Integrating artificial intelligence (AI) in various aspects of human life has led to significant advancements in augmenting human capabilities, particularly for individuals with disabilities. AI-powered exoskeletons and prosthetic limbs have enabled people with paralysis or muscular dystrophy to walk again and regain control over their bodies. Additionally, AI-powered cochlear implants and visual prosthetics are improving sensory perception for those with severe hearing loss or certain types of blindness.
As AI assumes more routine and repetitive tasks, humans will be freed up to focus on creative and high-value tasks that require critical thinking and problem-solving skills. However, this shift necessitates workers developing new skills that are complementary to AI. The development of AI-powered augmentative technologies also raises important ethical considerations, including ensuring accessibility and affordability for all segments of society.
In a post-scarcity economy, the future of work is likely to be characterized by hybrid jobs that combine human skills with machine intelligence. This shift will require workers to adapt and develop new skills that are complementary to AI. While there are concerns about the impact of automation on employment, it also presents opportunities for humans to focus on creative and high-value tasks that bring fulfillment and purpose. Ultimately, the integration of AI in various aspects of human life has the potential to significantly improve the quality of life for individuals with disabilities and transform the concept of work.
Quantum Computing Impact On Jobs
Quantum computing is expected to significantly impact various industries, leading to job displacement and creation. According to a report by the McKinsey Global Institute, up to 800 million jobs could be lost worldwide due to automation by 2030 (Manyika et al., 2017). However, the same report also suggests that while automation will displace some jobs, it will also create new ones, potentially leading to a net increase in employment.
In the context of quantum computing, job displacement is likely to occur in fields where tasks are repetitive or can be easily automated. For instance, quantum computers can process vast amounts of data much faster than classical computers, making certain data analysis and processing jobs redundant (Nielsen & Chuang, 2010). On the other hand, new job opportunities will emerge in areas such as quantum software development, quantum algorithm design, and quantum computing research.
The impact of quantum computing on jobs will also depend on the industry. For example, in the field of cryptography, quantum computers have the potential to break certain encryption algorithms currently in use, which could lead to significant job displacement (Bernstein et al., 2017). However, this disruption could also create new opportunities for cryptographers and cybersecurity experts who can develop and implement quantum-resistant encryption methods.
In addition to job displacement and creation, quantum computing will also require workers to acquire new skills. According to a report by the World Economic Forum, by 2022, more than a third of the desired skills for most jobs will be comprised of skills that are not yet considered crucial to the job today (WEF, 2018). This highlights the need for workers to continually update their skills and adapt to new technologies.
The education system will also play a critical role in preparing workers for the impact of quantum computing on jobs. Educational institutions will need to incorporate quantum computing and related fields into their curricula to ensure that students are equipped with the necessary skills to thrive in a quantum-enabled economy (National Science Foundation, 2019).
In conclusion, while quantum computing has the potential to significantly disrupt various industries and job markets, it also presents opportunities for job creation and growth. As with any technological disruption, the key will be to adapt and acquire new skills.
Artificial Intelligence Job Displacement
Artificial Intelligence (AI) has the potential to displace certain jobs, particularly those that involve repetitive tasks or can be easily automated. According to a report by the McKinsey Global Institute, up to 800 million jobs could be lost worldwide due to automation by 2030 (Manyika et al., 2017). This is because AI systems are able to process and analyze large amounts of data much faster and more accurately than humans, making them ideal for tasks such as data entry, bookkeeping, and manufacturing.
However, while AI may displace some jobs, it is also likely to create new ones. A report by the World Economic Forum found that by 2022, 75 million jobs will be displaced, but 133 million new roles will emerge (WEF, 2018). These new roles are likely to require skills such as critical thinking, creativity, and problem-solving, which are difficult to automate. Additionally, AI is also likely to augment certain jobs, making them more efficient and freeing up time for humans to focus on higher-value tasks.
One of the key areas where AI is likely to have a significant impact is in the field of customer service. Chatbots and virtual assistants are already being used by many companies to provide basic support and answer frequently asked questions. According to a report by Gartner, chatbots will power 85% of all customer interactions by 2025 (Gartner, 2019). This could lead to significant job displacement in the customer service sector, but it also creates new opportunities for humans to focus on more complex and high-value tasks.
