Introduction to Artificial Intelligence

            1. Ethical Gene Editing: The notion of personalized medical care to treat diseases or disorders caused due to gene mutations is achieved by precisely understanding the genetic blueprint of the patient. The analysis to identify the order of nucleotides is called Genome Sequencing. With the insights from Genome sequencing, susceptible mutations would be identified to prescribe a sufferer-specific line of treatment.
            2. Intelligent Disaster Response System: Modern rescue systems use AI-powered drones, robots, sensors to quickly gather precise information regarding the extent of damage, the exact location of trapped victims, topographical details of the landscape during crisis times. Intelligent systems assist rescue workers to identify the nearest and safest assemble points while evacuating people from disaster-hit areas. AI-equipped disaster management modules effectively stimulate mock disaster drills to identify potentially vulnerable locations, plan precautionary actions, to monitor and govern resource allocation seamlessly.
            3. Recommendation Systems: The best recommendation systems identify or predict users’ preferences to items based on items’ profiles and inferences about users behaviour. The willingness of users towards various items is represented as user-item pairs in the utility matrix.

•Advantages of Artificial Intelligence

Given below are the advantages of artificial intelligence:
1.Minimal Human Intervention: AI-powered systems are the best-fit solutions in environments where human life is more likely to be at risk. Few examples of such scenarios are space exploration, defense operations like bomb defusing, workplaces characterized by intense heat, Mineral mining, etc.
2.Faster and Accurate: The performance of well-trained AI-enabled applications drastically reduces the chance for human errors to creep in. These AI versions proved to be faster at computationally expensive tasks, especially in the field of scientific research and at time-consuming tasks. Most of the routine, trivial and repetitive tasks can be automated with proper AI drove the technology to improve operational efficiencies.

•Challanges:

1. Need for Massive Data Corpus: In general, intelligent systems, before getting deployed as a real-world solution, learn an optimized model with the help of a large amount of data while training and validation. The availability of huge data volumes and the ability to handle them are the major limitations for the conventional systems and software applications to evolve as AI-enabled editions. The need for sophisticated modeling techniques that can estimate the model parameters with high precision using limited data samples is imminent.
2. Multimodal Interactions: The efficiency and precision of perception-based recognition applications that encompass computer-vision methods can be improved by leveraging the ability to interpret and process multiple modes of data simultaneously. This enables the recognition paradigm to ideally emulate human intelligence that works in conjunction with various senses like touch, vision, hearing, etc.
3. Beyond Human Control: With the exceptional capability of AI technology to understand and learn vast libraries of information at a faster pace, there are few threatening instances where an AI framework gained an emotional quotient and surpassed the extremities of human logical thinking. In such unregulated cases, the unusual behavior of AI systems would lead to irreparable catastrophe

• Introduction to Types of Artificial Intelligence

The following article provides an outline for the most important type of Artificial Intelligence. The main aim of Artificial Intelligence aim is to enable machines to perform a human-like function. Thus the primary way of classification of AI is based on how well it is able to replicate human-like actions. AI can, by and large, be classified based on two types, both of which are based on its ability to replicate the human brain. One type of classification, which is “Based on Functionality”, classify AI on the basis of their likeness to the human mind and their ability to think and feel like humans. The second way of classification is more prominent in the tech industry, which is” Based on Capabilities” of AI vis-à-vis Human Intelligence.

• Types of Artificial Intellegence

There are mainly two types of AI which are based on Functionality & Capabilities:
★ Type 1 – Based on Functionality:
           1. Reactive Machine: They are the most basic and oldest type of Artificial Intelligence. They replicate a human’s ability to react to different kinds of stimuli. This type of AI has no memory power, so they lack the capability to use previously gained information/experience to obtain better results. Therefore, these kinds of AI don’t have the ability to train themselves like the ones we come across nowadays. Example: Deep Blue, IBM’s chess-playing supercomputer, is the perfect example of these kinds of machines. It is famous for defeating international grandmaster Garry Kasparov in the late 1990s. Deep Blue can identify different pieces in the chessboard and how each moves. It can identify all the possible legal moves for itself and its opponents. Based on the option, it selects the best possible move. However, it doesn’t have the ability to learn from its past moves as these machines don’t have any memory of their own.
            2. Limited Theory: This type of AI, along with the ability of Reactive Machines, have memory capabilities so they can use past information/experience to make better future decisions. Most of the common applications existing around us fall under this category. These AI applications can be trained by a large volume of training data they store in their memory in a reference model. Example: Limited Memory technology is used in many self-driving cars use. They store data like GPS location, speed of nearby cars, size /nature of obstructions, among a hundred other kinds of data to drive just like a human does.
            3. Theory of Mind: Theory of Mind is the next level of AI, which has very limited to no presence in our day-to-day lives. These kind of AI are mostly in the “Work in Progress” stage and are usually confined to research labs. These kinds of AI, once developed, will have a very deep understating of human minds ranging from their needs, likes, emotions, thought process, etc. Basis their understanding of Human minds and their whims, the AI will be able to alter its own response. Example: Researcher Winston in his research, showed a prototype of a robot that can walk down the small corridor with other robots coming from the opposite direction; the AI can foresee other robots movements and can turn right, left or any other way so as to avoid a possible collision with the incoming robots. As per Wilson, this Robot determines its action based on its “common sense” of how other robots will move.
            4. Self-Aware AI: This is the final stage of AI. Its current existence is only hypothetical and can be found only in Science fiction movies. These kinds of AI can understand and evoke human emotions and have emotions of their own. These kind of AI are decades, if not centuries, away from materializing. It is this kind of AI which AI skeptics like Elon Musk are wary of. This is because once it is self-aware, the AI can get into Self-Preservation mode; it might consider humanity as a potential threat and may directly or indirectly pursue endeavor to end humanity.

