Great report from Accenture. AI and ML are shaping human capabilities to ensure a better human/machine collaboration. This is how CEOs and CIOs should look at talent to take fully advantage of the new technologies.
Teaching Machine Learning with Physical Computing - July 2023Hal Speed
This document provides an overview of resources for teaching machine learning and artificial intelligence concepts to K-12 students. It discusses machine learning concepts and workflows. It then lists and briefly describes various hardware platforms, software tools, curricula, and online resources that can be used to teach machine learning, including platforms for visual programming languages like Scratch and Blockly.
Artificial intelligence (AI) is defined as making computers do tasks that require intelligence when done by humans. There are two main types of AI: weak AI, where machines act intelligently to accomplish specific tasks, and strong AI, where machines have general human-level intelligence. AI works using artificial neurons and logic-based rules. It has many applications in areas like finance, medicine, manufacturing, customer service, and gaming. While AI provides benefits like speed and accuracy, it also faces limitations such as a lack of common sense and difficulty handling emergencies. The future of AI is uncertain but technology improvements may allow it to become more human-like over time.
Artificial Intelligence and mobile robotics are transforming businesses and the economy: this deck explores possible futures for companies and workers.
Augmented intelligence is new way forward ! Steve Ardire
1. Augmented Intelligence transcends current narrow AI and allows machines and humans to work together pervasively and competitively.
2. Augmented Intelligence provides faster insights into operations too complex for human minds alone by understanding them better.
3. Augmented Intelligence with causal AI has the greatest potential to change industries and competition by how businesses operate.
What Is Artificial Emotional Intelligence?Bernard Marr
Artificial emotional intelligence uses computer vision, sensors, and deep learning to analyze facial expressions, speech, gestures and other data to identify human emotions. As the field continues to mature, companies are using it to provide better customer service, help advertisers optimize content, and develop technologies to assist those with autism or monitor mental health. By 2025, the global market for affective computing technologies is predicted to reach $174 billion.
A public talk "AI and the Professions of the Future", held on 29 April 2023 in Veliko Tarnovo by Svetlin Nakov. Main topics:
AI is here today --> take attention to it!
- ChatGPT: revolution in language AI
- Playground AI – AI for image generation
AI and the future professions
- AI-replaceable professions
- AI-resistant professions
AI in Education
Ethics in AI
Artificial Intelligence (Current state and future of A.I) by Mudasir KhushkMudsaraliKhushik
Artificial Intelligence(Current state and future of A.I) by Mudasir Khushik student of University Of Sindh Campus at Thatta.
This Presentation will be really helpful for those students who are researching on Artificial Intelligence, these slides will tell you all about Artificial Intelligence its current state as well as its future.
Advancing the Missing Middle Skills for Human-AI Collaborationaccenture
This document discusses skills needed for human-machine collaboration. It identifies 10 core intelligences like physical, strategic, practical, and interpersonal that need development. These intelligences underlie "fusion skills" like lead, create, judge and empathize. The document argues that developing these skills requires mutual readiness for change, accelerated learning using science/technology, and shared value between employers and employees to motivate lifelong learning.
Teaching Machine Learning with Physical Computing - July 2023Hal Speed
This document provides an overview of resources for teaching machine learning and artificial intelligence concepts to K-12 students. It discusses machine learning concepts and workflows. It then lists and briefly describes various hardware platforms, software tools, curricula, and online resources that can be used to teach machine learning, including platforms for visual programming languages like Scratch and Blockly.
Artificial intelligence (AI) is defined as making computers do tasks that require intelligence when done by humans. There are two main types of AI: weak AI, where machines act intelligently to accomplish specific tasks, and strong AI, where machines have general human-level intelligence. AI works using artificial neurons and logic-based rules. It has many applications in areas like finance, medicine, manufacturing, customer service, and gaming. While AI provides benefits like speed and accuracy, it also faces limitations such as a lack of common sense and difficulty handling emergencies. The future of AI is uncertain but technology improvements may allow it to become more human-like over time.
Artificial Intelligence and mobile robotics are transforming businesses and the economy: this deck explores possible futures for companies and workers.
Augmented intelligence is new way forward ! Steve Ardire
1. Augmented Intelligence transcends current narrow AI and allows machines and humans to work together pervasively and competitively.
2. Augmented Intelligence provides faster insights into operations too complex for human minds alone by understanding them better.
3. Augmented Intelligence with causal AI has the greatest potential to change industries and competition by how businesses operate.
What Is Artificial Emotional Intelligence?Bernard Marr
Artificial emotional intelligence uses computer vision, sensors, and deep learning to analyze facial expressions, speech, gestures and other data to identify human emotions. As the field continues to mature, companies are using it to provide better customer service, help advertisers optimize content, and develop technologies to assist those with autism or monitor mental health. By 2025, the global market for affective computing technologies is predicted to reach $174 billion.
A public talk "AI and the Professions of the Future", held on 29 April 2023 in Veliko Tarnovo by Svetlin Nakov. Main topics:
AI is here today --> take attention to it!
- ChatGPT: revolution in language AI
- Playground AI – AI for image generation
AI and the future professions
- AI-replaceable professions
- AI-resistant professions
AI in Education
Ethics in AI
Artificial Intelligence (Current state and future of A.I) by Mudasir KhushkMudsaraliKhushik
Artificial Intelligence(Current state and future of A.I) by Mudasir Khushik student of University Of Sindh Campus at Thatta.
This Presentation will be really helpful for those students who are researching on Artificial Intelligence, these slides will tell you all about Artificial Intelligence its current state as well as its future.
Advancing the Missing Middle Skills for Human-AI Collaborationaccenture
This document discusses skills needed for human-machine collaboration. It identifies 10 core intelligences like physical, strategic, practical, and interpersonal that need development. These intelligences underlie "fusion skills" like lead, create, judge and empathize. The document argues that developing these skills requires mutual readiness for change, accelerated learning using science/technology, and shared value between employers and employees to motivate lifelong learning.
GENERATIVE AI, THE FUTURE OF PRODUCTIVITYAndre Muscat
Discuss the impact and opportunity of using Generative AI to support your development and creative teams
* Explore business challenges in content creation
* Cost-per-unit of different types of content
* Use AI to reduce cost-per-unit
* New partnerships being formed that will have a material impact on the way we search and engage with content
Part 4 of a 9 Part Research Series named "What matters in AI" published on www.andremuscat.com
HR analytics refers to applying analytic processes to human resource data to improve employee outcomes and return on investment. It aims to provide insights into HR processes by gathering and analyzing data to make decisions that enhance these processes. HR analytics interacts business and employee data to find important connections and relationships between what HR does and business results. It helps optimize acquisition, compensation, development, and optimization of the workforce. HR analytics enables HR to make better data-driven decisions, create business cases for interventions, test intervention effectiveness, and become more strategic partners within organizations.
Issues on Artificial Intelligence and Future (Standards Perspective)Seungyun Lee
This document provides an overview of artificial intelligence standards. It begins with a brief history of AI and discusses some of the major issues with AI, including bias, lack of oversight, and safety concerns. It then examines the need for AI standards in areas like interoperability, security, privacy, and ethics. The document reviews related standardization activities from organizations like IEEE, ISO, IEC, and ITU. It analyzes the results of a JTC 1 survey that identified interoperability, domains, and security as top priority areas for AI standards. Finally, it recommends that JTC 1 urgently develop standards for AI to help guide its development and application in a safe, fair, and beneficial manner.
This document discusses artificial intelligence (AI) in teacher education. It begins with an introduction to AI and provides a short survey of the topic. It then discusses the importance of students learning basic AI concepts like computational thinking and machine learning. The document outlines various measures being taken in teacher education to incorporate AI training, including teaching AI skills and testing AI literacy programs for young students. It also examines social, cultural and application-oriented perspectives on AI. In conclusion, it emphasizes that students must continue learning and adapting to new technologies throughout their lives.
Why we need more women in science and techShiftbalance
Women represent less than 20% of tech leadership positions worldwide. In the EU, the average of women graduating in STEM is 11% and numbers are dropping. As professionals, women leave the tech industry twice as much as men.
WHY?
Because we socialize them to. Because the work environment is still massively hostile. Because they lack mentorship and role models.
And yet, the future is in code. 80% of the jobs in the next decade will require technological skills. And because technology will shape the future. With the rise of AI and robots, what will happen if we let women out of it?
So let's check which initiatives exist to shift the balance in tech!
Lower barriers to entry and the rise of ecosystems and integrated value chains are eroding organizational boundaries and spurring convergence. Automation, the need for digital skills, demographic shifts and globalization are upending labor supply and demand. The future is in flux.
Advances in talent analytics are finally allowing workforce planning to become as dynamic as this environment. Prescriptive analytics tools help organizations go beyond describing “what is happening now” or “what might happen in the future” to control “what should happen.”
Scenario-based strategic workforce planning is a digital playground for the business to constantly optimize workforce choices—testing, learning and tweaking across dimensions and futures—to deliver the business strategy.
What is People Analytics - PPT | SplashHRSwati Gupta
People Analytics is a process in which the company’s data is transformed into insights. People analytics, also known as HR or talent analytics, uses analysis to help decision-makers interpret business and people data to improve the impact on business goal – and assess human resources initiatives’ effectiveness.
