Daily Archives: November 29, 2020

Recently a news item caught my eye: there is a project to use artificial intelligence to recreate the smells of Europe of 500 years ago. The idea is to have AI scan all that has been written in that period to gather information related to the description of smells and collate them for a composite scent scape of those times. The team will then collaborate with chemists and perfumers to recreate those smells, to display them at interactive museums and historical sites.

My mind, of course, jumped to Queen Elizabeth Is oft-quoted remark that she bathed only once a month, as also The Great Stink rising from the Thames river in London in 1858. Would Londoners want to be reminded of these glorious incidents from their past, if only to be more grateful for their modern-day bath showers and clean(er) air? Even Europes smells may not be exactly what its current residents want to remember or experience.

That said, the link between smells and memory is deep and universal. There is nothing like a familiar whiff to set off all sorts of thoughts. For me, every place has a smell, as do events. People also have specific smells grandparents and parents, spouses, children, friends, enemies and so on. My mother used to tell me that when I was a toddler, if my father was out of town I would sniff all the pillows till I found one that smelt of him and sleep on that!

While many of my generation write about 4711 the famous Kolnisch Wasser or Eau de Cologne, of which my mother always had a bottlein relation to their female forbears, for me my mother is inextricably linked to sandalwood oil, the only perfume she used throughout her life abroad, confounding men and women alike with that mysterious spicy aroma! And I associate my grandmother with the oddly era-specific aroma of paan and talcum powder.

New cars, new notebooks, new clothes all have special aromas. The Durga Puja I attended in London over 20 years ago did not assuage my homesick heart simply because the venue was not redolent of marigolds, camphor, and coconut coir smoke. Delhi sarkari offices generally smell of badly plumbed toilets and damp airconditioning, and their Calcutta counterparts reek of stale mustard oil. Even pollution smells distinctly different in both. And of course, there is food. Mas and Didas signature dishes, the whiff of heeng and saffron emanating from kachoris and biryanis, the sulphurous goodness of rock salt, the sweet floral bouquet of Bengals short-grained rice, the complex scent of garam masalas many spices and panchphorons five seeds, fermented bamboo shoots and curry leaves each evokes a memory for me.

Yet there is also a timelessness to some smells. The onset of autumn in northern India has always smelt of harsinghar shiuli for Bengalis. The aroma of cool rain on parched soil, so unromantically called petrichor in English, evokes the same emotion today in Indians as it did millennia ago. And agarwoods resinous bouquet is as heady today as during the time of Kalidasa, who wrote of ladies perfuming their tresses with its smoke 1,500 years ago.

But the long olfactory link between past and present is weakening. As we become more urban, globalised and I daresay deracinated, the ancient smells of flowers, wood, resins and the seasons etc are supplanted in collective memory by aromas of perfumes, detergents, pesticides, pollution and other chemical emanations. If the scents of the 21st century are recreated in the 26th century, they may be even less pleasant than those unearthed from the 16th century.

DISCLAIMER : Views expressed above are the author's own.

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AI to give us a scent of the past - Economic Times

Written by Abhishek Singh | Updated: November 26, 2020 7:20:22 pmIndian farms and farmers provide vast and rich data to help create AI solutions for not just the country but the world at large. And this is one of the factors that makes the opportunity for AI in Indian agriculture unparalleled.

I see a big role for AI in empowering agriculture, healthcare, education, creating next-generation urban infrastructure and addressing urban issues, Prime Minister Narendra Modi said while inaugurating the Responsible AI for Social Empowerment Summit, RAISE 2020. Artificial Intelligence-based agri-tech applications are set to unleash value in agriculture, especially in wake of the recent farm reforms that have opened doors to private sector investments in agriculture.

In the financial year 2019-20, Indian agri-food tech start-ups raised more than $1 billion through 133 deals. Indias exports of agricultural products rose to $37.4 billion in 2019 and with investments in supply chain and better storage and packaging, this is set to increase further. All these steps will go a long way in ensuring remunerative prices for farmers and reduce agrarian stress.

