As machines eclipse human capabilities, we find ourselves searching for meaning in a world no longer centered on us.
While the wave of machines taking over human tasks is growing exponentially, it is being predicted by some of the global experts that 82% of global companies are either using or exploring the use of Artificial Intelligence (AI) in their organization.
Over the years, AI has become a driving force in reducing both operational and physical load on multiple industries. AI is certainly not just being restricted to computer science but spreading its wings across other industries; For instance, the use of neural networks in healthcare to chatbots or algorithmic trading in finance and more!
The question is “What do we really understand about AI?” If not, then it’s high time that we uncover the complex forces at the crossroads of humans and machines.
Let’s begin from the very beginning!
How did this all begin?
Artificial Intelligence has evolved remarkably since its early beginnings. The journey traces back to the 1950s when British mathematician and visionary Alan Turing posed a groundbreaking question: “Can machines think?” Turing’s work marked a pivotal shift—from abstract theories to practical experimentation—laying the foundation for the intelligent systems we continue to develop today.
The well-renowned Turing Test has machines on one side and humans on the other both hidden from view. The interrogator will have a natural language conversation with both and if the interrogator is not able to distinguish between machine and human – The machine is considered to be human intelligent enough to pass the test. This was a milestone for Artificial Intelligence development using modern computational techniques by igniting the spark of “Machine having human intelligence”. This was an exceptional pivotal moment in the history of AI.
Coming into existence – In 1956 in the Dartmouth Conference “Artificial Intelligence” was coined for the first time and this was the beginning of AI as a formal field of study. Scientists taking inspiration from human intelligence and aiming to impart it to machines has always been a thing in the field of AI.
The AI winters – The 1960s to 1970s are termed AI winters. Due to technology and data availability limitations there was almost no funding & research in the field of AI. AI was a vanished topic from newspapers, magazines, and books until in 1997 IBM’s Deep Blue defeated world chess champion Garry Kasparov. This opened the jammed research gates for AI.
Data Explosion That Fueled AI’s Evolution
The shift toward an internet- and data-centric world in the 1990s and 2000s marked a pivotal turning point in the evolution of artificial intelligence. As vast amounts of digital data became available and computing power advanced rapidly, momentum in AI research surged. In 2006, Geoffrey Hinton co-authored a seminal paper that reignited interest in neural networks through the use of deep learning techniques.
By 2012, researchers from Stanford and Google—including Jeff Dean and Andrew Ng—demonstrated the potential of multi-layer neural networks. Their system gained attention for its ability to recognize images, famously identifying cats without prior labeling, showcasing the emerging power of unsupervised learning.
In 2017, the Google Brain team introduced a major breakthrough in natural language processing (NLP) with the development of the Transformer architecture. This innovation leveraged a self-attention mechanism, enabling AI systems to more effectively process sequences of data such as text.
Building on this momentum, OpenAI released the first version of GPT in 2018. This generative AI model utilized the Transformer framework to create powerful large language models (LLMs), setting the stage for the AI revolution we are witnessing today.
With these landmark achievements, artificial intelligence is rapidly transitioning from cutting-edge research to an essential part of everyday life.
The way we don’t remember life before smartphones, trust me we won’t remember life before AI too!
Inside the Mind of AI: Its Branches and How They Work
AI consists of different branches and each branch uses a different method to inculcate human intelligence into machines. These branches focus on a specific problem where machines are intelligent enough to get a breakthrough. Let’s understand some of the AI branches offered.
- Machine Learning: Machine Learning is the core branch of AI which enables machines to learn from data autonomously without explicitly programming. A machine learning model is a program that can find patterns or make decisions from a previously unseen dataset. The primary machine learning models fall into broad categories – Unsupervised learning, Semi-supervised learning, and Reinforcement learning. ML powers recommendation systems that personalize user experiences in e-commerce and entertainment platforms like Netflix and Amazon.
- Natural Language Processing: AI’s ability to understand, interpret and generate human language. Natural language processing (NLP) combines computational linguistics, machine learning, and deep learning models to process human language. Computational linguistics – Computational linguistics is the science of understanding and constructing human language models with computers and software tools. Deep learning is a specific field of machine learning that teaches computers to learn and think like humans. Applications like chatbots provide seamless customer interactions leveraging the power of NLP.
- Neural Networks/Deep Learning: Neural networks are inspired by human brain structure and function. Artificial neural networks are computational models that mimic the structure and function of the biological brain to understand complex patterns. Neural Networks find applications across diverse fields, including image recognition, natural language processing, recommendation systems, and fraud detection.
- Computer Vision: Making machines capable of interpreting visual data. Computer Vision (CV) focuses on enabling machines to analyze and interpret images and videos, mimicking human visual perception. This branch of AI is widely applied in tasks such as facial recognition, which enhances security systems, and medical imaging, where it aids in diagnosing diseases through X-rays and MRIs.
