AI, ML, GenAI & Workday - This "iPhone moment" could last for-AI-ver!

This is an illusion. This is not real!

Yes, those creepy faces are not real. but are meant to mimic one—as are the hundreds of thousands of others made by Datagen, a company that sells fake, simulated humans.

The article published a couple of years back on MIT Technology Review was quite a revelation and provided a great glimpse of the capabilities of the synthetic data companies’ capabilities. These humans are not gaming avatars or animated characters for movies. They are synthetic data designed to feed the growing appetite of deep-learning algorithms. Firms like Datagen offer a compelling alternative to the expensive and time-consuming process of gathering real-world data. They will make it for you: how you want it, when you want—and relatively cheaply.

?To generate its synthetic humans,?Datagen ?first scans actual humans. It partners with vendors who pay people to step inside giant full-body scanners that capture every detail from their irises to their skin texture to the curvature of their fingers. The startup then takes the raw data and pumps it through a series of algorithms, which develop 3D representations of a person’s body, face, eyes, and hands.

?The market:

When OpenAI introduced its artificial intelligence chatbot, ChatGPT3, on November 30, 2022, the value of Microsoft, one of the main investors in this artificial intelligence (AI) company, jumped by $115 billion (€108 billion), or around 6.5%.

Tech giant Google, perceiving a threat, hastened to introduce its own AI engine, Bard, on February 8. Unfortunately, the program made an error, attributing to the James Webb space telescope the first photograph taken of a planet outside the solar system. Google quickly suffered a heavy penalty, losing $100 billion in market value, or 9% of its total worth.

According to Next Move Strategy Consulting ,?the market for artificial intelligence (AI) is expected to show strong growth in the coming decade. Its value of nearly 100 billion U.S. dollars is expected to grow twentyfold by 2030, up to nearly two trillion U.S. dollars. The AI market covers a vast number of industries. Everything from supply chains, marketing, product making, research, analysis, and more are fields that will in some aspect will adopt artificial intelligence within their?structures. Chatbots, image generating AI, and mobile applications are all among the major trends improving AI in the coming years.

Generative AI - A growing market:

Generative AI, also known as Creative AI, is being adopted by a wide range of industries, including healthcare, finance, retail, and entertainment. Generative AI applications include content creation, personalized customer experiences, and drug discovery. The generative AI market is expected to continue its rapid growth in the coming years, driven by the increasing demand for AI-generated content and personalized experiences.

In 2022, the release of ChatGPT 3.0 brought about a new awakening to the possibilities of generative artificial intelligence. A good understanding of this trend comes from observing the difference in interest in generative AI on Google, with interest growing rapidly from 2022 to 2023. It is to be expected that this interest will continue as both ChatGPT and others aim for updated chatbot versions in the future and further generative AI programs are in development.

It is to be expected that this interest will continue as both ChatGPT and others aim for updated chatbot versions in the future and further generative AI programs are in development.

The Global generative AI market size was valued at?USD 8.2 Billion?in 2021 and is projected to reach?USD 126.5 Billion?by 2031, growing at a?CAGR of 32% from 2022 to 2031.

Below are the projections across the different kinds of AI:

?The?Large Language Model (LLM) Market ?was valued at?10.5 Billion USD?in 2022 and is anticipated to reach?40.8 Billion USD?by 2029, witnessing a CAGR of 21.4% during the forecast period 2023-2029.

–??Conversational AI market ?size was valued at?USD 5.78 billion?in 2020 and is projected to reach?USD 32.62 billion?by 2030, registering a Compound Annual Growth Rate (CAGR) of 20.0% from 2021 to 2030.

–??Chatbots market ?size is projected to reach?USD 3892.1 Million?by 2028, from?USD 1079.9 Million?in 2021, at a CAGR of 20.0% during 2022-2028.

Deep Learning Artificial Intelligence market ?is projected to reach?USD 101260 million?in 2029, increasing from?USD 15240 million?in 2022, with a CAGR of 31.1% during the period of 2023 to 2029.

?Artificial Intelligence Software System market ?is projected to grow from?USD 30320 million?in 2023 to?USD 156800 million?by 2029, at a Compound Annual Growth Rate (CAGR) of 31.5% during the forecast period.

?AI Governance market ?size is projected to reach?USD 116.3 million?by 2028, from?USD 23 million?in 2021, at a CAGR of 25.1% during 2022-2028.

