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The management of talent has always been and continues to be a major challenge for most industries. This is particularly true for knowledge based industries like information technology. The dramatically changing dynamics of the Indian Technology industry compound the challenges and opportunities faced by the industry.
Never since the advent of mass production has an industry seen such dramatic volatility in such short period of time. The revolution before primarily added to the productivity of the labor and moved across the globe. The current revolution is not merely transcending national borders – it is redefining jobs, eliminating others and creating new opportunities.
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We are truly entering the Golden Age of Artificial Intelligence. With data and computational power making giant strides year on year, AI promises to unlock untold benefits for business and transform human life as we know it – a transformation that will play out in our professional and personal lives. Data Science and AI careers are in high demand as students and working professionals flock to courses in these subjects to ride the wave and build their careers. Enough has been written and consumed around the potential of AI and how corporations and universities need to enable their students to make the most of this emerging new technology.
Actively Seek Private and Public Funding for Research
Many countries globally do provide public funding programs for educational institutions. However, at the present level, this may be insufficient, and the exchequer may not be in a position to fill the massive capital gap required to improve research capabilities and labs.
To this end, it is critical that universities actively seek out ways to secure funding from public and private sector institutions. Several creative collaboration opportunities are surfacing to the instrument such partnerships. Corporations are always interested to seek inputs from the leading scientific minds to add to their portfolio of cutting-edge solutions and intellectual property. Some of the commonly seen engagement models include – securing research grants for topical research allied with a challenging business problem, setting up technology incubation labs to work on bleeding-edge technologies with exponential potential and sponsoring hiring hackathons to identify the best of talent.
To stem the brain drain from academia to corporate, universities need to offer corporations a model where academicians can add value to corporations while staying inside the university and keep the pipeline brimming with young talent. Privately funded research from a corporate perspective could be a useful way to engage professors while keeping them available to be able to develop fresh professionals. Data Science and AI professors at institutions may not simply be interested in studying, but also generating research with wide applicability. Universities with a strong financial muscle and backing of public and private agencies would be able to support such aspirations of professors and help them continue to stay relevant in the subjects that are highly relevant to the workforce today.
Re-Educate Academicians in Data Science and Artificial Intelligence
While universities make strategic moves required to increase their muscle to improve research capabilities, they also need to consider training more of their faculty members to address classroom requirements of students wanting to study AI. Universities need to augment their training curriculum for faculty to infuse subjects that can help them take up AI as a subject for students.
For instance, technical institutions are typically rich in academics that impact computer science curriculum; additional subjects such as machine learning, deep learning, statistical methods and data engineering will help them become better-rounded professors, able to teach AI concepts to students. Similarly interested candidates from the pure science faculty – such as math and statistics – can be trained in computer science methods. Such cross-pollination of skills would help create a better talent pool available to serve a larger base of students.
Engage Industry for Academic Internship Programs
Finally, universities need to promote hands-on skills in artificial intelligence among academia by developing corporate internship programs. Through this intervention, university faculty will be able to broaden their understanding of real-life applications of AI – the application of topical AI solutions to solve relevant business problems.
At present, a small number of universities do provide their professors with opportunities to collaborate on industry-specific use cases. For faculty that gain exposure to such programs, it can be a truly transformational learning experience – and one that they can replicate in their classrooms for enabling better guidance for their students. Universities that boast of such industry connects become automatically more appealing to prospective students – as they enter the campus knowing that they will learn material that is truly relevant to the age that we live in, rather than having just a cursory, booking understanding of AI-related concepts.
Reorienting existing academia and bringing in a supply of talented young researchers in the field of Artificial Intelligence should be the top priority for universities today globally. With the high demand for this technology today and abundance of impactful use cases, it is critical that we keep the tap running and bringing in more researchers and academicians is a critical part of the solution that can help keep the AI revolution going.
