Data annotation and labelling represents over 25% time consumed in most AI/ML projects, a challenge that all enterprises struggle with and are now resorting to third party solutions My previous article on The Achilles’ Heel of AI – Training Data, highlighted how training data can make or break your AI model. I further emphasized on the growing demand for data annotation and labelling services in supporting enterprises for their data related needs. Let us dive a level deeper to understand what makes data annotation services delivery a success and what are the prevalent business models in the annotation services space. Source: Data Annotation – Billion Dollar Potential Driving the AI Revolution Data Annotation Building Blocks Successful data annotation services delivery is a function of multiple factors, but the two main building blocks comprise of a trained workforce capable of annotating tool and an annotation platform to enable them to do so. Trained workforce needs to have knowledge and context of the annotation problem and flexibility to deliver basis client’s feedback and the AI model requirement. The annotation platform on the other hand usually has two pieces, the actual annotation tool that operationalises the annotation process and an analytics dashboard to manage workforce and tasks. Let us have a look at different business models that prevail that are a function of different workforce requirements coupled with the annotation platform. Data Annotation Business Models Global data annotation landscape comprises different business models. Organizations can opt for outsourcing options including crowdsourced platforms or managed service providers, while the ones that want to carry out in-house labelling leverage SaaS offerings. I. Outsourcing Data Annotation Options Crowdsourced Platforms This model utilizes a crowd of annotators on a platform Workforce can be scaled with the flexibility in hiring annotators depending on task load Compensation is per task or per hour of annotation Managed Service Providers (MSPs) A managed service provider (MSP) provides project-based services Services are provided either using client’s platform, 3rd party tools, or MSP self-developed annotation platform The services are supported by a service level agreement (SLA) II. Subscription-based Model for In-house Annotation SaaS-based Offerings SaaS based offering enables the provision of annotation platforms via a subscription-based model The platforms combine annotation capabilities and operations management Platforms are utilized by companies carrying out in-house annotation or by MSPs However, enterprises seeking data annotation services often struggle with the outsourcing dilemma between crowdsourcing and managed services. As integral parts of the outsource model, crowdsourced platforms and managed service providers offer different value propositions and advantages based on the requirement of the client. Watch out for my next article for more details on the data annotation landscape.

Artificial intelligence holds the key to an era of innovation and is increasingly becoming pervasive in our lives. Businesses across sectors are leveraging the transformative potential of AI for data-driven decision-making. However, at the core of the AI revolution lies the need for large training datasets, something that most enterprises are struggling to address. Data annotation and labelling services play a critical role in bridging this gap by helping enterprises with quality training data for their AI models. This report attempts to highlight the huge potential that India has to become the data annotation and labelling hub for the world. The study demystifies the data annotation landscape, market serviced by India, and the potential impact that the industry can create both in terms of job creation and accelerating India’s AI readiness. Key Highlights Data Annotation Overview: A Global Perspective Global data annotation spend on third-party solutions is estimated to be 7X by 2023 as compared to 2018, constituting about 1/4th of the total spend on annotation The two building blocks needed for data annotation services are a trained workforce and an efficient annotation platform to operationalize labelling Crowdsourced platforms and managed service providers offer different value propositions depending on client requirements across cost, scale, security, quality and agility ~70% of global players are in the intermediate to advanced phase of maturity with sustainable at-scale and diversified offerings The India Story: Annotation Landscape and Trends The data annotation market serviced by India in FY20 valued at ~USD 250 Mn – with ~60% of the revenues derived from US clients The India market revenues are derived from multiple business models with managed services contributing ~65-70% of the overall market Indian MSPs leverage either a dedicated workforce or a BPM partnered model with >80% of the employees from non-metro cities India’s competitive edge stems from its decades of service delivery experience and is driven by key pillars of cost, infrastructure, talent and innovation Indian Players: Current Maturity, Challenges and Opportunities Data annotation industry in India is still gathering pace with ~75% players in the initial and growth phase Challenges restricting Indian MSPs’ access to markets include data privacy, lack of cultural context & growing demand for non-English language data labelling COVID-19 posed several challenges for MSPs with an FTE workforce; requiring the MSPs to alter their operating model to ensure business continuity Opportunities for players are driven by advanced market access, expanded offerings, catering to advanced annotation and sector specific needs Outlook and Recommendations Data annotation market serviced by India can exceed USD 7 Bn. by 2030 with a potential of up to 1 Mn. workforce engaged via full-time and part-time employment models Roadmap for service providers comprises of ensuring existing capabilities and developing new capabilities for them to unlock maximum value Impact of data annotation on India – Job growth and accelerated AI readiness, transitioning India to an AI-ready nation Boosting domestic AI demand, unlocking public sector datasets, developing strong data policies and infrastructure and capital support to MSMEs will fuel the India data annotation market growth

The vision of Robots and Humans working together has been popularized by numerous Hollywood movies, comic books and the media for many decades now. Success stories about the application of automation in the manufacturing industry date back to the early 1980’s, when welding robots demonstrated efficiency and resilience to deliver on time and increased the overall supply. And ever since, robots have been used for a variety of applications such as painting, assembly, disassembly, pick and place for printed circuit boards, material handling, product inspection, and testing, all accomplished with high speed and precision. In the past few years, however, the world is seeing a significant shift towards AI-enabled robots that are changing the game by bringing various levels of autonomy into the picture. If we consider the example of the labor-intensive task of warehouse piece picking,  AI-enabled robots are learning to handle millions of objects with minimum help from humans. What once required individual item registration and programming of robots, can now be accomplished with self-directing or autonomous robots using deep learning algorithms. This in turn, is helping industries cut costs and improve profits in the long run. Further, with the dawn of COVID-19, the way we patronize restaurants and shops and see our doctors has changed drastically. From collaborative robots (co-robots) to robots with high levels of autonomy, the trend continues towards the application of Autonomous Mobile Robots (AMR) in every industry and retail setting; more so as societies are restructuring places of work and pleasure to minimize human contact. The accelerated adoption of AMRs is being seen in many frontline roles from spraying disinfectants to delivering & serving food to customers, in outdoor and indoor urban hotspots. A key advantage of using AMRs is that they are able to reliably do these repetitive physical tasks when many workers aren’t safely able to or willing to set foot in these areas. As AMRs are becoming more and more competent, a few popular ones that are gaining traction during this pandemic are discussed below.   