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Artificial intelligence is fast making its way into mainstream education. I do not infer as part of the standard technical curriculum. But several schools, colleges, universities and other academic institutions are adopting AI in the process of delivering impactful education to students and their numbers are rapidly increasing.
Across the world, we are seeing AI augmentation in different facets of the education system – from automating routine tasks that teachers have to perform to crafting personalised education curriculum that is line with a student’s aptitude and areas of interest.
The education sector in India suffers from deep-rooted challenges that need wholesale solutions. The bulk of our students is compelled to go through archaic pedagogical methods that are employed to deliver static and outdated curricula.
For a while now, Bill Gates and other tech stalwarts have been excited by the idea of infusing AI into the education system. Bill Gates calls this bouquet of technology-driven, impactful delivery of coursework as ‘Artificially Intelligent Tutoring Systems’ and hopes that it leads to better internalisation of course content. This column shares some of the areas where AI can leave its mark on the education system and revolutionise the way the next generation of students learn.
Freeing up Teacher’s Time
Teachers are burdened with several menial, low-value tasks that are ripe for an AI augmentation. These tasks neither deliver better learning outcomes nor improve student experience. The time our teachers spend performing hygiene activities – from taking the attendance of the class, evaluating and grading tests and assignments and performing peer reviews – is enormously wasteful.
The time spent by teachers can be easily unlocked through AI, helping them focus on what they do best – teaching and coaching for success. Bringing in AI into the core way-of-working of schools today will help eliminate these burdensome tasks in the following ways:
• By curating tests for students automatically based on the aptitude of students in the classroom. Rather than relying on teachers to conjure up questions in the classroom, AI can help tutors assess the learning level of students and contextually bring up questions. Teachers will be able to administer tests much more easily by using a gradational question bank powered by AI
• Grading the administered tests and assignments. This is another time-consuming and often low-value task that can easily be taken up by AI administered-tests. AI can help automate the repetitive task of grading tests, thus helping teachers focus more on how they can create a better platform for learning by coaching and solving questions from students. AI-graded tests can also help bring up commonly occurring patterns of errors (ie, are students mainly making the same mistakes?), in effect providing input to teachers on which lesson plans require more impetus in the next class
• Ease out repetitive administrative tasks. Teachers also spend hours over the year submitting periodic reviews to their supervisors and coordinators, taking attendance and peer reviewing the efficacy of other teachers. This workload can also be supported by AI – by maintaining automated attendance logs, summarising the test scores of students and reporting the performance of teachers
Curricula, Content Planning
The present-day curricula delivery process is largely inefficient. The current paradigm requires a teacher to deliver pre-designed, standardised content to a classroom full of students with diverse aptitudes and interest levels. The negative impact of current pedagogical methods can still be manifested through the employability score of the current generation.
By leveraging the variegated applications powered by AI techniques, academia will not only be able to deliver more personalised curricula and lesson plans but also improve students’ understanding and retention of the coursework, leading to an improvement in educational outcomes. Here are a few examples of how we can enable those:
• AI can be instrumental in creating a culture of continuous improvement among teachers. By tracking their performance across different key metrics, the educational system will be able to uncover the areas where teachers need support and coaching more effectively. AI can also help curate the coursework for teacher improvement, thus making sure that teachers are continuously updated and can continuously refine their craft
• By infusing AI into the skills and aptitude assessment process for students, schools will be able to better judge the current level of understanding among students for a particular subject area as well as where their innate inclinations lie. Often, students are unclear or unsure about how they can make the most of their talents and how they can channel them into a trade. AI can help schools map out the data of previous students, their career achievements and tie that back to educational research. This will allow schools to accurately predict the subjects for which a student has a natural inclination and then coach her in that direction
• AI can also use data around student attention, interest combined with their aptitudes and abilities to recommend customised coursework. This will help students build a structured career path. This AI-centric approach will foster personalised training pathways and provide students with the skills needed to succeed in their future professions, rather than burdening them and staggering their confidence as the current system does
Optimising Classroom Experience
To fully unleash the creativity and expertise of teachers, the education system needs to also imbue AI-led applications in the classroom on a day-to-day basis. This will enable teachers to work at full throttle. Time spent on minding students and reorienting classroom methods to ensure better student engagement can be saved by using AI in the following ways:
• AI can help improve the tracking of students’ attention levels and help teachers intervene before a student loses interest in the classroom content. While teachers are conversant in minding students that actively disrupt the classroom, engaging students who are quietly inattentive is a comparatively difficult task. By employing attention tracking devices, teachers can much easily monitor the attentiveness of the class and mind them before they tune out
• By aggregating the attention scores of the classroom, AI can help teachers devise a more potent mix of teaching, testing and activities – to continuously ensure better class performance and engagement
AI can bring a plethora of benefits to the education system at large, providing improved educational outcomes to all stakeholders – students, teachers and parents. Through personalised curricula, improved efficiency in the time management for teachers and effective in-class monitoring and assistance, AI can shift the paradigm of how the education system works and how coursework is consumed and leveraged by the next generation of students.
