We are pleased to share reflections from Patrice Chazerand , a director at DIGITALEUROPE in 2010-2023, on the discussions at the latest AI4People Summit. The session, titled ‘Building Sustainable AI for a Greener Future,’ brought together speakers dedicated to examining AI’s ecological footprint, discussing practical approaches to reduce the environmental impact of AI systems.
This included Ganesh Bukka, Vice President & Global Head Industry 4.0 at Hitachi Digital Services; Antoine Rostand , President and Founder of Kayrros; and Ricardo Vinuesa a, Associate Professor in the department of Aerospace Engineering, University of Michigan.
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These three experts agreed on the critical points below
However good a business or a university, working together will secure better results. To this effect, co-creation is the name of the game to improve on silos plagued with vain, closed feedback loops. AI is the unsung hero of the battle for containing climate change: too much focus on its merits as a marketing tool, not enough on what it does to secure energy efficiency.
Whatever its prowess, AI is only a tool: “The right tool for the right purpose” provides a more appropriate guidance than betting our future exclusively on AI. Quality data drives effective AI. Quality data needs quality people. The uptake of AI with people is a function of how it is perceived: enabler of inhibitor? Proper communication has yet to start to this effect.
Setting the scene
Ricardo referred to a 6-year old study documenting enabler/inhibitor perception relative to SDG targets. While the positive outruns the negative by far (79% vs 35%), the breakdown of positives by category (70% in economy, 93% in environment, 82% in society) tells that there is room to mitigate the negatives on society by enhancing the positives on environment.
Antoine made it clear that satellite imagery gets considerably boosted by unsupervised models, LLMs, etc. For instance, hyperspectral satellite imaging uses hundreds of bands to detail emissions of CO2, methane, etc. AI works wonders in mitigation as well as in adaptation.
Ganesh explained that Hitachi wants to bring harmony into innovation, e.g. via clean energy, smart mobility, etc. Hitachi is a major player in Europe, its second largest market after Japan, as testified by a number of leading-edge tech labs or its world headquarters for energy. Innovative infrastructures enhance energy efficiency. There is no dearth of challenges though: digital services, as they grow, make a growing contribution to a greener planet; however, data centers keep increasing their environmental footprint; furthermore, some CO2 emission sources happen to be embedded in hardware. In order to meet these challenges, sourcing should be better documented; goals should come with detailed KPIs and proper measurement tools; transparency should be ensured via optimal public disclosure. No doubt AI helps energy optimization: smart grids secure a better balance between supply and demand at all times; smart mobility helps minimize investment via dedicated platforms; AI-powered tools enable energy saving, from raw materials throughout the entire lifecycle of products.
The way forward: Sharing
For Ganesh, sharing is the way to go. Silos are the enemy since they are impervious to meaningful feedback. In contrast, co-creation between government, business and civil society (whether academia, NGOs, or other structures) spurs on creativity; it needs only proper platforms to thrive, and AI is there to help. Europe is doing well in this respect.
Ricardo observed that academia-business relations are more productive in the US. It is more a matter of mindset than money: in Europe, universities are all too often seen as either intimidating partners or cheap consulting whereas they have no match when it comes to unbiased, out-of-the-box thinking.
While collecting data from the very top of outer space, Antoine praised the merits of bottom-up creativity born out of engaging with local governments (in wild fires prevention and containment, for instance) and leading universities across the region.
Quality matters
For Antoine, AI-driven productivity is a direct function of the quality of algorithms and of the data they feed on. Year after year, you can see the impact of improved technology on how your services will perform. For example, foundation models have significantly decreased needs for energy. Suppliers of high-quality data cannot see the demand for it plateau anytime soon.
Gamesh sees merits in using the right tech for the right purpose. For instance, edge computing may provide extremely competitive solutions with smaller resources. Near-zero failure operations are enabled by quick progress towards AGI, synthetic data modelling, etc. However, reality checks are often needed for better performance, which sort of debunks the belief that AI systems operate mostly on their own: people are actually the systems’ gateway to the myriad aspects of human life.
Europe vs rest of the world
Antoine mentioned the magnitude of AI-related investment made in the US, Asia or the Middle-East: Europe pales in comparison. “We think too much”, he suggested. Only action can make a difference. In this regard, bureaucracy lurks at every step of the way: State aid and other regulations, for example, may act as a serial killer on projects that look promising to their supporters. AI leadership depends on quality models. The EU has yet to fully grasp this notion and to shape a global vision accordingly.
Ricardo agreed that the EU could be more flexible in the way it manages its generous funding: scaling up – often quoted as a liability in Europe’s industrial landscape – would be easier.
Gamesh contributed an interesting explanation on how Japan combines its time-honoured reverence for the benefits of taking the long-term view with the need to meet the goals of the next quarter. Different targets are set on 3-year, 10-year, 50-year; they are addressed and managed independently by business and government, although properly coordinated as illustrated in Society 5.0.
To Conclude
Antoine suggested that Brussels needs a better plan driven by modern-day reality.
Ganesh confirmed that open models based on sharing skills and experience will make AI more effective, hence our future greener. Ricardo stated that AI-powered dialogue cannot but help our data-driven societies to steer the course they choose.
