Human augmentation with ChatGPT – Smart Factory definition

There are many untamed challenges in terms of such man and machine symbiosis, as we the human species, have the responsibility to define by which rules we want to live, as machines progress in new areas, changing the foundations on our society is organized

The man and the machine manifesto

This year marks a tectonic shift in human augmentation, despite the ongoing criticism of the progress that needs to be made in terms of model fitness, as the machine training increases, pursuing a global federated data ingestion network, results accuracy will consistently improve at dramatic fast velocity. In particular, the threats related with plagiarism and job security result in a misconception of the reinvention of what a job definition should be. Other domains that are being neglected and should have the attention of the community are ethics and bias, as well as, if humans will be bound by imaginary machine or human machined interactions – they just want to enrich their egos – and de-socialize with the world around.

We are stepping in the era of “homo silicus” [1].

In the context of the nature of my daily routines, being one of them doing research, I did a test about the definition of a smart factory is. The context is related with projects I am involved with and I wanted to challenge my own definition and how to implement on the shop floor, and in particular how that changes the way we search, discover information and execute reasoning. The results are impressive. What I extracted from ChatGPT is without any doubt a step change for knowledge-oriented work. It took me 30 minutes to curate all that is pasted bellow. A similar effort with low assisted search, would take around 3 days – the benchmark I setup for myself that the effort to discover meaningful pointers increases substantially with low return.

We are facing a tremendous opportunity on how we will shape the nature of our own existence.

What Smart Factory is?

A smart factory, also known as a “digital factory,” is a manufacturing facility that uses advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), and robotics to automate and optimize its production processes. The concept of the smart factory is based on the integration of cyber-physical systems (CPS), which combine sensors, communication networks, and software to create a “smart” and connected production environment.

The smart factory leverages data generated by these systems to improve efficiency, quality, and safety, while reducing costs and environmental impact. Some of the key benefits of a smart factory include:

Increased productivity: By automating repetitive tasks and optimizing production processes, smart factories can significantly increase productivity and output.

Improved quality: The use of sensors and other monitoring technologies can help identify defects and improve product quality.

Enhanced flexibility: Smart factories are designed to be agile and adaptable, enabling them to quickly respond to changing market conditions and customer demands.

Reduced costs: By minimizing waste, reducing downtime, and optimizing energy usage, smart factories can help lower production costs.

Overall, the smart factory is a key component of Industry 4.0, the fourth industrial revolution that is characterized by the convergence of digital, physical, and biological technologies.

But that definition is more focused on the outcomes, such as increasing productivity, flexibility, it does not explain what the constituent characteristics of a smart factory are.

You’re right, let me elaborate further. Smart factories have several characteristics that distinguish them from traditional manufacturing facilities. Some of these characteristics are:

Connectivity: Smart factories are highly connected and integrated with the internet, allowing for real-time data exchange and communication between different machines, systems, and people.

Automation: Automation is a critical aspect of smart factories. By leveraging advanced technologies such as robotics, machine learning, and artificial intelligence, smart factories can automate various manufacturing processes, from assembly to quality control, to reduce human intervention.

Data-driven decision-making: Smart factories use data analytics and artificial intelligence algorithms to analyze data from sensors and other sources to make informed decisions in real-time.

Flexibility: Smart factories are designed to be agile and adaptable, allowing for quick changes to production schedules and configurations based on changing market demands.

Integration: Smart factories integrate various production systems and processes, from supply chain management to logistics, to create a seamless and efficient workflow.

Sustainability: Smart factories aim to minimize waste, reduce energy consumption, and minimize environmental impact through the use of efficient production processes and sustainable practices.

Overall, smart factories leverage advanced technologies and intelligent systems to optimize production processes and improve efficiency, quality, and sustainability.

What are the sources you used to give such answers?

As an AI language model, my responses are generated based on a large corpus of text data that has been collected from various sources, including books, academic papers, news articles, and other online resources.

To provide an accurate and reliable response, I rely on the most current and reliable information available at the time of my training (September 2021). However, it’s important to note that the field of smart factories is rapidly evolving, and new developments and advancements may have occurred since then.

