Know the difference between the buzzwords “AI” “ML” “Deep Learning” “Data Science”
Artificial intelligence is one of the most misused terms in tech today, and a new study apparently confirms how hyped the technology has become.
“According to the survey from London venture capital firm MMC, 40 percent of European startups that are classified as AI companies don’t actually use artificial intelligence”
It seems the world as become obsessed with these buzzwords AI, ML, Deep Learning, Data Science Even though extensively used, even tech people often confuse them with what each of the words means, may be because these terms are not quite concrete in the tech industry and are quite often used interchangeably.
The reason for this confusion could be multi-pronged – starting from a lack of clear understanding of the concepts, or even businesses trying to use these hype words to promote their operations; thereby increases sales & revenues.
Through this article I will try to give a simpler overview of these buzzwords and if you too are one of them who get confuse between these, then this article will resolve it for you.
1. Artificial Intelligence (AI)
The term artificial intelligence was coined in 1956.
AI leverages computers to think like humans and emphasizes the creation of intelligent machines that mimic decision-making capabilities of the human mind.
AI works by combining large amount of data, iterative processing and intelligent algorithms which makes possible for machines to learn from experience, adjust to new inputs and perform human-like tasks.
In a nutshell AI enables machine to mimic human behaviour.
2. Machine Learning (ML)
The term Machine Learning was coined in the late 1960s.
ML is a subset of artificial intelligence where computers learn from data and improve from experience without being explicitly programmed.
It uses statistical tools and methods to enable machines to improve with experience and make data-driven decisions to carry out any kind of task. It’s like teaching machines using examples.
In a nutshell ML is learning with experience using data
3. Deep Learning (DL)
The term Deep Learning was coined in the late 1980s.
Deep Learning is a special area in Machine Learning which is based on Artificial Neural Networks architecture (inspired by the functionality of our brain cells called neurons). ANN uses layers of interconnected nodes called neurons that work together to process and learn from the input data.
Computer programs that use deep learning go through much the same process as a toddler learning to identify any thing.
With Deep Learning humans have made most of the advances that we hear about in AI in the applications of Siri, Self-Driving Cars, Speech Recognition, etc.
In a nutshell DL is self learn with more data using Artificial Neural Networks.
4. Data Science (DS)
Data Science is a quite old concept but the term was popularized in 2008 by DJ Patil and Jeff Hammerbacher and became a buzzword, and eventually a part of the language.
Data science is a field of studying data with scientific methods, processes, algorithms, and systems to extract knowledge and meaningful insights to reach actionable conclusions.
This analysis helps data scientists to ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results. Data science is considered a discipline, while data scientists are the practitioners within that field
In a nutshell DS is understanding and finding hidden insights in data to reach actionable conclusions
Now you might have a got a fair idea about these terms and why these are often used interchangeably by many people due to similarities, although there is a fair amount of correlation between the two; still the terms & their concepts are not similar to each other.
In an actual sense, artificial intelligence is a super-set of machine learning as illustrated in the figure above. Many of the AI products we see around us today rely heavily on deep learning and natural language processing ( NLP is a branch of artificial intelligence that helps computers understand, interpret and manipulate human language).
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