Hi Reader!
Woke up this morning and realized it’s been a while I’ve written something in here and this not only decreased my views (hardly I’ve any), but also I’ve stopped learning and exploring new things. So today I’ve decided to write something on what is said to be our FUTURE!
Today, we hear a lot of heavy words like Artificial Intelligence, Machine Learning, Deep Learning, Neural Networks etc. and often seem to be used interchangeably. They are not quite the same thing, but the perception that they are can sometimes lead to some confusion. This blog will try to explain the differences between the two very hot buzzwords right now, AI and Machine Learning. This blog will be helpful for those who have just started exploring these buzzwords and for the one’s who are experts in these domains, KUDOS to them!
Let’s start by taking a one liner definition of the two terms:
Artificial Intelligence — Human Intelligence Exhibited by Machines
Machine Learning — An Approach to Achieve Artificial Intelligence
The concept of Artificial intelligence is broader than that of machine learning, the latter uses computers to imitate the cognitive human functions. Artificial intelligence, therefore, can be defined as machines carrying out various tasks based on algorithms in a perfectly intelligent way.
Machine learning is a subset of AI and its’ focus lies on the capability of machines to not only receive data sets but also learn and relearn for themselves, change the algorithms according to the information that they are processing.
Neural networks help in the training of computers to think like humans.(We’ll not go into the details of Neural Network)
For example, symbolic logic – rules engines, expert systems and knowledge graphs – could all be described as AI, and none of them are machine learning. One aspect that separates machine learning from the knowledge graphs and expert systems is its ability to modify itself when exposed to more data; i.e. machine learning is dynamic and does not require human intervention to make certain changes. That makes it less brittle, and less reliant on human experts.
“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.” –Tom Mitchell
Broadly the algorithms of AI/ML are classified into three categories:
1.Supervised Learning
This algorithm consist of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data. Examples of Supervised Learning: Regression, Decision Tree, Random Forest, KNN, Logistic Regression etc.
2.Unsupervised Learning
In this algorithm, we do not have any target or outcome variable to predict / estimate. It is used for clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention. Examples of Unsupervised Learning: Apriori algorithm, K-means.
3.Reinforcement Learning
Using this algorithm, the machine is trained to make specific decisions. It works this way: the machine is exposed to an environment where it trains itself continually using trial and error. This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions. Example of Reinforcement Learning: Markov Decision Process.
List Of Common algorithms are:
– Naive Bayes
– kNN
– K-Means
– Decision Tree etc.
From programming perspective, python and R are the languages which are commonly used because they have enormous and reliable libraries.
AI and IoT are Inextricably Intertwined
I think of the relationship between AI and IoT much like the relationship between the human brain and body.
Our bodies collect sensory input such as sight, sound, and touch. Our brains take that data and makes sense of it, turning light into recognizable objects and turning sounds into understandable speech. Our brains then make decisions, sending signals back out to the body to command movements like picking up an object or speaking.
All of the connected sensors that make up the Internet of Things are like our bodies, they provide the raw data of what’s going on in the world. Artificial intelligence is like our brain, making sense of that data and deciding what actions to perform. And the connected devices of IoT are again like our bodies, carrying out physical actions or communicating to others.
Unleashing Each Other’s Potential
The value and the promises of both AI and IoT are being realized because of the other.
Machine learning has led to huge leaps for AI in recent years. Machine learning require massive amounts of data to work, and this data is being collected by the billions of sensors that are continuing to come online in the Internet of Things.
IoT makes better AI.
We hear a lot of negative things about AI and Machine Learning that it will destroy our future and other bullshit.Everything in this world comes with a trade-off, but with AI and ML, I can see more of good things.
AI Plus Human Intelligence Is The Future Of Work!
Happy Reading,
Chao!
n00b301 
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