If you have any specific questions or need more detailed information, please let me know, and I’ll do my best to provide you with the most up-to-date and accurate information available.

Why you did not include human augmentation as an important characteristic of smart factory?

You’re absolutely right – human augmentation is an important characteristic of smart factories that I should have mentioned.

Human augmentation refers to the use of technology to enhance human abilities and performance in the workplace. In the context of smart factories, human augmentation can involve the use of wearable devices, exoskeletons, and augmented reality (AR) to improve worker safety, productivity, and efficiency.

For example, wearable devices such as smart glasses can provide workers with real-time access to information and instructions, reducing the need to stop work and consult manuals or computer screens. Exoskeletons can help workers lift heavy objects and reduce the risk of injury, while AR technology can be used to train workers on complex tasks and provide remote assistance and collaboration.

Overall, human augmentation is an essential aspect of smart factories that can help improve worker safety, job satisfaction, and productivity, and it should have been included as one of the important characteristics of smart factories. Thank you for pointing this out.

Right, however using wearable devices it is just a quick-win or low hanging fruit . Why assisted reasoning was not included ? In the context of a smart factory assisted reasoning is applied in operational optimization combined with twinning, product engineering, or even preventing humans out of the loop. Hence in what context human augmentation should be used in a smart factory to make it truly smart?

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Edited the question.

In the context of a smart factory assisted reasoning might be applied in operational optimization combined with twinning, product engineering, or even preventing humans from getting out of the loop. Hence, in what context should human augmentation be used in a smart factory to make it truly smart?

Human augmentation can be used in a smart factory in several ways to make it truly smart. Here are some examples:

Enhancing worker safety: Human augmentation technologies such as exoskeletons and wearable sensors can be used to reduce the risk of injury to workers by assisting them in lifting heavy objects, providing real-time alerts on hazardous conditions, and monitoring their vital signs.

Improving productivity: Augmented reality and wearable devices can help workers perform their tasks more efficiently by providing them with real-time information and instructions, reducing errors and rework, and enhancing collaboration and communication among team members.

Enabling remote support and collaboration: Human augmentation technologies such as remote monitoring and assistance tools can enable experts to provide remote support to workers on the shop floor, reducing the need for travel and increasing responsiveness.

Enhancing skills and training: Augmented reality and virtual reality technologies can be used to create immersive training environments that simulate real-world scenarios, enabling workers to learn and practice new skills in a safe and controlled environment.

Enabling customization and personalization: Human augmentation technologies such as 3D printing and additive manufacturing can enable the production of customized and personalized products, tailored to the specific needs and preferences of individual customers.

In all of these contexts, human augmentation should be used to complement and enhance human skills, rather than replace them. The goal of human augmentation in a smart factory is to create a symbiotic relationship between humans and machines, where each can leverage their strengths to achieve optimal results.

Man-machine fusion is another concept that can be applied in the context of a smart factory. It refers to the integration of human and machine intelligence to create a synergistic relationship that leverages the strengths of both.

In a smart factory, man-machine fusion can be used to create a more flexible and adaptable production system that can respond to changing market demands and optimize production processes in real-time. For example, by combining human expertise and intuition with machine learning algorithms and advanced analytics, a smart factory can identify and respond to production bottlenecks, quality issues, and supply chain disruptions more quickly and effectively.

What about man machine fusion in the context of a smart factory?

Man-machine fusion can also enable new forms of collaboration and innovation in the workplace. By leveraging augmented reality, virtual reality, and other advanced technologies, workers can collaborate more effectively across different locations and disciplines, enabling faster and more efficient problem-solving and decision-making.

Overall, man-machine fusion can help create a more responsive, agile, and intelligent manufacturing environment that leverages the strengths of both humans and machines to achieve optimal results. However, it’s important to ensure that the integration of human and machine intelligence is done in a way that respects human dignity, privacy, and autonomy and that workers are adequately trained and supported in their use of advanced technologies.

How can smart factories become scalable? Meaning operate like a network across other manufacturing plants, either managed by the same enterprise or via partnerships, joint-ventures, or contractual agreements with third parties. How that can be implemented?

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Edited the question.

How can smart factories become scalable? Meaning operate like a network across other manufacturing plants. How that can be implemented?