- Robotics: Robotics is about designing machines to perform tasks autonomously or semi-autonomously. Machine learning, artificial intelligence (AI), and robotics are three interconnected fields transforming the world around us. These systems combine sensors, actuators, and intelligent algorithms to execute operations in dynamic environments. Industrial robots enhance manufacturing efficiency by automating assembly lines, while autonomous vehicles revolutionize transportation with real-time navigation and decision-making. Robotics also extends to healthcare, where robotic surgical assistants improve precision and patient outcomes.
- Expert Systems: AI programs that simulate human expertise in specific fields. Expert systems are AI-based programs designed to emulate human decision-making in specialized domains. They rely on rule-based frameworks and knowledge bases to provide recommendations or solutions. In healthcare, diagnostic systems analyze patient data to identify diseases and suggest treatments.
AI has more branches such as fuzzy logic, Evolutionary Computation, Cognitive computing, Swarm Intelligence, and more worth giving a read about. You can check them out here.
Beyond the Buzz: Tangible AI Use Cases Transforming Everyday Operations
Okay!!!! So you are not gonna take me seriously until AI-powered robots take over – screamed the AI virtual BOT.
Everyone, everywhere is really talking about AI. But have you ever wondered if AI is really that helpful in business to increase operational efficiency? OR is it just following the flock’s opinion?
Let’s discuss some AI business implementations and I will let you pick a side.
Bryte is the leading restorative sleep technology platform powered by AI.
The Chief Producer officer of Bryte, Rex Harris, shared a use case where he spoke about getting swamped with survey results and chat messages from users. They applied Correlation to build a Correlation machine called Claude. (In machine learning, correlation is a statistical analysis that measures how related two variables are) .Claude connects the dots in every survey result and provides you with a detailed summary report. It also looks through chat sessions to categorize and prioritize bugs and feature requests.“Interesting, 20 hours of survey work? Gone, thanks to the power of AI!”.
Let’s transition to the next use case.
Taskade Automates tasks and supercharges workflows by crafting, training, and deploying your virtual AI Agents.
Dionis Loire (Co-Founder at Taskade) shared that before AI implementation, on average they could create 2 to 3 templates a week for customized template requests. But now with AI in place, they jumped it to 100 templates a week.
OKAY! That’s a 35x improvement using AI! They also noticed better consumer experience and organic traffic.
One more implementation by Taskad was in the support industry. We know different sets of consumers often end up asking the same doubts and questions, and we keep answering them again and again! Taskade developed AI software for drafting and sending replies based on studying previous email responses.
Interprets New Support Emails -> Does Remote Search -> Drafts Response-> TADA Customer Query Resolved!
That was a 6/60x Improvement using AI. Moving on to our next.
Pulley is all about everything you need to manage Equity.
We all tend to forget small meeting pointers or tasks we are about to complete. Mark Erdmann Co-founder of Pulley shared a fascinating use case where they build an AI tool that instantly searches instances of you mentioning your work on Slack and recaps it for you. This saved at least one hour of work per quarter per person. The second use case they shared was creating a knowledge bot for the Slack channel. Any question asked will be answered from analysis of chat history, pre-processing raw chat before going to machine learning language models. The team of 30 users uses this 30-40 times a day. AI Saving 40 hrs of Leadership time! Let’s turn our attention to the next use case
Zapier is a workflow automation software for everyone.
Have you ever used Google Gemini to take meeting notes? Or you are still noting down the Minutes of Meeting the old way. Google’s useful feature takes meeting notes for you. If you join late, you can tell it “Catch me up during the meeting with Summary so far”. As an organizer, you also receive an email with a link recap shortly after the meeting ends. Pretty cool Check this out.
Zapier co-founder Mike Knoop took it one step ahead and built an OpenAI gong that extracts transcripts, gives out detailed summaries, recommends next steps, adds back data to CRM, and prompts sales representatives on how they should continue the deal.
Result ??? $100 ARR per month
The Road Ahead: AI as a Tool, Partner, and a Catalyst for Change
In conclusion, AI has evolved from its early theoretical roots to become a transformative force across various industries today. With its branches ranging from machine learning and natural language processing to computer vision and robotics, AI has opened up new frontiers in problem-solving and automation. Businesses worldwide are leveraging AI for a myriad of use cases. As AI continues to advance, its potential for reshaping industries and improving efficiency remains boundless, promising a future where intelligent systems work alongside humans to tackle complex challenges.
One of the CEO’s in AI Public Consciousness 2022 said – “CEO’s have bought Ferraris in the shape of state of art AI systems. They just haven’t given any driving lessons to the staff!” …….Thoughts?
