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The last few years have seen Artificial Intelligence capturing the imagination of corporate executives and catapulted into the mainstream of the business world. With a myriad, and ever-expanding set of applications, AI promises to provide a quantum leap in enterprise efficiency, profitability, and competitiveness. Due to decreasing costs of storage, increasingly efficient algorithms running atop chipsets more powerful than over before, AI is witnessing a huge surge in interest and applicability. As companies rush to co-opt AI into their processes, practitioners of this technology are in high demand – which easily outstrips high-quality supply. With a soaring growth in demand and supply struggling to catch up, it is natural for professionals of the current and future workforce to ask – how to get started with a career in AI?
It is important to put down some context. Before answering where one can get started, it is important to first define AI. A simple, yet comprehensive working definition for AI is – the ability of machines to mimic human intelligence and functions. Going one step downstream, building a truly artificially intelligent machine is to equip it with the ability to sense and comprehend ‘stimuli’ within its environment, identify and weigh response options for acting on the stimuli, performing the suggested action, and continuously learning from the impact of the action taken, in a way that informs future decision-making.
Parsing this definition further, AI happens at the intersection of data (represented as the stimulus provided and the feedback loops for learning), mathematics (represented through models which weigh up decision-points and payoffs for each prospective action) and computer science (the technical and logical backbone that governs the flow of data and codifies potential action points). These are the three key ingredients of building powerful AI – and the three areas aspirants to this industry need to master.
Let us double-click on these three areas to understand their criticality to AI systems, and how the workforce can build competencies in each area.
While we can split hairs over the appropriate terminology (some prefer to call it Data Science, while others call it Data Engineering – depending on how teams are structured), it is important to focus more on the nature of the skill required in the AI arena.
Data skills encompass the entire range of tasks associated with data management for AI – the collection, sorting, storage, and extraction of data for meaningful use. It is data that fuels the growth of an AI application, and therefore the ability to sense incoming data, identify patterns therein and make informed decisions is a crucial building block for a career in AI.
Given the criticality of core data skills, it is not surprising to see data-literate employees – analytics professionals and data engineers – try their hand at reinventing their careers in this domain. Those who do not have a background in these two techniques should get started with courses in business analytics – to understand how businesses slice-and-dice data to inform their decision-making process. Those who have some background of computer science should upskill in data engineering areas i.e. how to effectively leverage emerging concepts in database management to improve storage, management, and extraction of data to feed AI applications in the most efficient manner. Alternatively, computer engineers could also learn business analytics to understand the applications and implications of data for business decisions.
Put simply, numeracy is the ability or skill to work with numbers and mathematical concepts. This is the second key ingredient for a successful career in AI. As I previously mentioned, a key building block of AI is to build the ability to weigh multiple options, probabilities, and payoffs across multiple options, to take the most optimal decision. These are essentially mathematical concepts of inference, probability, decision trees and game theory – and fine-tuning these skills are a critical part of building a great career in AI.
Developing advanced numeracy skills is a natural option for those who are mathematically inclined and have an education therein. Those who don’t have formal education in these areas can rely on numerous online courses that teach statistics and probability, before moving towards more advanced concepts. The takeaway from your education in numeracy should be the ability to formulate optimal pathways to decisions, identifying and accurately scoring multiple options, suggesting responses and continuously informing the mathematical model through a feedback loop, based on the results of responses delivered.
The final piece is to ramp up existing computer science skills to align with the needs of AI application development. There are two sub-areas at play here, namely – conceptualizing the logic (algorithms) and writing the language (code). Computer science provides the fundamental backbone required for improving the scalability and resilience of AI applications. It dictates how the data is operationalized and provides the logical base for mathematical models to process the data.
Python and R are two widely accepted languages in the field of AI. As a lot of existing developing in this domain has been done in these two languages, they provide rich libraries which are a key starting point to AI applications. Those who have a strong inclination and education in programming are highly advised to pick up online courses that provide hands-on skills in these two languages. Computer scientists well-versed with these two languages can also consider expanding their breadth into the numeracy skills – as these two works well in tandem and offer much better job opportunities in AI.
Like AI itself, a career in AI requires one to commit to continuous learning. This field, like any other emerging field, is rapidly evolving with new models and applications coming to light almost every day. While mastery of the above three skills is a good start, it is important to stay constantly updated to stay ahead of the curve. One way to do that is to keep an eye on research papers submitted therein. Additionally, it is equally important to keep an eye on the business end and staying updated on emerging use cases in this arena.
Disclaimer: The views expressed in the article above are those of the authors’ and do not necessarily represent or reflect the views of this publishing house
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It is critical that public services ramp up their data sets, identify partners for ideation and leverage technology
For all its growth and development since independence, India faces many systemic problems. From our complex and labyrinthine legal system to the inefficiencies in our agricultural sector, large-scale problems still abound.
