ARTIFICIAL INTELLIGENCE
SUBMITTED TO DR. OSSAMA MOSSALM
STARS GROUP
Doaa Alnawawy, Reporter
Mohamed Ali Abou Zeid
Nehal Samy Shafik, Facilitator
Contents
Definition
Early History
Current status of AI
Challenges for AI
Future of AI
Pros & Cons
Conclusion
WHAT IS ARTIFICIAL INTELLIGENCE?
Early History
 While exploiting the power of the computer
systems, the curiosity of human, lead English
mathematician Alan Turing In 1950 to wonder
“Can machines think and behave like humans do??”
 Further work came out of a 1956 workshop at
Dartmouth sponsored by John McCarthy. In the
proposal for that workshop, he coined the phrase a
“study of Artificial Intelligence”
PHILOSPHY OF AI
 AI is accomplished by studying how human brain
thinks, and how humans learn, decide, and work
while trying to solve a problem, and then using the
outcomes of this study as a basis of developing
intelligent software and systems.
 Thus, the development of AI started with the
intention of creating similar intelligence in machines
that we find and regard high in humans.
ARTIFICIAL INTELLIGENCE
Definition
 The study of computer systems that attempt to
model and apply the intelligence of the human mind.
 A branch of computer science dealing with the
Goals of AI
 To Create Expert Systems
The systems which exhibit intelligent behavior, learn,
demonstrate, explain, and advice its users.
simulation of intelligent behavior in computers.
 The capability of a machine to imitate intelligent
human behavior
 To Implement Human Intelligence in
Machines
Creating systems that understand, think, learn, and behave
like humans.
DEFENITIONS

Machine learning (ML): A subset of AI that often uses statistical techniques to give machines the ability to "learn" from data without being explicitly
given the instructions for how to do so. This process is known as “training” a “model” using a learning “algorithm” that progressively improves model
performance on a specific task.

Reinforcement learning (RL): An area of ML that has received particular attention from the research community over the past decade. It is
concerned with software agents that learn goal-oriented behavior by trial and error in an environment that provides rewards or penalties in response
to the agent’s actions towards achieving that goal.

Deep learning (DL): An area of ML that attempts to mimic the activity in layers of neurons in the brain to learn how to recognise complex patterns
in data. The “deep” in deep learning refers to the large number of layers of neurons in contemporary ML models that help to learn rich
representations of data to achieve better performance gains.

Algorithm: An unambiguous specification of how to solve a particular problem.

Model: Once a ML algorithm has been trained on data, the output of the process is known as the model. This can then be used to make predictions.

Supervised learning: This is the most common kind of (commercial) ML algorithm today where the system is presented with labelled examples to
explicitly learn from.

Unsupervised learning: In contrast to supervised learning, the ML algorithm has to infer the inherent structure of the data that is not annotated
with labels.

Transfer learning: This is an area of research in ML that focuses on storing knowledge gained in one problem and applying it to a different or related
problem, thereby reducing the need for additional training data and compute.

Good old fashioned AI: A name given to an early symbolic AI paradigm that fell out of favour amongst researchers in the 1990s
CURRENT STATUS OF A.I.
CURRENT STATUS OF A.I.
AI have taken many shapes and forms over recent years

Mobile Phones( Siri/Cortana)

Video Games Characters

GPS/Voice Recognition

Robotics
EXAMPLES OF A.I.
 Siri
 Alexa
 Tesla
 Cogito
 Netflix
Google has been a major play on AI transcendence and
Deep Learning.

Deep learning is a machine learning based on algorithms
 Amazon.com
 Nest
 Pandora
ARTIFICIAL INTELLIGENCE IN TODAY'S WORLD
AI in todays world
See AI in Every Industry

Peek inside an AI-enabled hospital, an AI-assisted retail store and a predictive
analytics system that talks. This report from Harvard Business Review examines
the AI landscape, takes a look at the AI workforce – and explains why you
shouldn’t swear at Siri
AI and the Internet of Things

Data is all around us. The Internet of Things (IoT) and sensors have the ability
to harness large volumes of data, while artificial intelligence (AI) can learn
patterns in the data to automate tasks for a variety of business benefits.
Integrate AI into your Analytics Program