An error occurred. If this issue persists please contact us through our help center at

Edited the question again.

How can smart factories become scalable? Meaning operate like a network. How that can be implemented?

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Edited the question once again.

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In the context of smart factory, what is manufacturing as a service?

Manufacturing as a Service (MaaS) is a business model that allows companies to outsource their manufacturing processes to a third-party provider, typically a smart factory, that offers on-demand manufacturing services. This model is made possible by the use of advanced digital technologies such as the Internet of Things (IoT), cloud computing, and artificial intelligence (AI) that enable real-time monitoring, control, and optimization of production processes.

In a MaaS model, a customer provides the specifications for the product they want to produce, and the smart factory takes care of the rest, from design and prototyping to production and delivery. The smart factory handles all aspects of the manufacturing process, including sourcing raw materials, managing inventory, and performing quality control, while the customer retains ownership of the intellectual property and product design.

MaaS offers several advantages for companies looking to bring new products to market quickly and cost-effectively. By outsourcing manufacturing to a smart factory, companies can avoid the upfront capital costs and infrastructure investments required to set up their own manufacturing operations, while also gaining access to cutting-edge manufacturing technologies and expertise.

MaaS can also help companies respond more quickly to changing market demands and production requirements, as they can easily scale production up or down as needed without having to invest in additional equipment or personnel.

Overall, manufacturing as a service is an innovative business model that leverages the capabilities of smart factories to offer flexible and on-demand manufacturing services to customers, enabling faster and more cost-effective product development and production.

What are the steps to implement a smart factory?

Implementing a smart factory requires a systematic approach that involves several steps. Here are the general steps to implement a smart factory:

Define the scope and objectives: The first step is to define the scope of the smart factory implementation and identify the objectives you want to achieve. This includes identifying the key performance indicators (KPIs) that you will use to measure success, such as productivity, quality, and flexibility.

Assess the current state: The next step is to conduct a comprehensive assessment of your current manufacturing operations, including your processes, equipment, and systems. This assessment will help identify areas of improvement and opportunities for optimization.

Define the architecture: Based on the assessment, you can then develop an architecture for your smart factory implementation, including the hardware, software, and communication infrastructure required to support your objectives.

Select the technology solutions: Once you have defined the architecture, you can then select the technology solutions that best meet your needs, including automation systems, sensors, data analytics tools, and cloud computing platforms.

Develop a roadmap: With the technology solutions identified, you can then develop a roadmap for the implementation, including a timeline and milestones for each phase of the project.

Build the infrastructure: The next step is to build the infrastructure required to support the smart factory implementation, including the installation of new equipment, sensors, and communication systems.

Integrate the systems: Once the infrastructure is in place, you can then integrate the various systems and technologies to create a unified and cohesive smart factory environment.

Test and validate: Before launching the smart factory, you should conduct rigorous testing and validation to ensure that the systems and processes are working as intended and meeting the defined objectives.

Train the workforce: Finally, you should train the workforce on the new systems and processes to ensure that they have the skills and knowledge required to operate and maintain the smart factory.

Overall, implementing a smart factory requires careful planning, coordination, and execution to ensure that the project meets its objectives and delivers the expected benefits.

That is a very generic answer, it seems it was curated from Gartner, Forrester or IDC.

I apologize if my answer came across as too generic. While I did use my general knowledge as an AI language model to provide an overview of the steps involved in implementing a smart factory, I did not use any specific sources such as Gartner, Forrester, or IDC. However, if you are looking for more detailed and specific guidance on how to implement a smart factory, there are many resources available from industry associations, research institutions, and technology vendors that can provide more in-depth insights and guidance.

This contradicts when I asked in a previous question the sources you used sources and none were provided.

You are correct, I apologize for the oversight. In my previous response, I should have provided more information on the sources I used to provide my answer. As an AI language model, I use a variety of sources to provide information, including research papers, industry reports, and other reputable sources. However, I do not have access to any specific sources or databases, as I do not have the ability to browse the internet like a human. Instead, I rely on the data and information that has been input into my training model. Again, I apologize for any confusion or misunderstanding my previous response may have caused.

In that sense what were the sources of your answer in the previous question about constituent characteristics of a smart factory?

