The history and basic functionality of Artificial Intelligence (AI):

From programming to machine learning

The use of the term ‘Artificial Intelligence’ is often criticized because it is quite unclear what human intelligence exactly is and how it refers to attributes and functions of the human nervous system. From its beginnings computer science or informatics had been divided by different views. For one group of scholars the bases had been logic, and symbol transformation; computing had been derived from conscious human thinking and the idea to use computers to mimic human intelligence by itself had been not considered. Instead computers had been seen as an extension of human capabilities in regard of memory and capacity for logical sequences of operations, thus computers had not been seen as intelligent by themselves. But two scientific discoveries enabled another group of scholars to think differently about intelligence and computers, first a basic understanding of general functional principles of complex nervous systems inclusive of the human brain, e.g. learning or neural and synaptic plasticity described by Donald O. Hebb (1949).

The huge impact of Hebb on the interdisciplinary discussion about artificial intelligence might be based on the fact that his findings are easy to summarize through the (simplified) Hebbian (learning) rule:  „neurons wire together if they fire together“. In principle it is a bold simplification to interpret neurons as electro-chemical switches but this view later on triggered the development of networks of electronic switches (transistors) parallel to each other, so called ‘perceptrons’ or one-layer neural networks as predecessors of nowadays convoluted neural networks. Perceptrons had been able to recognize different patterns, e.g. handwritten numbers and to transform into the correct input for computer-based data processing.  (cf Seijnowski, T., 2018, The deep learning revolution, San Diego, Cal.)

Another decisive step towards a serious scientific discussion of ‘Artificial Intelligence’ or ‘brain style computing’ had been the seminal paper of Alan Turing on ‘Computing, Machinery, and Intelligence’ (1950) proposing to consider the question: Can machines think? On page 19 Turing noted his view that that computers might be able to learn in a similar way like children do:

• “Structure of the Child Machine = Hereditary Material
• Changes of the Child Machine = Mutation
• Natural Selection = Judgment of the Experimenter”
• (Turing, A., 1950, p. 19)

Indeed, nowadays machine learning as the base of the development of artificial intelligence are following the evolutionary principles outlined by Turing and Hebb, multilayered complex neural networks are mimicking structures of the human pre-frontal cortex (initial algorithms) e.g. responsible for the recognition of patterns and objects. Random variations (mutations) in weighing the sensor inputs through neurons (transistors) and interference of the experimenter (or teacher), judging right or wrong are the principle factors to achieve improved results (better trained algorithms) or more intelligent behavior of the neural network. This simple process is called supervised learning and may be compared with training or schooling. The figure below displays a simple four layer neural network for supervised machine learning. Supervised learning means that the network is trained by a set of training data labeled by humans e.g. by pictures from a publicly available data base ImageNet containing more than 14 Million pictures with labeled objects suitable to train algorithms of neural networks in classifying objects as cars, bicycles, motorcycles, pedestrians, children or traffic lights, fire hydrants etc. which of course is vital for the development of driving assistance systems.

Learning algorithms based on complex math regulating but not determining the weights of ‘neurons’ expressed by the thickness of the lines between the neurons (in fact electronic switches in the state of 0 or 1).

The screenshot of an artificial multi-layer, convoluted, neural network (CNN) from the open source tensorflow.org platform in figure 5.3 is displaying a small cognitive machine working as a rough simulation of what might go on the visual cortex of mammal and human brains when telling different object classes from each other e.g. cats from dogs. However, compared with a two y.o. child the performance of even more sophisticated CNN’s is weak.  A nursing child can distinguish dogs from cats or his mother from other ‘objects’ around after having seen only one or a few examples and it also develops a mysterious sense of ‘object permanence’ a kind of model of the world where the ‘objects’, e.g. the mother still exists, even when she is not in sight. (You can play hide and seek with toddlers, they have fun). But even the model of such a simple cognitive machine e.g. a neural network recognizing handwritten numbers meant an enormous progress towards the development of artificial intelligence as alternatively it seems to be impossible to mimic or achieve advances in object recognition and guidance of machine behavior based on suitable models of its environment. Nobody would know how to write a program that would let a computer with sensors (eyes) see like a human or even on superhuman level, as our consciousness doesn’t have any access to the processes in our visual cortex which are making us recognizing things.

Despite all shortcomings of multilayered or convoluted neural networks ‘CNN’s’ and the so-called deep learning algorithms, the range of applications already developed and influencing our daily lives is amazing. All of the 4 Billion smartphones, all computers and tablets and more and more cars and machines are connected with it. Neural networks and the related applications of specialized AI, e.g. natural language recognition and processing had become the base for the business models of the world’s most valuable companies, from Amazon, to Apple, to Microsoft, to Google, to Facebook, to Alibaba, to Tencent, to Baidu, or the Japanese Softbank Group and including countless start-ups in different industries from automotive, transport, tourism, health care, diagnostics, education and higher education etc.. AI should be viewed as the ‘new electricity’, is a famous quote from Andrew Ng, co-founder of Google’s Brain deep learning division, as well as of the online education platform Coursera, chief scientist of the Chinese Search engine Baidu, now director of the AI lab at Stanford University (cf. Ford, M. 2018).