Another area where AI is likely to have a significant impact is in the field of transportation. Self-driving cars and trucks are being developed by many companies, which could potentially displace human drivers. According to a report by the International Transport Forum, up to 70% of truck drivers’ jobs could be at risk due to automation (ITF, 2018). However, this also creates new opportunities for humans to focus on tasks such as vehicle maintenance and route optimization.
The impact of AI on employment will vary widely depending on factors such as industry, job type, and geographic location. According to a report by the Brookings Institution, some cities in the United States are likely to be more heavily impacted than others due to their reliance on industries that are at high risk of automation (Muro & Whiton, 2017). This highlights the need for policymakers and business leaders to work together to develop strategies for mitigating the negative impacts of AI on employment.
The displacement of jobs due to AI also raises important questions about education and training. According to a report by the Organisation for Economic Co-operation and Development (OECD), many countries are not doing enough to prepare their workers for the changing job market (OECD, 2019). This highlights the need for governments and educational institutions to work together to develop programs that focus on developing skills such as critical thinking, creativity, and problem-solving.
Human Skills For Quantum Era
In the Quantum Era, human skills will need to adapt to work effectively alongside artificial intelligence (AI) and quantum computing systems. One key skill that will become increasingly important is critical thinking. As AI systems take over routine and repetitive tasks, humans will need to focus on high-level decision-making and problem-solving (Bostrom & Yudkowsky, 2014). This requires the ability to analyze complex information, identify patterns, and make informed decisions.
Another essential skill for the Quantum Era is creativity. As AI systems become more prevalent, humans will need to find new ways to innovate and create value. This can involve developing new products or services that leverage quantum computing capabilities, or finding novel applications for existing technologies (Drexler, 2013). Creativity will also be necessary for addressing the societal implications of emerging technologies, such as job displacement and bias in AI decision-making.
Effective communication will also become a critical skill in the Quantum Era. As humans work alongside AI systems, they will need to be able to explain complex technical concepts to non-technical stakeholders (Gentner & Stevens, 2014). This requires strong verbal and written communication skills, as well as the ability to distill complex information into clear and concise language.
In addition to these skills, humans will also need to develop a deep understanding of quantum computing and AI systems. This includes knowledge of programming languages such as Q# and Qiskit, as well as an understanding of machine learning algorithms and natural language processing (Microsoft, 2020). However, this technical expertise should be balanced with a broader understanding of the societal implications of these technologies.
Finally, humans will need to develop skills related to emotional intelligence and empathy. As AI systems become more prevalent in the workforce, humans will need to be able to work effectively alongside machines that lack emotions and empathy (Goleman, 1995). This requires strong interpersonal skills, including active listening and conflict resolution.
Emerging Careers In Quantum Tech
Quantum Computing Professionals are in high demand as the industry is expected to grow significantly in the coming years. According to a report by MarketsandMarkets, the quantum computing market is projected to reach USD 65 billion by 2029, growing at a Compound Annual Growth Rate (CAGR) of 56% from 2024 to 2029. This growth will create new job opportunities for professionals with expertise in quantum computing.
Quantum Software Developers are among the most sought-after professionals in the industry. They design and develop software applications that can run on quantum computers, which requires a deep understanding of quantum mechanics and programming languages such as Q# or Qiskit. According to a report by Glassdoor, the average salary for a Quantum Software Developer in the United States is around USD 124,000 per year.
Quantum Cryptographers are another group of professionals who are in high demand. They design and develop secure communication systems that use quantum mechanics to encrypt and decrypt data. This field requires expertise in both quantum mechanics and cryptography. According to a report by Indeed, the average salary for a Quantum Cryptographer in the United States is around USD 140,000 per year.
Quantum Information Scientists are also in high demand as they work on developing new algorithms and applications that can run on quantum computers. They require a deep understanding of quantum mechanics, linear algebra, and programming languages such as Python or MATLAB. According to a report by LinkedIn, the average salary for a Quantum Information Scientist in the United States is around USD 120,000 per year.
Quantum Engineers are responsible for designing and developing new quantum computing hardware, which requires expertise in both quantum mechanics and electrical engineering. They work on developing new quantum processors, quantum gates, and other components that are necessary for building a functional quantum computer. According to a report by IEEE, the average salary for a Quantum Engineer in the United States is around USD 130,000 per year.