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The adoption and benefit realisation from cognitive technologies is gaining increasing momentum. According to a PwC report, 72% of business executives surveyed believe that artificial intelligence (AI) will be a strong business advantage and 67% believe that a combination of human and machine intelligence is a more powerful entity than each one on its own.
Another survey conducted by Deloitte reports that on an average, 83% of respondents who have actively deployed AI in the enterprise see moderate to substantial benefits through AI – a number that goes further up with the number of AI deployments.
These studies make it abundantly clear that AI is occupying a high and increasing mindshare among business executives – who have a strong appreciation of the bottom line impact delivered by cognitive systems, through improved efficiencies.
Having said that, with AI becoming more and more mainstream in an organisational setup, piecemeal implementations will deliver a lower marginal impact to organisations’ competitive advantage. While once early adopters were able to realise transformational benefits through siloed AI deployments, now that it is fast maturing as a must-have in the enterprise and we will need a different approach.
To realise true competitive advantage, organisations need to have an AI-first mindset. It is the new normal in accelerating business decisions. It was once said that every company is a technology company – meaning that all companies were expected to have mature technology backbones to deliver business impact and customer satisfaction. That dictum is now being amended to say – every company is a cognitive company.
To deliver on this promise, companies need to weave AI into the very fabric of their strategy. To realise competitive advantage tomorrow, we need to embed AI across the organisation today, with a strong, stable and scalable foundation. Here are three building blocks that are needed to create that robust foundation.
1. Enrich Data & Algorithm Repositories
If data is indeed the new oil (which it is), organisations that hold the deepest reserves and the most advanced refinery will be the ones that win in this new landscape. Companies having the most meaningful repository of data, along with fit-for-purpose proprietary algorithms will most likely enjoy a sizeable competitive advantage.
So, companies need to improve and re-invent their data generation and collection mechanisms. Data generation will help reduce their reliance on external data providers and help them own the data for conducting meaningful, real-time analysis by continuously enriching the data set.
Alongside, corporations also need to build an ‘algorithm factory’ – to speed up the development of accurate, fit-for-purpose and meaningful algorithms. The algorithm factory would need to push out data models in an iterative process in a way that improves the speed and accuracy.
This would enable the data and analysis capabilities of companies to grow in a scalable manner. While this task would largely fall under the aegis of data science teams, business teams would be required to provide timely interventions and feedback – to validate impact delivered by these models, and suggest course-corrections where necessary.
Another key aspect of this process is to enable a transparent cross-organisation view into these repositories. This will allow employees to collaborate and innovate rapidly by learning what is already been done and will reduce needless time and effort spent in developing something that’s already there.
2. AI Education for Workforce
Operationalising AI requires a convergence of different skill sets. According to the above-cited Deloitte survey, 37% of respondents felt that their managers didn’t understand cognitive technology – which was a hindrance to their AI deployments.
We need to mix different streams of people to build a scalable AI-centric organisation. For instance, business teams need to be continuously trained on the operational aspects of AI, its various types, use cases and benefits – to appreciate how AI can impact their area of business.
Technology teams need to be re-skilled around the development and deployment of AI applications. Data processing and analyst teams need to better understand how to build scalable computational models, which can run more autonomously and improve fast.
Unlike a typical technology transformation, AI transformation is a business reengineering exercise and requires cross-functional teams to collaborate and enrich their understanding of AI and how it impacts their functions, while building a scalable AI programme.
The implicit advantage of developing topical training programmes and involving a larger set of the workforce is to mitigate the FUD that is typically associated with automation initiatives. By giving employees the opportunity to learn and contribute in a meaningful way, we can eliminate bottlenecks, change-aversion and enable a successful AI transformation.
3. Ethical and Security Measures
The 4th Industrial Revolution will require a re-assessment of ethical and security practices around data, algorithms and applications that use the former two.
