slide 1: Artificial
Intelligence
slide 2: Introduction HHHHHHHHH ionDEIntroduction G Artificial intelligence AI is the simulation of human intelligence
processes by machines especially computer systems. ... When
presented with an unfamiliar task a strong AI system is able to find a
solution without human intervention. ● Speech recognition ● Learning ● Planning Introduction
slide 3: Types of artificial intelligence Type 1: Reactive machines Type 2: Limited memory. Type 3: Theory of mind. Type 4: Self-awareness
slide 4: Examples of AI technology 1 . Automation: 2 . Machine learning: 3 . Machine vision: 4 . Natural language processing NLP 5 . Robotics: 6 . Self-driving cars:
slide 5: Automation: What makes a system or process function automatically. For example
robotic process automation RPA can be programmed to perform
high-volume repeatable tasks that humans normally performed. RPA is
different from IT automation in that it can adapt to changing
circumstances.
slide 6: Machine learning: The science of getting a computer to act without programming .
Deeplearning is a subset of machine learning that in very simple terms
can be thought of as the automation of predictive analytics
slide 7: Machine vision: The science of allowing computers to see. This technology captures and analyzes visual
information using a camera analog-to-digital conversion and digital signal processing. It is
often compared to human eyesight but machine vision isnt bound by biology and can be
programmed to see through walls for example. It is used in a range of applications from
signature identification to medical image analysis. Computer vision which is focused on
machine-based image processing is often conflated with machine vision.
slide 8: Natural language processing NLP: The processing of human -- and not computer -- language by a
computer program. One of the older and best known examples of NLP is
spam detection which looks at the subject line and the text of an email
and decides if its junk. Current approaches to NLP are based on
machine learning. NLP tasks include text translation sentiment analysis
and speech recognition.
slide 9: Robotics: A field of engineering focused on the design and manufacturing of
robots. Robots are often used to perform tasks that are difficult for
humans to perform or perform consistently. They are used in assembly
lines for car production or by NASA to move large objects in space.
Researchers are also using machine learning to build robots that can
interact in social settings.
slide 10: Self-driving cars: These use a combination of computer vision image recognition and
deep learning to build automated skill at piloting a vehicle while
staying in a given lane and avoiding unexpected obstructions such
as pedestrians.
slide 11: AI applications AI in business. AI in education. AI in finance AI in law. AI in manufacturing
slide 12: Regulation of AI technology Despite these potential risks there are few regulations governing the use AI tools and where
laws do exist the typically pertain to AI only indirectly. For example federal Fair Lending
regulations require financial institutions to explain credit decisions to potential customers
which limit the extent to which lenders can use deep learning algorithms which by their
nature are typically opaque. Europes GDPR puts strict limits on how enterprises can use
consumer data which impedes the training and functionality of many consumer-facing AI
applications. For more visit at-https://technologymoon.com/