Since this is an Engineers’ day Special, we have used some quotes from different professionals
“On this Engineers’ Day, we pledge to make engineers intelligent designers with ideas instead of making them screwdrivers,” says. Ravi Raj, Brand Head, Director, Sales & Support at NetRack
With the advancement of technology, both the industry and the government is focusing and welcoming the fourth state of Industry revolution: Industry 4.0 which enables the wide range of digital concepts especially in ESDM Industry in multiple ways by making engineers and the technology leaders more flexible to adapt and meet the new demands of the market easily. On this special occasion of Engineers day, we at NetRack would like to congratulate all engineers across the globe for bringing the wave of innovation and solution leading to faster sustainable and profitable future of India.
Every year, more than 20 lakh engineering graduates passed out from their colleges but without having their practical or skillful experience to contribute to the industry as a whole. And, in this dynamic industry, the scenario is witnessing more in a magnified way and which needs specialized and skills to cater its requirements. The only solution is emphasizing on their skills and offering them specialized training from the operational level to even the engineers’ level. We have also come across, very few colleges/ engineering schools have not stressed this issue so far.
On this special occasion, we as one the key Industry leader should take the pledge to not only focus to make them skillful but intelligent designers with new ideas. However, this, in turn, helps in fulfilling make & create (in)n India initiative with innovation.
However, we are thankful to all the engineers for their highly valuable expertise and dedication and wish them all the very best for future endeavors!!
“Emphasizing more on hands-on training to expose engineers’ to the real world to make them job ready”, says Adam Paclt, CEO, IceWarp on this Engineers’ Day
It is the fact that science and technology are the spine of any country to scale-up its growth development. Similarly, for any country’ economy, investment in skilling and reskilling the engineers’ is the necessity to enhance their knowledge both technical and vocational skills along with transferable and digital skills to make them job ready
we have to train our young and aspiring engineers who are committed to driving development by adopting the best practices of Industry 4.0 to transform the industry. For this, the major area where we at IceWarp believes that the Industry and academia have to jointly take a step forward in building and filling the Industry-academia gap by incorporating skills-based courses in their curriculum of engineering degree.
On this Engineers’ Day, we pledge to help the young engineers to unleash their true potential and discover their true self by giving more emphasis on the principle of hands-on practical training exposing them to real-world situations and reasoning.
Companies should also change their working culture by offering an apprenticeship programme which in turn will provide hands-on exposure to high-value engineering skills in an industrial environment. Moreover, Industry ’s the mission must promote the cooperation, not competition by adopting the holistic approach to connect with a variety of personas and to become an agent of change.
“We salutes the spirit of all Indian Engineers, whose innovations have contributed to the world’s Digital Transformation journey across industries,” says Mr Krishna Raj Sharma, Director & CEO at iValue InfoSolutions:
We at iValue have solution offerings which cater to the Digital transformation needs of the customers. It is important to skill the engineers and re-skill them time and again on the latest technologies so that they are abreast and capable of giving better and optimum solutions in order to address a customer’s DX journey. We firmly believe in enabling our women employees on technology and we began this exercise by hiring campus recruits and ensured they travel through the complete training cycle of solution sales journey and are ready for facing customers and partners addressing Industry Revolution 4.0. across multiple continents. There is a paradigm shift in the way the business is done in the IT fraternity. Hence, it is of prime importance that the channel community ensures there is a constant innovation in GTM and technology adaption as it will play a major role in creating a differentiator in the market. iValue salutes the spirit of all Indian Engineers, whose innovations have contributed to the world’s Digital Transformation journey across industries.”
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We are well and truly in the midst of the AI revolution. Research houses, academicians, think-tanks, business and technology leaders all agree upon the significant value waiting to be unlocked through the positive and progressive use of Artificial Intelligence – by re-engineering the old and envisioning the new. According to a research by Gartner, organizations using cognitive ergonomics and system design in new artificial intelligence projects will achieve long term success four times more often than others. Citing research by the MIT Center for Digital Business, from a competitive standpoint, companies that embrace digital transformation are 26 percent more profitable than their average industry competitors and enjoy a 12 percent higher market valuation.
