The artificial intelligence field is rapidly changing and maturing. The field is well-suited for candidates that are interested in computers, think analytically and enjoy reading and learning about the latest technological developments. Artificial intelligence specialists work with massive amounts of information, so analytical thinking is especially important for success in the field. In the future, artificial intelligence specialists will automate not only minute task, but entire business processes. Tomorrow’s AI leaders will man the helm of change initiatives designed to make better use of existing enterprise benchmarks, metrics and data stores.
With blockchain-based technology like bitcoin taking up news headlines, awareness and excitement about other potential uses for blockchain is increasing. One of the best use cases for blockchain may end up being healthcare and medical records. By having a distributed database for healthcare-related information, healthcare providers can benefit from increased accessibility, accuracy, and safety, all of which will result in better healthcare outcomes for all.
The biotech field has been held back by the technological limitations, but ML and AI programs have programs have broken through the barriers into new possibilities impacting recent biotech and healthcare developments. Security and healthcare trends suggest future generations will utilize biotech on a daily basis, either to thwart identity theft or to cure cancer. We may still ask the big questions, but AI programs are finding the solutions. Public opinion is mixed about biotech. The manipulation of organic materials at the microscopic level has already produced new drugs and treatments while ethics committees discern what is acceptable.
Most big data programs are focused on certain types of data. These essential data types are considered most relevant to the organization’s overall goals. But what about all the data left over? Data exhaust can offer businesses significant value – if it’s leveraged properly. No, it doesn’t have to do with being exhausted by the amount of data your business collects although that’s a common sentiment among executives. Instead, it has to do with the amount of “leftover” data produced by an organization. When you set out to collect specific types of data, other information is collected at the same time.
More practical applications of AI are on the horizon. Those applications will include helping the transportation industry to solve some of our biggest challenges in getting from place to place. AI is getting smarter thanks to innovative programmers—but it’s also making our transportation system better by building on the building blocks these programmers provided. AI can assist with urban design and traffic control in several ways, including adjusting variable speed zones based on traffic, traffic light timing, and smart pricing for vehicle tolls. Here are 4 important ways AI will help improve our transportation system.