TELEPRESENCE ROBOT Telepresence Robots or Virtual Assistance Robots, now more than ever, are revolutionizing the way we work or learn remotely. They enable telecommuters, doctors, remote workers, and students to feel more connected to their colleagues by giving them a physical presence where they can’t be in person.   Image Credit: Using robots to enable patients to be inspected remotely In recent times, Telepresence robots are being put to use to enable interactions with COVID-19 patients in isolation wards. These semi-autonomous robots could be teleoperated over the Wi-Fi, to visit the patients and provide a live video link to their loved ones or to deliver their food and medication.   DISINFECTANT ROBOT  In the post pandemic world, disinfecting shared public places has been a tricky problem to solve. A non-intrusive solution widely adopted in recent times is UV-C light, but due to its adverse effects on human skin, trolleying it would look somewhat like the image below.   Image credit: UV-C based disinfection trolley by rapid cleaning of hospital environment, helping in the fight against COVID-19 Enter AMR, mounted with a UV-C lamp as a payload, which would traverse autonomously in a designated area.                                                                                     Image Credit: Weston Robot   This is accomplished by using the mobile app where the user creates a map of the area and chooses the waypoints for the robot to traverse. The robot then follows the waypoints and stops at each waypoint, till the disinfection job is done. Also, it returns to the charging station once the battery is below a threshold eliminating the need for human intervention. A swarm of these robots could be used in and around the areas where the most interactions occur such as hospitals, railway stations, airports, etc. An intrusive solution would be to air-blast disinfectant liquids similar to the ones used in agricultural spraying, to increase the chlorine content in the air and reduce the possibility of aerosol transmission.   Image Credit: Robots deployed to disinfect open spaces This fully autonomous solution is basically an AMR, mounted with a sprayer mechanism, which can navigate to the decontamination area using pre-built maps giving  the technical staff less exposure to these highly concentrated chemicals while working in an entirely safe and risk-free environment. The range of its spray devices can reach up-to 30 feet.   LAST-MILE DELIVERY ROBOTS As door delivery businesses saw a huge increase in demand during the pandemic, Last-Mile delivery robots found application as a reliable and safe contactless delivery system. These robots are an alternative to human food delivery drivers from companies like Uber Eats and DoorDash, which perform tasks that a person cannot do safely. These companies have created and deployed cool new robots with the intelligence to navigate city streets to deliver orders from selected restaurants to the customer location using a mobile app, while avoiding dynamic obstacles like pedestrians.   Image Credit: Autonomous delivery robot   The robot’s body is equipped with a storage bin with a locking compartment where the restaurant stores the delivery package and the bin only unlocks upon authentication by the customer at her location during delivery. Typically, these robots are equipped with cameras and computer vision driven by machine learning. They can detect and classify what they see, and tell the difference between a car, a person, or a wall. While these robots are definitely cool tech; procuring, running and maintaining fleets of robots can be prohibitively expensive. To circumvent this, the solution that small restaurant owners are turning to is the clever ‘Robot-as-a-Service’(RaaS) business model for food delivery, which is becoming more crucial as Covid-19 reshapes the gig economy.   ROBOT AS A SERVICE Many are now familiar with the concept of Software as a Service (SaaS) or Big Data as a Service (BDaaS) or Platform as a Service (PaaS) where the intent is to democratize technology while lowering the barrier to entry for businesses, large and small alike. One of the new areas, this philosophy is becoming more prevalent, is “Robot as a Service” (RaaS), a cloud-based “robotic rental” solution used for both B2B or B2C businesses. RaaS takes the capabilities of robotics and removes the upfront cost of robot installation with large amounts of computing power, utilities, and knowledge.  And so, small- and medium-sized businesses are increasingly experimenting with RaaS because of its flexibility, scalability, and lower cost of entry compared to traditional robotics programs. For example, finding sufficient  numbers of workers in a warehouse, for online retailers during seasonal surges is quite the challenge. With the RaaS model, these seasonal labor shortcomings can be mitigated without investing in equipment that won’t be used in slower periods while still being able to quickly scale up to meet the high demand. RaaS might be the answer for businesses, trying to figure out how to improve productivity or reduce risk, but always thought, robots were out of their price range.   ROBOTICS AT IGNITARIUM Robotics and in particular Autonomous Mobile Robots is an area where Ignitarium has been developing technology solutions for the warehouse use cases. Robotics as a theme started as a R&D thread to leverage the existing skills in computer vision and AI/ML. In the past year, we have demonstrated various use cases on sensor fusion, integration of Lidars & Radars, visual odometry using RGB-D Cameras, path planning, obstacle avoidance, integrating with deep learning modules for object Classification / Detection / Tracking. Our vision is to create a software package which is hardware agnostic, for use  across hardware platforms with minimal customization.     CONCLUSION The COVID-19 pandemic has accelerated the adoption of Autonomous Mobile Robots (AMRs) such as Telepresence, Disinfectant, Last-mile delivery robots. Roboticists are also seeing adaptation of AMRs to new niches and are exploring new avenues. Although the robotics wave is changing the way services and products are sold, there are still challenges to overcome like the amount of customization required, both in hardware and software for the robots to adapt to customer specific needs. Regardless of the hurdles, RaaS will be the inevitable solution many organizations seek either with hardware & software or software-only flavors.

