Start small, scale up: AI’s ‘not-so-secret’ to success

Starting small and scaling for success

A small-scale natural language processing (NLP) pilot by a French Cancer Research Organization resulted in a transformative set of initial results and learnings that gave it the basis from which to expand the use case’s scope.

Before the organization developed its solution, it took 30 people six months to review patient records and identify patients suitable for clinical trials. The pilot project learned from data from 24 million records and 1.25 million patients, and classified new and previously unseen data based on these learnings. The organization believes this system could cut the whole process down to a day.

The team is now looking to scale its solution, which runs on high-performance Intel® Xeon® processor-based clusters, by deepening the search capabilities, refining the user interface, further optimizing performance, and expanding the number of users and locations.  

Automation at the speed of life

Another example of how, once scaled up from successful initial proofs of concept, AI projects can have a transformative impact is found in image recognition.

In the healthcare industry, again, radiologists make clinical judgments every day about whether patients’ scans are symptomatic of cancer. However, it would be difficult for them to describe how to identify all cancers in any images associated with any patient, irrespective of image quality or rotation. Yet this is exactly what a deep learning image recognition system is able to learn and automate, given sufficient data – resulting in the ability to process images and flag potential cancers with life-saving speed and accuracy.¹

Watch: Artificial intelligence is transforming the way enterprises automate image recognition to build better business solutions and lower operating costs. 

The three technology factors underpinning AI performance²

AI performance is driven by a combination of compute, software optimizations and processing memory bandwidth, and the Intel® architecture in your data center can give you the AI you need on the hardware you already own.

Intel’s data science team recently wrote of its work on image classification for health and life sciences:

On the software side, by optimizing a number of deep learning libraries for many of the most popular AI frameworks, Intel has made it possible for data scientists and developers to work with their preferred tools on Intel hardware. These frameworks include TensorFlow*, Theano*, and more.

Additionally, BigDL is a distributed deep learning library for Spark* that can run directly on top of existing Spark or Apache Hadoop* clusters. It allows for loading of pre-trained Torch* models into the Spark framework and can efficiently scale out to perform data analytics at big data scale.

How ready is your organization for AI?

Intel works with many organizations looking to deploy artificial intelligence and its continued optimization of both hardware and software means AI is within reach of almost any business.

Wherever you are in your AI journey, Intel’s broad portfolio of hardware and software offers a rich toolkit for building the most cost-effective deployment architecture for AI workloads, and you can start today.