What Industries Need — AI only?. Is really AI a bane or boon for…
By: Awadesh Kumar
Tags: Automotive, Data Science, Infrastructure, Artificial Intelligence, Aerospace

Is really AI a bane or boon for industries and what it means when it comes to reaping the benefits or paying the penalties of utilizing such disruptive technologies stand alone.

Photo by Sepp Rutz on Unsplash

Typical problems that we encounter across industries are predictive maintenance, enhancing user experience, proactive alerting, cost optimizations, accuracy of predictions, reduction of false operating scenarios, availability of resources (input supply included), establishing effectiveness and sustaining that for control systems, maintaining robust performance under process drifts and changes, slowing down the ageing of components, manpower optimization, enhanced penetrations and application of disruptive technologies (AI/ML), increasing degree of virtualization, forced outage reductions etc.

There are all kinds of data available from sources as IOT, texts, voice, video, behaviors, environments. These data sources could be structured, unstructured, pixel, written, unwritten, logs etc.

Assuming that problems listed above are primarily applicable for all industries but BFSI (cost beats all though!), let’s take one example and discuss if AI only solution works best or there are limitations and boundaries that we can draw.

Automotive: Degree of autonomous is challenge dealt today using AI/ML, this problem requires modeling of user behavior and create millions of such behavioral scenarios, yes, Bayesian Belief network or multi-layered deep learning or CNN, adaptive learning models work in tandem here to create highly complex model and solutions, but is this possible to account for all combinations of human thoughts and emotions? If someone has COVID-19, driving to hospital or from hospital, think of his/her mindset, can AI model be trained to counter behavioral issues due to such pandemic scenarios? Thus limitations of AI only approach and solution are evident here.

If AI alone handles , then question comes when do I retrain the models, how do I ensure accuracy consistently of these models. Thus AI training and accuracy sustenance requires physics based simulations of system that constantly will act as retraining data and course correction standards.

Aerospace: Recent accident of Air India “Vande Bharat” flight, that skid on Kozhikode table top airport, what role AI could have played all alone? Can AI in this scenario be able to avert the accident? It was Pilot who acted sensibly that he emptied most of the fuel to prevent the fire, he attempted landing twice. Most likely anti-skid, forced auto-landing system that ensures exact touchdown point and AI in ensemble could be good solution to address such scenarios, but AI alone would fail as physics and characteristics of aircraft, conditions and degradation of components and devices and sentiments of passengers inside needs to be modeled to have complete foolproof solutions. Here again complete end-2-end simulators needs to feed the data for retraining and re-computations of AI models, also simulators should provide maintenance factor to AI models in terms of bias and variance accounting.

Amount of data required (read as relevant data!) to train AI models and despite that having sufficient supervised labels is huge challenge!

IT: This industry is revolving around logs, metrics, dashboards, data management, monitoring tools, cloud, migrations, speed, bandwidth, volume, solution architecture, protocols, data acquisitions etc. Challenges aplenty, AIOPs are roped in to address multi-dimensional challenges but obvious drawbacks are fundamentally same as other industries, those are do we have sufficient failure or anomaly labels to have anomaly detection models? impact radius model accurate enough to capture all the ripples with limited relevant data? continuous SME involvement not needed? dependency on ticket data isn’t too high? ChatOps, ChatBots are as effective as person who trained them! Thus to create effective AI system SME/domain knowledge is needed and that needs to be fed cognitively into AIOPs system.

I shall touch upon other industries in my next blog.

Questions we should ask while applying AI in an industrial ecosystem are:

Do I have right problem to solve? Do I have enough data to create AI framework and train the models Do I have right infrastructure to deploy and operationalise the models Do I understand what ROIs I can reap out of AI solutions?

Photo by Joshua Sortino on Unsplash

Conclusion : AI alone with no domain is bound to fail as evident from various experiments (pl read autonomous!). AI is very effective and disruptive technology but it has been there 10–15 years back in more linearity form with finite domains, in the form of golden searches and likes of gradient optimization techniques. Processing technologies have evolved and thus AI model computations are being addressed but when it comes to accuracy and robust outcomes, domain, physics, ecosystem behavior, characteristics, knowledge of controllable and uncontrollable factors are key!

In my view bring forth and push forward “SCIENCE” part in “DATA SCIENCE”!