Beginners lets start AI learning ..

In the 1950,s time frame at the Dartmouth Conference may consider the term coined "Artificial Intelligence" by John McCarthy while researchers interest on that time was on the neural networks and automation. Alan Turing, a British mathematician pioneered working on the AI with so many other scientist , researches , academics and mathematicians on the support are in the list ......

Thought the history of AI is not just 1950's rather it goes way earlier when René Descartes, a French philosopher and mathematician had an idea of machines mimicking human behaviour the foundation and definition of AI itself is what we are using today. Let's define AI fast forward in today's time " Enabling machines to mimic human intelligence where they learn the task, execute and understand new inputs with minimum human intervention"

Simply put the AI will have types as Machine Learning | Deep Learning | Neural Network as terms to understand the foundations.

Machine Learning : When machines can predict based on data provided (historical cab be patterns , trends , matrix etc.) This is the foundation of today's AI evolution. Predicting trends of weather, forecast in businesses, profits, growth or stocks performances, healthcare trends are few use cases to apply to the market.

Deep Learning: When we start analysing the data sets and make meaningful patterns not just static historical data patterns rather the neural networks create patterns through complex data sets. Best we all know is Hey Alexa , Hey Siri ..... ( Best of the examples quoted several times are the use cases of Deep Learning). DL being the subset of ML is all about neural networks to study ...

Neural Networks: Inspired by Human Brain, Neural network is a type of ML process called Deep Learning with layers of neurons so they can process it as human will do - like computer vision, speech recognition, natural language processing use cases.

Lets focus on Concern of AI: There are millions of documents to learn on AI , use cases ,tech adoption , here let's just look at what are the concerns to address and continue the innovation as well as adoption of AI at fast pace.

Jobs will be taken by AI : May be YES for the jobs which are repetitive, mundane jobs or risky for humans to perform(high altitudes , high heat , high energy to extreme conditions where human existence to intelligence may be difficult to reach). Jobs where humans are lagging behind to harness and educate themselves on AI in their benefits will loose to AI empowered workforce for tomorrow.

Bias and Fairness: After all its humans who will provide data sets , what data you give will further be utilized to perform human mimicking task – There has to be framework , regulatory , compliance around using AI for Good , AI to be non biased and should cater all the data outcomes with fairness.

Ethics in AI : The fundamentals of utilizing AI is to simplify task and accelerate growth with ethical principles in place so to outperform business use cases as optimization , cost reductions , scalability , high performance like AI agents for tomorrow to have Ethics at its core.

Security and Privacy: There cannot be any innovation , any digital transformation without a security transformation and maintaining privacy of users , machines , ecosystem. In AI the trainings of data , data modelling and sources of data all need to be well integrated with privacy by design principles. The security should be paramount from the DAY ZERO of AI development till final implementation and usage.. this shall be a continuous assessments process to make sure AI is secure in itself , on the platforms it runs ( on prem , hybrid ,cloud SaaS apps wherever it runs ) and for the end users privacy and transparency are always ON

Industry specific attention: While its just tip of the iceberg and scratching the surface on AI revolutionary adoption for customers , enterprises , governments across the world HOWEVER the DATA is the backbone specific to industry based AI applications. The future of AI shall see variations when it is deployed in Healthcare (AI in Health ) vs Manufacturing ( AI in Automation) or AI in public sector will have its own nuances and concerns which require focused governance , deep know how like a bank or a Fintech will need tighten parameter than an NGO adopting a AI use case. Both are sensitive still impact of AI can cause various financial to human threats as per industries.

AI is also utilized equally or may be more by the underground economy – Cyber Criminals:

Its not just one way traffic ... AI empowerment , education and learnings must be at peak with cyber criminals , sophisticated nation state threat actors, hacktivist and so on..Remember they only need one out of ten score in their AI driven tools and tech to disrupt to bring critical infra at halt while Defenders have to score 10 out of 10 every time to be safe in the AI augmented security world - THE GAME IS TOUCH now that there are evolutionary new data creation in progress ( welcome to GENAI) which have never been seen and its creating content , images , voice , with zettabytes of data [processed in fraction of milliseconds at high speed , high energy consumptions to be also controlled.....

Lets keep Learning AI and Transformation ahead

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