Thank you for stopping by to read this. I appreciate you! I’m Tempted to introduce you to how Artificial Intelligence (AI) works, and its various use cases and applications. But I’m keeping it real simple in this post by just differentiating some of
the closely related terms and concepts of AI that most people are sort of getting it twisted. Artificial intelligence, machine learning, deep learning, and neural networks. These terms are sometimes used interchangeably, but they do not refer to the same thing.
AI, as people call it. Some people are like, yes, it’s the greatest technology of all time, and some people are like, this will be the
downfall of humanity. I’d say that neither of those responses would be correct. The reason I say that is because this is technology. It’s a very advanced technology, it helps us do things that we never could have done before. But the thing is AI technology or at least, for example, the basic perceptron and these sorts of mathematical techniques have existed since even before computers or calculators became popular.
So when we were creating these sorts of machine learning concepts and AI, people started to create literature and movies on the future of technology and computers. When we barely had any idea of not only where technology would go in the future, but also what technology really is. And Because of that, people have this very common misconception of artificial intelligence being the human mind within a computer, the human intelligence simulated wholly within a computer. But that couldn’t be farther from the truth. Machine learning or AI is not simulating a human mind, but what it does try and do, is it tries to open up new doors for computers. It tries to enable computers to understand certain kinds of data that they couldn’t
have understood before.
Who I’m I?
I know from the title most of my readers would be like, who’s this lady? She should be talking about the latest fashion, not this, well gone are those days. How does she even know about all this stuff? So here’s the deal. My supervisor from my final year project pushed me into deep-learning with no mercy. I had to swim all alone in that pool learning, un-learning and re-learning, with no physical support community. To cut long story short with support from quite a few mentors virtually explaining things. I started my machine learning project and finished in less than a month after suffering for like 2months. But, Alhamdulillah I’m very grateful for what my supervisor did and now I realized why she did what she did. Enough! Now let’s dive in.
Artificial intelligence is a branch of computer science dealing with a simulation of intelligent behavior. AI systems will typically demonstrate behaviors associated with human intelligence such as planning, learning, reasoning, problem-solving, knowledge representation, perception, motion, and manipulation, and to a lesser extent social intelligence and creativity.
I like to think of AI as augmented intelligence because I believe that AI should not attempt to replace human experts, but rather extend human capabilities and accomplish tasks that neither humans nor machines could do on their own. The internet has given us access to more information, faster. Distributed computing and IoT have led to massive amounts of data, and social networking has encouraged most of that data to be unstructured.
With Augmented Intelligence, we are putting information that subject matter experts need at their fingertips, and backing it with evidence so they can make informed decisions.
Machine learning is a subset of AI that uses computer algorithms to analyze data and make intelligent decisions based
on what it has learned, without being explicitly programmed. Machine learning algorithms are trained with large sets of data and
they learn from examples. They do not follow rules-based algorithms. Machine learning is what enables machines to solve
problems on their own and make accurate predictions using the provided data.
Deep learning is a specialized subset of Machine Learning that uses layered neural networks to simulate human decision-making. DL algorithms can label and categorize information and identify patterns. It is what enables AI systems to continuously learn on the job, and improve the quality and accuracy of results by determining whether decisions were correct.
Artificial Neural Networks
Artificial neural networks often referred to simply as neural networks take inspiration from biological neural networks, although they work quite a bit differently. A neural network in AI is a collection of small computing units called neurons that take incoming data and learn to make decisions over time. Neural networks are often layered deep and are the reason deep learning algorithms become more efficient as the datasets increase in volume, as opposed to other machine learning algorithms that may plateau as data increases.
So, now that you have a broad understanding of the differences between some key AI concepts, there is one more differentiation that is important to understand, that between artificial intelligence and data science.
Data science is the process and method for extracting knowledge and insights from large volumes of disparate data. It’s an interdisciplinary field involving mathematics, statistical analysis, data visualization, machine learning, and more. It’s what makes it possible for us to appropriate information, see patterns, find meaning from large volumes of data, and use it to make decisions
that drive business. Data Science can use many of the AI techniques to derive insight from data. For example, it could use
machine learning algorithms and even deep learning models to extract meaning and draw inferences from data.
There is some intersection between AI and data science, but one is not a subset of the other. Rather, data science is a broad term that encompasses the entire data processing methodology.
Well, AI includes everything that allows computers to learn how to solve problems and make intelligent decisions. Both AI and Data Science can involve the use of big data that is significantly large volumes of data.
Hoping you don’t find this too technical, more posts related to how you can dive into the field are coming up. Do enjoy and do share your thoughts in the comment box below. Thank You.