The Future Is Faster Than You Think Book SummaryPrasad Kaushik
The document discusses how exponential technologies are accelerating at an increasing rate due to factors like Moore's Law, convergence of technologies, and new business models. It outlines seven forces that are further amplifying this acceleration, including saved time, availability of capital, and communications abundance. The document also predicts five major migrations over the next 100 years as a result of these trends, such as climate migrations, virtual worlds, and a potential "meta-intelligence" through brain-computer interfaces.
The document provides an overview of artificial intelligence, including its definition, history, approaches, tools for evaluation, applications, and predictions for the future. It discusses topics such as the traits of an intelligent system, methods like cybernetics and symbolic/statistical approaches, tools including search algorithms and neural networks, and applications in fields like medicine, robotics, and web search engines.
HR Transformation-The Digitization Impact: The Future is NowManish Mohan Misra
Business Environment is massively impacted by VUCA, Technology & New workforce. New business models are fast emerging and are totally different from the current ones. The new dynamics will require HR to undergo a metamorphosis in order to deliver value. The Future of HR is NOW.
This is a general interest talk on how we can use artificial intelligence (specifically machine learning) to screen literature in support of systematic reviews for chemical risk assessment.
In this talk I go over some of the challenges in using machine learning to screen the literature, how we tend to approach this type of challenge (using term frequency-inverse document frequency) and several caveats. Also included is a brief introduction to undersampling, oversampling, and bagging to try to prevent challenges posed by imbalanced data.
M. Farhan is an HR professional interested in automation and improving the employee experience. Their contact information is provided. The document discusses how focusing on the employee experience is important for organizational success and the future of work. It suggests using chatbots, automation, and artificial intelligence to improve HR processes like recruitment and development. This can enhance the employee experience, engagement, productivity and reduce costs.
This document provides an overview of artificial intelligence, including:
- A brief history noting the term was coined in 1956.
- Comparisons between human and computer intelligence in terms of speed/memory versus understanding of intellectual mechanisms.
- Categories of AI including narrow/weak AI, general/strong AI, and super intelligence.
- Applications like expert systems, natural language processing, speech recognition, computer vision, robotics, and automatic programming.
- Both positive and negative potential impacts are imagined, such as robots assisting with tasks but also potentially being programmed with antisocial intentions.
Prompt engineering is a concept in artificial intelligence, particularly natural language processing. In prompt engineering, the description of the task that the AI is supposed to accomplish is embedded in the input, e.g. as a question, instead of it being explicitly given.
This document summarizes a presentation about using metrics to drive workforce transformation. It discusses how metrics can help leaders make strategic decisions about human resources with the same rigor as finance or operations. Metrics can identify areas for improvement and focus, and demonstrate the link between human capital activities and business results. Real-world examples are provided of how metrics have helped organizations like the US Navy and FBI optimize workforce allocation and planning.
7 Amazing Examples of Digital Twin Technology In PracticeBernard Marr
Digital twins are a virtual simulation of real-world objects. By using Internet of Things sensors that feed data from the physical object to computers, digital twins provide the exact same situation to study and test without the consequences of doing the test in the real world. The uses for the technology are nearly limitless.
Artificial intelligence (AI) is the ability of machines to mimic human intelligence through learning, reasoning and interacting with their environments. The document discusses the history and definitions of AI, types of AI including narrow, general and super AI, how AI works using artificial neural networks and algorithms, benefits like reducing human risk but also drawbacks like costs and job disruption. Examples of AI in use include predictive search in Google, personalized recommendations in Netflix using viewing history, and spam filtering in Gmail's inbox.
The document discusses artificial intelligence and how it works. It defines intelligence and AI, explaining that AI aims to make computers as intelligent as humans. It describes how AI uses artificial neurons and networks to function similarly to the human brain. Examples of AI applications are given, like expert systems used in various domains. The document also compares human and artificial intelligence, noting their differing strengths and weaknesses.
Advancing the Missing Middle Skills for Human-AI Collaboration accenture
This document discusses skills needed for human-AI collaboration. It identifies a "missing middle" of skills required for roles that involve both human and machine work. These middle skills include creativity, complex reasoning, and socio-emotional skills. The document outlines 10 core intelligences needed for human-AI collaboration, including physical, strategic, practical, and interpersonal skills. It argues that developing these skills requires mutual readiness for change from workers and employers, accelerated ability through new learning methods, and shared value in developing skills.
GENERATIVE AI, THE FUTURE OF PRODUCTIVITYAndre Muscat
Discuss the impact and opportunity of using Generative AI to support your development and creative teams
* Explore business challenges in content creation
* Cost-per-unit of different types of content
* Use AI to reduce cost-per-unit
* New partnerships being formed that will have a material impact on the way we search and engage with content
Part 4 of a 9 Part Research Series named "What matters in AI" published on www.andremuscat.com
HR analytics refers to applying analytic processes to human resource data to improve employee outcomes and return on investment. It aims to provide insights into HR processes by gathering and analyzing data to make decisions that enhance these processes. HR analytics interacts business and employee data to find important connections and relationships between what HR does and business results. It helps optimize acquisition, compensation, development, and optimization of the workforce. HR analytics enables HR to make better data-driven decisions, create business cases for interventions, test intervention effectiveness, and become more strategic partners within organizations.
Issues on Artificial Intelligence and Future (Standards Perspective)Seungyun Lee
This document provides an overview of artificial intelligence standards. It begins with a brief history of AI and discusses some of the major issues with AI, including bias, lack of oversight, and safety concerns. It then examines the need for AI standards in areas like interoperability, security, privacy, and ethics. The document reviews related standardization activities from organizations like IEEE, ISO, IEC, and ITU. It analyzes the results of a JTC 1 survey that identified interoperability, domains, and security as top priority areas for AI standards. Finally, it recommends that JTC 1 urgently develop standards for AI to help guide its development and application in a safe, fair, and beneficial manner.
This document discusses artificial intelligence (AI) in teacher education. It begins with an introduction to AI and provides a short survey of the topic. It then discusses the importance of students learning basic AI concepts like computational thinking and machine learning. The document outlines various measures being taken in teacher education to incorporate AI training, including teaching AI skills and testing AI literacy programs for young students. It also examines social, cultural and application-oriented perspectives on AI. In conclusion, it emphasizes that students must continue learning and adapting to new technologies throughout their lives.
Why we need more women in science and techShiftbalance
Women represent less than 20% of tech leadership positions worldwide. In the EU, the average of women graduating in STEM is 11% and numbers are dropping. As professionals, women leave the tech industry twice as much as men.
WHY?
Because we socialize them to. Because the work environment is still massively hostile. Because they lack mentorship and role models.
And yet, the future is in code. 80% of the jobs in the next decade will require technological skills. And because technology will shape the future. With the rise of AI and robots, what will happen if we let women out of it?
So let's check which initiatives exist to shift the balance in tech!
Lower barriers to entry and the rise of ecosystems and integrated value chains are eroding organizational boundaries and spurring convergence. Automation, the need for digital skills, demographic shifts and globalization are upending labor supply and demand. The future is in flux.
Advances in talent analytics are finally allowing workforce planning to become as dynamic as this environment. Prescriptive analytics tools help organizations go beyond describing “what is happening now” or “what might happen in the future” to control “what should happen.”
Scenario-based strategic workforce planning is a digital playground for the business to constantly optimize workforce choices—testing, learning and tweaking across dimensions and futures—to deliver the business strategy.
What is People Analytics - PPT | SplashHRSwati Gupta
People Analytics is a process in which the company’s data is transformed into insights. People analytics, also known as HR or talent analytics, uses analysis to help decision-makers interpret business and people data to improve the impact on business goal – and assess human resources initiatives’ effectiveness.
The Future Is Faster Than You Think Book SummaryPrasad Kaushik
The document discusses how exponential technologies are accelerating at an increasing rate due to factors like Moore's Law, convergence of technologies, and new business models. It outlines seven forces that are further amplifying this acceleration, including saved time, availability of capital, and communications abundance. The document also predicts five major migrations over the next 100 years as a result of these trends, such as climate migrations, virtual worlds, and a potential "meta-intelligence" through brain-computer interfaces.
The document provides an overview of artificial intelligence, including its definition, history, approaches, tools for evaluation, applications, and predictions for the future. It discusses topics such as the traits of an intelligent system, methods like cybernetics and symbolic/statistical approaches, tools including search algorithms and neural networks, and applications in fields like medicine, robotics, and web search engines.
HR Transformation-The Digitization Impact: The Future is NowManish Mohan Misra
Business Environment is massively impacted by VUCA, Technology & New workforce. New business models are fast emerging and are totally different from the current ones. The new dynamics will require HR to undergo a metamorphosis in order to deliver value. The Future of HR is NOW.
This is a general interest talk on how we can use artificial intelligence (specifically machine learning) to screen literature in support of systematic reviews for chemical risk assessment.
In this talk I go over some of the challenges in using machine learning to screen the literature, how we tend to approach this type of challenge (using term frequency-inverse document frequency) and several caveats. Also included is a brief introduction to undersampling, oversampling, and bagging to try to prevent challenges posed by imbalanced data.
