DATA PROCESSING
Therefore to do this , companies need to call upon increasingly greater processing power , which requires significant computational resources . This can not only be time consuming , but expensive .
This ability to refine these data for these ever-growing AI models may be out of reach for some .
THE MULTIMODAL FUTURE Equally , as AI models evolve , they are increasingly processing image and audio data to support language models .
Although this brings a wealth of new data and understanding , this multimodal approach requires the development of more sophisticated data processing techniques to handle the diverse data types effectively .
“ Processing unstructured data like images and audio introduces additional challenges , such as feature extraction , representation learning , and integration
with textual data ,” notes Richard Fayers , Head of the Data and Analytics Practice at Slalom . “ Techniques like computer vision , speech recognition , and multimodal fusion will become increasingly important to enable AI models to understand and generate rich , multimodal content .”
THE BALANCING ACT : QUANTITY VS . QUALITY While the volume of data available is staggering , the true challenge lies in striking a balance between quantity and quality .
“ Without the right amount and quality of data , there ’ s no AI ,” explains Rajagopal .
Poor data quality can have severe consequences , with MIT estimating it to cost most companies a staggering 15-20 % of revenue . However , being too puritanical in pursuit of data quality can also lead to slower development rollouts or more limited models , which could
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