“It costs five times more to acquire a customer than to retain a customer” – goes a famous adage. With due respect to this adage, if you don’t acquire new customers, your bank would cease to grow. So, one cannot overemphasize the importance of customer acquisition. Today Artificial Intelligence has permeated almost every aspect of the banking and financial industry. It looks like customer acquisition just got the Conversational AI reinforcement. The banking customer acquisition process slowly shifted gears from physical to digital channels in the last decade. Today, banking and financial players can speed up the customer acquisition cycle and reduce the costs associated, thanks to Conversational AI. But legacy banks need to move fast as there is a constant threat of upstart fintech players giving banks a run for their money! Meet your customers where they are The distribution-led ‘bank branch’ growth model has few takers today. In the first decade of this millennium, the number of physical branches closely linked to banking customer base and revenues. Not anymore. Today’s digital-first customers expect you to meet them where they are – web, social, instant messenger, apps, etc. Conversational AI can help you be omnipresent on digital channels to capture your customer’s mindshare and, ultimately, the wallet share in its many avatars. Reduce the friction in customer journeys Every prospective banking customer would go through a handful of predefined digital customer journeys. It could start searching on Google, comparing competitor offerings, chatting with reps online, checking for social proof, etc. Banks should map these journeys and offer extensive customer touchpoints and compelling reasons for customers to move to the next stage. AI-powered voice bot or chatbot helps familiarize the customers with banking products and services and remove friction in banking journeys, thus driving new revenues and deepening relationships. Offer personalized products and services Banking enterprises are sitting on a treasure trove of customer data. Utilizing this data to offer more personalized customer service removes friction and delivers a transformed customer experience. Credit card companies have for long benefited by providing targeted services to their customers. Leveraging AI and predictive analytics, demographic-specific offers, location-based discounts, and brand loyalty benefits are other ways credit card providers reap rich benefits. Listen to the voice of the customer Gone are the days of one-size-fits-all banking services. Customers today have their primary goals laid bare, and then they have specific goals nested within these larger goals. Banks need to pay special attention to what exactly the customers are seeking. AI-based platforms like Conversational Service Automation help deliver a superior banking experience, thus empowering banks to deepen relationships with their customers. What is the customer’s implied financial needs, service expectations, apprehensions, unspoken priorities, ‘wow moment’ triggers, etc.? Machines can pore over the customer conversations across all channels for nuggets of information using conversational analytics. This way, you not only reduce the cost of customer acquisition but pave the way for a long-term relationship with banking customers based on mutual understanding.

While healthcare organizations are on their digital journey to provide accessible, affordable and quality healthcare services, the COVID-19 pandemic aggravates the existing challenges faced by the sector. In addition to low doctor patient ratio and inadequate healthcare infrastructure, the pandemic resulted in shortage of beds, ventilators, insufficient testing capacity, resources, biosafety measures and a surge in patient contacts and queries, deepening the woes of the sector. As the pandemic redefines the new normal, healthcare organizations are accelerating their digital transformation journeys to be able to address the need for increasing tele consultations, remote diagnostics, customer support chatbots and wearable patient care. Here are my 10 picks for AI use cases that can be prove to be extremely useful (specifically amid the pandemic) and help organizations across the healthcare value chain advance through their digital journeys. Drug development – Healthcare organizations, specially pharma and life sciences players along with research centres are now heavily using AI to accelerate the drug discovery process which becomes even more critical in case of the COVID-19 vaccine Disease Simulation – AI is also being utilized to replicate the behaviour of disease causing microbes and organizations are seeing benefit in analysing it to help in coming up with possible cure of diseases Population health Insights – Use of AI to understand drug prescription patterns and propensity of diseases or comorbidities in case of COVID-19, predict drug demand and estimate potential market to ensure availability of required medicines Medical Image Analysis – AI-powered solutions for assisted medical imaging analysis tools for pathology, Radiology, radiotherapy, etc. and very useful amidst the pandemic Smart POS for pharma retail – An integrated AI and cloud based POS & billing solution for billing, payment, and inventory management, intelligent reporting, in-store marketing, and customer management for maintaining supply of required medicines Telemedicine – AI-powered analysis of patient symptoms remotely to deliver accurate prognosis post assessment of patients can assist doctors in making the diagnosis quicker and accurate Prognosis Assistant – AI-powered cognitive analysis to help in treatment selection, to recommend first actions and prognostic prediction. It also store patients’ medical records and share securely with doctors that helps in getting the correct diagnostic path Hospital Patient Flow – AI to determine the length of stay of patients and aid in discharge process that will also help in efficient management of hospital beds specially amid the pandemic Smart wearable sensors – Use of AI to link natural language and sensor data to identify potential health issues through changes in activity, communication and sleep patterns as tracked passively from handheld devices Patient Assistance Chatbot – Personal medical assistant app providing automatic answers to conversations, scheduling appointments, diagnosis & treatment, medical questions answering, medical image recognition, personalized medicine. NASSCOM Research, LHIF along with EY released a report titled “Unravelling AI for Healthcare in India” focusing on AI opportunities in the healthcare space. The report provides a unique periodic table of 150+ AI use cases across the healthcare value chain. The use cases identified in this article are also a part of the report. The report also highlights best practices across healthcare organizations that have implemented these use cases.

