AI – Artificial Intelligence refers to the intelligence demonstrated by computers. In today’s world, AI has become highly popular. It is the replication of human intelligence in computers that have been programmed to learn and replicate human activities. These computers can learn from their mistakes and do jobs like humans. AI will have a significant influence on our quality of life as it develops. And It’s only natural that everyone nowadays wants to engage with AI technology in some way. weather as a consumer or as a professional in the field.
Introduction to Artificial Intelligence
What is Artificial Intelligence? The quick answer is that it depends on who you ask.
A person with only a basic grasp of technology might associate it with robots. AI, they believe, is a Robocop figure that can act and think for itself.
If you ask an AI researcher what AI is. He or She will tell you that it is a set of algorithms that can create outcomes without being explicitly directed to do so. And they’d all be correct.
To summarise, the meaning of AI is:
- An intelligent entity created by humans.
- Capable of performing tasks intelligently without being explicitly instructed.
- Capable of thinking and acting rationally and humanely.
How do we measure if AI is acting like a human?
Even if we get to the point where an AI can behave like a human. How can we be sure it will do so in the future? The following criteria can be used to assess an AI entity’s human likeness:
- Turing Test
- The Cognitive Modelling Approach
- The Law of Thought Approach
- The Rational Agent Approach
How does AI Works?
Building an AI system is a complex process of reverse engineering human traits and capabilities in a machine and then leveraging its computing strength to outperform our abilities.
To understand fully how AI works. One must first dig into the many sub-domains of AI and understand how those domains maybe apply to various industries of the industry.
- Machine Learning (ML): ML teaches a computer how to make inferences and conclusions based on previous experience. It recognizes patterns, analyses previous data, and infers the meaning of these data points without relying on human experience to draw a decision. This automation of reaching conclusions by analyzing data saves firms time and allows them to make better decisions.
- Deep Learning: DL is a machine learning approach. It trains a machine to categorize, infer, and predict outcomes by processing inputs through layers.
- Neural Networks: Human neural cells and Neural Networks both function on the same principles. They are a set of algorithms that capture the relationship between numerous underpinning variables. And analyze the information in the same way that a human brain does.
- Natural Language Processing (NLP): NLP is the science of a machine reading, understanding, and interpreting a language. When a computer understands what the user is trying to say, it reacts appropriately.
- Computer Vision (CV): CV algorithms attempt to discover a picture by splitting it and analyzing various elements of the objects. This enhances the machine’s classification and learning from a group of photos, allowing it to make better output decisions based on prior observations.
- Cognitive Computing: Cognitive computing algorithms attempt to emulate the human brain by analyzing text, voice, pictures, and objects. In the same way, as humans do, and then attempting to produce the appropriate output.
Where is AI Used?
AI is being applied in a variety of fields to get insights into user behavior and provide data-driven suggestions.
Google’s predictive search algorithm. For eg: Analyze user data from the past to forecast what a user would put next in the search field.
Netflix leverages previous user data to suggest what movie a user should watch next. Keeping them hooked on the site and increasing their viewing duration.
Facebook utilizes historical user data to automatically propose tags for your friends based on their facial traits in their photos. Large corporations employ AI to make the lives of their customers easier.
AI’s applications are widely classified as data processing. Which includes the following:
- Searching within data, and optimizing the search to give the most relevant results.
- Logic-chains for if-then reasoning, that can be applied to execute a string of commands based on parameters.
- Pattern-detection to identify significant patterns in large data set for unique insights.
- Applied probabilistic models for predicting future outcomes.
Advantages of Artificial Intelligence
There’s no doubt that technological advancements have improved our lives. AI and other technologies have taken over everything from music suggestions to map directions, mobile banking to fraud protection. The line between development and destruction is fine. There are usually two sides to every coin, and AI is no exception. Take a look at some of the benefits of AI.
- High Accuracy with fewer errors: AI devices or systems are less prone to mistakes and have higher accuracy. Since they make judgments based on prior knowledge or experience.
- High-Speed: AI systems may be incredibly fast and make quick decisions, and as a result. They can beat a chess champion in a game of chess.
- High reliability: AI robots are extremely dependable and can repeat actions with great precision several times.
- Useful for risky areas: In instances where employing a person would be unsafe. Such as defusing a bomb or researching the ocean below, AI computers can be useful.
- Digital Assistant: AI may be highly beneficial in providing consumers with digital assistants. For eg: numerous E-commerce websites are presently using AI technology to offer items based on client requirements.
- Useful as a public utility: AI may be highly valuable for public utilities such as self-driving cars. Which can make our journeys safer and more convenient. Face recognition for security, natural language processing, which allows us to speak with humans in our own language, and so on.
Disadvantages of Artificial Intelligence
Every technology has drawbacks, and AI is no exception. Despite being such a beneficial technology, it still has several drawbacks that we must consider when developing an AI system. The following are some of AI’s drawbacks:
- High Cost: AI’s hardware and software needs are quite expensive. As it requires a lot of maintenance to match current global requirements.
- Can’t think out of the box: Even though AI is allowing us to create smarter machines. They cannot work out of the box since robots will only accomplish the task for which they have been trained or designed.
- No feelings and emotions: AI machines can be excellent performers. But they lack emotion and hence cannot build emotional attachments with humans. They can also be dangerous to users if proper measures are not followed.
- Increase dependency on machines: People are becoming more reliant on electronics as technology advances, and as a result, their mental capabilities are decreasing.
- No Original Creativity: Humans are very creative and can come up with new ideas. But AI computers will never be able to match this level of intellect or be as creative and imaginative as humans.
Prerequisites for AI
As a beginner, here are some essential requirements that will assist you in getting started with AI.
- A stronghold on Mathematics such as Calculus, Statistics, and probability.
- A good amount of experience in programming languages such as Java, or Python.
- A stronghold in understanding and writing algorithms.
- A strong background in data analytics skills.
- A good amount of knowledge in discrete mathematics.
- The will to learn ML languages.