To explore ways in which amalgamation of technologies can bring digitization of the traditional grid.

Keywords

AI, Covid-19, Sustainable, Electricity, Smart Grid.

Bio

Leena Patel is Founder and CEO of Global entrepreneurial system GES one soul army certified by CERN & NASA. She is from Ahmedabad, Gujarat, India. Leena Patel’s hard work and dedication had resulted in being awarded 19 International Awards and several Titles. In addition, Leena Patel is a Brand Ambassador at 4 International/ National Associations, and she is a World Record Holder for being an EDUCATOR & EDUPRENEUR.

Abstract

The global coronavirus (covid-19) pandemic resulted in humans taking a pause from their mundane lives. It has facilitated individuals to act in retrospect and react thoughtfully to the new normal way of living on earth. In addition, they have started to think about how to share common resources due to the rise in cost of living. Electricity has played an important role in fueling industrial, commercial, and household appliances. Simultaneously, it has contributed to the running of technology, social media, and communication equipment, which was highlighted during the lockdown period, as it was utilized to keep humans informed and connected. Not all energy generation techniques are sustainable, resources such as natural gasses, coal, and nuclear resources used in electricity generation are limited. Therefore, finding sustainable alternatives for electricity generation, will help humanity greatly in future events like covid-19 pandemic. In conclusion, this work presents a cumulative analysis of sustainable alternatives through which electricity generation can occur, highlighting limitations and presenting novel AI-driven approaches to conserve different forms of renewable energy and use them to generate electricity. Ultimately, we all look for the innovative dimensions of life-standard, to achieve and aspire with the goals of 5th industry revolution as well in the future.

Introduction

Fulfilling demands of electricity has always been a challenge for several developing countries. This has become more complex and difficult as pandemic has created a great impact on communities, including the use of electricity patterns in our day-to- day life. As per a study conducted by Abdeen, Kharvari, O’Brein and Gunaya (Abdeen, Kharvari, O’Brien, & Gunay, 2021), a few areas in Canada have increased significantly heightening from 16.3% to 29.1% every day after COVID-19. Such trends have been observed by major distributors across the globe and have resulted in emergence of newer ways of electricity generation (Abdeen, Kharvari, O’Brien, & Gunay, 2021) to cope with the rise in demand. Global push towards reducing carbon emissions resulting from using traditional fossil fuels for electricity generation has drawn significant attention towards renewable energy resources such as wind power, solar energy, hydropower, tidal energy, hydrogen etc. as an alternative means to generate electricity. Despite having numerous benefits – never ending & replenished time to time; one time cost of installing needed machineries; less maintenance; promote well-being of remote areas as chances of generating renewable energy are higher over such regions; capacity of recycling waste in the form of biomass energy; lesser reliance on imported energy – of renewable sources, integration of renewable energy into electric grid is facing challenges mainly due to their variable and uncertain nature (Shi, et al., 2020).

Uncontrollable power output generated from Variable Renewable Energy (VREs) (IRENA, 2019) has necessitated the initiation of novel methods of energy storage and dispatching energy to the grid later to handle peak load duration (Shi, et al., 2020). Also in the traditional grid structure, the transmission and generation are the dominant elements which are monitored in real-time and controlled. This is due to the fact that there are numerous unauthorized “connections to the power grid”. This indicates colossal energy is not being accounted for through meter readings with financial implications (Shi, et al., 2020). This contributes to a major challenge due to heightened CO2 emissions, decreased efficiency and increase financial investment to help find solutions. To integrate these VREs and overcome limitations of electric grid structures, many developed countries have started investing in a new version of grid – “Smart Grid” (Shi, et al., 2020).

The Smart Grid is defined as “an electric system that uses information, two-way, cyber-secure communication technologies, and computational intelligence in an integrated fashion across electricity generation, transmission, substations, distribution and consumption to achieve a system that is clean, safe, secure, reliable, resilient, efficient and sustainable” (Shi, et al., 2020). The characteristics of smart grid mentioned in below figure require installation of new devices at each stage of grid – Smart Meter & Home Area Networks, Photovoltaics, Electric vehicle Charging Stations & Micro grids, Newer means of energy storage, Heat & Power Co-Gen Facilities, Solar Thermal & Wind Farm Generation, etc. (Shi, et al., 2020).

Smart meter can be perceived as a heterogeneous that measures electricity which is inputted into a grid. There are multiple benefits of such an advanced energy system and users. Using smart meters, the major issues like unjustifiable bills, back billing etc. can be resolved because of the advantages of measurement accuracy. It provides the profits including the lowest and precise measurements of the energy use at regular intervals. The financial commitments only pertain to energy utilized. Also, the smart meters are

• Vast amount of data generated due to use of IoT- empowered smart meters (IRENA, 2019), replacement of traditional SCADA systems by Phasor Measurement Units (PMUs), smart home appliances,
• Decentralization with increased deployment of small renewable power generations
• Added demands of electricity load i.e. electric vehicles, boilers, etc.
• Intermittent & discontinuous nature of renewable energy resources (particularly wind & solar)
• Bi-directional flow of electricity has enabled new challenges to ensure smooth operation.

Figure 1 (shi, et, al., 2020)

Figure 2 (IRENA, 2019)

Figure 3. (Mhlanga, 2006)

(IRENA, 2019) presents how technologies like, Artificial Intelligence and Big Data, Internet of Things (IoT) and Blockchain. When used together might result in powerful tools to deal with complexity introduced because of the above factors (IRENA, 2019), (Ahmad, et al., 2022) represent this amalgamation of technologies that can significantly contribute to the modern power sector at different stages of the energy industry i.e. the production of electricity, delivering power, storing energy and electric distribution networks.

Artificial intelligence and machine learning can facilitate optimum generation of power. Figure 3 demonstrates how applying AI and ML within energy sectors can be advantageous within countries like Africa.  According to figure 4 some solutions include “performing predictive maintenance of turbines, the ability to accurately predict energy prices, AI and ML to correctly determine energy demand” (Mhlanga, 2006).

This paper presents a survey on three major key points on the effectiveness of AI algorithms in ensuring stability and reliability of the power grid. Those key points are mentioned and discussed in the next chapter.

Figure 4. (Mhlanga, 2006)

2 Role of Technology in Integration of Variable Renewable Energy (VREs) into Power System:

This chapter presents a few key points of review done on effectiveness of AI algorithms in ensuring stability and reliability of power grid by:

Figure 5. (Ahmad, et al., 2022)

Accurately forecasting renewable energy generation

It helps in safe grid operation & minimizes the operational cost of energy sources (Ahmad, et al., 2022). In addition, “AB-Net” is a new architecture that is formed pertaining to a forecast consisting on one step toward regenerating for horizons in the short-term. This can be achieved through integrating an autoencoder (AE) together with a bidirectional long short-term memory (BiLSTM).

According to this research done by researchers of Sejong University of Korea, to solve the forecast problem a new architectural development was made. It ensures that a hybrid connection is initiated between the BiLSTM network and AE. The process entails data clearance through refinement and preprocessing. Feature collection is conducted through the refined sequence as it is processed to the AE. The featured attained from the AE are then provided to the BiLSTM so the concluding forecast can be attained. RES power can be forecasted accurately through this process because this proposed approach can learn compressed representation from the sequential input data. The method that has been proposed will facilitate the avoidance of wasting energy production via reducing the production of excess energy power. The algorithm that has been proposed will aid smoother cooperation between the smart grid and the consumers. Through utilizing data that is available on the public domain, there was an increase in performance levels in comparison to other techniques. Figure 5 highlights the framework of the architecture proposed.

Incorporating predictive maintenance

The maintenance and deterioration of a turbine can be accurately predicted via analytics. This can be achieved through sensor data recorded from a wind turbine (Xu, Pan, Chen, & Fu, 2019).

This paper (Rodriguez, et al., 2023) talks about three types of maintenance as below:

• Corrective
• Preventive
• Predictive

In Corrective maintenance when the product was damaged then and only maintenance was performed so it is already prompted that seems such an inefficient way to apply. Corrective maintenance is not that much effective so preventive maintenance and predictive maintenance are widely used and because of that both are popular too. The feasible way to perform the maintenance is between two strategies, proactive and reactive is known as condition-based maintenance (CBM). It is focused on constant monitoring and prior to any failures happening they are detected by condition monitoring systems (CMS). This is achieved through obtaining data from sensors which is then pre-processed, after which data is evaluated and interpreted. The predictive maintenance can also be referred to as proactive maintenance. There are three types of predictive maintenance observed with real time data to be or not: “1. Based on existing sensors 2. Based on Supply sensors 3. Based on signal techniques” (Rodriguez, et al., 2023). When predictive maintenance is applied it subsequently links to the big data paradigm. This then deals with the data

Figure 6 (Eseye, Lehtonen, Tukia, Uimonen & Millar, 2019)

management methods which include the Cross Industry Standard Process for Data Mining (CRISP-DM), Sampling, then exploration, modification, modelling and accessibility processes are followed after which the Team Data Science Process (TDSP) is completed (Rodriguez, et al., 2023).

Predicting consumer demands

It talks about effective application of Genetic Algorithm (GA) in forecasting the demand for electricity within smaller decentralized energy systems that are being initiated in smart grids (Khan, et al., 2021).

BINARY GENETIC ALGORITHM (BGA)

The theory of evolution and genetics by Charles Darwin has inspired the GA population-based heuristic type optimization method. It is based upon the survival of the fittest (Eseye, Lehtonen, Tukia, Uimonen, & Millar, 2019).

FS RESULTS EVALUATION: IMPROVED FORECASTING

As per the forecast results of Feature Selection approach in Figure 6, the model testing has been completed on a randomized selection of dates. This included the following: during the summertime Wednesday 26th July, 2017, summer weekend Sunday 16th July 2017, a fall weekend included Thursday 12th October,2017, another fall weekend Sunday 1st January 2017, a Spring weekend included Tuesday 18th April 2017 and another Spring weekend selected was Saturday 8th April 2017. Information gathered from these dates highlighted the importance of forecasting models in ensuring the optimal quality of the energy is supplied but at a low cost. Figure 6 highlights Machine Learning Based Integrated Feature Selection Approach for Improved Electricity Demand Forecasting in Decentralized Energy Systems.

Conclusion

This paper has explored ways in which amalgamation of technologies can bring digitization of traditional grid. When combined machine learning & Internet of Things with smart grid, it will result in better analysis and tighter control at all the stages of the energy sector from power generation to power distribution. Also, the goal of this research was to make the energy industry realize the possible contributions of AI and machine learning ML technologies. This may help to develop future advancements and tools with the formulation of small-scale decentralized systems for the growing nations across the globe.

Funding: This research received no external funding.

Data Availability Statement: We have done a survey on the researched data for the research paper title.

Conflicts of Interest: Author declares no conflict of interest.

References

Abdeen, A., Kharvari, F., O’Brien, W., & Gunay, B. (2021). The impact of the COVID-19 on households’ hourly electricity consumption in Canada. Energy and buildings, 250.

Ahmad, T., Hongyu, Z., Dongdong, Z., Rasikh, T., Bassam, A., Ullah, F., . . . Sultan, A. (2022). Energetics Systems and artificial intelligence: . Applications of industry 4.0. Energy Reports.

Eseye,  A.  T.,  Lehtonen,  M.,Tukia, T., Uimonen, S., & Millar, R. J. (2019). Machine Learning Based Integrated Feature Selection Approach for Improved Electricity Demand Forecasting in Decentralized Energy Systems. IEEE Access, 13.

IRENA. (2019). Artificial intelligence and big data innovation landscape brief. Irea , 1–24.

Khan, N., Ullah, F. U., Haq, I. U., Khan, S. U., Lee, M. Y., & Baik, S.W.(2021). AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting. Mathematics, 2456.

Mhlanga, D. (2006). Artificial Intelligence and Machine Learning for Energy Consumption and Production in Emerging Markets. Johannesburg: The University of Johannesburg.

