AI is a branch of computer science that endeavors to replicate as much as possible or to simulate human understandings and intelligence in machines.
Providing a final and complete version of artificial intelligence meaning is almost impossible due to its continuous development and the new approaches embraced.
According to Britannica Dictionary AI is defined as “ the ability of a computer or a robot controlled by a computer to do tasks that are usually done by humans because they require human intelligence and discernment.”
To avoid confusion we should go back to 1955 and remind us of one of the earliest definitions of AI from the time when it was first coined officially by John McCarthy.
John McCarthy is known as one of the founders of the discipline of artificial intelligence, and his definition of artificial intelligence was as follows;
Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.
From this definition, we can say that AI engines demonstrate the intelligence of machines and have the ability to solve problems that usually are done by us humans using our natural intelligence.
The 1955 Proposal for the Dartmouth research project on artificial intelligence, defines the 7 areas of AI;
1- Simulating higher functions of the human brain.
2- Programming a computer to use general language
3- Arranging hypothetical neurons in a manner so that they can form concepts
4- A way to determine and measure problem complexity
6- Abstraction: defined as the quality of dealing with ideas rather than events.
7- Randomness and creativity.
Few examples of currently existing types of AI consist of; machine learning, computer vision, natural language processing, robotics, speech, expert systems, and other subordinate types of AI.
This list of types and models of artificial intelligence will expand over time always depending on the new developments and technologies that are yet to be found out.
There are two most known types of AI, Strong and Weak.
It is a system that behaves like a human but it doesn’t give us an insight into how the brain works as the strong AI does.
An example of this was IBM’s Deep Blue chess play. It processed million of moves before it made any actual move on the chessboard.
It achieves the results of a human, just a lot faster and in a larger amount of possible moves.
It simulates the human brain by building systems that think and give us insights into how the brain works, basically, it can do anything as well as humans or even better.
For now, scientist, researchers are giving efforts in developing this AI level of knowledge and pave the way to the next more advanced level of AI.
Currently the researches and developers worldwide are focused on developing machines the General AI, hence, Super AI is still far for being achieved.
Super AI, is a level of advanced machines that can surpass the human intelligence, furthermore it can perform tasks better than human with cognitive abilities.
Characteristics of Super AI include the ability to solve the puzzle, make judgements, learn, plan, reason and communicate by its own. This stage of AI development is still a hypothetical concept.
Maybe you are already familiar with different types of artificial intelligence, some of them far more sophisticated than others. Some of them aren’t scientifically possible yet.
For your refreshment, we will try to shortly explain each one of them.
This type of AI is the most basic type of artificial intelligence which is programmed to provide a predictable output based on the input it receives. Reactive machines are designed to complete only a limited number of specific tasks.
An example of reactive artificial intelligence is Deep Blue, designed by IBM in the 1990s, a chess-playing supercomputer that defeated international chess grandmaster Gary Kasparov.
AlphaGo, a reactive machine from Google is another example of reactive AI. AlphaGo is capable of evaluating moves just according to its neural network for the present game and is incapable of evaluating future moves.
Learning from the past and building knowledge by observing actions or data is what Limited Memory AI is capable of. Limited memory AI obviously is more complex and possibilities it presents are much greater than Reactive machines.
We have three major machine learning models that utilize limited memory AI:
Learn how to make better predictions through repeated trial-and-error.
It utilizes past data to predict future item in a sequence.
Based on previous experiences it evolves over time and it grows to explore more modified paths.
An example of Limited Memory AI use, is shown at autonomous vehicles. Through Limited Memory AI vehicles are able to observe other cars’ speed, positioning and direction, helping them to understand the moves and ation done in the road and adjust as needed.
Machines with this AI will be acquired a real and true decision-making capabilities that are similar to humans.
Machines will be able to understand and remember emotions (in contrast to previous types of AI) and be able to express adjusted behaviour based on emotions by interacting with humans.
This technology is yet only on the development stage because of the difficulties when it comes to transferring the behaviour and feelings at machines, whereas at humans the process of shifting behaviour based on shifting emotions is fluid and fast.
This type of AI is the most advanced type of intelligence that engineers imagine to be developed in the future. After Theory of mind AI reaches its potential, only then we can see self-awareness get established.
This kind of AI possesses human-level consciousness and understands its own existence in the world (creepy right) and can understand emotional state of others.
Meaning, it is able to understand what are the needs of a human based on not only what they communicate but also how they communicate it.
Self-aware AI will have emotions, desires and needs. A machine with self-aware AI can get angry because someone cut him off in traffic (because this is how humans feel like, and we want to mimic human behaviours and feelings).
For this type of AI to be developed we don’t have the right tools and algorithms to support it.
After explaining the meaning and different types of Artificial Intelligence, now it’s time to talk about AI tools, what are the best AI apps and what features do they provide.
But first, let us remind ourselves that an AI software basically is used to build an intelligent application with the help of Machine Deep Learning capabilities.
Google cloud contributes in training, analyzing and tuning different models which later can be deployd. Google Cloud ML consists 3 components, a.Google Cloud Platform Console (the UI interface for deploying and managing models & versions), b.Gcloud and c. Rest API.
Though, the platform isn’t ease to master.
Being a Google based program, you will have access to all Google’s top notch AI technologies like Tensorflow, TPUs, etc., and Google’s open-source platform Kubeflow with which you can create portable machine learning pipelines.
This AI software offers a wide range of possibilities to help data scientists and developers build, train, and deploy ML models faster than other AI software.
No programming skill are needed to use Azure ML. It is extremely user-friendly, only with drag and drop features you are able to maneuver across the platform and deploy the models in cloud from the application itself.
This software is scalable and it can be integrated with open source technologies.
The idea behind IBM Watson software is to help making business processes smarter. It is a question-answering system which can learn from small data.
Training your model with Watson, it helps to deeply understand the real concepts. IBM Watson is a robust system which supports distributed computing and it provides an API for application development.
H2O is an advanced machine learning platform. It is used for banking, telecom, insurance, healthcare etc.
Programming languages such as R and Python are used in this platform to build models. H20 it helps in simplifying and accelerate making, operating and innovating applications with AI in any environment.
The platform is easy to use, it’s scalable and it follows a distributed in-memory structure.
TensorFlow is an open-source machine learning platform used to develop and deploy ML programs.
TensorFlow offers many levels of abstraction, allowing developers to choose from a wide array of tools and libraries with the common theme of making ML accessible.
TensorFlow’s APIs by using Keras allow users to build their own machine learning models and helps load the data to train that model and deploy it using the TensorFlow Serving.
It's ideal for data scientists and researchers to advance AI research and developer-friendly for deploying ML programs.
AI is going to impact businesses of all shapes and sizes across all industries.
Following the trends and exploring the great possibilities that AI provides should be an inevitable part of our business strategy.
If you’re struggling with the implementation of AI or don’t know how to make the most of it, reach us out.