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1. Very Insightful!
   Great book that everyone in a people or technology role should read.

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2. Future is now – the machine behind humans! Excellent!
   #RadicallyHuman is a great book for executives and business leaders looking for real world scenarios and a framework to start innovations small and scale fast! The book very well articulates with IDEAS(Innovation, Data, Expertise, Architecture and Strategy) framework and customer examples on how leaders are creating living systems and adopting technology to innovate and fast forward growth! #FutureIsNow

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3. What could well prove to be “the most radically human hope for the future”
   In their previous collaboration, HUMAN + MACHINE, Paul Daugherty and James Wilson suggest that “the key to understanding AI’s current and future impact is its transformation of business processes…To see how we got here, it helps to understand some historical context. The first wave of business transformation involved standardized processes…The second wave consisted of automated processes…Now, the third way involves adaptive processes.” Daugherty and Wilson share the revelations of their research that involved more than 1,500 organizations.In 1859, Charles Darwin observed: “It is not the most intellectual of the species that survives; it is not the strongest that survives; but the species that survives is the one that is able to adapt to and to adjust best to the changing environment in which it finds itself.”More recently, Alvin Toffler observed (in 1970): “The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn.” Daugherty and Wilson say this: “The simple truth is that machines are not taking over the world, nor are they obviating the need for humans in the workplace.” On the contrary, all manner of research studies stress the importance of human+machine collaborations.What we have in Daugherty and Wilson’s latest book is a continuation, expansion, and (in some instances) revision of the lessons they shared in HUMAN + MACHINE. I was especially interested in what they have to say about some trends that were just beginning to come into view four years ago — “trends that, like human-machine collaborations, overturn longstanding assumptions about AI and innovation.” Then the virus intervened…Obviously, new technologies — artificial intelligence (AI); sensors and the Internet of Things (IOT); autonomous machines — robots, cobots, drones, and self-driving vehicles; distributed leaders and blockchains; virtual, augmented, and mixed reality; and connecting everything and everyone: 5G networks and satellite constellations —  are radically disrupting and transforming the global business world and therefore are shaping its future while also transforming what workers do and how they do it.These are among the other passages that caught my eye, also listed to indicate this brilliant book’s coverage:o Radically Human — Innovation turned Upside Down [or Downside Up] Pages 5-10)o The Trouble with intelligence (18-21)o Generalizing in Real-World Settings (21-24)o Callingon Common Sense (28-30)o Giving Machines a Theory of Mind (32-34)o The Trouble with Maximum Data (38-43)o How to Do More with Less Data (52-54)o Three Dimensions of Expertise (60-62)o Collective  Expertise: Teaching AI Social Contexts (67-75)o HAzrnessing Expertise in Your Organization (79-80)o The Cloud Continuum (89-91)o The New Wave of Frontier Technology (99-101)o The Evolution of Technology (113)o Technology-Integrated Strategies for the Age of IDEA (117-132)o “Talent: Humans and Radically Human Technology” by Charles Smith (141-159)o Five Essentials of Trust for the Digital Age (162-163)o Four Types of Radically Human Experiences for the Digital Age (186)o Empowering Experiences (187-191)o Rewarding Experiences (192-199)o The Most Important Differentiator of All (226-228)When concluding their latest and, in my opinion, their most valuable book thus far, Paul Daugherty and James Wilson cite three “truths” that have emerged with undeniable clarity during recent years: “First, all companies are now technology companies. Second, companies have proved that they can wield technology to innovate and change with unprecedented speed. Third, in the human-technology nexus, the human is in the ascent.” I agree about the first two but not the third because I am convinced now — and expect to remain convinced in years to come — that a superior human in combination with a superior machine cannot be outperformed by either one without the other.Here’s their final paragraph. “The stakes couldn’t be higher; the opportunities couldn’t be greater. All of the phenomena we examine through this book…make technologies more recognizably human. As these new terms of our relationship with technology unfold, we will find ourselves moving deeper and deeper into reflections about what makes us truly human. In the final analysis, that may be the most radically human hope for the future.”* * *Paul Daugherty is an author, business technology futurist, speaker and Chief Technology & Innovation Officer of Accenture. He advises the world’s top technology and business leaders on how to continually innovate in an age of unprecedented technology change. Also, as indicated, he is co-author of HUMAN + MACHINE and Radically Human.H. James Wilson is Managing Director of Information Technology and Business Research at Accenture Research. Also as indicated, he is co-author of HUMAN + MACHINE and Radically Human.Both books were published by Harvard Business Review Press.