Rodriguez, P., Marti-Puig, P., Caiafa,C., Serra-Serra, M., Cusidó, J., & Solé-Casals, J. (2023). Exploratory Analysis of SCADA Data from Wind Turbines Using the K-Means Clustering Algorithm for Predictive Maintenance Purposes. Machines.

Shi, Z., Yao, W., Li, Z., Zeng, L.,Zhao, Y., Zhang, R., . . . Wen, J. (2020). Artificial intelligence techniques for stability analysis and control in smart grids. Methodologies, applications, challenges and future directions Applied Energy.

Xu, W., Pan, Y., Chen, W., & Fu,H. (2019). Forecasting corporate failure in the Chinese energy sector: A novel integrated model of deep learning and support vector machine. Energies, 2251.

The post AI Based Sustainable Electricity Generation (AI-SEG) appeared first on GRJ.

To analyse AI’s role in fostering sustainability and resilience in the modern world. Explore ethical concerns surrounding AI, such as algorithmic bias, data privacy, and potential job displacement.

Keywords

AI, Sustainability, Resilience, Ethics, Transformation

Bio

Roxanne Boodhoo is an accomplished professional with a diverse and versatile background. Her extensive academic training has equipped her with various skills and knowledge, enabling her to excel in multiple roles. Roxanne is known for her strong work ethic, diligence, and commitment to undertaking any responsibilities assigned to her. She is deeply passionate about helping and supporting others, making her a compassionate and empathetic individual. Throughout her career, Roxanne has consistently demonstrated a dedication to making a positive impact through her professional work or community involvement, striving to uplift those around her.

Abstract

As artificial intelligence (AI) permeates the digital world, its transformative potential for a sustainable and resilient future is undeniable. This review examines the existing literature on AI applications, exploring both their transformative capabilities and the ethical challenges that must be addressed.

Aim: To analyse AI’s role in fostering sustainability and resilience in the modern world. The review investigates how AI can be leveraged to address environmental challenges, optimise resource management, and reinforce societal preparedness for disruptions. It concurrently explores ethical concerns surrounding AI, such as algorithmic bias, data privacy, and potential job displacement. The study employed a systematic review of academic research on the intersection of AI, sustainability, and resilience. The analysis focuses on AI applications in areas like climate change mitigation, disaster response optimization, and its contribution to building a more sustainable future.

This study highlights AI’s potential to revolutionise sustainability efforts. This includes advancements in renewable energy production, disaster prediction modelling, and personalised environmental solutions. While acknowledging AI’s transformative power, the study emphasises the need for robust ethical frameworks. Mitigating bias in AI development, ensuring data privacy, and fostering human-centric AI applications are critical for maximising positive outcomes.

AI presents a powerful tool for building a more sustainable and resilient future. However, responsible and ethical development is paramount. The review underscores the importance of continuous research and collaboration to harness AI’s potential while mitigating its risks, ultimately creating a future where technology and ethics work in harmony.

Introduction

The fundamental objective of this paper is to address the potential of AI in a global landscape and the challenges of AI ethics to determine the path for a sustainable and resilient future. We are going through rapid technological advancements in AI which is driven by big data, powerful computing infrastructure, and advanced software algorithms. The rapid development of AI can indeed greatly facilitate tackling global challenges such as healthcare, lifestyle, agriculture, energy, safety, and infrastructure. But this fast-growing technology merely makes it an extremely challenging task to stay current with and effectively guide the changes with respect to societal values and ethical behaviour. The worldwide acceleration of AI research and applications has ignited a need for institutional, regulatory, and legal initiatives to ensure that AI development and deployment are not only compliant with laws and regulations but also sensible to robust ethical considerations (Kazim & Soares   Koshiyama,   2027) (Dignum,   2022) (Pastor- Escuredo, 2020).

Artificial intelligence (AI) research aims at creating systems capable of making human-like inferences, achieving goals with minimal human intervention, and exhibiting flexible, autonomous behaviour in real-world environments. AI is revolutionising our day-to- day activities concretely through the extensive data collection that enables machine learning, deep learning, natural language processing, and visual recognition from internet search engines, game- playing automata, chatbots, driverless cars, and robot caregivers. Human interaction with AI systems occurs through social platforms such as the Google search engine, Facebook, Twitter, Instagram, LinkedIn, and so forth, where AI algorithms filter content, create posts, and manage interactions. As a society, we are progressively embedding AI in our lives with expectations of improved quality of life.

2. Aim Various anticipate majority of artificial intelligence used today are relaxing experience in areas such as loan qualification, job interviews for decision making, media and customer controlling, leading to benefits such as risk, reduction, improved utilisation of resources, enlarged personalisationand a more convenient and seamless overall experience. However, it is undebated that there also are numerous significant concerns ago this quick brake association among unmarked AI. These embody a deficit on transparency, fairness, quality and accountability, excessive data protection and the registers of ambivalent suggestions which might innocently give unequitable personal analysis. It’s no surprise the testing of misuse such artificial intelligence has opened an encompassing creative intelligence to power regulators in different parts of the globe. If AI transformations manage well meet this enthusiasm and among this publication we provide views and consent specifications to that sign, it should become practical in achieving numerous global sustainable advancement signals something which broadly do many think is possible, in theory.

Artificial Intelligence or AI adoption in numerous digital capacities is growing at a semiautomatic pace. The adoption is mainly due to its potential to transform and streamline numerous digital processes including information recognition, recommendation and deduction. These characteristics have led to increasing use of AI technologies in diverse areas, including autonomous vehicles, advanced communication tools, and robotics. In addition to this, some of the recent developments in AI along with the imminent start of the 5G-oriented mobile era are projected to speed up the overall growth of AI around the globe. In fact, the already growing popularity of AI technologies in emerging applications and involving business emergent has further fueled the growth of artificial intelligence. According to a recent market study, the overall market development for AI is predicted to grow at a CAGR of whether 40.7% between 2020 and 2026. This rapid growth is directly associated with specifically drive uses of AI, majority by many firms, purporting to realise a high return on their investment. It is due to the increasing growth of AI technologies, that we need to comprehend their marketing journey.

Data Collection

The data collection scheme for this study consists of two parts: (1) the secondary collected electronic works, industries reports, and analysis and (2) selected the comparative case study actual practices (Li et al., 2022). A wide range of quantitative and qualitative data collection from secondary sources are considered. Its major limitations notwithstanding because the data were publicly available. Method for the primary data collection is employed through a structured interview with project in-charge or/and process and policy owner of not more than ten of the selected case artist profiles with a maximum of two respondents where required. The number of interviews was conceived to achieve maximum diversity in terms of the artists, gender and their talents. The interviews were semistructured with open-ended questions and were conducted en face in a quiet location, to ensure an efficient and comfortable atmosphere of dialogue. The collected data are subsequently analysed in measurement ranges and rate agreement of hypothesis test and verified by the expert comments by analyse into the consequent statement.

This section presents an overview of the research method that was employed in this study. The method followed in this research attempts to combine diverse approaches in gathering evidence and insights to put forward mixed design and to increase the interpretation that is derived. Proposed research will be following mixed method; the concurrent approach of Mixing applies to different kinds of research in different ways (Muhlenbach, 2020).

Results and Discussion

AI seems to be focused neither on data acquisition and quality aspects, nor on the secure management of data.

It is seen as important, although their importance rate from the respondents (50.00%) can be evaluated as only moderate. Here AI acts as a “blind” method. This means all methods in that area just use the primary heuristic information from input signal, as it does not reduce asymmetry of information during the decision-making process. For “enlightenment” a deeper AI-extended method and mode of cooperation between collaborative humans and such system configuration, which has a low complexity in content and information management as in (Bano et al., 2023). We point out that the system can make decisions at different levels without the explanation of “black box” and the AI- supported modes of cooperation in group systems.

Although the overarching goal is to manage the digital transformation, the AI projects help digital transformation projects to a higher degree (Giralt Hernández, 2024). Approximately 79% of all interviewees estimate that AI projects support the primary digital transformation initiatives, and over 35% of the participants even believe that they have a very high alignment. The digital transformation management challenges of an AI- enhanced project management that result from these findings include the following: AI-enhanced project management should reflect external and internal signs; there are calls for an AI-enhanced project management design that supports organisational agility; it is important to integrate AI project management and the primary digital transformation initiatives; the evaluation of guidelines, models, and standards for AI security and ethics has to be embedded in everyday project work and is rarely a core task of project management planning; and an increased effort is expected in PMI areas in AI- enhanced project management. AI as an evolutionary approach requires the closer examination of the AI- related epistemic ethical decision process at a number of points and key challenges (Samuel et al., 2021).

4.1 AI Applications in Climate Change Mitigation

AI technology can potentially spur innovations and policy solutions by identifying relationship patterns between causal and impactful sequences societal processes and environmental outcomes. It can uncover intricate and non-linear dependencies between climate-relevant circuits, like greenhouse gas emissions, atmospheric parameters, and climate conditions, including seasonal variations and the long-term climate change, evolution. In relation to AI in the digital era, AI models and big data analysis tools have been used to successfully warn people and organise rescue teams before and after natural disasters before extensive loss occurred. AI has also been used in urban planning and to analyse and assess future climate-related challenges, like housing, social, and eco-healthy construction and living conditions. AI technology in health care, namely in PM- related fields potentially acts as the most critical segment of the SDGS’ achievement. Technology can be harnessed even further for recognising and addressing a globally critical societal change factor, subject to climate change.AI can enhance monitoring of wildfire-prone regions and help technological development of fire suppression and rehabilitation strategies (Sirmacek et al., 2022) (Cowls et al., 2021). AI has been applied to early detection and real-time disaster relief support through layer processing of SAR and thermal infrared (TIR) satellite imagery. Research has shown that AI can improve the prediction accuracy of floods, enhance water quality monitoring, and optimise water supply networks and complex chemical models. AI models have been deployed to help corporations reduce greenhouse gas emissions, to manage organisational carbon footprint data, and to forecast methane and CO2 emissions from fossil fuel combustion and land use change.

4.2 AI in Disaster Response Optimisation

Network-oriented AI of the future of electric systems can increase user-centricity and situational awareness, considering all users equally in the future energy regime, to minimise differences between producer, control centre, government, and user. To derive these goals, the paper delves into social identity theory, contextualising the convergence between social relations and technology in the network-oriented and transactive electric systems. The corona pandemic in 2020 underlined the challenges and vulnerabilities in global emergency management and yet emphasises the necessity of connectedness, the awareness of the other, and the global character of humanity. Success in future systems, such as network- oriented governance, will not only depend on the behind- the-scenes intelligence and general rules, but also the prevalent integration of the users and operators of network-owned assets (Pastor-Escuredo, 2021).

The recent development in renewable and decentralised energy resources are a result of emerging global problems, such as resource scarcity and infrastructure weakness, along with advances in digital and artificial intelligence technology. These technologies enabled the transformation of the current centralised energy systems into decentralised, network-oriented, intelligent micro-grids, in addition to challenging big energy corporations as well as small- scale energy cooperatives. Using a social identity perspective, this research aims to investigate the contribution of network- oriented AI that governs beyond corporate management, incorporating the understanding, integration of users, smoothing out differences, and achieving security and sustainability between all the players in and around the grid (Lee et al., 2022).

4.3 AI’s Contribution to Building a More Sustainable Future

Regarding these considerations, it is hypothesised that AI technologies can become integral to achieving the UN’s Sustainable Development Goals (SDGs) by strengthening responses to AI-related challenges, in particular, by orchestrating mechanisms which allow computational decision-aids for decision-critical areas of human societies, also balancing transformations and ethical considerations as a dwelling environment for future generations. Furthermore AI can assist in developing a more sustainable portfolio of everyday life choices and sustainable options ,. Moreover, it fosters changes to the very cultural landscape in order to integrate these options as common/ingrained, broadening its perspective. Now, to work towards a technically sound sustainable AI, a comprehensive toolkit of sustainability criteria and indicators for AI system design-as well as management decisions has to be elaborated from a holistic and theoretically and empirically validated perspective.