Artificial Intelligence (AI) as a research field can be traced back to a conference organized by the computer scientist John McCarthy in the summer of 1956. Among the participants of the two-month event, the famous Dartmouth Conference at Dartmouth College in New Hampshire had been Marvin Minsky and Allen Newell. (Crevier: 1993; McCorduck 2004: 259-305; Roberts 2016) Overtime, from 1956 to 2018, Terry Sejnowski (2018) reports in his account of the ‘Deep Learning Revolution’, there had been several periods of AI winter, where high hopes on break through applications of machine learning had been disappointed and investments as well research budgets had been cut down. But advances in computing hardware and information transmission, as well as in neuroscience and mathematical modeling of multidimensional learning, helped for instance solving the problem of ‘over-fitting’, in-between training data sets and the interpretation of data (means e.g. objects not in the training data set would not be recognized because there is no generalization possible). In consequence ‘Artificial Intelligence’ isn’t programmed, it evolves and is trained like a dog or more sophisticated it is educated like children or students and even can acquire capabilities their educators don’t have. The state of the art of technology, a combination of reward learning with back propagation algorithms, already allows that an experimenter (trainer, educator) can be substituted by a machine too. The tech-magazine wired devoted its June 2016 issue question if the end of traditional programming and coding is thinkable in the near future, a question which is out of scope of this introductory text as well as the rather speculative debate on superhuman artificial general intelligence (AGI).

However, the basic artificial intelligence technology has advanced and commercial applications of machine learning took off with overwhelming success in the first decade of the 21th century. ‘Brain style’ computing not only had become complementary to traditional computing on the bases of programmed logical symbol transformation process but dominant in business models of the digital tech platform companies stretching from web search and digital marketing, nearly every kind of industry and corporate function, from HR to finance, from health care to ‘intelligent’ or service robots.

The evolution of commercial applications of machine learning

Recent progress in AI have been achieved by applying machine learning to very large data sets and the development of deep learning algorithms to detect patterns enabling them to make predictions and recommendations by processing data in ‘brain style computing’ rather than by programming instructions. The algorithms also adapt in response to new data and feed back to improve efficacy over time and can be used for descriptive analysis, means to identify patterns, structures trends (to describe what happened), for predictive analysis, means anticipating what is likely to happen (mainly practiced and adopted in corporations with systems of data driven decision making implemented) and prescriptive analysis and decision making where decisions are made autonomous by AI based systems, e.g. in the operations of Amazon or Alibaba fulfillment centers.

Please note that there are many related technologies and that machine learning is not synonymous with artificial intelligence. Left out of considerations for instance are the psychological and social frames of the way we think and interact. Humans seems to have biases towards causality and intention in regard auf the connection and interpretation of data which enables them to learn, transfer and generalize knowledge just from one example and even also without direct experience, often with better predictive validity than technical neural networks trained with billions of data points and millions of examples. Simplified constructs about how the world functions or how it should be, imagined realities, seems to play a major role as new frontier of AI. (cf. Tenenbaum, J. in: Ford, M., 2018 pp. 463)

Major types of machine learning the technology platform strategies of the leading transnational corporations are based on are supervised learning, unsupervised learning and reinforcement or reward learning. The figure below explains their basic features and lists some applications widespread in business.

Some might even still think about the question if the rise of the new tech giants and their dominance in the global economy is really a novel phenomenon, technologies as pointed out above had been also a decisive factor for the emergence of the big industrial conglomerates of the 19^th and 20^th century; thus, one of the big questions remaining is what the innovation dynamic and strategies corresponding with makes so special. Of course, the founders and founding teams of the contemporary tech giants didn’t exactly plan or aim at what they have achieved in the recent decades.

None of the new entrants into internet-based businesses could know about the future technological development and breakthrough in machine learning around the turn of the millennium. Analyses of the extraordinary success of Amazon e.g. Bard Stone’s ‘The Everything Store’ (2013) are routinely stressing the genius or business acumen of the founder Jeff Bezos as one of the decisive factors. But there is much more.

Personality and the background in investment banking of course had been crucial to identify the potential of the internet with its rapid growing numbers of users. Bezos obviously knew well about positive network externalities of platforms and platform growth. The growth of the numbers of users and the unique cost structures of software, high cost for the development of the first copy and cost close to zero for any other copy provided a unique chance. Traditional book retailers like the previous US market leader Barnes & Noble didn’t take the new competition serious and hesitated to invest and boost online sales and cannibalizing their stationary business and continued believing in their business model. Traditionally readers needed and appreciate the consultations with knowledgeable sales staff or to make their pick by seeing a selection of books. Amazon never employed people knowledgeable of literature, it left the function of giving recommendations about books and their content to the growing community of customers or ‘users’ of its platform. As more users Amazon had as more perfect the system could become. Introducing eBooks, music services, venturing into complementary hardware, leveraging the value of Amazons infrastructure by evolving into a market place for every kind of internet retail and services was more a consequent evolution than revolution, obviously based on core competence resource and market-based strategy evolution. But that Amazon became an AI powered data driven corporation of course had been only possible by the adoption of machine learning technology. Aside from online retail and logistics Amazon had become a major player in cloud computing infrastructures and services with its Amazon Web Services division (AWS).