M. Farhan is an HR professional interested in automation and improving the employee experience. Their contact information is provided. The document discusses how focusing on the employee experience is important for organizational success and the future of work. It suggests using chatbots, automation, and artificial intelligence to improve HR processes like recruitment and development. This can enhance the employee experience, engagement, productivity and reduce costs.
This document provides an overview of artificial intelligence, including:
- A brief history noting the term was coined in 1956.
- Comparisons between human and computer intelligence in terms of speed/memory versus understanding of intellectual mechanisms.
- Categories of AI including narrow/weak AI, general/strong AI, and super intelligence.
- Applications like expert systems, natural language processing, speech recognition, computer vision, robotics, and automatic programming.
- Both positive and negative potential impacts are imagined, such as robots assisting with tasks but also potentially being programmed with antisocial intentions.
Prompt engineering is a concept in artificial intelligence, particularly natural language processing. In prompt engineering, the description of the task that the AI is supposed to accomplish is embedded in the input, e.g. as a question, instead of it being explicitly given.
This document summarizes a presentation about using metrics to drive workforce transformation. It discusses how metrics can help leaders make strategic decisions about human resources with the same rigor as finance or operations. Metrics can identify areas for improvement and focus, and demonstrate the link between human capital activities and business results. Real-world examples are provided of how metrics have helped organizations like the US Navy and FBI optimize workforce allocation and planning.
7 Amazing Examples of Digital Twin Technology In PracticeBernard Marr
Digital twins are a virtual simulation of real-world objects. By using Internet of Things sensors that feed data from the physical object to computers, digital twins provide the exact same situation to study and test without the consequences of doing the test in the real world. The uses for the technology are nearly limitless.
Artificial intelligence (AI) is the ability of machines to mimic human intelligence through learning, reasoning and interacting with their environments. The document discusses the history and definitions of AI, types of AI including narrow, general and super AI, how AI works using artificial neural networks and algorithms, benefits like reducing human risk but also drawbacks like costs and job disruption. Examples of AI in use include predictive search in Google, personalized recommendations in Netflix using viewing history, and spam filtering in Gmail's inbox.
The document discusses artificial intelligence and how it works. It defines intelligence and AI, explaining that AI aims to make computers as intelligent as humans. It describes how AI uses artificial neurons and networks to function similarly to the human brain. Examples of AI applications are given, like expert systems used in various domains. The document also compares human and artificial intelligence, noting their differing strengths and weaknesses.
Advancing the Missing Middle Skills for Human-AI Collaboration accenture
This document discusses skills needed for human-AI collaboration. It identifies a "missing middle" of skills required for roles that involve both human and machine work. These middle skills include creativity, complex reasoning, and socio-emotional skills. The document outlines 10 core intelligences needed for human-AI collaboration, including physical, strategic, practical, and interpersonal skills. It argues that developing these skills requires mutual readiness for change from workers and employers, accelerated ability through new learning methods, and shared value in developing skills.
Crowdsourced topic rankings at Snowforce 2017 in Salt Lake City drove this one-hour "Top 10" -- from evolving role of CIO, up through AI-leveraged connection, into a culture of innovation. (Peter Coffee, VP for Strategic Research at Salesforce)
The Symbiosis of Humans and Machines: Planning for Our AI-Augmented FutureCognizant
With human workers increasingly collaborating with intelligent machines, here's how organizations can develop the disciplines needed to optimize the capabilities of both.
Accenture Workforce of the Future: Humanizing Work through Digitalaccenture
This new wave of technology is far from dehumanizing. In fact, it’s precisely what will make work radically more human: more tailored to individual strengths, more flexible and portable, more collaborative and more meaningful to employees throughout the organization.
Level 1 Individual EcologyWe will measure 3 characteristics o.docxsmile790243
Level 1: Individual Ecology
We will measure 3 characteristics of individuals in 3 locations along the Upper Winter Creek trail. We will measure DBH (Diameter at Breast Height), tree height, and leaf size. Each team will have to choose their own methods for each measurement and be sure to verify the precision, accuracy and bias. There is a freeware Image J program developed by the NIH described in a file attached to this module for leaf area measurement but you are welcome to try any app or other method you prefer.
Level 2: Population Ecology
We will document age structure using the DBH data and we will measure dispersion of the population. Once again each team will choose a method for each. 2 methods for calculating dispersion are described in file attached to this module.
Level 3: Community Ecology
We will measure species richness and species diversity using a species count and a calculation each of which, once again, will determined by each team.
The final product will be a scientific poster with all of your data and and explanation of the synthesis of all 3 levels of ecology we sampled. This will be communicated as a concept map with graphs of your data verifying the relationships among the components. This is the first step in making a predictive systems model, like a climate model.
Small tree height: 3.5814 m medium tree height:7.875m tall tree height : 18.02m
Small tree leaves length 3.81 cm mid tree leaves length: 5.08 width 2.54cm
mid tree perimeter80
Width 2.54 cm tall tree leaves length 10.16cm width 6.36 cm
Small Tree perimeter 50cm tall tree perimeter 290cm
Small Shurb Community
Butterfly
50
Black Bee
27
Yellow Bee
4
Lizard
5
Fly
25
Gnat
40
Beetle
4
snake
1
Medium Tree Community
Birds
5
Catepillar
3
Gnats
20
Flys
15
Mouse
1
Snake
1
Mosquito
3
Spider
1
Tall Tree Community
Woodpecker
2
Bluejay
3
Lizard
5
Beetle
3
Butterfly
34
Ladybug
300
Squirrel
4
Gecko
2
Waterbugs
27
Birds
7
As the prominent philosopher Jerry, Kaplan puts it “Viewpoint Artificial Intelligence Think Again” (Jerry, 2017). The purpose is that we need to use more hand-working and we do not need Artificial Intelligence replace our brain. Firstly, Social and cultural conventions are an often-neglected aspect of intelligent-machine development. (1) The DOMINANT PUBLIC narrative about artificial intelligence is that we are building increasingly intelligent ma- chines that will ultimately surpass human capabilities, steal our jobs, possibly even escape human control and kill us all. This misguided perception, not widely shared by AI researchers, runs a significant risk of delaying or derailing practical applications and influencing public policy in counterproductive ways. (1) Secondly, Machines don’t have minds, and there is precious little evidence to suggest they ever will. (2) Finally, So the robots are certainly coming, but not in the way most people think. So the robo ...
1. The document discusses artificial intelligence (AI) and provides an overview of key concepts related to AI including its goals, significance, applications, and challenges.
2. It outlines the objectives of AI as creating technology that allows machines to function intelligently and discusses the scope of AI in areas like agriculture, banking, education, and more.
3. The document acknowledges challenges of AI such as potential exploitation of data privacy and security but also provides suggestions to encourage research and workforce development to address challenges.
The Jobs That Artificial Intelligence Will Create (2) (1).pdfSindhu Adini
The document summarizes the findings of a study that identified three new categories of human jobs that will be created by artificial intelligence:
1. Trainers - Jobs that involve teaching AI systems, such as training chatbots to detect sarcasm or teaching empathy to digital assistants.
2. Explainers - Jobs that help communicate how complex AI systems work, such as algorithm forensics analysts who investigate mistakes or unintended outcomes.
3. Sustainers - Jobs focused on ensuring AI systems are operating as intended and addressing unintended consequences, such as ethics compliance managers who intervene if an AI system behaves in a discriminatory manner.
Navigating the AI Era Three Pathways to Long-Term Relevance for Every Individ...🔷Tisha Jackson🔷
In a time when artificial intelligence (AI) is growing at an unprecedented rate, finding relevance becomes a never-ending journey. For the average person, staying relevant is a major concern as technology continues to reshape industries and traditional work environments. However, in the midst of this technological revolution, there are opportunities as well as challenges—ways for people to thrive in an AI-driven world, not just survive it.
Victoria G. Axelrod presented on the impact of technology and automation on the future of work. Key findings from Oxford University and McKinsey reports show that while many jobs can be automated to some degree, fully automated jobs make up less than 5% of total jobs. Specifically, 45% of work activities within jobs can currently be automated, including in high-wage occupations like CEOs and physicians. However, tasks involving complex perception, creativity and social intelligence are unlikely to be replaced by computers in the next 10-20 years. The presentation highlighted that automation will both automate activities and redefine jobs and business activities, while creativity and meaning at work will still be important human skills.
The document provides an introduction to knowledge graphs. It discusses how knowledge graphs are being used by large enterprises and intelligent agents to capture concepts, entities, and relationships within domains to drive business, generate insights, and enhance relationships. The presentation will cover an overview of what knowledge graphs are, who uses them, why they are used, and how to use them. It then provides some examples of how knowledge graphs are applied, including in intelligent agents, semantic web, search engines, social networks, biology, enterprise knowledge management, and more.