For AI to be used effectively, it’s important that the strategy around it feeds
into your larger business strategy, always taking into account the convergence
of people, process and technology
Separate Hype From Reality

AI is helping to embed "greater smartness into machines" but it is not taking
over the world, says Oliver Schabenberger, SAS Executive Vice President and
Chief Technology Officer.
13 skills AI can already do today
1)
2)
3)
4)
5)
6)
7)
8)
9)
10)
11)
12)
13)
Read
Write
See
Hear and understand
Speak
Smell
Touch
Move
Understand emotions
Play games
Debate
Create
Read your mind
AI TODAY
HOW ARTIFICIAL INTELLIGENCE WORKS
AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms , allowing the
software to learn automatically from patterns or features in the data. AI is a broad field of study that includes many theories,
methods and technologies, as well as the following major subfields:

Machine Learning automates analytical model building. It uses methods from neural networks, statistics, operations research and
physics to find hidden insights in data without explicitly being programmed for where to look or what to conclude.

A neural network is a type of machine learning that is made up of interconnected units (like neurons) that processes information by
responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find
connections and derive meaning from undefined data.

Deep Learning uses huge neural networks with many layers of processing units, taking advantage of advances in computing power
and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and
speech recognition.

Cognitive computing is a subfield of AI that strives for a natural, human-like interaction with machines. Using AI and
cognitive computing, the ultimate goal is for a machine to simulate human processes through the ability to interpret images and speech – and
then speak coherently in response.

Computer Vision relies on pattern recognition and deep learning to recognize what’s in a picture or video . When
machines can process, analyze and understand images, they can capture images or videos in real time and interpret their surroundings.

Natural Language processing (NLP) is the ability of computers to analyze, understand and generate human language ,
including speech.The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal,
everyday language to perform tasks.
HOW ARTIFICIAL INTELLIGENCE WORKS
Additionally, several technologies enable and support AI:
 Graphical processing units are key to AI because they provide the heavy compute power that’s required
for iterative processing. Training neural networks requires big data plus compute power.
 The Internet of Things generates massive amounts of data from connected devices, most of it
unanalyzed. Automating models with AI will allow us to use more of it.
 Advanced algorithms are being developed and combined in new ways to analyze more data faster and
at multiple levels. This intelligent processing is key to identifying and predicting rare events,
understanding complex systems and optimizing unique scenarios.
 APIs, or application programming interfaces, are portable packages of code that make it possible
to add AI functionality to existing products and software packages. They can add image recognition
capabilities to home security systems and Q&A capabilities that describe data, create captions and headlines, or
call out interesting patterns and insights in data.
In summary, the goal of AI is to provide software that can reason on input and explain on output.
AI will provide human-like interactions with software and offer decision support for specific tasks, but it’s not a
replacement for humans – and won’t be anytime soon
NEURAL NETWORK
DEEP LEARNING
WHY IS ARTIFICIAL INTELLIGENCE IMPORTANT?

AI automates repetitive learning and discovery through data.
But AI is different from hardware-driven, robotic automation. Instead of automating manual tasks, AI performs frequent, high-volume, computerized tasks reliably and
without fatigue. For this type of automation, human inquiry is still essential to set up the system and ask the right questions.

AI adds intelligence to existing products. In most cases, AI will not be sold as an individual application.
Rather, products you already use will be improved with AI capabilities, much like Siri was added as a feature to a new generation of Apple products. Automation,
conversational platforms, bots and smart machines can be combined with large amounts of data to improve many technologies at home and in the workplace, from
security intelligence to investment analysis.

AI adapts through progressive learning algorithms to let the data do the programming.
AI finds structure and regularities in data so that the algorithm acquires a skill: The algorithm becomes a classifier or a predictor. So, just as the algorithm can teach itself
how to play chess, it can teach itself what product to recommend next online. And the models adapt when given new data. Back propagation is an AI technique that allows
the model to adjust, through training and added data, when the first answer is not quite right.

AI analyzes more and deeper data using neural networks that have many hidden layers.
Building a fraud detection system with five hidden layers was almost impossible a few years ago. All that has changed with incredible computer power and big data. You
need lots of data to train deep learning models because they learn directly from the data. The more data you can feed them, the more accurate they become.