As doctoral students of computer science the Google founders Sergey Brin and Larry Page, both with a strong background in mathematics hadn’t have any intention to make a business out of their project to improve web search for internet users. In the 90’s of the last century web search had been dominated by tag words provided by the websites themselves to search engines, by programs called meta-crawler searching for marked tag words, by expert judgments of internet portals and by paid listings. Internet search was time consuming and very biased by questionable marketing practices, deception (gambling and pornographic sites, fraudulent businesses and information) and paid listings as well as by the idea to keep users as long as possible the own websites, web portals and their affiliates. The internet was already at this time the largest knowledge base humankind ever had created but was nearly defunct with only a low value for users trying to retrieve information according to their own preferences. Brin and Page wanted to change this situation dramatically by improving the user experience with web search. The project supported by Stanford University was centered on the development of a novel search algorithm for page ranking by measuring how dense are the connections in-between websites with similar subjects or profiles and how often this links and back-links are used. The algorithm in principle would encompass a model of the whole world wide web with all the links and back-links in-between the web pages and measuring their ‘objective’ relevance independent of tag word bias, paid listing and advertising purposes, enabling every user to find what he or she is searching for fast and without harassment by unwanted results.

Relying on computing resources of Stanford University Brin and Page could demonstrate the principle functionality of their algorithm and its improvement by time and scale, but as the consumption of computing power became unbearable for the University infrastructure; the supervisor of the project computer science Professor Terry Winograd recommended to sell the algorithm to media companies on the verge to venture into the internet or to established portals and search engines like Yahoo and Altavista. But none of the first-generation internet companies was interested in a functional web search bringing the users directly to websites related to their interests. Because keeping users as long as possible on their own websites or routing them only to paid listings had been the prevailing, dominant business models where the new technology didn’t fit in. Even a price tag as low as one million Dollar had been refused by Yahoo!

The digital transformation already has changed how successful corporations are working

Due to the fame the technology savvy students and their prominent supervisor enjoyed on Stanford campus contact with venture capitalist provided a suitable escape from the deadlock Brin and Page had been in. With Rav ShriRam and Andreas von Bechtolsheim, two prominent venture capitalists of the Silicon Valley Tech Scene didn’t hesitate to finance Google to start up and to become incorporated 1998. The early investors just trusted in a revolutionary idea and the disruptive potential of a new technology without having a business model and business plan at hand. Google became the most popular search engine by its superior technology to improve user experience in web search, but it took 6 years before Google introduced a business model, ironically based on paid listings, on top of the search results. But they are at least optimized to match with user interests and behavior by intelligent algorithms and clearly marked as paid. The evolution of Google/Alphabet holding towards becoming a leading tech conglomerate placing large bets on future breakthrough applications of artificial intelligence is well documented by Eric Schmidt and Jonathan Rosenberg’s (2017) insider book on ‘How Google Works’.

Google/ Alphabet had been from the beginning a data, science and technology driven company. Thinking about and knowledge of business strategies in line with the models of corporate strategy and traditional leadership and management practices are not in the center of the specific culture of tech companies and how the work of their core employees is organized.

Please note that the number of core employees in new tech companies driving business by developing and monitoring AI applications is quite small. The older hardware and software giants with their sales networks like Apple and Microsoft or corporations with major operations in logistic and shipment tend to have as much employees as traditional industrial conglomerates, Amazon for instance has globally more than 500.000 employees but most of them are not working in tech but in logistics and fulfillment center operations, services and maintenance which are already guided and monitored by AI applications. Worker in Amazon stores, ‘fulfillment centers’, are monitored and navigated by getting their information about what to do by smart watches and scanners. These explains low median salaries (median means 50% of the employees have a higher and 50% a lower salary as indicated) in comparison with the social network Facebook or Alphabet / Google Holding with which run business based on a much smaller number of employees without major operations in retail and logistics. The figue below displays employee numbers and medium salaries of public corporations listed on the US stock markets according to their 2018 filings to the Federal Stock Exchange Commission in comparison with traditional companies like JP Morgan Chase (largest US Bank), McDonalds and General Electric.

In his foreword to Eric Schmidt and Jonathan Rosenberg’s account on ‘How Google Works’ (2017) Larry Page explains the special role of the core employees of Alpahabet/ Google:

 „Over time I’ve learned, surprisingly, that it’s tremendously hard to get teams to be super ambitious. It turns out most people haven’t been educated in this kind of moonshot thinking. They tend to assume that things are impossible, rather than starting from real-world physics and figuring out what’s actually possible. It’s why we’ve put so much energy into hiring independent thinkers at Google, and setting big goals. Because if you hire the right people and have big enough dreams, you’ll usually get there. And even if you fail, you’ll probably learn something important. It’s also true that many companies get comfortable doing what they have always done, with a few incremental changes. This kind of incrementalism leads to irrelevance over time, especially in technology, because change tends to be revolutionary not evolutionary. So, you need to force yourself to place big bets on the future.“ Larry Page, quoted from Schmidt, Eric. How Google Works 2017 (Kindle Locations 105-112).