  As  per WHO one in   four people  in the world  will be affected by mental or neurological  disorder at some point in  their lives. Around  450 million people  currently suffer  from such conditions, placing  mental disorders  among the leading causes of  ill health and disability  worldwide. Mood Disorders – These disorders are also called   affective disorders which  involves persistent  feeling  of sadness or  period of  feeling overly happy , or fluctuations  from extreme happiness to  extreme  sadness. The most common  mood disorders are  depression , bipolar disorder and cyclothymic disorder. Another type of  disorder is the  Psychotic  disorder – This involves  distorted awareness and thinking. Two of the  main common  symptoms of psychotic disorders are  hallucination, where the patient experiences  images or  sounds that are  not real. Delusions- These are false  fixed beliefs that the  patient accepts as  true,  despite the  evidence to the contrary . Schizophrenia is an example of  psychotic  disorder. These  disorders cause detachment from reality. The  question today is how technology advancement in the  field of Artificial Intelligence can help in  the diagnosis of the mental disorders. Therefore it is important to understand what Artificial Intelligence is ? Artificial Intelligence  is a  software  program which can think and act like human . Basically we  are designing  programs which  acts like  our brain but with a  higher level of computing power. The Artificial  Intelligent program have multiple tools and subsets  which have  different  functions, but they combine  together to  create an Artificial Intelligent  program. One of the important  subset of AI is Machine Learning  – Machine Learning are algorithms that learn complex patterns from data and make predictions from it. Machine learning  programs have the following  steps:- It takes  data to  train the system. This  data  can be in the form of structured or unstructured data .  The data  can be  extracted from the data base.  It can be in  the form of text, it can be in the form  of images. After  processing this  data , the  algorithm understands   and learns the pattern  shown by this vast  data . It  can classify  the data that it has not seen before. Machine learning  is    trained by the features  or the traits of the  subjects. In  case of  patients  who suffer  from  a mental health issue, this data can be in the form of text data  that  a  patient may write on social media  site,  the  spoken data , language and data  captured  through spoken media and then converted to  text through the use of Natural Language processing. Artificial intelligent program  can be used  to detect  the Depression , we  take an example  of a  research paper where  the  researchers  accessed the Facebook status which  was posted by  683 patients  who visited  a large urban academic  emergency  department, 114 of whom  had a diagnosis of  depression in their medical record. The research  was   undertaken  to detect and predict the diagnosis of the  depression problem from the language used in the Facebook  posts. Prediction performances of future diagnosis of depression in the EMR based on demographics and Facebook posting activity, reported as cross-validated out-of-sample AUCs. With the  Facebook data  in  hand  and using the  ML model, researchers  could identify the  depressed patients with a  fair degree of  accuracy  at AUC=0.69, approximately matching  the accuracy of screening surveys bench marked  against medical  records. They  found that the language  predictor  of depression include emotional(sadness), interpersonal(loneliness, hostility) and cognitive(preoccupation with  self, rumination) process.  From the  result , it was  also observed that the  user  who  ultimately had a diagnosis of depression  used   more  first  person singular  pronouns( I , My , me)suggesting a preoccupation with self.  The  results  show that the  Facebook  language based prediction model  performs similarly to  screening surveys in identifying the patients with   depression when using  diagnostic codes in the EMR to identify diagnosis of depression. Growth of social media and the continuous improvement of machine learning  algorithm  suggest that social media based  screening methods for depression  may become  increasingly  feasible and more accurate.  The present analysis  therefore also suggests that the  social media based  prediction of  future  depression status  may be possible as  early as  3 months before  the  first documentation of depression  in the medical record.  Novel avenues are also becoming  available to  detect depression.  These methods also include  algorithmic analysis of  phone sensors , GPS position on the phone,  facial expression in images and videos shared on social platforms. The predictive model of Logistic  regression was  used. Ten language topics most positively associated with a future depression diagnosis controlling for demographics (*P < 0.05, **P < 0.01, and ***P < 0.001; BHP < 0.05 after Benjamini–Hochberg correction for multiple comparisons). As per WHO close to 800 000 commit suicide every year. Some of the companies are also involved in building healthcare applications. Ginger is a  chat application that  is used by the employers that provide direct  counselling to its employees. The algorithm  analyses the words someone uses and then relies on the training from more than  2 billion behavioural data samples , 45 million chat  messages and  2 million  clinical assessments to provide a recommendation. The CompanionMX system has an app that allows patients being treated with depression, bipolar disorders, and other conditions to create an audio log where they can talk about how they are feeling. The AI system analyses the recording as well as looks for changes in behaviour for proactive mental health monitoring. Bark, a parental control phone tracker app, monitors major messaging and social media platforms to look for signs of cyber bullying, depression, suicidal thoughts and sexting on a child’s phone. Advantages of Artificial Intelligence in Healthcare Support Mental Health professionals –  AI  can  act as  a support  for the  health professionals in doing their  jobs. Algorithms can analyse data much faster than humans can suggest possible treatments, monitor a patient’s progress and  alert the human  professional  to any concern. 