QUALCOMM
“ The key focus will be advancing distributed compute, enabling more intelligent, personalised and responsive experience across the connected ecosystem”
Vinesh Sukumar VP of Product Management, AI / Gen AI Qualcomm
“ For example, in environments such as agriculture where robots must continuously interpret terrain, detect anomalies such as weeds or crop conditions and act in real time, edge-based solutions such as autonomous weed detection and removal systems, supported through Qualcomm for Good, demonstrate how AI can process sensor data locally to make immediate decisions without relying on constant cloud connectivity.”
Personalisation is another key benefit – models can adapt locally to individual user behaviour.“ For example, smart home devices are learning your routine,” Vinesh says.“ You’ re trying to create a digital twin that can mimic and be exactly like you.”
Edge AI is also more efficient – reduced bandwidth usage and lower infrastructure costs become extremely critical for industrial IoT deployments with thousands of sensors that only send actionable insights, not raw data, all the way to the cloud.
In the shift towards more personalised AI models, there is an argument for the increasing need for user privacy.
With user-specific AI algorithms, there are risks of severe privacy erosion, psychological manipulation and algorithmic bias, while offering hyper-relevant personalised experiences to the user.
These risks can be tackled using edge AI, which can help protect privacy by processing and analysing
16 qualcomm. com