But not only the American ‘big five’ internet corporations Alphabet Holding (Google), Apple, Facebook, Amazon, and Microsoft are impacting major industries on a global scale and the way how knowledge is produced, used and organized as well as we work and live together, others like the Chinese Alibaba Group or the Japanese Softbank conglomerate shouldn’t be overlooked and the demise of Kodak, Nokia, Philips or IBM from leadership positions in their industries can’t be ignored.

The rise of social media from Facebook to Byte Dance’s Tik-Tok and the share economy known by Uber or AirBnB is based on machine learning

The impact of Social Media on the organization of societies is largely unknown and not explored yet, but there is no doubt that Facebook, Twitter, YouTube, Baidu, Renren, Weibo, WeChat, or WhatsApp are some of the most important global players. The social media are operated by large, transnational corporations with AI powered platform strategies: Not only Facebook Inc. (Facebook, WhatsApp), Twitter Inc., Alphabet/Google Inc. (e.g. YouTube), but by players unknown in the West like Sina Corp (Weibo), Tencent (WeChat), Baidu Inc. (Baidu), and Renren Inc (Renren) or the Japanese Softbank Group not only with Yahoo Japan but financing and investing dozens of Social Media and Share Economy Start Ups (e.g. Uber, Lyft, AirBnB). The social media companies had become the largest advertising agencies in the world that have access to millions or billions of users’ personal data to customize advertising to specific groups of users with till now unknown efficiency. (Fuchs, 2017)

The huge shift in value creation to a small group of novel companies will continue. Never in the history of the economy had companies reached dominant market positions in such a short time. After all, these monopolies serve the customers, all of us, says the eccentric investor Peter Thiel, who earned billions with PayPal and early venture capital funding of Facebook. These companies are driving progress, the transformation of the economy and society on a global scale.

Although criticism of the new monopolies, data misuse and tax avoidance, manipulation of search results is sporadically voiced and has led to penalties by national competition authorities or the EU Commission, neither destruction nor strict regulation of the data companies is likely. The opposite is true; politics and society are increasingly aligning themselves with the new social media and tech giants.

The defense of the traditional multinational corporations and of many small and medium sized companies is weak up to now. Most managers neither understand artificial intelligence technologies nor platform strategies based on them. They are not eager and capable to explore the potential of the accelerated technological progress especially in the fields of machine learning, deep learning and other fields relevant for digital transformation;  e.g. the blockchain technology for data security, records management, transaction processing and for authentication by securing data integrity based on distributed and public accessible ledgers with the potential to disrupt public administration, legal services and the facilitation of payments.

No wonder many are annoyed by the constant talk about disruption, artificial intelligence, machine learning, platform strategies, big data, open innovation and the like. Is it hyped? Just buzzword bingo? Some people who have a lot to lose might wish. Of course, there is much lip service to digitization, but seldom talk is combined with fundamental changes. The reason for ignorance and idleness is quite simple: Today’s Business is based on experience and expertise from the last century.

Successful digital transformation requires new approaches toward management

Where something different can be learnt? Most Business Schools and training programs are confined to the past and present and not capable to contribute to a future which is significantly different from the past and present; teaching and training is conceptualized as cramming and rigorous instruction to exert control and power over resources and people within hierarchical social systems. Or as Sumantra Ghoshal has put it: (Bad) management practice is taken as a criterion of truth. For most computer scientists, software engineers and mathematicians CEO’s, CFO’s, HR, Marketing Directors and alike from established ventures are mostly clueless persons used to structures and to perform functions in a way which hardly make sense in a new digital world based on machine learning, platforms, and crowd. Where in corporate hierarchies and meetings the highest paid persons opinion (Hippo) counts more than an argumentation based on scientifically validated facts organizational inefficiency is rising at high cost.

Google’s former Executive Chairman Eric Schmidt and Jonathan Rosenberg’s account on how leadership at Alphabet is used to work differently from the traditions of corporate America is telling us something about the future of work and management, not only in Mountain View, California. And indeed, when we had been in talk with ‘smart creatives’ from large established corporations as well as with the leadership of exponentially growing startups or the new tech giants and champions of digital transformation, it didn’t matter from where they had been: the US, China, Japan, India, Germany, the UK or Malta. It was agreed that social organization relevant for digital transformation is different from corporate hierarchies laid out as social machines based on process management. Innovation is an offspring of structures known from excellent research universities where small groups of extraordinary talented minds working on subjects they are really interested in.

