Artificial Intelligence

Undoubtedly, we are going through another AI summer where we see a rapid growth in Artificial Intelligence based interventions. Everyone is so keen on using applications like ChatGPT and want to know what’s the next big thing. On my last post, I was discussing the underlying technicalities of large language models (LLMs) which is the baseline of ChatGPT.

Through this post, I’m trying to give my opinion on some of the questions raised by my colleagues and peers recently. These are my own opinions and definitely there can be different interpretations.

What’s happening with AI right now?

Yes. AI is having a good time! 😀 Recently almost all the mainstream media started talking about AI and it’s good and bad. Even people who are not very interested in technology started their research on AI. In my point of view, it all happened since major tech companies and research institutes came out with AI base products that general public can use not only for business operations, but also for entertainment purposes. For example, AI base image generation tools (mostly based on artificial neural models as Dall-E) started getting popular in social media. ANN based image filters and chatting applications conquered all around social media.

All these fun stuffs didn’t come overnight. Those products have been under research and development for years. There have been more interesting stuff happening in research world and the problem was giving an easy accessibility of their abilities for general public.

Right now, we can see a clear motivation from enterprises to make AI more accessible and user-friendly. The barrier of having extensive understanding on mathematics and related theories to use AI and machine learning is getting reduced day by day.

Yes. I see that as a good trend.

ChatGPT is the only big thing happened right?

No, it’s not! ChatGPT is an application build upon the capabilities of GPT-3 which is a LLM. OpenAI has announced they are coming up with GPT-4 soon, which is going to be much more powerful in understanding human language and images.

With the large computation capabilities opening up with technological advancements like cloud, we can observe research is going towards training massive neural networks to understand the non-linear data such as natural language, image and videos. The recent advancements of architectures like Transformers (which was introduced in 2017), has making very big changes in computer vision too.

Applications such as GitHub Copilot, an AI pair programming application gives software developers the ability to code faster and produce more reliable outcome efficiently.

Microsoft announced, Copilot is going to be a part of their office365 suite, which is widely used in many industries. It’s going to be a definite big change for the way that people work with computers today.

I’m a tech folk. How AI is going to affect my career?

I work in the AI domain. The advancement of AI directly affects my career and I always keep myself busy reading and grabbing new trends as much as I could (which is impossible with this rate!)

AI developers or the machine learning engineers are not the only groups getting affected directly with these advancements. Remember how cloud technologies changed the way we operate in enterprises, completely eliminating the excessive usage of cumbersome networking devices and in-house servers. I feel something similar is going to happen with AI too. IT folks may have to adapt for using AI As a Service in application development life cycle. You can be a dinosaur. Let’s get updated!

I’m a non-tech person. How AI is going to affect me?

It’s not only for tech savvy folks. Since, the trend is all about making AI more accessible, the AI based interventions are going to be there in almost all the industries. Starting from retail business, the knowledge focused industries such as legal and education going too going to get affected with the ways of working.

If you are a non-tech person definitely you should research on the ways of using AI and machine learning in your career.

Can AI displace people from job?

Yes and no! According to a McKinsey study, Artificial intelligence could displace roughly 15% of workers, or 400 million people, worldwide between 2016 and 2030! Most the monotonous jobs will get replaced with robots and AI applications. For example, the manual labour needed for heavy lifting in factories will be replaced with computer vision assisted robots. Some monotonous tasks in domains like accounting will be replaced by automated applications powered with AI based applications.

In the meanwhile, new job will be generated to assist AI powered operations. The term data scientist was not widely used even a decade ago. Now it has become one of the most demanding professions. It’s just an example.

Isn’t excessive use of AI is harmful?

Excessive use of anything is harmful! It’s the same with AI too. There are many discussions going on related to regulating human-competitive machine intelligence. Enterprises are working towards concepts such as responsible AI to make sure AI systems are not harmful for the betterment of mankind and the nature.

To be frank, one can argue that AI base systems are reducing the critical thinking ability and the creative power of humans. In a way that can be true in some contexts. We should find ways to keep it as only a tool, but not totally rely on it.

