We all know how the Internet of Things has made it possible to turn everyday devices into sources of raw data for analysis in order to generate business insight. Machines have since the beginning set themselves as important because of their ability to enhance human efficiency. Forward-looking organizations are embedding AI and machine learning technologies into their infrastructure and fundamental business processes.
Microsoft’s deep investments in the field are advancing the state of the art in machine intelligence and perception, enabling computers that understand what they see, communicate in natural language, answer complex questions and interact with their environment.
What these examples make clear is that in any system that might have bugs or unintended behavior or behavior humans don’t fully understand, a sufficiently powerful AI system might act unpredictably — pursuing its goals through an avenue that isn’t the one we expected.
While it is very important to eliminate social bias from the data we use to train ML algorithms and verify their behavior, companies must understand that there’re different types of AI biases and be aware of them. 3 further reading on reinforcement learning, see Michael Chui, James Manyika, and Mehdi Miremadi, What AI can and can’t do (yet) for your business ,†McKinsey Quarterly, January 2018.
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Artificial Intelligence (AI) is a specialised branch of robotic control engineering applied to the human-machine interface. If done right, RPA and Intelligent Process Automation (IPA) would be an opportunity to redesign and automate the underlying processes for the new workforce where humans and machines collaborating intelligently and closer together. We’re already having intelligent conversations with our AI equipped cell phones, and in the months and years to come, artificial intelligence will materially change our world.
Training these systems typically requires vast amounts of data, with some systems needing to scour millions of examples to learn how to carry out a task effectively – although this is increasingly possible in an age of big data and widespread data mining.
In Computer Science, work termed AI†has traditionally focused on the high-level problem; on imparting high-level abilities to use language, form abstractions and concepts†and to solve kinds of problems now reserved for humans†(McCarthy et al. 1955); abilities to play intellectual games such as checkers (Samuel 1954) and chess (Deep Blue); to prove mathematical theorems (GPS); to apply expert knowledge to diagnose bacterial infections (MYCIN); and so forth.
How Artificial Intelligence Is Transforming Business
The research program of the Center is directed toward understanding the design and operation of systems capable of improving performance based on experience; efficient and effective interaction with other systems and with humans; sensor-based control of autonomous activity; and the integration of varieties of reasoning as necessary to support complex decision-making. For example, Ernest Rutherford, arguably the greatest nuclear physicist of his time, said in 1933 — less than 24 hours before Szilard’s invention of the nuclear chain reaction — that nuclear energy was moonshine.†And Astronomer Royal Richard Woolley called interplanetary travel … Read More..