By introducing renewed standards and ethical codes, enterprises can address two important concerns people typically raise – how much power can/should AI exercise and how can we stay protected in cases of overreach.
We are already witnessing teething trouble – with accidents involving self-driving cars resulting in pedestrian deaths, and the continuing Facebook-Cambridge Analytica saga.
Building a strong grounding for AI systems will go a long way in improving customer and social confidence – that personal data is in safe hands and is protected from abuse – enabling them to provide an informed consent to their data. To that end, we need to continue refining our understanding around the ethical standards of AI implementations
AI and other cyber-physical systems are key components of the next generation of business. According to a report by semiconductor manufacturer, ARM, 61% of respondents believe that AI can make the world a better place. To increase that sentiment even further, and to make AI business-as-usual, and power the cognitive enterprise, it is critical that we subject machine intelligence to the same level of governance, scrutiny and ethical standards that we would apply to any core business process.
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In the academic, industry forums and conferences, India is being positioned as a potential superpower in the field of AI on the world stage. It is our collective vision to see India as the premier destination of AI in the foreseeable future. There is seemingly a lot of work to be done if we are to overtake our formidable competitors and eventually the current leaders in the space of AI – the US and China. But no intervention is perhaps as urgent as the need to promote natively developed AI research and intellectual property within our academic institutions, universities and corporate enterprises.
China spends 2.07% of its GDP on core research and development. In India, that number is a meagre 0.6%. Given this, it should not be surprising that China produced 4.5 times the number of citable research documents in the field of AI between 2010 and 2016. In terms of citations, the H-index of papers published in India (100) lags well behind China (195) and the US (413) – for the same period. At this juncture, it is evident that India’s contribution to the overall body of knowledge of Artificial Intelligence has been both quantitatively and qualitatively disappointing.
The contribution from Indian corporate enterprises has also been found to be wanting. For the period between 2001 and 2016, corporates contributed to merely 14.42% of the AI research done in India, with almost 70% of that being done by foreign multinationals doing business in India – the likes of Microsoft, Google, IBM and others.
There is a definite need for both academia and Indian industry to step up and contribute to India’s vibrancy in the domain of artificial intelligence. Here are 5 critical interventions needed urgently to take our research capabilities to the next level:
Foster a collaborative approach to IP development
There is often a pattern of multiple research projects happening in silos – through collaboration between like-minded researchers from the same field of science. Artificial Intelligence on the other is an interdisciplinary subject. It essentially requires researchers from different walks of life – data engineers, machine learning scientists and those conversant with real-life challenges that AI can solve – to come together to solve common problems and add to the overall body of knowledge.
Universities need to quickly recognize that and build research capabilities in AI in an interdisciplinary, collaborative manner. The good thing is, universities by their very nature tend to host brilliant minds from multiple fields, but it is imperative that these minds be brought together for real, cutting-edge research to be published in the field of AI
Boost Funding for Research at Universities
Whilst India does not lag in the number of STEM graduates, our research capability is still inadequate. This is partly because of a lack of funding for research. For millennials, the allure of a corporate job or opting for entrepreneurship is far too high due to the compensation and benefits on offer.
It is critical that local and national governments intervene to help academic research in the field of AI be a viable career option for those who are interested in research. By expanding the budget allocated towards research and development, more researchers would be able to tap into public grants to expand the research in AI.
Promote Corporate Funding and Empanelment
The onus of funding AI research cannot only fall on public institutions. Private Indian corporations would also be a huge beneficiary of locally developed AI competency. It is critical that we continue the work in building the bridge between academia and corporations to fund and promote AI research.
Several large companies, as well as startups, recognize the need for indigenously developed research in Artificial Intelligence. Corporate empanelment programs take multiple forms – from the low touch speaker arrangements to corporations helping setup topical centers of excellence at universities in a technology area that is key to their business success. More arrangements of the latter form are necessary, in addition to individual research grants and scholarships that corporations provide.
Release government datasets for AI algorithm development
Activating the development and learning for AI algorithms requires access to a lot of data. Here again, the government should step in to provide the relevant data sets to researchers working on complex, India-specific problems.
In multiple circumstances, the government holds a treasure trove of data which can be hugely beneficial for the learning cycle of algorithms and promoting their development. The government should take a positive stand on sharing data sets, all the while keeping in mind data security measures and privacy rights of citizens.