24/7 access- Due to lack of human mental health professionals, it can  take months to take an appointment. AI provides a tool that an individual can access without waiting for an appointment. Not expensive –  The cost of care prohibits some  individuals from seeking help. This is more affordable. Comfort  talking to a bot-   It is easier to disclose  an information to  a bot than to a human. Cognitive computers will analyse a patient’s speech or written words to look for tell-tale indicators found in language, including meaning, syntax and intonation. Combining the results of these measurements with those from wearable devices and imaging systems (MRIs and EEGs) can paint a more complete picture of the individual for health professionals to better identify, understand and treat the underlying disease, be it Parkinson’s, Alzheimer’s, Huntington’s disease, PTSD or even underdevelopment conditions such as autism and ADHD. In a study  with Columbia University psychiatrists, were able to predict, with 100 percent accuracy, who among a population of at-risk adolescents would develop their first episode of psychosis within two years. In other research with our Pfizer colleagues, we’re using only about 1 minute of speech from Parkinson’s patients to better track, predict and monitor the disease. We’re already seeing results of nearly 80 percent accuracy. In five years, we hope to advance the study of using words as windows into our mental health. IBM is building an automated speech analysis application that runs off a mobile device. By taking approximately one minute of speech input, the system uses text-to-speech, advanced analytics, machine learning, natural language processing technologies and computational biology to provide a real-time, overview of the patient’s mental health. Artificial Intelligence will play a pivotal role in creating ground-breaking tools to analyse and detect mental health problems and will play a substantially positive role in increasing the treatment coverage by early diagnosis and possibly be able to reduce the death rates due to mental health problems. REFERENCE Eichstaedt, Johannes C., et al. “Facebook Language Predicts Depression in Medical Records.” PNAS, National Academy of Sciences, 30 Oct. 2018, www.pnas.org/content/115/44/11203. Marr, Bernard. “The Incredible Ways Artificial Intelligence Is Now Used In Mental Health.” Forbes, Forbes Magazine, 22 May 2019, www.forbes.com/sites/bernardmarr/2019/05/03/the-incredible-ways-artificial-intelligence-is-now-used-in-mental-health/#74cf5137d02e. Cecchi, Guillermo. “With AI, Our Words Will Be a Window into Our Mental Health.” With AI, Our Words Will Be a Window into Our Mental Health- IBM Research, www.research.ibm.com/5-in-5/mental-health/. IBM Research Editorial Staff. “IBM 5 in 5: With AI, Our Words Will Be a Window into Our Mental Health.” IBM Research Blog, 5 Jan. 2017, www.ibm.com/blogs/research/2017/1/ibm-5-in-5-our-words-will-be-the-windows-to-our-mental-health/. Bedi, Gillinder, et al. “Automated Analysis of Free Speech Predicts Psychosis Onset in High-Risk Youths.” Nature News, Nature Publishing Group, 26 Aug. 2015, www.nature.com/articles/npjschz201530. WHO. “Mental Disorders Affect One in Four People.” World Health Organization, World Health Organization, 4 Oct. 2001, www.who.int/whr/2001/media_centre/press_release/. Goldberg, Joseph. “Mental Health: Types of Mental Illness.” WebMD, WebMD, 6 Apr. 2019, www.webmd.com/mental-health/mental-health-types-illness#1.

The excitement of using Artificial Intelligence has not dwindled from the time it has been unfolded. In KPMG study on “living in the AI world 2020: achievements and challenges of AI across 5 industries (retail, financial service, healthcare, transportation, and technology), revealed that 92% of respondents agreed that leveraging the spectrum of AI technologies will make their companies run more efficiently. Amidst the admiration towards AI, IBM created the Women Leaders in AI program in 2019. This was a way to acknowledge the women leading in AI and encourage females to lend a hand in the field of AI. Through this IBM, planned to make the efforts of the honourees more visible to the world. 2020 IBM women leaders were honoured for outstanding leadership in the AI space. Here is the list of women leaders in AI 2020 honorees:- Aarthi Fernandez Who is a Global head of Trade Operations and SEA Trade COO at Standard Chartered Bank? She is a C-suite executive with deep insight on how digitalization can positively disrupt US$17 trillion global trade. She is into deploying AI/Machine learning to make trade financing simple, faster, and better for corporate clients and mitigate compliance risk. Piera Valeria Cordaro She is a commercial Operations Innovation Manager, Wing Tre S.p.A., Italy. She is a speaker, advocating the use of AI in customer operations. Along with her team and with support by IBM Watson, implemented two chatbots, to improve customer experience. Both bots have made it possible to handle a million queries efficiently. Amala Duggirala Who is the enterprise Chief operation and Technology officer, Regions Bank, United States. To handle customers’ inquiries she deployed IBM Watson’s assistant- virtual banker persona, ”Reggie”. From the time of its implementation 4.3 million customer calls have been answered, with 22% of them being handled by AI. Mara Reiff Vice President, Strategy and Business Intelligence, Beli Canada, Canada. She used AI to improve operations, loyalty, and brand. She worked with IBM to install Watson studio Local using Red Hat open shift. This resulted in smarter, fast decision-making with improved customer experience leading to increased sales. Mara suggests everybody to “Make sure to stop and smell the roses. Take each opportunity to learn something new and embrace change”. Amy Shreve- McDonald She is lead Product Marketing Manager for Business Digital experience, AI&T, USA. EVA (Enterprise Virtual Agent) was launched in February 2019, to improve customer chat experience, it uses Watson assistant. This system has been able to handle 45% chats on its own, resulting in reduced costs and expanding 24/7 support. She also received AT&T’s 2019 Visionary Award for her work advocating EVA. Ryoko Miyashita Manager, customer service department, customer service section JACCS CO., LTD Japan. She launched a Watson-enabled operator onboarding tool, that resulted in reduced new operator training period by 30%. The tool has increase customer satisfaction. Her advice to the younger self is “It is important to believe in yourself, but it is equally or more important to believe in people around you. I would encourage myself to have many experiences and garner knowledge to objectively evaluate things, not blindly accept or exclude others’ opinions”. Carol Chen She is Vice President for Global Marketing, Global Commercial, Royal Dutch Shell, United