The writing is on the wall. Intelligent business interventions made through AI will, to a large extent, define if your business will be an industry leader or a laggard tomorrow. And with that end in mind, businesses are rapidly changing their mindset and approach to AI – from topical experiments performed by forward-thinking business units, to more of a strategic mandate for enabling competitive differentiation. Businesses realize that for truly unlocking business value, they need to not only weave AI into the fabric of their enterprise, but also operationalize it – with the right personnel and change management initiatives. Given that AI can bring both cost efficiencies to business as well as potentially new revenue streams, businesses today are exploring an ‘AI Transformation’ – moving the dial on what is truly possible through a business model, engineered around AI. To enable your organization to do so, here are three powerful ideas to map the AI Transformation journey of your business.
Ensure Enterprise Readiness to Build and Adopt AI
The first step in the journey to AI Transformation for your enterprise is to understand and address if there are any disparities between your vision for AI and the ability of your organization to follow through with it. To that end, it is important to assess just how ready your enterprise is, in its current state, to build, deploy, adopt and benefit from AI-centric solutions. Ideas for AI Transformation need to be communicated clearly and grounded in the realities of organizational capabilities. When they are not, even the best intentions can go awry.
To do so, it is critical that business leaders measure their current AI maturity and assess the availability of internal skills. This will enable you to baseline just how empowered your current workforce is to develop industry-leading AI solutions. Once such a baseline is established on workforce readiness for building and adopting AI-led solutions, organizations need to start improving on these metrics – through internal trainings and external capability augmentation.
By developing this baseline score for AI readiness – organizations can have an objective view of where they are, how far they need to go and what the potential milestones to be achieved are in the journey to AI Transformation. This sort of pre-survey, combined with relevant training and assessment can help organizations craft a relevant roadmap with realistic timelines, as well as concrete actionables.
Build an AI ‘Win Team’
An AI Transformation is not unlike an extremely complex business re-engineering exercise. It entails massive changes – from the way you do business to how you run internal processes and staff multiple business units. Not only is it important to reskill a huge section of the workforce, there is also an important aspect of enabling change management to reinforce the importance of an AI-centric mindset.
To overcome this challenge, enterprises need to foster the consensus and engagement of a ‘win-team.’ This win-team would typically comprise functional and technical leaders who would be responsible for enabling the AI Transformation within their business units – from orienting the employees to the new mindset and ensuring capability readiness for the tasks at hand. On one hand, functional leaders can help their teams identify the processes that can be re-imagined using AI and manage resistance to change. On the other hand, technical leaders would lead the solutioning of technical components, while setting the training priorities and calendars for the workforce.
On change management, enterprises need employees to clearly appreciate the topline and bottomline benefits of an AI Transformation and focus towards enabling it. Employees stand to benefit themselves – as the professional benefits of making this transformation will accrue for their future. To further explore how companies can reduce the defensiveness in implementing AI-led processes further, they could also set innovation objectives for stakeholders as part of their performance metrics. Doing this will help create a strong alignment between individual, team and organizational objectives. A key aspect of AI transformation is ensuring large-scale adoption and usage of AI-powered solutions. AI applications typically fare better with every incremental user feedback and enriched data sources. Adoption and continuous use is a key parameter for the success of this transformation.
Integrated Business Processes over Siloed Business Functions
For years, the view of technology transformation and procurement has been of one that happens at a department / functional level – HR teams buy talent management software, finance teams sanction the purchase of accounting software, and CRMs get implemented to aid the efforts of sales teams. While this serves small technology initiatives, a sea-change is required for progressing an AI Transformation. To foster this, enterprises need to make a shift from a siloed, function-centric mindset to an integrated, process-centric mindset.
This is because AI use cases can often span multiple business units and functions, while tapping into multiple data sources for providing cross-team value, seamlessly. The very nature of AI deployments thus requires a process-centric view, with a strong consensus and buy-in from multiple stakeholders. Furthermore, the budget for purchasing AI services / applications is likely to come from the allocations of multiple beneficiaries across functions. This makes it all the more imperative that enterprises deprioritize functions in favor of processes.