Two-Pronged Approach – Core AI as well as Applications-Led IP Development
Research in AI needs to be supported at two levels. First, the development of patented core AI algorithms that will have broad, cross-industry applicability. The second is the development of intellectual property that is topical to industry or sector-specific problems.
India suffers from numerous topical problems that need an AI-led solution – from providing health care services to our burgeoning population, support for those engaged in the agricultural sector and provision of basic public infrastructure – roads, hospitals, schools and sanitation facilities. In addition to developing core mathematical capability, significant value can be unlocked through by developing expertise around these large, complex problems in AI, with solutions that can be applied to help countries facing similar problems.
Building core R&D capabilities and more IPs in Artificial Intelligence is key to cementing India’s position and competitiveness in this space. Building strong capabilities in AI is now a mandate for most of the world’s most powerful countries and it is imperative that India does not fall by the wayside. The good news is that NITI Aayog, MEITY, NASSCOM and others are already taking concrete steps to engage the stakeholder community – researchers, educational and corporate institutions – for AI research. The foundational aspects – an inclination towards STEM streams and an orientation towards key subject areas among the current students is there as well. It is time we harness their wisdom and knowledge to catapult India to the forefront in the field of AI.
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Artificial intelligence is slowly, and steadily, making its way into mainstream education. And not simply as part of educational curricula. We are seeing increasing instances of schools, colleges and other academic institutions leveraging AI as a crucial part of the process in which they deliver education to their students. In the West, numerous examples abound of these educational institutions leaning heavily on AI – from delivering personalized educational curricula to automating routine tasks that classroom teachers have to routinely perform.
Tech luminaries such as Bill Gates are enthused by the idea of Artificially Intelligent Tutoring Systems – which can ensure impactful delivery of course content and improved internalization of that content among students. The education sector in India, currently reeling from endemic problems – from static curricula to dated pedagogical methods – has much to gain through an AI-driven facelift. Let us look at some of the areas where AI can make its way into education and revolutionize the way the next generation of students learns.
Augment Planning of Curricula and Lesson Plans
The present-day paradigm of a teacher delivering pre-designed, standardized content to a classroom of students with diverse aptitudes and interest levels – is remarkably inefficient. We’ve seen the negative impact that the current pedagogical methods have had on the employability levels of the current generation. To this end, by leveraging the variegated applications of artificial intelligence techniques, academia would be able to deliver more personalized curricula and lesson plans, improve students’ understanding and retention of the coursework and in turn improve educational outcomes. Here are a few examples of how we could enable those:
By infusing AI into the skills assessment and aptitude assessment process for students, schools and universities will be able to better judge both – the current level of understanding among students for a specific subject area and where their innate inclinations lie. Often, students are unclear or unsure about where they see their career graph moving and what they would like to do in the future. Through AI, schools and universities can map out the data of previous students and their career achievements and tie that back to educational research. This way, schools, and universities may be able to accurately predict which subjects a student has a natural inclination towards and then coach them for a career in that direction.
Going in the same vein, AI can also use data around student attention, interest, aptitude, and ability to recommend customized coursework. This will help build the capability of students towards a specific career path and bring better value to the time of students. This AI-centric approach would help foster more personalized training pathways and enable students with the skills they need to succeed in their future professions, rather than burdening our students and staggering their confidence as done by the current system.
Furthermore, AI can also be instrumental in enabling continuous improvement for teachers. By tracking their performance across a variety of metrics, schools will be able to better uncover the areas where teachers need support and coaching. AI can also help curate the coursework for teacher improvement, thus making sure that teachers are continuously updated and continuously refine their craft
Automating Routine, Low-Value Tasks
Teachers today are overburdened by all manner of menial, low-value tasks that neither improve student experience nor deliver better learning outcomes. Enormous time is spent by our teachers worrying about and performing hygiene activities – from taking the attendance of the class, evaluating and grading tests and assignment and performing peer reviews. We can unlock this time spent by teachers and help them focus on what they do best – teaching and coaching for success. By incorporating AI into the core way-of-working of schools today, we can eliminate these burdensome tasks in the following ways:
By automatically curating tests for students based on the aptitude of students in the classroom. Rather than relying on teachers to conjure up questions in the classroom, AI could help understand the learning level of students and fire up the questions. By using a gradational question bank, teachers would be able to administer tests much more easily.
The other related time-consuming area for teachers tends to be grading the administered tests and assignments. These tasks can much easily be eliminated by using the AI administered tests. AI can help automate the repetitive task of grading tests, thus helping teachers focus more on coaching, solving questions from students and helping create a better platform for learning. AI-graded tests can also help surface patterns of errors (i.e. are students mainly making the same mistakes?), thus providing input to teachers on which areas of training require more impetus in the next class.
