Table of Contents
What is the background of artificial intelligence?
AI was a term first coined at Dartmouth College in 1956. Cognitive scientist Marvin Minsky was optimistic about the technology’s future. The 1974-1980 saw government funding in the field drop, a period known as “AI winter”, when several criticised progress in the field.
What are the new challenges that arise from the use of artificial intelligence in the decision making process?
Top Common Challenges in AI
- Computing Power. The amount of power these power-hungry algorithms use is a factor keeping most developers away.
- Trust Deficit.
- Limited Knowledge.
- Human-level.
- Data Privacy and Security.
- The Bias Problem.
- Data Scarcity.
What happened to AI in the 1990s?
Regardless, funding of the FGCP ceased, and AI fell out of the limelight. Ironically, in the absence of government funding and public hype, AI thrived. During the 1990s and 2000s, many of the landmark goals of artificial intelligence had been achieved.
What are the potential risks of artificial intelligence?
Such potential risks may arise in whole or in part from sources including the data used to train the AI system; potential risks arising from the AI system itself; potential risks arising from the usage of the AI system; and potential risks arising from poor overall governance of the AI system.
How will artificial intelligence change the world?
With massive improvements in storage systems, processing speeds, and analytic techniques, they are capable of tremendous sophistication in analysis and decisionmaking. Artificial intelligence is already altering the world and raising important questions for society, the economy, and governance.
Is Moore’s law slowing down for Artificial Intelligence?
We’ve seen that even if algorithms don’t improve much, big data and massive computing simply allow artificial intelligence to learn through brute force. There may be evidence that Moore’s law is slowing down a tad, but the increase in data certainly hasn’t lost any momentum.