Monthly Archives: december 2016

To contemplate….

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We stand at the threshold of a terrific challenge, but also a unique opportunity, and a fantastic adventure—never before has a single human generation had the potential to make such a difference. Arguably one serious aspect of the global issues is the loss of ecological diversity—the other living things with which we share Earth. This affects human well-being by interfering with crucial ecosystem services such as crop pollination and water purification and by destroying humanity’s beautiful, fascinating, and culturally important living companions. An important point is that we focus exclusively on species, ignoring the extirpation of populations—the units relevant to ecological functioning and the delivery of ecosystem functioning.

The road ahead will be bumpy and full of potholes. Be hopeful that we actually get wiser. However, we must prepare for a disruptive, dangerous future. In order to navigate wisely, we must remain anchored and true to the best of our collected lore and wisdom. We must not let ourselves forget the complex critical thinking, moral reasoning, aesthetic appreciation, and emotional gifts that make us human and our lives worth living. As we gain successes in improving technologies, we must not lose sight of our keenest skills, creativity, of our friendship, kinship, empathy, love, and our individuality. Now, more than ever, we need to cultivate a solid grounding in the humanities to bring out and hold on to the better angels of our nature. To secure the blessings of liberty to our posterity calls for recognizing the special contributions that the humanities make to our collective culture. This is necessary for giving our grandchildren and their grandchildren a better future.

Meta analyses of biodiversity-ecosystem function studies suggests that the impact of biodiversity losses on ecosystem functions is comparable in scale with that of other global changes (such as pollution and nutrient deposition). However, most efforts to quantify this relationship have focused largely on effects of reduced producer diversity, which may typically have much lower functional impacts than does consumer loss.

Time to reexamine the relationship between humans and the rest of the natural world. There has been a long-standing narrative of humanity and nature being separate, but human activity is intrinsically linked to nature, and is part of it. From the land we live on to the resources we use to the trash we throw away, everything we do is tied into and impacts our surroundings.

Perhaps most importantly, we have self-awareness of the impact of our activities. We are aware of the finiteness of Earth’s natural resources and we have the capacity to create innovative solutions.. Having this self-awareness along with our creative problem-solving will be critical to be conscious of the effects of our behavior into the future.

The decisions we collectively make, or fail to make, in the next few years will affect the course of life on Earth. It’s not too late to stop the mass extinction if we rethink our priorities, face our responsibilities, organize, and act.

The story of human evolution features a unique ability to adapt in the face of changing environments, and this will be no different for human-shaped environment of today and the future. With our own growing awareness of how our actions impact the natural world, the question is how best we can shape our actions so that the consequences of our activities are purposeful and positive.

Altering our surroundings is fundamental to human survival. In this light, how may we come to alter the world that we’ve created in a conscious and productive way? Community and global collaboration, along with innovation, will be the keys to creating a new path for the future of our species and our environment. By looking at it from a human origins viewpoint, the narrative of our collective humanity and the qualities that unite us as a species with a common origin can give us a sense of communal purpose in developing solutions for the problems of today and tomorrow.

Contemplating these questions will help us begin to determine our common future. The themes of self-determination, community, and action will all be parts of the human-driven innovation for the future of the planet. As we look to the future, we will see not only the planet change, but we may even see changes in ourselves as a species. An invitation to contemplate: what will it mean to be human in the future of a human dominated planet.

dwelling thoughts after creativity sessions with students (be warned… long thoughts)

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2030. No cause for concern. No machines with self-sustaining long-term goals and intent have been developed, nor are they likely to be developed in the near future. Instead, increasingly useful applications of disruptive technologies conversions, including AI, with potentially profound positive impacts on our society and economy are likely to emerge between now and 2030. At the same time, many of these developments will spur disruptions in how human labor is augmented or replaced by robots, cyber-systems and AI, creating new challenges for the economy and society more broadly.

Sessions with 22 students in 2 three hour session focused on 2030 and meant to highlight specific technology changes affecting the everyday lives of us.

The boundaries between disciplines, such as natural sciences and informatics, are becoming increasingly blurred. As disciplines converge, so do the technologies The convergence of technologies can disrupt existing business models, but also creates completely new markets and novel application fields. Bioinformatics is an example of a rapidly growing interdisciplinary scientific field that derives knowledge from computer analysis of biological data, using techniques and concepts drawn from informatics, statistics, mathematics, chemistry, biochemistry, physics, and linguistics.

As digitalization becomes pervasive in production, autonomous, decentralized and local production systems and factories are within reach, ushering in a new era of industrialization. The development continues to be characterized by performance increases, miniaturization and nanotechnology. The amount of data generated by the digital economy is growing rapidly. Analyzing this data offers tremendous potential for efficiency gains and new business models and opportunities.

Technology convergence is revolutionizing industry, products, markets. Once composed solely of mechanical and electrical parts, products have become complex systems that combine hardware, sensors, data storage, microprocessors, software, and connectivity in myriad ways. These smart, connected products—made possible by vast improvements in processing power and device miniaturization and by the network benefits of ubiquitous wireless connectivity—have up to 2030 unleashed a new era of competition.