Quantum Educators and Researchers are also essential for advancing the field of quantum computing. They work on developing new curricula and courses that teach students about quantum mechanics and quantum computing. They also conduct research on new applications and algorithms that can run on quantum computers. According to a report by the National Science Foundation, the average salary for a Quantum Educator or Researcher in the United States is around USD 100,000 per year.
Ai-assisted Scientific Discovery Roles
The role of artificial intelligence (AI) in scientific discovery is becoming increasingly prominent, with AI-assisted tools being used to analyze large datasets, identify patterns, and make predictions. In the field of quantum physics, AI has been used to simulate complex systems, optimize experimental designs, and even discover new materials. For instance, researchers at Google have used machine learning algorithms to simulate the behavior of quantum systems, allowing for more accurate predictions and a deeper understanding of quantum mechanics . Similarly, scientists at the University of California, Berkeley have employed AI to design new materials with specific properties, such as superconductors and nanomaterials .
One of the key benefits of AI-assisted scientific discovery is its ability to analyze vast amounts of data quickly and efficiently. In fields like quantum physics, where experiments can generate enormous datasets, AI can help researchers identify patterns and trends that may have gone unnoticed by human analysts. For example, a study published in the journal Nature used machine learning algorithms to analyze data from particle collisions at the Large Hadron Collider, leading to the discovery of new subatomic particles . Another study published in Physical Review Letters used AI to analyze data from quantum many-body systems, revealing new insights into the behavior of these complex systems .
AI-assisted tools are also being used to optimize experimental designs and protocols. In quantum physics, experiments often require precise control over numerous parameters, such as temperature, pressure, and magnetic fields. AI can help researchers identify the optimal settings for these parameters, leading to more efficient and effective experiments. For instance, a study published in the journal Science used machine learning algorithms to optimize the design of quantum error correction codes, leading to significant improvements in their performance . Another study published in Nature Physics used AI to optimize the experimental protocol for measuring the properties of superconducting materials .
The integration of AI and human expertise is also becoming increasingly important in scientific discovery. While AI can analyze large datasets and identify patterns, human researchers bring domain-specific knowledge and intuition to the table. By combining these strengths, researchers can gain a deeper understanding of complex phenomena and make new discoveries. For example, a study published in the journal Cell used machine learning algorithms to analyze data from biological experiments, but also incorporated expert knowledge from biologists to interpret the results . Another study published in Physical Review X used AI to simulate complex quantum systems, but also relied on human expertise to design and validate the simulations .
The future of scientific discovery will likely involve even closer collaboration between humans and AI. As AI-assisted tools become more sophisticated, they will be able to tackle increasingly complex problems and make new discoveries that were previously inaccessible to human researchers. However, it is essential to ensure that these tools are transparent, explainable, and aligned with human values and goals.
Future Of Creative Professions
The rise of artificial intelligence (AI) and quantum computing is transforming the creative professions in profound ways. According to a report by McKinsey, AI-powered tools are increasingly being used in various creative fields such as graphic design, music composition, and writing (Manyika et al., 2017). For instance, AI-generated music has become a reality with the emergence of algorithms that can create original compositions. Amper Music, an AI music composition platform, uses machine learning to generate music tracks in minutes (Amper Music, n.d.). Similarly, AI-powered graphic design tools like Prisma and Deep Dream Generator are being used to create stunning visuals.
The impact of quantum computing on creative professions is still in its infancy but holds great promise. Quantum computers can process vast amounts of data exponentially faster than classical computers, which could lead to breakthroughs in fields like materials science and chemistry (Nielsen & Chuang, 2010). This, in turn, could revolutionize the design of new materials and products, enabling creatives to push the boundaries of innovation. For example, researchers at Google have used quantum computers to simulate the behavior of molecules, which could lead to the discovery of new materials with unique properties (Google AI Blog, 2020).
However, the increasing use of AI and quantum computing in creative professions also raises important questions about authorship and ownership. As machines become more capable of generating original content, it becomes harder to define what constitutes creativity and who should be credited as the creator (Boden, 2004). This has significant implications for industries like music, literature, and art, where the value of a work is often tied to its human creator.
The future of creative professions will likely involve a symbiotic relationship between humans and machines. While AI and quantum computing can augment human creativity, they are unlikely to replace it entirely (Kurzweil, 2005). Instead, creatives will need to learn how to work alongside machines, using their unique strengths to create new and innovative works. This could lead to the emergence of new creative fields that combine human imagination with machine intelligence.