AI and broader data science methods and technologies have attracted interest across multiple sustainable development sectors (Rohde et al., 2023) (Hermann et al., 2021). Among potential building blocks for sustainability, research efforts have focused on designing and developing AI algorithms, digital solutions, technologies and applications, such as healthcare data classification. In another realm, environmental monitoring systems integrating, for example, Internet of Things (IoT), remote sensing, and data

analytics, aim to obtain and interpret environmental information. Figure 1 illustrates the four foundational technologies underpinning IoT applications in the Metaverse are:

Responsible AI: This enables engineers and data scientists to clearly understand and explain the inner workings of AI algorithms.High-Speed Data Communication: This supports real-time connectivity through advanced technologies like millimeter wave (mmWave), non-orthogonal multiple access (NOMA), massive multiple-input and multiple- output (MIMO), terahertz (THz), and visible light communication (VLC).

MEC (Multi-access Edge Computing): This provides the Metaverse with substantial server resources to sustain the virtual 3D environment and offers a network with ultra-low latency for nearly instantaneous responses.

Digital Twins: These create virtual replicas of physical objects or services, delivering users an immersive experience that mirrors the real world.

Hence, AI offers opportunities to develop and maintain sustainable and balance ecosystems. Moreover, AI might solve deep societal issues and ensure common welfare as a response to anticipated challenges in an ever-changing and interconnected world. For business stakeholders, it supports new product development, competitive disruption and risk management.

Conclusion

AI, if properly harnessed and combined with human compassion and communication, will drive a monumental leap forward for cancer care in the UK and beyond. It is however possible that the speed of technological development has overtaken the pace of professional moral and ethical education, as society has not evolved at such a tempo (LaCroix & J. D. Prince, 2023). Consequently, the balance of technological development with the cultural, moral, and ethical complexity of our world is at risk of falling into disarray. The Allen Fairbairn Vision for Ethics Series Paper took the opportunity to consider the ethical implications across a range of healthcare settings for AI using the Stackelberg Anticipative Game formalism with a mean field approach. Key perspectives from international contributors are used to explore ethical considerations in oncology. Suitable definitions, and argument, are advanced for the necessity of a robust prioritisation framework that adapts to evolving data. Ethical frameworks necessitating observing the basic tenets of responsible AI, addressing biases, highlighting the ethical need for the translation of a “trust understanding” and Ability conditions are considered.

The integration of AI in various domains, including healthcare, brings unprecedented potential for human advancement, pioneering new treatment modalities, supporting prioritisation and management of patient care, as well as enabling operational efficiencies and cost savings (Fabrice Djete, 2023). The value has been considerably recognised as solutions have been used to save lives, particularly during the COVID-19 response, and are potentially pivotal for delivering care during future pandemics. It opens up exciting possibilities for translation of AI system / technology integration and will require further queries into safe and effective deployment of advanced digital applications in “routine” practice from an efficiency, economic and ethical standpoint. Amongst the key ethical considerations are organisational responsibility for maintaining standards to safeguard informed consent, ensuring human oversight of AI decisions, transparency in AI decision-making, and addressing biases and impact on workforce (Zhu et al., 2021).

References

Bano, M., Zowghi, D., Shea, P., & Ibarra, G. (2023). Investigating Responsible AI for Scientific Research: An Empirical Study.  [PDF]

Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2021). The AI gambit: leveraging artificial intelligence to combat climate change— opportunities, challenges, and recommendations. ncbi.nlm.nih.gov

Dignum, V. (2022). Relational Artificial Intelligence. [  PDF]

Fabrice Djete, M. (2023). Stackelberg Mean Field Games: convergence and existence results to the problem of Principal with multiple Agents in competition. [  PDF]

Giralt  Hernández,  E.  (2024).  Towards an Ethical and Inclusive Implementation of Artificial Intelligence in Organizations: A  Multidimensional Framework.  [PDF]

Hermann, E., Hermann, G., & Tremblay, J. C. (2021). Ethical Artificial Intelligence in Chemical Research and Development: A Dual Advantage for Sustainability. ncbi.nlm.nih.gov

Kazim, E. & Soares Koshiyama, A. (2021). A high- level overview of AI ethics. ncbi.nlm.nih.gov

LaCroix, T. & J. D. Prince, S. (2023). Deep Learning and Ethics. [  PDF]

Muhlenbach, F. (2020). A Methodology for Ethics-by- Design AI Systems: Dealing with Human Value Conflicts.

Pastor-Escuredo, D. (2020). Ethics in the digital era (PDF)

Pastor-Escuredo, D. (2021). Future of work: ethics.  [PDF] Rohde, F., Wagner, J., Meyer, A., Reinhard, P., Voss, M., Petschow, U., & Mollen, A. (2023). Broadening the perspective for sustainable AI: Comprehensive sustainability criteria and indicators for AI systems. [PDF]

Samuel, G., Chubb, J., & Derrick, G. (2021). Boundaries Between Research Ethics and Ethical Research Use in Artificial Intelligence Health Research. ncbi.nlm.nih.gov Sirmacek, B., Gupta, S., Mallor, F., Azizpour, H., Ban, Y., Eivazi, H., Fang, H., Golzar, F., Leite, I., I. Melsion, G., Smith, K., Fuso Nerini, F., & Vinuesa, R. (2022). The potential of artificial intelligence for achieving healthy and sustainable societies.  [PDF]

Zhu,  L.,  Xu,  X.,  Lu,  Q., Governatori, G., & Whittle, J. (2021). AI and Ethics — Operationalising Responsible AI. [  PDF]

The post AI In The Digital Era: Balancing Transformation And Ethics For A Sustainable And Resilient Future appeared first on GRJ.

The aim of this research study is to examine the differences and intersections between AI, ML, and CI, highlighting their roles in technological advancements. It also seeks to guide students in understanding and applying these fields to develop intelligent systems.

Keywords:

artificial intelligence; software; dichotomy; computational intelligence; metamorphosing

Bio

Dr. P. Prabhavathy is currently serving as an Associate Professor of English in the Department of Science and Humanities, KGiSL. She has authored technical textbooks, workbooks, reference books and contributed to articles, chapters, research papers etc., for publication in the international conferences, Journals, Magazines etc., She is a speaking cum Written Examiner of BULATS – ESOL Examinations, British Council, Cambridge Assessment and Evaluation, EBEK. Dr. Prabhavathy has been honoured with many awards and recently recognised as an AICTE certified UHV Mentor.

Abstract

Artificial Intelligence and Machine Learning are two emphasising branches of computer science, authorities have acknowledged their discrepancy and the roles that they both offer in advancement of computer applications. Both fields have upended industries, reasserting the way we interact with technology and metamorphosing how business operates. In the convoluted domain of technology, the juxtaposition between Artificial Intelligence and Software Engineering emerges as a perpetual enigma, akin to untangling a Gordian knot. This discourse endeavours to delve into the intricacies of this dichotomy, peeling back the layers that differentiate these two formidable domains.  Computational Intelligence is the design, theory, application and linguistically motivated computational framework. CI act a major role in building a successful intelligent system, games and cognitive development of system in this regard mock interview guides the students of Engineering and Technology to kickstart with trends and application of Machine Learning (ML), Natural Language Processing (NLP) and Computational Intelligence (CI).

Introduction

The dichotomy between AI and Software Engineering manifests in their divergent methodologies and objectives. While AI strives to imbue machines with human-like cognitive prowess, Software Engineering is focused on crafting robust, efficient software solutions. The fusion of AI-driven algorithms with software engineering principles yields groundbreaking applications, from autonomous systems to intelligent virtual assistants. AI are booming although in certain Industries are started to appoint AI in a position of software Engineer and also in the post of Human Resource (HR). Nowadays, the question frequently raised is do AI replace human? To answer this question, we need to know about efficiency of AI and software Engineer. Design and creativity thinking are the fundamental skill of IT professionals as increasing these steps generative AI and low code take a great space on coding load  like speech recognition. The primary goal of the students pursuing professional courses is to learn the technical aspects of their respective professions. Its fundamental essence lies in mirroring human cognitive faculties, traversing the labyrinthine terrain of intricate decision-making and pattern recognition. As we navigate this intricate web of algorithms and heuristics, we encounter the words of Alan Turing echoing through the corridors of innovation, “We can only see a short distance ahead, but we can see plenty there that needs to be done,” encapsulating the perpetual quest of AI to transcend the boundaries of artificial intelligence. On the other hand, software engineers are in the process of upgrading themselves. It adheres to structured methodologies such as Agile or Waterfall, emphasising precision, reliability, and scalability.

1.1 A Confluence of Changes and Beneficiary of Machine Learning and Natural Language Processing

The words of Frederick P. Brooks Jr. reverberate in this realm, “The programmer, like the poet, works only slightly removed from pure thought-stuff,” elucidating the creative yet disciplined approach inherent in software engineering.  AI has developed in the recent years as an emerging career towards students. Nowadays, everything is handled by machine. By introducing machine learning algorithms, we can design the model to our requirements. Modern times building a robot (AI) becomes facile, as it is used in every field all over the society. It also provides us with the evolution of skills that are in high demand. The roles and responsibilities towards the career path are expected to be superior. Nevertheless, as the software engineering society observes the escalate need for AI talent. Software engineers aspire to move their profession towards AI. Undoubtedly, both Software Engineers and AI will continue to be in leading demand. As software Engineers are required to innovate new techniques and methodology in the technical world. The point of divergence is the converging and differing skill sets between AI and software Engineers.

AI has the competence to create and manage the development, creation of automation and also do statistical analysis. It is well developed to provide the organisations decision making spirit. By training the AI model it also helps the manager and stockholders in the analysis process. We can also introduce the Machine Learning models into programming, and it can be integrated with our application.  Computational intelligence has the key component known as fuzzy logic, they handle with the checking process of value whether it is true or false. Fuzzy logic allows the user to represent the concept of fuzzy by allowing the system to make decisions based on the given instructions.

Accessibility of fuzzy logic involves defining the fuzzy sets, fuzzy rules to undoubtedly get into real world problems. Evolutionary computation within CI have inspired biological evolution and natural selection. The working of evolutionary algorithms involves creating a solution over mutation, fitness, genetic crossovers. It is effective in finding solutions to given tasks. At its core, CI works on various computational models and approaches to solve complex problems based on the algorithms. The fundamental feature of CI is neural network. They are mathematical models inspired by structures and functions of neural networks, interconnected by nodes and arranged in layers. The working of neural network involves in strengthening between connections and nodes from the input. Swarm intelligence is another fascinating field within CI, where it collects the activity of social insects such as bees, ants. There are two optimisations, namely ant colony optimisation and particle swarm optimisation. The working of swarm intelligence involves in interacting generally with their environment to provide the optimal solution towards the problems. There are plenty of methodologies available to create intelligence in taking decisions and solving the problem by self-organized process. These are the key methodologies used in CI while working on the projects. CI is also efficient in finding fraud detection, moreover in robotics and autonomous systems. It has numerous advantages especially in areas such as hiring, lending, and law enforcement. CI techniques such as machine learning algorithms have empowered business and research to follow the valuable data by optimisation process to create innovative solutions. Some of the drawbacks are seen in CI is risk of reliance, where humans trust the AI without an evaluation of themselves. This case will lead to errors in the program, the development and maintenance of CI needs to be expertise in the data science domain. Although they ensure the transparency and accountability of the system.

1.2 Decoding the Dichotomy: AI vs. Software Engineer

Software Engineering is in top demand in industry from the survey of past few years. Most of the MNC’s are welcoming and offering a job role for software engineers, they also provide internship for new graduates and students. The action taken by these companies is greatly beneficial for students to work in Hands-on projects and to develop their skill accordingly to their interests. Software architecture is acknowledged as a different expertise Category from software design. The roles of software Engineer are to test and compose software application to evaluate the requirements, estimate the cost of deployment and to implement the system software. They also review the project that they build with peers and stack holders to decide the current tools. software Engineers are highly skilled, they can do the necessary outcome that is expected from the industry. A distinct opportunity is over the software engineers. The society of software Engineers must adopt a realistic approach, avoiding the temptation to transform all software Engineering programs into AIfocused ones.