Among other several administrative tasks – teachers also spend hours over the year taking attendance, peer reviewing the efficacy of the other teachers and submitting periodic reviews to their supervisors and coordinators. This workload can also be supported by artificial intelligence – by maintaining automated attendance logs, summarizing the test scores of students and reporting the performance of teachers.
Optimizing the Classroom Experience
AI in education can go well beyond simply personalizing course content and unburdening teachers. To fully inform and unleash the creativity and expertise of teachers, we also need to imbue AI-led applications in the classroom on a day-to-day basis so that teachers can work at full-throttle. Time spent on minding students and reorienting classroom methods to ensure better student engagement can be saved by using AI in the following ways:
AI can help improve the tracking of students’ attention levels and help teachers intervene before students lose interest in the classroom content. While teachers are conversant in minding students that actively disrupt the classroom, engaging students who are quietly inattentive is a comparatively difficult task. Using attention trackers, teachers can much easily monitor the attentiveness of the class and mind them before they tune out.
Finally, by aggregating the attention scores of a particular classroom, AI can help teachers devise a more potent mix of teaching, testing, and activities – to continuously ensure better class performance and engagement
Using AI to augment classroom and educational institutions is of interest to everyone – students, teachers, and parents – and can help bolster educational outcomes. By personalizing the curriculum, optimizing the time of teachers and effective in-class monitoring and assistance, AI can be a game-changer in the way coursework is consumed and leveraged by the next generation of students.
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A combination of economic, business and technology factors have led to a steady rise in synergistic partnerships between industry and academia in India. Whilst a strong academia-industry partnership model has existed for several years in USA, UK, Canada, Singapore and few other countries. India Inc. is catching up quickly to the transformative benefits that academia-industry engagements can bring to both parties. Rather than co-opt existing technology, corporates are under increasing pressure to incubate and deliver bleeding edge technology solutions to ensure continued competitive advantage and they are turning to some of the brightest minds in universities today for ideas on how to do that.
While there may be several drivers for corporates inking strategic, operational partnership modes with an academic institution; I see three common themes: First, academic institutions are under increasing pressure to deliver bleeding edge research that has commercial viability and real-world applications. They simply cannot ignore events in the business spheres anymore. For this, they rely on enterprises to provide contextualized understanding within which they can frame their problem statements and hypotheses. Second, we are also witnessing a muted public-sector funding for research, combined with the proliferation of private universities. As a result, academics need the patronage of corporates to fund their long-term research endeavors and goals. Lastly, R&D departments at organizations need the bright minds from academia to deliver results in a shorter time-frame and lower cost. These three critical drivers are spurring a healthy interest in developing academia-industry engagements.
From AI, analytics perspective; research and innovation are the key differentiators. Let us dig deeper into the academia-industry engagements and dwell on building robust and synergistic engagement model and framework between academia-industry:
Collaboration for Data Sets
This engagement is primarily intended for gaining access to data for running analysis and hypothesis building. Usually, an organization may need access to multiple varieties of data sets that are available with universities, to refine and improve their statistical models. These engagements can be often seen between enterprises and university hospitals – a hot-bed of structured and unstructured patient data. Healthcare-focused technology companies typically need access to tons of data to build and improve their AI systems – to capture every possible variation of the data and ensure that their model accounts for the best results.
An example of such a collaboration can be seen between Google DeepMind and University College London (UCL) for the use of AI in radiotherapy. The key to this partnership is UCL’s hospital and the availability of researchers in a real-world medical environment.
The second area of collaboration is for industry and academia to create real-world applicability for research. Academics tend to be extremely visionary in their ability to add to a body of knowledge through thorough and intelligent research but may often lack visibility into challenges faced by businesses. By leveraging business context provided by corporates, they can add a flavor of high applicability to their research. Additionally, solving relevant, business-critical problems, researchers can also improve their visibility among their community, while potentially improving their H-Index scores through highly citable research.
An example of this collaboration is CA Technologies and IIIT Hyderabad engagement, they recently signed an agreement to set up a co-innovation lab. They intend to work together on topical problems in areas of Natural Language Processing, AI and Machine Learning, as per the company statement. For the researchers, this agreement would help improve their visibility through publications in scientific journals and CA Technologies can identify reference architectures and prototypes that will enable faster development timelines.