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4. One of the most important reads of this century!
   As the authors make clear in the introduction, all companies are now technology companies. Whether you are seeking work, gainfully employed or run your own enterprise, this book offers a totally new perspective on how to collaborate with machines in the 21st century.Machines are a part of the workforce these days, and thankfully the authors waste no time making this point. Instead, they demonstrate how humans are learning from machines and how machines are learning from humans. The examples provide clear illustrations of how humans can genuinely collaborate with smart machines to leverage both human and machine strengths. I was surprised to see how many of these examples reduced the amount of data needed for analytics, which makes the lessons from the book relevant to both data-hungry enterprises and smaller businesses alike.If you want to get the most out of your human and technological resources, read this book for a radical take on the future of work.

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5. A fascinating perspective on the future of technology
   As the past years have demonstrated, change can happen at a remarkable rate. The authors show that even as technology accelerates to meet the changing times, it is the leaders and companies who recognize the human (and humane) potential in these shifts who will succeed. The book also provides an excellent roadmap to young people looking to navigate the changing landscape of work in the coming years. A must-read.

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6. Radically Human makes a case for human-centered tech!
   Radially Human makes an original, provocative business case for human-centered tech. Wilson and Daugherty show how far technology has come in transforming the traditional definitions of invention and innovation!

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7. Great read
   An important read! A well written and well researched look into how AI and other technologies are changing the future of work.

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8. Nueva entrega tras la anterior de Human+Machine. Visión anticipadora de lo que viene con la inteligencia artificial en la que se pone al humano en el centro.

