As a concept, Artificial Intelligence has time and again pushed its boundaries – be it to create medical drugs faster or explore space science. However, the core idea remains the same – It is always about machines that can mimic human thinking and reasoning, problem-solving, show action, and be socially adaptable. If you want to learn artificial intelligence, there is no better time than now, because believe it or not, AI is the force behind the current industrial revolution.
Artificial Intelligence is defined as one of the most complicated creations by humans. Yet this field continues to be largely unexplored. All the AI applications that we see today are just the tip of the iceberg. Despite that, the impact of artificial intelligence is grabbing eyeballs every passing day. The possibilities of artificial intelligence are numerous and potentially huge. However, to learn artificial intelligence, you need to understand its classifications.
Types of Artificial Intelligence
Researchers are tirelessly working towards making machines so capable that they emulate human-like functioning effortlessly. The extent to which a machine can replicate a human determines the types of artificial intelligence. Broadly, artificial intelligence is categorized in two ways –
First is the ability of a machine to ‘think’ and maybe ‘feel’ like humans. There are four classifications under this type:
- Reactive machines
Reactive machines are the oldest form of artificial intelligence. These have limited capabilities and mostly resemble the human brain’s ability to respond to various types of stimuli. These, unlike modern machines, do not have memory-based functionalities. This means that these machines do not learn from their previous experiences and do not incorporate them. In short, these machines cannot ‘learn.’ Ideally, reactive machines are used to generate an automated response for a certain combination of inputs. A popular example is IBM’s Deep Blue, a machine that defeated Chess Grandmaster Garry Kasparov in 1997 by making calculated moves as pre-fed.
- Machines with limited memory
One step ahead of the reactive machines, these machines had the ability to learn from historical data and make decisions. All applications of AI that we see today come under this category. For instance, an image rendition AI machine is trained using thousands of reference images. When a new image is brought under the scanner, it uses its pre-fed reference images to make a match and understand its contents. Popular examples are the present-day virtual assistants.
- The theory of mind
This type of artificial intelligence hasn’t made it to practical implementation, unlike the above two. Theory of mind AI is the futuristic AI concept that is being innovated. It is being developed to understand the entity with which it interacts by discerning its beliefs, thought processes, needs, and emotions. Although artificial emotional intelligence is already gaining pace, it will take more time for researchers to perfect AI theory.
- Self-aware Artificial Intelligence
Another type of AI still in the making is self-aware intelligence. In fact, this concept exists only hypothetically as of now. The idea is to develop AI in a self-explanatory way – i.e., awareness in a human brain. This could be termed as the ultimate goal of AI researchers – to develop a self-aware machine.
The second broad classification is actually the classification of the technology. Primary there are three types of AI in this type –
- ANI or Artificial Narrow Intelligence: This represents almost all the AI applications that exist as of today. These machines are programmed to do exactly a single task.
- AGI or Artificial General Intelligence: This represents the ability of an AI agent to understand and function as a human.
- ASI or Artificial Super-intelligence: The development of ASI will surely take AI to newer heights as and when it comes into existence. The concept is to replicate multi-faceted human intelligence that will perform exceedingly well with enhanced memory and faster data processing.
Artificial intelligence is rapidly evolving. Reports, amidst the novel coronavirus pandemic, show that the global artificial intelligence market is predicted to reach $99.94 billion by 2023. This number is not surprising, given that the total number of AI startups has grown by 14 times in over two decades, and about 36% of executives look to incorporate AI in their internal business operations.
This clearly points to the evolving dynamics in the AI job domain. Businesses recognize AI’s power and are constantly looking for skilled people to fill in a large gap. If you thought AI would end up killing jobs, you know the half story. The fact that robotics can replace several job roles also comes riding high on the flip fact that – many new job roles are coming up for which skilled people are limited. So, put on your learning cap because this is the right time to learn artificial intelligence.
How to get started with Artificial Intelligence?
Before I tell you how to learn artificial intelligence from scratch, you must know about a few terms used interchangeably with AI. Terms like machine learning, data science, and artificial intelligence are often used interchangeably. But they are all distinctly different from one another from a micro-perspective.
On a broader level, Data Science incorporates several concepts like machine learning, statistics, AI, et al. Data science revolves around creating a powerful algorithm that receives input and generates an output based on the predictive model.
In layman’s terms, Artificial Intelligence is based on machine learning (the ability of machines to learn), and machine learning is based on data science (the ability of machines to learn and process data inputs and generate outputs). So, Data Science is the umbrella term that includes AI and machine learning.
So, what should I learn first – AI or ML?
Artificial intelligence and machine learning (ML) are often used together, sometimes even interchangeably. However, AI is about machines that can perform a task like humans, and ML is the source from where computers ‘learn’ data and make predictions without being explicitly told how to do it. With every new data set, a machine learning algorithm adjusts its predictions.
In other words, ML is a sub-category of AI. The ML algorithms enable a machine to perform a specific task well, but they cannot do tasks for which they are not trained. For instance, a machine learning algorithm used to recommend courses to people will not recommend videos unless data related to how people search for videos are added.