Co-Curriculum development and learning programs
This alliance between industry and academia is for cross-pollinating and co-creating AI, analytics academic curricula. Given the dynamic nature of business today, enterprises are collaborating with universities for providing continuous AI, analytics training to their employees across disciplines. This ensures that their employees have a contemporary understanding of the best practices in their field of work, while also promoting employee satisfaction. On the other hand, universities carry this understanding of the needs of the corporate sector and incorporate the same into their AI, analytics academic curriculum. For universities, this is a critical way in which they can create a comprehensive coursework that is exceeding relevant in the job market today.
Whilst, these are few prevalent areas of collaboration; other ones may look at mutually inking long-term strategic initiatives that involve academic institutions adding a cross-dimensional flavor to multiple analytics projects and requirements at organizations. The journey between academic – institutions collaboration has evolved and will witness several novel engagement models in the future. The continuous evolution of learning, unlearning and relearning phase will usher a new paradigm in academia-industry collaboration
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We are on the definitive cusp of the 4th Industrial Revolution. Earlier industrial revolutions ushered mechanization of previously manual tasks, leading to a huge shift in production output and increased operational efficiencies while creating a new range of skills for the workforce to master. According to Klaus Schwab, Founder and Executive Chairman of the World Economic Forum, the transformation driven through this technological revolution will be unlike anything that humankind has experienced before and will require an integrated and comprehensive response involving all stakeholders of the global polity – from the public and private sector businesses, academia, and civil society.
Industry 4.0 – defined by breakthroughs in emerging technologies such as Robotics, Artificial Intelligence, Internet of Things, 3D Printing, Autonomous Vehicles and Quantum Computing – will yet again create a massive shift. It is increasingly common news that a manufacturing major is introducing robots on the production line. With smarter factories, smarter production and smarter supply chains, running autonomous production and delivery of manufactured goods, the question is bound to arise – what are the engineers supposed to do?
Engineering has long been a highly sought-after stream of education in India. Consider this – approximately 1.5mn students graduate out of 3,000+ AICTE-affiliated institutions in India, every year. [However, endemic problems surround their quality and technical output. Research after research confirms the disconnect between the education imparted to students, and the skills required on the job. According to the National Employability Report 2016, conducted by Aspiring Minds, 80% of engineers are considered unemployable. Even in India’s highly vaunted software industry, 95% of engineers are thought to be unfit to take up software jobs.
If the average Indian engineer is unfit to perform the tasks expected from him today, what hope is there for him to be able to perform the jobs of tomorrow? The 4th Industrial Revolution is only going to complicate matters by contracting the number of available jobs, while looking for specialized skills that Indian engineers most likely won’t have. One example, according to Talent Supply Index 2017 published by hiring startup Belong, there are only 8 data scientists for every 10 data scientist jobs in India.
It is undoubtedly a matter that needs urgent attention from educational institutes. The need for alignment between the skill-suppliers (colleges) and skill-consumers (businesses) is greater than ever before, and it is critical that educators stay in step with this new wave of industrialization, or risk falling by the wayside.
Embedding AI in engineering streams
Artificial Intelligence is the cornerstone of this new wave of industrialization. Embracing emerging technology areas will ensure that the engineering workforce is relevant for the jobs of the future and their knowledge needs to be embedded in traditional engineering syllabi. It is commonly assumed that AI only happens at the intersection of computer science and mathematics. While that is somewhat true at present, other streams too are looking at developing topical AI programs. Let’s look at these other engineering fields and how AI can be embedded into their existing coursework.
Civil or Construction Engineering is often considered to be far removed from AI disruption. However, AI is already making in-roads into this field. With geo-spatial intelligence and historical earthquake data, civil engineers can make better decisions on assessing the landscape available for projects, understand the materials required to withstand environmental conditions, or at times drop a risky project that might be too dangerous to develop. AI-driven predictive maintenance helps engineers optimally predict maintenance schedules for civil infrastructure developed – mitigating the risks posed by damaged infrastructure to civilians. AI can also help parse image data to detect damage to property, assess the extent of repairs required, and the costs of that repair work. Beyond these, AI can also help design smarter buildings – optimally utilizing electricity and water resources, while also bringing efficiencies to construction costs by automating inventory procurement decisions.