AI achieves incredible accuracy through deep neural networks – which was previously impossible.
For example, your interactions with Alexa, Google Search and Google Photos are all based on deep learning – and they keep getting more accurate the more we use
them. In the medical field, AI techniques from deep learning, image classification and object recognition can now be used to find cancer on MRIs with the same accuracy as
highly trained radiologists.

AI gets the most out of data. When algorithms are self-learning, the data itself can become intellectual property.
The answers are in the data; you just have to apply AI to get them out. Since the role of the data is now more important than ever before, it can create a competitive
advantage. If you have the best data in a competitive industry, even if everyone is applying similar techniques, the best data will win
WHY IS ARTIFICIAL INTELLIGENCE IMPORTANT
HOW ARTIFICIAL INTELLIGENCE IS BEING USED
Every industry has a high demand for AI capabilities – especially question answering systems that can be used for legal
assistance, patent searches, risk notification and medical research. Other uses of AI include:
 Health Care
AI applications can provide personalized medicine and X-ray readings. Personal health care assistants can act as life coaches, reminding
you to take your pills, exercise or eat healthier.
 Retail
AI provides virtual shopping capabilities that offer personalized recommendations and discuss purchase options with the consumer.
Stock management and site layout technologies will also be improved with AI.
 Manufacturing
AI can analyze factory IoT data as it streams from connected equipment to forecast expected load and demand using recurrent
networks, a specific type of deep learning network used with sequence data.
 Banking
Artificial Intelligence enhances the speed, precision and effectiveness of human efforts. In financial institutions, AI techniques can be used
to identify which transactions are likely to be fraudulent, adopt fast and accurate credit scoring, as well as automate manually intense
data management tasks
ARTIFICIAL INTELLIGENCE TYPES
TYPES BASED ON CAPABILITY
Suppresses
brainpower of
Mouse
Suppresses
brainpower of
Human
Suppresses
brainpower of
All Humans
combined
TYPES BASED ON FUNCTIONALITY
TYPES BASED ON FUNCTIONALITY
 Reactive Machines:
The artificial Intelligence is mostly reactive, they implement current decisions without using the form of memories or past
experiences. Example machines: Deep blue, IBM’s chess-playing supercomputer. Deep blue can find the pieces on the chessboard and
make moves.it can exactly perform what moves might be next for its and its opponents. but it doesn’t have any past memory and
experience taken place before.
 Limited Memory:
In this type machines look into the past. example: self-driving cars, they observe other cars speed and direction, within a moment
this can be done. But they need to identify specific objects and monitoring over time. They observe lane markings, traffic lights signals
and pedestrians to avoid accidents.
 Theory of mind:
This type can understand people’s emotion, thoughts, expectation, and interactions this is very advanced but this kind of AI not
completely developed
 Self-Awareness:
We can say this is the future of AI. this can be most high in intelligence, they will have own conscious self-awareness and sentiments.
These machines will act very smart and accurate than humans. This concept is not developed still and not exist.
PROS & CONS
 ADVANTAGES
 DISADVANTAGES
 Less Errors: Errors are reduced and the chance of
reaching accuracy with a greater degree of precision is
a possibility.

High Costs: The hardware and software need to get updated
with time to meet the latest requirements.

Unemployment: As machines are replacing human resources,
the rate of people losing their jobs will increase.
just like Apple’s Siri, Google’s OK Google. Using these
type of applications we can communicate with our
device using our voice. Which makes our work easy.

Cannot think out of box: Robots can only do the work that
they are programmed to do. They are not able to act any
different.
 No Breaks: Unlike humans, machines can work 24*7

Cannot feel Compassion and Sympathy: There is no doubt
that machines are much better when it comes to working
efficiently but they cannot replace the human connection that
makes the team.