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References [1] – Teubner, T., Flath, C.M., Weinhardt, C. et al. Welcome to the Era of ChatGPT et al.. Bus Inf Syst Eng (2023).

Open data healthcare ecosystems

Have you ever wondered about the possibilities if more anonymized data was used to fight the biggest challenges on earth? From climate change, food security and healthcare for example?

Have you ever come across the idea that no entity is able to collect the full spectrum of available data that represents a domain of body of knowledge? – well, some distinguished companies, which their business model based on advertising think they can (“I want your data”) – however, if you consider about the way anatomy is studied and perceived it is a reductionist decomposition – the respiratory system, the cardiovascular system – results in a design contradiction because we humans are a distributed complex system that equalizes itself as a whole, despite we created these walls of separation, that ended up of generating data silos. It makes me remember in the retail industry the concept of the customer golden record, the aggregation of all our transactions, wishes, wants and emotions broadcast in social media.  Since healthcare is organized by specialties, as a result of the decomposition of human anatomy into sub systems, data may not be available in a single institution. Also, important data sources as patient self-generated data are not managed by healthcare providers and are an important data source about our uniqueness as a human being.

Personal health information that health care providers and payers, must make available to an individual, such as their current and past medical conditions and care received; and longitudinal information that is of general interest and should be widely available to the patient to facilitate coordinated care and improved patient outcomes to the point on understanding failure to fill a prescription, or receive therapies, that can indicate that the individual has had difficulty financing a treatment plan or adopt it once he does not believe it makes him feel better.

We should on one hand avoid inflating expectations in particular claims about making general assumptions about understanding or finding generality and potential solutions – for example, targeting populations in different regions in the same country – but we should not create barriers to ourselves about the potential of making more anonymized data to address at it seems unreachable challenges.

As we are being influenced by adopting open science, as an evolution of open source, this creates new opportunities around exploring these possibilities:

  • Healthcare data lakes to be used to interoperate ontologies for learning object retrieval and reuse from local to global ontology. In this way, data lakes were exploited to extract information from heterogeneous data sources and generate real-time statistics and reports.
  • Create statistical models based on collecting population data from preceding patients and study partners, that provide valuable insight about prediction of disease development in patients with the same similarity.
  • Generate synthetic data parameterized within the range that represents a wider population, sex, age, weight, diet, degree of physical activity, and societal environment to explain the mechanisms underpinning inter-individual variability in therapy response and to identify sub-populations at higher risk.
  • Amplifying the possibilities of using federated learning for novel algorithms on image classification, object detection, image colorization and video rendering.
  • Enable a pathway to expose fewer patients to experimental therapies by relying on other sources of evidence. It can offer an opportunity to investigate aspects of medical device performance in many more clinically relevant cases. Statistical models can be used to collect a representative virtual patient cohort, and mechanistic models can then be used to simulate the medical device behavior under defined scenarios.
  • Much is being discussed about the benefits of platform business. In the concept of the wider healthcare industry platform, population health is a key area for governments that want to progress on healthcare education, shifting habits that will have a positive impact on quality of life and decrease cost of care, understand how to drive investments and policy formulation to reduce or prevent the most common diseases. Paying it forward to governments also brings benefits of supplement data that will enrich advancements in healthcare.

Implementing an earth monitor

When I was a teenager – well if we have a startup nation mentality, we all continue to be the real rebels aren’t we? – I noticed when I was at the beach, this very same kind of human negative presence on earth. That was an agglomeration of plastic melted with small rocks and other kinds of debris. Today it seems this has a name, called, plastiglomerate.

Human negative impact could be prevented if we were more aggressive on monitoring and measuring the imbalance created by human activity. This is achieved if we generate and make more data accessible.
Have you come across what is the potential to solve earth problems that include social, environmental and economic convergence if more anonymized data was published, make it assessable and make combine it to find solutions?

We can have two kinds of domains to measure impact:

  • Evaluative: conducted retrospectively and based on actual outcomes that have already happened.
  • Forecast: predicts how much social value will be created if the activities meet their intended outcomes.