So when you ask what I should learn first – AI or ML, I’d recommend starting with ML. For more insights, follow our guide on How to learn AI and Machine Learning by yourself.
Now that you know how to learn artificial intelligence from scratch or, rather, should I say, you now know the starting point – I will quickly address another common query that I hear – why learn artificial intelligence.
Why learn artificial intelligence?
Way back in 1955, Artificial Intelligence was coined by the father of AI- John McCarthy. He was the pioneer to set the stage for future researchers to explore all the possibilities of training machines to think and reason like humans.
Fast-forward to the present day, we see the application of AI in almost everything – be it the automated chatbots on websites and apps or in cyber defense or converting data in texts, et al.
Learning AI is not just a fancy idea. If you are good with mathematics and love data, AI is for you.
AI has managed to disrupt the technology industry but has also resulted in huge skill gaps. Businesses are continuously searching for skilled AI engineers who can handle business automation and work around data-based predictive models.
They need experts who can handle quintillion bytes of data generated and acquired by organizations every day. As a data expert, you will use your AI and ML skills to dig out crucial information from this data to enable a business to make a data-driven decision.
In short, when you look for jobs, you will apply for data analyst roles as well, but roles that have AI and ML as their required skill sets.
AI, Data science, machine learning – all may seem confusing to you right now because each one at one point overlaps with the other. Please read our detailed guide to understand the differences (and similarities) between these three concepts for clarity.
Scope + Tips of AI for beginners
AI is evolving rapidly, which is why having a career in AI has a huge potential. As a beginner, to learn artificial intelligence, you must be aware of the three main stages of AI (and machine learning)-
- Machine learning consisting of intelligent systems that use algorithms to learn from historical data.
- Machine intelligence, the stage where all current AI applications exist. In this stage, machines learn from experiences based on false algorithms – an evolved form of ML that improves the machine’s cognitive abilities.
- Machine consciousness is a stage where machines can do self-learning from their past experiences without any external data set.
If you find yourself intrigued by this field and want to get your hands on an artificial intelligence tutorial – here’s what you need to know. Each industry has specific role requirements, but at the core, you must be good with communications, mathematics, and computing skills. These are basic requirements before you jump onto any artificial intelligence tutorials or AI courses.
A beginner can shine in this domain as much as an experienced person – all it takes is the right kind of skills.
I assume you have some questions in mind already, like should I learn AI with Python? Should I learn artificial intelligence online? Which course is perfect for me? What should I learn before I learn artificial intelligence? And so on.
In this FAQ section, I will go over a few of such commonly asked questions to help you with all your queries so that you can have clarity on where, how, and with what to start.
- Who can learn AI?
Theoretically, anyone interested can learn artificial intelligence. However, there is more to just being interested. You must have a basic knowledge of mathematics, programming language, and data analytics. As a beginner, you need not know all of these at advanced levels, but a basic overview is essential to kickstart your AI career. In case you don’t have this basic knowledge and still want to pursue a career in AI, it will be difficult for you but surely not impossible.
- How can I self-study artificial intelligence?
It is surely possible to self-learn with the right kind of resources. Opting to learn AI online is a ubiquitous choice since people want to learn at their comfort and pace. To know how you can self learn AI, read our guide on the same.
- Is learning AI easy?
It depends on your approach. There is nothing as very hard or very easy – if you have basic knowledge prior to learning AI, it may be easier for you than for someone trying to acquire all the basic skills. There is no one way to learn AI. It is important you take the approach that is most suited for you. That will make your path easier.
- Can I learn AI without knowing to code?
Despite talking about knowledge of computer programming, you can learn AI without coding. However, you will not be able to build an application from scratch. There are many online courses or artificial intelligence tutorials that offer AI courses without the pre-requisite of coding. However, if you look carefully, almost all course materials include a section on how to learn AI with Python or any other such programming language. To sum it up, you can start learning AI without knowing to code because eventually, you will be taught its basics.
- How can I get a job with AI?
If you have the right skills and are confident with it, getting a job with AI is not rocket science. Like I repeatedly said, the skill gap is huge in the market. Businesses are looking for experts in AI and ML frantically, and if you can fit into their requirements, there is no other career as lucrative as that in AI.
- Is AI a good career option?
Yes. Undoubtedly, AI is an amazing career option. However, getting into a role that gives you enough scope to explore your skills and hone them largely impacts your career path. The job demand is high and skilled people are limited – so if you can tap into the right opportunity, you are good to go.
Since you are looking for a career in AI, you must also be familiar with the concepts of R, Hadoop, and deep learning. In the realm of AI, once an open-source community of coders and data, scientists would trace thousands of line codes for various applications that beginners would use. But times are changing. The new demand is for smart AI engineers who can supervise and steer machines to learn fasters and deliver real-time results. If you want to learn artificial intelligence, aim to become a smart one. To start, you can explore our AI online courses and also classroom training sessions on AnalytixLabs.
If you have any other queries, write them in the comments section. Till then, all the best.