Another stream of engineering assumed to be immune from AI intervention is Chemical Engineering. Chemical Engineers with an understanding of AI can reduce the time for new chemical development, by modeling the impact of chemical combinations. AI can help predict and test the quality and resilience of new formulations. Chemical engineers with a knowledge of how to operationalize robotics technology for combining potentially dangerous chemicals – will again be an important intervention in this area of engineering.
Even across diverse engineering domains – metallurgy, oceanology and aerospace engineering, knowledge of artificial intelligence will be critical. Metallurgists with a knowledge of AI can run models to understand the properties of various metals and build stronger and more purpose-driven alloys. Oceanographers can leverage AI technology to parse geospatial information to better understand sea-beds and model the chemical and physical properties of oceans. In Aerospace Engineering, AI can bring untold efficiencies through robotics for assembling components. AI can predict failures and maintenance schedules required for aerospace equipment. In each of these domains, knowledge of AI, Robotics, Predictive Analytics, Computer Vision and Deep Learning will help ingest large volumes of unstructured disparate data, autonomously generating insights in a much lower time span – while improving the speed of the production process.
Finally, in certain streams within engineering – Mechanical, E&TC and Electrical – AI lends itself more naturally. Mechanical Engineers need to upskill themselves to develop and run autonomous robots that can do complex assembly and integration tasks. Education in Electronics Engineering needs to tend more in favor of developing Industrial IoT, Quantum Computers and advanced chipsets that can handle the large-scale processing required to run cross-platform AI applications. Electrical and Telecommunications engineers, given an education in Artificial Intelligence, can automate, monitor and improve the uptime and performance of their respective systems.
Leading the Way
A substantial chunk of upskilling needs of existing engineering graduates are handled by online courses. We are seeing an increased proliferation of AI, Machine Learning, cybersecurity, IoT, and Robotics courses delivered by online educational platforms: Coursera, Udemy, Udacity, UpGrad, while their programs are well serving the current crop of engineers, some of the other prominent academic institutions and academies – ISB , Manipal global education , Jigsaw Academy , IFIM , Institute of Product leadership (IPL) ,UPES, Praxis Business School , Shiv Nadar engineering school, IITD are few listed ones that have taken vantage position in imparting AI , Analytics programs . A structured learning and innovative pedagogy approach are needed from traditional educational institutes for skilling new engineering graduates, to be able to master these new means of learning. They need to alter their curriculum to ensure that the next generation of engineers is equipped to handle the next generation of opportunities. However, it is very important that other institutions also follow suit and promote the cause of AI education.AI will usher a new beginning in the education arena and the ones that have the ability to learn, unlearn and relearn will succeed in the professional spheres.
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Several pieces of research, studies and content have been published about the exponential growth witnessed in emerging technologies – AI, blockchain, RPA, cybersecurity, IoT, AR/VR. There is no doubt that these are the emerging technologies of the future, which will help catapult the next growth spurt of enterprises. Professionals across the information technology landscape are queuing up to upskill, reskill themselves across these mentioned areas to continue to be relevant in the workforce for tomorrow. But for those who already have learning, skills and experience in these emerging technologies, how can they take their career to the next level?
The flux of deployment-ready and value-generating use cases across industries suggest that a cross-technology expertise across these emerging technology areas would be the next big source of career growth for incumbent professionals. We witnessed few years ago, software developers were keen to reinvent themselves as full-stack developers. High performing technologists wanted to develop proficiency across the software architecture and the development life-cycle – from database to UI, and from infrastructure set-up to deployment. Similarly, IT professionals today should seek the synergistic benefits of combining areas across emerging technologies. This article will focus on what emerging technology areas are being effectively combined by enterprises.
AI + Blockchain
Artificial intelligence is the set of technologies that help machines mimic human functions. Blockchain is the emergent technology paradigm that helps build a distributed, immutable sequence of financial events and transactions. With a strong uptick in the dispersion of blockchain use cases, enterprises are also looking for a robust way to surface potential fraud and other anomalous events in real-time. The events of fraud attempt and security threats to blockchain systems are often very high-speed and require immediate attention and analysis to ensure that the perceived anomalies are rooted out. Using AI, specifically machine learning, we can rapidly parse through a log of events to find anomalous situations and flag them off in real-time, protecting the integrity of the blockchain.
Internet of Things is the network of physical devices that exchange data. This very definition makes the case for combining artificial intelligence and IOT plainly clear. IOT-enabled sensors usually are a source of multitudinous data – based on the use case employed – which is increasingly being sent to the central controlling server in real-time, rather than in batches. Picking out key inferences from voluminous data, sent in real-time by numerous sensors is a task that is again handed over to AI systems – typically machine learning systems. While IOT systems can ably sense, transmit and store data, ML systems are required for making sense of the data and providing input on whether any action is required to be taken, and potentially even suggesting what best-case action could be taken.