Artificial Intelligence
Navya Reddy Karnati (556139)
Venkateshwara Reddy Allu (559524)
Savan Ramparaiya (554616)
Sreehasha sunkara (548576)
Sai Venkat rathan Ravula (550732)
BA63473H4
Introduction:
Artificial intelligence is a new development platform which is able to make tasks with human intelligence. Artificial intelligence plays an important role in coming future to make things much faster without human force. There are lot of advantages using the artificial intelligence. Here the advantages below explained in detail. Here are the examples AI can perform tasks like visual identification, speech recognition, making the decisions and language translations.
Before knowing more about the Artificial intelligence, we need to know about the intelligence, types and components of intelligence.
What is Intelligence?
it is an ability to perform a task or an activity to learn from the experience, store and retrieve information from memory, resolve issues and adopt new situations. There are different types of intelligence detailed in below.
Here are the types below. Linguistic intelligence, Musical intelligence, logical mathematical intelligence, spatial intelligence, Bodily-Kinesthetic intelligence , Intra-personal intelligence, Interpersonal intelligence.
There are more real life examples with use of Artificial intelligence. One of the famous motor company TESLA has announced self-driving cars that are going to drive with using human intelligence so person may not be needed to drive any vehicle. This is the most trending innovation with the help of artificial intelligence. Another important feature here is Navigation System. This is also an important feature that helps us to reach any destination with the help artificial intelligence. With the help of artificial intelligence designing robots which will he be helpful to control terrorist attacks without human force. Robots can be much helpful for the military. Google is also working on the artificial intelligence feature which will be helpful to the public in the form of providing benefits to the common people. There are several google applications everybody is using in today’s world like google maps, drive for sharing the data in the cloud and securing the data and back up the data. To conclude there are many more advantages using the artificial intelligence which can perform the tasks with human intelligence and also explained the real time examples detailed above.
Here are some weak points about the Artificial intelligence. The most weak point about the machine learning is , machines with weak Artificial intelligence are made to respond to specific situations but cannot think for themselves. On the other hand, there are more points about the artificial intelligence. A machine with strong Artificial intelligence is able to think and just act like a human which is an extra ordinary thing. The best real time example here is how the Hollywood movies can have portrayed their movies wi.
As the prominent philosopher Jerry, Kaplan puts it Viewpoint Arti.docxdavezstarr61655
As the prominent philosopher Jerry, Kaplan puts it “Viewpoint Artificial Intelligence Think Again” (Jerry, 2017). The purpose is that we need to use more hand-working and we do not need Artificial Intelligence replace our brain. Firstly, Social and cultural conventions are an often-neglected aspect of intelligent-machine development. (1) The DOMINANT PUBLIC narrative about artificial intelligence is that we are building increasingly intelligent ma- chines that will ultimately surpass human capabilities, steal our jobs, possibly even escape human control and kill us all. This misguided perception, not widely shared by AI researchers, runs a significant risk of delaying or derailing practical applications and influencing public policy in counterproductive ways. (1) Secondly, Machines don’t have minds, and there is precious little evidence to suggest they ever will. (2) Finally, So the robots are certainly coming, but not in the way most people think. So the robots are certainly coming, but not quite in the way most people think. Concerns that they are going to obsolete us, rise up, and take over, are misguided at best. Worrying about super intelligent machines dis- tracts us from the very real obstacles we will face as increasingly capable machines become more intricately intertwined with our lives and be- gin to share our physical and public spaces. (3)
David himself writes, “CAN INTELLIGENT MACHINES IN THE WORKFORCE LEAD TO A NET GAIN IN THE NUMBER OF JOBS” (David,2016) ? The purpose is that how to choose a job in Artificial Intelligence times. We can meet kinds of problem in the future. The first is that Innovation for jobs and growth. Innovative firms are more competitive, able to capture increased market share and more likely to increase employment than their competitors. Over the period 2006-2011, 1.4 million new jobs were created by firms aged less than three years old. Employment in mature businesses, in contrast, fell 400,000(1). The second is that Jobs of the future. A recent report sponsored by the National Broadband Network (NBN) and the Regional Australia Institute makes the case that by 2030 fully half of Australians will need advanced IT skills, in addition to having well- developed soft skills like communication, creativity and critical thinking if they are to flourish in the labor market (2). The third is that The report predicts three classes of work in the world of 2030. Changing jobs – those that exist now but which have evolved beyond their current form, sometimes radically, through the integration of technology, and Fading jobs – those replaced by intelligent machines. (3) Finally, Tomorrow’s Jobs. When The Future Laboratory teamed up with Microsoft to bring some clarity for career planners they produced Tomorrow’s Jobs, a report that predicts some of the more important IT-related jobs of the future. The Future Factory used a method that all of us can use to good effect. First you look at the patterns coming forward from t.
"Understanding Humans with Machines" (Arthur Tisi)Maryam Farooq
At NYAI #16, Arthur Tisi explores deep neural networks that dominate advanced approaches to pattern recognition. Today neural networks transcribe our speech, recognize our pets, understand linguistics and fight our trolls. Recent advances by Geoff Hinton and the introduction of capsule networks only ups the ante. But despite the results, we have to wonder… why do they work so well?
In this session, Arthur Tisi, CEO and Founder of MeaningBot, will share some extremely remarkable results in applying deep neural networks to natural language processing (NLP), particularly in the areas of determining human traits in the areas of leadership, team building, personality, consumption preferences and more. Arthur will cite real world examples and share some of the math and science behind these advances including different variants of artificial neural networks, such as deep multilayer perceptron (MLP), convolutional neural network (CNN), recursive neural network (RNN), recurrent neural network (RNN), long short-term memory (LSTM), sequence-to-sequence model, and shallow neural networks including word2vec for word embeddings.
Talent Intelligence: Unlocking People Data to Redefine How Humans Need to WorkCognizant
With the influx of intelligent machines, conventional work models are shifting. Using insights from workforce intelligence, businesses can redefine the employee experience while also driving collaboration, innovation and success across the enterprise.
Artificial Intelligence Role in Modern Science Aims, Merits, Risks and Its Ap...ijtsrd
Artificial Intelligence AI is a growing field at the intersection of computer science, mathematics, and engineering, focused on creating machines capable of intelligent behavior. Over the years, AI has evolved from rule based systems to data driven approaches, prominently leveraging machine learning and deep learning. This evolution has led to AI systems capable of complex tasks such as pattern recognition, natural language processing, and decision making. The applications of AI are vast and diverse, permeating industries like healthcare, finance, automotive, retail, and education. AI driven technologies enable efficient automation, precise data analysis, personalized experiences, and improved decision making. However, with these advancements come ethical and culture concerns, including biases, data privacy, job displacement, and the responsible development and deployment of AI. Striking a balance between AIs potential and its associated risks necessitates a holistic approach, incorporating transparency, fairness, robust regulations, and ongoing research. This abstract encapsulates AIs transformative potential, emphasizing the importance of responsible AI development to ensure a positive impact on society while mitigating risks. Manish Verma "Artificial Intelligence Role in Modern Science: Aims, Merits, Risks and Its Applications" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-5 , October 2023, URL: https://www.ijtsrd.com/papers/ijtsrd59910.pdf Paper Url: https://www.ijtsrd.com/computer-science/artificial-intelligence/59910/artificial-intelligence-role-in-modern-science-aims-merits-risks-and-its-applications/manish-verma
The document discusses artificial intelligence and its implications for business leadership. It makes the following key points:
1) AI is advancing rapidly but also means different things to different people, from machine learning to data analysis. While AI may transform business, its full potential and impact are still unknown.
2) Companies are increasingly using AI to assist with tasks like data analysis, decision making, and talent management. However, AI also raises ethical issues that executives must address.
3) While AI can outperform humans on specific narrow tasks, humans retain advantages in areas like strategic decision making, creativity, emotional intelligence, and balancing different stakeholder needs. Successful companies will combine AI with human capabilities.
GDG Cloud Southlake 28 Brad Taylor and Shawn Augenstein Old Problems in the N...James Anderson
GDG Cloud Southlake #28: Brad Taylor and Shawn Augenstein: Old Problems in the New Frontiers of AI
• Brad discusses how decades-old laws and expanding regulation have new implications in the ML and Large Model age, and will touch on:
• Legal and Regulatory: Data usage rights, cautionary tale of stability.ai and Getty Images, EU's planned expansion of GDPR re models
• How Neural Networks, zero and one-shot learning, and LLMs have increased the need for better data governance, lineage management
• Shawn speaks on the coming "Data Renaissance"
• The New IP: Prompts and Internatl Interaction Data
• Where GenAI can be used right now and where it maybe shouldn't be used yet
• The Power of the Diversity of Insight
• What is making the future look bright!
Brad has been an intrapreneur and entrepreneur in data, AI, and IoT and has led teams in the creation of NLP, data products and predictive analytics for retention, churn, driver safety, traffic, CX and fleet risk. He has built solutions on global hyperscalers GCP, AWS, Azure, and IBM. Brad is a former founding partner at Tech Wildcatters, and worked with dozens of mobile, SaaS and AI start-ups, many of which became both job creators and profitable exits for TW investors. He is currently a Senior Manager in Pepsico's global Strategy and Transformation group, where he focuses on delivering AI/ML driven solutions.