We need to better connect our burgeoning population with basic facilities. While Artificial Intelligence may not be the panacea in itself, we need to harness its potential to improve living conditions. Thankfully, we have the intellectual capital – our information technology peers – that can bring substantial dividend in this arena. By combining our inherent technological prowess and the keenness of our government in promoting technology-led interventions, AI can truly be a game-changer for India. Here’s an India-specific perspective on how AI can be a force for good for our country.
Though the agricultural sector sees piecemeal improvements, numerous problems go unresolved – from low yield, low predictability of yield, poor access to institutional credit and financing to lack of transparency around pricing for produce. Using AI, agriculture can be transformed by:
• Provision of on-demand information on quality of seeds, fertilizers, pesticides and the track record of providers and opportunities for mechanisation through better equipment. This can be done through bot-enabled ‘Kisan Helplines’ that can provide guided advice for improving productivity
• Improving predictability of yield by ingesting data on soil health, equipment quality, farmer activity and weather conditions
• Improving visibility of market price trends for crops produced (domestic and international) so that they can make informed decisions on pricing, while exploring going to market without intermediary interference
• Leveraging data from productivity, yield and forecasts and potential prices to assess creditworthiness of individual farmers. This will speed up disbursement of finance and ensure farmers get better rates for crop insurance
Indian cities have grown in an extremely unplanned manner, with public infrastructure and services struggling to catch up. Consider this – the cost of traffic congestion alone in just four major cities is estimated to be $22 billion annually. With AI, urban planners can:
• Track movement of traffic and people to identify opportunities for ‘decentralising’ major hubs and develop future-ready public infrastructure to facilitate smoother movement of people, vehicles and goods
• Model population density and availability of sanitation facilities to improve access. Additionally, by applying image analytics on drone surveilled images can help determine quality of sanitation facilities and accelerating their upkeep
• Identify and improve access to current and emergent residential and commercial hubs by creating more optimal public transport networks
• Align consumption of resources – energy, water, cooking gas – to actual needs
• Crowdsource, store and take action to improve infrastructure by directly soliciting participation from citizens
• Improve planning and forecasting for infrastructure development through better coordination between public works departments, leveraging traffic data and streamlining supply chains
The education system in India is among the most outdated and unequitable when compared with the developed world. Problems abound from a prominent level of student dropouts, to quality and methodology of teaching, lack of workforce readiness among students and outdated curricula. Here’s how AI can help improve certain facets:
• Track the demand for skills in the market and the educational infrastructure available to supply those skills through a National Skills Repository. This will help keep education concurrent with current market demands
• Automate routine, time-consuming tasks – from creating and grading test papers, developing personalised benchmarks for each student, identifying gaps in student development, tracking aptitude and attentiveness within each subject – and enabling teachers to focus on curriculum development, coaching and mentoring and improving behavioural and personality aspects of students
• Identify potential dropouts and root-causes, enabling educational institutions to take proactive steps to ensure student retention and course completion
The doctor-to-patient ratio in India is quite poor – with 0.62 doctors available per 1,000 people (WHO recommends a ratio of 1:1,000). When you add to that the inadequate spread of doctors across the country, we have a poorly served population, ranking 125th in the world for life expectancy. Using AI, we can:
• Identify areas with a high population density, which are underserved by public hospitals. Further, improve the deployment and availability of doctors, medical equipment and medication to better serve the population
• Track patient histories and clinical notes to prescribe evidence-based treatment
• Speed up routine processes such as scanning X-rays and CT-scans for malignancies using image analytics
• Improve public health studies by identifying early warning signals through alternative methods such as social media tracking
• Identify individuals without health insurance and incentivise their usage to improve patient medical adherence
When adjusted for VIP protection, India claims an extremely poor police-to-people ratio with 1 police for every 663 people. There are 27 million cases pending with courts, of which six million have been pending for over five years. AI can be a crucial enabler for our crumbling governance system and can help:
• Speed up review and summary writing of long drawn cases and their
history using natural language processing and voice recognition
• Use image analytics for surveillance and identification of wrong-doers in areas recognised for high criminal activity
• Surface fraudulent deals – especially among land deals – using anomaly detection frameworks to speed up delivery of justice
• Improve public services and transparency by routing RTI requests through intelligent bots, thus making it more efficient to get critical information
With a population of over 1.3 billion people, distributed across a huge landmass, public services urgently need technology-centric solutions that are both intelligent and scalable. AI will effectively address a number of these problems. To this end, it is critical that public services act sooner than later and ramp up their data sets, identify technology partners for ideation and apply AI techniques to power the India’s next leap forward.