Artificial Intelligence-The Complete Guide(2021)

AI is a machine with the ability to solve problems that are usually done by us humans with our natural intelligence a computer will demonstrate a form of intelligence when it learns how to improve itself at solving these real-life problems. Artificial intelligence is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals. Remember artificial and machine learning are not the same. If I were to name a technology that completely changed the 21st century, I would name Artificial Intelligence and lets know more about it. 

Lets dive Right In.

How much people know about AI

 A survey conducted  by Mozilla Foundation in which they asked people about AI gave the following results.-

how much people know about Artificial Intelligence

Since last few years Artificial Intelligence has gained massive popularity but very few people actually know what is Artificial Intelligence because of World Wide Web .Today you are going to know everything about AI from how it originated to where is at current scenario                     

Types of Artificial Intelligence

Type I Artificial Intelligence

Narrow AI or Weak AI 

Weak Artificial Intelligence is AI that executes a restricted piece of psyche, its is centered around one tight undertaking. Slender AI is a term used to portray Artificial Intelligence frameworks that are indicated to deal with a solitary or restricted undertaking.

Examples of Narrow Artificial Intelligence

The direct opposite to Narrow AI, in some cases alluded to as powerless AI, is called solid AI. Solid AI, in contrast to Narrow AI, is equipped for taking care of a wide scope of undertakings instead of one specific errand or issue.Solid AI, in contrast to Narrow AI, is fit for dealing with a wide scope of assignments as opposed to one specific errand or issue. This variety of computerized reasoning can be generally conceptualized as an establishment for neural organizations copying awareness or cognizance.

General AI

General AI is the theoretical knowledge of a machine that has the ability to comprehend or become familiar with any intelligent assignment that an individual can. It is an essential objective of some man-made consciousness research and a typical subject in sci-fi and fates contemplates. Fake general insight (AGI), additionally alluded to as solid AI or profound AI, is the idea of a machine with the general knowledge that copies human knowledge as well as to conduct, with the capacity to learn and apply its knowledge to take care of any issue. AGI can think, comprehend, and act in a way that is vague from that of a human in some random circumstance.

Strong AI 

Robot Sophia

Strong AI,for eg Robot Sofia otherwise called counterfeit general insight (AGI) or general AI, is a hypothetical type of AI used to portray a specific attitude of AI improvement. In the event that analysts can create Strong AI, the machine would require an insight equivalent to people; it would have a mindful awareness that can take care of issues, learn, and plan for what's to come.

General AI intends to make shrewd machines that are indistinct from the human brain. However, much the same as a kid, the AI machine would need to learn through information and encounters, continually advancing a lot its capacities after some time.
While AI analysts in both scholarly community and private areas are put resources into the production of counterfeit general insight (AGI), it just exists today as a hypothetical idea versus a substantial reality.

Type II Artificial Intelligence

Reactive Machines 

This is the most basic type of AI which neither stores memory nor uses its past experience to make its future moves. The best example would The  IBM's Deep Blue chess-playing supercomputer which defeated international chess champion ,Gary Kasparov, at chess. It is a reactive machine that sees the pieces on a chessboard and reacts to them. It cannot refer to any of its prior experiences, and cannot improve with practice. 

IBM Deep Blue vs Gary Kasparov

This type of Artificial Intelligence directly reacts to surroundings without taking the prior experiences or history in the count. Another example of this type is Google's AlphaGo defeated  the best human at its own game. These have a specific algorithm which they follow. 

Google's AlphaGO AI model

Limited Memory

These type of AI machines can retain the memory of a short time and cannot add it to their experience. They can only use this data for a specific time. Self-driving cars are a good example of limited memory AI which keep in mind the cars around them, distance from other cars, speed limits etc all this information helps in navigating roads. 

Autonomous car

With Limited Memory, machine learning architecture becomes a little more complex. Every machine learning model requires limited memory to be created, but the model can get deployed as a reactive machine type.

This Type II class contains machines that can investigate the past. Self-driving vehicles do a portion of this as of now. For instance, they notice other vehicles' speed and heading. That isn't possible in an only one second, yet rather requires distinguishing explicit items and checking them after some time. The self driving cars operate on LIDAR(Light detection and Ranging).

These perceptions are added to oneself driving vehicles' prearranged portrayals of the world, which likewise incorporate path markings, traffic signals and other significant components, similar to bends in the street. They're incorporated when the vehicle chooses when to move to another lane, to abstain from removing another driver or being hit by a close-by vehicle.