This growth in agricultural output and productivity is being further enhanced by investments in technology. Disruptive technologies like AI are making big positive changes across Indian agriculture, and an increasing number of agri-tech startups in the country are working to develop and implement AI-based solutions. Globally, AI applications in agriculture reached a valuation of $852.2 million in 2019 and this is estimated to grow to almost $8.38 billion by 2030, a nearly 25 per cent growth. The Indian agri-tech market, presently valued at $204 million, has reached just 1 per cent of its estimated potential of $ 24 billion.

Use of technology in agriculture will improve farmers access to markets, inputs, data, advisory, credit and insurance. Timely and accurate data coupled with analytics can help build a robust demand-driven efficient supply chain. With the use of sensors, photographs through phones, IoT devices, drones and satellite images, agricultural data can be collected and matched with weather data, soil health card data, mandi prices and help build predictive models that can greatly enhance decisions about seeds, fertilisers, pesticides that are of critical importance in both pre-harvest and post-harvest stages. Most of these AI models are low-cost and affordable and can add a lot of value to the agriculture ecosystem.

India has made rapid strides in the services sector, yet, agriculture continues to employ 49 per cent of the workforce and contributes 16 per cent of the countrys GDP. Improvement in agriculture would, therefore, positively impact the well-being of a very large section of the Indian population, apart from delivering food security to our country. Feeding over a billion Indians on limited land resources is a big challenge, a task that requires technological intervention on a large scale, to enable a giant leap in agricultural productivity.

Indian agriculture is faced with multiple challenges like high dependence on monsoon, resource intensiveness heavy use of resources (water, inorganic fertilisers and pesticides), degradation of land and loss of soil fertility, and low per hectare yield, among others.

AI can play a catalytic role in improving farm productivity, removing supply chain constraints and increasing market access. It can positively impact the entire agrarian value chain. It is estimated that AI in global agriculture could be a $4 billion-opportunity by 2026.

Greater use of AI would increase mechanisation of Indian agriculture. It would increase productivity by introducing precision agriculture. Indian agriculture technology startups are trying to integrate AI-based technological solutions across a range of use cases such as monitoring crop productivity and soil fertility, predictive agricultural analytics and ensuring supply chain efficiencies.

In predictive agricultural analytics, various AI and machine learning tools are used to predict the right time to sow seeds, get alerts on impending pest attacks etc. AI in agriculture powers the optimum utilization of farming data to help devices like smart drones, autonomous tractors, soil sensors and Agri-bots function and deliver superlative efficiency in farming.

In what is a great example of innovation for agriculture using AI, industry has joined hands with the government to develop an AI-powered crop yield prediction model to provide real-time advisory services to farmers. The system employs AI-based predictive tools to help increase crop productivity, enhance soil yield, control the wastage of agricultural inputs and warn of pest or disease outbreaks.

This system uses remote sensing data provided by the Indian Space Research Organisation (ISRO), data from soil health cards, the India Meteorological Departments (IMD) weather prediction and analysis of soil moisture and temperature etc. to give accurate information to farmers.

This project is being implemented in 10 aspirational districts in the country across Assam, Bihar, Jharkhand, Madhya Pradesh, Maharashtra, Rajasthan and Uttar Pradesh.

Similarly, an increasing number of Indian startups are already implementing AI-based solutions in agriculture. A startup has used data science, AI and machine learning algorithms, along with data sets from ISRO to assess damage to crops, compensation payable based on value of the damage that has taken place. Questions around what is being grown, what is the damage, what would the value of the crops damaged be, are answered with reasonably high accuracy.