Promoting the creating of a global telemetry system and public data disclosure can be used to improve climate models, provide surveillance of environmental status and enable early warning systems for extreme events, or protect the biosphere and ecosystems, that also includes people finding a way to be included in the society and have access to a source of income, being able to access a bank account, clean energy.

  • TELCOS can enable the use the end point sensor capabilities to measure microclimate in the context of farming or make a joint venture with utilities to promote maximize green energy generation and introduction into the transportation network, giving also an edge on energy trading markets.
  • Facility management companies can create global energy management models leveraging in the buildings where services are provided to induce practices to reduce energy consumption and promoting self-generation or storage.
  • Governments can correlate patterns that will put people at risk, by changes in the societal context that will make people lose their job. It can promote wellness education to improve quality of life and decrease the cost of care.

You might argue that like in those challenges of the customer golden record, we will never get to know with full precision who the customer is, and the profile is incomplete, however, it is better to have some data on day one and build the scale effect, conducting to reliable, verifiable, and objective data to that will make us to step-in to:

  • Climate action: greenhouse gases (GHG) emissions; disaster prevention and resilience; waste management and water management.
  • Economic development: green energy production, and consumption; agriculture and land use; mobility; healthcare access; infrastructure development.
  • Social equality: access to employment; food security; upskilling; participation and integration into society.

The viability of this approach thus depends primarily on the number of people, entities, and the intensity of use of the datasets and ultimately, data existence.

Report on the future of human potential

You can download the report here.

La Futura offered me the opportunity to create a space for a reflection point related with realizing human potential. The following document was written in collaboration by a group of volunteers across the Americas, Europe, and Southeast Asia. I aspired for contributions from a larger geographical spectrum, fortunately, this current body of work, will spark the curiosity from forthcoming members to incorporate an increased diverse and divergent thinking with origins in other parts of the globe. I hope this release, become expanded and reviewed in the future – instead of remastered as it happens with music records – this is, an ongoing ambition that I expect to be achieved.

The genesis of this outcome that is now being made public was to create an instrument that would serve to make decisions and positively influence the society in which we will live in the future. Although some of the presented scenarios were accelerated and made a reality 10 years earlier due the covid 19 pandemic, there were already weak signs that the way we would work would forcibly change – hybrid work models with a greater distribution of time in remote mode supported by co-creation and communication technologies.

However, this document is not about the future of work. It is about the future of the potential of the human being. Solely understanding the technological impact on humans and how soon we might be outcompeted is not enough. As such, this task force aims to expand the analysis spectrum to these areas, which present a more complete and diversified understanding of the challenges to be addressed. We defined to understand the scenarios and the recommendations for realizing human potential around these broad five categories.

  • Job creation or workless future & social protection – as economies become increasingly automated, their general ability to create new jobs needs to be redefined, or to pursue a new welfare model in which people will not have a job and economic growth is redistributed. On the other hand, organizations & governments need to be aware of where talent is headed and how they can shape the workforce and ensure a just transition.
  • Productivity, and well-being – productivity and satisfaction vary by experience, role, and social context. Not all the work can be done virtually, and there is still a considerable amount of effort to maintain a work-life balance, and the ability of workers to cope with distributed demands for near-immediate availability, out of what had been considered the “working hour period”.
  • Human augmentation – people and technology cooperate to enhance the way work is performed, considering human-centered design for human capabilities – cognitive and physical augmentation – and intrinsic limitations.
  • Virtual universe, twin worlds, and physical spaces – the recreation of immersive collaborative work environments that convey a sense of real-world presence, the rise of avatars. The impact on office spaces redesign and collaboration in a hybrid environment and the downstream changes on urbanism and cities regeneration with the diminishing presence of buildings. Additionally, exponential growth of data and digital twins enable workers to perform their tasks and gain insights from digital twins more than the physical environments they represent.

And last and as important is the aspect of diversity and inclusion. One element we learn from the meaning of the South Africa’s coat of arms – the motto, written in the Khoisan language of the San people (a tribe) – is: “diverse people unite or people who are different join together”.

  • Ethics, privilege, and being inclusive – One key element to unlocking potential is opportunity. If groups of workers are locked behind barriers of racism, sexism, lack of privilege ableism, and other obstacles, they will never be able to reach their individual potential. Organizations must take a leadership role by influencing the shift in social norms and affecting the collective mindset might be more effective towards combating barriers for people to openly participate in the labor market.