Shawn Augenstein is a dynamic and highly experienced professional, who is driven by educating, providing equal access to technology and equitable access to information. Currently, Shawn serves as Principal Data & AI Consultant at CDW, where he develops the curriculum and architectures for understanding and furthering the use of AI, as well as developing solutions for both partners and clients. In his spare time, he enjoys exploring new frontiers of Diffusers, capturing moments through photography, and listening to music as a passionate melophile.
Slides from today's presentation in Brussels at LT-Accelerate. The short talk covered the core ideas underpinning a recent patent for a method known colloquially as CoderRank. I focus on the challenge of human annotation, the role of measurement, and the tools we have built, both free/open source and commercial, to make it easier for teams to create gold standard annotation sets quickly and accurately.
Similar to Human, AI collaboration, Accenture (20)
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Natural Language Processing (NLP), RAG and its applications .pptxfkyes25
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
2. 2ADVANCING MISSING MIDDLE SKILLS FOR HUMAN – AI COLLABORATION
Contents
HUMAN SKILLS SURGING
IN DEPTH: FUSION SKILLS AND INTELLIGENCES
GETTING THE RIGHT SKILLS
MUTUAL READINESS
ACCELERATED ABILITY
SHARED VALUE
IN DEPTH: TRAIN OR HIRE?
THE WAY FORWARD
ABOUT THE RESEARCH
4
8
10
12
15
18
21
23
24
3. What will the future of work look like? Will machines be on one
side of the room, performing certain jobs, while on the other side,
humans carry out different roles? Our research reveals that such a
stark division of labor is unlikely. Instead, the in-between space—
what Accenture calls “the missing middle”—is where intelligent
technology and human ingenuity will come together to create new
forms of value. Robots, by and large, will not be taking our jobs.
Instead, human-machine collaboration will reconfigure most of
the work we do, making uniquely human skills more important
than ever.
This paper explores the nature of these skills—higher-level
intelligence skills—that we all possess but could use more.
Developments in the science of learning show how these
exclusively human capabilities can be developed and applied so
we can achieve the full potential of the missing middle.
To grow these skills, employees must develop a lifelong learning
mindset, while employers must establish conditions to nourish the
desire and capability of people, and invest in skills development.
4. HUMANSKILLS
SURGING
As companies rapidly adopt intelligent
technologies, the complex impact of
these changes on work is coming into
focus. While it is true that some jobs will
exclusively be done by humans while
other roles will be taken on by machines
via intelligent automation, most of the
emerging roles will be fulfilled by both
working together in the dynamic space
Accenture calls “the missing middle.”
This human-machine collaboration will
augment human capabilities, unleashing
productivity advances along with more
creativity, innovation and growth. We are
leaving the “Information Era”, when
machines delivered data that improved
processes and products, and entering the
“Experience Era”, during which uniquely
human skills will deliver more personalized
and adaptive customer experiences (see
Figure 1: The Human Skills Surge).
The Information Era required legions of
smart engineers to build software,
networks and algorithms. But the
Experience Era will need people with social
and leadership abilities who can improvise
and show good judgment, such as those
who can train Artificial Intelligence (AI)
systems and make sense of the data
generated by AI. Training chatbots to be
empathetic to customers is the kind of
complex, creative activity that will
characterize more roles in the future.1
5. 5ADVANCING MISSING MIDDLE SKILLS FOR HUMAN–AI COLLABORATION
Figure 1: THE HUMAN SKILLS SURGE
Our analysis of the U.S. Department of Labor’s O*NET database of
occupational data provides evidence of this surge in skills.2
We analyzed the evolution of more than 100 abilities, skills, tasks,
and working styles in the U.S. over the last decade and found that
creativity, complex reasoning and socio-emotional intelligence
have sharply increased in importance for many jobs.
Specifically, more than half of U.S. jobs need higher-level
creativity, over 45 percent require more complex reasoning, and
35 percent need more socio-emotional skills than in the past.
The relative importance of physical, operational and simple
analytical skills has waned, even in job profiles focused on physical
strengths and machine operation (see Figure 2: Evidence of
Human Skills Surge in U.S. Jobs).
For instance, industrial engineers now find themselves interacting
more with leadership and shop-floor personnel to develop
production and design standards, rather than maintain equipment.
Similarly, because of automation, sales personnel write client
reports and maintain paperwork less frequently than in the past.
Consequently, socio-emotional intelligence for connecting with
clients is now more germane to the job.
Source: Accenture Research
PRE-INDUSTRIAL
Machine-like
humans
Resource Ingenuity
• Creative use of natural resources
• Managing materials and manual labor
• Physical strength, stamina, body
equilibrium abilities
INDUSTRIAL ERA
Machine automation aids
humans
Operations Management
• Creativity of product and process
design
• Managing operations, personnel,
finances
• Communication, coordination and
organization skills
INFORMATION ERA
Machine data informs
humans
Knowledge Management
• Creativity of service design and
delivery
• Managing data, information
• Analytical reasoning and STEM
(science, technology, engineering,
math) or technical skills
EXPERIENCE ERA
Machine intelligence amplifies
humans
Collaborative Intelligence
• Unconstrained creativity of
Human+Machine
• Managing hyper-relevant experiences
• Socio-emotional intelligence and HEAT
(humanities, engineering, arts,
technology) or multidisciplinary skills
1760s
1970s
2010s
6. 6ADVANCING MISSING MIDDLE SKILLS FOR HUMAN–AI COLLABORATION
Change in the importance of skill type in U.S. from 2004 to 2016
(100% = 151M jobs in U.S. as of 2016)
Source: Accenture Research analysis of O*NET database
Figure 2: EVIDENCE OF HUMAN SKILLS SURGE IN U.S. JOBS
The surge of socio-emotional and multidisciplinary skills is
reflected not only in what workers do, but also in the way
companies assemble teams. Consider the example set by
Autodesk. The leading 3D design software creator is making its
AI-enabled Autodesk Virtual Assistant (AVA) more emotionally
intelligent. The team includes application engineers and data
scientists as well as user-experience designers, creative writers
and conversational engineers who design personas, shape
linguistic patterns, and improve the user experience. Today, AVA
handles about 15 percent of English customer support
conversations. Call-resolution times have decreased, and human
customer service representatives can now focus on more
complex issues.
“The companies that do not associate AI
with EI (emotional intelligence) are going to
miss the mark.”—Rachael Rekart, Director
of Machine Assistance, Autodesk
44%
56%
6%
47%
47%
21%
44%
36%
64%
33%
67%
33% 33% 33%
9%
58%
53%
14%
53%
41%
14%
86%
86% 100% 100%
Less important Equally important More important
3%
6% 14%
7. For many workers, traditional forms of work have comprised routine and repetitive tasks.
The new AI-based workplace, however, will require employees to tap higher-level
human-only intelligences (see Fusion Skills and Intelligences). While the skills in the
“missing middle” enable working with machines, they don’t necessarily require
additional expertise in machine learning or robot programming. They require thoughtful
people who are better able to apply socio-emotional, creative and complex reasoning
skills to the specific needs of the business.
New forms of training are therefore urgently required to help people develop and apply
these higher-level intelligences.
Academic studies are proving that training focused on mindset and socio-emotional
intelligence is more effective than technical education. In one recent study, MIT Sloan
evaluated a 12-month in-factory training program on communication, problem solving,
decision-making and stress management. It found the training returned roughly 250
percent on investment within eight months of its conclusion, mainly from boosts in
worker productivity and speed with complex tasks.3
When creative humans and machines work together as allies instead of adversaries, they
enhance each other’s strengths. With support from machines, humans are free to apply
advanced socio-emotional intelligence to develop a better understanding of customer
needs and create entirely new customer experiences. Human conscience and self-
discipline will also help companies use AI in an ethical and human-centric way.
8. 8ADVANCING MISSING MIDDLE SKILLS FOR HUMAN–AI COLLABORATION
IN DEPTH FUSION SKILLS AND INTELLIGENCES
“Human+Machine”, written by Accenture’s Paul Daugherty and James H. Wilson,4
describes eight fusion skills—abilities for combining the relative strengths of a
human and machine to create a better outcome than either could achieve alone.
These fusion skills guide managers and workers in designing and developing a
workforce capable of thriving in the “missing middle.”
Three of these skills allow people to help machines (the left side of the missing
middle); the other three enable people to be augmented by machines (the right
side of the missing middle). The last two help people skillfully work across both
sides of the missing middle (see About the Research).
We identify the underlying core intelligences of each fusion skill. Intelligences
(or competencies) relate to a person’s unique aptitude, set of capabilities and
ways they might prefer to demonstrate intellectual abilities. We assess these
intelligences in the context of academic literature on the many approaches
toward human potential. Among them is the theory of multiple intelligences
developed by Howard Gardner, the triarchic theory by Robert Sternberg and the
growth mindset theory by Carol Dweck. Recent studies highlight the significance
of intrapersonal intelligences, including intuition and a growth mindset as the
most influential on worker performance.
Andy Clark, David Chalmers and others are on the forefront of an exciting new
field of study known as embodied or extended cognition—the theory that what
we think of as brain processes can take place outside of the brain.
They propose that people naturally use technologies to augment themselves,
incorporating tools and technologies as part of their own cognition. From
eyeglasses to bicycles to fighter jets, these tools, when used often enough and
at an expert level, can feel like extensions of our bodies and minds. AI brings
another dimension to this kind of biotechnical symbiosis.
