AI Definitions

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AI:

Artificial Intelligence is the simulation of human intelligence processes by machines, that include learning, reasoning, self-correction and solving problems.
It is an important part in technology industries, and this field is improving more and more over the years.

- Machines that simulate intelligent behaviour.
- Collection of technologies that allow the computers to think like humans.
- Science of making intelligent machines.

Involves the development of complex algorithms incorporated to the computer infrastructure which can perform tasks like: 
- Visual awareness
- Speech recognition
-  Language translation
- Decision making

1. Reactive Machines AI
2. Limited Memory AI
3. Theory of Mind AI
4. Self-Aware AI

AI systems can be divided (based on functionalities) into four types:

Is a level of Intelligence of systems at which machines could surpass human intelligence, and can perform any task better than human with cognitive properties.
Some key characteristics include the ability to think, reason, solve the puzzle, make judgments, plan, learn, and communicate by its own.
Still a hypothetical concept of AI. Development of such systems in real is still world challenging task.

3. Super AI:

A machine that handle many tasks, considered to be human-like. 
Tend to be more complex, they are programmed to handle situations in which they may require problem solving without having a human intervene.
Examples: 
-Self-driving cars and robots in hospital operating rooms.
 -Google Alpha Go it has the ability to train itself purely through self-play.

2. General AI:

Not intelligent enough, a machine that handle one particular task. 
Examples: 
-Video games (chess).
-Virtual assistants such as Apple's Siri, Microsoft Cortana, Amazon's Alexa. You ask the virtual assistant a question, it answers it for you. Uses natural language and voice queries to answer questions.

1. Narrow/Weak AI:

Are the fundamental types of AI systems.
Are quite reactive and they are not able to use previous experiences to advise current decisions or to configure memories. 
Example:
IBM’s chess-playing computer Deep Blue that defeated chess grandmaster Garry Kasparov is a reactive machine that sees the chessboard pieces and reacts to them.

1. Reactive Machines AI:

2. Limited Memory AI:

Train themselves from previous data and can make decisions for a specific period of time, but they cannot add it to a library of their experiences. 
The memory of such systems is short-lived.  
Example:
Is mostly used in self-driving cars. 
They detect the movements of vehicles around them constantly. 
The static data such as lane marks, traffic lights and any curves in the road will be added to the AI machine. This helps autonomous cars to avoid getting hit by nearby vehicles.

3. Theory of Mind AI:

Is a very advanced technology.
In terms of psychology, the theory of mind represents the understanding of people and things in the world that can have emotions which alter their own behavior. 
Still, this type of AI has not been developed completely in the society. 
But research shows that the way to make advancements is to begin by developing robots that are able to identify eye and face movements and act according to the looks.

Example 1:
One real-world example of the theory of mind AI is Kismet. 
Kismet can mimic human emotions and recognize them, but can’t follow glimpses or carry attention to humans.

Example 2:
Another example is Sophia from Hanson Robotics.
Cameras are used in Sophia's eyes, with the help of computer algorithms, allow her to see. 
She can sustain eye contacts, recognize individuals, and follow faces.

4. Self-Aware AI:

Is a supplement of the theory of mind AI. Only exists hypothetically.
Is not developed yet, but when it happens, it can configure representations about themselves. 
It means particular devices are tuned into cues from humans like attention spans, emotions and also able to display self-driven reactions.

https://www.pngwing.com/en/free-png-xxmuo

Stands for: Advanced Step in Innovation Mobility.
Humanoid robot.
 Designed and developed by Honda.
Designed to be a multi-functional mobile assistant.

Google AI:

Is a division of Google dedicated solely to Artificial Intelligence.
 They're conducting research that advances the state-of-the-art in the field of computer science.
Applying AI to products and to new domains, and developing tools to ensure that everyone can access AI.
They have stories about how AI is helping people everywhere to solve problems in exciting new ways.
They publish hundreds of research papers each year.
At Google AI you can learn from ML experts from google like Introduction to Machine Learning Problem Framing, Data Engineering on Google Cloud Platform Specialization, etc.

These cognitive tasks include:

Natural language processing:

For communication with humans.

Knowledge representation:

To store information effectively & efficiently.

Automated reasoning:

To retrieve & answer questions using the stored information.

Machine learning:

To adapt to new circumstances.

Computer vision:

To perceive objects (seeing).

Robotics:

To move objects (acting).

• Machine Learning.
• Deep Learning.
• Neural Networks.

Programming languages for building models:

• C/C++
• Python
• Java
• TensorFlow (platform)

Software/Hardware for training and running models:

• Parallel processing tools (like Spark).
• Cloud data storage and computing platforms.
• Graphics Processing Units (GPUs).

GPUs

GPU computing is the use of a GPU (graphics processing unit) as a co-processor to accelerate CPUs for general-purpose scientific and engineering computing.