–??Artificial Intelligence in Manufacturing and Supply Chain market ?is projected to grow from?USD 1165.4 million?in 2023 to?USD 7671.9 million?by 2029, at a Compound Annual Growth Rate (CAGR) of 36.9% during the forecast period.

–??Algorithmic IT Operations (AIOps) market ?size is projected to reach?USD 23.9 Billion?by 2027, from?USD 4.0 Billion?in 2020, at a CAGR of 30% during 2021-2027.

–??Artificial Intelligence in healthcare market ?size was valued at?USD 8.23 billion?in 2020 and is projected to reach?USD 194.4 billion?by 2030, growing at a CAGR of 38.1% from 2021 to 2030.

–??Artificial intelligence market ?size was valued at?USD 65.48 billion?in 2020, and is projected to reach?USD 1,581.70 billion?by 2030, growing at a CAGR of 38.0% from 2021 to 2030.

–??Artificial Intelligence as a Service (AIaaS) Market ?size (AIaaS Market size) is expected to reach?USD 77,047.7 million?in 2025, from?USD 2,397.2 million?in 2017, growing at a CAGR of 56.7% from 2018 to 2025.

–??Synthetic data generation market ?was valued at?USD 168.9 Million?in 2021, and is projected to reach?USD 3.5 Billion?by 2031, growing at a CAGR of 35.8% from 2022 to 2031.

–??Call

AI market ?was valued at?USD 959.80 million?in 2020, and is projected to reach?USD 9,949.61 million?by 2030, registering a Compound Annual Growth Rate (CAGR) of 26.3%.

Key Trends in the Generative AI Industry

1. The Rise of Large Language Models:?The development and deployment of large language models, such as GPT-3 and its successors, have significantly advanced generative AI capabilities. These models can generate human-like text and have applications in content creation, chatbots, and more.
2. The Emergence of New Generative AI Applications:?Generative AI is finding new applications across various industries. It's being used for creative content generation, data augmentation, personalized marketing, and even drug discovery. As the technology evolves, we can expect even more innovative use cases to emerge.
3. The Increasing Focus on Responsible AI Development:?With the growth of generative AI, there is also a growing emphasis on responsible AI development. Ethical considerations, bias mitigation, and ensuring AI-generated content meets regulatory standards are becoming crucial aspects of AI development.

The Key Segments are

By Applications:

• Content Creation and Marketing
• Human Resource Management
• Research and Development
• Customer Relations and Support
• Others

By Type:

• Visual
• Audio
• Text-Based
• Others

By Technology:

• Generative Adversarial Networks (GANS)
• Variational Autoencoder (VAE)
• Transformer
• Diffusion Networks

By Offering:

• Natural Language Processing (NLP)
• Machine Learning-based Predictive Modeling
• Computer Vision
• Robotics and Automation
• Augmented Reality (AR) and Virtual Reality (VR)
• Others

Key Market Players

The key market players in the generative AI market are:

1. OpenAI
2. NVIDIA
3. Adobe
4. IBM
5. Google
6. Microsoft
7. Facebook (Meta Platforms)
8. Salesforce

What’s driving the resurgence of ML and AI?

The amount of available data has massively increased due to mobile phones, IoT, improvements in SaaS applications and more. Cloud-based infrastructure and platform services have made analysing the volume, variety and velocity of data more cost-effective. The algorithmic approaches to predicting and prescriptive approaches to decision-making are on the rise.

How Workday is focusing on the next in ML and AI and what favours Workday?

Workday’s customers’ data is unified under a single umbrella that provides structure and semantics for everything.

• Using LLMs – have been using large language models (LLMs), like those which power generative AI, for years and are currently building capabilities that leverage generative AI for various language and image-related tasks, including natural language generation, document understanding, and content search, summarization, and augmentation. These new capabilities will enable their customers to unlock increased productivity through streamlined tasks and processes, increased efficiency, and better decision-making.
• Unrivalled dataset - The effectiveness of generative AI hinges upon the quantity and quality of the data it is built on. As evidenced by the many stories highlighting how generative AI chatbots have provided biased or incorrect responses, LLMs are only as good as the data that feeds them.?

