AI Types & Approaches: ANI, AGI, ASI, Reactive Machines, Limited Memory, Theory of Mind, S | Schemes and Mind Maps Artificial Intelligence

Various approaches to AI / Types of AI

A) Based on Capabilities: Based on capabilities of a machine, there are three

types of Artificial Intelligence approaches:

1. Artificial Narrow Intelligence (ANI) / Weak AI / Narrow AI

It has a narrow range of capabilities.

Weak AI focuses on performing a specific task, such as answering questions based

on user input or playing chess. It can perform one type of task, but not both.

More Examples - Virtual assistants (Siri, Alexa, Cortana), Image/facial recognition

software, Email spam filters, Self-driving cars.

2. Artificial General Intelligence (AGI) / Strong AI / Deep AI / General AI

It is on par with human capabilities.

Strong AI can perform a variety of functions, eventually teaching itself to solve for

new problems.

It is the concept of a machine with general intelligence that mimics human

intelligence and/or behavior, with the ability to learn and apply its intelligence to

solve any problem.

AGI can think, understand, and act in a way that is indistinguishable from that of a

human in any given situation.

In theory, then, anything a human can do, a strong AI can do too.

AI researchers and scientists have not yet achieved strong AI.

3. Artificial Super intelligence (ASI)

It is more capable than a human.

ASI is the hypothetical AI that doesn’t just mimic or understand human intelligence

and behavior; ASI is where machines become self-aware and surpass the capacity of

human intelligence and ability.

Super AI is purely speculative at this point.

B) Based on Functionalities: Based on the ways the machines behave and

functionalities, there are four types of Artificial Intelligence approaches:

1. Reactive Machines

These machines are the most basic form of AI applications.

Such AI systems do not store memories or past experiences for future actions.

These machines focus only on current scenario and react on it as per possible best

action.

Example: Games like Deep Blue & IBM’s chess- playing supercomputer.

2. Limited Memory

Limited Memory machines can retain data for a short period of time.

While they can use this data for specific time period, they cannot add it to a library

of their experiences.

Many self-driving cars use Limited Memory technology: they store data such as the

recent speed of nearby cars, the distance of such cars, the speed limit, & other

information that can help them navigate roads.

3. Theory of Mind

Theory of mind is AI should understand the human emotions, beliefs and be able to

interact socially like humans.

Resources are making lots of efforts and improvement for developing such AI

machines

4. Self-Awareness

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Various approaches to AI / Types of AI

A) Based on Capabilities: Based on capabilities of a machine, there are three types of Artificial Intelligence approaches:

1. Artificial Narrow Intelligence (ANI) / Weak AI / Narrow AI  It has a narrow range of capabilities.  Weak AI focuses on performing a specific task, such as answering questions based on user input or playing chess. It can perform one type of task, but not both.  More Examples - Virtual assistants (Siri, Alexa, Cortana), Image/facial recognition software, Email spam filters, Self-driving cars. 2. Artificial General Intelligence (AGI) / Strong AI / Deep AI / General AI  It is on par with human capabilities.  Strong AI can perform a variety of functions, eventually teaching itself to solve for new problems.  It is the concept of a machine with general intelligence that mimics human intelligence and/or behavior, with the ability to learn and apply its intelligence to solve any problem.  AGI can think, understand, and act in a way that is indistinguishable from that of a human in any given situation.  In theory, then, anything a human can do, a strong AI can do too.  AI researchers and scientists have not yet achieved strong AI. 3. Artificial Super intelligence (ASI)  It is more capable than a human.  ASI is the hypothetical AI that doesn’t just mimic or understand human intelligence and behavior; ASI is where machines become self-aware and surpass the capacity of human intelligence and ability.  Super AI is purely speculative at this point. B) Based on Functionalities: Based on the ways the machines behave and functionalities, there are four types of Artificial Intelligence approaches: 1. Reactive Machines  These machines are the most basic form of AI applications.  Such AI systems do not store memories or past experiences for future actions.  These machines focus only on current scenario and react on it as per possible best action.  Example: Games like Deep Blue & IBM’s chess- playing supercomputer. 2. Limited Memory  Limited Memory machines can retain data for a short period of time.  While they can use this data for specific time period, they cannot add it to a library of their experiences.  Many self-driving cars use Limited Memory technology: they store data such as the recent speed of nearby cars, the distance of such cars, the speed limit, & other information that can help them navigate roads. 3. Theory of Mind  Theory of mind is AI should understand the human emotions, beliefs and be able to interact socially like humans.  Resources are making lots of efforts and improvement for developing such AI machines 4. Self-Awareness

 Self awareness AI is the future of artificial intelligence. These machines will be super intelligent and will have their own consciousness, sentiments and self- awareness.  These machines will be smarter than human mind.  Self awareness AI does not exist till now and it is a hypothetical concept.