Of course, this kind of freedom is not for everyone. The still human work-drones in the logistic and fulfillment centers of Amazon and Alibaba wearing devices similar to smart watches and are closely monitored and directed by processes and algorithms. Management here is already automated on a large scale. Indeed, the bulk of management as we know it from the 19th and 20th century corporations is no longer needed. Disrupted. The last days of 20^th century Management and strategies are coming soon.

From our ongoing comparative studies on digital transformation projects, comprising major players of the new type of corporations, start-ups and established large, medium sized and small firms, we can delineate that the failure rate is highest in large and medium sized established firms. This is only partly due to a lack of access or due to a lack of understanding of technology or big data or artificial intelligence. Especially large corporations do not have too much problems with that. But traditionally-managed firms typically get meager results. Because they are good in doing what they are doing and in improving that incrementally; disruptive innovations and changing themselves drastically is out of scope for them (but is needed to stay in the game). Thus, it’s not technology or data or AI that make the difference. The difference lies in the inability to delegitimize an existing structure or social order and to develop a new, credible narrative to legitimize different forms of social organization. Instead of being precise by using the language of social systems theory we express it here in simple words:

The required leadership practice seems to be counterintuitive to narratives learnt and believed. What is necessary is:

• to let talent drive strategy,
• to establish small self-organizing teams equipped with the necessary resources to solve big problems,
• complex systems of decision making must be slashed and de-scaled.
• Extra efforts to get the Hippos out of the way, they are stubborn, inflexible, huge and dangerous, have to be made; in general managers can’t tell experts and talents what to think and to do; or to stick to micromanagement and controlism as they are used to.
• The potential of an idea should be assessed at first place by competent people, the logic of cost cutting and economic value added has to be abandoned.
• In the digital economy you make more money by not focusing on money, but on technology, the users, politics and society.

Google/ Alphabet is not only dominating the search engine industry they are also technology leader in artificial intelligence and in a broad scope of applications like medical diagnosis, self-optimizing software systems and alike. Facebook is dominating social networks, both together accounting for more than 60 percent of global online advertising revenue and 25 percent of the total spending on advertising. New competitors hardly have a chance in these markets. Therefore, the data scandal around Cambridge Analytica (see chapter 1), manipulating the US elections of 2016 with micro-targeting through Facebook feeds, caused only limited damage for Facebook: A competitor who could profit was and is not in sight. Users who leave the network lose all their digital contacts and their virtual existence.

The major challenges for the corporations successfully leading digital transformation is of political nature

Albeit widespread critique on the impact of social networks on the society and fines amounting to billions of dollars demanded from EU and US institutional watchdogs the impact of social media on the public seems to grow. Systematic deception driven by political and commercial interests is a part of reality. Big data and AI powered platforms are leveraging and reinforcing these practices to influence human cognition. Media and public communication in the 20^th century already had been used based on cognitive and psychology and behavioral for systematic development and advancement of political propaganda and marketing techniques but the emergence of the so-called social networks and the amount of instantly available data has multiplied its effects. The subsequent figure displays the major features of contemporary critiques of social activists on the commercial character of social networks like Facebook.

Social media and machine learning applications are enabling control and manipulation of communication and social behavior in an extend which is hard to comprehend. The website socialcooling.com compares the data driven surveillance capitalism, Zuboff, (2014), The Age of Surveilance Capitalism, with the major driver of the environmental crisis, global warming.

The philosopher Jeremy Bentham (1791), “Panopticon or the Inspection House”, imagined a prison, in which there would be a tower in the middle, from which a guard could look into each cell at any time while the prisoners would not be able to know whether or not they were observed by the guard. Given this uncertainty Bentham assumed that in order to avoid the risk of being caught breaking a rule, they would always behave AS IF they were observed. The mechanism of the Panopticon can be imagined to be leveraged towards the society as a whole. They would display a high level of self-control. Already Bentham had speculated about the usefulness of it for other types of organizations such as companies or schools.

Commercial and political actors are exploiting a plethora of cognitive biases to influence human cognition and behavior through communication in social networks, the most important of more than 700 known biases are:

• the anchoring bias (means to build expectations based on the environment you are used to),
• availability bias (to value information you have above information you don’t have),
• confirmation bias (perceiving only information confirming what is believed),
• framing bias (the situation or the person is more important than the information itself),
• optimism bias and planning fallacy (overestimating favorable outcomes, underestimating difficulty and necessary resources),
• loss aversion or sunk cost bias (continuing to ride a dead horse).

