and-error process of tuning these things correctly . AutoML tunes them for you automatically .”
When it comes down to the AI accelerator , distributed training is another tactic of Samsung ’ s as Datta explains : “ We utilise more than one graphics processing unit ( GPU ). To help speed up the process , we may use many hundreds of these GPU processors , simultaneously distributed over many computers to execute a single training task .”
With the AI accelerator as the go-to toolkit for Samsung SDS , the aim is to work on all the areas of efficiency , labelling , tuning of the models and distributed training in parallel , so they can “ execute the artificial intelligence workflow a lot more quickly and efficiently and actually arrive at a much more accurate model in the end .” said Datta .
Challenges in healthcare imaging Nasim Eftekhari , director of applied AI and data science at City of Hope , a worldrenowned cancer research and treatment organisation near Los Angeles , says , “ All supervised models that we use today have been trained on labeled data . Regardless of industry , labelling the data for training is always the most expensive part of any AI solution . It is very time-consuming and expensive because doctors and healthcare professionals have to annotate these images , and each image takes hours and hours of time . City of Hope wanted to explore
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