Another AI startup in India maps farmers zones in remote areas, answering questions like who has been farming which land, what is being grown, what is the quality of soil on the land, with great accuracy. Crop insurers, seed suppliers, state governments all want this data and its possible to build a business model around this as the data and information has value for everyone. Farmers are also able to get all this valuable information and insights which helps them in making better decisions about their agricultural practices and create value.

other Agri-tech startups who are using predictive analytics and machine learning to solve the problem of volatility in input prices and suboptimal input utilization. Imaging and AI, traceability solutions are being developed for large scale quality testing and post-harvest produce handling and monitoring.

Data is helping to create platforms for price transparency to check malpractices in the supply chain. Similarly, agricultural bots (ag-bots) and drones are being developed to ensure seamless execution of cultivation and harvesting.

In order to help these AI solutions scale, what is needed is to increase investments both public and private especially from venture capitalists.

With the recent reforms in the Agriculture sector, there is a likelihood of increased investments in contract farming and infusion of technology for better yields and productivity. This will further push the adoption of AI in agriculture. The recently concluded Responsible AI for Social Empowerment Summit RAISE 2020 Summit has helped provide a platform for global stakeholders to come together to finalize the roadmap for using AI for public good. As many as 321 global AI experts from 21 countries, and sectors including agriculture, converged onto the RAISE 2020 platform to firm up plans for developing path-breaking AI-based tools and for improving the adoption of AI across sectors.

Thanks to the diversity of its soil types, climate and topography, India provides a great opportunity for the data scientists and AI experts to develop state of the art AI tools and solutions for agriculture. Indian farms and farmers provide vast and rich data to help create AI solutions for not just the country but the world at large. And this is one of the factors that makes the opportunity for AI in Indian agriculture unparalleled.

The writer is CEO MyGov; President & CEO NeGD; MD & CEO Digital India Corporation (DIC), Government of India

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AI for the farmer - The Indian Express

Lexology GTDT Market Intelligence provides a unique perspective on evolving legal and regulatory landscapes. This interview is taken from the Artificial Intelligence volume featuring discussion on various topics including, government national strategy on AI, ethics and human rights, AI-related data protection and privacy issues, trade implications for AI and more, within key jurisdictions worldwide.

1 What is the current state of the law and regulation governing AI in your jurisdiction? How would you compare the level of regulation with that in other jurisdictions?

According to our observations, Taiwans government sector is aware of the AI trends and has proceeded to explore whether any adjustment to the current regulatory regimes in multiple aspects would be needed. In early 2018, to promote fintech services and companies, the legislators in Taiwan passed the Financial Technology Development and Innovative Experimentation Act (the Fintech Sandbox Act), which was enacted to allow fintech businesses to test their financial technologies in a controlled regulatory environment. Although the Fintech Sandbox Act is not specifically designed for AI, the creators of new financial-related business models with AI technology may test their new ideas and applications under such a mechanism while enjoying exemptions from certain laws and regulations.

Adopting a similar spirit to the Fintech Sandbox Act, the legislators in Taiwan passed another sandbox law for autonomous and self-driving vehicles, the Unmanned Vehicle Technology Innovation and Experiment Act (the Unmanned Vehicle Sandbox Act), in late 2018, which took effect from June 2019. The act is to provide a friendlier environment to test the applications of AI and the Internet of Things (IOT) in transportation. Vehicle, as defined in this act, covers cars, aircraft, ships or boats, and any combination thereof.

In addition to the above-mentioned legislation, the impacts on the current regulatory regimes as a result of the application of AI technologies have also been widely discussed, such as whether AI technology can be protected by intellectual property rights, what would be the consequences of and how to mitigate algorithmic bias in AI systems, whether data protection issues will be triggered when personal data are shared for the purpose of AI-related developments, among other things. However, as at the time of writing, no explicit court precedents or rulings have been issued on such topics.