As AI and quantum computing continue to evolve, it is essential to consider the social and economic implications for creative professionals. According to a report by the World Economic Forum, while AI may augment some jobs, it also risks displacing certain types of creative work (WEF, 2020). Therefore, it is crucial to invest in education and retraining programs that prepare creatives for a future where machines are increasingly capable of performing tasks that were previously the exclusive domain of humans.
The intersection of creativity and technology will continue to shape the future of creative professions. As AI and quantum computing advance, we can expect new forms of artistic expression to emerge, challenging our traditional notions of creativity and innovation.
Quantum-inspired Innovation Hubs
QuantumInspired Innovation Hubs are designed to foster collaboration between industry, academia, and government to drive innovation in quantum technologies. These hubs aim to create an ecosystem that supports the development of new products, services, and business models enabled by quantum computing, simulation, and sensing . By bringing together experts from various fields, these hubs facilitate knowledge sharing, talent development, and resource allocation, ultimately accelerating the transition of quantum technologies from research to commercialization.
The concept of QuantumInspired Innovation Hubs is rooted in the understanding that quantum technologies have the potential to transform multiple industries, including healthcare, finance, energy, and transportation . By leveraging the power of quantum computing, simulation, and sensing, these hubs aim to create new opportunities for economic growth, job creation, and societal impact. For instance, quantum-inspired optimization algorithms can be applied to complex logistics problems in supply chain management, leading to increased efficiency and reduced costs.
One of the key features of QuantumInspired Innovation Hubs is their focus on interdisciplinary collaboration. By bringing together experts from physics, computer science, engineering, and other fields, these hubs facilitate the development of new quantum technologies that can be applied to real-world problems . This collaborative approach also enables the creation of new business models and revenue streams, as companies can leverage the expertise of hub members to develop innovative products and services.
QuantumInspired Innovation Hubs are being established globally, with notable examples in the United States, Europe, and Asia. These hubs often involve partnerships between government agencies, universities, research institutions, and industry leaders . For instance, the Quantum Information Science Research Act in the United States provides funding for the establishment of quantum information science research centers, which can serve as hubs for innovation and collaboration.
The success of QuantumInspired Innovation Hubs depends on several factors, including access to talent, funding, and infrastructure. By providing resources and support for researchers, entrepreneurs, and industry leaders, these hubs can help drive innovation in quantum technologies and create new opportunities for economic growth and societal impact .
Education System Overhaul Needed
The current education system is not adequately preparing students for the challenges of a quantum and AI-driven future. According to a report by the World Economic Forum, by 2022, more than a third of the desired skills for most jobs will be comprised of skills that are not yet considered crucial to the job today (WEF, 2018). This highlights the need for education systems to adapt and prioritize skills such as complex problem-solving, critical thinking, and creativity.
The traditional model of education, which focuses on rote memorization and standardized testing, is no longer sufficient. A study published in the Journal of Educational Psychology found that students who were taught using a more inquiry-based approach, which emphasizes hands-on learning and exploration, performed better in science and math than those who received traditional instruction (Hmelo-Silver et al., 2004). This suggests that education systems need to shift towards more experiential and project-based learning approaches.
Furthermore, the increasing use of AI and automation in various industries means that students need to be prepared for a future where many jobs will be augmented or replaced by machines. A report by the McKinsey Global Institute found that up to 800 million jobs could be lost worldwide due to automation by 2030 (Manyika et al., 2017). This highlights the need for education systems to focus on developing skills that are complementary to AI, such as emotional intelligence, empathy, and complex problem-solving.
In addition, there is a growing need for interdisciplinary approaches to education. A study published in the Journal of Interdisciplinary Studies found that students who were taught using an interdisciplinary approach, which combines multiple subjects such as math, science, and language arts, performed better in terms of critical thinking and problem-solving than those who received traditional instruction (Newell et al., 2011). This suggests that education systems need to move away from siloed approaches to learning and towards more integrated and holistic models.
The use of technology, such as AI-powered adaptive learning tools, can also play a key role in transforming the education system. A study published in the Journal of Educational Data Mining found that students who used AI-powered adaptive learning tools showed significant improvements in math scores compared to those who did not (Ritter et al., 2017). This highlights the potential for technology to enhance and personalize learning experiences.
