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Semelhante a Introduction to Artificial Intelligence (20)
Introduction to Artificial Intelligence
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History
Turing in his 1950 paper: “Computing Machinery and Intelligence”
It opens with the question: “Can Computer Think”
The word Artificial Intelligence was coined in 1955
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AI can draw logical conclusion, prove some theorems, create simple
plan. Some initial work on neural networks
History 1950-60 Success Stories
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━ Overhyping
─ researchers promised funding agencies spectacular progress, but started running
into difficulties
─ Ambiguity: highly funded translation programs (Russian to English) were good a
syntactic manipulation but bad at disambiguation
“The spirit is willing but the flesh is weak” becomes “The vodka is good but the meat is
rotten”
─ Scalability/complexity: early examples were very small, programs could not scale to
bigger instances
History 1950-60 Success Stories
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History
1970 - 80
Expert System
Creation of expert systems
(systems specialized for one
particular task based on
experts’ knowledge), wide
industry adoption.
1980 - 90
AI as an Industry
AI becomes an industry,
neural network returns to
popularies, AI becomes a
science
1990 - 2010
Intelligent Agents
Emergence of an intelligent
agents, Machine Learning
results
2010 - present
Deep Learning
More machine learning
projects and products. AI is
working, deep learning is
everywhere.
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What is AI ?
The science of making
machine that think or
act like human
What if we can make a
system that is better and
smarter than us ?
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What is AI ?
The science of making
machine that think or
act like human
What if we can make a
system that is better and
smarter than us ?
The science of making
machine that act
rational
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What does it mean for a system to act rational ?
… Always make the right
decision.
… A decision that will
maximize his chance of
achieving the predefined
goals
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Machine Learning and Artificial Intelligence
Machine Learning and Artificial Intelligence are often
used interchangeably but they are not the same
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Machine Learning and Artificial Intelligence
Machine Learning is
the ability to learn
without been explicitly
programmed.
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Machine Learning and Deep Learning
Deep Learning is a
subcategory of Machine
Learning that is based on
Artificial neural networks.
Currently, it works well when
you have enough data. They
are designed to process
information like human brain.
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State of the art in Artificial Intelligence
Watson defeats
Jeopardy champions
(2011)
DeepMind achieves
human-level
performance on
many Atari games
(2015)
AlphaGo defeats Go
champion (2016)
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State of the art in Artificial Intelligence
CMU’s Libratus
defeats top human
poker players (2017)
Self driving car taxi
for selected
customer
Robot that can
handle objects and
climb stairs
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Artificial Intelligence in Health
ML models can be used to find patients who are
likely to crash based on their medical records,
laboratory results, and vital signs. I.e Jvion claim
ML model can be used to provide a proactive
assessment of readmissions risk from patient’s
recent care, their current condition, treatment, their
home life and other risk factors from electronic
medical records.
Yes, company like BIOBEATS is already doing that
using wearable gadget and mobile app
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Artificial Intelligence in Manufacturing
Microchips and circuit boards companies are
making use of ‘machine vision’ to pick out minute
details and defects far more reliably than the
human eye
LG are making use of Machine Learning to detect
and predict defects in their machinery before
issues arise.
Siemen has been using neural networks to monitor
its steel plants and improve efficiencies.
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Artificial Intelligence in Banking
Analyze brand sentiment from social media
platforms.
ML can be used to appraise customer for loan.
oneFi
ML Model is employed to identified fraudulent
transaction
Many Fintech companies are using AI model to
conduct autonomous trading
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Artificial Intelligence in Agriculture
Predictive Analytics
Machine learning models are being developed to
track and predict various environmental impacts
on crop yield such as weather changes
Crop and Soil
Monitoring
Companies are leveraging computer vision and
deep-learning algorithms to process data captured
by drones and/or software-based technology to
monitor crop and soil health
Agricultural Robots
Companies are developing and programming
autonomous robots to handle essential agricultural
tasks such as harvesting crops at a higher volume
and faster pace than human laborers.
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How to start learning Artificial Intelligence
Fast.ai
Coursera Andrew Ng Courses
IBM Cognitive Courses
Youtube
Udacity AI and ML Courses
Edx
Khan Academy for Math