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9. The message is crystal clear.  The paradigm is shifting again and is becoming human-centric. It is not about technology replacing humans but how to enhance each other. I know quite a few people who would love “Radically Human: How New Technology Is Transforming Business and Shaping Our Future”. Particularly if you are interested in citizen development.Intelligent technologyIntelligent technologies have unfolded in three stages. The first stage was machine-centric. The second stage of human-machine interaction was collaborative. The third stage, underway now, is the human-centric. Machines adapt to humans. Citizen development (ish) on steroids. Towards a more human—and humane—future.Every business is a technology businessTo say that the pandemic accelerated the pace of tech adoption is, to put it mildly. Compared to before the pandemic, the pace picked up by 70%. First-time adoption of digital, AI, cloud and related technologies averaged 63%. IT changes planned for over 12 to 18 months occurred in a matter of days. The bottom line: every business is now a technology business.Innovation A large majority of companies used technology as a lifeline, not as an engine of innovation. Laggards adopt technologies unsystematically, isolate them in unconnected silos, and fail to harness their innovative potential. By contrast, leaders adopt a wide range of cutting-edge information technologies and weave them into “living systems” that blur boundaries, afford agile adaptability, and create seamless human-machine integration.Human-centricLeading organisations are not only out-innovating their competitors but taking an even more decisive turn toward human-centred technology. A radically human turn that is upending the very nature of innovation as it was practised over the previous decade. Revolutionary in that it is rewriting the terms of competition. Rooted in the deepest attributes of humans—how we understand, feel, and think.IDEASThe authors introduce an innovation framework they call IDEAS, Intelligence, Data, Expertise, Architecture, Strategy. It offers a new innovation framework for companies large and small that they can use to chart a new course to the future, turbocharge revenue growth, and prepare to compete in a world where the human—and the humane—will be the means by which companies will succeed and the measure by which they will be judged.Living systemsIn the future, we will have top-down systems that don’t require as much data and are faster, more flexible, and more affordable. Rather than training systems with bottom-up machine intelligence, people are guiding them with top-down human knowledge, imparting natural intelligence to what was previously artificial. Innovative companies are creating “living systems”—boundaryless, adaptable, and radically human architectures that bring an elegant simplicity to human-machine interaction. AI has enabled humans and machines to complement each other, transforming mechanistic processes into highly adaptive, organic, and human-centred activities. Some 80% of respondents of the author’s research believe that systems of the future will provide seamless interaction with humans, and 78% believe these systems will adapt to suit human styles of work. Companies can now reimagine systems to empower new human + machine relationships with natural conversation, simple touches, and abundant personalisation.The problems with AIThere are a few problems with AI at the moment. No machine powered by AI can yet match the ease and efficiency with which even the youngest humans learn, comprehend, and contextualise.AI is a black box (we don’t know how they work).Context is very hard.Genuine comprehension is hard.Knowledge frameworks are hard.Common sense is hard.Emotion and intuition are even harder.Correlation is not causation (machines find that hard to understand)Every decision needs a huge data set (the first computer program to defeat a professional player in the ancient board game Go was trained on 30 million games). There is a lack of existing big datasets for AI. For most business problems or opportunities that exist, there are no big datasets “cleansed” and ready for use by AI systems. In fact, in most organisations, the biggest obstacle to comprehensive AI solutions remains noisy, sparse, or incomplete data, much of it semi- or unstructured. For example, Walmart collects 2.5 petabytes of unstructured data (2.5 million gigabytes) from 1 million customers every hour—equivalent to 167 times the number of books in the Library of Congress. By 2025, each human being will create an estimated 3.4 exabytes of data per day (1 billion gigabytes), mostly through social media, video sharing, and communications.The computing power needed growing exponentially. Since 2013, the amount of computational power required to train a deep learning model has increased 600,000-fold.Costs are rising astronomically. Deep learning requires resources that lie beyond the reach of many organisations. Only a limited number—Alibaba, Amazon, Apple, Google, Microsoft, and some Global 1000 companies—can keep up.Less artificial and more intelligentThe solution in the core is straightforward. You can (and need to) give an AI huge data sets to learn (an almost impossible task), or you can teach the AI to ask a human for feedback or learning. In other words, AIs learning from humans (and vice versa). Becoming less artificial and more intelligent,TerminologyDoing more with less data. This is where I got lost. The book introduces terms such as few-shot learning, one-shot learning, edge computing, generative adversarial networks, data echoing, dynamic filtering, simultaneous training, federated learning, data lakes, digital decoupling, adaptable cloud-to-edge architecture, edge analytics, edge intelligence, holistic stacks,Terminology IICombine that with data engineering, batch processing, synthetic data, small data, digital twinning, brain interfaces, VR, edge computing, microservices, RPA, cloud, technology debt, IoT, GPT-3, 5G, single-digit millisecond latencyMachine teachingFinally, the book arrives at machine teaching. Humans tutoring AI. Machine teaching includes three distinct areas of human expertise that AI has long struggled to incorporate: professional experience, collective social experience, and personal experience. The overlay of human expertise creates a significant multiplier effect. A no brainer. From explicit to tacit. Creating immediate emotional context.AugmentationBut also machines augmenting humans by amplifying our powers, as in providing otherwise unattainable data-driven insightsinteracting with us through intelligent agentsembodying us, as with robots that extend our physical capabilities.Citizen developersThis next generation of AI will do at least three things for workers:It will enhance creative work through smarter apps that can, for instance, anticipate the needs of designers, salespeople, and others.It will augment language-based tasks.It will lower the barriers for scaling digital innovations, making it even easier for non-technical staff to use everyday natural language to program,Enabling in-house expertsFinding new ways to build human professional experience into value-creating systems unique to their businesses. Thus, companies can use in-house experts to rapidly build customised