Skills Required to Become an AI Engineer

1. Programming Skills: The first skill required to become an AI engineer is programming. For this, it’s crucial to learn programming languages, such as Python, R, Java, and C++ to build and implement models. 2. Linear Algebra, Probability, and Statistics: To understand and implement different AI models, you must have detailed knowledge of linear algebra, probability, and statistics. 3. Spark and Big Data Technologies: AI engineers work with large volumes of data, which could be streaming or real-time production level data in terabytes or petabytes. For such data, these engineers need to know about Spark and other big data technologies to make sense of it. 4. Algorithms and Frameworks: Understanding how machine learning algorithms like linear regression, KNN, Naive Bayes, Support Vector Machine, and others work will help you implement machine learning models with ease. Additionally, to build AI models with unstructured data, you should understand deep learning algorithms and implement them using a framework. 5. Communication and Problem-solving Skills: AI engineers need to communicate correctly to pitch their products and ideas to stakeholders. They should also have excellent problem-solving skills to resolve obstacles for decision making and drawing helpful business insights. 6. Necessary Business Skills: The following are some of the business skills required to be a successful AI engineer: → Creative thinking → Effective communication → Analytic problem-solving skills → Industry Knowledge

Other Emerging Technologies

1. The Internet of Things (IoT): Refers to a system of interrelated, internet-connected objects that are able to collect and transfer data over a wireless network without human intervention. The Internet of things describes the network of physical objects—“things”—that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the Internet.

The creation of a 3D printed object is achieved using additive process in which an object is created by laying down successive layers of material until the object is created. 3D printing is the opposite of subtractive manufacturing which is cutting out / hollowing out a piece of metal or plastic with for instance a milling machine. 3D printing enables you to produce complex shapes using less material than traditional manufacturing methods.

5. Augmented reality ( AR): AR is an interactive experience of a real-world environment where AR combines the physical world with computer-generated virtual elements overlay. These 2D or 3D virtual content are projected in reality within people’s field of view (through smartphone camera or smartglasses. Augmented reality is a technology that virtually places a 2D/3D visual into a “real- world” experience. This gives the user the appearance that the virtual object is co- existing with them in the physical world. In a few words, AR is the real world with an added layer of virtual content (2D/3D). 6. Virtual Reality (VR): Virtual reality (VR) refers to a computer-generated simulation in which a person can interact within an artificial 3D environment using electronic devices, such as special goggles with a screen or gloves fitted with sensors. In this simulated artificial environment, the user is able to have a realistic-feeling experience. It uses software to produce images, sounds, and other sensations to create a different place so that a user feels like he or she is really part of this other place. 7. 5G technology: 5G is the latest upgrade in the long-term evolution (LTE) mobile broadband networks. 5G mainly works in 3 bands. a) Low band spectrum : It has shown great promise in terms of coverage and speed of internet and data exchange, the maximum speed is limited to 100 Mbps. b) Mid band spectrum : It offers higher speeds compared to the low band, but has limitations in terms of coverage area. This spectrum doesn’t penetrate buildings very well, but it does deliver speeds around 1 Gbps. c) High band spectrum : It offers the highest speed of all the three bands, but has extremely limited coverage and signal penetration strength. Internet speeds in the high-band spectrum of 5G has been tested to be as high as 20 Gbps. 8. Brain Computer Interface (BCI): BCIs acquire brain signals, analyze them, and translate them into commands that are relayed to output devices that carry out desired actions.

BCIs measure brain activity, extract features from that activity, and convert those features into outputs that replace, restore, enhance, supplement, or improve human functions.

AI and Ethical Concerns

1. Unemployment: As AI become more and more advance, it will obviously take over jobs that were once performed by humans. People will move from physical and repetitive jobs to jobs that actually requires creative and strategic thinking. 2. AI is Imperfect : AIs are not immune to making mistakes and machine learning takes time to become useful. If trained well, using good data, then AIs can perform well. However, if we feed AIs bad date or make errors with internal programming, the AIs can be harmful. 3. Biasness : Human being are sometimes biased against other religion, gender and nationalities. This bias may unconsciously also enter into AI system that are developed by humans. There are many companies that are working towards creating unbiased AI system. 4. Artificial Stupidity: Intelligence come from learning, systems usually have a training phase in which they learn to detect the right patterns and act according to their input. Obviously, the training phase can not cover all the possible examples that a system may deal with in the real world. So this systems can be fooled in a ways that humans wouldn’t be. 5. Loss of skills: We lose more and more human skills due to the use of computers and Smartphone’s. 6. Security: The more powerful a technology becomes, the more can it be used for unfair reasons as well as good. AI system can cause damage if used maliciously. In terms of Cyber security, in future we will deal with AI system that is faster and more capable than us by order of magnitude. 7. Technological singularity: Technological singularity is a point when artificial intelligence may become more intelligent than human. It would make AI the dominant species on earth and lead to huge changes in human existence or human extinction. 8. Humanity: AI Bots becoming better and better at modeling human conversation and relationships. Tech addiction is the new frontier of human dependency. In future, we will interact frequently with machines as if they are human; whether in customer services or sales. 9. Everything becomes unreliable : For Examples, fake news and fake videos & audios of an individual. Smart systems are becoming increasingly capable of creating content – they can create faces, compose texts, produce tweets, manipulate images, clone voices and engage in smart advertising.

AI Types & Approaches: ANI, AGI, ASI, Reactive Machines, Limited Memory, Theory of Mind, S, Schemes and Mind Maps of Artificial Intelligence

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Download AI Types & Approaches: ANI, AGI, ASI, Reactive Machines, Limited Memory, Theory of Mind, S and more Schemes and Mind Maps Artificial Intelligence in PDF only on Docsity!

Various approaches to AI / Types of AI

Skills Required to Become an AI Engineer

Other Emerging Technologies

AI and Ethical Concerns