9. 9ADVANCING MISSING MIDDLE SKILLS FOR HUMAN–AI COLLABORATION
Core intelligence underlining human-only and missing middle roles
Figure 3: FUSION SKILLS AND INTELLIGENCES MATRIX
We propose that the following 10 intelligences will gain prominence
in the AI workplace:
(1) physical/sensory abilities
(2) embodied or extended cognition
(3) strategic intelligence
(4) practical
(5) analytical
(6) creative
(7) interpersonal
(8) intrapersonal
(9) moral intelligence, and
(10) growth mindset (see Figure 3: Fusion Skills and Intelligences
Matrix).
While these 10 intelligences are not mutually exclusive or exhaustive,
they provide a broad guideline for business leaders directing
training efforts and forming teams. In the age of human-machine
collaboration, these core intelligences will be critical to the future
workforce.
Core Intelligences
Fusion Skills
Human-
only
activity
Lead
Create
Judge
Empathize
Humans
manage
machines
Re-humanizing Time
Responsible Normalizing
Judgment Integration
Machines
augment
humans
Intelligent Interrogation
Bot-based Empowerment
Holistic Melding
Humans manage machines+
Machines augment humans
Dominant Basic
Source: Accenture Research deconstruction of fusion skills described in “Human+Machine” book authored by Paul R.Daugherty and James H. Wilson. Refer to the appendix for research approach & definitions.
Interpersonal
10. 10ADVANCING MISSING MIDDLE SKILLS FOR HUMAN–AI COLLABORATION
GETTINGTHERIGHTSKILLS
Today, 61 percent of activities in the
missing middle require employees
to do different things, and to do
things differently. That means
reimagining processes and
committing to lifelong learning.5
Almost half of the executives Accenture
surveyed recently said they consider the
growing skills gap as one of the top three
trends affecting their workforce
strategy.6
Our research reveals that
workers and business leaders have very
different perceptions of the skills gap:
• Business leaders, on average, believe
only about a quarter of their workforce
is prepared to work with AI and
machines. Many leaders find it
challenging to encourage employees to
make time for learning new skills.
• Yet, over 60 percent of workers have a
positive view about the impact of
intelligent technologies on their work.
Over two-thirds of workers recognize
the importance of developing their own
skills to work with intelligent machines.
• Many workers feel their companies
should do more to help. They cite lack
of time (48 percent), lack of
sponsorship (37 percent) and lack of
resources (36 percent) as the biggest
barriers to developing new skills.
11. 11ADVANCING MISSING MIDDLE SKILLS FOR HUMAN–AI COLLABORATION
How can companies and employees find common
ground and reconcile their differences?
Based on our research and analysis, we believe
three dimensions are essential to successfully
developing the human skills and higher-level
intelligences that will enable human-machine
collaboration:
1. Mutual Readiness: Everyone must be ready to
change and invest in training to prepare for a world
of human-machine partnerships.
2. Accelerated Ability: Educators and learners must
call upon scientific techniques and smart
technologies to learn faster, stretch thinking and tap
latent intelligences.
3. Shared Value: Together, employers and workers
must create and maximize the motivation to learn
and adapt.
Let’s look at some examples of companies that have
found success in executing one or more of these
three dimensions.
Figure 4: THREE DIMENSIONS OF SKILL DEVELOPMENT
12. 12ADVANCING MISSING MIDDLE SKILLS FOR HUMAN–AI COLLABORATION
MUTUALREADINESS
Companies should commit to long-
term investments in workforce skills
development, while employees
should start adapting their skills for
AI. But this readiness to change is
feasible only when both company
and worker have opportunities to
realize their common aspirations in
the new workplace.
Prepare for change
Executives need a clear long-term
strategy and should be able to outline the
top business initiatives for the coming
years, communicate the possible
workforce implications and build support
structures to transition employees.
Change-management planning must
precede any AI-driven business initiative.
Consider how a large fintech company
prepared for AI-led change: When the
company implemented an AI-based call
center agent to handle simple “low-
severity” customer service issues, many
job tasks became redundant. The
company had to let some employees go,
but gave the remaining majority an
opportunity to learn how to handle “high-
severity” cases alongside the chatbot.
Success hinged on careful planning and
focused execution. The AI initiative was
communicated to employees between
one and two years ahead of time. Top
management made sure to outline a
strategy and fully explain which skills
would be required and which would
become unnecessary for alternative roles
within the company.
Employees factored the changes into their
own career planning and mapped their
forward route with line managers—
electing either to stay and upskill, move to
a different role with training or move to a
similar role elsewhere.
13. 13ADVANCING MISSING MIDDLE SKILLS FOR HUMAN–AI COLLABORATION
Reimagine work
Leaders should shift from “planning the workforce” to “planning the work.”
They must assess which tasks will be needed and which skills will be
required to fulfill those tasks. They also need to map internal capabilities to
new roles and then develop new skills to bridge talent gaps.
PlainsCapital Bank, one of the largest independent banks in Texas,
reimagined the nature of the work its people were doing. After it introduced
digital banking services, demand for human bank tellers started to decrease.
The company then combined the tasks of on-site teller, adviser and
customer service agent, creating the role of Universal Banker. Universal
Bankers need better interpersonal skills, strong problem-solving abilities and
more creativity, as well as knowledge of the products and the customer
experience.
To fill these new jobs, the bank changed the selection process to behavioral-
based interviewing, based on the belief that how someone acted in past
situations can be a predictor of future performance. Once PlainsCapital finds
a good match from a pool of internal candidates and identifies where they
need skills development, the organization provides the relevant training,
whether in technical skills, soft skills or both.
14. 14ADVANCING MISSING MIDDLE SKILLS FOR HUMAN–AI COLLABORATION
Use AI to tap potential and align
aspirations
Predictive AI algorithms can recommend job
tasks and training programs suited to each
worker. As such, companies should seek out
everyone’s hidden talents, transferrable
skills and life aspirations with the help of AI.
The algorithm’s baseline assessments can
then be validated through discussions
between employees and their managers or
mentors before charting a course of action.
At Nestlé, leadership clearly communicates
why workforce change is positive for both
individuals and the business, and empowers
employees to facilitate decision-making.
Joint decision-making between employees
and line managers accounts for 90 percent
of the effort to successfully transition
employees to new work.
While the organization provides work
opportunities and support structures, the
employees plot out career paths that align
with their personal interests and aspirations.
Employees cultivate a growth mindset about
technological change, which helps them
persist through learning challenges.
“Our approach to finding
the right person for the job
is focusing on the potential
of the individual for the
future challenge(s), rather
than existing skills and
capabilities.”—David Gaal,
Nestlé Italy Head of Talent
15. 15ADVANCING MISSING MIDDLE SKILLS FOR HUMAN–AI COLLABORATION
ACCELERATEDABILITY
Use scientific methods
Executives can draw on neuroscience
techniques to reduce learning time,
increase retention and optimize the
brain’s capacities.
Neuroscience is illuminating how the
brain acquires, stores and uses
information. Researchers are learning
how the brain responds to rewards and
feedback, how the use of game-play (or
gamification) can forge emotional
connections and reduce cognitive
overload, and how to adjust for
differences in learning ability.
Above all, neuroscience is challenging
the conventional notion that our brains
are static after age 20 or so. Recent
studies on neuroplasticity have
discovered that our brains, including the
“social brain,” continue to change in both
structure and function. These findings
suggest that higher levels of cognitive
and socio-emotional intelligence can be
developed with the right mental training
at any age.
“Mindfulness” interventions that
encourage present-moment awareness
have proven benefits for cognitive
processing, raising IQ levels and
improving resilience.7
Participants in
Accenture’s Mindfulness program, for
example, reported notable
improvements in their ability to focus
and prioritize tasks and collaborate
within teams.
16. 16ADVANCING MISSING MIDDLE SKILLS FOR HUMAN–AI COLLABORATION
Enable learning with smart
technologies
Leaders should offer experiential and
interactive programs so workers can learn
at convenient times. Training with
intelligent, mobile technologies can
accelerate progress on the learning curve.
In an Accenture survey, 77 percent of
organizations said they are planning to
introduce, or are already using, digital
personal assistants and other AI tools to
help improve personal productivity.8
Indeed, the potential to enhance learning
and productivity using smart technologies
is both substantial and inclusive: With the
pervasiveness of technology, almost
anyone can access a robot tutor or virtual
teaching assistant matched to their learning
need, style and pace. AI-based adaptive
learning platforms with built-in analytics
can personalize coaching and feedback.
Virtual reality (VR) and augmented reality
(AR) are being used to simulate real-world
situations, onboard new employees and
even train people in the development of
socio-emotional skills. Fidelity Investments,
for example, uses VR for empathy training.
A Fidelity employee is “transported”
between a call center and the customer’s
living room so they can understand the
impact of listening and helping the
customer through real scenarios.9
Not only do they lower training costs, VR
and AR technologies are more effective. A
global industrial services company found a
34 percent improvement in workers’
performance when they used an AR
headset to communicate information
instead of a traditional instruction manual.10
Education companies are bringing the best
of AI and neuroscience to corporate
learning and development. Startups like the
Silicon Valley-based Socos Labs, the
Lausanne-based Coorpacademy and the
Paris-based Insideboard offer adaptive
learning experiences using AI algorithms
and scientific principles. These companies
customize modules to make learning more
entertaining and easier to absorb.