Let us focus on the key features where AI focuses on building artificial intelligent systems that can perform human tasks. AI software includes natural processing, machine learning, deep learning, and computer vision of the system. Software Engineers focus on consistent, efficient and adaptable software solutions using programming languages, deployment and building model methodologies and frameworks. AI uncovers the applications in diverse domains like finance, Hospitals and health care, driverless vehicles and support services. Most of the AI system are build using the principle of software engineers by Integrating AI techniques into their solutions. AI sets up the system and tools to make decisions under precise standards. AI is generally trained during the time habitual beyond supervision.

On the contrary of AI replacing the software Engineer, deployment of machine learning algorithms skilled by software Engineers will provide the outcome of software Engineer performance by intensifying required practices. Let me give a live example that has already been on board, Cognition is a steering company where they have built the world’s first AI software engineer called DEVIN that can do any technical work assigned to it. As a virtual software engineer it is well known for the accuracy of the outcome with or without an assistance, they have the ability to code and operate the code, plans, design and finally they can also deploy software projects.

Devika, Indian AI engineer manifests to hurdle Devin. Devika AI was spearheaded by Mufeed VH of Lyminal and station, where it aims to compete the efficiency of Devin the AI coder. Similarities of Devin are introduced in Devika such factors are power of machine learning and natural language processing by understanding the human instructions. In spite of that Devika district itself deconstructed these institutions into actionable tasks. Here Devin’s accessibility and features remains obscured in mystery, Devika’s functionality is transparent as they are open-source nature of evolution.

The key feature of Devika is that the model is trained in a way to interact in a feedback loop, explore, decision making, research accordingly, coding a program, it also answers to the queries raised by users to archive the project outcome. It is capable of rectifying the error generated from the code autonomously without the involvement of user as a motive to minimise the human power. Devika has also created static websites on Netlify. As it is a python – based project any user needs to install the latest version of python to their system, if they need to work with Devika.

While coming for the benefits of using Devika AI over Devin AI, it has increased productivity as they focus on more complex aspects of software development for faster completion of the project before the deadline submission. It is also beneficial as they are reducing errors spontaneously without the need of the human. Devika breaks down the assigned tasks and works on them to improve the learning curve structures. Finally, accessibility and collaboration make Devika contribute its development and practice towards the outcome.

The fact about the virtual software engineer they can solve the problem in well skilled manner by providing the efficient result. The founder of Devin states that they will not replace software engineers, instead they are freeing up the developer for higher level thinking and creative solutions for the problems. However, navigating this dichotomy is not without its challenges. Ethical quandaries surrounding AI’s decision-making capabilities and the imperative for stringent software engineering practices underscore the need for a harmonious convergence of these domains.

The collaboration between both the fields will bridges the dichotomy by leveraging the strength of each discipline with intelligence functionalities. Even though there are plenty of innovations emerging in AI technology, they cannot be as accurate as humans. Trusting AI will obviously not advisable as they make faults often, humans need at least to monitor the work of the AI. The duty of humans is still in demand in all fields to check over and to operate AI in wise way. The necessity of prompt engineers is at peak to deal with these machines. Prompt engineers play a crucial role in designing the behaviour and capabilities of AI models, ensuring that they produce accurate and relevant results.

While examining the job crisis between AI versus software Engineer, it is crucial to consider the sophisticated interplay in the evolving technologies. AI, with its advancement in technique has undoubtedly disrupted traditional job roles across various industries. AI on phrase has introduced new job opportunities particularly in certain domains of its major such as data science, AI research and warehousing. The work done in the industry is now handled by AI, resulting in job losses in many sectors such as manufacturing, customer service role.

2. Mock Interview

There are more and more chances for engineers and technical professionals to convey technical information in English for various purposes. The primary goal of the students pursuing professional courses is to learn the technical aspects of their respective professions. Similarly, the practical suggestions for developing language skills in the learners and each item followed by tasks that the students motivated to do on their own. Teaching aids prove effective only when it suits the teaching objectives and group of learners. The aid should be displayed properly so that all the students are able to see it, observe it and derive maximum benefit out of it. English for Academic Purposes (EAP) entails training students, usually in a higher education setting, to use language appropriately for study. It is a challenging and multifaceted area within the wider field of English Language Teaching (ELT) and is one of the most common forms of English for Specific Purposes (ESP). English for Academic Purposes programme focuses instruction on skills required to perform well in an Englishspeaking academic context English for Specific Purpose (ESP) is to meet the specific needs of the learners. It makes use of the methodology and activities of the discipline it serves, and it is purpose. It is the teachers’ responsibility to propose a variety of exercises, both written and oral, to improve the learner’s accuracy, fluency and communicative ability. At times the teachers’ should translate-if they know both languages very well and believes it is the most efficient way to provide the meaning of a new concept in that moment, especially abstract ideas and also the teachers have to correct errors immediately if the scope of the classroom activity is accuracy, but if the scope of the activity is fluency these errors will be corrected later on.

2.1 QUESTIONS WITH ACADEMICIAN(S) AND ADMINISTRATOR (S)

1. Is it good to kickstart with NLTK journey?
2. Few suggestions about ML, NLP and CI
3. Share some credits about Text Summarisation
4. Opinion about Language Modelling Module
5. Do you find Masked Language Model really aids Software Engineers and others? If so… How?
6. Whether the text corpus in NLP enrich language skills?
7. Your appreciation with regards to paper cum paperless work
8. Do you agree with the title decoding the dichotomy AI versus Software Engineers?
9. Share some unique features about Devin’s and Devika’s AI
10. About Quantum computing and its sustainability

Language learning is done best in a non–threatening atmosphere. Learner errors are dealt through self–monitoring and peer correction. Through understanding the teachers, it can help the students to overcome their fears and work more positively towards learning a new language. Remedial teaching is different from the other kind of teaching in the sense it has only one main purpose that of correction of errors. Language games and communicative activities help a lot to learn the language interestingly. Pair work and group work should be encouraged where the students get opportunities to interact with their pairs without fear. Language lab helps the students to remedy the errors in pronunciation. These are the some of the remedies where the students could feel free in learning the language without fear and inhibitions.

The teachers have to develop all four linguistic capabilities (reading, writing, listening and speaking). The role of teacher at first is to identify and find out the needs of the individual learners. Then, the teacher has to find out effective strategies to be implemented to provide an active, interesting and interactive process of learning for the students with different levels of ability. Focusing on both intellectual and social goals, the teacher should figure out stages, roles, and problem-solving strategies that support student competence. Teachers should feel responsible for teaching; shaping; moulding; and motivating the students to learn English language. If English language teachers can make students feel successful and not give up their efforts in acquiring skills, there lies the success of teaching. The teacher’s responsibility is help to students to develop the skill in written.

Conclusion

To conclude, the mock interview with regard to Computational Intelligence is an activity conducted among Engineering & Technological students with higher officials as a practical teaching aid to create awareness and gain knowledge about the classifications of AI. The contrast between AI and Software Engineering reflects a dynamic blend of innovation and discipline, creativity, and precision. As we makeover through this intricate landscape, a holistic understanding of their overlaps and distinctions becomes crucial, mirroring the essence of Leonardo da Vinci’s saying, “Simplicity is the highest form of sophistication.” In addressing the scarcity, software Engineering is not limited to the industry but rather has applications and demand in multiple fields, by providing a wide range of options. To effectively navigate the job crisis AI and software engineering realms, individual and organisations must prioritise continuous learning and developing the necessary latest skills and to be updated about each and every newly emerging technology, is essential for maintaining relevance and adaptability in a rapidly changing professional environment.

References

IEEE – Author: Ipek Ozkaya with reference to S. Overmyer,  Jobs of the future: Emerging trends in artificial intelligence, Aug. 2022, [online] Available:  https:// www.indeed.com/lead/ artificial-intelligence-report

WEBSITE – Dzone: AI and software engineer differences Available: mp;url=https:// dzone.com/articles/Artificialintelligence-vs-Softwareengineering%23

NDTU: Indian AI Engineer Devika Emerges to challenge Devi, The World First ‘AI Coder’. Available: https://www. ndtv.com/feature/artificialintelligence-indian-aiengineer-devika-emerges-tochallenge-devin-the-worldsfirst-ai-coder-5355983

Cognition: Introducing Devin, the first AI Software Engineer. Available: https:// www.cognition-labs.com/ introducing-devin

The post A Mock Interview With Regard To Computational Intelligence: Decoding The Dichotomy appeared first on GRJ.

This study aims to explore strategies to secure the jobs of neurodivergent employees within Contact Management (CM) industries, such as Business Process Outsourcing (BPOs) and Knowledge Process Outsourcing (KPOs).

Keywords:

Artificial Intelligence (AI), Contact Management (CM), Neurodiversity, Dry Promotion, Neurotypical (NT) Population, Job Security

Bio

Dr. Amit Phillora is driven by “Service Acumen, Operational Excellence, and Continuous Improvement.” With expertise in project management methodologies, psychometric analysis, recruitment analysis, yield ratio techniques, and escalation management, he excels in deriving optimal value. Trained in recruitment analysis and psychometric testing, Dr. Phillora specialises in emotional intelligence and has worked extensively in neurodiversity, neuromarketing, and breast cancer. His leadership, analytical, problem-solving, and communication skills, combined with the ability to network with stakeholders at all levels, have delivered extraordinary results. Recognised with multiple awards, including Pillars of India and the UN 75 Peace Award, he is also a member of InSc.

Abstract

In 2024, Artificial Intelligence (AI) has increasingly taken over a significant portion of the workforce, leading to widespread job insecurity among employees. Major multinational corporations like Amazon, Google, and Microsoft have had to downsize, resulting in the loss of numerous jobs. This situation has created a heightened sense of anxiety, particularly among neurodivergent employees, such as those with autism spectrum disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). This study aims to explore strategies to secure the jobs of neurodivergent employees within Contact Management (CM) industries, such as Business Process Outsourcing (BPOs) and Knowledge Process Outsourcing (KPOs). Specifically, the research will investigate whether dry promotions—promotions without a corresponding increase in pay—serve as a positive motivational factor for neurotypical employees by providing a sense of job security or if they contribute to negative motivation. To achieve this, the study will employ various psychometric tests to assess the personality traits, IQ levels, and emotional stability of neurodivergent employees. Understanding their current mental health status will be crucial in designing an AI-based recession strategy aimed at ensuring their job security in the evolving job market. This research hopes to contribute to the development of more inclusive workplace practices that support neurodivergent individuals in the face of AI-driven changes.

Introduction

To understand how to work with neurodivergent population specially with Autistic and ADHD Employees we will first have to understand D.E.I model.

Diversity is the combination of unique skills, experience, perspective, and cultural backgrounds that make us who we are and ultimately benefits our global customers. It’s full range of visible and invisible identities, including but not limited to gender, race, status, race, ethnicity, nationality, physical and cognitive ability, sexual orientation, military status, education, age/generation, social class, language etc. individuals and groups are not one dimensional, and in fact are shaped in multiple and intersecting identities.

Equity is the fairness of access, opportunity, and advancement for all. Equity looks to identify and eliminate barriers that have prevented the full participation of some groups. It is also about ensuring that policies, practices, and systems provide all individuals access to the opportunities, resources, and recognition to be successful.

Inclusion is providing an environment where our employees feel valued, trusted, connected, and informed. It’s about recognising a d valuing the different lived experiences of our teams and leveraging their unique competencies and perspectives, so that everyone may experience ownership and empowerment.