Kingdom. Along with her team, Carol is partnering is planning for digital transformation with the creation of “Oren”- a Smart Minning Platform, by partnering with IBM. This platform will offer an innovative and creative experience for users in the sector to deliver connectivity and integration across the ecosystem. To use AI, she advice commencing with analyzing the business outcome that one wants and customer pain points that one can cater to. The next step would be to determine how to leverage AI and data to solve the problem. Rosa Martinez Cognitive Project Manager, CiaxaBank, Spain. For those who consider using AI, her advice to them is ‘first to understand the business case as it may take time more than expected. This phase can result in a non-AI project example a ‘software as usual’. But moving further with the project there can be more AI application for sure to work on’. Lee- Lim Sok Know Deputy Principal, Temasek Polytechnic, Singapore. Under the leadership of Sok Keow, The higher education institution in Singapore ‘Temasek Polytechnic’ launched the “Ask TP” chatbot in January 2018. The chatbot helped current as well as prospective students to get answers to the questions asked about Temasek and also gave personalized course advice. In the 1st two weeks of 2020, ‘Ask’ TP’ responded to more than4,351 questions. She suggests everybody “deeply appreciate ‘people’ as they are the most critical asset in an organization, and a leader must develop a team”. Itumeleng Monale Executive Head of Enterprise Information Management Personal and Business Banking, Standard Bank of South Africa, South Africa. By deploying many analytical tools in her organization, she can uplift the revenue of the company. Through models of analytics relationships, bankers are experiencing a 40% revenue uplift when comparing to their peers. She sees AI as a tool through which business delivery can be accelerated, value could be added to human capital and relationships can build further. With this AI era, Research has postulated that corporate giants still have less percentage of women in the technical department. Facebook’s diversity report suggests that there are 22 % of women in the technical department and 15 per cent of women work in the AI research group. Similarly, Google’s diversity report suggests that only 10% women are working on  “machine intelligence”. There is a need to encourage women participation as there are many more women around the world, stepping out of the pre-existed sheathe and going beyond the walls to shape the future. Opening up the AI platform for all will fetch us more talented beings which can help us celebrate the use of AI in different fields and different ways. Reference:- https://www.ibm.com/watson/women-leaders-in-ai/2020-list https://advisory.kpmg.us/content/dam/advisory/en/pdfs/2020/technology-living-in-an-ai-world.pdf   About the author:- Kirti Kumar is a budding HR professional currently pursuing PGDM in HR and Marketing at New Delhi Institue of Management. She looks forward to opportunities that can hone her skills. She is agile in her attitude with versatility in her action

When the whole world seems to come to a standstill because of the COVID-19 pandemic, the two sectors that have become extremely critical are agriculture and healthcare. Agriculture is the backbone of any economy, and it has become all the more important during this crisis to keep the global food supply chain running smoothly to ensure that there is no food crisis. On the other hand, the healthcare sector is the most stressed out sector right now that is working hard to ensure the health and safety of the population. While technology has played a critical role in ensuring business continuity across various sectors, let us take a look at how the latest technologies like AI and ML are set to transform these two critical industries. AI in Agriculture The food crisis has plagued the world for long. Add on top of that the havoc being unleashed on agriculture due to climate change, and we have a ripe case for AI and ML application to solve the food crisis. This is being touted as the next major agricultural revolution. By leveraging such next-generation technologies and by working smarter, a lot of ground can be covered. It is estimated that the AI in the agriculture market will grow to USD 4.0 billion by 2026. So, what are the various use cases of AI in agriculture? Let’s take a look Yield Optimization: Yield optimization is beyond just historical data analysis. There are components such as data about weather predictions, soil data, and even the economic conditions of the regions which come into play. Using AL/ ML models to analyze these parameters, farmers can not only predict the yield but also know how to optimize the same. Livestock Management: Cattle and livestock are important assets for farmers. They not only help in the farming process but also provide other sources of income in the form of meat as well as dairy. Their health is of utmost importance, and the whole herd can be affected adversely by diseases of foot and mouth. Farmers now deploy infrared sensors and other smart monitors to detect anomalies in the herd movement or temperature reading to identify and cure the particular animal and prevent the disease from becoming a catastrophe. Weed Detection: The same principle can be extended to crop disease and weed detection. Farmers can use smart devices, robotics, and machine learning to constantly monitor and identify the changes in the vitals of the crop and see if unwanted weeds are growing on the soil. AI bots can help farmers cull out the weed at the nascent stages and harvest a higher volume crop at a faster pace. Soil and Water Management: AI-powered smart metering can help farmers in the economical usage of water resources and help them in cost-saving. Similarly, these technologies can help them with the optimal utilization of soil. Over usage of the soil can lead to depletion of its natural resources and optimal usage can ensure that the soil retains its vitality. By adopting AI and ML, farmers can know when the soil might need replenishment and how the intervals need to be spaced out considering the weather, yield, and soil time. This can help them make more accurate decisions regarding sowing, seed selection, and fertilizer usage to get a higher yield per hectare. AI in Healthcare AI has come a long way in healthcare and has already delivered a high impact on this industry. It has played a pivotal role in giving rise to a patient-centric model. Here are some of the many ways AI and ML are transforming the healthcare industry – Drug Discovery: On average, $2.7 billion