This is a comprehensive review of the Types of Artificial Intelligence and also a detailed guide to enlighten you on the bedrock and history of Artificial Intelligence relatively.
Artificial intelligence was founded as an academic discipline in 1955, and in the year’s things has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an “AI winter”), followed by new approaches, success, and renewed funding.
For most of its history, AI research has been divided into subfields that often fail to communicate with each other.
These sub-fields are based on technical considerations, such as particular goals (e.g. “robotics” or “machine learning”) the use of particular tools (“logic” or artificial neural networks).
Or deep philosophical differences. Sub-fields have also been based on social factors (particular institutions or the work of particular researchers).
Artificial intelligence is sometimes called machine intelligence, its intelligence is demonstrated by machines, unlike the natural intelligence displayed by humans and animals.
Leading AI textbooks define the field as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.
Colloquially, the term “artificial intelligence“ is often used to describe machines (or computers) that mimic “cognitive” functions that humans associate with the human mind, such as “learning” and “problem-solving”.
What are the examples of artificial intelligence?
- Manufacturing robots
- Smart assistants
- Proactive healthcare management
- Disease mapping
- Automated financial investing
- Virtual travel booking agent
- Social media monitoring
- Inter-team chat tool
- Conversational marketing bot
- Natural Language Processing (NLP) tools
How is the Artificial Intelligence Used?
The use of artificial intelligence cannot be under emphasized
Artificial intelligence is the use of computer science programming to imitate human thought and action by analyzing data and surroundings.
Solving or anticipating problems and learning or self-teaching to adapt to a variety of tasks. Chances are, you’re exposed to artificial intelligence every day.
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What are the applications of artificial intelligence?
Artificial Intelligence has various applications in today’s society.
It is becoming essential for today’s time because it can solve complex problems in an efficient way in multiple industries, such as Healthcare, entertainment, finance, education, etc.
AI is making our daily life more comfortable and fast.
Here are some sectors which have the application of Artificial Intelligence.
1. AI in Astronomy
- Artificial Intelligence can be very useful to solve complex universe problems.
- AI technology can be helpful for understanding the universe such as how it works, origin, etc.
2. AI in Healthcare
- In the last, five to ten years, AI becoming more advantageous for the healthcare industry and going to have a significant impact on this industry.
- Healthcare Industries are applying AI to make a better and faster diagnosis than humans.
- AI can help doctors with diagnoses and can inform when patients are worsening so that medical help can reach the patient before hospitalization.
3. AI in Gaming
- AI can be used for gaming purpose. The AI machines can play strategic games like chess, where the machine needs to think of a large number of possible places.
4. AI in Finance
- AI and finance industries are the best matches for each other. The finance industry is implementing automation, chatbot, adaptive intelligence, algorithm trading, and machine learning into financial processes.
5. AI in Data Security
- The security of data is crucial for every company and cyber-attacks are growing very rapidly in the digital world.
- AI can be used to make your data more safe and secure. Some examples such as AEG bot, AI2 Platform, are used to determine software bugs and cyber-attacks in a better way.
6. AI in Social Media
- Social Media sites such as Facebook, Twitter, and contain billions of user profiles, which need to be stored and managed in a very efficient way. AI can organize and manage massive amounts of data.
- AI can analyze lots of data to identify the latest trends, hashtag, and requirement of different users.
What are the Types of Artificial Intelligence?
Here are the types of artificial intelligence and a comprehensive review unlike the natural intelligence displayed by humans and animals. And this post buttresses the types of artificial intelligence
1. Reactive Machines
The most important basic types of AI systems are purely reactive and have the ability neither to form memories nor to use past experiences to inform current decisions.
This type beat international grandmaster Garry Kasparov in the late 1990s is the perfect example of this type of machine.
Deep Blue can identify the pieces on a chessboard and know how each moves.
It can make predictions about what moves might be next for it and its opponent. And it can choose the most optimal moves from among the possibilities.
But it doesn’t have any concept of the past, nor any memory of what has happened before.
Apart from a rarely used chess-specific rule against repeating the same move three times, Deep Blue ignores everything before the present moment.
All it does is look at the pieces on the chessboard as it stands right now, and choose from possible next moves.
This type of intelligence involves the computer perceiving the world directly and acting on what it sees.
It doesn’t rely on an internal concept of the world. In a seminal paper, AI researcher Rodney Brooks argued that we should only create machines like this.
His main reason was that people are not very good at programming accurate simulated worlds for computers to use, what is called in AI scholarship a “representation” of the world.