Bias and limited rationality and opportunism plays a major role in contemporary economics, management and marketing science. Daniel Kahneman, author of the bestselling book ‘Thinking fast and slow’ (2011) had been awarded with the Nobel Prize in Economics for his groundbreaking empirical and experimental works aiming at making economic theory more realistic and linking it with realistic assumptions about human behavior. In this regard the commercial use of AI is dual faced. In corporation’s data driven strategies are bound to reduce irrational decision-making practices, depending on ‘group think’, traditions, outdated practice, worldviews and theories; in the field of marketing, lobbying etc. it is aiming at exploiting human bias. Yuval Noah Harari, a historian of Hebrew University who had become an influential and visionary popular science author, with its publications Sapiens – A Brief History of Humankind  (2014), Homo Deus – Brief History of Tomorrow (2016) and 21 lessons for the 21^st Century (2018) assumes that there are not only some striking similarities between influencing human behavior and hacking a human brain but the twin revolution of infotech and biotech also will enable us to hack the genetic code, meaning that social, psychological and biological processes are becoming available for design and modification according to political and economic interests. These far reaching assumptions indeed are enjoying the attention of prominent figures in the teach industry. (New York Times 2018.11.09: Tech CEO’s Are in Love with their Principal Doomsayer)

The fear of losing control and the essential exponential

Imagine the following situation. Two people walk 35 steps each. One person, which can be also seen as a traditional corporation, makes normal, but long steps of 1 m and is 10 km ahead. The other person, let us call it an ambitious start up, which organized itself around accelerating returns of technology starts very slow with a first step of 10cm only, but doubles the step lengths by any of its additional steps. For the first five steps the traditional company even gets further ahead, beyond the 10km advantage it already has. Hard to see any competition. Where are the two, after 35 steps. The traditional company stands proud at 10km and 35m. The initially slow and irrelevant competitor might have wet feet, it crossed oceans and orbited the globe more than 30 times.

There is no reasonable doubt about the fact that technology is a decisive factor in the long-term development of economies and societies. Of course, technology is not the only single and determining factor. The institutional framework of a society e.g. of the 19^th century China e.g. has a major impact too. In the 19^th century the governing Qing emperors and their administration had been opposed against the modernization of the infrastructures of the country. The first railroad built by a British trading firm 1876 from Shanghai to Woosung was bought by the Chinese government only to stop its operations and to remove the rails in the following year. The fear of a loss of control by rapid technological and social change and ‘Westernization’ might have been behind such policies which seem to be highly irrational from a contemporary perspective. (c.f. Mark, R. B., 2002, The Origins of the Modern World, London)

The fear of losing control obviously is not an attitude specific for the Chinese government of the 19^th century it is also a central factor and major impediment for political and economic institutions, societal groups, trade unions, workers and even consumers to adopt new technologies and to benefit from them.

The institutional and social context are determining decisions and the pace of change

Most of us think we make choices individually because of our specific personality. We feel it is us when we make decisions. This seems sensible, but what “feels” right is not necessarily the truth. Studies based on neuroscience, cognitive and behavioral psychology getting more influential in economics and are to read as a fundamental criticism of the economic theory of rational individual behavior. (cf. Glimcher, P.W./ Fehr, E. edit., 2014, Neuroeconomics, 2^nd edit., Neuroeconomics, Decision Making and the Brain, Amsterdam e.a..)

We are social creatures, and the context (rather than the individual calculus) dominates our decisions. If decisions can be forecasted (a) and regulated (b) by tweaking the situational design, then really, where did the choice originate? Who’s in charge? The theoretical foundations of a system based on the assumption of rational actors are based on folk psychology of the19th century and related theories of efficient markets are obviously undermined by their stark contrast to empirical evidence, experimental methods and the continuous progress in research about the neurological and socio-biological context of individual behavior and decision making.

• Three dimensions of societal change in the context of digital transformation
• Therefore, digital transformation based on modern empirical sciences inclusive of economics and management and organization research are bound to view all processes, physical, chemical, biological, psychological and social essentially represented by data and algorithms. Biologists have already defined life as based on code, DNA and DNA replication, and therefore neuroscience, computer science and social sciences are converging in their empirical bases.
• As a consequence of accelerating technological progress in computing and digital communication (analytical) intelligence decouples from the limitations of individual human consciousness. (AI as a system intelligence based on machine learning exceeding human capabilities by far).
• Algorithms (or machines serving and monitoring us in daily and work life) will soon know us much better than we know ourselves enhancing the capabilities of predicting and influencing behavior.
• The impact of digital transformation on economy and society

Digital transformation has a real impact on the global economy and society. Accelerating technological progress, access to data and enhanced technologies to process and analyze data based on machine learning through new methods of parallel computing, ‘neural networks’ simulating the synaptic plasticity of the human brain is known as ‘machine learning’ and provides a form of artificial intelligence which is already applied on a large scale. The business models of the US and Asian transnational corporations with the highest evaluations by the financial markets are based on machine learning technologies. Cloud computing and social networks as well as the marketing practices of the most valuable corporations like Microsoft, Apple, Amazon, Google, Facebook, Alibaba, Tencent, Baidu, Softbank etc. are focused on these new forms of data analysis and artificial intelligence.