are spent by the pharmaceutical companies for every drug. Pharma companies have started leveraging AI in drug design to better predict molecular dynamics. It is helping them improve development efficiency and reduce drug development costs. Improve Operational Efficiencies: Hospitals need to optimally manage various parameters such as temperature, humidity, and air regulation to run the facilities smoothly. AI is allowing hospitals to smoothly carry out facility management while ensuring the physical safety of the patients and staff members. These technologies are also enabling predictive maintenance of hospital assets and the tracking of healthcare devices to ensure proper allocation and deliver better care outcomes. Pandemic Management: Countries like Taiwan, Japan, and Singapore leveraged the power of AI and ML to curtail the spread of coronavirus. Borrowing from the principles of how communicable diseases spread, AI/ ML helped in predicting the spread of the coronavirus and allowed the government agencies to put in place the required logistics, ensure border controls, and protect their most vulnerable staff members. Remote Health Monitoring: Remote health monitoring and telemedicine have shifted care from a hospital to a more personal environment, such as the patient’s home. It is helping patients in saving their care costs and also helping in reducing the workload on hospitals. With wearable devices monitoring the basic vitals of a patient and periodically streaming those to the caregivers, the quality of care has improved. Precision Surgeries: AI robots are not new anymore. Those are being increasingly adopted in hospitals for carrying out precision surgeries. These medical robots are also being used in rehabilitation facilities. Today, a large volume of data is being generated from various sources. The future will be driven by technologies like AI and ML and their ability to crunch this data to deliver actionable insights. These insights will have a significant impact on the lives of people!

The Institute for Robotic Process Automation & Artificial Intelligence defines RPA as follows, “Robotic process automation (RPA) is the application of technology that allows employees in a company to configure computer software or a bot to capture and interpret existing applications for processing a transaction, manipulating data, triggering responses, and communicating with other digital systems.” In simple terms, RPA is the automation of repetitive, rule-based manual tasks (performed on windows) by the use of automation agents that can run attended or unattended without making any errors. RPA Segments: RPA is segmented into three major categories: 1. Attended RPA: In Attended RPA, the bot resides within the user’s machine & invoked by the user as per their need. Attended RPA is most prominently used by customer-facing functions such as customer service. This kind of RPA requires user intervention to make decisions or update based on conditions. Let us look at some scenarios where Attended RPA can be used: Making Decisions: A customer logs a trouble ticket for a solution to an issue within a software. There may be multiple ways to solve the problem. Basis the decision taken by the team, one can decide the particular RPA bot that needs to be run and trigger it manually Providing Input: While filling a CRM application, if a customer leaves a few fields blank by mistake, the RPA bot can stop and ask for those inputs to proceed further 2. Unattended RPA: Unattended RPA involves bots that perform tasks in batches based on automatic/timed triggers. There is absolutely no human intervention, and the bots run automatically to execute the tasks end to end. One of the scenarios that involve Unattended RPA is: Claim Settlement: Insurance claims validation and processing from analysis to updating for eligibility for a claim 3. Hybrid RPA: Hybrid RPA is a combination of both attended and unattended RPA. Hybrid RPA involves both attended and unattended bots working along with a human effort to achieve the business objective more effectively. It is generally touted as the most effective method of automation. It involves a fully integrated platform for intelligent automation that promises clear visibility, accountability, and governance across the process automation from start to completion. One of the examples of Hybrid RPA is: Ticket Solution: Updating tickets in the system based on errors and sending emails. Post the ticket log, ensuring a reply to the customer basis the resolutions/status Unit of automation – BOTS Bots are the automation agents that can be created by using various tools like Automation Anywhere, UI Path, Microsoft Power Automate, etc. These bots can run on from the local setup or can also be deployed on the cloud. Bots consist of the following two components that execute process automation: 1. Bot Program: The interface provided by RPA tools are claimed to be no-code-automation and can be done in a very short time. Some tools provide methods to create specific bots using programming languages like C#, Visual Basic, and Python. 2. Interaction with System: Bots interact with Windows/web and remote applications based on the functionality created and provide the desired output. Monitoring and Controlling the Deployed Bots Most of the RPA tools (Automation Anywhere, UI Path, Microsoft Power Automate, etc.) provide a control panel or a command module that can monitor the health of the bots. This process can help track statistics such as RPA Bot success rate and Bot failure reasons to take corrective actions. Let us look at some of the features of the Command Module of the RPA tools: 1. User Management: This panel provides the user the accessibility to control the bot functionality. There can be various roles assigned to different users within the tool, such as Admin, Developer, Sales, and Customer, to govern the bot. 2. Data Analytics: The Command center can process the data and run analytics on the data. The insights generated through analytics provide intelligence to the management and aide in making critical decisions. 