ARTIFICIAL INTELLIGENCE ......
Artificial intelligence (AI) is 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. Although there are no AIs that can perform the wide variety of tasks an ordinary human can do, some AIs can match humans in specific tasks.
Introduction to Artificial Intelligence
The short answer to What is Artificial Intelligence is that it depends on who you ask.
A layman with a fleeting understanding of technology would link it to robots. They’d say Artificial Intelligence is a terminator like-figure that can act and think on its own.
If you ask about artificial intelligence to an AI researcher, (s)he would say that it’s a set of algorithms that can produce results without having to be explicitly instructed to do so. And they would all be right. So to summarise, Artificial Intelligence meaning is:
Artificial Intelligence Definition
- An intelligent entity created by humans.
- Capable of performing tasks intelligently without being explicitly instructed.
- Capable of thinking and acting rationally and humanely.
How Artificial Intelligence (AI) Works?
Building an AI system is a careful process of reverse-engineering human traits and capabilities in a machine, and using it’s computational prowess to surpass what we are capable of.
To understand How Aritificial Intelligence actually works, one needs to deep dive into the various sub domains of Artificial Intelligence and and understand how those domains could be applied into the various fields of the industry. You can also take up an artificial intelligence course that will help you gain a comprehensive understanding.
- Machine Learning : ML teaches a machine how to make inferences and decisions based on past experience. It identifies patterns, analyses past data to infer the meaning of these data points to reach a possible conclusion without having to involve human experience. This automation to reach conclusions by evaluating data, saves a human time for businesses and helps them make a better decision.
- Deep Learning : Deep Learning ia an ML technique. It teaches a machine to process inputs through layers in order to classify, infer and predict the outcome.
- Neural Networks : Neural Networks work on the similar principles as of Human Neural cells. They are a series of algorithms that captures the relationship between various underying variabes and processes the data as a human brain does.
- Natural Language Processingc: NLP is a science of reading, understanding, interpreting a language by a machine. Once a machine understands what the user intends to communicate, it responds accordingly.
- Computer Vision : Computer vision algorithms tries to understand an image by breaking down an image and studying different parts of the objects. This helps the machine classify and learn from a set of images, to make a better output decision based on previous observations.
- Cognitive Computing : Cognitive computing algorithms try to mimic a human brain by anaysing text/speech/images/objects in a manner that a human does and tries to give the desired output.
What are the Types of Artificial Intelligence?
4 main types of artificial intelligence :
1. Reactive AI
Early AI algorithms had one thing in common; they lacked memory and were purely reactional. Given a specific input, the output would always be the same.
That is the case with many machine learning models. Stemming from statistical math, these models were able to consider huge chunks of data, then produce a seemingly intelligent output. For instance, it is extremely difficult (if not impossible) to write a math formula for movie recommendations. But machine learning models were able to yield great results by looking at the purchase history of other customers. Solving that problem became one of the factors behind Netflix's success.
The same mechanism works for spam filters, which can statistically determine if the presence and density of certain words should raise a red flag.
This kind of AI is known as "reactional" or "reactive AI," and it works great -- even performing beyond human capacity in certain domains. Most notably, it defeated chess Grandmaster Garry Kasparov in 1997. However, reactive AI is also extremely limited.
In real life, many of our actions are not reactive -- in the first place, we may not have all information at hand to react on. Yet, we are masters of anticipation and can prepare for the unexpected, even based on imperfect information. This "imperfect information" scenario has been one of the target milestones in the evolution of AI and is necessary for a range of use cases from natural language understanding to self-driving cars.
For that reason, researchers worked to develop the next level of AI, which had the ability to remember and learn.
2. Limited memory machines
As mentioned earlier, in 2012 we witnessed the deep learning revolution. Based on our understanding of the brain's inner mechanisms, an algorithm was developed which was able to imitate the way our neurons connect. One of the characteristics of deep learning is that it gets smarter the more data it is trained on.
Deep learning dramatically improved AI's image recognition capabilities, and soon other kinds of AI algorithms were born, such as deep reinforcement learning.
These AI models were much better at absorbing the characteristics of their training data, but more importantly, they were able to improve over time.
One notable example is Google's AlphaStar project, which managed to defeat top professional players at the real-time strategy game StarCraft 2. The models were developed to work with imperfect information and the AI repeatedly played against itself to learn new strategies and perfect its decisions.
In the StarCraft game, the decision a player makes early in the game may have decisive effects later. As such, the AI had to be able to predict the outcome of its actions well in advance.
We witness the same concept in self-driving cars, where the AI must predict the trajectory of nearby cars in order to avoid collisions. In these systems, the AI is basing its actions on historical data. Needless to say, reactive machines were incapable of dealing with situations like these.
Despite all these advancements, AI still lags behind human intelligence. Most notably, it requires huge amounts of data to learn simple tasks. While the models can be retrained to advance and improve, changes to the environment the AI was trained on would force it into full retraining from scratch. For instance, consider a language: Once we learn a second language, learning a third and fourth become proportionally easier. For AI, it makes no difference.
That is the limitation of narrow AI -- it can become perfect at doing a specific task but fails miserably with the slightest alterations.
3. Theory of mind
Theory of mind capability refers to the AI machine's ability to attribute mental states to other entities. The term is derived from psychology and requires the AI to infer the motives and intents of entities (e.g., their beliefs, emotions, goals).
Emotion AI, currently under development, aims to recognize, simulate, monitor and respond appropriately to human emotion by analyzing voice, image and other kinds of data. But this capability, while potentially invaluable in advertising, customer service, healthcare and many other areas, is still far from being an AI possessing theory of mind: The latter is not only capable of varying its treatment of human beings based on its ability to detect their emotional state, but is also able to understand them.
Indeed, "understanding," as it is generally defined, is one of AI's huge barriers. The type of AI that can generate a masterpiece portrait still has no clue what it has painted. It can generate long essays without understanding a word of what it has said. An AI that has reached the theory of mind state would have overcome this limitation.
4. Self-aware AI
The types of AI discussed above are precursors to self-aware or conscious machines, i.e., systems that are aware of their own internal state as well as that of others. This essentially means an AI that is on par with human intelligence and can mimic the same emotions, desires or needs.
Top Used Applications in Artificial Intelligence
- Google’s AI-powered predictions (E.g.: Google Maps)
- Ride-sharing applications (E.g.: Uber, Lyft)
- AI Autopilot in Commercial Flights
- Spam filters on E-mails
- Plagiarism checkers and tools
- Facial Recognition
- Search recommendations
- Voice-to-text features
- 9.Smart personal assistants (E.g.: Siri, Alexa)
- 10.Fraud Protection and Prevention
- Reduction in human error
- Available 24×7
- Helps in repetitive work
- Digital assistance
- Faster decisions
- Rational Decision Maker
- Medical applications
- Improves Security
- Efficient Communication
Advantages of Artificial Intelligence (AI)
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