• Using same platform for Generative AI and AI/ML allows them to rapidly leverage emerging technologies like foundational models to build new features quickly and easily, while maintaining a consistent experience throughout the entire Workday environment. It also helps them remain at the forefront of the rapidly evolving AI landscape by being able to embrace new models quickly.
• A hybrid, vendor agnostic approach Not only are they developing their own domain-specific LLMs, they’re also working with multiple leading third-party providers to create blended or ensemble models. This method lets them harness the best technologies available while delivering performant, cost-effective, trustworthy solutions to our customers. By augmenting these models with Workday data, they can provide responses that combine the strengths of leading LLMs with the accuracy of verified data. This will result in a more robust and dependable solution for the customers.

For their critical use cases, they focus on targeted, domain-specific models and high data quality above all else to provide outputs customers can have confidence in. "One of their key differentiators is that all customers are running on the same version of Workday, including the same data model, with over 60 million users who contribute to nearly 450 billion transactions processed by the system every year—and growing. With their permissions, they utilize that data as the fuel for our generative AI capabilities. This massive, high-quality dataset allows them to build models that consistently generate accurate, meaningful,?trustworthy?results" as stated by Jim Stratton in a blog published in Aug this year.

Responsible AI Governance at Workday

Incorporating a human review:

As published in one of the blogs a couple of months back by Workday Chief Legal Officer Rich Sauer, one of the most important guidelines is to always incorporate human review of any outputs generated from the AI technology that we release and the importance of transparency and disclosure. "It also highlights the importance of transparency and disclosure, which we operationalize primarily through our machine learning fact sheets, providing our customers with a clear understanding of how our AI and ML technologies are developed and assessed in order to help mitigate any risks associated with their use", he adds.

Leadership commitment:

Key executives from across the company, including their chief integrity and compliance officer, chief diversity officer, and chief technology officer participate on their RAI Advisory Board. This board meets regularly to review and approve new aspects of the RAI program and advise on novel issues as they arise.

RAI risk evaluation tool?

In order to implement and scale a risk-based approach to AI and ML development at Workday, they’ve created an RAI risk evaluation tool that their product managers (PMs) use at the ideation stage of any new AI and ML project. The tool walks PMs through a series of questions to determine the sensitivity level of the technology and the appropriate set of RAI guidelines to highlight, relevant to the intended use case.

Ongoing collaboration with government bodies

?They play a leading role?in AI-focused policy discussions at the federal, state, and local levels in the United States and also collaborate with other global governments to drive responsible AI practices internationally.

Areas getting impacted the most with ML/AI

• User experience - Machine learning capabilities centre around the user and are focused on helping to create business value for organisations. Workday uses machine learning to personalise each customer’s Workday experience and ensure it caters to their unique and evolving needs. With Workday People Experience, they consider more than what the user has done in the past. They are able to understand the user’s role and the behaviour of other users with that same role in the system. This allows them to predict the actions a user is going to take and make immediate recommendations, even if they are logging in to Workday for the very first time. This ensures users get the right recommendations at the right time.
• Human Capital Management -In the changing world of work, skills are the new currency. That’s why Workday is using machine learning to help businesses better understand their employees’ skills, while also creating personalised experiences that enable employees to better put their skills to work.
• Financial Management Machine learning is a critical part of their plan to bring finance into the future. With machine learning built deep into the core of Workday, they can seamlessly deliver more capabilities throughout all of their applications and make them more powerful and predictive.

Welcome to the thrill-ing world of Workday Skills Cloud!

Launched in 2018, Skills Cloud was built with data provided by our customers as well as massive industry-standard sets of training data, growing from 25 million skills being used across all customer tenants to more than 5 billion today as highlighted by David Somers in this blog published in Sep,22.

• Engaging experiences using Workday Skills Cloud - In a recent McKinsey global study, 87 percent of executives said they were experiencing skills gaps in the workforce or expected them within a few years. But fewer than half of respondents had a clear sense of how to address the problem. Skills, as they say, are the new currency!
• Over 25% of?Fortune?500 companies are live with Workday Skills Cloud.
• Recently, machine learning was leveraged to read and analyse countless documents to understand and graph the interrelatedness of more than 200,000 skills to create the skills cloud. This allows their customers to clearly see the type of skills within their workforce, analyse the strengths and gaps, and plan.
• Creating an engaging user experience requires behavioural awareness. Workday engages the user by creating an individualised engagement around a person’s career journey. It all starts with skills – the new currency that’s the basis for everything surrounding a worker’s journey. You can map workers to jobs, suggest learning and mentors, and much more using Workday Skills Cloud.