Theory of Mind

These types use analogical reasoning and are those who think like us, under us and their environment. Theory of Mind refers to the ability to attribute mental states such as beliefs, desires, goals, and intentions to others, and to understand that these states are different from one's own. ... A theory of mind makes it possible to understand emotions, infer intentions, and predict behavior.Theory of Mind AI includes Machine Learning systems, robotics and programs their decisions in languages that human beings understand.

Self-Awareness 

The final step of AI development is to build systems that can form representations about themselves. Ultimately, we AI researchers will have to not only understand consciousness but build machines that have it.

This is, in a sense, an extension of the “theory of mind” possessed by Type III artificial bits of intelligence. Consciousness is also called “self-awareness” for a reason. (“I want that item” is a very different statement from “I know I want that item.”) Conscious beings are aware of themselves, know about their internal states, and can predict feelings of others. We assume someone honking behind us in traffic is angry or impatient because that’s how we feel when we honk at others. Without a theory of mind, we could not make those sorts of inferences.

Applications of Artificial Intelligence 

Healthcare

AI in healthcare

Artificial Intelligence and high-performance computing are redesigning healthcare bringing in a new era of precision medicine. The healthcare industry has quickly taken advantage of artificial intelligence, machine learning and deep learning. Medical applications are using them to accelerate and improve patient outcomes.

Cloud computing in Healthcare

Automation, cloud technology and streamlined IT are more important than ever before. Machine learning diagnosis conditions through medical imaging crowdsource medical data and even suggest treatments and steps in drug development. This matters when diagnosis and treatment depend on quickly and accurately interpreting MRIs, CT scans and x-rays to identify tumors, fractures and medical conditions and with advances in deep learning driven by NVIDIA's powerful GPU accelerators the healthcare industry is heading towards even more sophisticated applications such as personalized medicine, wearable medical devices and automated robotic surgery.

Heart Scanning with machine learning

With researchers and startups already beginning to improve the accuracy of medical imaging and carry out analysis at unprecedented speeds researchers are using deep learning to compare patients with a broader population predicting heart conditions and cancerous genetic mutations before they even occur with significant implications for treatment options.

Finance

Applications of Artificial Intelligence in finance

Automation: It enables organisations to boost productivity and cut operational cost. Statistics say that AI helps companies save 70% of the cost associated with the competitive task.

Automation in Business Process

Credit Decisions: An AI-based software that helps banks access potential barriers it immediately analyzes countless factors thus saving costs and making the process much faster. Sounds exciting, doesn't it. 

Trading:AI-driven trading systems can analyze massive amounts of data much quicker. You won't believe it but predictions made by AI algorithms are much more accurate because they can analyze a lot of historical data. Moreover, AI can analyze specific investors long term and short term goals to provide recommendations on the strongest portfolios.

AI in trading

Risk Management: AI can handle risk management task much more efficiently and analyze various financial activities in real-time. 

Risk Management with Artificial |Intelligence

Fraud Prevention: AI-driven fraud detection tools can analyze a client's behavior track their locations and determine their purchasing habits therefore they can quickly detect any unusual activities.

Fraud Prevention with AI

Personalized banking: AI-powered chat-bots minimize the workload of call centres. There are also many apps that offer personalized financial advice so that users can achieve their financial goals, track regular expenses and purchasing habits. 

AI-Powered Chatbot

Agriculture

Smart-Farming

Infield Monitoring with computer vision: Artificial Intelligence can be used to monitor the health and progress of crops in the field. Pests are a major issue in agriculture. Being one of the main sources in the reduction of crops.                                                           

AI monitoring with computer vision

Pest causes two problems in agriculture, the first is by causing by damaging crops and food production. Second is indirect damage, some insects are able to transmit bacterial, viral or fungal infection to crops which can spread quite rapidly resulting in major losses this is where AI comes in with the use of computer vision through drone we are able to identify harmful pests in crops, once identified the drone will proceed to spray the crops with pesticides.

Pests

With the use of computer vision, the farmers will also be able to know when the disease is spotted. Early detection of diseases reduces the chances of spread and ensures better quality control computer vision is also used to check on crop readiness and also used to monitor livestock.

