billion searches done each day, the IEA says, the electricity demand would increase by 10 terawatt-hours a year — the amount consumed by about 1.5 million European Union residents. (Source: Vox)

Given stats like this, and the fact that demand for AI – and the power it consumes along the way – is rapidly growing, it’s no wonder that engineers and researchers are focusing on ways tamp down/slow down AI’s energy demands.

Writing recently in the EE Times, Simran Khoka notes that “data centers, central to AI computations, currently consume about 1% of global electricity—a figure that could rise to 3% to 8% in the next few decades if present trends persist,” adding that there are other environmental impacts that AI brings with it, such as e-waste and the water usage required for data center cooling. She notes that IBM is one of the leaders in creating analog chips for AI apps. These chips deploy phase-change memory (PCM) technology.

PCM technology alters the material phase between crystalline and amorphous states, enabling high-density storage and swift access times—qualities essential for efficient AI data processing. In IBM’s design, PCM is employed to emulate synaptic weights in artificial neural networks, thus facilitating energy-efficient learning and inference processes.

IBM is not alone. Khoka cites a couple of the little guys: Mythic, which:

…has engineered analog AI processors that amalgamate memory and computation. This integration allows AI tasks to be executed directly within memory, minimizing data movement and enhancing energy efficiency.

She also writes about Rain Neuromorphic which is developing chips “process signals continuously and perform neuronal computations, making them ideal for creating scalable and adaptable AI systems that learn and respond in real time.”

Applications well suited to analog chips include edge computing, neuromorphic computing, and AI inference and training.

A principal challenge that switching to analog chips presents is ensuring that they have the same precision and accuracy that digital chips yield. Another hurdle is that, at present, the infrastructure behind AI systems is digital.

It’s no surprise that MIT is keeping its eye on ways to reduce the energy consumption of voracious AI models. MIT’s Lincoln Lab Supercomputing Cener (LLSC) is finding that by capping power and slightly increasing task time, energy consumption of GPUs can be substantially reduced. The trade-off: tasks may take 3 percent longer, but energy consumption is lowered by 12-15 percent. With power-capping constraints in place, the Lincoln Lab supercomputers are also running a lot cooler, decreasing demand placed on cooling systems – and keeping hardware in service longer. (And something as simple as running jobs at night, when it’s cooler, or in the winter, can greatly reduce cooling needs.)

LLSC is also looking at ways to improve how efficiently AI models are trained and used.

When training models, AI developers often focus on improving accuracy, and they build upon previous models as a starting point. To achieve the desired output, they have to figure out what parameters to use, and getting it right can take testing thousands of configurations. This process, called hyperparameter optimization, is one area LLSC researchers have found ripe for cutting down energy waste.

“We’ve developed a model that basically looks at the rate at which a given configuration is learning,” [LLSC senior staff member Vijay] Gadepally says. Given that rate, their model predicts the likely performance. Underperforming models are stopped early. “We can give you a very accurate estimate early on that the best model will be in this top 10 of 100 models running,” he says.

Jettisoning models that are slow learners has resulted in a whopping “80 percent reduction in energy used for model training.”

Whatever your feelings about AI, it’s comforting to know that there are plenty of folks out there trying to ensure that AI’s power consumption will be held in check.

 

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Image sourc: Emerj Insights

Not that I’ve given it all that much thought to it, but if I’d been asked, I don’t think that I’d have put brewing beer very high on the list of candidates for AI involvement.

Not that the beer industry is any stranger to using up-to-date technology. It’s widely used in brewing’s production and logistics processes. But making beer that tastes better? I would have said that this is more art than science. Sure, the major market share industry players with the more widely-known and consumed brands are more focused on the “science” parts of production and logistics – after all, a Corona’s a Corona and a Bud’s a Bud. And the microbrewers (not to mention the homebrewers) would come down more on the “arts” side, using trial and error to come up with the ideal mix.

Of course, even Corona and Budweiser are always introducing new products, and whether you’re one of the big guys or one of the little guys, creating a beer that tastes good isn’t easy. Figuring out whether – to borrow from an ancient Miller ad – a beer tastes great and/or is less filling can involve drafting (and educating) employees and “civilian” beer drinkers to act as taste testers for their products. But, as a recent MIT Technology Review article said, “running such sensory tasting panels is expensive, and perceptions of what tastes good can be highly subjective.”

Enter AI.

Research published in Nature Communications described how AI models are being used to find not only how consumers will rate a beer, but also how to make a beer that’s better tasting.

This wasn’t an overnight process. Over a five-year period, researchers analyzed the chemical properties and flavor compounds in 250 commercial beers.

The researchers then combined these detailed analyses with a trained tasting panel’s assessments of the beers—including hop, yeast, and malt flavors—and 180,000 reviews of the same beers taken from the popular online platform RateBeer, sampling scores for the beers’ taste, appearance, aroma, and overall quality.

This large data set, which links chemical data with sensory features, was used to train 10 machine-learning models to accurately predict a beer’s taste, smell, and mouthfeel and how likely a consumer was to rate it highly.

The result? When it came to predicting how the RateBeer reviewers had rated a beer, the AI models actually worked better than trained tasting experts. Further, the models enable the researchers “to pinpoint specific compounds that contribute to consumer appreciation of a beer: people were more likely to rate a beer highly if it contained these specific compounds. For example, the models predicted that adding lactic acid, which is present in tart-tasting sour beers, could improve other kinds of beers by making them taste fresher.”

Admittedly, having lactic acid in a beer doesn’t sound all that appealing. But if the beer tastes fresher, well, just don’t read the fine print on the ingredients list.

One area where they anticipate the AI approach will prove particularly effective is in the development of non-alcoholic beers that taste as good as the real thing. This will be great news for those who want to enjoy a beer without having to consume any alcohol.

There are other instances of AI being used in brewing. Way back in 2016, a UK AI software startup IntelligentX, came out with four beers based on their Automated Brewing Intelligence algorithm. The release of Amber AI, Black AI, Golden AI, and Pale AI caused a brief flurry of excitement as the first AI developed beer. Unfortunately, it looks like none of them made much of an impact in the beer market. When I searched for them, I couldn’t find any references beyond 2019.

Maybe the models that the Belgian researchers produced will have more luck creating a successful AI beer.

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The full research report from Nature Communications can be found here.