The current intelligent machines we marvel at either have no such concept of the world, or have a very limited and specialized one for its particular duties.
The innovation in deep blue design was not to broaden the range of possible movies the computer considered.
Rather, the developers found a way to narrow its view, to stop pursuing some potential future moves, based on how it rated their outcome.
Without this ability, Deep Blue would have needed to be an even more powerful computer to actually beat Kasparov.
Similarly, Google’s Alpha Go, which has beaten top human Go experts, can’t evaluate all potential future moves either.
Its analysis method is more sophisticated than Deep Blue’s, using a neural network to evaluate game developments.
These methods do improve the ability of AI systems to play specific games better, but they can’t be easily changed or applied to other situations.
These computerized imaginations have no concept of the wider world – meaning they can’t function beyond the specific tasks they’re assigned and are easily fooled.
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They can’t interactively participate in the world, the way we imagine AI systems one day might.
Instead, these machines will behave exactly the same way every time they encounter the same situation.
This can be very good for ensuring an AI system is trustworthy: You want your autonomous car to be a reliable driver.
But it’s bad if we want machines to truly engage with, and respond to, the world. These simplest AI systems won’t ever be bored, or interested, or sad.
2. Limited Memory
This type of artificial intelligence class contains machines that can look into the past. Some Self-driving cars do some of this already. For example, they observe other cars’ speed and direction.
That can’t be done in just one moment, but rather requires identifying specific objects and monitoring them over time.
These observations are added to the self-driving cars’ pre-programmed representations of the world, which also include lane markings, traffic lights and other important elements, like curves in the road.
They’re included when the car decides when to change lanes, to avoid cutting off another driver or being hit by a nearby car.
But these simple pieces of information about the past are only transient. They aren’t saved as part of the car’s library of experience it can learn from, the way human drivers compile experience over years behind the wheel.
So how can we build AI systems that build full representations, remember their experiences and learn how to handle new situations? Brooks was right in that it is very difficult to do this.
My own research into methods inspired by Darwinian evolution can start to make up for human shortcomings by letting the machines build their own representations.
3. Theory of the Mind
We might stop here, and call this point the important divide between the machines we have and the machines we will build in the future.
However, it is better to be more specific to discuss the types of representations machines need to form, and what they need to be about.
Machines in the next, more advanced, class not only form representations about the world, but also about other agents or entities in the world.
In psychology, this is called “theory of mind” – the understanding that people, creatures and objects in the world can have thoughts and emotions that affect their own behavior.
This is crucial to how we humans formed societies, because they allowed us to have social interactions.
Without understanding each other’s motives and intentions, and without taking into account what somebody else knows either about me or the environment, working together is at best difficult, at worst impossible.
If AI systems are indeed ever to walk among us, they’ll have to be able to understand that each of us has thoughts and feelings and expectations for how we’ll be treated. And they’ll have to adjust their behavior accordingly.
The final step of AI development is to build systems that can form representations about themselves. Ultimately, we AI researchers will have to not only understand consciousness, but build machines that have it.
This is, in a sense, an extension of the “theory of mind” possessed by Type III artificial intelligence.
Consciousness is also called “self-awareness” for a reason. (“I want that item” is a very different statement from “I know I want that item.”) Conscious beings are aware of themselves, know about their internal states, and are able to predict the feelings of others.
We assume someone honking behind us in traffic is angry or impatient because that’s how we feel when we honk at others. Without a theory of mind, we could not make those sorts of inferences.
While we are probably far from creating machines that are self-aware, we should focus our efforts toward understanding memory, learning and the ability to base decisions on past experiences.
This is an important step to understand human intelligence on its own. And it is crucial if we want to design or evolve machines that are more than exceptional at classifying what they see in front of them.
5. Artificial Narrow Intelligence (ANI)
This type of artificial intelligence represents all the existing AI, including even the most complicated and capable AI that has ever been created to date.
Artificial narrow intelligence refers to AI systems that can only perform a specific task autonomously using human-like capabilities.
These machines can do nothing more than what they are programmed to do, and thus have a very limited or narrow range of competencies.
According to the aforementioned system of classification, these systems correspond to all the reactive and limited memory AI.
Even the most complex AI that uses machine learning and deep learning to teach itself falls under ANI.
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6. Artificial General Intelligence (AGI)
Artificial General Intelligence is the ability of an AI agent to learn, perceive, understand, and function completely like a human being.
These systems will be able to independently build multiple competencies and form connections and generalizations across domains, massively cutting down on time needed for training.
This will make AI systems just as capable as humans by replicating our multi-functional capabilities.
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