HPE – Is sustainability compatible with AI in the media and entertainment industry?
Matt Quirk, Director, WW HPE Channel & Partner Ecosystem, HPE OEM Solutions
When none other than Tyler Perry halts an $800 million studio expansion after seeing a text-to-video AI demo, you know something major is happening in media and entertainment. AI isn’t new to the industry—Netflix has used machine learning (ML) to serve up recommendations since the early 2000s—but generative artificial intelligence (GenAI) is changing more than distribution and marketing. GenAI is primed to change how film, television, and music are imagined and produced.
The risk to the artists, creators, and craftspeople who make the shows and songs we love is worrying, but it isn’t entirely clear yet. What is clear: Training AI and deploying AI services consume staggering amounts of energy and create tons of CO2 emissions. Here are some data points.
- Training OpenAI’s GPT-3 model produced an estimated 552 tons of CO2 and consumed an estimated 1287 MWh of electricity,1 enough energy to power a US household for 120 years.2
- Training just one AI model can emit nearly five times the lifetime emissions of an average American car.3
- Researchers estimate that ChatGPT needs 1 GWh of electricity to answer the queries it receives in one day. 1 GWh is the daily energy consumption of 33,000 US households.4
Every human-powered function that GenAI assists, improves, or replaces adds to the electricity bill. The question for media and entertainment is whether the efficiencies GenAI creates outweigh the energy it consumes. If it does, GenAI may be a net positive for sustainability. If not, it’s a sign we all need to heed.
What roles will GenAI play in media and entertainment?
AI is already hard at work throughout the media and entertainment industry. AI algorithms compress video, optimize streaming, and save energy every day. With the advent of GenAI, machine intelligence will contribute even more, and at every stage of media creation and distribution.
Creative – It may sound like fantasy, but GenAI is already helping film producers analyze scripts, predict box office potential, and even generate scenes and entire scripts. The same holds true in music, where singers and songwriters are using AI to generate lyrics, melodies, and finished songs.
Production – Synthetic, virtual worlds have always been part of filmmaking. Major motion pictures and television productions already shoot in virtual sets made of 360°LED walls that can display any location imaginable. With GenAI, set designers will be able to conjure worlds with minimal effort by typing a few words.
Post-production – Animation, editing, and sound design use AI today to automate tasks and generate sequences. GenAI allows editors to remove objects from a scene, turn a can of soda into a glass of wine, de-age actors, and create composite performances stitched from multiple takes.
Digital asset management – Using GenAI to search footage is another emerging use case that can save hours of manual searching. Because GenAI can understand the action, performances, and cinematography of a scene, editors can search conversationally for virtually any attribute. This super-search workflow speeds up editing whether an editor is scrubbing through dailies on a feature film or searching stock libraries for the perfect shot.
Distribution and streaming – With rare exceptions, like 70 mm IMAX, movies and television shows are delivered digitally to cinemas, TVs, and phones all over the world. AI plays a major role in video compression and data optimization from the cloud to packet processing and bit rates as video streams across wired, Wi-Fi, and 4G/5G networks.
Captioning, translation, and localization – GenAI services can combine automated speech-to-text with response generation to produce closed captioning, transcripts, and translations on the fly. AI also assists in reformatting to match local broadcast specifications, frame rates, and device aspect ratios.
Marketing – AI helps identify potential hits, trending songs, and hot shows, then places them in front of the right audience, at the right time, on the right device. Thanks to AI, streaming platforms’ recommendation engines combine a nuanced understanding of audience behaviors and preferences with real-time analysis of what’s hot.
General business intelligence – AI and GenAI services create value in both directions. The users of AI services receive help, have more efficient workflows, and produce labor-saving work. The service provider receives intelligence. Wherever AI supports a function or role, the business receives data about that role that GenAI can turn into knowledge and action.
The same GenAI service that helps a line producer optimize shooting schedules can review fleet data and map more efficient routes for studio trucks. It can scout locations to minimize travel, track carbon emissions, and calculate offsets all while optimizing cloud computing resources to the enterprise’s real-time needs.