Understanding basics of Neurodiversity

Neurodiversity is an idea that people experience and interact with world in different ways, and that differences are not viewed as defects. Neurodiversity is a term representing individuals who cognitively process differently than what society considers normal.

As per World Health Organisation (WHO):

• 15 % of humanity is livIntroduction ing in disability.
• Less than 12% of this group are included in diversity programs.

When we discuss or talk about an individual then it referred as Neuro-Divergent (ND) or Neuro-Typical (NT) or Atypical.

Let’s try to understand following different types of ND/NT who add to Neurodivergent populations:

• Autism
• Attention Deficit Hyperactivity Disorder (ADHD)
• Dyslexia
• Dysgraphia
• Other Learning Disorders

History about Hiring NT Workforce

Hiring Autistic candidates’ movement was started in March 2013, when SAP, one of the global enterprise software and technical company announced its intention to have 1% of its workforce to be composed of Autistic Employees by end of year 2020. That was one of the reasons why hiring of autistic candidates got increased in the US. Even though the hiring of autistic candidates got increased, however, 85% of autistic populations still remained unemployed.

In 2019, a group of employers with established autism hiring programs published the Autism@WorkPlaybook, in which they noted the most critical factor for the success of an Autism@Work program was the ability to source talent.

It was observed and learnt that the traditional processes for recruiting autistic talent was not good and in addition to that the US Vocational Rehabilitation System (VRS) was not an open platform. The state VRS restricted employers from accessing the database and find NT candidates for hiring. This also restricted them to find and identify the type of disability the person might be having. It was further found that not every autistic or NT person was registered under VRS. Further being registered with State VR system, didn’t give assurance that neurodivergent/disabled graduates would be to find enough opportunities for filing vacant positions. Therefore, the agencies looking for Neurodivergent or autistic talents must be equipped with strong resources for finding out and identifying NT candidates.

In 2017, Marcia Scheiner. President and founder of Integrate Autism Employment Advisors developed guidelines called” An Employers Guide to Manage professionals on Autism Spectrum”, to address employers needs in supporting the existing autistic employees. This was the time when Autism@work programme was just launched, usually with small pilot groups and employers needed guidance on creating a supportive and inclusive workplace for their autistic colleagues.

In 1998, journalist and autism activist Harvey Blume introduced this concept in broader way when he wrote in The Atlantic, “ND may be every bit as crucial for human race as biodiversity is for life in general. Who can say what form of wiring prove best at any given moment?”

The Myth of Normal Brain

The Bell-Shaped Curve of a Normal Distribution

“Average, Standard & Normal” are part of a concept we are apply every day – from height to IQ, blood pressure & even cloth size – to make sense of the world around us.

When we try to describe human traits, the normal distribution predicts that about two thirds (68%) of the people in a sample will fall within the “average”, range, with fewer people represented at the extremes.

Example

Random Sample Size = 1000 Average Score = 100 Expectancy for 680 People to have IQ Between = 85 – 115.

Fewer & fewer people will be represented as their IQ scores get further away from 100 in either direction.

Another example is of a women shoe store. If average shoe size is 7 then the shop would more like to keep more styles for shoe sizes ranging from size 5 to 9 and not of size 11 or above as rare large feet people would they get for selling and would know if larger feet come to their shop. In similar way its next to impossible to plot the complexity and variety of human cognitive & processing styles, yet in many ways society assumes that there is a standard or “typical” way of thinking & tends to accommodate the people who fall into that camp. Whereas there is no way of “normal” thinking, most of the people assume that others process information the same way they ever think of it at all. Due to this these brains work differently and can have significant barriers as well.

Neurodiversity

Judy Singer, Australian Sociologist & autism rights coined the word neurodiversity, which was a combination of neurological & diversity terms. It helped in articulating the needs of the autistic people and also helping them not to get labelled under disability but has now been seen as people who had different neurology

(Neurodiversity is a framework for understanding human brain function that recognizes the diversity of human cognition as a biological fact. The neurodiversity paradigm argues that diversity in human cognition is normal and that some conditions classified as mental disorders are differences and disabilities that are not necessarily pathological.)

The language of Neurominorities

In the context of work environment, there are few basic rules that can be applied to ensure the correct usage of the neurodivergent terminologies to communicate with Neurominorities or ND or NT workforce.

1. ND, on standalone basis, is an idea that there is a biological difference in all human minds. It’s not the characteristics of one individual.
2. Neurodivergent refers to neurological variations of a group, however some researchers advocate and uses the term for Neurodivergent individuals.
3. Autistic and Neurodiverse or Neurodivergent are not interchangeable terms.:

• An autistic individual is neurodivergent, but a Neurodivergent individual may or may not have autism.
• The term Neurotypical (NT) refers to someone who is not neurodivergent. However, a person who is not autistic is not necessarily neurotypical as they may be neurodivergent in another way.

Why Should we hire Autistic Talent?

A Recruiter, hiring manager or Diversity and Inclusion professional is said to be successful when he or she can hire and retain talents in their companies. In the US, at the beginning of year 2020 the labour force recorded was 164 million, it was assumed that to fill any vacancy by hiring talent is an easy fix. Soon when covid 19, pandemic hit all of us, the unemployment rate in the US itself got jumped to 3.5%. Still employers adjusted and resumed hiring and finding right talent for their works, hiring became tougher and difficult in comparison to the past. However, a large number of talents got noticed in autistic community.

2Let’s understand that statistical data: Secondary Data

The Neurodivergent Job Candidate, Recruiting Autistic Professional by Marcia Schener and Joan Bogden, Chapter 2

In the US, one in every 54, 8 years old is diagnosed with autism spectrum disorder (ASD): That is 1.85 % of Children population have ASD condition, whereas 5.4 million adults are estimated to be autistic. The employment condition of approximately 2.2% of Autistic population is not that good in comparison to other disable populations. By the time they hit their 20s, only 58% of these autistic population will have some form of earning income in comparison to other intellectual disable (74%), & 94% for those having learning disabilities. 35% of Autistic students who attended college are in bad economic status in comparison to their peer autistic people who never attended college. That’s how 85% of autistic population land into unemployment and under empowerment situations. Which makes a good number of autistic population available for employment.

What benefits do recruiters get by hiring a neurodivergent?

 Business Benefits

 Better Talent: A NT think different and have very creative way of problem solving.

 Increased productivity

 Lower Turnover

 Employee Engagement

 Brand Recognition

 3Tax Credits: Government programs targeted at encouraging employers to hire people with disabilities provide tax credits to small and large businesses at the federal and state levels. there is a concept of Disability Access Credit (DAC): which means that enterprises at federal levels who have earnings of $ 1 million or have ≥30 employees might be eligible for DAC. The amount can range between $ 250 to $ 10,000. All employers are eligible for Workforce Opportunity Tax Credit, which ranges from $1,100 to $ 9,600 per employee, which again depends on hiring of employee and length of employment.

 Economic4 Benefits Social Security Disability Income (SSDI) Vs Supplemental Security Income (SSI) Federal Financial Support Programs for individuals with disabilities For Every 1% of Autistic adult employed $222,000 (SSI, SNAP, and Medicaid) x (1% autistic adults)54,000 = $1.2B SNAP = Supplemental Nutrition Assistance Program

 Societal Benefits: J.K.Y Lai, E Rhee and Nicholas found that employment plays an important role in Mental health of people. It helps in issues related to suicide, depression and Anxiety happening due to unemployment. Studies shows Let’s try to understand how an Autistic Jobseeker is different from us Normal.

May also be eligible for SSI Medicaid: $3,000(Adult) to $20,000+(disabled)

Most hiring managers look for following qualities for their candidates:

• Strong Communication Skills,

• A “can-do” or Positive Attitude,

• Self – Awareness,

• Teamwork focus.

When interviewing the Autistic or NT candidate, it’s important to create inclusive and supportive environment. There are guidelines which needs to be considered while interviewing NT candidate, that unemployed people get 30% more negative emotional experience in comparison to a normal employed person, Autistic people are depended on their family for financial and other needs and live with aging parents. Financial independence helps them gaining self-esteem and confidence to live independently. 5History of Disability & Neurodiversity Hiring Initiatives: Timeline of Disability hiring programs few of them are listed below:

• Disclosure of Autism or Neurodiversity: As a recruiter you must disclose to the candidate will be checked for Autism and other Learning Disabilities tests, during the job interview. It’s your personal decision based on your needs and comfort level. Please note that, Under the Americans with Disabilities Act (ADA), you only need to disclose a disability when requesting accommodations for the application or interview process.
• Highlight Unique Skills: If it’s decided to disclose the Autism diagnosis, identify the unique skills that the person is bringing in with themselves. For example, mention, the ability to problem solving techniques blended with creative mind.
• Practice interview Questions: Prepare for common interview questions. Some examples include:
  o Tell me about yourself.
  o What are your strengths and weaknesses?
  o Describe a problem you’ve solved in the past. o What is your dream job?
  o How do you work in a team? o What type of work environment do you thrive in?

Communication Guidelines:

• Always be direct and upfront
• Be calm and patient and give adequate time for responding back to your question, don’t rush for answers.
• Encourage comfort and openness and support flexibility.
• Be open for alternative interview modes or venue
• Be observative and understand their body language and nonverbal cue.
• If something is not clear or in doubt, stop and ask for clarification o Provide writing surface if required.

Seek Advice: Talk to professionals who know the individual well to determine the best interview approach. Consider interviewing in a familiar place with a familiar person present.

To understand more about hiring Autistic Candidates, Wales Autism Research Centre have designed Guidelines for hiring Autistic Individuals by Ben Winn.

Let’s try to understand differences in Neurotypical Thinking Understand that disability and Disorder are not the same thing there is a difference between the two. Based on, “medical model” autism spectrum disorder (ASD) is considered as Neurodevelopmental disorder. Clinical Psychologist or Psychiatrist use Diagnostic & Statistical Manual of Mental Disorders (DSM) as a reference to study mental disorders and classify them. We should be able to distinguish between development disorders like autism and psychiatric disorders. As a recruiter or hiring manager you must know that ASD is not a Mental illness, and it falls under the category of development disorder.

There is a difference between developmental disorder and psychiatric disorder. Developmental disorders are lifelong and cognitive based conditions that affects persons social interactions, and their challenges may vary as per the different life stages of their lives.

Whereas Psychiatric disorders are more of emotional based and not cognitive based conditions.

Disability is a legal term that the Americans with Disability Act 1990 (ADA) use to determine eligibility for accommodations at workplace. As per ADA, disability can be metal or physical which creates hinderance performing daily tasks, social engagement. Invisible disability like Autism, bipolar disorder, multiple sclerosis, diabetes, epiclesis etc are not obvious, so at the time of hiring employer may ask for medical documentations, so that disable employee can special accommodations at workplace.

Executive Functioning Skills (EFS) are set of cognitive skills that help us in getting our things done and regulates our behaviour. It also helps us in accomplishing our goals.

Executive Functioning Skills (EFS) are set of cognitive skills that help us in getting our things done and regulates our behaviour. It also helps us in accomplishing our goals.

A survey of 2,000 hiring managers conducted in 2018 confirms the impact of 1st impressions Following behavior was observed in interviewers:

• 33% knew if they would hire a candidate or reject within 90 seconds of meeting them.
• 65% did not hire a candidate who had poor eye contact with them,
• 50% won’t consider the candidate’s dress sense, their body language or their odor or the way they opened or closed doors or the way they walked in.
• 40% felt that if a person is not wearing a smile on their face, then it’s a good reason to not hire that applicant.
• 40% would reject an applicant based on the quality of their voice and overall confidence. Like any candidate an NT must undergo Interview funnel and candidates need to pass through each level to avoid rejection and get recruited.

INTERVIEW FUNNEL FOR JOB APPLICANTS

After a rigorous hiring process, when the hiring manager shortlists the selected candidates then they pitch an offer to the selected candidates, now it’s up to them to accept the offer or leave the job opportunity offered to them. For that it’s important to understand what Offer Acceptance Rate (OAR) is.

