In the world of tech, “artificial intelligence” or “AI” has become one of the most popular search keywords. AI is the invention which allows machines to learn from given data and past experiences to perform very much like humans. From chess playing, to live chatbots and to driverless cars, AI has rapidly become an integral part of our daily lives.
By using the AI enabled technologies, computers can be efficiently trained to achieve detailed tasks by processing huge amounts of large amounts of data and by recognizing different patterns in the data.
Let’s explore through this read the different kinds of AI that explain how we can implement AI in our daily lives.
Artificial intelligence (AI) can be broadly categorized in four kinds, based on type, learning and complexity of a particular task which a system needs to able to perform, these are:
Reactive machines are the most elementary kind of unsupervised form of AI. Simply put, reactive machines are designed to react to current existing scenarios or situations. They cannot use past experiences and memories to draw conclusions, hence they are known as reactive machines.
Reactive machines have no concept of the real-world practicality and hence they cannot work beyond the very basic tasks. However, reactive machines always behave in a set way the way they have been programmed.
This kind of AI is considered as trustworthy and reliable, and it will react the same way to the same stimuli every time.
Examples of Reactive Machines
- Spam filters
- Netflix recommendation engine
- Chess playing supercomputers such as Deep Blue.
As opposed to reactive machines, limited memory is a kind of supervised AI system. These machines derive information from real-life events and from past experimental data. Limited memory machines learn from the data fed to them and observe actions to finally create a proper model. Essentially, limited memory stores the past data for clues and suggests what may come next. Limited memory AI is quite complex and has great potential in the magnificent world of AI.
Limited memory is created when the model is undergoing training so that models can be automatically proficient and renewed.
Following are the steps to be followed while utilizing limited memory AI in machines:
- Create data
- Establish the machine learning type and model
- The selected model should be able to make predictions
- Confirm the model can receive human response
- Accumulate the human and environmental response as data
- Then, repeat these above-mentioned steps as a cycle
Example of Limited Memory
Autonomous vehicles or self-driving cars. For instance, Tesla’s autopilot cars are powered with 40x more graphical processing power and cutting-edge sensor technology, making it the future of driving.
Self-drive or automated vehicles observe their environment and also detect location and catch traffic in hindsight. Before the launch of limited memory, such driverless cars used to take more time to react and make decisions based on peripheral factors. However, post the introduction of limited memory, the reaction time on machine-based observations has fallen sharply.
Artificial intelligence has even evolved to attain “Theory of Mind” at a stunning speed. As the name suggests, theory of mind takes theoretical concepts into consideration. Just like human beings, theory of mind mimics thoughts and emotions which any living being could possess. In the language of AI machines, ToM means that AI could grasp how humans, animals and other living beings feel and make rational decisions based on self-thought process.
Presently, there are few machines which could comprehend humanlike capabilities up to a certain level, but none of them are fully compatible with holding conversations as per human standards.
Theory of Mind is a very advanced form of AI, which requires machines to process commands and directions given by human beings and interpret the basic rules of communication and social interactions. This kind of AI technique requires a lot of testing and effort. ToM works on developing human emotions such as empathy, moral judgment, or self-consciousness via AI systems.
Theory of Mind in AI is definitely the next big boom in industry. For things such as self-driving cars, theory of mind can help to decide whether to put the driver’s life in danger or to save the life of a child crossing the street.
Example of Theory of Mind (ToM)
Robots which recognize human facial signals and can replicate emotions and also respond to interactions with appropriate facial expressions. Such robots do not have the same conversational ability as human beings, but they carry certain aspects of human emotions.
However, the AI research in this subfield of ToM is still ongoing as it is not that easy to comprehend human behavior and accurately model it AI system. Emotions are too very multifaceted and difficult to understand. Generally, AI systems struggle to interpret human emotions accurately and thus it is very challenging to achieve contextual understanding via ToM in an AI system.
Self-awareness or self-consciousness is another form of AI, which is considered as the most technologically advanced form of human innovation in AI space. Self-awareness of AI involves machines which can possess consciousness on par with human-levels. Consciousness is being mindful of one’s own situation and body and self-awareness means recognizing this kind of awareness.
Self-awareness in AI is an extension of the Theory of Mind. However, ToM focuses on the aspects of replication of human practices, but self-aware AI takes a dive a little deeper and cannot only mimic but is also aware of self-guided thoughts and reactions.
When self-aware AI would be fully achieved it would be similar to AI which has human-level consciousness equal to human intelligence with the same sentiments and desires.
Till now, this kind of AI has not been developed successfully, because of lack of hardware and algorithms. If this gets launched anytime in future, then this will be a case of artificial superintelligence (ASI) making it possible for machines to take over humans or help create and collaborate with humans.
These different types of artificial intelligence displays why AI is so talked about everywhere as it’s used everywhere. It is not wrong to say that AI has been an integral part of almost every industry. Many companies are using such hi-tech capabilities for automation, legal assistance, risk notification and for purely research and development purposes.
AI does not only analyze more and more deeper data, but also adds much power and intelligence to existing products and services as it adapts that data quickly via various learning algorithms. Today, every industry is trying their best to capitalize the advancements related to AI, and maybe they continue implanting AI technologies to seek the best possible solutions and outcomes.