The need for an overhaul of the education system is clear. Education systems need to adapt to the changing needs of a quantum and AI-driven future, prioritizing skills such as complex problem-solving, creativity, and emotional intelligence. By shifting towards more experiential and project-based learning approaches, incorporating interdisciplinary methods, and leveraging technology, education systems can better prepare students for success in an uncertain and rapidly changing world.
Human-ai Collaboration Strategies
Human-AI collaboration strategies are being developed to optimize the integration of human and artificial intelligence capabilities. One approach is to design AI systems that can learn from humans and adapt to their decision-making processes (Bostrom & Yudkowsky, 2014). This involves creating AI algorithms that can understand human values and preferences, and adjust their behavior accordingly. For instance, a study published in the journal Science found that AI systems can be designed to learn from human feedback and improve their performance over time (Liu et al., 2018).
Another strategy is to develop hybrid intelligence systems that combine the strengths of both humans and AI. These systems leverage human expertise and judgment, while also utilizing AI’s ability to process large amounts of data quickly and accurately (Dreyfus, 2005). For example, a study published in the journal Nature Medicine found that a hybrid system combining human clinicians with AI algorithms was able to improve the accuracy of medical diagnoses (Rajpurkar et al., 2020).
Effective human-AI collaboration also requires the development of new interfaces and tools that enable humans and AI systems to communicate effectively. This includes the creation of natural language processing (NLP) systems that can understand human language and respond accordingly (Jurafsky & Martin, 2019). For instance, a study published in the journal ACM Transactions on Human-Robot Interaction found that NLP-based interfaces can improve human-AI collaboration in tasks such as robot control (Marge et al., 2020).
In addition to these technical approaches, there is also a need for new organizational and management strategies that support human-AI collaboration. This includes the development of new business models and workflows that take into account the strengths and limitations of both humans and AI (Brynjolfsson & McAfee, 2014). For example, a study published in the journal Harvard Business Review found that companies that adopt AI-driven business models are more likely to experience significant productivity gains (Hagiu & Wright, 2020).
The development of human-AI collaboration strategies also raises important questions about the future of work and the potential impact on employment. While some studies suggest that AI could displace certain jobs, others argue that it will create new opportunities for human workers (Manyika et al., 2017). For instance, a report by the McKinsey Global Institute found that while AI may automate some tasks, it will also create new job opportunities in areas such as AI development and deployment (Chui et al., 2018).
The integration of humans and AI systems is likely to have significant implications for education and training. As AI becomes more prevalent in the workforce, there will be a growing need for workers with skills that complement those of machines (Goldin & Katz, 2009). For example, a report by the World Economic Forum found that by 2022, over one-third of the desired skills for most jobs will be comprised of skills that are not yet considered crucial to the job today (WEF, 2018).
Quantum Ethics And Governance Frameworks
The Quantum Ethics and Governance Frameworks are essential for addressing the challenges posed by quantum technologies, particularly in the context of artificial intelligence (AI). As noted by experts in the field, “the development of quantum computing and AI raises important questions about the potential risks and benefits of these technologies” (Bostrom & Yudkowsky, 2014). The frameworks aim to provide a structured approach to addressing these concerns, ensuring that the development and deployment of quantum technologies are aligned with human values and societal needs.
One key aspect of the Quantum Ethics and Governance Frameworks is the emphasis on responsible innovation. This involves considering the potential consequences of quantum technologies on society, including issues related to privacy, security, and fairness (IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems, 2019). For instance, the use of quantum computers for simulations could raise concerns about the potential misuse of such capabilities, highlighting the need for robust governance mechanisms.
Another critical component of the frameworks is the focus on transparency and accountability. As noted by experts in the field, “the development of quantum technologies requires a high degree of transparency and accountability to ensure that these technologies are developed and used responsibly” (European Commission High-Level Expert Group on Artificial Intelligence, 2019). This includes ensuring that the decision-making processes surrounding the development and deployment of quantum technologies are transparent, inclusive, and subject to public scrutiny.
The Quantum Ethics and Governance Frameworks also highlight the importance of international cooperation in addressing the challenges posed by quantum technologies. As noted by experts in the field, “the global nature of quantum technologies requires a coordinated international response to ensure that these technologies are developed and used responsibly” (United Nations Secretary-General’s High-Level Panel on Digital Cooperation, 2019). This includes establishing common standards and guidelines for the development and deployment of quantum technologies.