OAR = (Number of offers Accepted /Number of offers) X100

There are many reasons due to which a shortlisted candidate can turn down a job offer. according to a job seeking site called glassdoor.com, top3 reason for declining a job opportunity are:

• Better salary, perks & benefits.
• Poor recruiting/interviewing experience, experienced by job applicant.
• Poor or Negative image of employer.

Passing all gates when a Candidate enters inside an Organisation, then it becomes important for the Organisation to improve the retention ratios as recruitment and selection with training is a very expensive and tiring process.

Employee Retention Rate = [(Total # of Employees – # of Employees who left)/Total of # Employees)]

• As per World Institutes ’2020 report, in 2019 major reason for increased attrition rates in companies were:
• 20% People left because there was no Career Growth and poor Career Development in their existence companies.
• Over Workload and poor increments resulted in poor work life balance, due to which 12% of the workforce left their existing companies and switched to different companies which provided better working environment and opportunities to balance personal and professional lives of employees.
• Toxic work culture and bad managers created a situation which let to loss of another 12% of workforce.
• The major reasons for Neurodivergent to leave was:
• Bullying
• Sensory Overload
• Social Misunderstandings
• The percentage of such is 78%, the report was made by work institutes.

Retention Rate can be improved, and Attrition rates can be brought down in NT working talent. All we must do is taking care of following points:

• Maintain Supportive working environment.
• Education & Training on sensitive topics like POSH, D.E.I, & Neurodiversity.
• Generating Awareness about NT Workforce
• Train and Make Neuro-Recruiters who specialise in hiring NT Workforce & candidates who specialise in Neuro Marketing.
• Management Training
• Manager Support System.

Artificial Intelligence (AI), Dry Promotion and NT Workforce Job Security

As per recent reports shared by CNN in one of their articles stated that AI is closely linked to major layoffs in IT sectors because the companies are investing a huge amount in Machine learning, automation and AI based technology. Tech Giants like Google, Microsoft, IBM had to let their people go as they have invested a good amount of time and many in developing and using AI based applications like Chat GPTs. Due to this drift the job security is reduced even for top IT or Tech experts. Now, when companies are struggling hard in balancing human workforce and AI based applications. It’s time to upscale the existing talent so that they are able survive in this AI based world. As per layoffs, about 212,294 were laid off from IT sector in year 2023 which was far more than the numbers of people got laid off in 2022 numbering 164,709. Microsoft after firing 10,000 employees and announced that they will be investing on AI based applications, Chat GPT etc. Nov 2023, Facebook laid off their 10,000 employees after cutting 11,000 positions. A software engineer who is trained in AI and ML gets 12% higher salary in comparisons to a normal software engineer.

https://edition.cnn.com/2023/07/04/ tech/ai-tech-layoffs/index.html

Now when we see retaining a normal workforce is getting difficult then how we will be able to retain NT workforce who have cognitive and psychological disorders.

Dry Promotions

As we know NT specifically Autistics people are creative minds, socially remains disconnected and focus on primary goals, due to sensory overloading and selective mutism. They can do one task at time. We know that companies are investing a lot in AI and ML due to with salary hikes are almost negligible, so what we can do is, instead of finding a new NT talent who is equipped with AI skills, why not promote the existing NT Individual by upskilling them in AI, ML and Automation skills. By using Dry Promotion, we can give them Power and Authorities with Job security with same increment or lesser increment in comparison to the increments which use to happen in past.

This will act as positive motivating factor as promotion in hierarchy will boost their moral and having equip to work with AI and ML will be another positive factor.

This will help:

• Retention Rate in NT population
• Opportunity for Trainers to train Neurotypicals
• Cost saving in hiring and training new NT talents
• Existing NT talents of the company will be able to learn in faster way and productivity rates will be get improved because they know how their companies work.

We have targeted Contact Management Industries which are composed of BPOs and KPOs. Contact management companies are companies which work on Contacts. Contacts that are generated by Customer, Clients, their employees. They could be through Emails, Phones and Chats.

E-commerce giants like Myntra, Amazon etc uses Chat bots and Auto bots in dealing with customers. Our aim is to retain NT techies who can design, modify and update the AI tools and make them more user friendly. Most of the NT techies are depended on AI for their day to day lives, like Active Noice Cancellation (ANC) headphones, Chat bots, Virtual mode of communication in comparison to physical mode. So, the understand the user interface better and their ideas and design proposals will help in AI applications development keeping Normal and NT populations in mind.

Literature Review

The study is very much inspired from the book “The Neurodivergent Job Candidate, Recruiting Autistic professional by Marcia Scheiner & Joan Bogden”. The explains about neurodiversity and autism. The author had explained about the challenges which were faced by hiring managers in hiring neurodivergent candidates and explain how to create a working model for working with neurotypical working professionals. The book helps in identifying differences between traditional resume review and atypical resume reviews.

A health writer Ariane Resnick had written an article with title “What Does It Mean to Be Neurodivergent?” for Websites well mind, where she had explained about neurodiversity. The Article has a live poll on Question: Do you identify as neurodivergent? The result of this poll on June 2,2024 at 13:36 IST is:

In The Neurodiversity Edge, renowned Oxford-trained cognitive scientist, neurodiversity expert, and business leader, Dr. Maureen Dunne presents a pioneering framework to harnessing the power of neurodiversity to navigate the most important human resources revolution in the modern era.

Cherry Gupta, wrote an article with title: “Dry promotion’: All about the appraisal trend offering higher designation without pay hike”, which got published in Online Newspaper:” The Indian Express”

Roberta Matuson had written an article on Here’s What You Should Do If You Receive A ‘Dry Promotion’. Here she tried to explain what reason is behind getting dry promotion. She touched on topics like are there is any benefit in accepting a dry promotion and does no money really means no money. The article came in online site of Forbes

To understand the terminology related psychology and mental health like disorders, disabilities, help was taken from Penguin dictionary of Psychology by Arthur S. Reber, Second Edition.

K Ashwathapa had explained concepts related to Managing benefits and Wellbeing, A Safe and Healthy Environment, Labour Laws were very well explained in his book, Human Resource Management.

Mike Byon wrote a book: Ultimate Psychometric Tests, in which there were more than 1000 Verbal, Numerical, Diagrammatic and Personality tests.

Udai Pareek in his book Organisation Behaviour and Process, had explained different concepts related to the Perceptual process, Interpersonal styles, Personal effectiveness,

What Does It Mean to Be Neurodivergent? (verywellmind.com)

https://indianexpress.com/ article/lifestyle/workplace/ got-promoted-but-no-payraise-dry-promotions-appraisal-trend-9285951/

https://www.forbes.com/sites/ robertamatuson/2024/05/01/ what-is-dry-promotion/?sh=57b565b42a52

Leadership styles, work motivations, managing frustration, managing stress and burnouts etc.

Research Methodology

Sampling Methodology: Quota Sampling Target population: MNCs like Amazon.com, Accenture, Universities like APS, IGNOU. Industry type: Contact Management Age Group: 18 – 45 years

Parameters

ASQ: Anything Above 26 is Autism ADHD: Anything above and equal to 55 is ADHD Sample Population: Autistic, ADHD. Country: India.

Psychometric Tests:

Emotional Intelligence Scale (EIS), Personal Style Inventory/Indicator (PSI). Neuro Divergent Tests: Autism Test for Adults, ADHD Test for Adults Sample Size: 13 NTs (Either Autistic or ADHD or Both)

Hypothesis Testing

Generally, organisations want to retain the employees to avoid unnecessary expenditure towards hiring and training new candidates. Amongst various performance parameters of an employees the most important parameters to the organisations are Overall Performance (Productivity) and Quality of work. For this hypothesis testing main logic used was to study actual scores awarded by the organisation on these two performance parameters for known NT population /Sample, work out the Average Score X Bar on both parameters and apply T Test on both the samples. Here we have assumed that Population Mean =100% Hence if X Bar is =100% we will assume that Organisation can accept the retention of NT employees else not.

HYPOTHESES

First Set of Hypotheses. Productivity H0: Autistic and ADHD type of Neurodivergent individuals can be retained and be trained on new AI tools in an Organisation if Productivity X Bar=100%.)

H1 : Autistic and ADHD type of Neurodivergent individual cannot be retained in Organisation on new AI based working environment if Productivity X Bar≠100%.) Quality of Work

Neurodivergent Test | Free Am I Neurodivergent Quiz (exceptionalindividuals.com)

H0 : Autistic and ADHD type of Neurodivergent individuals can be retained and be trained on new AI tools in an Organisation if Quality X Bar=100%.)

›H1 : Autistic and ADHD type of Neurodivergent individual cannot be retained in Organisation on new AI based working environment if Quality X Bar≠100%.)

Sample Size of NT Population =13

Statistical Tool =Left tail T Test (T test was selected as the sample size was small) was used to calculate Critical Value. The average of marks (score) awarded to 13 individuals of the samples, by the organisations in Productivity and Quality of Work was checked against the critical vale of each parameter worked out based on One Tail T Test

Decision Rule: If average scores awarded by organisations on Productivity and Quality of work are more than Critical Value, statistically worked out by a T Test (T test was selected as the sample size was small) would mean that the organisation should not hesitate them to continue in the organisation. Proceedings of Hypotheses testing for Productivity and Quality of work

Table: 4

Steps Step 1 Productivity Quality Ho: Mean = 100 100 H1 : Mean≠ 100 100

Step 2

Data Sample size n 13 13 Population Mean 100 100 Estimator (x bar) 98.46 9.23 Sample SD 3.76 2.77 Population Infinite Infinite Confidence Level (CL) 0.95 0.95 Level of Significance (LOS) 0.05 0.05

Productivity: The Table 3 shows that 11 out of 13 NT employees have scored Cent percent, 100% in the assessment of their Productivity.

Quality of Work: The Table 3 shows that 12 out of 13 NT employees have scored Cent percent, 100% in their assessment of Quality of work.

Decision: Overall performance of these NT sample employees is X bar 98.46% More than CV of 98.14% in Productivity (98.46% > 98.14%) X bar 99.23% is More than CV of 98.63% in Quality of Work (99.23%>98.63%),

Hence, we can accept H0, that is, i.e. the organisation should not hesitate them to continue them in the organisation. Or They can be retained by the organisation, as they are meeting the performance standard set by them.

Second Set of Hypothesis

Dry Promotion.

The study also considered Dry promotion as the H0: Dry Promotion with adequate training is a very good option for improving retention rates. H2 : Dry Promotion is not a good option for improving retention rates for NT Population.

Dry Promotion

As discussed earlier in this paper it was opined or hypothesised that under the threat of AI conquering the contact management industries, that Dry Promotion and upskilling them in AI, ML and Automation skills, may give them sense of Power and Authorities with Job security. It was also felt that may act as positive motivating factor and at the same time benefit organisation for not incurring additional expenditure on ne hiring.

This hypothesis was primarily formulated to find the mind set of existing NT employees whether they are in favour or against on dry promotion concept.

Data Sample Size of NT Population =13 Statistical Tool = Counting Numbers of Employees in favour of Dry promotion and Numbers of Employees not in favour of Dry promotion. Decision Rule = If percentage of Employees in favour of Dry promotion (Y) is more than percentage of employees not in favour (N) then accept Ho.

Decision. As Percentage of employees Not in Favour of Dry Promotion (No) is more than Percentage of employees in Favour of Dry Promotion (Yes), We can does not accept H0,

Inference: The NT employees are not in favour of Dry Promotion as this will not give any financial benefits to them. The sample data shows that monetary benefits are more important than continuing in same organisations on dry promotions only. They rather prefer shifting to other organisations and face further struggle to improve financial status.