• Touchless automation - Workday uses the power of AI to automate and improve the accuracy of repetitive and predictable tasks. This has measurable business impact without removing humans from, or complicating, the process.
• AI-assisted insights and recommendations - Workday has made significant investments in the use of AI to deliver recommendations that enable better decision-making. They surface recommendations for the task at hand – such as determining your next learning or gig opportunity or choosing the best candidate for a job requisition.

Two features that support AI-delivered insights and recommendations to help improve decision-making are intelligent planning and candidate skills match.

1. Intelligent planning combines AI-driven forecast with a planner’s own forecast, and alerts the planner when inputs fall outside normal or historical ranges. Outlier reporting provides an easy way to identify anomalies, decrease planning cycle times and immediately surface potential problems.
2. Candidate skills match for recruiting uses AI to intelligently match candidates to open job positions. Recruiters receive a list of candidates and the strength of their match, as?well as details of the match factors that informed the results. This allows recruiters to focus their time on the right candidates, without the cumbersome pre-screening that often interferes.

How Workday has made this possible?

Mapping a successful skills strategy

Developing and executing a skills strategy is a journey that takes time. Once you know where you are on the maturity curve, you can take steps to lay the groundwork for mapping a successful skills strategy. These building blocks include:

1. Defining key characteristics for the future skills environment - This means getting cross-functional alignment on the key elements to successfully enable skills and create the necessary environment to?support this skills-based talent approach.

2. Designing guiding principles to structure skills-related decisions - This supports ongoing alignment to help projects and practitioners make decisions that are consistent with the key elements and characteristics that the organisation has set for the future of skills.

3. Highlighting operational impacts to support skills - Understanding and planning for these impacts enables the organisation to take a more holistic approach, as opposed to a technology-only solution. It also accounts for the necessary changes across the dimensions of people, process, data and technology.

4. Leveraging personas to help bring these ideas to life- - The focus is to define how skills support each persona, while also incorporating who interfaces with skills in enabling the persona – for example, the manager or HR. This activity gives us a truer sense of how skills are used in different ways by different roles and across different functions to drive business and individual value.

5. The key role of Data and analytics - Providing deep learnings and insights into what is working with your strategy, as well as areas for improvement. With Workday HCM, custom reports and dashboards can be built to better understand the current landscape within your organisation.

? Examples include:

• Skills inventory
• Recently acquired skills
• Recently lost skills and causes (attrition, internal transfers and so on)
• Relevancy of skills compared to job profiles
• Total number of skills assigned to a worker

6. Workday People Analytics - if enabled, it provides crucial insights into your skills strategy are surfaced without lifting a finger. Dashboards are pre-populated with information that makes it easy to quickly drill in and analyse core areas. Insights include:

• Skills gaps
• Skills categories
• Skills match
• Skills in demand

How Workday leverages Machine learning?for Skills Cloud?

Machine learning for skills at Workday looks at plain text across the platform to infer skills. This analysis can include information such as position history, job history, job requisition text fields, feedback, project history, certifications, candidate resumes, learning content titles and descriptions, gig titles and descriptions. It also looks at explicitly listed skills in the worker profile, skills interests, gigs and learning content. All of this together provides suggested skills, calculations on skills validation and match strength in Workday Talent Marketplace, and analysis.

To take it one level further, here are some examples of how machine learning supports a skills-based strategy:

? Suggested skills for candidates based on CV parsing

? Surface content in Workday Learning based on skills workers would like to?develop

? Match workers to Workday Talent Marketplace jobs and gigs based on skills strengths

? Identify workers for projects based on skills strengths

? Suggest skills for Workday Learning admins to add to content

? Suggest mentors and networking opportunities based on skills matching in career hub

?Connected applications

Workday customers have the ability to pair applications such as core Workday Human Capital Management (HCM), Workday Recruiting, Workday Learning, Workday Talent and Performance, Workday Talent Marketplace, Career hub, Workday Journeys and Workday People Analytics together to create a complete skills picture and strategy.

?How will this work for me as a customer?

Step 1 - Establish a skills governance model. Mature organisations often have a?federated governance model where there is a dedicated team responsible for the development and execution of an organisation-wide skills strategy.

Step 2 - Understand your workforce today. The skills cloud feature in Workday and machine learning algorithms provide you with a baseline of skills within your organisation. ??

• Turn on skills cloud – included with Workday HCM – in your tenant. When?you turn on skills cloud, Workday algorithms will parse your job and worker data to infer skills.
• Use the delivered Workday HCM skills dashboard to review inferred skills and identify the current skills within your organisation.

