Artificial Intelligence is a term of computer sciences that emphasize the creation of intelligent machines that works like a human.
History of Artificial Intelligence
The term Artificial Intelligence coin in 1956, but AI has become more popular today thanks to increase data volumes, advance algorithms, and improvements in computer power and storage.
Here i am telling you the history, stay with me. Early AI research in the 1950s explore topics like problem solving and symbolic methods. In 1960s, the US Department of Defense took interest in this type of work and began training computers to mimic basic human reasoning. For Example, the Defense Advance Research Projects Agency (DArPA) complete street mapping projects in 1970s. And DARPA produce intelligent personal assistants in 2003, long before Siri, Alexa or Cortana were household names.
Moreover, this early work paved the way of automation and formal reasoning that we see in computers today, including decision support systems as well as smart search systems that can be designed to complement and augment human abilities.
AI in Real World
While, Hollywood movies and science fiction novels depict AI as human-like robots that take over the World, the current evolution of AI technologies isn’t that scary or quit that smart. Instead, AI evolve to provide many specific benefits in every industry. Keep reading for modern examples of artificial intelligence in health care, retail, augmented reality and more.
Early work with neural networks stirs excitement for ‘thinking machines’ .
1980s – 2010s
Machine Learning becomes popular.
Deep learning breakthroughs drive AI boom.
Perhaps as a counter to the panic that artificial intelligence will destroy jobs, consulting firm KPMG today publish a list of what it predicts will soon become the five most sought-after AI roles. Moreover, the predictions are base on the company’s own projects and those on which it advises. They are:
- AI Architect – Responsible for working out where AI can help a business, measuring performance as well as, crucially, “sustaining the AI model over time.”Additionally, Lack of architects “is a big reason why companies cannot successfully sustain AI initiatives,” KPMG notes.
- AI Product Manager – Liaises between teams, making sure ideas can be implement, especially at scale. And Works closely with architects, and with human resources departments to make sure humans and machines can all work effectively.
- Data Scientist – Manages the huge amounts of available data and designs algorithms to make it meaningful.
- AI Technology Software Engineer – “One of the biggest problems facing businesses is getting AI from pilot phase to scalable deployment,” KPMG writes. Software engineers need to be able both to built scalable technology and understand how AI actually works.
- AI Ethicist – AI presents a host of ethical challenges which will continue to unfold as the technology develops. Creating guidelines and ensuring they’re upheld will increasingly become a full-time job.
While it’s all very well to list the jobs people’s training and hiring for, it’s another matter to actually create a pipeline of people ready to enter those roles. Brad Fisher, KPMG’s US lead on data and analytics and the lead author of the predictions, tells Quartz there aren’t enough people getting ready for these roles.