Trivergence (eBook)

Accelerating Innovation with AI, Blockchain, and the Internet of Things

Bob Tapscott (Autor)

eBook Download: EPUB

2024
John Wiley & Sons (Verlag)
978-1-394-22662-7 (ISBN)

Infos

A Breakthrough Introduction to The Next Phase of the Digital Age

In Trivergence, Bob Tapscott, writer, speaker, complex system designer, and former CIO introduces an exciting new concept in explaining how the intersection of artificial intelligence (AI), blockchain, and the Internet of Things (IoT) will transform business and society. He explains the synergies between these technologies and the disruptive potential that they will offer, as well as the challenges and risks to making it happen. He offers an insightful guide through the difficult decisions that businesses and society must make to thrive in a new era where decisions will be difficult, and uncertainties abound.

You'll discover how and why AI's power is now exploding, its growth driven by smarter approaches to neural networks trained on a new hardware architecture that can derive its intelligence from ever more massive datasets. You'll also find:

• Discussions of the multiplicative and exponential power of trivergence on the core technologies discussed in the book
• Explorations of IoT's tendency to bring the physical world to life as it harnesses the capabilities of AI and the blockchain
• How trivergence morphs Big Data into something new he calls 'Infinite Data', where thinking machines consider trillions of data points to generate their own content, value, and perspectives without programmed code or human intervention

A fresh and innovative guide, rich with case stories, on how the most critical technologies of this new phase in the digital age are combining to drive business transformation, Trivergence will become a critical handbook for forward-looking leaders, and anyone interested in the intersection of cutting-edge tech and business.

BOB TAPSCOTT's extensive background as a CIO, a speaker and as a consultant in successfully developing and implementing disruptive strategies from concept through systems design and implementation includes the training, organizational restructuring, and workflow redesign required to deliver, measure and improve overall corporate performance and customer satisfaction. His strong analysis, communication, mathematical, and leadership skills and out of the box thinking have ensured his success as an agent of change for his pedigreed clients. Bob's background in developing and deploying complex systems from nuclear power reactor maintenance to Wall Street derivatives, to flying commercial aircraft by computer has emphasized the prudent mitigation of risk.

Rezensionen

"In Trivergence, Bob Tapscott explains how the convergence of technology will drive innovation and success for the next period in business history. Tapscott has put the human (and the reader) in the center of all of these continuously ground breaking technologies. At the end of the day, it's people working together leveraging this ecosystem of tech, that can create better outcomes for our planet and for us."
--Susan Doniz, Chief Information and Data Analytics Officer, The Boeing Company

"Trivergence! Why didn't someone else think of this? This profound new concept, may well find its place in business hall of fame with terms like The Singularity, Cloud Computing, Mass Collaboration, Outsourcing, Wikinomics, and Big Data."
--Hunter Muller, CEO of HMG Strategy and host of the Global Innovation Summit

Textauszug

2
Artificial Intelligence

Imagine a not-too-distant future in which AI had transformed society in unimaginable ways. One remarkable example was its profound impact on education—a sector that had long been seeking innovative approaches to cater to diverse learning needs and enable lifelong learning.

In this transformed society, a young girl named Maya embarked on her educational journey. Maya's personalized education experience began before she stepped foot in a traditional classroom. AI algorithms had analyzed vast amounts of data about her cognitive abilities, learning styles, and interests. Armed with this knowledge, the AI-powered learning system tailored a unique curriculum to maximize her potential and foster her love for knowledge.

As Maya entered her AI-enhanced classroom, she found a vibrant and collaborative environment. Schools replaced the traditional rows of desks with interactive learning pods, where students engaged in hands-on activities and problem-solving exercises. Their AI-powered virtual assistant, AIVA, provided immediate feedback, answered questions, and offered guidance personalized to each student's needs.

In this transformed education landscape, Maya's teachers had become facilitators and mentors, working in tandem with AI technologies. The teachers leveraged AI-powered tools to gather real-time insights into student progress, identifying areas where students needed additional support. By analyzing the data, AI algorithms helped teachers identify specific concepts or skills that required reinforcement, allowing them to effectively tailor their instruction to individual students.

Beyond the classroom, AI extended Maya's educational opportunities. Virtual reality simulations allowed her to explore historical events, visit distant places, and experience scientific phenomena firsthand. AIVA's language translation capabilities enabled her to connect with students from diverse backgrounds and learn from global perspectives. AI-powered recommendation systems suggested personalized reading materials and educational resources, opening doors to new subjects and areas of interest.

Maya's story was not unique. Across society, AI democratized education, making quality learning accessible to all, regardless of geographical location or socioeconomic background. Along the way, educators, parents, and other stakeholders pulled together to address several concerns about the pervasive presence of AI in education. Educators worried that insufficient human interaction in the classroom could undermine students' social and emotional development and limit their critical thinking skills. Social justice advocates voiced concerns about the potential for bias and lack of diversity in AI algorithms to perpetuate existing inequalities. Fiscal conservatives demanded much higher student-faculty ratios. Parents and students feared that AI systems might collect and store profoundly personal information about students and redeploy it in hyper-targeted marketing campaigns.

In this stylized reality, AI did not replace human teachers but elevated their roles, enabling them to focus on fostering creativity, critical thinking, and emotional intelligence. Developers honed their systems by training AI models on diverse datasets, drawing on the educational experiences of a broad and inclusive population of learners. Strict privacy safeguards ensured that students and parents retained full custody of and control over their personal data. Most stakeholders saw AI as an indispensable ally, augmenting human abilities and revolutionizing the way knowledge was acquired, shared, and applied.