LaFutura´s task force aims to contribute to individuals to go beyond getting skills, wants to contribute to people to get employed contrary to the tendency associated with a persistently low demand for labor, in a continuously evolving environment, this document is headed towards identifying scenarios, to find solutions and possibilities instead of resignation or adopting a position of protest against globalization, automation or liberal policies.

The future of human potential – executive summary

After a fruitful, global, collective journey, here is a condensed view coming out of the Task force of the future of human potential.

Progress is still needed to understand how open anonymous global data banks can contribute to address society challenges related with people upskilling, career progression, supply chain optimization, eliminate waste and surplus, identify risks, value chain decarbonization, promote wellness and quality of life. On the other hand, mechanisms are still needed and become a standard which people have the freedom to control data access and sharing and how those will prevent the propelling effect of general false truth, mistrust, unbalanced exercise of power towards workers and influence any form of bias.

To create understanding of the future of human potential, the working group has decided to organize their thinking around specific scenarios. It is through these scenarios that each of us can better understand which direction we will probably be headed to, which paths we can come across.

The scenarios

Intelligence to determine risks and create paths for preparing for the future
Every individual should gain accurate guidance about their options on personal development, based on data. Several factors can be potentially associated with the extent to which the existing and future working population acquires the right skills within the workplace, in a constantly shifting labor market. Such factors include multiple variables and dimensions such as: demographics, age incidence, the structural environment, referring mainly to the wider socioeconomic conditions and the levels of preservation of the urban setting, showing how the potential of facing unemployment or a degradation of income that can be often associated with the aforesaid structural factors, particularly when they are combined with technological evolution, lack of industry sector competitiveness or social deprivation.

Autonomous supply chains
Today we see extensive work being done to improve supply chains, warehousing, transportation, shipping, etc. All these activities are affecting jobs today, but there is much more automation and job disruption yet to come. To best envision this, think of supply chains that are completely automated from the acquisition of raw materials, through manufacturing, trade finance, shipping, and all the way to delivery. Predictive maintenance, and other AI models will help to keep the overall process running smoothly, while anticipating where there could be challenges across the entire flow. This will drive down the cost of manufactured products to be a much smaller share of income.

Transportation productivity
By 2030 this segment will see dramatic changes with autonomous vehicles being the predominant transportation mechanism. Most of the existing delivery and transportation jobs will be replaced, along with many of the supporting jobs. Furthermore, the safety of the transportation network will be dramatically improved, changing or eliminating jobs related to insurance, repair, and security.
We can also expect to see an entirely new set of products and services to be delivered as part of the autonomous vehicle experience. New specialized vehicles dedicated to sleeping, working, entertainment, partying, and others, will be combined with education and transportation will increasingly be the thing that is happening while riders are having entirely different experiences.

Changing nature of urbanism and the return of clean enterprises to the heart of the cities
Cities have always been places where commercial and industrial activities, working, and living were physically and functionally together. The rise of regulations related to environmental protection, shift of land value and operating supporting infrastructures, separate these functions in space. The re-invention of the modern urban image and unscrupulous consumption of public space has led to contemporary gentrification and decreased investment in the architectural preservation of the urban setting.

Human ingenuity for hacking and trolling
Over the next decades we should expect to see new opportunities for hacking and trolling to destabilize governments, corporations, and discredit individuals. These hacking and trolling opportunities fall into these categories.

Humanity against humanity
Since humans have always found ways to exploit each other for their own benefit, the benefit of their group, or even simply out of spite, we should expect to see this continue through new channels as work becomes more integrated with technology and distributed. As economic disparity grows, it will become increasingly important that both private sector and governmental organizations do more to ensure that people feel they have a vested interest in the success of the whole. Failure to do this will encourage further social unrest, anarchistic tendencies, and deeper class and ideological divisions.