Step 3 - Identify business-critical skills for today and the future. It is easy to get overwhelmed with the volume of skills workers have, but there is often a?subgroup of critical skills required to meet business objectives.

• Consider labour market data to help identify future skills for relevant job functions.
• ?Share the inferred skills and labour market data with the business units and facilitate conversations to determine current and future critical skills.
• Update your job profiles or job requisition data with business-critical skills.
• Review the skills dashboard to understand how worker skills align with critical skills and help identify gaps. The dashboard is delivered in real time, allowing you to monitor your progress toward your strategic goals.

?Step 4 - Design a communications strategy to help workers and managers understand the role of skills within your organisation, and the benefits skills provide employees. Mature organisations often have robust communications plans that define the worker and manager benefits, such as internal mobility or upskilling opportunities.

?Step 5 - Bring it all together, and continue to build upon your foundation by exploring what’s possible with Workday.

And the magic begins!

• Career hub: The career hub in Workday is a machine-learning-driven career coach that brings together Workday Talent Marketplace, Workday Learning, and the Workday skills platform into a single experience to empower employees to take ownership in their career development. Employees receive suggestions for networking and mentorship opportunities, gig and internal job matches, and Workday Learning content based on skills-matching algorithms. Employees can also explore the skills in demand across the organisation and take steps to develop those skills.

• Workday Talent Marketplace: Workday Talent Marketplace creates transparency and connects employees to opportunities based on the skills they have or want to develop, allowing them to take an active role in their career development. Similar to career hub, employees receive suggestions for gigs and internal jobs based on skills matching and can review the skills-match analysis to see how well they are matched to these opportunities. Gig owners can view skills-match analysis for gig applicants, helping them to select the right people for their gig.

• Workday Recruiting: Workday Recruiting is an end-to-end talent acquisition application that helps you find, share, engage and select the best internal and external candidates for your organisation. Candidates’ resumes are automatically parsed to suggest skills for the candidates to add to their application. Recruiters are presented with candidate-matching scores, allowing them to quickly find the top talent for each job requisition.

• Workday Learning: Workday Learning is an enterprise learning management system that empowers organisations to deliver an employee-centric experience that transforms business outcomes by delivering learning in a consumer-like social environment. Learners receive machine-learning-recommended content based on skills. Content recommendations are surfaced within Workday Learning, career hub and Workday Talent Marketplace.

• Workday Journeys: Workday Journeys enables organisations to build tailored, concierge-style experiences for employees to help guide them through the moments that matter most. Workday Journeys can be used to guide employees through the organisation’s skills strategy in an easy-to-consume flow. Content can help employees understand the value of skills, learn how to manage their?personal skills profile, and see growth opportunities.

• Workday Prism Analytics: With Workday Prism Analytics, finance and HR now have a data hub built right into the system of record, bringing together the data they need while maintaining fidelity, providing access to decision-makers without compromising security, and supplying intuitive tools that make insights from disparate data sources easier to generate and consume. Workday skills data can be combined with external skills source data, such as labour market trends, to provide insights into skills coverage across the organisation and support your team in making informed workforce planning decisions.

• Workday People Analytics: Workday People Analytics enables organisations to make better people decisions faster with augmented analytics – helping you tackle three of the most pressing data-related challenges faced by HR: prioritisation, data literacy and scalability. The skills insights feature uses augmented analytics to surface trends and business questions to provide insight into the skills supply across the organisation and identify opportunities to develop skills in areas that have critical skills shortages.
• Extend: New AI capabilities are also coming to the Workday Extend developer platform, with an AI gateway that will give developers access to Workday AI and ML services, including skills analysis based on Workday Skills Cloud, sentiment analysis, intelligent document analysis to extract business-relevant data, and a time series forecaster. Workday Extend will also natively support several AWS AI services, including image processing and language translation.
• Workday App Builder: The low-code application is gaining a developer co-pilot feature, which creates application code from natural language prompts, along with other low-code and no-code capabilities. Workday is also looking into the use of generative AI to extend the natural language capabilities of a conversational user interface.

Let's hear "Workday Peakon Employee Voice"!