It is also worth noting that, according to the Taiwan AI Action Plan, announced by the Executive Yuan in 2018, the Taiwan government has been evaluating relevant issues so as to further determine whether any laws need to be enacted or amended to address AI development. Such issues include, among others, the rights and obligations derived from the application of AI technology (eg, whether AI should be considered a person from the perspective of certain legal fields, whether there will be intellectual property rights in an AI-created work, among other things), open data, restrictions on AI applications, government procurement (eg, the outsourcing concerning AI issues), industry regulatory challenges and approaches to AI, among other things. Given so, we think that Taiwan has been actively examining the current regulatory regime in relation to AI in order to establish a good foundation for developments of AI technology.

2 Has the government released a national strategy on AI? Are there any national efforts to create data sharing arrangements?

The Executive Yuan announced the Digital Nation and Innovative Economic Development Plan and the Taiwan AI Action Plan in 2016 and 2018 to declare Taiwans goal to become an important partner in the value chain of global AI technology and intelligence systems and to leverage the advantages in software and hardware techniques to promote AI technology across industries with, among others, test fields, regulations and a data-sharing environment. According to the Taiwan AI Action Plan, the governments view is that Taiwan is well positioned to take advantage of the opportunities in developing AI-related industries.

In addition, the Taiwan government views AI as having an indispensable role in the 5+2 Industrial Innovation Plan (5+2 Plan), as declared by the Taiwan government in 2018. The 5+2 Plan is considered the core generator for Taiwans next generation of industrial development, which mainly focuses on seven industries: intelligent machinery, Asia Silicon Valley, green energy, biomedicine, national defence and aerospace, new agriculture and the circular economy. To facilitate the 5+2 Plan, the government has launched the AI Talent Programme, which aims to:

With respect to data sharing, the National Development Council prescribed the Guidelines for Trial Operation of Data Interface on MyData Platform to promote personalised digital services called MyData in February 2020. The main purpose of this service, in a similar to open data and open banking, is to create a platform for individuals to authorise the government or the participating companies to collect their personal data in order for the government and such companies to develop and render more personalised services to the individuals with such data.

3 What is the government policy and strategy for managing the ethical and human rights issues raised by the deployment of AI?

The Ministry of Science and Technology under the Executive Yuan announced the AI Technology R&D Guidelines in September 2019 to demonstrate the Taiwan governments commitment to improve Taiwans AI R&D environment. Pursuant to the AI Technology R&D Guidelines, considering that AI developments may bring changes to various aspects of human existence, the Taiwan government expects the participants to always pay attention to when conducting relevant activities and endeavouring to build an AI-embedded society with three core values: human-centred values, sustainable developments and diversity and inclusion.

Deriving from the three core values, eight guidelines were given under the AI Technology R&D Guidelines for all AI participants to follow so that a solid AI R&D environment and society that connects to the global AI trends may be established. The eight guidelines are:

4 What is the government policy and strategy for managing the national security and trade implications of AI? Are there any trade restrictions that may apply to AI-based products?

To date, no laws or regulations have been specifically promulgated or amended to deal with the national security and trade implications of AI. These matters are still handled in accordance with the existing regulatory regime (eg, the National Security Act, Cyber Security Management Act, trade regulations, among others).

5 How are AI-related data protection and privacy issues being addressed? Have these issues affected data sharing arrangements in any way?

In Taiwan, personal data is protected by Taiwans Personal Data Protection Act (PDPA). The collection, processing and use of any personal data are generally subject to notice and consent requirements under the PDPA. Pursuant to the PDPA, personal data is defined broadly as the name, date of birth, ID card number, passport number, characteristics, fingerprints, marital status, family information, education, occupation, medical record, medical treatment and health examination information, genetic information, information about sex life, criminal record, contact information, financial conditions, social activities and other information that may directly or indirectly identify an individual.

Under the PDPA, unless otherwise specified under law, a company is generally required to give notice to (notice requirement) and obtain consent from (consent requirement) an individual before collecting, processing or using any of said individuals personal data, subject to certain exemptions. To satisfy the notice requirement, certain matters must be communicated to the individual, such as the purposes for which his or her data is collected, the type of the personal data and the term, area and persons authorised to use the data, among other things.