IOT + Smart Cities
While the concept of a Smart City is a sub-segment of IOT, it’s the other way around. IOT forms only one of the component of powering a smart city. In addition to IOT, the Smart City stack would typically also include cloud (for running processes and storing data), Artificial Intelligence (for data analysis and learning) and an element of urban planning (for deciding the what and how of a Smart City design). By combining knowledge of IOT with these other ancillary areas can help IOT professionals become valuable and irreplaceable resources in this fast-growing technology area.
AI + Behavioral Sciences
This final combination may sound surprising, but one of the most valuable and high-impact grouping of skills might just be the combination of data science and behavioral science. While AI and data science can provide the what (‘What happened?’ And ‘What should we do now?’) of a business scenario, behavioral sciences inform the how part. Consider the example of Amazon, which has numerous examples of the coming together of behavioral sciences and AI. The recommendation engine uses artificial intelligence to answer the what part of ‘what are other people buying’. But, the idea that it will lead to continued stickiness on the website, serve as a wide showcase of SKUs available on it, while promoting product bundling and larger cart sizes is a clear behavioral sciences intervention and contributes massively to the success of AI applications.
If you are a professional already conversant with one of the emerging technology areas, combining that with another emerging area can be hugely beneficial. IT professionals today in these areas should strongly consider leveraging synergies across multiple technology areas – which can help them be better-rounded, high-value practitioners in an ever evolving areas of technology.
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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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Technology proliferation and changing socio-economic factors is ushering tumultuous change in the old paradigm of work. Today, with the anvil of ‘gig-economy’ – a collective talent marketplace of independent workforce working on recurring short-term assignments; we are now at the definitive cusp of a new reality of the workforce – working professionals will not only change jobs but will take multiple career switches while being expected to continuously unlearn and relearn new skills along the way.
The future of work is here. It is time to shape the future of education.
According to multiple studies, success in the gig economy will be centered around 3 competencies which I call the 3 C’s – Creativity, Curiosity, and Collaboration. While children are naturally curious and creative, it is more important than ever before for academia nurture and sharpens these two qualities, while adding a core competency of collaboration, by imbibing them in their teaching methods.
In the continuum, I strongly believe that education in the time to come will go in for a fundamental change. Here’s my take on the future of education:
According to a World Economic Forum report, there is enormous potential to improve the social and emotional skills of students by incorporating the use of play in their education – which in turn can provide a boost to their collaborative skills and drive curiosity. Developing these skills will require three types of games, namely
Role-playing Games -creating a narrative arc through a sequence of events and providing them with a variety of options for interacting with the game through their characters. Role-playing games also allow students to explore multiple paths and revisit previously explored times and experiences.
Strategy Games -multiple students partaking in a quest to manage the strategic planning and deployment of scarce resources
Sandbox Games – focusing on open-ended exploration, being resourceful and taking initiative among a group of players to achieve a shared goal.
It may no longer be productive to attend 3 -4 years of graduate school, followed by post-graduate education. In the gig economy, students and corporates will unlock shared benefits of skills-centric learning, followed by a stint at the workplace, before going back to school and acquiring new skills. While this will reduce the time and cost of learning; it will also help students apply their skills in the workforce and gain the much-needed hands-on experience. By seeing their classroom learnings in action, it will also spur curiosity to learn more and do more in the future.
Increased Mobility Between Institutes
While our generation uses MOOCs for furthering our education, MOOCs will become mainstream for future generations. MOOCs provide a wonderful counterweight to the natural curiosity of students while helping institutions extend their curricula into subjects they currently do not have the capacity to address. MOOCs will also become more social and collaborative, encouraging students to learn with each other and improve their overall performance.
We will also see a rise in Virtual Reality (VR) and robots in the classroom. VR will help create more immersive learning experiences for students, thus stroking their natural curiosity to learn. Robots, on the other hand, will take the scud work from teachers – and provide inputs on skills assessments, personalized curriculum pathways, and attention tracking – allowing teachers to focus more on coaching and mentoring.
The future of education will define how our next generations shape up and succeed in the workplace. It is critical that we understand the value of developing the 3C’s – Creativity, Curiosity, and Collaboration – from an early age so that our next generation can achieve their full potential and value in the workforce.