3. Exceptions Reporting: It can capture the anomalies in the defined rule-based process, and then the bot can be modified to handle that specific scenario making it easy to catch exceptions in the system. RPA Use-cases in the Industry: RPA technology integrates smoothly with the existing IT infrastructure within an organization and does not require any large installations. Companies do not need to make significant investments to automate essential processes. Some of the everyday use cases in the industry around automating processes through RPA are: 1. Business processes: RPA can help in automating business processes such as Customer/Employee boarding, Invoice & Quotes processing, updating CRM, etc. 2. Web Tasks: Tasks such as logging into a website and performing operations to generate reports. Feed data into a system from sources like MS Excel, Mail, databases, etc. 3. Data Transfer: Transfer of data and automatic pull of relevant data from email or filled-in forms. This ensures that all departments across the organization can access current and correct data. 4. Web Data Extraction: RPA is very efficient in tasks that involve web scraping and navigating through pages. Web Data Extraction has applications such as resume screening, records validation, etc. 5. IT and System Administration Tasks: Tasks such as a regular check of software for bugs on employee systems and distribution of software for different employees. RPA can help in running a periodic audit within an organization to save repetitive efforts. 6. Data Backup and File Management: RPA can quickly help achieve automatic data backup on cloud or databases system logs. It can be used to organize files based on specific rules and, thus, help in organized record keeping. 7. Job Scheduling: Schedule jobs to run based on various triggers like time, mail, file available in a folder, etc. 8. Batch Data Processing: Activities such as restart and recovery, integration with security systems, sending alerts, classification of service types, etc. can be easily managed via RPA. 9. Automated Software Testing: RPA can help to automate manual testing processes, maintain the highest product quality, increase productivity, and free up QA testers to work on strategic projects. The Future of RPA: From Intelligent Automation to Hyper-Automation: RPA becomes more interesting with the addition of the capability and scope of hyper-automation. Gartner defines Hyper-automation as, “Hyper-automation deals with the application of advanced technologies, including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans. Hyper-automation extends across a range of tools that can be automated, but also refers to the sophistication of the automation (i.e., discover, analyze, design, automate, measure, monitor, reassess.). It involves a combination of tools, including robotic process automation (RPA), intelligent business management software (iBPMS), and AI, with a goal of increasingly AI-driven decision making.” Some areas of hyper-automation include: IDP (Intelligent document processing) where the bots can utilize ML and OCR to process unstructured data and learn patterns in the data Supervised Machine learning modes can be used to support decision making Virtual Assistant, smart speakers, and chatbot-integration with RPA Conclusion: Even though RPA software can be found across all industries, significant adopters include insurance companies, banks, and telecom companies, and utility companies. RPA tools should consist of role-based security capability to ensure action specific permissions. They should also offer configuration as well as customization of encryption capabilities for securing certain data types. References: https://www.uipath.com/company/rpa-analyst-reports/2020-gartner-rpa-hyperautomation-predictions https://www.ibm.com/in-en/products/robotic-process-automation https://www2.deloitte.com/us/en/pages/operations/articles/global-robotic-process-automation-report.html https://research.aimultiple.com/what-is-robotic-process-automation/ https://www.processexcellencenetwork.com/rpa-artificial-intelligence/reports/intelligent-automation-rpa-and-ai-report-2020 https://imagine.automationanywhere.com/presentations/the-intelligent-automation-journey-at-sprint/ https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2020/ (This blog was originally published here)

AI’s true potential emerges from its ability to drive transformation across multiple sectors through diverse range of applications. Research suggests that we can best understand the implications of AI by viewing AI as a General Purpose Technology (GPT). AI as a GPT implies, that AI led innovations will be reflected not only as direct contribution in the sectors it is applied to, but also drive complementary innovations and spillover benefits across the economy. Source: Implications of AI on Indian Economy “For more than 250 years the fundamental drivers of economic growth have been technological innovations…… The most important general-purpose technology of our era is artificial intelligence, particularly machine learning.” —Erik Brynjolfsson and Andrew McAfee, 2018 Three critical features characterizing GPTs that AI fulfils: GPTs are pervasive, evolve and improve with time, and play role in enabling innovation. Pervasiveness: AI pans across various “application sectors” including automotive, banking, consumer goods, healthcare, insurance, pharmaceuticals, retail, telecommunications, and transport and logistics sectors, etc., making it pervasive Technical Improvements: Field of AI continues to undergo significant transformations, not just in terms of performance and applicability but also changing trends in various AI techniques like the rise of game theory, machine learning and natural language processing Enabling Innovations: Diffusion of AI has enabled a wide range of activities that were unimaginable before. AI’s predictive capabilities are reducing costs and altering organizational costs across verticals Like other GPTs in the past, the effects of AI will not be fully realized until waves of complementary innovative solutions are developed, implemented and deploying. GPTs have unlocked the growth potential and played a significant role in driving economies. AI fulfils certain fundamental characteristics of GPTS as highlighted above, and thus to is expected to validate the future promise of AI-driven economic growth. When one thinks of AI as a GPT, the implications for output, welfare and productivity gains are huge. Conceptualising AI as a GPT and its adoption will: Drive innovation across sectors Generate social benefits and improve welfare/productivity Result in spillover benefits throughout economy NASSCOM, ICRIER and Google conducted a joint study on Implications of AI on Indian Economy. The report traces the impact of AI on the Indian economy using an econometric model to estimate the impact of GPTs, such as AI, on firm productivity. Watch out for more interesting articles on AI ! References [1] Implications of AI on Indian Economy [2] Facebook AI Research [3] Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics [4] http://ide.mit.edu/sites/default/files/publications/2019-04JCurvebrief.final2_.pdf