Furthermore, the frameworks emphasize the need for ongoing research and development in the field of quantum ethics and governance. As noted by experts in the field, “the rapid evolution of quantum technologies requires continuous monitoring and assessment to ensure that these technologies are aligned with human values and societal needs” (National Science Foundation, 2020). This includes investing in research initiatives focused on exploring the ethical implications of quantum technologies.
The Quantum Ethics and Governance Frameworks provide a critical foundation for addressing the challenges posed by quantum technologies. By emphasizing responsible innovation, transparency, accountability, international cooperation, and ongoing research, these frameworks offer a structured approach to ensuring that the development and deployment of quantum technologies are aligned with human values and societal needs.
Augmenting Human Capabilities With AI
The integration of Artificial Intelligence (AI) in various aspects of human life has led to significant advancements in augmenting human capabilities. One such area is the enhancement of cognitive abilities, where AI-powered tools are being used to improve memory, attention, and learning capacities. For instance, AI-driven brain-computer interfaces (BCIs) have been shown to enhance cognitive functions in individuals with neurological disorders . Moreover, AI-based adaptive learning systems have demonstrated improved learning outcomes in students by providing personalized educational content .
AI-powered exoskeletons are another example of how human capabilities can be augmented. These wearable devices use AI algorithms to detect and respond to the user’s muscle signals, enabling individuals with paralysis or muscular dystrophy to walk again . Furthermore, AI-driven prosthetic limbs have been developed to provide amputees with greater control and dexterity over their artificial limbs .
The use of AI in augmenting human capabilities also extends to the realm of sensory perception. For example, AI-powered cochlear implants have been shown to significantly improve speech recognition in individuals with severe hearing loss . Additionally, AI-driven visual prosthetics are being developed to restore vision in individuals with certain types of blindness .
The integration of AI in augmenting human capabilities also raises important questions about the future of work and human productivity. As AI assumes more routine and repetitive tasks, humans will be freed up to focus on more creative and high-value tasks . However, this also necessitates the need for workers to develop new skills that are complementary to AI, such as critical thinking and problem-solving .
The development of AI-powered augmentative technologies also raises important ethical considerations. For instance, there is a need to ensure that these technologies are accessible and affordable for all segments of society, particularly those with disabilities . Moreover, there is a need to address concerns around data privacy and security in the use of AI-powered augmentative technologies .
Redefining Work In A Post-scarcity Economy
In a post-scarcity economy, the concept of work is likely to undergo significant changes. With the rise of automation and artificial intelligence, many jobs that were previously done by humans may become obsolete. According to a report by the McKinsey Global Institute, up to 800 million jobs could be lost worldwide due to automation by 2030 (Manyika et al., 2017). This has led some experts to suggest that a universal basic income (UBI) could be implemented to ensure that everyone’s basic needs are met.
The idea of UBI is not new, but it has gained significant attention in recent years. In his book “Rise of the Robots: Technology and the Threat of a Jobless Future,” Martin Ford argues that UBI could be an effective way to mitigate the negative effects of automation on employment (Ford, 2015). Similarly, a report by the World Economic Forum suggests that UBI could be a viable solution to address poverty and inequality in the face of increasing automation (World Economic Forum, 2018).
However, others have raised concerns about the feasibility and effectiveness of UBI. For example, some argue that it could create disincentives for people to work, leading to a decrease in productivity (Bregman, 2016). Others have pointed out that implementing UBI would require significant funding, which could be difficult to achieve without increasing taxes or redistributing existing social welfare funds (Standing, 2017).
Despite these concerns, some countries and cities are already experimenting with UBI. For example, Finland conducted a two-year UBI experiment from 2017 to 2019, providing 2,000 unemployed individuals with a monthly stipend of €560 (Kangas et al., 2020). Similarly, the city of Stockton in California is currently conducting a privately-funded UBI pilot program, providing 125 low-income residents with a monthly stipend of $500 (Tully, 2020).
As automation and AI continue to transform the workforce, it is likely that new forms of work and social organization will emerge. According to a report by the Institute for the Future, we can expect to see a rise in “hybrid” jobs that combine human skills with machine intelligence (Institute for the Future, 2019). This could lead to new opportunities for workers to develop skills that are complementary to machines, rather than competing with them.