Sound far-fetched? Not really, because AI's growing impact on education is already evident today. Edtech companies such as Eightfold, Gloat, Docebo, Squirrel AI, Cognii, and Degreed are currently incorporating advanced AI capabilities to accurately identify content that matches skill requirements, offer personalized tutoring and coaching, and provide learners at all levels with highly targeted educational materials and experiences. Whether educational institutions are ready or not, AI will quickly redefine the very nature of teaching and learning. And yet, education is just one domain where artificial intelligence is shaking up the world as we know it.

Sped by the exponential rate of technological progress, powerful artificial intelligence systems are creating a new era of superintelligence that will reshape the social and economic landscape. From robotic surgery to autonomous vehicles, from revolutionary biotech research to reading cat scans, the applications for increasingly smart machines will span healthcare, legal and financial services, transportation, construction, agriculture, manufacturing, and much more. With companies such as Facebook, Google, Amazon, Microsoft, Alibaba, and others in and outside the software industry making multibillion-dollar investments in talent and research, state-of-the-art AI capabilities will advance even more rapidly in the years to come, opening up new possibilities and applications that we can scarcely imagine today.

The Evolution of AI

Artificial intelligence (AI) can be defined as the ability of computers to perform tasks that had previously required human intelligence, such as perception, learning, reasoning, complex problem-solving, and decision-making. It is a broad field that encompasses a wide range of techniques and approaches, including machine learning, natural language processing, computer vision, robotics, and expert systems.

The notion that computers can “think” as we do is a concept originally envisioned in the 1950s, and is rooted in the work of computing pioneers like Alan Turing and John McCarthy. Early AI research focused on developing algorithms and symbolic logic to simulate human intelligence. By the middle of that decade, Arthur Samuelson had programmed a computer to play a reasonable game of checkers. He simply defined artificial intelligence as the “field of study that gives computers the ability to learn without being explicitly programmed.”1

Realistically, computers were far too slow to deliver on the dream that someday computers would think. The only remaining question was when?

In the 1960s and 1970s, computerized AI development efforts began in earnest with rules-based systems, game theory, and mathematical methods where if-then-else statements were used to mimic human decision-making processes. Expert systems, such as DENDRAL for chemistry and MYCIN for medical diagnosis, were notable achievements during this period.

In the 1980s, AI researchers focused on knowledge-based systems, which utilized large knowledge bases to solve complex problems. These systems used inference engines to reason and draw conclusions based on available knowledge. For many, the aspiration that computers could use neural networks to reason was tried and failed and re-tried and re-failed. To most, it was clearly time to pronounce them dead.

The 1990s witnessed a resurgence of interest in AI with the rise of machine learning. Another attempt at getting ever-larger neural networks to think was tried and failed again. Using different AI approaches, researchers explored various algorithms and approaches that allowed computers to learn from data and make predictions or decisions (see the sidebar “The Five Tribes of AI”).

THE FIVE TRIBES OF AI

In his paper “A Few Useful Things to Know About Machine Learning,” Professor Pedro Domingos identifies five main “tribes” or approaches within machine learning.2 These “tribes” represent different perspectives and methodologies within the field of machine learning, each with its own strengths, weaknesses, and areas of application.

Symbolists/rule-based learners: Symbolists focus on representing knowledge in explicit rules or logical formulas. They emphasize the interpretability and understandability of the learned models. In symbolic reasoning, the logic is analyzed and then hard-coded into a static program. Its roots are in logic and philosophy. Symbolic AI systems use knowledge representation, reasoning, and inference to make decisions. Examples include classical expert systems that are still used today to provide advice in specific domains, such as medicine or law. This approach was first successfully implemented in the 1970s.

Evolutionaries/genetic programmers: Evolutionary algorithms are inspired by the process of natural evolution. They use techniques like genetic algorithms to evolve populations of models over generations. The fittest models survive and reproduce, gradually improving their performance through selection, crossover, and mutation. Defined by the research of John Holland at the University of Michigan in 1960, useful implementations became popular in the 1990s.

Bayesians/probabilistic learners: Bayesians approach machine learning from a probabilistic perspective. They use probability theory and statistical inference to model uncertainty and make predictions. Bayesian methods incorporate prior knowledge and update beliefs based on observed data, enabling the calculation of posterior probabilities for different outcomes. This method was first published by Thomas Bayes in 1763. It was first computerized in late 1950s to early 1960s. A modern example today is political polling.

Analogizers/instance-based learners: Analogizers learn by identifying similarities between new instances and instances encountered during training. They rely on measures of similarity and distance to...

Produkt-Details

• Erscheint lt. Verlag: 15.1.2024
• Sprache: englisch
• Themenwelt: Sachbuch/Ratgeber ► Beruf / Finanzen / Recht / Wirtschaft ► Wirtschaft
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management
• Schlagworte: AI • Artificial Intelligence • Blockchain • Business & Management • ChatGPT • cloud processing • Computer Science • Computer Science Special Topics • convergence • Creativity & Innovation Management • Deep learning • generative AI • generative artificial intelligence • Informatik • Innovations- u. Kreativitätsmanagement • Internet der Dinge • internet of things • IOT • KI • Künstliche Intelligenz • Large Language Models • LLM • LLMS • Neural networks • Spezialthemen Informatik • technology convergence • Wirtschaft u. Management
• ISBN-10: 1-394-22662-4 / 1394226624
• ISBN-13: 978-1-394-22662-7 / 9781394226627