As society dances with the transformative power of artificial intelligence (AI), a pressing question arises: what can we do for AI rather than the other way around? With the potential benefits come risks, and it’s crucial we find a balance. To maximise the value from AI while keeping dangers at bay, we must develop grounded, practical approaches rather than get lost in the hype.
According to Elena Fersman of Ericsson, three fundamentals are key to creating a safe environment for AI: infrastructure, ecosystem, and governance. First up is the infrastructure—everything from data centres to the tiny sensors embedded in our devices plays a vital role. These components collectively dictate the economic and environmental footprint of AI, so they certainly shouldn’t be overlooked.
An interesting trend is the shift toward edge computing—placing computing power closer to where data is generated, instead of solely relying on centralised systems. This means quicker decisions, enhanced privacy, and less lag time overall. For example, agricultural drones may swiftly analyse data for immediate decisions but still need a robust connection to support more complex data tasks combined with cloud systems, like updating models and spotting broader trends.
This local-global interplay has its parallels in human cognition as per Daniel Kahneman’s insights in “Thinking, Fast and Slow.” Like our brains, AI systems must operate within two modes: intuitive responses and reflective analysis. The challenge is ensuring seamless communication between edge devices and cloud systems—that big nervous system of AI. A high-performance network is non-negotiable, especially when dealing with heavy data types like video or sensory data.
The power requirements of AI aren’t just a tech problem; they’re an environmental one too. Consider the electricity it takes to train models like GPT-4. Millions of kilowatt-hours! As AI grows in complexity and scale, its environmental impacts need urgent attention. More efficient model designs and new ways to source power are vital conversations going forward.
Next, we dive into the ecosystem surrounding AI. Today’s developers are not just builders but stewards of a new AI landscape. They bear responsibility for making ethical choices that shape AI’s future. Luckily, the emergence of low-code and no-code platforms is opening doors, allowing non-tech folks to engage with AI tools—this is big for narrowing the digital divide and forging real, meaningful community involvement.
One cannot discuss AI without addressing data. The decisions surrounding its collection and usage lie in human hands. Without diverse, high-quality data, any AI efforts run the risk of entrenching biases, undermining their intended purpose. Let’s also spotlight the device ecosystem: smartphones, wearables, industrial gear—these are AI’s eyes and ears, collecting vital data to learn from but also needing to deliver insights on the fly. Innovations in hardware are critical to keep pace and ensure these devices can support AI workloads effectively.
Lastly, let’s consider governance. It’s not merely about creating a set of rules. The responsibility for oversight must intertwine top-down frameworks with grassroots understanding. Effective governance should allow for adaptability, adjusting to new observations while enshrining our values—fairness, transparency, accountability. We can’t simply outsource this responsibility to machines; it’s on us, as a society, to shape how AI interacts within our lives.
In summary, to harness AI for our collective future, we must cultivate the right infrastructure, encourage a diverse ecosystem, and establish conscientious governance. Our responsibility is clear – it’s time to ask the right questions and steer this powerful technology toward a beneficial and inclusive path.
Elena Fersman of Ericsson urges society to rethink its approach to AI, focusing not only on what AI can do but also on how we can leverage it responsibly. Key elements for AI success include robust infrastructure, an inclusive ecosystem, and adaptive governance that reflects human values. As AI technology evolves, responsible oversight becomes essential to mitigate risks and increase the technology’s benefits for all.
In conclusion, navigating the AI landscape demands a mix of sound infrastructure, a collaborative ecosystem, and thoughtful governance. The conversation shouldn’t solely revolve around the capabilities of AI; rather, we must focus on how we manage and utilize it appropriately. The onus is on society to ensure that AI serves everyone, not just a select few, while balancing innovation with ethics and responsibility.
Original Source: www.weforum.org