As GenAI spreads in media and entertainment, will the net effect be good or bad for sustainability?
At this stage, the tradeoffs are difficult to calculate. We only have estimates of GenAI energy use and a limited view into how rapidly studios, streamers, and infrastructure providers are adopting GenAI. However, we can make some educated guesses.
Replacing location shoots with virtual, AI-generated sets should reduce net energy use. Traveling to a location means moving talent, crew, and equipment. Powering a set usually means diesel generators, although there are greener alternatives that run on propane or natural gas. Even with green shooting practices in place, a location shoot is an energy and time-consuming proposition.
Using AI to improve file compression, encoding, and transmission should make file transfers and streaming faster, less expensive, and less energy-intensive. Compared to printing and shipping thousands of reels of film or Blu-rays, digital delivery is clearly more sustainable. GenAI promises to make it even more efficient.
General operational efficiency—from faster post-production to insight into a project’s carbon footprint—should improve steadily as GenAI takes on more roles and functions. Over time, automated, continuous improvements in processes should add up to significant energy savings.
Whether these hypothetical efficiency gains outweigh the energy cost of GenAI is an open question. One way to make sure GenAI does have a positive impact is to improve the energy efficiency of model training and deployment.
How to make LLMs and GenAI more sustainable
The energy costs of GenAI are built-in to the cost of the service, which makes GenAI’s energy impact opaque to the end user. Changing the energy equation for AI rests with the companies providing AI services and manufacturers like HPE who build the supercomputers and data centers that power GenAI. There are levers we can pull to reduce the energy overhead of GenAI, and HPE is working to create new ways to make GenAI sustainable for every industry.
Model training takes massive amounts of time, computing power, and electricity. Every improvement we make in the training process pays immense dividends. HPE has developed the HPE Machine Learning Development Environment. It includes tools for tuning training workloads, so they use hardware more efficiently. The environment runs on machine-learning-specific accelerators that are up to 5x more efficient than off-the-shelf systems.2
Even though model training is energy-intensive, AI services consume 90% of the energy used to deliver AI.2 Where those services run can be a major factor in energy consumption and carbon footprint. For example, the average private data center can be half as efficient than a cloud data center.2 Newer, more energy-efficient servers like the HPE ProLiant Gen11 come equipped with workload-specific accelerators that can deliver up to 10x better performance per watt.[1]
The physical location of a data center matters, too. It takes far more energy to move electricity than it takes to move data. Locating data centers near power generation facilities minimizes energy lost to power transmission. If those facilities use solar, wind, or hydropower, the data center’s carbon footprint will be significantly reduced. This is why many hyperscalers and service providers, including HPE, are shifting data center infrastructure to regions with abundant hydropower and colder climates that can reuse the data center’s waste heat.
Optimizing AI models to run more efficiently can also drastically improve energy performance, regardless of what hardware these models run on. Sparsely activated deep neural networks can consume <1/10th the energy of similarly dense deep neural networks (DNNs) without sacrificing accuracy.2
Taken together, increasing supply-side efficiencies through optimization, more efficient hardware, and renewable energy can collectively reduce AI carbon emissions 1000x.2 With efficiency gains like that, GenAI in media and entertainment—and other industries—may prove to be a gain for sustainability. At HPE, we are working to ensure that this is the case.
Learn more about how we can help you build solutions that are both sustainable and AI-driven at hpe.com/solutions/oem.
Footnotes
1. David Patterson, et. al, Carbon Emissions and Large Neural Network Training, arxiv.org.
2. Zachary Champion, Optimization could cut the carbon footprint of AI training by up to 75%, Michigan News, University of Michigan.
3. Bernard Marr, Green Intelligence: Why Data and AI Must Become More Sustainable, Forbes.
4. Sarah McQuate, Q&A: UW researcher discusses just how much energy ChatGPT uses, University of Washington News.
5. Based on MLPerf v3.0 inference results for Offline Throughput/Average Watts against comparable accelerators (with similar TDP)
