Deep Tech

Wholesome Flavors Of Artificial Intelligence

John McCarthy, an American computer scientist, pioneer, and inventor is called the “Father of Artificial Intelligence”. He coined the term in 1955. He also invented the first programming language for symbolic computation, LISP (which is still used as a preferred language in the field of AI), and established time-sharing. Human-level AI and common sense reasoning were two of his major contributions.

The developments in AI have sky-rocketed since then. The progress in AI can be categorized into 3 stages, 7 branches, and 4 systems.

Let’s discuss each of them in detail.

3 Stages of Artificial Intelligence:

Narrow AI or ANI

Narrow or weak AI refrains a computer from performing more than one operation at a time. It has a limited playing field in performing multiple intellectual tasks in the same time frame. Narrow AI can compile one particular instruction in a customized scenario. Some examples are Google Assistant, Alexa, and Siri.

Artificial general intelligence or AGI

Artificial general intelligence or AGI is the future of digital technology where self-assist robots or cyborgs will emulate human sensory movements. With AGI, machines will be able to see, respond to, and interpret external information like the human nervous system. The advancements in artificial neural networks will drive future AGI loaders, which will run businesses with the passage of time.

Strong AI or ASI

Strong AI is a futuristic concept that has only been the premise of a sci-fi movie until now. Strong AI will be the ultimate dominator as it would enable machines to design self-improvements and outclass humanity. It would construct cognitive abilities, feelings, and emotions in machines better than us. Thankfully, as of now, it is just a proposition.

7 Branches of Artificial Intelligence

These are the techniques with which AI is put into practice.

  1. Machine learning is the main branch of AI that enables machines to analyze, interpret and process data from all angles to generate correct output.
  2. Deep learning is a convolutional neural network consisting of different layers to extract and classify different components of data.
  3. Natural language processing is a self-evolved technology for basic human-computer communication. It is mainly used to design conversational chatbots.
  4. Robotics deals with designing, constructing, and operating robots that impersonate human actions and converse with other humans.
  5. Expert systems learn and imitate a human being’s decisions using logical notations and conditional operators.
  6. Fuzzy logic or hypothesis testing exhibits the degree of truth of an output. Say, if TRUE equals 0 and the output says 1, it is inferred that the null hypothesis is untrue.
  7. Random forest algorithm is often known as an “ensemble” or “decision tree” as it combines different decision trees to measure output accuracy.

4 Systems of Artificial Intelligence

The 4 systems provide a narrative of the progress of AI. They outline the timeline of AI development.

Reactive machines

Reactive machines are the most basic type of unsupervised AI. This means that they cannot form memories or use past experiences to influence present-made decisions; they can only react to currently existing situations – hence “reactive.”

In practice, reactive machines can read and respond to external stimuli in real-time. This makes them useful for performing basic autonomous functions, such as filtering spam from your email inbox or recommending movies based on your most recent Netflix searches.

Most famously, IBM’s reactive AI machine Deep Blue was able to read real-time cues to beat Russian chess grand master Garry Kasparov in a 1997 chess match. But beyond that, reactive AI can’t build upon previous knowledge or perform more complex tasks.

Limited memory

Limited memory consists of supervised AI systems that derive knowledge from experimental data or real-life events. Unlike reactive machines, limited memory learns from the past by observing actions or data fed to them to create a good-fit model.

Limited memory AI can be applied in a broad range of scenarios, from smaller-scale applications such as chatbots, to self-driving cars and other advanced use cases. One example is image recognition technology, which uses a massive database of photos to build memory and practice labeling to improve visual accessibility online.

When an image is scanned by such an AI, the training images are used as references to understand the contents of the image presented to it.  Based on its ‘learning experience’, it labels new images with increasing accuracy.

Theory of mind

As the name suggests, theory of mind is a technique of passing the baton of your ideas, decisions, and thought patterns to computers. While some machines currently exhibit human-like capabilities, none are fully capable of holding conversations relative to human standards. Even the most advanced robot in the world lacks emotional intelligence factor (sounding and behaving like a human).

This future class of machine ability would include understanding that people have thoughts and emotions that affect behavioral output and thus influence a “theory of mind” machine’s thought process. Social interaction is a key facet of human interaction. So to make the theory of mind machines tangible, the AI systems that control the now-robots would have to identify, understand, retain, and remember emotional responses.

Theory of mind could bring plenty of positive changes to the tech world, but it also poses its risks. Since emotional cues are so nuanced, it would take a long time for AI machines to perfect reading them, and could potentially make big errors while in the learning stage.

Some elements of the theory of mind AI currently exist or have existed in the recent past. Two notable examples are the robots Kismet and Sophia, created in 2000 and 2016, respectively.


Self-aware AI involves machines that have human-level consciousness. This form of AI is not currently in existence but would be considered the most advanced form of artificial intelligence known to man.

Facets of self-aware AI include the ability to not only recognize and replicate human-like actions, but also to think for itself, have desires, and understand its feelings. Self-aware AI, in essence, is an advancement and extension of the theory of mind AI. Where the theory of mind only focuses on the aspects of comprehension and replication of human practices, self-aware AI takes it a step further by implying that it can and will have self-guided thoughts and reactions.

We are presently in tier three of the four types of artificial intelligence, so believing that we could potentially reach the fourth (and final?) tier of AI doesn’t seem like a far-fetched idea.

But for now, it is important to focus on perfecting all aspects of AI.



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