AI technology has not changed the said requirements. If a company wishes to collect, process and use any individuals personal data using AI technology or exploring the data with AI technology, it will be subject to the obligations under the PDPA as advised above. Please note that the MyData platform described in question 2 should also be subject to the PDPA regime.

6 How are government authorities enforcing and monitoring compliance with AI legislation, regulations and practice guidance? Which entities are issuing and enforcing regulations, strategies and frameworks with respect to AI?

In the past few years, the Executive Yuan has published several guidelines and plans for AI developments, such as the Digital Nation and Innovative Economic Development Plan, the Taiwan AI Action Plan, the AI Technology R&D Guidelines and the 5+2 Plan, as stated in the answers to previous questions.

However, considering AI is more of a technology, which could be applied in various industries, there is no single central competent authority for the actual enforcement and monitoring of AI technology and such enforcement and supervisory tasks fall under the jurisdictions of relevant competent authorities. For example, the Ministry of Economic Affairs is assigned as the competent authority for the Unmanned Vehicle Sandbox Act, while the Financial Supervisory Commission is the authority for the FinTech Sandbox Act.

7 Has your jurisdiction participated in any international frameworks for AI?

To our knowledge, the Taiwan government has not participated in any international frameworks for AI. However, according to the relevant public announcement by the Ministry of Science and Technology, the AI Technology R&D Guidelines, as outlined in question 3, were set out to, among other things, follow the international trends with respect to AI developments and were prescribed by referencing the relevant principles and guidelines of the European Union, Japan, the Organisation for Economic Co-operation and Development, among others.

8 What have been the most noteworthy AI-related developments over the past year in your jurisdiction?

Taiwan is well known for its information and communications technology and semiconductor industry, and it is reported that there have been AI-related developments in these areas, such as AI-related chips, integrated circuit design, systems, software and relevant peripheral products. In recent years, there have been also certain AI and data analysis-focused start-ups reportedly having the potential to become the next unicorns, with main products used in the areas of digital marketing, advertising, video analytics, among others. It is also noteworthy that there are more and more associations and non-profit organisations offering educational and training programmes and courses to various industry players that are interested in exploring the possibility of exerting AI as a tool for improving their existing products or operations or to create new applications or business models with AI as underlying technology.

9 Which industry sectors have seen the most development in AI-based products and services in your jurisdiction?

In addition to those stated in question 8, please see below the recent trends relating to developments of AI-based products and services in Taiwan.

Transportation

As mentioned above, the aim of the Unmanned Vehicle Sandbox Act is to provide a friendlier environment for testing the application of AI in transportation. Pursuant to the news releases in November 2019, the Ministry of Economic Affairs plans to invest around US$8 million in four years to expedite the industrialisation of unmanned vehicle technology.

Healthcare

The Ministry of Science and Technology has driven the AI for Health plan, which assisted major medical research institutions in Taiwan in developing AI algorithms to be used for cardiovascular risk assessment, diagnosing cancer lesions at an early stage, accelerating the image recognition, among other things.

Financial services

The main application of AI in financial business in Taiwan involves correspondence with clients, such as ChatBot and Robo-Adviser Services. In June 2017, the Securities Investment Trust and Consulting Association of Taiwan, the self-disciplinary organisation of the asset management industry, issued Operating Rules for Securities Investment Consulting Enterprises Using Automated Tools to Provide Consulting Service (the Robo-Adviser Rules). Pursuant to the Robo-Adviser Rules, securities investment consulting enterprises may provide online securities investment consulting services by using automated tools through algorithm (Robo-Adviser Services).

10 Are there any pending or proposed legislative or regulatory initiatives in relation to AI?

As indicated in question 1, according to the Taiwan AI Action Plan, the Taiwan government is still evaluating the following issues so as to further determine whether any laws need to be enacted or amended to address AI development:

In addition to the above, some legislators proposed the draft Basic Act for Developments of Artificial Intelligence in 2019, which is intended to set out fundamental principles for AI developments, to drive the government to promote the development of AI technologies. The draft is still under review by the Legislative Yuan and it is uncertain whether this draft will be passed.

