As I shared in part one of this four-part AI series, artificial intelligence is a growing field that is adding jobs at a rapid pace. Recruiting is accelerating for engineers, scientists, R&D (Research and Development), IT (Information Technology) and technical manufacturing talent for job candidates both: a) directly in artificial intelligence and b) indirectly in peripheral fields influenced by AI including Big Data, IoT (Internet of Things) and robotics.
Wide Range Of Companies And Industries Employing AI Technology!
Artificial intelligence (AI) is set for exponential growth in the coming years. According to a 2016 report from CB Insights, equity financing in the AI space skyrocketed from $282 million to $2.4 billion from 2011 to 2015. It is expected to continue to grow exponentially this year and beyond. AI is now influencing nearly every industry from manufacturing and robotics to the Internet of Things (IoT), finance, healthcare, legal and even agriculture.
Within this expansion, the technology industry has seen a plethora of new AI related startups, as well as acquisitions and investments from major companies like Google, Samsung, GE, and Intel. In this article, I will share two extraordinary artificial intelligence examples, a unique acquisition by Intel and an unusual application in healthcare.
Intel Artificial Intelligence Acquisition
Intel joined the race to create autonomous vehicles with its recent acquisition of Israeli car camera pioneer Mobileye. The $15.3 billion acquisition was their second largest ever. It also signaled that they are joining the race to create driverless cars.
According to several studies, the market for autonomous driving systems, services and data will reach $70 billion by 2030. Mobileye is at the center of this artificial intelligence R&D, IT, technical, engineering and scientific innovation and technology, which allows a car to see and understand the space around it. This includes 360-degree vision and mapping which integrates various sensor elements such as cameras, radar, sonar and laser-sensing technology known as LiDAR.
Healthcare Artificial Intelligence: Heart Attack Diagnosis
AI scientists have now demonstrated that computers are not only capable of teaching themselves, but also they can produce better diagnosis rates than even trained doctors! One such area is predicting heart attacks.
Each year, nearly 20 million people die from the effects of cardiovascular disease, including heart attacks, strokes, blocked arteries and other circulatory system malfunctions. In an effort to predict these cases, many doctors use guidelines similar to those of the American College of Cardiology/American Heart Association (ACC/AHA). Those are based on eight risk factors, including age, cholesterol level and blood pressure that physicians effectively add up.
Unfortunately, this system is way too simplistic and cannot account for the many ingested medications, other disease and lifestyle factors influencing the patient. According to Dr. Stephen Weng, an epidemiologist at the University of Nottingham in the United Kingdom, “There’s a lot of interaction in biological systems. That’s the reality of the human body. What computer science allows us to do is to explore those associations.”
In a new study, Weng and his colleagues compared use of the ACC/AHA guidelines with four machine-learning algorithms: random forest, logistic regression, gradient boosting, and neural networks. All four techniques analyzed lots of data in order to come up with predictive tools without any human instruction. The data was derived from the electronic medical records of 378,256 patients in the United Kingdom. The goal was to find patterns in the records that were associated with cardiovascular events.
First, the artificial intelligence (AI) algorithms had to train themselves. They used about 78% of the data (some 295,267 records) to search for patterns and build their own internal “guidelines.” They then tested themselves on the remaining records. Using record data available in 2005, they predicted which patients would have their first cardiovascular event over the next 10 years, and checked the guesses against the 2015 records. Unlike the ACC/AHA guidelines, the machine-learning methods were allowed to take into account 22 more data points, including ethnicity, arthritis, and kidney disease.
All four AI methods performed significantly better than the human doctor implemented ACC/AHA guidelines. Using a statistic called AUC (in which a score of 1.0 signifies 100% accuracy), the ACC/AHA guidelines hit 0.728. Weng’s team reported the four new methods ranged from 0.745 to 0.764. The best one, neural networks, correctly predicted 7.6% more events than the ACC/AHA method and it raised 1.6% fewer false alarms. In the test sample of about 83,000 records, this amounts to 355 additional patients whose lives could have been saved if artificial intelligence was used! That’s because prediction often leads to prevention, Weng says, through cholesterol-lowering medication or changes in diet.
Next Two Artificial Intelligence Articles!
These are but two of the growing numbers of AI applications that will change the world. As employer technology and engineering staffing increases, more artificial intelligence developments will occur. Next week’s installment will focus on ways AI will be used for recruiting candidates and managing workers. Finally, I will discuss the need for and steps to take in hiring a Chief Artificial Intelligence Officer (CAIO) in order to fully harness the power of AI for your company and industry. I hope you enjoy.