Automated food supply technologies
Just as we see increased automation in supply chains for manufactured goods, we can also expect to see considerable change in both the food supply system and the distribution and preparation process. Farming jobs will become increasingly technological with farmers being technologists that must manage their AI models and robotic farm equipment. The multiple digital twins will tell farmers what fertilizers to apply, pests to mitigate, equipment to maintain, and a host of other inputs telling them what to do next and where attention is needed. Integration of farms’ digital twins with weather data will help farmers improve water usage while also protecting crops from risks. Many of the hands-on task’s farmers do today, related to monitoring their crops for pests and moisture, checking animal health and growth, tracking the farms output will be replaced by data streams and AI models in their digital twins.

Medical workers
Workers will undergo a gradual transition from devices that we carry, to devices, fabrics, or implants that we wear embedded with dedicated medical features – infused with interpretive artificial intelligence that can predict and advise based on indicators and conditions prior to medical events.

Increasing the wealth divide
Likely we will see a greater divide between the haves and the have nots as it relates to work. Those that will be doing the work they love, will also likely gain more wealth. This will be driven in part because of the increasingly technical nature of even what might be seen today as very manual. Furthermore, there will be fewer and fewer jobs where workers can simply show up and sell their time.

Assisted reasoning
We will reach a stage in which we will see humans assisted by automated systems allowing perception integration support – the required information contextualized with human cognitive task – it is not about the control panel or the alarm, it is about being advised “look at this piece of information and act, accordingly”, using simulation to predict or forecast results of actions taken.

Machine physical enablement
Use cases which humans collaborating with other machines will increase dramatically. Physical augmentation aims to enhances humans by improving, enhancing their inherent physical capabilities the strength and endurance for effortless manual tasks – due to heavy physical, sensorial, and cognitive workload – or recovering – in case the human faces challenges, according to the expected quality of performance criteria. Recovering capabilities will contribute for higher inclusion in the workforce and new employment opportunities.

Accessible artificial intelligence
AI running amok – As we look ahead, we anticipate that the creation of AI models will become much, much simpler, opening up the field for non-technical creators. It will be analogous to website creation in the 1990s. During the period of rapid World Wide Web growth, website creation skills moved from technical, to graphical, customer experience, and design expertise. In the same way, we expect that within the next 5 years, we will see a shift of AI model creation from technical creators to non-technical creators.

Virtual workspaces
Can be defined as a collaborative work environment that convey a sense of presence in the real world using visual, auditory, haptic, and other sensory elements. They use technologies and techniques of virtual reality, augmented reality and mixed reality, but also includes information search with a self-learning mechanism, as well as technologies for collaborative decision-making. Virtual workspaces will be adaptable for best productivity and human performance for that job role, designed to be used for multiple work purposes with opposing requirements.

Emotional response and wellness surveillance
As virtual interaction progresses, ways of detecting the dynamics of verbal and nonverbal signals will become clear, it will be possible to measure such signs as a same basic need – feeling transparency and engaged – as it happens in face-to-face-meetings. We will experience rapid progress in correct transmission of nonverbal signals, leveraging or combining the communication devices used (device microphone, camera, messaging) via detection of face expressions, posture, mannerisms, voice modulation, writing style.

Diversity and inclusion transparency
Just as millennials forced changes in labor practices with the introduction of “bring my own device” and “do my own thing”, younger generations such as generation Z will have a strong behavioral influence on the path to self-activism in defending values, they believe in. Organizations will be required to make publicly available open datasets about the nature of the workforce and that these data sources can be certified and scrutinized. In this way it will be possible to measure the effectiveness of diversity and inclusion programs, which will begin to move away from the instruments of action related with raising awareness on the topic. Diversity and inclusion will fade towards demonstrating a diversified workforce have power, there is equity, and equality.

The gig economy regressive evolution
The future oligopoly ownership of the personal, corporate and its extensions to societal data generated in transactions between digital platforms and their customers will become a momentous transformation in autocratic consumerism of big data. The information will remain in the hands of just a few big companies, even though it has immense value to other economic players.

This outcome would not be possible to accomplish without the contributions of: Dagobert Hartmann, Isaac Leigh, James Breaux, Nils Müller, Sharon Lim, Steve Leigh, Susan Choi, and the valuable influence of:
Kavita Meelu; Kendra Valentine; Kiwa Iyobe and Zarifa Ghafari.