11 What best practices would you recommend to assess and manage risks arising in the deployment of AI?

As stated in question 1, Taiwan promulgated two regulatory sandbox laws, the FinTech Sandbox Act and the Unmanned Vehicle Sandbox Act. These regulatory sandbox laws were enacted to allow the relevant businesses to test their new ideas and technologies within a safe harbour. After completion of the approved experiments, the relevant competent authority will analyse the result of the experiment. If the result is positive, the relevant competent authority will actively examine the existing laws and regulations to explore the possibility of amending them with a view to making feasible the business models or activities previously tested in the sandbox. Therefore, for any business models that will involve the application of AI, relevant risks, especially legal risks, may be mitigated in case they are tested under either of the two sandbox laws.

As to any proposed business models or activities not falling within the sandbox scope permitted by the above sandbox laws, we would recommend that relevant risks, especially legal risks, be analysed as early as possible, and certainly well before the time any product is officially launched. The application of AI technology or AI-related products may involve various issues under traditional as well as emerging legal areas such as potential liabilities under civil and criminal laws, the ownership of AI products-related IP rights, privacy, among others. With respect to any products to be sold to end-customers, more detailed analysis on issues such as consumer protection and product liabilities, product inspection and testing, and liability insurance are also advised.

The Inside Track

What skills and experiences have helped you to navigate AI issues as a lawyer?

We were engaged by the National Development Council to conduct a research project, focusing on exploring the necessary adjustments to the existing legal regime to create a more friendly environment for AI developments. As to AI technology and its applications and its various areas of legal practice, Lee and Li, known for expertise in all legal fields and offering a full range of services, has the competitive advantage in offering valuable insight and best solutions from a Taiwan law perspective in every legal practice area.

Which areas of AI development are you most excited about and which do you think will offer the greatest opportunities?

AI applications may be used across many industries. As lawyers, what we are most excited about is that we may see industry experts come up with creative ideas associated with the technology and assist clients in exploring how to put technology innovations and actual AI applications into practical use in the real world. With respect to Taiwan, Taiwans well-known information and communications technology has established a good foundation for AI development. Taiwan has also been one of the major players in the semiconductor manufacturing industry. Given this, we think Taiwan has great opportunities to play an important role in AI trends.

What do you see as the greatest challenges facing both developers and society as a whole in relation to the deployment of AI?

We think an important challenge facing the developers would be how to commercialise AI technology and make AI applications address the needs of industry players and the general public. From the perspective of wider society, we think that the greatest challenge might be the replacement of human resources. Take the legal profession as an example, where topics widely discussed include how AI may impact the legal profession (eg, whether AI will replace some of the jobs that lawyers do). One can imagine AI applications to replace some jobs in multiple professional settings in many industries. Where AI applications can replace most of the jobs currently done by humans, it would be inevitable that the whole society would have to face issues arising from human resource surplus.

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An interview with Lee and Li Attorneys at Law discussing artificial intelligence in Taiwan - Lexology

AI is still in its early stages in Southeast Asia. Some countries are implementing initiatives to build the digital infrastructure and data ecosystems, with Singapore, Malaysia, and Vietnam leading the charge, according to a McKinsey report.

Machine learning, a subset of AI that trains computers to find patterns in big data sets to draw conclusions and make predictions, is the bedrock of most use casesfrom chatbots to driverless cars. Annotating one hour of video content can take eight hoursor morewhile the AI software has to process millions of data inputs in order to be effective and reliable.

Malaysia-based Supahands has carved out a niche for itself as an end-to-end data labeling partner that provides training data for AI and machine learning. It does so via a crowdsourced workforce of 16,000 SupaAgents across Southeast Asia, as well as their own proprietary technology to prepare different types of training data sets.

