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What Exactly is AI and Machine Learning

What is Artificial Intelligence?

Specific practical applications of AI comprise modern web search engines, personal assistant programs that comprehend spoken language, self-driving vehicles, and suggestion engines, such as those used by Spotify, Youtube, and Netflix.

There are four types of Artificial intelligence, two of which we already have achieved, and two are which remain still ream at the theoretical stage.

Artificial intelligence typically refers to operations and algorithms that have the ability to simulate human intelligence, including mimicking cognitive functions such as perception, learning, and complex problem-solving. Machine learning and deep learning (DL) are subgroups of Artificial intelligence logistics.


What is Machine Learning?

To put it briefly, machine learning is a sub-category of Artificial intelligence that falls within the "limited memory" category in which the AI (machine) can learn and develop its skills and knowledge over time.

There are a variety of different machine learning algorithms, with the three primary types being supervised learning, unsupervised learning, and reinforcement learning

3 types of machine learning algorithms

As with the different types of Artificial intelligence, these different types of machine learning cover a range of complicatedness. And while there are several other types of machine learning algorithms present in the world, most are a combination of—or based on—these primary three Machine Learning Algorithms.

Supervised Learning is the simplest of these. When an AI is actively supervised throughout the learning process. Researchers or data scientists will provide the machine with a quantity of data to process and learn from, as well as some example results of what that data should produce (more formally referred to as inputs and desired outputs).

The result of supervised learning is an agent that can predict results based on new input data. The machine may continue to refine its learning augmented reality by storing and continually re-analyzing these predictions, improving its accuracy over time.

Supervised machine learning applications include image recognition, media recommendation systems, predictive analytics, and spam detection.

Unsupervised learning involves no help from humans during the learning process. The agent is given a quantity of data to analyze, and independently identifies patterns in that data. This type of analysis can be extremely helpful because machines can recognize more and different patterns in any given set of data than humans. Like supervised machine learning, unsupervised ML can learn and improve over time.

Unsupervised machine learning applications include things like determining customer segments in marketing data, medical imaging, and anomaly detection.

Reinforcement Learning is the most complex of these three algorithms in that there is no data set provided to train the machine. Instead, the agent learns by interacting with the environment in which it is placed. It receives positive or negative rewards based on the actions it takes and improves over time by refining its responses to maximize positive rewards.

Some applications of reinforcement learning include self-improving industrial robots, automated stock trading, advanced recommendation engines, and bid optimization for maximizing ad spending.


The 4 types of AI

Going from the simplest to the most advanced type of Artificial Intelligence, the four types of AI include Reactive Machines, Limited Memory, Theory Of Mind, and Self-Awareness.

Limited memory AI systems can store incoming data and data about any actions or decisions it makes, and then analyze that stored data to improve over time. This is where machine learning really begins, as limited memory is required for learning to happen.

Since limited memory AIs can improve over time, these are the most advanced AIs we have developed to date. Examples include self-driving vehicles, virtual voice assistants, and chatbots. 

Reactive machines possess the ability to perform basic operations based on some form of input. At this level of AI, no "learning" happens—the system is trained to do a particular task or set of tasks and never deviates from that. These are purely reactive machines that do not store inputs, have any ability to function outside of a particular context, or have the ability to evolve over a certain period of time.

Examples of Reactive Machines include most suggestion engines, IBM’s Deep Blue chess AI, and Google’s AlphaGo AI (arguably the best Go player in the world).

Self-Awareness is considered the ultimate goal for many AI developers, wherein AIs have human-level consciousness, aware of themselves as beings in the world with similar desires and emotions as humans. As yet, self-aware AIs are purely the stuff of science fiction. 

Theory of Mind is the first of the two more advanced and (currently) theoretical types of AI that we haven’t yet achieved. At this level, AIs would begin to understand human thoughts and emotions and start to interact with us in a meaningful way. Here, the relationship between humans and AI becomes reciprocal, rather than the simple one-way relationship humans have with various less advanced AIs now.

The "theory of mind" terminology comes from psychology, and in this case refers to an AI understanding that humans have thoughts and emotions which then, in turn, affect the AI’s behavior.

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