Deep Learning for Executives
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What is Deep Learning?
Deep learning, also known as deep neural learning or deep neural network, is an artificial intelligence (AI) function that mimics how the human brain works to process data and create patterns that facilitate decision making. A subset of machine learning in artificial intelligence, deep learning has networks capable of learning unsupervised from unstructured or unlabeled data.
Deep learning uses a layered approach to make better decisions by constantly curating the data it is fed. Consider this: Machine learning is comparable to cramming for a college exam by re-reading notes. Deep learning is more in line with a child who is continually presented with the letters of the alphabet and slowly learns the trillions of ways to sequence those letters into words. In the first example, previously identified data is interpreted. In the second, the interpreter realizes the potential of the data they are given.
This is important for business because it can deliver data insights at a broader scope and higher level of fidelity for more complex use cases.
As deep learning grows in sophistication, it is integrating into concrete business use cases. Deep learning, especially in the field of computer vision, is ready for the global industrial stage.
businesses equate parsing through data as a solution to business problems. And with deep learning, they now have smart machines to parse through their most complex, multidimensional data to gain new insights.
Markets Benefiting from Deep Learning
Health care
Deep learning holds the most immediate promise in health care, an industry full of data.
The primary app with health care is computer vision. Many 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 far too much 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 are working to discover how deep learning can better predict optimal pharmaceuticals to battle diseases based on their molecular structure, even uncovering uses for existing drugs that weren’t originally intended by creators.
Manufacturing
Manufacturing facilities of the future will see the convergence of many leading-edge fields such as robotics, cloud computing, the internet of things (IOT) and additive manufacturing. Many of these areas require significant visual work.
Rather than humans doing all the assembling, defect identification and such, tasks are passed through a deep learning algorithm that leverages sensor information to enable better decision making.
Automotive
Google, Tesla, Volvo and other automotive OEMs are working to get self-driving cars into market. Automated vehicles could hold huge promise for how to quickly and efficiently move goods with a very low environmental impact.
Applying deep learning to cargo-based automotive can increase profit margins for any company that relies on roadway logistics.
Retail
Retail also involves a significant amount of visual information. Some companies are decoding what types of clothes someone would like to purchase, using their previous style choices as a template for making recommendations.
For customers who 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.
Deep learning uses a layered approach to make better decisions by constantly curating the data it is fed. Consider this: Machine learning is comparable to cramming for a college exam by re-reading notes. Deep learning is more in line with a child who is continually presented with the letters of the alphabet and slowly learns the trillions of ways to sequence those letters into words. In the first example, previously identified data is interpreted. In the second, the interpreter realizes the potential of the data they are given.
This is important for business because it can deliver data insights at a broader scope and higher level of fidelity for more complex use cases.
As deep learning grows in sophistication, it is integrating into concrete business use cases. Deep learning, especially in the field of computer vision, is ready for the global industrial stage.
businesses equate parsing through data as a solution to business problems. And with deep learning, they now have smart machines to parse through their most complex, multidimensional data to gain new insights.
Markets Benefiting from Deep Learning
Health care
Deep learning holds the most immediate promise in health care, an industry full of data.
The primary app with health care is computer vision. Many 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 far too much 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 are working to discover how deep learning can better predict optimal pharmaceuticals to battle diseases based on their molecular structure, even uncovering uses for existing drugs that weren’t originally intended by creators.
Manufacturing
Manufacturing facilities of the future will see the convergence of many leading-edge fields such as robotics, cloud computing, the internet of things (IOT) and additive manufacturing. Many of these areas require significant visual work.
Rather than humans doing all the assembling, defect identification and such, tasks are passed through a deep learning algorithm that leverages sensor information to enable better decision making.
Automotive
Google, Tesla, Volvo and other automotive OEMs are working to get self-driving cars into market. Automated vehicles could hold huge promise for how to quickly and efficiently move goods with a very low environmental impact.
Applying deep learning to cargo-based automotive can increase profit margins for any company that relies on roadway logistics.
Retail
Retail also involves a significant amount of visual information. Some companies are decoding what types of clothes someone would like to purchase, using their previous style choices as a template for making recommendations.
For customers who 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.