The in-house software includes SupaAnnotator, an image interpretation tool with customizable labels such as 2D bounding boxes and polygon annotations, and SupaMiner, a data management platform for image-to-text, video, and audio transcription projects, which can determine metadata for tagging and categorization.

Looking at the machine learning process as a whole, data labeling is an important part of the puzzle and simultaneously one of the most time-consuming and laborious tasks to be done when developing a model, said co-founder and COO Susian Yeap. Given the more repetitive nature of the tasks, the workforce we already had on hand, the infrastructure we could put together, and the general market opportunityit was the perfect fit.

Supahands wants to free up the bulk of an AI engineer or data scientists time by taking over the labor-intensive tasks of data preparation and labeling, helping their clients deploy their AI models faster. Use cases range from autonomous vehicles to facial landmark recognition and geospatial imaging across several industries, though a chunk of the revenue comes from retail AI and agtech verticals.

To give you an example, we have a retail client whom we helped with real-time shelf tagging, said Yeap. For them, we reviewed over 750,000 images a month with a team of more than 350 SupaAgents and an image annotation accuracy of over 95%.

For all the glitz and glamour associated with the promises of AI, the collection and labeling of huge, localized datasets are the necessary grunt work in the AI supply chain. But it presents a large market opportunity. Industry analyst Cognilyticaputs the third-party data labeling services market at USD 1.7 billion in 2019 and is predicted to grow to over USD 4.1 billion by 2024.

Supahands has helped Ibotta, a US-based consumer tech company process 300,000 to 600,000 receipts per month and AI firm Visionary to categorize 200,000 images saving its client 2030% of time. In 2019, it raised an undisclosed amount of Series A funding led by social venture capital firm Patamar Capital alongside Cradle Seed Ventures, for further expansion in the region.

But theres also another side of the technology Supahands is not shying away from. The AI/ML world has a history of not only injecting gender bias into software, but also to amplify it in the algorithm. While the future of AI shines bright, it is especially worrying that women will not only have to contend with everyday sexism, but also from that of technology.

If five 20-year-old men were to create a chatbot, the finished chatbot will sound like a 20-year old man, Yeap asserts. How could it not?

In an industry where professionals are overwhelming men, Supahands is setting an example. Female employees make up about 50% of the workforce, and this is consistent across its leadership, in tech, as well as in the SupaAgent teams.

From an AI development standpoint, its crucial that the technology provider is aware of the high tendency to create biased machines and to actively ensure as many variances and as much diversity as possible in the process, explains Yeap. We play our part by providing quality training data to our clients with as little bias as possible. In fact, our tech team consists of 51% women and five different nationalitiesan accomplishment that is rare in the industry.

However, when asked if Supahands hires with a diversity objective in mind, she responds that they dont. The company emphasizes the importance of hiring people who think differently, and along with that, naturally, come those who have different backgrounds in terms of culture, race, and gender.

We dont just look for female engineers and female business operation managers to tip the scales, said Yeap. We are just looking for the best engineers and the best designers, and based on these hiring principles and the people who walked through our door, this happens to be how our team looks like today.

This starts for her with the individuals mindset. Its crucial for women to stop putting a label on ourselves and seeing ourselves as women in tech,' she said. We should present ourselves as the working professionals and industry experts we really aresoftware engineers, data scientists, programmers, and CEOs.

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In the glamorous new AI world, it pays to do the tedious work: Startup Stories - KrASIA

Expected to be the most significant business advantage in the future by 72% of executives, artificial intelligence (AI) is predicted to be incorporated into 47% of organisations HR functions by 2022.

While it is feared that employment rates will drop as the use of intelligent technologies rises, when speaking with Business Chief in October, Arun Shenoy, SVP Global Sales and Marketing at Serverfarm reflected on the best way to deploy technology, software and hardware tools. Most organisations find this challenging because they are only solving one part of the problem - the technology. Simply buying and deploying a platform isnt enough; you have to change and refine the processes and ensure that you have the right people, commented Shenoy.

