Artificial Intelligence is a technology aimed at creating intelligent machines that work and react like humans. Although there are many ways that AI can be implemented, the real purpose for AI in the work place is to assist and enhance human work, improve medical emergency response, increase productivity, and making the world intelligent. Most people don’t realize that AI is already in many of the products we use every day.
Being a part of our everyday existence; in the workplace, home, during our commutes, etc.- we rely on AI closely how we rely on the internet for communication, work and research. But there is also a tremendous demand for automation hardware from several industries.
AI can deliver an authentic intelligence technology that requires no human complexities once trained allowing meaningful extractions from large data sets. Even then, artificial intelligence requires a very different type of IT architecture: one that’s built on a solid data communications framework.
Adding intelligent agents to our life could have an enormously positive impact.”
-Thomas Frey, Futurist
Self-Driving Cars
There are many benefits and speculations on the functions of self-driving cars. To avoid accidents, an autonomous, or self-driving car can sense its environment with very little human input. Greater road safety is always a top concern. With an increasing volume of self-driving cars, there is greater potential to reduce dangerous driving behaviors reducing devastation of drugged driving, impaired driving, speeding, distraction and passengers not wearing seat belts.
A NHTSA study looked at major accident causes and found:
- 2% - Environment
- 2% - Vehicles
- 2% - Unknown
- 94% - Driver Error
AI in the Medical Field
The primary aim for health-related artificial intelligence applications is to analyze the relationship between prevention or treatment techniques and a patient’s outcome. The acknowledgment of powerful tools that help find disease just before a patient is symptomatic, treat it early, and achieve a higher survival rate with far less patient suffering. With the technology for diagnostics and imaging tools like MRIs, CT and PET scans, algorithms can be trained to accurately measure symptoms, done faster than humans can accomplish. These algorithms can assess thousands of data points covering the extent of strokes and how they are characterized. With time, the algorithm learns how to read images with extreme accuracy.
Components of Artificial Intelligence
Machine Learning
Machine learning is a discipline of computer science that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model based on inputs and using that to make predictions or decisions, rather than following only explicitly programmed instructions.
Machine learning enables an enterprise to discover valuable insights hidden within their big data and accelerate the decision cycle to outperform the competition.
Deep Learning
Deep Learning is a subset of machine learning. It means that systems are capable of unsupervised learning from data that is unstructured or unlabeled. With the power of machine learning, deep learning automatically learns image features required for detection tasks.
While artificial intelligence is becoming more mainstream and advanced, the public can be apprehensive about the technology. One fear is that AI will take on jobs and making human less needed, leading to job loss. But we’ve seen this kind of fear in technology before. In the turn of the 20th century, people feared cars would take away jobs for blacksmiths and wagon makers, but those professions are still being utilized today.
AI will change the workplace as we know it. The technology will take over repetitive job responsibilities and leave workers to focus on more critical tasks.