Cloud data storage

used to store data

Machine learning :

the ability of devices to learn without programming.

deep learning:

is what is based on deep neural networks.

Error Reduction:

Decreasing mistakes.

Difficult Exploration:

Handle difficult tasks in harsh areas i.e., can be put to mining and fuel exploration process.

Virtual Assistants:

In daily Application. We have our lady Siri or Cortana to help us out.

Repetitive Jobs:

Speed of computations i.e., faster than humans, multi-tasking.

No Breaks:

Don’t need any breaks and refreshments.

Absence of Emotions:

Makes the AI system think logically.

Risk of loss of important data.

Can’t think out of box:

AI systems cannot make the judgment of right or wrong as machines do not have any emotions and moral values.

Can’t be improved with experience.

Replacement of human’s job leads to increase unemployment.

No Original Creativity:

Human creativity and imagination is difficult to be replicate.

Addiction:

Humans can become too dependent i.e., we depend on machines to form everyday tasks.

High Costs.

Self-Driving Cars:

- Have you ever heard of cars that drive without a driver? That are solely guided by Artificial Intelligence technologies and automatic learning.
- Tesla was one among the primary automotive brands to launch a self-driving vehicle, and Audi, Cadillac, and Volvo are already developing their own models. 
- Autonomous rovers.

Virtual Assistance:

Everyone is familiar with the personal assistant, Apple’s Siri or Microsoft’s Cortana. 
She's the friendly voice-activated computer that we interact with on a daily basis. 
She helps us find information, gives us directions, add events to our calendars, helps us send messages and so on.

Health Care:

It help doctors with diagnoses and tell once patients are deteriorating therefore medical intervention can occur sooner before the patient needs hospitalization. 
It's a win-win for the healthcare industry, saving costs for both, hospitals and patients. 
Example: Automated image diagnosis: 
Computer vision capabilities of AI benefit healthcare a lot.
Hospitals and clinics use AI to recognize abnormalities in different kinds of medical images — from CT to MRI to radiology scans.

Health Monitoring:

Wearable health trackers – like those from FitBit, Apple, Garmin and others – monitors heart rate and activity levels. 
They can send alerts to the user to get more exercise and can share this information.

Doing Repetitive Jobs:

Robots can be used in production assembly lines or in any repetitive tasks that done quicker and more carefully.

Artificial Intelligence vs. Human Intelligence:

UAE AI Strategy Aims

UAE AI Strategy Themes

1- Machine learning as a service (MLaaS) will be deployed more broadly.

MLaaS is sold primarily on a subscription or usage basis by cloud-computing providers. For example, Microsoft Azure's ML Studio provides developers with a drag-and-drop environment to develop powerful machine learning models. In 2019 and beyond, be prepared to see MLaaS offered on a much broader scale. Transparency Market Research predicts it will grow to US$20 billion at an alarming 40% CAGR(Compound annual growth rate) by 2025.

2- More explainable or "transparent" AI will be developed.

AI democratization has been led by a lot of open source tools and libraries, such as Scikit Learn, TensorFlow, and more. The open source community will lead the charge to build explainable, or "transparent," AI that can clearly document its logic, expose biases in data sets, and provide answers to follow-up questions.

3- AI will impact the global political landscape.

In 2019, AI will play a bigger role on the global stage, impacting relationships between international superpowers that are investing in the technology such as the US and China, will struggle to balance self-interest with collaborative R&D(Research and development). Countries like that will experience tremendous growth in areas like predictive analytics, creating a wider global technology gap. That will lead to more conversations about the ethical use of AI.  Naturally, different countries will approach this topic differently, which will affect political relationships.

Humans are needed to support AI implementation and oversee its application. Next year, more manual labour will transition to management-type jobs that work alongside AI, a trend that will continue to 2020. Gartner predicts that in two years, AI will create 2.3 million jobs while only eliminating 1.8 million.

5- AI assistants will become more pervasive and useful

In 2018, Technology company X.ai has built two AI personal assistants, Amy and Andrew, who can interact with humans and schedule meetings for their employers. In 2019, we will see AI assistants continue to grow in their sophistication and capabilities. As they collect more behavioral data, AI assistants will become better at responding to requests and completing tasks. With advances in natural language processing and speech recognition, humans will have smoother and more useful interactions with AI assistants.

6- AI/ML governance will gain importance

In 2019, more organizations will create governance structures and more clearly define how AI progress and implementation are managed. Given the current gap in explainability, these structures will be tremendously important as humans continue to turn to AI to support decision-making.

7- AI will help companies solve AI talent shortages

A survey released last year from O'Reilly revealed that the biggest challenge companies are facing related to using AI is a lack of available talent. organizations will—ironically—use AI and machine learning to help address the talent gap in 2019. For example, Amazon Personalize is another machine learning service that helps developers build sophisticated personalization systems that can be implemented in many ways by different kinds of companies.