Machine learning meets human learning. Through the power of advanced intelligence technology and robust, validated research, this platform helps organisations collect employee feedback, deliver customised action plans and fuel engagement. The continuous listening platform gives you the real-time insight you need to take action to engage and empower your teams. It impacts and enables the below areas:

• Employee Engagement: Listen to and understand your employees so you can help them perform at their best and drive positive business outcomes.
• Employee Retention: Retain top talent by understanding when they’re at risk of leaving and then increase their engagement at work.
• Leadership Development: Empower leaders at every level with science-backed action plans and training that have been proven to work.
• Diversity, Equity and Inclusion: Identify opportunities to focus your diversity and inclusion efforts and make more strategic, evidence-based decisions.
• Employee Health and Well-Being: Get proactive about the health of your people, and adapt your initiatives based on the changing needs of your employees.
• Transformation and Change: Put employees at the centre of your transformation efforts to sustain positive change and improve business performance.
• Personalised surveys: It leverages intelligent follow-up that tailors questions based on the most recent feedback and the stage in their employee lifecycle.
• Global benchmarks: Their True Benchmark technology adjusts for demographic variations such as age and location, so managers can accurately assess expected engagement levels.
• The power of machine learning: Instantly surface trusted insights with Semantic Intelligence, using natural language processing (NLP) to automatically extract meaning from employee feedback.

The new AppStore/Playstore - AI Marketplace

Hosting certified third-party apps and services that can be accessed in Extend via the AI Gateway. Set for availability at latest in Q2 next year, the first wave of 15 early adopter partners was unveiled at Rising, including consulting partners. The Workday AI Marketplace aims to help Workday customers harness the power of generative AI and other cutting edge technologies to the fullest extent by bringing the best of Workday AI and solutions from third-party partners together in one place. The marketplace will feature AI and ML apps that integrate with Workday data via APIs, as well as apps built through Workday Extend using Workday-trained large language models.

Other AI-assisted salient points:

• A new Manager Insights Hub uses AI and ML to surface personalized recommendations for employee career development, such as suggesting connections, mentors, and gigs based on their skills interests.
• A new Flex Teams capability helps to rapidly assemble teams for specific tasks and projects. This uses AI within Workday Skills Cloud to quickly identify suitable talent from across the organization, assemble a team, and define roles.
• A new Home and Insights feature delivers curated insights from Workday to provide managers with a holistic view of relevant information related to their teams, including important dates such as birthdays, anniversaries and time off. A My Tasks section on the home page enables quick actions to complete tasks, and these capabilities are also integrated into Microsoft Teams and Slack.
• A new UI integration to Adaptive Planning brings workforce planning directly into the Workday HCM user interface. Other new capabilities here include automated headcount reconciliation and a new planning configuration manager that simplifies setup and maintenance.
• Adaptive Planning also gets an upgrade to?its AI-assisted intelligent modeling engine , bringing faster dashboard performance and OfficeConnect reports, a predictive forecaster, and the ability to create personal what-if scenarios and automated report scheduling and distribution.

It is not perfect!

AI has limits and some of the major ones are as per this blog and other sources :

• Data is the king! It's important to remember that there are limits to what it can do. One major limitation is that AI is only as good as the data it's trained on. If the data is biased or incomplete, the AI system will reflect those biases and limitations.?
• Creating original ideas: AI is also incapable of true creativity or innovation. While AI can generate new ideas and solutions based on existing data, it cannot think outside the box and create original ideas. This is because AI is based on algorithms and patterns, whereas human creativity is driven by intuition, inspiration, and imagination. Therefore, AI can be a valuable tool for augmenting human ingenuity but can never replace it.
• Showing empathy : Additionally, AI is not capable of empathy or emotional intelligence. While AI can recognise and analyse emotions, it can't truly understand them or respond to them in a meaningful way. This means that AI can't replace human relationships or social interactions, as these require a deep understanding of human emotions and behaviours. Therefore, while AI can be a powerful tool for many applications, it's important to remember that it's not a substitute for human intelligence, empathy, and creativity. Jealousy is the feeling of resentment or anger towards someone else's success or possession. It is often directed towards a person who is perceived as having something that the jealous person wants or desires. Jealousy can be triggered by a romantic partner, a friend, or a colleague who is doing well in their life or career.

Envy, on the other hand, is the feeling of wanting something that someone else has. It is often directed towards a person who is perceived as having something that the envious person wishes they had. Envy can be triggered by someone's possessions, status, or accomplishments.

In summary, jealousy is an emotion that arises when someone feels like they are losing something they have, while envy is an emotion that arises when someone wants something they don't have.