High dependence on machines: In future with the heavy use
of application of artificial intelligence, human may become fully
dependent on machines, losing their mental capacities.
 Faster Decisions: Using Artificial intelligence,
decisions can be taken very fast.
 Daily Applications: A.I. is used in many applications
without any break.
 Taking risks on behalf of humans: Robots can be
used instead of Humans to avoid the risks.
 Public Utilities: Facial recognition can be used for
security in self driving cars.
CHALLENGES
Artificial intelligence is going to change every industry, but we have to understand its limits.
 The principle limitation of AI is that it learns from the data. There is no other way in which knowledge can be
incorporated. That means any inaccuracies in the data will be reflected in the results. And any additional layers of
prediction or analysis have to be added separately.
 Today’s AI systems are trained to do a clearly defined task. The system that plays poker cannot play solitaire or
chess. The system that detects fraud cannot drive a car or give you legal advice. In fact, an AI system that detects
health care fraud cannot accurately detect tax fraud or warranty claims fraud.
In other words, these systems are very, very specialized. They are focused on a single task and are far from behaving like
humans.
 Likewise, self-learning systems are not autonomous systems.
The imagined AI technologies that you see in movies and TV are still science fiction. But computers that can probe complex
data to learn and perfect specific tasks are becoming quite common.
WHAT CHALLENGES DO COMPANIES FACE WHEN IMPLEMENTING AI?
Common challenges of AI implementation
 Data
 Data quality and quantity
 Data labeling
 People
 Lack of understanding of AI among non-technical
employees
 Scarcity of field specialists
 Explainability
 Case-specific learning
 Business
 Bias
 Lack of business alignment
 How to deal with model errors
 Difficulty assessing vendors
 Legal issues
 Integration challenges
WHAT CHALLENGES DO COMPANIES FACE WHEN IMPLEMENTING AI?
The Biggest Business and Social Challenges For AI
 Lack of compute power
 Lack of people power
 Building Trust
 One-track minds
ETHICAL ISSUES IN ARTIFICIAL INTELLIGENCE
Most Pressing Ethical Issues in AI

Unemployment.What happens after the end of jobs?

Inequality. How do we distribute the wealth created by
machines?
 Should AI Systems Be Allowed to Kill?

Humanity. How do machines affect our behaviour and
interaction?
 Rogue AIs

Artificial stupidity. How can we guard against mistakes?

Racist robots. How do we eliminate AI bias?

Security. How do we keep AI safe from adversaries?
 How Should We Treat AIs?

Evil genies. How do we protect against unintended
consequences?
 AI Bias

Singularity. How do we stay in control of a complex
intelligent system?

Robot rights. How do we define the humane treatment of
AI?
 Job Loss and Wealth Inequality
 AI is Imperfect — What if it Makes a Mistake?
 Singularity and Keeping Control Over AIs
THE ROLE OF CORPORATIONS IN
ADDRESSING AI’S ETHICAL DILEMMAS
Recommendations
1.
Hiring company ethicists
Hire ethicists who work with corporate decisionmakers and software developers
2. Having an AI code of ethics
Develop a code of AI ethics that lays out how various issues will be handled
3. Instituting AI review boards
Have an AI review board that regularly addresses corporate ethical questions
4. Requiring AI audit trails
Develop AI audit trails that show how various coding decisions have been made
5. Implementing AI training programs
Implement AI training programs so staff operationalizes ethical considerations in their daily work,
and
6. Providing a means of remediation for AI damages or harm
Provide a means for remediation when AI solutions inflict harm or damages on people or
organizations.
IMPORTANT ARTIFICIAL INTELLIGENCE PREDICTIONS (FOR 2019)
1) AI increasingly becomes a matter of international
politics
2) A Move Towards “Transparent AI”
3) AI and automation drilling deeper into every
business
4) More jobs will be created by AI than will be lost
to it.
5) AI assistants will become truly useful
WORKING TOGETHER WITH AI
Artificial intelligence is not here to replace us.
 It augments our abilities and makes us better at what we do.
Because AI algorithms learn differently than humans, they look at
things differently. They can see relationships and patterns that escape
us. This human, AI partnership offers many opportunities. It can:
 Bring analytics to industries and domains where it’s currently
underutilized.
 Improve the performance of existing analytic technologies,
like computer vision and time series analysis.
 Break down economic barriers, including language and
translation barriers.
 Augment existing abilities and make us better at what we do.
 Give us better vision, better understanding, better memory
and much more.
THANK YOU
STARS GROUP