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AI is a very broad umbrella term with applications varying from text analysis to robotics. Artificial Intelligence is all about decision making based on available data, be it self-driving cars, virtual personal assistants, calculating business investment risks, or examining medical samples. AI is all about doing human intelligence tasks but faster and with reduced error rate.
Machine learning is a subset of AI that makes software applications more accurate in predicting outcomes without having to be specially programmed. An application of artificial intelligence that automatically learns and improves over time when exposed to new data.
Data Science is not exactly a subset of machine learning but makes use of ML for data analysis and future predictions. Data Science is interdisciplinary in nature -an amalgamation of machine learning with other disciplines like cloud computing, big data analytics, statistics, and more.
Right, so you might have a question here? Aren’t AI and data science one and the same? The answer is a big NO. Data science gets solutions and results to specific business problems using AI as a tool.
If data science is to insights, machine learning is to predictions and artificial intelligence is to actions.
Imagine you are building a self-driving car, and you are working on solving the problem of stopping the car at stop signage boards. You would require skills from all three of these emerging fields –
Machine Learning -The foremost step will be to identify the presence of stop signs from the images of street-side objects. You will have to train a machine learning algorithm on a dataset of millions of images to predict which images have stop signs in them.
Artificial Intelligence -The moment the car identifies a stop sign ahead, it needs to take action of applying brakes, which is a problem of control theory.
Data Science – During street tests, you discover that the car’s performance is not good enough as it drives right by a stop signage board (false negative). Street test data analysis gives insight that false negatives are dependent on the time of the day – the car is most probably missing a stop signage board after sunset or before sunrise. This could have happened because most of the images in your dataset have been clicked in full daylight. Having gained insights, you can construct a better dataset that has nighttime images. You now move back to step 1 (machine learning) and re-train the model.
Data Science
Artificial Intelligence, Machine Learning, and Data Science are inextricably intertwined. Rather than giving a verdict on which one should you learn in 2019, we suggest before you get started with learning artificial intelligence subjects, master your skills in machine learning, data analytics, and data science. This will give you the power to pursue artificial intelligence and build a rewarding and lucrative career in either of these.
Me: “Hey Siri, what should I learn in 2019– AI, ML or Data Science?
Siri – The crystal ball is clouded, I can’t tell. AI, ML, and Data Science will remain the most in-demand skills.
My conversation with Apple’s virtual assistant very well sums up that having specialization in AI, ML and Data Science will make you most desirable to employers.
What’s in it for me? Why AI, ML, and Data Science are great skills to learn in 2019?
With so many articles doing rounds on the Internet that “AI and Robots will take over our Jobs.”. Do you fear AI will take your job and learning artificial intelligence and other interrelated skills might not be an intelligent move? You’re wrong, that’s not the real story.
AI is creating more jobs than it destroys with an overall increase of more than 2 million jobs by 2025. Machine Learning in Mining will take over boring tasks so humans can focus on high-level tasks.