Artificial Intelligence — Start with AI right here
AI has become the face of modern computing and why not? Today, everyone uses services that are monitored or run by AI. Search systems, recommendations, gaming, health care, predictions, robotics, NLP, etc are some of the numerous AI applications we use.
Since the early 19th century, we have been into developing programmable computers. The first major contribution towards developing computers was made by Charles Babbage, the “father of the computer”, who invented the first mechanical computer. Two centuries later, we have CPUs the size of fingernails. Computers perform logical operations, complex computations, have the best accuracy in performing tasks and have been through evolution, just like us. The only aspect that sets us apart is Intelligence — the ability to think, learn and solve problems, make decisions through experiences and calculations, react to uncertain and new events. Bridging this gap would transform life on the face of this planet.
Definition
- Artificial Intelligence is the ability of machines to independently display human-like intelligence in making decisions, perceiving the environment, interpreting it and acting accordingly.
There are numerous definitions of AI. However, everything comes down to decision making, human-like behaviour and learning.
The high throughput and computing capability of computers coupled with AI would lead machines to mimic natural intelligence and solve complex problems in a better way than us.
Classifications
There are two main classifications:
Type-1
This classification is based on the capability of AI.
— Narrow AI
Narrow AI or Weak AI is both the most limited and the most common of the three types of AI. It’s also known as narrow AI or artificial narrow intelligence (ANI). It refers to any AI tool that focuses on doing one task really well. It has a narrow scope of what it can do, hence the name. The idea behind weak AI isn’t to mimic or replicate human intelligence. Rather, it’s to simulate human behavior. They carry out tasks they are programmed to do.
A common misconception about weak AI is that it’s barely intelligent at all. But even the smartest seeming AI of today are only weak AI. In reality, it is very intelligent at completing the specific tasks it’s programmed to do.
Examples: iPhone’s Siri, Amazon’s Alexa, Microsoft’s Cortana etc.
— General AI
General AI is the concept of a machine with general intelligence that mimics human intelligence and behavior. It solves problems by the ability to learn and apply intelligence and can think, understand, and act in a way that is indistinguishable from that of a human under any given situation.
AI researchers and scientists are yet to achieve general AI. To succeed, we would need to find a way to make machines conscious, programming a full set of cognitive abilities. Machines would have to take experiential learning to the next level, not just improving efficiency on singular tasks, but gaining the ability to apply experiential knowledge to a wider range of different problems.
Examples: None. Yet to be achieved
— Strong AI
Strong AI is a hypothetical AI that doesn’t just mimic or understand human intelligence and behavior. It is where machines become self-aware and surpass human intelligence and ability.
This concept has been depicted very well in science fiction in which robots overrun, overthrow, and/or enslave humanity. The concept here sees AI evolve to be so akin to human emotions and experiences, that it doesn’t just understand them, it evokes emotions, needs, beliefs and desires of its own.
Examples: None. Too far away.
Type 2
This classification is based on the functionality of AI.
— Reactive Machines
These machines react on a present moment basis and have no memory or learning from past experiences in order to make present decisions. IBM’s chess-playing supercomputer Deep Blue, which beat international grandmaster Garry Kasparov in the late 1990s, is the perfect example of this type of machine.
Examples: Deep Blue, AlphaGo
— Limited Memory AI
Machines with limited memory AI use past experiences to make future decisions. They work on drawing relevant information from surroundings, processing it and making informed decisions. Self-driving cars do this already. They observe other cars’ speed and direction, curvature of the road, terrain and monitors them over time. Before making a move, analysis with respect to all the above is done and decision is evaluated.
Example: Self-driving cars, digital assistants (Siri, Cortana, Google assistant)
— Theory of mind AI
Theory of mind reacts according to human thoughts and emotions. It involves the idea that people, creatures and objects in the world have thoughts and emotions that affect their decisions. These machines understand our thought process, emotions, beliefs and expectations and make human-like decisions while also interacting socially.
Example: None
— Self-Awareness AI
These machines have consciousness. They are aware of themselves, know about their internal states, and are able to predict the feelings of others. At this stage, the machines not only master the human abilities but surpass them.
Example: None. Far away.
Implementation and use cases
AI is implemented in many ways and can be incorporated in systems depending on the task it specializes in.
— Machine Learning
Machine Learning is a part of AI that learns from the data and works on information gathered from previous experiences. It is a simple concept where the machine takes data and learns on certain tasks to maximize the performance of the machine on this task. ML allows the system to learn new things from data.
Machine Learning and Artificial Intelligence are buzz words today. These terms are often used interchangeably. However, there is a stark difference between the two. ML is a subset of AI.
— Natural Language Processing(NLP)
Natural Language Processing is defined as the understanding and interpretation of natural languages such as speech and text by machines. One of the well-known examples of this is communication with digital assistants and email spam detection.
The machine is able to categorize and understand grammar, phrases, connotations, meanings, linguistics and then process all of them to generate informed decisions.
— Speech
Speech is more of a subset of NLP that focuses on converting between text and speech. Due to the complexity of this process, it is usually referred to as a totally different use case of AI.
— Expert system
Expert systems are computer applications developed to solve complex problems in a particular domain, at the level of extra-ordinary human intelligence and expertise. It uses efficient procedures and rules, specific to the domain, and deduces a correct, flawless solution. Expert systems are capable of advising and instructing and assisting humans in decision making.
Robots are artificial agents acting in a real-world environment.
— Robotics
Robotics is a branch of AI, which is composed of Electrical Engineering, Mechanical Engineering, and Computer Science for designing, construction, and application of robots. It is aimed at manipulating the objects by perceiving, picking, moving and modifying its physical properties.
Robots are often used to perform tasks that are difficult for humans to perform or perform consistently. Examples include car assembly lines, in hospitals, office cleaner, serving foods, and preparing foods in hotels, patrolling farm areas and even as police officers.
— Vision
Computer vision is a field that enables the machines to see, identify, process, perceive and generate conclusions. CV captures and analyses visual information using a camera and is not bound by the human limitation, thus it can see through wavelengths of light the naked eye cannot.
ML bosses the room
Even though the subfields are discretely labeled above, everything is somewhere related to ML, with NLP having the smallest share.
Start creating AI❗️❗️❗️
Even though we are at the initial steps of the AI ladder, current technologies are overwhelmingly yielding the desired results and it is nothing less than a charm to be able to create them. The only things that are required to work on AI are logic and the ability to code it.
Prerequisites for working on AI: Fundamental knowledge of a programming language(python), a computer, internet connection, dedication and time.
You can follow along with my other blogs for a total hands-on experience with AI.
Cheers!!!!! 🙌