Further studies done

Apart from administrating ASQ and ADHD testing on NT sample employees, additional psychometric test on this sampled employee were also administered to study the associated traits from behavioural science or psychological aspect to help the researcher: –

(a) to find out strengths and weaknesses of the sample with an aim to build up favourable strategies and recommendation on NT population for the organisation to strengthen their HR to have win- win situation for not only retaining the NT Talent and suitably promoting them but at the same time for overall sustenance, development, and growth of the organisation.

(b) To generate some likely relationship between these/or some of these traits to have some idea of the NT Employee or normal employees in the presence of nondisclosure act for purely his understanding to help the individuals/ provide some more attentions. These Tests are discussed as follows:

EIS/ EQ: There are 10 Parameters on which the test is performed to find out EQ. The study of scores in each can also help us to find their strengths and weaknesses. They can be used for designing and developing training programme. Or putting them in suitable positions, where they can really excel and bring laurels to the organisation PSI. This test gives the Personality type. This can also help organisation to place them suitably. Apart from these out puts it may be possible to guess whether an employee may be an NT. If some correlation between these factors of EQ and PSI with NT, can be made, it will help the managers to correctly address the training and developmental needs of the employees. This may help him and guessing a person whether he may be NT or Not and take suitable corrective action to deploy him for best performance in the interest of organisation. This will help him against the act of nondisclosure also.

Challenges and Suggestions for Retention of NT Employees who are under the threat of Contact Management Industry, getting invaded by AI Technology.

However, when organisations are shifting towards complex AI Platforms, then even existing employees may need certain special training including NTs. It is felt that the expenditure incurred on training them will be definitely costing less than hiring new NTs as Organisations still will have to employ NT on social grounds/calls.

Limitation of the study

The non-disclosure act on NT, prohibits to access data bank of NT persons, hence large data sample from NT population could not be obtained. Study with large sample size (of at least normal Distribution (Sample size at least >30) could have given better outcomes particularly in case of Dry Promotion hypothesis.

No records could be accessed where any out outstanding work of NT Employees was recorded may be due to nondisclosure act. Hence no evidence could be generated to support the retention case for NT Employees.

Suggestions for further studies.

Industries specially Contact Management Organisations/ Industries may consider doing a in depths in house study on such matters for NT employees within the bounds of non-disclosure act as they have all the required information and wherewithal to generate better future for their talented NT employees and implement the finding after thorough study in the favour of NT Population.

The post Development Of Research Based Working Model For Neurotypical In The Era Of AI Influencing Contact Management Industry appeared first on GRJ.

The post Facilitating Cyber Security In Contemporary Life appeared first on GRJ.

The post The Secrets of Barred Spiral Galaxies appeared first on GRJ.

– Innovation in current practices in auto-mobile sector
– How machine learning can enhance the applicability of the automobile sector

Keywords:

Autonomous Vehicle, Artificial Intelligence, Machine Learning, Deep Learning

Bio

Academician, mentor, trainer and entrepreneur, MD port eight, Director strategic management, Mangalmay group of institutions, having strong credentials in rolling out innovative teaching methodologies, working for automation of logistics by enabling technology based things.

Abstract

Maximum manufacturing operations in automotive industries are none the less largely dependent on experience-based human choices. The emergence of huge information, alongside gadget learning in automotive organizations, has paved a manner this is assisting covey operational and commercial enterprise variations, thereby leading to an increased degree of accuracy in choice making and advanced overall performance.

The car industry keeps facing a dynamic set of demanding situations. Transferring market conditions, extended competition, globalization, price stress and vitality are main to an alternate in the marketplace landscape. Self-driving motors and changing usage fashions have heightened client expectations. It is pointless to mention that the automotive industry is getting ready to a revolution. One vicinity that has demonstrated an opportunity to supply substantial aggressive benefit is analytics. The car is getting transformed by means of technologies. AI and system learning algorithms have found a growing degree of applicability in this industry. The collaboration of big information analytics and device gaining knowledge of has boosted potential to system massive volumes of facts, thereby accelerating growth of AI structures. Gadget studying in automobile industry has an amazing capability to carry out hidden relationships among facts units and make predictions.

Introduction

One of the most thrilling era breakthroughs inside the previous few years has been the upward push of deep studying. Ultra-modern deep mastering models are being widely deployed in academia and enterprise, across a spread of areas, from photo evaluation to herbal language processing. These models have grown from Fledgling research topics to mature strategies in real-global use.

The growing scale of records, computational Strength and the associated algorithmic improvements are the main drivers for the development we see in this field. Those traits actually have a huge potential for the automobile industry and therefore the hobby in deep Mastering-based technology is growing. Lots of the product improvements, inclusive of self-using motors, parking and Lane-alternate assist or protection functions, which includes autonomous emergency braking, are powered by deep studying Algorithms. Deep mastering is poised to provide profits in overall performance and functionality for maximum ADAS (advanced driver assistance machine) solutions. Virtual sensing for automobile dynamics software, vehicle Inspection/heath tracking, automatic riding and records-driven product development are key regions which might be anticipated to get the most attention. This text gives an overview of the recent advances and some related challenges in deep learning techniques in the context of automobile packages.

Statement of the problem: Autonomous vehicle test and development

A self-driving automobile, additionally known as an self-sufficient automobile (AV), related and autonomous car (CAV), driverless vehicle, robo-vehicle, or robotic automobile, is a car this is capable of sensing its surroundings and moving effectively with little or no human enter. Self-using cars combine a selection of sensors to perceive their environment, such as radar, lidar, sonar, GPS, odometry and inertial measurement gadgets. Advanced control systems interpret sensory data to identify suitable navigation paths, as well as limitations and applicable signage. Long-distance trucking is visible as being at the leading edge of adopting and enforcing the generation.

Purpose: Deep learning in Automobiles

From voice assistants to self-riding automobiles, deep studying (DL) Is redefining the manner we engage with machines. DL has been Capable of reap breakthroughs in historically hard areas Of machine gaining knowledge of along with textual content-to-speech conversion, Image category, and speech reputation. DL commonly Refers to a category of models and algorithms primarily based on deep Artificial neural networks (ANN). Perceptron, the building Block of an ANN was first presented within the Fifties. ANNs had been a chief area of research in each neuroscience and laptop science till the late 1960s and the approach Then enjoyed a resurgence in the mid-1980s. At Bell Labs, Yann Le-Cun developed several DL algorithms within the Past due Eighties, together with the convolutional neural network (CNN). Pioneering deep neural networks via Yann Le-Cun ought to classify handwritten digits with properly speed and Accuracy and have been extensively deployed to read over 10% of all the cheques inside the U.S. inside the late 1990s and early 2000s. Even though the primary theories associated with ANN have existed because Nineteen Fifties, the field of DL has matured loads in the ultimate Decade and changed loads in the previous few years. The “deep” in Deep studying is not a connection with any kind of deeper Information accomplished through the approach; as a substitute, it stands for the many layers inside the neural community that contribute to a Version of the records.

There are 4 key factors that are riding the development and uptake of DL.

1. Extra Compute energy: e.g., Photographs processing unit (GPUs), tensor processing unit (TPUs) and so on.
2. Prepared large Datasets: e.g., Image Net and so forth.
3. Higher Algorithms: e.g., Activation features like RELU, optimization schemes and so forth.
4. Software & Infrastructure: e.g. Git, robot operating System (ROS) , Tensor Flow [9], karas and so on.

Figure 1

Significance of the study: Perception systems for object detection

The belief system of a self-reliant car is answerable for mapping sensor observations into a semantic Description of the automobile’s surroundings. 3D item detects a common feature inside this gadget and outputs a listing of 3-D bounding boxes around gadgets of interest. Diverse 3-D Object detection techniques have depended on fusion of different Sensor modalities to conquer barriers of person sensors. However, occlusion, limited subject-of-view and low-factor density of the sensor facts cannot be reliably and value-correctly addressed through multi-modal sensing from a single factor of view. Alternatively, Cooperative belief carries information from spatially various sensors distributed across the surroundings as a manner to mitigate those obstacles. This paper proposes two schemes for cooperative three-D item detection. The early fusion scheme combines point clouds from a couple of spatially diverse sensing factors of view before detection.

In assessment, the late fusion scheme fuses the independently envisioned bounding boxes from more than one spatially diverse sensors. We evaluate the performance of each schemes using an artificial cooperative dataset created in two complicated using scenarios, a T-junction and a roundabout. The evaluation show that the early fusion approach outperforms overdue fusion via a good-sizedmargin at the fee of better communication bandwidth. The outcomes demonstrate that cooperative belief can consider greater than ninety-five% of the items as antagonistic to 30% for unmarried-point sensing within the maximum challenging scenario to offer practical insights into the deployment of such device, we document how the variety of sensors and their configuration impact the detection overall performance of the device.

Motion planning system

ARIA is an item-oriented automobile control utility that has a programming library (SDK) for C++ programmers who need to access their P3-DX platform and add-ons. However, the automobile firmware does no longer carry out any high-level robotic responsibilities. As an alternative, it’s miles the activity of an clever purchaser walking on a connected pc to carry out this utility-level robotic control techniques and obligations, such as impediment detection and avoidance, localization, mapping, clever navigation and digicam manipulate. Indoor movement making plans of mobile robot deals with the troubles of localization, route making plans and the movement manage hows the motion making plans system used in this observe. Localization is the willpower of the positions of mobile robotic, boundaries and the target. Normally positions are given via a user or recognized by way of a digital camera. In the case of actual-time and dynamic working, consisting of moving obstacles or goals are used inside the environment, localization via a digital camera ought to be favored.

Because actual-time and dynamic packages are made in this study, a digital camera and photo processing techniques are used for localization. The digital camera is installed on the ceiling and it sends the actual-time pix of the surroundings to a pc. Each item is labeled with a extraordinary coloration on it; inexperienced for the boundaries, blue for the goal and yellow for the mobile robotic. So as to decide the heading angle of the mobile robotic, it’s also categorized with a purple color. Course of the road that connects the centers of yellow and purple colored circles at the robotic equals to the heading attitude of it.

Figure 2

Photograph processing program going for walks on the pc, which is written in MATLAB, takes the immediately pix from the digicam. The program determines the coordinates of each item which has a coloration extraordinary from the bottom color, as proven within the higher proper photograph of the figure. Coordinate willpower is a complex hassle, due to the fact the real environment and its picture do not overlap because of the path of the digital camera. Therefore, coordinate transformation must be done with the aid of the usage of corner coordinates of the real surroundings and the photo. At the closing, photo processing software suits the coordinates of colours with a grid based totally map, as proven at the lower proper picture. On this way, localization is found out and the map is produced. The second one step of the motion making plans device is to send the map to the path planning technique. On this have a look at, genetic set of rules is used to decide the route which mobile robotic goes via it. Course planning with the genetic set of rules is explained in detail inside the following phase. Within the last step, with the intention to manipulate the robot platform, a patron software, which is written in MATLAB, sends command packets thru the robotic connection. This may be finished the use of direct instructions. Direct commands encompass -byte packet header, one-byte byte count, one-byte command wide variety, one-byte argument type, n-byte argument and two-byte checksum, as described by way of the robotics’ running machine ARCOS. The direct command technique lets in sending any unusual or unique command immediately to the robot platform, without any intervening processing.

Figure 3

Path planning and optimization

Path making plans is a totally important task for the self sustaining cell robotic. It is desired to discover a collision-loose motion in an obstacle inclined environment that allows you to navigate properly from the begin configuration to the aim configuration.

In lots of static and dynamic environments, cellular robots are more and more being hired. Typically, there are numerous possible paths for a robot to attain the goal from the begin region,

however in situation, the first rate feasible route is decided on in line with some guiding principle such as shortest distance, smoothness of the route, minimum energy consumption and many others. Or the most followed standards are the shortest distance with the minimal possible time.