“If designed well, AI can make
work itself a growth
experience—projects that
offer life-building knowledge
and support the individual’s
all-around development.”—
Vivienne Ming, Founder and
Chair, Socos Labs
17. Bring people together to teach
each other
People learn best from others—whether
through one-on-one coaching or from
knowledge exchanges in a larger group.
As such, companies should nurture
in-person engagements, share
knowledge in the community and
welcome outside-in learning, while
providing role models to inspire learners.
Google helps employees keep their skills
current and valuable by fostering peer-
to-peer connections. Workers move
around the organization and learn new
skills on the job, not by attending
mandatory training seminars. Some 80
percent of all tracked training at Google
is now done through g2g (Googler-to-
Googler), a voluntary network of 6,000-
plus employees who dedicate a portion
of their time to helping peers learn.
Volunteers can participate in numerous
ways—teaching courses, providing
one-on-one mentorship and designing
learning materials. In one example,
thousands of Googlers went through an
Android training boot camp run by the
people who developed Android. Google
notes that an employee-to-employee
learning program is not about cutting
costs, but about creating a culture that
values knowledge sharing.11
Danone, the French multinational food-
products corporation, also encourages
learning from the outside in and enables
cross-fertilization of ideas through
“learning expedition” programs with
startups, universities, non governmental
organizations and the public sector.12
To explore new careers, experienced
individuals are now opting for internships
inside the company or even outside. A
key part of AT&T’s retraining effort is the
experienced internship program, which
lets workers who have added skills try
out a new position for a limited test run.
For example, one 20-year veteran of the
company used the program to make a
switch from billing systems to team
facilitator in the software interface
development unit.13
Accenture’s Talent Platform
accelerates skills in New IT
The Accenture Future Talent Platform
provides a personalized, interactive
and on-demand learning solution,
helping companies develop their
workforces in critical areas such as
digital, cloud, security and artificial
intelligence. Subject matter experts
can curate learning boards for a
better, faster learning experience,
and users can interact with each
other by following, commenting and
contributing to their favorite boards.
A mobile app enables learners to
pick up new skills anytime, anywhere
and allows leaders to track learning
progress—in real time.
Accenture has been using the Future
Talent Platform internally to reskill
more than 165,000 people globally
in the latest digital technologies—
or New IT—for the past two years.
Users can explore more than 3,500
learnings boards curated by emerging
technology experts within Accenture
and from its partners. Now, Accenture
is bringing these learning capabilities
to other companies to help them
reskill their employees and run agile,
intelligent businesses.
18. 18ADVANCING MISSING MIDDLE SKILLS FOR HUMAN–AI COLLABORATION
SHAREDVALUE
Companies must recognize and uphold
the value of education and lifelong
learning, which includes giving
employees time to adjust and extend
their abilities. Employees, in turn, must
co-invest their time and effort to acquire
new ways of working.
Danone recognizes the intangible value
associated with a trained, engaged
workforce. The company has set a
strategy called “one learning a day” to
give each employee opportunities to
learn.14
Danone has invested significantly
in upskilling to nurture the company’s
culture as a competitive advantage. So
far, Danone says, employees are
motivated by the availability of career-
development guidance and training.
Discover Financial sees tangible value in
paying for workers to go back to school.
Discover’s Tuition Reimbursement
Program has yielded a 144 percent return
on investment in the form of lower talent-
management costs (people stay longer
and are being promoted into new jobs,
saving recruitment costs) and higher
productivity (less absenteeism).
Employees participating in the program
receive, on average, annual wage
increases that were at least 41 percent
greater than those for non-participating
employees, achieve more promotions
(+21 percent) and lateral transfers (+9
percent), stay longer (+0.5 percent) and
take fewer unplanned days off (-0.4
percent). Discover estimates the total
financial benefit at US$18.5 million
across all four factors assessed, or net
savings of US$10.9 million after
deducting the investment cost.15
19. 19ADVANCING MISSING MIDDLE SKILLS FOR HUMAN–AI COLLABORATION
Create a sense of safety and
well-being
Executives should foster well-being among
employees about the AI transition, and
understand that each person is unique and
will be motivated and inspired by different
things at different points in time.
To learn and create, people need to feel
psychologically safe. If an AI-driven change
to the workplace makes employees
anxious, even the most-thought-out
retraining programs may flounder.
Give employees ample time to adapt and
prepare for new tasks and support them
with wellness and counseling programs.
The online mental health services provided
by AI-human hybrid platforms like Wysa
and Juno Clinic to IT professionals in
response to job loss concerns in India’s
technology outsourcing industry are one
example.16
Then, to maximize workers’ potential,
organizations must inspire them by stoking
their passions and interests and helping
them grow. Studies have shown that
extrinsic factors are less effective for
learning and creativity than intrinsic
motivation.17
Walmart has spent around US$2.7 billion in
education, training and higher wages—an
investment that includes 200 brick-and-
mortar Walmart Academies, which have
produced 225,000 graduates as of the end
of 2017. Its Pathways training program
offers associates a clear career path from
entry-level positions to management jobs
with more responsibility and higher pay.18
Co-fund learning
Leaders should co-develop (i.e.,
crowdsourced learning content) and co-
fund (i.e., shared financing) skill
development programs with employees as
well as with educational institutions,
governments and nongovernmental
organizations.
For example, the Aspen Institute Future of
Work Initiative has proposed tax-
advantaged “Lifelong Learning and Training
Accounts” in the United States.19
These
accounts would be funded by workers,
employers and government, and would be
available to workers anytime during their
careers to pay for education and training.
Lifelong Learning and Training Accounts
would provide a better-trained workforce,
help retrain mid-career workers, improve
unemployed workers’ job prospects and
ease reliance on the safety net.
Consider Singapore, which has established
a national movement called the
“SkillsFuture.” The government offers a
variety of resources, including study
subsidies and direct credits, to help
citizens attain mastery of skills at any stage
in life—schooling years, early career, mid-
career or silver years. The government has
also set up a dedicated “Task force for
Responsible Retrenchment and
Employment Facilitation.” Seven in 10
retrenched workers who were helped by
this task force in 2017 were able to find jobs
within six months.20
20. Encourage lifelong learning
Leaders should track performance outcomes, engagement and evolving aspirations to
continuously reimagine work and lifelong learning, while keeping a long-term
perspective on the workforce.
Salesforce’s learning platform, Trailhead, provides employees and customers with an
interactive way to learn at their own pace and navigate new career paths. Trailhead offers
more than 450 virtual badges and super-badges, which prove the learner has applied
their skills to advanced real-world business tasks.
The platform has helped several Salesforce employees acquire “remote” skills to change
roles and break into careers that otherwise would not have been available to them. For
instance, after learning how to code on Trailhead, one employee moved from a recruiter
job to an engineering role. Another with specialization in nursing used Trailhead to get
up to speed at her new job as a Salesforce solution engineer. Importantly, employees
can display these badges on their online profiles to showcase transferrable skills.21
21. 21ADVANCING MISSING MIDDLE SKILLS FOR HUMAN–AI COLLABORATION
IN DEPTH TRAIN OR HIRE?
Accenture research and the 2019 World Development Report found that
the most-sought-after trait nowadays is adaptability: the readiness to
respond to unexpected circumstances and learn quickly.22
For many, it’s a
challenging trait to aquire.
More than 40 percent of executives surveyed by Accenture said hard-to-
change processes are hindering their efforts to train employees in new
skills. And one-third of employees said they expect AI will make their
jobs more complex, adding to their workload and increasing pressure to
perform.23
A fear of being displaced by new hires looms large for many
workers.
Leaders can alleviate the situation by striking the right balance between
training and hiring. Companies are under so much pressure to fend off
disruptive competitors that they often decide to hire talent from the
outside rather than make a long-term investment in retraining their own
people.
An Accenture survey found that only 3 percent of companies are planning
to significantly increase their investment in skills-development programs
in next three years. But long-term competitiveness requires a highly
engaged workforce made up of both existing employees (who are likely to
be motivated to learn and develop) and outside hires (who bring in new
expertise and perspectives).
When determining the right balance, executives should consider several
factors, such as the type of skills that are needed, whether those skills
are in adjacent roles and the urgency of the business need. Sometimes
an outside hire may be the most sensible option. An executive at a large
financial services company we interviewed says the task of teaching
quantitative skills to a person with a non-quant background can be very
difficult and he prefers to hire from the outside (see Figure 5: Essentials for
finding the common ground).
But favoring excessive hiring over retraining may not lead to positive
outcomes. For example, the move into online banking forced one midsized
bank to close almost half of its branches, affecting thousands of staff
members. Rather than invest in retraining, the company chose to make
90 percent of staff redundant and retrain only 10 percent to work in
call centers. To fill other new roles in the online banking business, the
company hired new people. The move damaged morale among remaining
workers, even those whose jobs were not at risk.