Digital transformation means that firms have been moving to an increasingly digital core based on software, data, and digital networks for more than a decade now and are requiring a fundamentally new operating architecture and strategic framework

Recent changes in strategies and operations of extraordinary successful transnational corporations

When it comes to transnational corporations we can see a dramatic change of their capitalization in terms of evaluation through the international financial markets. Figure 5.1 below displays what hurts managers of large established corporations most: The bureaucratic dinosaurs of the industrial age, laid out hierarchically as efficient social machines, lose the favor of the capital markets on a permanent basis. A new-type of companies with a new type of strategy are threatening to take over large portions of future profits based on human creativity and technological change. Most of the companies on the list of the world most valuable corporations made it to the top within less than two decades after they had been founded (Amazon, Alphabet, Facebook, Alibaba and Tencent). The older Software and technology giants Microsoft and Apple founded 1975 and 1976 managed to adopt major features of the technology-based platform strategies in the last two decades and multiplied their market capitalization in the last decade nearly as fast as the newcomers.

Such a change in market capitalization in only one decade is historical unprecedented in regard of pace but also can be seen in line with the rise of the first transnational corporations, the international trading and shipping companies which based their strategies and structures on the discoveries of the 15^th century and the emerging colonial world order of the 16^th, 17^th, 18^th and 19^th century. They had been sidelined by conglomerates based on technological infrastructures like railways, coal and steel, chemistry and electricity in the 19^th century during the so called ‘gilded age’ in the second half of the 19^th and the beginning of the 20^th century. Transnational companies like Royal Dutch Shell, Mitsubishi, General Electric, Siemens and Daimler as well as many other automotive maker and industrial conglomerates can be rooted back to the extraordinary innovation dynamics of the ‘gilded age’.

It seems to not too farfetched to assume that we again live in a kind of new ‘gilded age’ with an even more rapid change of institutions, political and economic structures in the context of accelerated technological change, to summarize as ‘digital transformation’ of the economy and society on a global scale. Of course, it is ruled out that we conduct comprehensive analysis of all aspects of digital transformation in these lecture materials, we need to limit our investigation towards the most significant impacts on transnational corporations by exemplary analysis. The next subchapter 5.2 is linking the appreciation of the financial markets with the phenomenon of accelerated technological progress and institutional change as a consequence of digital transformation, comparable with the gilded age of industrialization.

The link between digital transformation and corporate strategy

Two of the Massachusetts Institute of Technology brilliant minds, Erik Brynjolfsson and Andrew MacAfee promised us 2017, that we ain’t have seen nothing yet of the further acceleration of returns on technology and point out:

„Machine learning systems get better as they get bigger as they run on faster and more specialized hardware, get access to more data, and have improved algorithms – all these improvements are happening now, so machine learning is developing rapidly.“ Brynjolfson, E., MacAfee, 2017, pos 982

In 1998 Google’s servers had been able to perform 100 million floating point operations (FLOPS) per second. The Graphic Processing Units (GPU) Google was using 2017 already performed 20 trillion of such computations a second; nowadays Google’s revolutionary Tensor Processing Units (TPU) for accelerated machine learning, artificial intelligence (AI) and cloud computing are capable of 180 trillion FLOPS a second. While being successful with its prototype of a quantum computer project the two founders of Google/ Alphabet gave an outlook towards the development of a potentially 10 to the power of 105 times faster processing technology (Brin, S. 2018). But how this is to be converted in a sustainable competitive advantage in nearly every industry you can imagine?

The Figure below shows an impressive (please note the logarithmic scale) display of progressing efficiency in major digital technologies principally in line with ‘Moore’s law’ which is not a law of nature but an observation which Gordon Moore (1965) made in regard of technological progress in field of microelectronics already in a paper published 1965. He observed that the number transistors which can be placed on a microprocessor or a chip, the Central Processing Unit (CPU), doubles in an average rhythm of 18 to 24 month. From there he extrapolated that the capacity or performance of a computer will double roundabout every two years or 18 months at constant cost or that the same capacity will be available in similar time spans at only half of the previous cost. Moore’s law had been quite prescient for five decades and seems to be applicable on a wide range of progress in digital technology. Of course, there are physical boundaries for chipsets, the number of transistors to be put on limited space can’t be endless, but there are also new ways of computing in sight, which might accelerate computing power not only further but even faster.

Ray Kurzweil (2013: 177) calls this broader version of Moore’s law not only in respect of artificial intelligence the law of accelerating returns (LOAR), applicable to both biological and technological evolution. His visionary claims are based on the emergence of a new paradigm guiding contemporary research. The new and comprehensive scientific paradigm, encompassing social sciences, psychology and biology is based on the pictured rapid development of information and communication technologies.  It defines or depicts the basic phenomena of life (DNA replication), cognition and thinking, behavior, and communication through algorithms (mathematical formulas describing processes of system replication, emergence of processes and change of processes).

Already perceptible in everyday and professional life, cognition and data processing as bases of intelligent behavior are decoupled from uncontrollable human consciousness (perceptions, sensations and the flow of thought). Applications of artificial intelligence are impacting, supporting and directing private activities as well as professional work in organizations. One is more inclined to ask Google or his smart phone than a knowledgeable person. Consequently, many tasks, even classic management tasks of planning, organization and control are either better or more reliably taken over by machines or supported by digital assistants.

