When most people think about Artificial Intelligence, they instantly picture robots and machines like the Transformers and big machines that wreak havoc on earth. It is what high-budget movies have led us to believe but, it’s far from the actual picture. However enigmatic and impenetrable the science of Artificial Intelligence might sound; it is easy to understand when broken down into simpler terms.
In 1956, the term ‘Artificial Intelligence’ was coined by John McCarthy. He defined AI as:
“The science and engineering of making intelligent machines.”
Artificial intelligence (AI) is the ability of a computer or a computer-controlled system to perform tasks that would normally be performed by intelligent life forms. In its most basic form, it is an area that incorporates computer science and large databases to solve problems.
Based on the idea that a set of algorithms can be used to replicate actions and simulate human thoughts in machines, it is widely used to mimic anything from the simplest to the most complex tasks.
While it might still be science fiction for many, but you will be surprised to know that it has not been a new term for researchers and developers. Let us know a bit about the history of Artificial Intelligence before diving deeper into it. This flowchart depicts the timeline of advancements in the field of AI over the past century:
Before going further, let us answer some basic questions related to artificial intelligence.
AI vs Machine Learning vs Deep Learning
In today’s tech industry, these terms are amongst the buzzwords and often used by industry experts. While most people are unaware of the relation and difference between them, let us try to understand it in simple terms.
Machine Learning and Deep Learning fall under the umbrella of Artificial Intelligence. Furthermore, as seen in the diagram, Deep Learning is a subset of Machine Learning. As a result, AI, Machine Learning, and Deep Learning are subsets of one another.
To explain the difference between the three:
- AI is a technique that enables computers to behave like humans by mimicking their behaviour and thoughts.
- Machine Learning is a subset of AI which is used to program a machine to learn and predict outcomes based on its experience(data).
- Deep Learning is a specific field under Machine Learning, which utilises a neural network to attempt and stimulate behaviour similar to that of the human brain in processing data and decision making.
Apart from these, AI covers several other domains including – Neural Networks, Robotics, Expert Systems, Fuzzy Logic Systems, Natural Language Processing.
Click here to read more about these domains
How to get started?
STEP 1 – Learn Python & SQL
Start by learning a programming language. While there are a considerable number of languages that are used for programming worldwide, many prefer to start with Python for building AI models because its libraries are better suited for Machine Learning. SQL is a language that is used to acquire data and is easy to learn.
STEP 2 – Take up Machine Learning Courses
Several online courses are available that provide quality education and hands-on experience to begin your journey with.
STEP 3 – Learn Basics of Statistics and Mathematics
As most of the algorithms and models are used for handling data, it is useful to have clear fundamentals in Mathematics.
STEP 4 – Practice-Learn-Practice
Keep learning and practising consistently and at your own pace to improve every day and become an AI programmer. One can participate in online competitions like hackathons and Kaggle competitions to improve their skills.
INITIATIVES / OPPORTUNITIES IN AI
1) AI Podcast by NVIDIA – Weekly podcasts with some of the smartest people in the world to talk about today’s more trending topics and technologies.
2) DeepLearning.AI – It is an education technology company that seeks to equip the global workforce with world-class education, hands-on training, and a shared community to help them create an AI-powered future.
3) Google AI – The main aim of Google AI is to conduct research and create AI applications that help in solving problems everywhere. Students can access courses, blogs, guides and podcasts from the Education section, to learn for free and advance their skills.
4) IBM AutoAI – AutoAI with IBM Watson Studio can be used to find and deploy top-performing models within minutes.
5) INSPIRIT AI – It is an intensive program designed for High School students, to expose them to the technology of Artificial Intelligence, taught by a team of alumni and graduate students from MIT and Stanford.
6) Kaggle – It is the world’s largest community of Data Science and Machine Learning practitioners. It provides powerful tools and resources to help one in becoming an AI programmer.
7) Lobe – A product by Microsoft – Lobe is a free and easy to use tool for creating Machine Learning models.
8) Microsoft AI Classroom – It is a no-cost initiative launched by Microsoft in association with NASSCOM FutureSkills®, supported by GitHub to train students and future developers on future technologies.
9) OpenAI – Powered by Microsoft Azure – Based in San Francisco, they aim to create a secure artificial general intelligence (AGI) that benefits everyone.
10) UDACITY – The School of Artificial Intelligence – Provide professional online courses related to Artificial Intelligence and its industrial applications.
** Click on the underlined name to access the website link of the respective program **
AI ASSOCIATIONS
1) AI Student Society – It is a non-profit association founded by Data Science and Scientific Computing graduates of the University of Trieste, Italy, to create a thriving community for students in AI and Data Science.
2) DAMA International – The Global Data Management (DAMA) Community is a non-profit community that aims to promote the understanding, development and practice of data management.
3) Association for Uncertainty in Artificial Intelligence (AUAI) – It is a non-profit organization that develops research-based principles and applications in Artificial Intelligence.
4) Allen Institute for AI (AI2) – The main aim of this community is to contribute to social welfare through high-impact AI research and engineering.
5) Partnership on AI – The association aims at conducting AI research and organizing discussions, shares insights and creates educational material to promote the understanding of AI technologies across its various domains.
6) Association for Advancement of Artificial Intelligence (AAAI) – It is a non-profit scientific society that aims to promote responsible use and research in Artificial Intelligence.
7) International Society of Applied Intelligence (ISAI) – It is a non-profit organization that aims to publicise research on Applied Intelligent Systems and provide a forum for discussions and exchange of ideas.
8) International Association for Pattern Recognition (IAPR) – It is an international association of non-profit, scientific or professional organizations concerned with pattern recognition, computer vision and image processing.
9) AI Inclusive – It is an international organization that aims at improving the representation and participation of gender minority groups in AI.
10) World Economic Forum (WEF) Global AI Actions Alliance – It is a project of the World Economic Forum that aims to harness the potential of AI by accelerating the transparency and inclusivity in AI systems globally.
** Click on the underlined name to access the website link of the respective organization **
AI is one of the most dynamic and advanced disciplines of Computer Science today. It is now being used for real-time applications in the fields of medicine, engineering, marketing, defence and banking. It is the Gmail auto-reply prompter, Spotify music recommendations, Amazon product recommendations, the news headlines on our phones and of course. our precious AI assistants. AI applications are also being used to transform the education sector.
Often regarded as “The Skill of the Century”, learning Artificial Intelligence opens up a pool of futuristic opportunities that can impact the world. It is still growing and can only get better as it provides competitive advantages in all market sectors by preparing people for the future.
Sources:
- How to get started
- The difference in AI, ML, DL
- Images created using Canva, also included from Google Image search
Great article