Livestock Monitoring with IOT

 Livestock can be monitored 24 7 and checked for sicknesses or diseases which can help farmers take action immediately when spotted computer vision can also be applied to the problem of soil diagnosis startups such as p-e-a-t have developed an algorithm that can identify the strength and wellness of soil the goal is to reduce the chances of growing underdeveloped crops while optimizing the potential for healthy crop production.        

Predictive Analytics: The current state of agriculture involves a lot of guesswork with 
farmers applying water and pesticides uniformly to crops. The issue with this is there may be some plants in need of pesticides or water other plants will not benefit from the additional water or pesticides this causes a massive waste in both water and pesticides.

Artificial Intelligence in agriculture

 but with the help of data-driven agriculture, farmers are able to utilize precision farming which detects and identifies crops that require water or pesticides and provide it only to the plants that are in need of it this a substantial method will substantially reduce cost and wastage of water and pesticides.

Spraying Pesticide with Machines

With the use of big data, we are able to use it as actionable insights helping farmers make decisions in order to improve sustainability efficiency reduce costs and increase profitability this consists of collecting data from soil sensors, GPS, equipped tractors and other external sources such as local weather data this allows farmers to drastically increase productivity while decreasing manpower.  

Internet of Things in Farming

Autonomous Robots: autonomous robots one of AI's major applications in agriculture
are autonomous robots, they help increase productivity while allowing farmers to focus on more important aspects.
Harvesting and picking is one of the most popular robotic applications in agriculture due to the accuracy and speed that robots can achieve to improve the size of yields and reduce waste from crops being left in the field 

Harvesting with Artificial Intelligence

With the use of autonomous robots combined with computer vision the machine will be able to identify the ripeness of the crops and pick based on a few merits such as readiness disease-free and many more criteria so there you have it it's safe to say that ai's applications in agriculture have come a long way and be needed to feed the world's growing population.   

Crop Monitoring with AI

Advantages and disadvantages of Artificial Intelligence 

Advantages

  • AI causes Reduction in human error.  
  • AI operates 24x7 without interruption or breaks and has no downtime.
  • AI augments the capabilities of disabled individuals. 
  • AI leads to new inventions. 
  • AI saves time by automation. 
  • AI gives much more accurate results.    

Disadvantages 

  • AI has taken jobs away from people. 
  • AI has high costs of implementation. 
  • AI can't replace humans. 
  • AI Machines can easily lead to destruction if the implementation of machine put in the wrong hands the results are hazardous for human being. To know more read this book Life 3.0 from Max Tegmark .


History of A.I.

The official idea and definition of Artificial Intelligence were first coined by Jay McCartney in 1955 at the Dartmouth Conference of course of that plenty of research work done on A.I. such as Alan Turing before this but they were working on was an undefined field before 1955 okay so here's what McCarthy proposed, "Every aspect of learning or any feature of intelligence can in principle be so precisely described that a machine can stimulate it. An attempt would be made to find out how many machines use language, form abstractions and concepts, solve kinds of problems now served for humans, and improve themselves."

So in definition, we heard intelligence but what exactly is intelligence according to Jack Copeland who is written several books on AI the most important factors of AI are :

1. Generalization learning i.e. learning that enables the learner to be able to perform better in situations not previously encountered.

2. Reasoning: To reason is conclude appropriate to the situations in hand.

3. Problem solving: Given such and such data then find X.

4. Perception: Analyzing asks and environment and analyzing features and relationships between the objects self-driving cars are an example.

5. Language understanding: understanding language and syntax rules similar to a human.

So now we have an understanding of A.I. and intelligence so to bring it together a bit and solidify the concept in your mind of what Artificial Intelligence is here's a few examples of A.I.
machine learning, computer vision, natural language processing, robotics, pattern recognition and knowledge management. 

The Seven Patterns of Artificial Intelligence

The seven patterns of AI

1. Hyper-Personalisation

2. Recognition.                                                           

3. Conversion and human Interaction. 

4. Predictive Analysis and Decision Making. 

5. Goal-Driven Systems

6. Autonomous Systems

7. Patterns and Anomalies

Now I’d Like to Hear From You

 hope you found this AI guide helpful.

Now I’d like to hear what you have to say:

Which portion today’s post did you like the most ?

Did I miss anything in any part you wanted me to write about ?

Or maybe you wanted to know more about some section.

Either way, let me know by leaving a comment below right now.

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