Here’s a dictionary.com explanation (presumably written by a human and easier to understand): Jealousy and envy both involve a feeling of desire for what another person has, but jealousy is usually thought to be more negative—it often involves resentment toward the other person. Envy is also a negative feeling—like a mix of admiration and discontent—but the word doesn’t usually imply hostility. Another difference is that envy can be used as both a noun and a verb.

They can only process data in a logical and structured way. They can recognise patterns in data that may indicate certain emotions, such as facial expressions or tone of voice, but they do not experience emotions themselves. In other words, AI does not have consciousness or emotions, it does not have the subjective experience of feeling happy, sad or angry.

• Lack of robustness: Another limitation of AI systems is the lack of robustness, which makes them susceptible to manipulation. AI systems are based on large amounts of data and complex algorithms, which can make them difficult to interpret and understand. As a result, they can be easily fooled by malicious actors who may use techniques such as adversarial examples to manipulate the system's decisions.?
• Bias: Biases can be introduced in the data through various means, such as human error, sampling bias, or social and historical factors. For example, an AI system trained on a dataset of job applicants that is mostly composed of men will likely be biased towards men and make less accurate predictions for women.?
• Lack of common sense: AI systems currently lack the ability to apply common sense reasoning to new situations. They are only able to make predictions and decisions based on the data they have been trained on, meaning they are not able to apply their knowledge in a flexible way to new situations. This lack of common sense can make AI systems prone to errors, particularly when dealing with novel situations. For example, an AI system trained to identify objects in images may not be able to recognise an object that it has not seen before, meaning it will still require human input to feed it the new item and programme it for future experiences
• Limited understanding of context: AI systems have a limited understanding of context and the nuances of human language and communication.? Machines are often trained on large amounts of text data and are able to identify patterns and make predictions based on that data. However, they lack the ability to understand the nuances and subtleties of human language and communication.?For example, they may struggle to understand sarcasm, irony, or figurative language. They also lack the ability to understand the context in which language is used, which can lead to errors or unexpected behaviour.
• Not a seeker: As per Cristian Randieri , , Forbes Council member , another limitation of AI is its difficulty asking questions and seeking new knowledge. Unlike humans, AI systems cannot wonder and be curious, essential for scientific exploration and discovery. Human researchers can question and seek new knowledge by examining the world, while AI systems can only process pre-programmed information.
• Understanding abstract concepts: AI systems are also limited in understanding abstract concepts due to their limited training using specific data sets and algorithms, making it difficult to grasp complex and abstract concepts. In contrast, human researchers can understand and work with abstract concepts, essential for scientific advancement and discovery.

Conclusion:

Ultimately, AI is not a replacement for human intelligence, it's a tool that can help us achieve our goals, but we need to ensure that we use it responsibly and ethically.

Furthermore, humans bring a wide range of experiences, creativity, and intuition to the decision-making process that AI cannot replicate. While AI can process vast amounts of data and identify patterns that humans may miss, it cannot replace the value of human intuition and creativity in decision-making.

?As Karim Lakhani, a professor at Harvard Business School states it “AI is not going to replace humans, but humans with AI are going to replace humans without AI. This is definitely the case for generative AI. The first step is to begin, start experimentation, create the sandboxes, run internal bootcamps, and don’t just run bootcamps for technology workers, run bootcamps for everybody. Give them access to tools, figure out what use cases they develop, and then use that as a basis to rank and stack them and put them into play.” He’s done pioneering work in identifying how digital transformation has remade the world of business, and he’s the co-author of the 2020 book?Competing in the Age of AI.?He emphasizes that change and change management are skills that are no longer optional for modern organizations.

?As per Cristian Randieri “Combining AI and human curiosity can lead to even more outstanding results. Although AI may eventually improve and replicate certain aspects of human curiosity, interest is an integral part of being human and is necessary for scientific progress. In the future, AI and human curiosity will work together in a complementary way to achieve even more impressive scientific discoveries”

I believe AI should be leveraged to bolster our capabilities as humans rather than looking at it as a potential replacement. The future is exciting, and it certainly is time to embrace rAInbow thinking , stay hopeful and welcome AI into our lives with open arms! ?

Sources:

Workday.com

Statista.com

https://www.lemonde.fr/

https://www.prnewswire.com/

https://www.macaubusiness.com/

https://www.calls9.com/

https://hbr.org/

https://www.forbes.com/

https://www.technologyreview.com/

?https://www.edcatalogue.com/

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