An AI Transformation is doubtless the most strategic subject to be tackled by organizations today. Successful transformations will ensure enterprises go beyond mere automation and cost-cutting strategies and unveil previously unseen business and revenue opportunities. It is also extremely important to consider the role of digitization in building a new technology infrastructure that is AI-ready – possibly decentralized, cloud-based and highly available. There is now an urgent need for business leaders to have more than just a superficial understanding of AI and its successes. They will now be tasked with building and delivering a concrete, value-oriented roadmap for enabling a key transformation in the history of their organizations.
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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.
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Numerous studies in the past have shown that an engaged workforce significantly out-produces an unmotivated one. And even a greater number of those studies reinforce the common notion that employee creativity is the key to innovation and eventual customer or client satisfaction. To a great degree, for most industries and organizations, talent is one of the most treasured asset and to an extent a key differentiator in the marketplace. Every company needs creative team, decision makers and visionaries, but on a similar note, it’s equally important to have a motivated workforce who can give their all for the cause day-by-day. As clichéd it may sound, Analytics truly is one of the industries where the average churn or attrition rate has typically been on the higher side (as compared to industry average in general) due to great demand for Analytics experts in the industry across the globe and dearth of skills required. I presume all my readers would concur on the perennial challenge of analytics talent crunch most businesses are grappling with today. And even if you find the so-called right analytics resource that align with your skills requirements, you’re bound to chart out engaging career prospects and develop that talent over what could amount to a decade before that person is achieving optimum results for your company.
Analytics is all about working with an extremely talented & creative set of people, who need constant care and attention from their leaders & mentors. And undoubtedly, most businesses yearn to maximize output and increase topline, they need to have their team working as effectively as possible. But how should they go about this? A generally accepted phenomenon that happy workers tend to be productive ones – meaning there are clear benefits to keeping people engaged and motivated.
When it comes to job satisfaction, financial rewards may be lower on the list than most people think and as I reflected in my previous posts. Being happy with the job seems to depend more on the intangibles: feeling part of a team and being valued and appreciated consistently outrank money when employees are polled about job satisfaction. It’s all about keeping employees highly engaged & give them the due respect they deserve. Engaged analytics talent learns, grows, displays high leadership quotient, deliver heightened ROI and significantly decrease your organization’s turnover rate. However, keeping a team that approaches work with vigor and passion intact is easier discussed than executed.
Every other company which employs analytics services or is outsourcing it wants to hire and keep the best of the breed talent – how do you stay a step ahead? Clarity on company’s mission that they are driven to lead themselves is all commonplace and is a sanity in Analytics as with every other industry. And if an employee isn’t engaged creativity is not present! So what truly keeps this evolved species engaged enough?
- Where in the Organization your Analytics Talent sits
First & foremost, as leaders, we must acknowledge that Analytics talent is different from rest of the organizations and placing them rightly within the organization is crucial to ensure they are truly able to create the real impact. Bright minds cannot be chained or siloed or put in a bureaucratic hierarchy, lest they tend to lose their sheen and may end up attriting or be sitting ducks stuck up in the usual business stream where their skills are definitely not put to the best of use. Analytics talent should be led by analytic leaders who know this industry in & out; because they are the ones who understand how this talent needs to be groomed & nurtured, and shielded from the political bureaucracies and the analytics leaders should effectively communicate those differences throughout the organization constantly. Separate HR policies, working culture and operating rhythm is required to give these prodigies a conducive environment to deliver their best. They should acknowledge different processes for them, they should have different technical ladders, different job expectations. It must be thoroughly acknowledged that they have different motivations and is the organization in the capacity to carve out that special niche for thriving Analytics talent. Whether you keep them together in their own close knit group so they can keep their skills sharp with constant interaction with each other, or should they be spread across company’s business units because that is where they must have an impact?