Recently, an open letter titled “Pause Giant AI Experiments” was published enforcing the need of proper rule sets and legislations around AI systems. It was signed by profound scientists including Yoshua Bengio and known tech folks as Elon Musk.

Personally, I strongly believe a framework on regulating the usage of AI interventions should be there and the knowhow on such applications should be made widely available for general public.

That’s not something can be done overnight. Global organisations and governments should start working together soon as possible to make it happen.

What’s the future would look like with AI?

It’s something we actually can’t predict. There will be more AI applications which may have human-like abilities and even more than that in some domains. The way people work will change drastically with the wide accessibility of AI. As we use electricity today to make our lives easy, the well-managed AI systems will be assisting us with our daily chaos. Definitely we may have to keep our eyes open!

Artificial Intelligence, Machine Learning, Neural Networks, Deep Learning….

Most probably, the words on the top are the widely used and widely discussed buzz words today. Even the big companies use them to make their products appear more futuristic and “market candy” (Like a ‘tech giant’ recently introduced something called a ‘neural engine’)!

Though AI and related buzz words are so much popular, still there are some misconceptions with people on their definitions. One thing that clearly you should know is; AI, machine learning & deep learning is having a huge deviation from the field called “Big Data”. It’s true that some ML & DL experiments are using big data for training… but keep in mind that handling big data and doing operations with big data is a separate discipline.

So, what is Artificial Intelligence?

“Artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals.” – Wikipedia

Simple as that. If a system has been developed to perform the tasks that need human intelligence such as visual perception, speech recognition, decision making… these systems can be defined as a intelligent system or an so called AI!

The most famous “Turing Test” developed by Alan Turing (Yes. The Enigma guy in the Imitation Game movie!) proposed a way to evaluate the intelligent behavior of an AI system.

Turing Test

There are two closed rooms… let’s say A & B. in the room A… we have a human while in the room B we have a system. The interrogator; person C is given the task to identify in which room the human is. C is limited to use written questions to make the determination. If C fails to do it- the computer in room A can be defined as an AI! Though this test is not so valid for the intelligent systems we have today, it gives a basic idea on what AI is.

Then Machine Learning?

Machine learning is a sub component of AI, that consists of methods and algorithms allows the computer systems to statistically learn the patterns of data. Isn’t that statistics? No. Machine learning doesn’t rely on rule based programming (It means that a If-Else ladder is not ML 😀 ) where statistical modeling is mostly about formulation of relationships between data in the form of mathematical equations.

There are many machine learning algorithms out there. SVMs, decision trees, unsupervised methods like K-mean clustering and so-called neural networks.

That’s ma boy! Artificial Neural Networks?

Inspired by the neural networks we all have inside our body; artificial neural network systems “learn” to perform tasks by considering many examples. Simply, we show a thousand images of cute cats to a ANN and next time.. when the ANN sees a cat he is gonna yell.. “Hey it seems like a cat!”.

If you wanna know all the math and magic behind that… just Google! Tons of resources there.

Alright… then Deep Learning?

Yes! That’s deep! Imagine the typical vanilla neural networks as thin crust pizza… It’s having the input layer (the crust), one or two hidden layers (the thinly soft part in the middle) and the output layer (the topping). When it comes to Deep Learning or the deep neural networks, that’s DEEP PAN PIZZA!

DNNs are just like Deep Pan Pizzas

Deep Neural Networks consist of many hidden layers between the input layer and the output layer. Not only typical propagation operations, but also some add-ins (like pineapple) in the middle. Pooling layers, activation functions…. MANY!

So, the CNNs… RNNs…

You can have many flavors in Deep Pan Pizzas! Some are good for spicy lovers… some are good for meat lovers. Same with Deep Neural Networks. Many good researchers have found interesting ways of connecting the hidden layers (or baking the yummy middle) of DNNs. Some of them are very good in image interpretation while others are good in predicting values that involves time or the state. Convolutional Neural Networks, Recurrent Neural Networks are most famous flavors of this deep pan pizzas!

These deep pan pizzas have proven that they are able to perform some tasks with close-to-human accuracy and even sometimes with a higher accuracy than humans! deep-learning