As deep learning grows in sophistication, it is time to integrate it into concrete business use cases. Deep learning, especially in the field of computervision, has gotten so good that it is ready for the global industrial stage.
The amount of data produced every day sits around 2.5 exabytes, and businesses now equate parsing through all this data as a solution to their business problems. And with deep learning, they now have smart machines to parse through their most complex, multidimensional data to gain new insights.
In past blogs in this series, we have reviewed what deep learning is and how it’s a valuable business investment. It’s time to get get into the nitty-gritty — which markets are ripe for deep learning right now. One of them could be yours.
Above all other applications, deep learning holds a the most immediate promise in health care. It is an industry full of data and one that is on track to account for $1 out of every $5 spent in the United States.
The killer app here is computervision. A lot of expensive and skilled procedures in health care involve imaging. From MRIs to CAT scans and even simple X-rays, doctors use visual observation to determine a diagnosis from a picture. But the cognitive load on doctors is staggering. One journal study assessed that the typical radiologist is tasked with reviewing so many images a day that it amounts to one new image every three to four seconds over an eight-hour period. That is far too high of a workload for a human to remain effective.
Deep learning, however, currently excels at image recognition and can perform this task faster than humans. By showing a program millions, even billions, of scanned images and how they correlate with diagnosis, deep learning could take the human out of the loop, bringing the doctor back in to come up with a treatment plan. In the future, even simple treatment recommendations could become automated through an AI-based health care assistant, freeing doctors up to perform work that takes skill plus imagination, like researching the cure for cancer.
Outside of oncology, deep learning holds promise for drug discovery. Startups like Atomwise are honing in on how deep learning could better predict optimal pharmaceuticals to battle diseases based on their molecular structure. The company used a virtual search to discover two drugs that would prove effective at preventing Ebola infections, though that was not the intended use by their creators.
Manufacturing is going through a pivot, from simple, introductory automation to what the market is terming Industry 4.0. The factory of the future will see the convergence of many leading-edge fields like robotics, cloud computing, the internet of things and additive manufacturing. Many of these areas require a lot of visual work.
Instead of humans doing all the assembling, defect identification and the like, those tasks are passed through a deep learning algorithm that leverages sensor information to enable better decision making.
By now, everyone has heard of Google, Tesla, Volvo and all the other automotive OEMs racing to get ahead in self-driving cars. But automated vehicles could hold huge promise for how to quickly and efficiently move goods with a very low environmental impact.
Europe has been pouring testing funds into truck platooning, an effort where a human is driving the leader truck in a lineup of autonomous follower semis. Back in October, the United States ushered in its self-driving truck era in the most American way — through a self-driving Budweiser truck that delivered more than 50,000 cans of beer in Colorado.
Applying deep learning to cargo-based automotive will increase profit margins for any company that relies on roadway logistics.
Lastly, retail is another another area where there’s a lot of visual information. Some companies are honing in on exactly what types of clothes someone would like to purchase, using their previous style choices as a template for making recommendations.
For customers that already own or see a product they like in the real world, a reverse image search function could enable them to purchase the exact dress or shirt they see someone donning on the street.
In the far future, retailers could provide shoppers with assistant bots that could interpret natural language and provide shoppers with a personalized shopping experience.
I’ve covered just a handful of industries that could see real impact from deep learning, but there are a host of others — agriculture predictions based off of satellite imagery, fraud detection and cybersecurity, financial modeling at banks. Going forward, it will be imperative that companies keep an eye out for potential disruption from deep learning. Staying ahead of this curve could give your business agility in the face of uncertainty.
Mo Patel, Senior Data Scientist, Teradata
Mo Patel, based in Austin Texas, is a practicing Data Scientist at Teradata. In his role as the Practice Director, Mr. Patel is focused on building the Artificial Intelligence & Deep Learning consulting practice via mentoring and advising Teradata clients and providing guidance on ongoing Deep Learning projects. Mo Patel has successfully managed and executed Data Science projects with clients across several industries, notably Major Cable Company, Major Auto Manufacturer, Major Medical Devices Manufacturer, Leading Technology Firm and Major Car Insurance Provider. A continuous learner, Mr. Patel conducts research on applications of Deep Learning, Reinforcement Learning, and Graph Analytics towards solving existing and novel business problems.