Even if companies are hiring outside for certain skills, they can
simultaneously transition current employees to “the new.” When a
large payments company introduced a predictive AI solution to better
understand employee engagement and retention, it added new people to
the HR team—70 percent were new hires. The external hires brought in the
necessary data and computer science skills, but also helped retrain the
remaining 30 percent on statistical analysis, creating a new department:
HR Analytics.
For any transformation to be successful, companies must consider the
impact of technology on the individual worker. Consider the experience of
a global logistics company that launched a digital transformation program
to automate business processes.
“Ultimately, we will need to reskill existing
employees as there will soon be a huge
imbalance in supply and demand. We also
have a moral obligation to train the future
workforce in association with the broader
education system.”—Kees van der Vleuten,
former Global Transformation Director, VEON
22. 22ADVANCING MISSING MIDDLE SKILLS FOR HUMAN–AI COLLABORATION
The initiative backfired because the company focused too much on the
benefit to shareholders and not enough on employees. Some people’s work
became a lot more complex, but it didn’t benefit them personally. In fact, a
post-transformation assessment revealed that the majority of workers had
not adopted the new processes and were still using Excel spreadsheets—
defeating the very purpose of digital investment.
Additionally, a lack of investment in the workforce can have negative
consequences on employee engagement, productivity and, ultimately,
company reputation and brand. If employees feel that employers are invested
in their development, they are more likely to be motivated to work harder
and stay longer in the workplace, improving productivity and retention. This
creates a virtuous cycle, attracting new and better talent who know they will
have opportunities to learn and grow.
Source: Interview-survey of executives in Accenture and Aspen network
Figure 5: ESSENTIALS FOR FINDING THE COMMON GROUND
Readiness
Ability
Value
Change
Openness
Career
Ownership
Skill
Proximity
Skill
Time
Intangible
Value
Tangible Return
on Investment
Reticent about workforce
impact or resistant to
giving support
Evades responsibility for
people development
Has only distant tasks to
offer in new setup
Urgent need of
new skills
Values experience and
diversity of new hires
New hires are lower
overhead or quicker return
Supports people on change
in work within or outside
company
Actively shapes people
development
Makes adjacent tasks
available
Reasonable lead time to
close skill gap
Values intangibles
associated with retained
individual
Views training as a
long-term investment
Employer Employee
Open to change in work
within or outside company
Actively shapes people
development
Persistent to learn new
adjacent (or remote) skills
Fast learning curve on
new skills
Values intangibles
associated with company
Ready to co-invest time
and resources
Lacks confidence or
concerned by future
workload/pressure
Passively dependent on
employer for career path
Lacks persistence or
aptitude to learn
even adjacent skills
Slow to learn and transition
Otherwise disengaged or
values new experience
outside company
Expects employer to fully
fund skill development
23. THEWAY
FORWARD
There is no substitute for uniquely human qualities and
characteristics like empathy, creativity and the ability to provide
context for decision-making processes. But that does not mean
the human workforce can stand idly by during this AI revolution.
To make the most effective use of AI, workers need to develop
different, deeper skill sets.
Advancing the missing middle skills is a matter of cultivating the
right mindset, and that will prove to be the greatest responsibility
for leaders. But the transition to the future workforce cannot be
successful unless both executives and employees walk this
journey together. We must all get ready to change, call upon
scientific techniques and smart technologies, and embrace a
lifelong learning mindset. Companies must reinvent organizational
structures and processes to allow workers to be part of the
process, while workers must embrace every opportunity to
learn—for the long run.
24. 24ADVANCING MISSING MIDDLE SKILLS FOR HUMAN–AI COLLABORATION
ABOUTTHE
RESEARCH
Accenture and the Aspen Institute Business and Society
Program (BSP) jointly conducted this research between
January and August 2018 via interview surveys of nearly
40 company executives and subject matter experts.
Accenture is an exclusive 2017-2018 Artificial Intelligence
Content Partner for the Long-Term Strategy Group of the
Aspen BSP. The objective of our partnership is to
encourage an innovative approach to developing future
workforce strategies that allow the collaboration of people
with artificial intelligence to create business value and
wider societal benefits.
To develop the Fusion Skills and Intelligences Matrix, we
adopted a three-step approach to build on the fusion skills
framework described in “Human+Machine, Reimagining
Work in the Age of AI”, written by Accenture’s Paul Daugherty
and James H. Wilson and published in March 2018.24
Approach to map fusion skills &
intelligences
DECONSTRUCT
For each human-only activity and missing-
middle role, identify the long list of personal
aptitudes required.
RECONSTRUCT
Broadly categorize the long list of personal
aptitudes into core intelligences as described
in academic literature.
MAPPING 1 & 2
For each human-only activity and missing-
middle role, map the core intelligences as
dominant or basic to create the “Fusion Skills
and Intelligences Matrix” (see Figure 3 on
page 9).
STEP 1
STEP 2
STEP 3
25. 25ADVANCING MISSING MIDDLE SKILLS FOR HUMAN–AI COLLABORATION
STEP 1 Deconstruction of Fusion Skills in the Missing Middle
Missing middle Fusion skill Definition Aptitudes identified
Humans manage
machines
Re-humanizing time
The ability to increase the time available for distinctly human
tasks, like interpersonal interactions, creativity and
decision-making in a reimagined business process
• Interpersonal
• Creative
• Analytical, Practical
Responsible normalizing
The act of responsibily shaping the purpose and perception
of human-machine interaction as it relates to individuals,
businesses and society
• Ethical
• Contextual awareness, Analytical, Strategic
• Enterprising, Gritty
Judgment integration
The judgment-based ability to decide a course of action
when a machine is uncertain about what to do
• Analytical, Perceptive
• Intuition
• Ethical
Humans manage
machines
Intelligent interrogation
Knowing how best to ask question of AI, across levels of
abstraction, to get the insights you need
• Business-savvy, Curiosity
• Strategic, Imaginative
Bot-based empowerment
Working well with AI agents to extend your capabilties and
create superpowers in business processes & professional
careers
• Tech-savvy
• Practical
Holistic melding
The ability to develop robust mental models of AI agents to
improve process outcomes
• Intuition
• Perception
Humans manage
machines +
machines augment
humans
Reciprocal apprenticing
Performing tasks alongside AI agents so they can learn new
skills; and on-the-job training for people so they can work
well within AI-enhanced processes
• Practical
• Inspiring
• Trustworthy, Ethical
Relentless reimagining
The rigorous discipline of creating new processes and
business models from scratch, rather than simply automating
old processes
• Imaginative, Creative
• Enterprising
Source: “Human+Machine” book authored by Paul R. Daugherty and James H. Wilson, published by Harvard Business Review Press (2018)
26. 26ADVANCING MISSING MIDDLE SKILLS FOR HUMAN–AI COLLABORATION
Intelligence Accenture description adapted from academic literature Academic literature reference
Physical/sensory Physical movement, coordination, flexibility and sensory perception Howard Gardner (1983)
Embodied or extended cognition Ability to internalize technologies and objects in human thought
Andy Clark and David Chalmers (1998), Weisberg
and Newcombe, Dror and Harnard
Strategic
Foresight, visioning, systems thinking, critical thinking, partnering, motivating and
empowering others
Michael Maccoby, Oxford and Harvard Business
Press (2001, 2007)
Practical Ability to use existing knowledge and skills to find solutions and achieve goals Robert Sternberg’s triarchic theory (1997)
Analytical
Ability to analyze, critique, judge, compare, evaluate, assess, including
logical-mathematical intelligence
Robert Sternberg’s triarchic theory (1997)
Creative Ability to use knowledge and skills to create, invent, discover, imagine, suppose, predict Robert Sternberg’s triarchic theory (1997)
Interpersonal
Capacity to detect and respond appropriately to feelings, motivations and
desires of others; cooperation and communication ability
Howard Gardner (1983), Harvard University (1999)
Intrapersonal
Capacity to be self-aware and aware of inner feelings, values, beliefs and thinking
processes; understanding of collective intelligence or intuition
Howard Gardner (1983), Gerd Gigerenzer, Max
Planck Institute for Human Development (2011)
Moral
Capacity to understand right from wrong; empathy, compassion, conscience, self-control,
respect, kindness, tolerance, fairness
Michele Borba (2001), Doug Lennick-Fred Kiel
(2005), Beheshtifar, Esmaeli, Moghadam (2011)
Growth mindset
Adaptability and a love of learning; belief that effort or training can change one’s qualities
and traits
Carol Dweck (2000), Stajkovic and others (2015)
STEP 2 Academic Theories of Multiple Intelligences
27. 27ADVANCING MISSING MIDDLE SKILLS FOR HUMAN–AI COLLABORATION
References
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2
Occupational Information Network (O*NET) of the US Department of Labor and from the International Labour Organization (ILO)
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Paul Daugherty and James H. Wilson, Human + Machine. https://www.accenture.com/gb-en/insight-human-machine-ai
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Brian Besanceney, 100 Training Academies, Endless Opportunties, LinkedIn, April 2017. https://www.linkedin.com/pulse/100-training-academies-endless-possibilities-brian-besanceney/?arti
cleId=6260085091255533568
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