Type 2 – Based on Capabilities

1. Artificial Narrow Intelligence (ANI): All the existing AI applications which we see around us falls under this category. ANI includes an AI system that can perform narrowly defined specific tasks just like humans. However these machines cannot perform tasks for which it was not programmed before-hand, so they fail at performing an unprecedented task. Based on the classification mentioned above, this system is a combination of all reactive and limited memory AI. AI algorithms that we use in today’s world to perform the most complex Prediction Modelling fall under this category of AI.
2. Artificial General Intelligence (ANI) AGI has the capability to train, learn, understand and perform functions just like a normal human does. These systems will have multi-functional capabilities cutting across different domains. These systems will be more agile and will react and improvise just like humans while facing unprecedented scenarios. There is no real-world example of this kind of AI, but a good amount of progress has been made in this field
3. Artificial Super Intelligence (ASI) Artificial Super Intelligence will be the top-most point of AI development. ASI will be the most potent form of intelligence to ever exist on this planet. It will be able to perform all the tasks better than humans because of its inordinately superior data processing, memory, and decision-making ability. Some of the researchers fear that the advent of ASI will ultimately result in “Technological Singularity”. It is a hypothetical situation in which the growth in technology will reach an uncontrollable stage, resulting in an unimaginable change in Human Civilization. At present, it is very hard to foresee how our future will look like when a more dexterous form of AI materializes. However, with great certainty, we are still a long distance apart to reach that stage as we are just in the very nascent stage of the development of advanced AI. For the proponents of AI, we can say that we are just scratching the surface to unearth the true potential of AI, and for the AI skeptics, it is too soon to get chills about Technological Singularity.

• Examples Of Making AI

Most of the programming languages used in AI are as follows. Python is unique and most favorite for computer programmers because of its syntax, which is simple and versatile. It is very comfortable and applied in all OS like Unix, Linux, Windows, and Mac. As Python has a systematic arrangement, it is applied in OOPS, neural network, NLP development and various types of programming. It is so unique and has a wide variety of Library functions. C++ is applied mostly in AI programming tasks because of its time-sensitive feature. It has minimum response time and a quick execution process that is important for developing games and search engines. It is reusable because of its inheritance and data hiding properties. It is widely used to solve AI statistical techniques. Java is another mostly used AI programming language, and it does not need any special platform for recompilation because of Virtual Machine Technology. It combines the features of C and C++ and makes it more simple and easy to debug. In addition, the Automatic memory manager in Java reduces the work of the developer. LISP is used in part of AI development. LISP has a specific macro system that alleviates implementation and exploration of multiple levels of Intellectual Intelligence. It is mostly applied in solving logic tasks and Machine learning. It favours Liberty and fast prototyping to programmers and makes LISP as more standard language and User-friendly in AI. PROLOG is used for basic algorithm automatic backtracking, tree-based structuring and Pattern matching, which is mandatory for AI. In addition, it is extensively applied in medical science.

• Conclusion

Artificial intelligence is successfully set its milestones in all industries such as e-commerce, biotechnology, diagnosis of diseases, military, mathematics and logistics, heavy industry, finance, transportation, telecommunication, aviation, digital marketing, telephone customer services, agriculture, and gaming.
Artificial intelligence holds a much higher significance and importance than what is read in this article. This will continue to grow in the future to come. Don’t miss out, get involved, and have fun with the technology as much as you can. Do write to us about how you felt about the article. Stay tuned to our blog.