In fact, speaking with executive experts in HR operations, the consensus highlights that benefits of AI in HR operations come from a collaborative approach between AI and humans, with a core use case being to provide efficiency gains. It has allowed us to do the same thing we always did - but faster and more cost effective, comments Andi Britt, Senior Partner at IBM Talent & Transformation, IBM Services Europe. While the internet brought the capability of fast recruitment, both Britt and Chris Huff, CSO at Kofax identify that AI can apply the same speed to the assessment of potential candidates, the likelihood of future success and the expected timeframe to fill a given role. This is an example of the ways in which AI is changing the situation so that technology enables the HR function to solve critical business challenges, building on earlier contributions from workforce analytics, added Britt.

With COVID-19 placing organisation and business operations on the edge of a pivotal moment when it comes to innovation and digital transformation, AI and automation have transitioned from a nice to have to a necessity for survival. COVID-19 has created a digital awakening that has accelerated the adoption of AI and automation technologies, comments Huff. It is expected by those in HR that COVID-19 will not only accelerate the overall digital journey for organisations, but the role of HR in the modern workplace. This acceleration will ultimately move organisations closer to HR 3.0 with employee experience at its centre. CHROs at high performing organisations are taking immediate action to achieve this vision. They are leveraging real-time unstructured data from inside and outside organisations, and pairing that with analytics and AI to improve talent and workforce decisions while enabling more personalised employee experiences, says Britt.

Statistics reported during the height of the pandemic, identified that many organisations are embracing AI tools to attract diverse talent and to enhance and personalise recruitment. In an IBM HR executive survey, the company identified that more than half of high performing companies are using AI to identify behavioral skills to build diverse and adaptable teams. Currently, High performing organisations are leveraging AI across talent acquisition at a rate of 6 times more than all other companies. During the pandemic, IBM saw its clients rely heavily on AI enabled HR applications such as chatbots and skill building recommendation platforms. These technologies enable organisations to free up HR leaders time for more meaningful work. C-Suite leaders surveyed expect to see nearly tenfold growth with regard to automating HR processes between 2018 and 2022, comments Britt. However, while the rate of adoption has increased, IBM found that only 30% of companies have the skills and capabilities in AI in the HR function.

To be successful in adopting AI in HR operations - or any technology - culture is identified as an all to often underestimated barrier. It is important for organisations to ensure that they include their employees in the transformation journey. When employees understand the reasoning for change they are more receptive, making it easier to implement and adopt technology. Ultimately, Progress has to start from the top, with good leadership and open conversation to dispel fears and misunderstandings about the technology, states Britt. Not only is it important to engage with employees to showcase the business needs, it is also important to listen to the needs of the employees conducting the tasks.

By combining the best of what AI can provide, with employee hopes for the technology, Huff explains that this approach is a win-win that will increase adoption of AI and lead to a collaborative person-machine future to drive productivity for the organisation and individual. With this collaborative approach to AI and humans, HR is on the cusp of a new digital era in which employees adopt a more behind-the-scenes role to create the scenarios carried out by AI. People will find themselves in more creative, strategy, problem defining and problem escalation roles as opposed to transactional activities, concludes Huff.

The benefits of artificial intelligence (AI) in HR

Today, AIs capabilities are being used to augment business operations and consumer solutions, comments Andi Britt, Senior Partner at IBM Talent & Transformation, IBM Services Europe. At IBM, the company has identified five reasons for implementing AI in HR operations:

The challenges of artificial intelligence (AI) in HR

Current HR and AI trends point to a promising Future of Work thats richer in experience, but also brings with it the need for strong governance to account for unintended consequences, comments Chris Huff, CSO at Kofax. When it comes to the successful adoption of AI to deliver on its promising future, IBM identifies four key prevention barriers:

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Reinventing HR operations with humans and AI collaboration | Human Capital | Business Chief North America - Business Chief