The path planning may be categorized into folds:

1. Neighborhood direction making plans and,
2. Worldwide direction making plans.

In neighborhood route making plans strategy, the robot has a restrained know-how (both in part-recognized or unknown) approximately the navigational environment. But, in worldwide course planning, the robotic has entire understanding approximately the navigational environment and thereby robotic can attain the goal by using following a predefined direction. However, worldwide direction planning techniques display constrained applications due to much less robustness in terrain uncertainty whereas, nearby route making plans techniques show greater flexibilities in partially known/unknown environments and presents an optimized course. It could be in addition categorized as classical technique and heuristic method (synthetic Intelligence method). The uses of cell robotic path making plans/arranging are included into restorative and surgical makes use of, person assist, safety, stockroom and movement packages, and moreover ocean and area investigation, robotized guided vehicles for shifting products in a plant, unmanned bomb switch robots, and planet investigation robots.

Addressing the Policy Issues Associated with Automated Driving Systems (ADS)

The convergence of records and communication technology (ICT) with automotive technology has already led to automation features in road vehicles and this fashion is expected to continue within the destiny owing to client call for, losing costs of additives, and stepped forward reliability. Even as the automation capabilities that have taken place thus far are specially in the form of data and driver warning technologies (labeled as level I pre-2010), destiny trends within the medium time period (level II 2010–2025)are anticipated to exhibit connected cognitive car features and embody increasing degree of automation inside the shape of advanced driving force assistance systems. Although autonomous automobiles were developed for research functions and are being tested in managed riding missions, the independent using case is most effective a long term (degree III 2025 +) state of affairs. It’s on technological forecasts regarding automation, coverage challenges for each level of era improvement and alertness context, and the important device of fee-effectiveness for coverage analysis which enables policy choices at the automation systems to be assessed in a regular and balanced way. The fee of a machine in keeping with car is considered against its effectiveness in meeting policy objectives of enhancing safety, performance, mobility, convenience and lowering environmental results. Instance programs are supplied that illustrate the contribution of the technique in supplying facts for helping coverage selections. Given the uncertainties in device costs in addition to effectiveness, the device for assessing policies for destiny generation features probabilistic and utility-theoretic analysis capability. The coverage problems described and the evaluation framework permit the decision of policy challenges while allowing worth revolutionary automation in driving to enhance future road transportation.

Figure 4

In recent years, fast trends in automobile technology have positioned public coverage in the capture-up mode. Advances in records and verbal exchange generation (ICT) have enabled the profession to go beyond the unique motive of the smart auto-mobile and toll road ma-chine (IVHS) initiative of many a long time ago and now we’re within the technology of developing era for related cognitive cars. Further, experimental independent vehicle era has these days been tested efficaciously. The development and the scenario of huge-unfold applications of more and more automated vehicles in public street networks pose coverage demanding situations. Despite the fact that an economically possible self-sustaining vehicle is not possibly to be within the marketplace for decades, self-sufficient using as a public coverage problem has already emerged.

Legal issues surrounding cyber security and privacy

Cyber security and facts safety are buzzwords in the meanwhile and for top motive. Banks and different financial establishments face constantly evolving cyber threats. The nature of the threat and the means through which cyber attacks are perpetrated are developing ever more sophisticated and the capacity fallout from a main cyber protection breach may be big.

The branch for business and skills’ 2015 statistics protection Breaches survey observed that ninety percent of large firms had suffered a safety breach in the previous 12 months. The average price of the worst unmarried breach suffered by way of a big agency changed into a watch-watering £1.46m to £three.14m. Statistics protection breaches bring about loss of patron agree with, that can have an immediate impact on sales. However, the fees of a security breach will even include enterprise disruption costs, compensation bills and regulatory fines.

The survey also observed that the character of cyber attacks experienced by using establishments has shifted, with a decreased number of denial of service assaults and a growth in attacks regarding malicious software. Possibly especially with the developing awareness of cyber security risks, inadvertent human error turned into mentioned because the principal cause of the worst security breaches, up to 50 percentage from 31 percentage in the preceding year.

Felony requirements with regards to cyber protection in the U.K rise up commonly from the statistics safety Act 1998, which requires organizations to take “appropriate technical and organizational measures” to guard non-public facts from unauthorized get admission to, damage, loss or disclosure. Such measures need to ensure a degree of protection that is suitable, considering the harm that may be precipitated to people in the event of a facts security breach and the character of the information. While determining which safety features to install area, the Act in addition specifies that companies ought to keep in mind the kingdom of technological improvement and the fees of enforcing the measures.

Human factors of automated driving systems

Automated riding can essentially exchange road transportation and improve nice of existence. However, at gift, the function of humans in automated automobiles (AVs) isn’t always honestly established. Interviews had been conducted in April and might 2015 with twelve professional researchers inside the discipline of Human elements (HF) of computerized using to identify commonalities and exclusive views regarding HF challenges within the improvement of AVs. The experts indicated that an AV as much as SAE degree four have to tell its driver about the AV’s abilities and operational repute and ensure safety whilst converting between automated and manual modes. HF research should mainly address interactions between AVs, human drivers, and susceptible road users. Moreover, motive force education packages may additionally need to be modified to make sure that human beings are capable of the use of AVs. Subsequently, a mirrored image on the interviews is furnished, showing discordance among the interviewees’ statements—which seem like consistent with an extended records of work on human factors studies, and the speedy improvement of automation era. We count on our angle to be instrumental for stakeholders worried in AV improvement and instructive to other parties. Automated using should essentially change road transportation and improve first rate of lifestyles. But, the position of the human driver inside the computerized automobile isn’t always yet without a doubt established. This work affords the results of an interview examine among 12 HF scientists worried in automated driving studies. A consensus become revealed many of the researchers regarding the HF demanding situations that need to be resolved previous to the deployment of AVs on public roads. Such demanding situations consist of the synergy among the human driving force and automation, capacity changes in driving behaviour due to automation, and the form of records that the human drivers will be receiving from the automatic riding device. Alternatively, a disparity was recognized between the researchers’ concerns regarding the AVs improvement and deployment and the AVs technological advances: even though the researchers expressed that AVs should no longer be delivered except proven secure, reality indicates that enterprise is now near the advent of level three and stage 4 AVs on public roads.

Figure 5

Autonomous Vehicle Applications

Automatic cars are an increasing number of present in modern-day society. Already, prototype motors had been automatic and judged dependable enough to force autonomously under widespread using situations. Beyond motors, there is a range of motors suitable for automation and throughout vehicle type there are not unusual hints recognized to standardize the procedure of making an autonomous car. In this text we overview what the commonplace elements are and illustrate with a few examples of automated floor vehicles developed with Freelance Robotics Pty Ltd.

Automatic motors

The DARPA urban project did provide an incentive in the quest towards a totally computerized vehicle. DARPA has been at once related to a success development of independent motors via many studies college groups in addition to private sector. Industrial availability of a completely automat zed car seems to be just around the corner. The remaining hurdles to business cognizance may be the important updates to road safety laws and, possibly greater tough to define, the overcoming of individual and network fears to accept as true with inside the wheels of an automated device on our roads. However, checks on present auto-mated vehicles are proving their reliability, with looking at outcomes higher than human judgments made on the road. Inside the cutting-edge marketplace, hooked up car agencies upload autonomous parts while they comply with the regulation. Common-place examples of this emergence of automation in industrial cars encompass automated parking, computerized correction of the automobile’s trajectory if the driving force crosses a continuous line, and alerts sounding to warn while other vehicles get too close.

Car automation in different fields

The enterprise of vehicle automation is a whole lot broader than business vehicle automation. Freelance Robotics has worked on automation throughout many types of motors. As an example, we’ve evolved automation components for farming cars together with irrigators, tractors, and buggies, mining automobiles inclusive of drilling rigs, and also industrial vehicles like forklifts and car crash checking out automobiles. Civil engineering is an extra place of utility, with a success robots having been evolved for pipes inspection. Within the wider context of the automatic marketplace, those applications are only a few examples. Given the ability range and utility of applications, automatic vehicles include a clear increase marketplace.

Landmark help in nearby positioning structures

Any other tool for automated car localization is the usage of landmarks. Landmarks can be natural or artificially delivered to the automobile’s environment.Whilst herbal, the vehicle identifies shapes which might be purported to be there, along with bushes, limitations, posts, et cetera normally herbal landmarks can be complicated to pick out, reducing machine reliability. For example, a tree can alternate form, or boundaries appear exceptional from distinctive angles. In evaluation, artificial landmarks provide a clean, steady way for the automated vehicle to become aware of its function. Examples of appropriate artificial landmarks encompass reflectors, RF IDS, bar codes, traces on the floor, panels with geometric shapes and colors, lighting, Wi-Fi, or IR beacons. When the automobile has correctly recognized positioning thru landmarks, it is able to update and music its position over time. In this way, dependable correction of positioning on its map turns into possible when the vehicle is a) in motion or b) moved in regarded surroundings.

Conclusion

New era cars have severe tendencies and car brands are in opposition. Due to this opposition, sensible motive force assistance systems are playing a key role at the same time as automobile enterprise is being more automated. Research shows that finding a solution to parallel parking is one of the maximum wanted enhancements for drivers. Because parking is a very difficult subject matter for novice drivers. In crowded cities this problem is getting larger due to the fact number of the automobile is increasing every day. [1] The purpose of the park assistant system is to assist the drivers have more fun and more efficient riding stories. Additionally, some other intention is decreasing damages for the duration of the parking operation. Parking damages have very horrific effect on international’s economic system. Due to the fact international locations and coverage organizations can pay cash for easy parking damages and proprietors of the cars’ sell their motors below its value due to harm records. Systems need ultrasonic sensors that placed on the corners of the automobile. These sensors wanted now not only for doing parking moves, however additionally scanning the park area. There are a few problems about ultrasonic sensors. For instance, thin objects are now not seen through these sensors and some environmental adjustments as temperature, pressure, humidity, air turbulence, airborne particles and so on. Effect on ultrasonic reaction. Despite those risks ultrasonic sensors are the most suitable sensor for these systems.

In this paper, we have discussed about machine learning in automobiles. This paper discussed about autonomous vehicle test and development, deep learning in Automobiles. In this paper we discussed about legal issues surrounding cyber security and privacy and autonomous Vehicle Applications also.

References

1. Gang LH, Zhang MH, Zhang LF, et al. Automatic roadmarking recognition for intelligent vehicle systems appli-cation. Adv Mech Eng 2017; 9: 1–11.
2. Wang B, Gao LJ and Juan ZC. A trip detection modelfor individual smart-phone-based GPS records with anovel evaluation method. Adv Mech Eng 2017; 9: 1–10.
3. Zhou CR, Jia HF, Gao JX, et al. Travel mode detectionmethod based on big smartphone global positioning sys-tem tracking data. Adv Mech Eng 2017; 9: 1–10.
4. Escobar C and Morales-Menendez R. Machine learningtechniques for quality control in high conformance man-ufac-turing environment. Adv Mech Eng 2018; 10: 1–16.
5. Gao JJ, Xie YN, Gu F, et al. A hybrid optimizationapproach to forecast auto-mobile sales of China. AdvMech Eng 2017; 9: 1–10.
6. Hu YW, Wang YP, Zhang JF, et al. Cor-relation degreeanalysis of arterial adja-cent intersections for coordinatedcontrol subunit partition. Adv Mech Eng 2018; 10: 1–12.
7. Huang H, Tang QR, Zhang GC, et al. A novel approachto integrate potential field and interval type-2 fuzzylearning for the formation control of multiple AUVs.Adv Mech Eng 2017; 9: 1–11.
8. Ji YF, Hu X, Jiang LY, et al. Algorithmic design andapplication of feedback con-trol for coiling temperature inhot strip mill. AdvMechEng2017; 9: 1–7.
9. Men YZ, Yu HT and Yu HB. Development of blockloading spectrum for car power-train rig test correlatedwith customers’ usage. Adv Mech Eng 2017; 9: 1–10.
10. Shen XP, Chen F, Su BJ, et al. Optimization of park-and-ride system: a case study of Shunyi in Beijing. AdvMech Eng
    2017;9;1-8.

The post Machine Learning In Automobiles appeared first on GRJ.