It’s important for your analytics talent to garner complementary skill sets. Obviously, you intend to build a team with eclectic mix of skills instead of having all people who are good at data massaging or modelling or all people who do optimizations or visualizers. Analytics is a broad space and there are umpteen specialties, and piecing together these different puzzle elements is the key to generating impactful business insights. As an analytics leader because you understand the space, you understand these specialties. So to be a good leader of such a talented team, you really have to focus on the individuals on your team and help them succeed.
- A Robust Career Path
Talent always needs a clear future vision on their career trajectory & growth within the organizations with distinctive career paths through career ladders and lattices. A clarity/transparency on roles, career tracks and skill expectations has to be in place to affirm that your analytics professionals have a well thought through career roadmap charted out. Structured capability building, systematic learning and development frameworks could be crucial to ensuring that career architecture plan is laid out appropriately.
- A Rock-solid Training Regime
An effective training and development plan is one of the best ways to convey to your valued employees the commitment and faith you have by investing in them. And, when you consider this investment in the long run with a way higher ROI, the expense of a solid training program suddenly seems measly. With the pace the Analytics industry is maturing, it’s crucial that your talent is in tune with the current needs in the industry, has hands-on experience with the topical tools & technologies and is abreast with the latest and greatest techniques being used today to deliver business impact. Just to substantiate with an example here, R being open-source has tons of pre-built libraries & many more keep getting added to the repository, avoiding duplicity of effort and ensures optimal procedures/techniques are being employed which have proven to be effective & accredited by experts in the wider public analytics community. Smart talent is always hungry for more and it’s imperative for the analytics leaders to keep feeding their talent with all the trending skills/tools and keep their talent’s arsenal up-to-date.
- Empower them
Give your talent the wings of freedom, to innovate, to be creative, basically get out of their way and empower them, give them the requisite tools to deliver. Don’t keep them shielded from the real-world all the time & get them the exposure to be deeply embedded in the C-suite. The senior executives want to make decisions based on data and they trust this talent tremendously. Get them in front of the clients or the C-suite and give them the platform to talk to them directly as to what they are doing. That’s what keeping them there, as they feel that they are in the driver’s seat helping businesses navigate in a highly competitive, relentless environment. Highlight in appropriate forums about their achievements & the impact they have made.
- Business Exposure
Analytics professionals need to be spending a sizable time(between 6-10 years) to industry-specific challenges before they can have the right context to understand the problem well and know exactly know what’s needed & what not to go about finding a solution. This is among the scariest challenges which majority Analytics leaders across the globe are trying to figure out and still poses a big question mark for most on how to get their analytics talent a business bent of mind. Continuous exposure to the business, ongoing domain-specific trainings are hence an important ingredient to their success & keeping them relevant
- Challenging Work Environment
Throw all sorts of challenges and varied projects at your talent. Be it as far away from their comfort zones & let them drown in it. This is what they truly enjoy doing and they love finding their way out from the middle of nowhere. Organize week long hackathons, competitions & give them stringent timelines to deliver. Give them the opportunity to make the impossible possible. The sense of achievement & gratification after working their way out from challenges unimagined in their wildest dreams is what keeps them going. Let them mingle up with the wider Analytics talent beyond the walls of the organization; let them participate in global competitions organized by Kaggle, 5th Elephant, VCCircle, Analytics Vidya etc. Motivate them to join interesting discussions pertaining to Analytics online (twitter, blogs, linkedin communities) and make a contribution, be an expert advisor in the area they specialize in.
- Be the “Employer of Choice”
Value your talent, get them the requisite support and developmental environment from mentors & other experienced professional in your Analytics team, pay them well, recognize their contributions, and give them the opportunity to rise up the career ladder. Start collaborating with academic institutions from where you hire, increase interventions, do guest lectures, seminars, workshops, mentoring sessions, case competitions, “Shadow a Leader” program etc to be constantly engaged with your pipeline talent.