The concepts of machine learning and artificial intelligence have been around much longer than people may realize. The first idea of machine learning was as early as 1943 as a mathematical model presented in a scientific paper, but the term was not coined until 1952 by Arthur Samuel to describe his computer program that played championship-level checkers and got better the more it played. He defined machine learning as a “field of study that gives computers the ability to learn without being explicitly programmed”. Artificial intelligence was even earlier with the first mainstream mention in the film Metropolis released in 1927 that features an evil robot double of the protagonist Maria to cause chaos in Berlin in 2026.
A machine learning training course will give you a firm understanding of the fundamentals of how AI learns and interacts in a digital space and how computers learn and adapt through experience to do specific tasks without explicit programming. The Ashton College Machine Learning course is taught in a live online format that enables students to connect and collaborate with other students from anywhere in the world.
- Artificial intelligence (AI): human-like intelligence exhibited by a computer, robot, or another machine to mimic the capabilities of the human mind and learning from examples and experience to perform functions a human might perform.
- Deep Learning (DL): is a subset of machine learning applications that teaches itself to perform a specific task with increasingly greater accuracy, without human intervention.
- Supervised Learning: a machine learning approach that is defined by its use of labelled datasets that can measure its accuracy and learn over time.
- Unsupervised Learning: the use of machine learning algorithms to analyze and cluster unlabeled data sets to discover hidden patterns in data without the need for human intervention.
AI and Machine Learning: The Beginning
- Automotive Industry
In 1961 the first industrial robot was created by George Devol to be used in operation on the General Motors assembly line, completing the task of transporting die casts and wielded them to auto bodies, a potentially dangerous task for the workers. The next big step from building cars was to using AI and machine learning to drive cars. The first driverless car, a Mercedes-Benz van, drove the empty streets of Munich in 1986. In 1994, two gray Mercedes 500 SELs were driving with other vehicles at high speeds autonomously in Paris thanks to machine learning scientist Ernst Dickmanns.
AI and Machine Learning: The Present
- Automotive Industry
Waymo, a sister company to Google, started testing autonomous cars in the US with backup drivers only at the back of the car in 2017. Later the same year they introduce completely autonomous taxis in the city of Phoenix that in the coming years was only available to the first users. By using Lidar, cameras, Radar, and an onboard computer, Waymo became the first publicly accessible driverless car-hailing service in 2020. That said, the future of fully autonomous cars is uncertain due to moral and safety concerns.
With the use of pattern recognition and algorithmic processes, AI technology can diagnose and improve the accuracy of treatment protocols and health outcomes making for more efficient and effective decision making. Outside of the doctor’s office smart devices can track vital data that would otherwise be missed. Smartwatches, such as the Apple Watch Series 4, enables users to get an electrocardiogram directly from their wrist. Last year Fitbit released their signature Charge 3 wristband which uses AI to detect sleep apnea. We cannot forget the infamous video of the surgical system da Vinci Xi doing surgery on a grape to show the advancements of robotic technology in precision and dexterity.
AI and Machine Learning: The Future
The future of healthcare and the future of AI are deeply interconnected. Combining AI and predictive analytics with social determinants of health, the WE Forum envisions that by 2030 healthcare systems will be able to anticipate when a person is at risk of developing a chronic disease and suggest preventive measures.
Another area of interest is genome sequencing. Machine learning in health informatics enables genetic mutations to be analyzed much faster and helps in diagnosing conditions that can lead to disease. Genome sequencing can impact cancer diagnosis and treatment and mitigate the impact of infectious disease. As genome sequencing becomes more affordable and machine learning becomes smarter, health informatics professionals can help advance genomic medicine to treat the world’s deadliest diseases.
- Virtual Reality and AI
In the past few years advancements have been made to combine VR and AI technology to create a single form of technology that offers endless possibilities and experiences. VR headsets have already made gaming more exciting, adding AI to the VR gaming experience will make background characters more intelligent with the ability to react to real-life players. This same technology can be used in filmmaking creating games and films that are interactive and can be different every time you watch or play.
To become part of the exciting future of machine learning and AI technology, take your first step with a machine learning training course at Ashton College. You may find yourself amazed at just how beneficial continuing education courses such as this can be to your future growth and career development.