The enormous scope, speed and significance of digital transformation obviously fueled by the broader version of Moore’s law, Kurzweil’s LOAR, has of course also an enormous impact on the global economy and its organizations. Just within a couple of years new corporations and businesses with global impact and unprecedented growth emerged while other players failed to develop business models and forms of social organizations suitable to exploit and further drive the dynamics of the LOAR within the global economy. Our hypothesis is that new forms and changes of social organization are playing a major role in development, adoption and use of technologies and that it is a misleading way of thinking to construct a cause and effect relationship between technological artifacts and social structures. Instead the platform strategies of the leading transnational tech companies are based on a novel concept of innovation based on technological and institutional change at the same time and comprehending the follow three elements:

• Scenarios to shape the future: What happens or should happen at the level of society, politics and economics, and in the working and everyday lives of individuals, groups as well as in regard of institutions, organizations, markets, global and regional cultures and the public, if – as foreseeable in the near future – the cognitive capacity (intelligence) of machines and networks are developed far beyond of those of individuals with perceptions, feelings and largely uncontrollable thoughts?

• Leading and managing change: How are social structures based on the acceptance of interpretations of reality („narratives“), by individuals, social groups, institutions, markets, and organizations changing? (Think, for example, of the moon-shot projects of Alphabet/Google venturing into mobility services by self-driving cars, medicine by diagnosis and development of treatments driven by data analysis through algorithms or through payments systems e.g. like Alibaba Ant or other apps.

• Analyzing and evaluating alternatives, implementing viable solutions: What are the concrete consequences of data-driven, largely automated process and behavioral control in business and society? For example, in relation to the organization of mobility and traffic, or in regard to financial services and banking system. (E.g.: blockchain technology applications are calling traditional institutions such as financial markets and national sovereignty over currencies into question).

From incremental improvement to disruptive innovation

The underlying concept of strategy development is based on linking technological progress with the possibility of futures fundamentally different from the past. Unlike normal or incremental innovation strategies aiming at improving

1. existing products and services,
2. organizational structures and processes
3. or expanding markets and customer bases
4. or sources of supply gradually

And as a fifth exceptional mode of innovation

(5) strategies based on breakthrough technologies like electric power in the late 19^th century or digital transformation and platform strategies of the 21^st century aiming at fundamental or radical changes of industries on a global scale or the establishment of new industries

These five types type of innovation already had been distinguished and exemplified by Joseph A. Schumpeter (1911; 1948). Schumpeter considered type 5 of innovations as unique and typical for the ‘gilded age’ because the application of a large number of novel technologies in transport, energy, physics and chemistry, led to the formation of new monopoles and the destruction of traditional structures.

Nowadays we are not talking about a fifth, fundamental type of innovation like Schumpeter, but about radical or disruptive innovation vs. incremental innovation or like Peter Thiel, the eccentric libertarian PayPal founder and Silicon Valley Venture Capitalist, expressed it at in his lecture series on start-ups at Stanford University: The vision or idea to do new things differently vs. scaling up and improving things that already works are the fundamental difference between promising start up and longtime established ventures in various industries. (Thiel, P., Masters B., 2015).

Schumpeter argued that the dynamics of the early days of large corporations the late 19th and early 20th century won’t occur again because the created large bureaucratic structures are interested in gradual improvement only. He didn’t believe that new revolutionary scientific discoveries would come up; corporations would compete on efficiency improvements only based on the existing set of basic technologies and their improvement. In his landmark volume on Capitalism, Socialism and Democracy (1942) he also foresaw the differences between capitalism and socialism dwindling as the innovation dynamics of capitalist market economies inevitably would slow down by time: A creative destruction of existing economic structures and ways of social life was no longer to be expected. Nowadays we know that Schumpeter had good arguments but he was wrong! The current generations of humans are experiencing life changing radical, disruptive innovation on a global scale not only in the economy but also in other institutional context of the society and in their daily lives.

Defining digital transformation and linking it precisely to the historical development in digital computation and communication is nearly impossible. Terms like the ‘fourth industrial revolution’ (coined by Klaus Schwab and the World Economic Forum, WEF) are as complex as the ‘second machine age’ announced by the MIT Economists Brynjolfson and MacAfee. However, the idea that ‘Artificial Intelligence’ or ‘Machine Learning’ is at the core of the current technology driven innovation strategies of extraordinary successful transnational or multinational corporations has arrived in the mainstream. The consultancy giant McKinsey and its MGI Global institute are dishing out guides to AI applications on regularly base (McKinsey, 2019) Business Faculties and Business Schools across the globe launching programs to cope with the current innovation dynamics in fields like digital leadership, strategy, organization, marketing, finance etc..

When it comes to a comprehensive account of the science behind the technological development the reader is referred to the seminal book of Terry Sejnowski (2018) ‘The Deep Learning Revolution’ or the compilation of interviews with leading scientists in the field by Martin Ford (2018), ‘Architects of Intelligence’. But for now, it is enough to point out that all of the 7 MNC’s with the highest market evaluation are organized around the idea of digital transformation as explained above.