solutions using machine teaching. Amazon and Google are working on machine teaching techniques that enable engineers without AI expertise to program complicated AI models. Instead of extracting knowledge from data alone, putting the specialised knowledge of human experts to full use. That knowledge includes their functional and domain expertise and their fine-grained understanding of the business itself: how it makes money, how it competes, and where it could be improved.ModellingAmazon’s SageMaker Autopilot, for example, can be used by people without machine learning experience to easily produce a model. Teaching a machine what a human expert would do in the face of high uncertainty and little data can beat data-hungry approaches for designing and controlling many varieties of factory equipment.StrategyNowhere is the human—and human agency—more central than in strategy. That is where the capacity to make a difference in performance is limited only by the imagination of humans. The challenge lies in the fact that technology, business strategy, and execution are becoming so closely intertwined as to be nearly indistinguishable (magic?).Three core strategiesThree strategies stand out: Forever Beta, Minimum Viable IDEA (VMI), and Co-lab.Forever Beta strategies are seen in products like the Tesla, digitally undateable through the cloud,VMI strategies use one or more elements of the IDEAS framework to precisely target weak links in a traditional industry, provide a superior customer experience, and make immediate inroads in the market.Co-lab strategies produce superior results in the sciences or other knowledge-intensive environments through human-guided, machine-driven discovery.Business modelsI don’t understand much about the technology, but I understand how it impacts business models by being able to implement remote diagnostics and repair, remove friction, deliver consistent customer experiences, making companies a utility and/or a platform, become more efficient, more anticipatory and fully data-enabled across the value chain. It is also good for the climate (there is a circular element). Basically, marrying strategy and execution.Four critical areasAccording to the book, there are four key areas that will be critical for companies to compete successfully in the radically human future: talent, trust, experiences, and sustainability.TalentTechnology democratisation ensures that as many of your people as possible are empowered to become drivers of change, igniting grassroots innovation by equipping every employee with the tools and skills to build technology solutions at the point of need. Natural language processing, low-code platforms, and robotic process automation (RPA) are just a few capabilities and services that make technology more accessible. Technology democratisation could not come at a more critical time for businesses. When access to powerful technology spreads throughout an organisation, every employee can be an active and vital part of digital transformation. Other requirements:DiversityA superior employee brandSafety research shows that when employees feel psychologically safe and can act fearlessly at work, productivity increases by 50%, turnover drops 27%, workers are 40% less likely to experience burnout, and companies become 11 times more innovative compared to their peers.Moving from digital literacy to digital fluency (a higher TQ, Technology Quotient)AutonomyTrustNever have people been more alert—in a more deeply personal way—to matters of trust. And in the commercial world, a rising tide of mistrust had been building for years, driven by a succession of corporate scandals and malfeasance, including the worldwide financial meltdown of 2008. What’s more, we judge companies just as we do human beings. Brands are now judged so strongly along the lines of warmth and competence dimensions that these judgments explained nearly 50% of all purchase intent, loyalty, and likelihood to recommend a brand or product.”ExperiencesThe difference between the scent-free pouch and the aroma-enriched cup is the difference between efficiency-first engineering and radically human-centred design. Radically human-centred design gives us the ability to “stop and smell the coffee” (literally, in NASA’s case), providing richly human experiences. More and more companies are pushing past traditional notions of customer experience to a more expansive notion of experiences that includes employees and sharply differentiates the company from the competition.3RsThe journey begins with three R’s: re-platforming, reframing, and reach. Replatforming means fully committing to the cloud— Reframing requires shifting your organisation’s mindset about technology. Technology should not be seen as a mere facilitator of more efficient transactions and smooth touchpoints but as the driver of innovation. Reach means moving beyond traditional business priorities to exceptional customer and employee experiences and new value propositions.SustainabilityThe radically human turns represented by IDEAS come together in the most radically human turn of all: sustainability, the existential struggle to save our planet and those who inhabit Remote sensors and other edge technologies make it possible to track water pollution, deforestation, and “dark fleets” of vessels whose fishing practices breach environmental regulations. A few examplesThe Porsche, Audi, and Volkswagen brands are using an AI-powered early warning system to identify sustainability risks such as environmental pollution, human rights abuses, and corruption among direct business partners and at the lower levels of the supply chain.Dendra drones, using swarm technology, map every square inch of the area to be restored and gather data, including erosion trends, that the company’s ecologists and AI use to determine what seeding plan to pursue.Using FarmGrow, suppliers’ field teams—the humans in the loop—can provide customised coaching on farming practices, help farmers prioritise investments, share information about crop management, and monitor adoption methods to improve farmers’ yields.Intelligence is a Netherlands-based company whose technology combines remote sensors, satellite imagery, and cloud-based AI.In the United Kingdom, the “Spatial Finance Initiative” was established in 2019 to find ways to analyse geospatial data and translate it into financial decision making for impact investors. Tiny satellites, augmented by drones, will be used to take high-resolution images of every point on the globe daily. The resulting data will be scanned and interpreted by AI.Digital twins are used to model complex systems—from cars to cities to human hearts—and simulate their functioning accurately. Singapore has created Virtual Singapore. Meanwhile, the European Union is creating a digital twin of the Earth.AI is being widely employed to speed up the development of batteries across a wide variety of areas. A team of researchers from Stanford, MIT, and the Toyota Research Institute has developed a machine-learning approach that cuts the time required to evaluate batteries by 98%.An AI that is human-centric and environmentally aware. How bad…….? Also read:Small Data (Anthropology as the last mile of data)Scary Smart (Interesting perspective on AI)Unchartered (Decision making and data)

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