Smart, connected industries and products offer exponentially expanding opportunities for new functionality, far greater reliability, much higher product utilization, and capabilities that cut across and transcend traditional product boundaries. The changing nature of products is also disrupting value chains, forcing companies to rethink and retool nearly everything they do internally. Another leap in productivity in the economy will be unleashed by these new and better products. In addition, producing them will reshape the value chain yet again, by changing product design, marketing, manufacturing, and after-sale service and by creating the need for new activities such as product data analytics and security.

These new types of products itself alters industry structures and the nature of competition, exposing companies to new competitive opportunities and threats. They are reshaping industry boundaries and creating entirely new industries. In many companies, smart, connected products will force the fundamental question, ‘What business am I in?’ Smart, connected products raise a new set of strategic choices related to how value is created and captured, how the prodigious amount of new (and sensitive) data they generate is utilized and managed, how relationships with traditional business partners such as channels are redefined, and what role companies should play as industry boundaries are expanded.

There are clear examples of industries in which digital technologies have had profound impacts, good and bad, and other sectors in which automation will likely make major changes in the near future. Many of these changes have been driven strongly by routine digital technologies, including enterprise resource planning, networking, information processing, and search. Understanding these changes should provide insights into how technology conversions and AI will affect future labor demand, including the shift in skill demands. It is also creeping into high end of the spectrum, including professional services not historically performed by machines.

These developments will likely replace tasks rather than jobs in the near term, and will also create new kinds of jobs. But the new jobs that will emerge are harder to imagine in advance than the existing jobs that will likely be lost. Changes in employment usually happen gradually, often without a sharp transition, a trend likely to continue as technology and AI slowly moves into the workplace. A spectrum of effects will emerge, ranging from small amounts of replacement or augmentation to complete replacement. AI may also influence the size and location of the workforce. Many organizations and institutions are large because they perform functions that can be scaled only by adding human labor, either horizontally across geographical areas or vertically in management hierarchies. As technology and AI takes over many functions, scalability no longer implies large organizations. The technological development divides jobs into smaller tasks, automates the routine work and then recruits freelance workers through crowdsourcing platforms to perform the non-routine work. As the AI monitors the workers it learns from them, meaning that over time it can automate more of the non-routine tasks. In other words, the freelance workers train the system to replace them.

Importantly, the expanding scope of technology, automation is not suggestive of the end of work, despite of significant advances in machine learning and mobile robotics, several bottlenecks remain: more challenging forms of social intelligence, including coordination, teaching, negotiation and mentoring creative intelligence, social intelligence, as well as perception and manipulation, perception and manipulation skills of perceiving and interacting with unstructured physical environments.

Creative work, involving the development of novel ideas and artifacts — requiring the ability to achieve desired goals without explicit instruction — is difficult to automate by definition as computerization usually requires some explicit instruction.

To be sure, there are humanoid robots that can interpret music and make improvisations on the part of other performers, and drawing programs, have generated thousands of drawings being exhibited in galleries worldwide. Nevertheless, human workers still maintain the comparative advantage in creative work as replicating the kind of implicit reservoirs of knowledge and judgment drawn upon by human creators is extremely difficult, largely because we find it difficult to define creativity ourselves. Thus, although it is clearly possible to design an algorithm that can churn out an endless sequence of paintings, it is difficult to teach the algorithm to differentiate between the emotionally powerful and the rest. The reason tasks requiring social intelligence are difficult to automate is largely similar: social intelligence requires a wealth of tacit knowledge, about social and cultural contexts, and thus involve a wider range of subtleties that are difficult to specify and often give rise to misinterpretations, even among humans.

So, if a routine task can be performed cheaper, faster and better by a robot, there is a chance it will be. What is also true – and even more certain – is that machines push us to specialize in our competitive advantages: more ‘human’ work, creative and social intelligence, interpersonal and non-routine tasks are what makes us resilient and adaptive to change. The future of work is about much more than automation. It is about equitable access to opportunities and accessibility. It is as much about finding employment as being entrepreneurial. The future of work is about new ways to better preserve, share, spread and generate economic and social value. Advances in knowledge and technology have already empowered us with novel skills and solutions. They come down to circular, collaborative and connective.

Preserving and uncovering value is not enough. It must be shared and accessible. This is ever more possible in an increasingly collaborative economy. Communities are creating a shared economy for peer-to-peer energy exchange to be more resilient and take neighbors out of energy poverty. Circular and collaborative innovations would not run as efficiently – if at all – had we not entered the connective economy. Connectivity has extended our imagination, allowing us to engineer solutions to seemingly unsolvable problems, and invent new professions that not long ago only belonged to science fiction.

Connectivity also means that we can invite more talent – all talent – into the future workplace. Initiatives use virtual exchanges to build cross-cultural skills and facilitate the integration of today’s immigrant children into tomorrow’s ever more diverse workforce. The elderly and people with disabilities can overcome physical limits and enter digital workplaces. Tear down the last barriers of discrimination.

The economic effects of technology and AI on cognitive human jobs will be analogous to the effects of automation and robotics on humans in manufacturing jobs. Large fractions of the total workforce may, in the long run, lose well-paying ‘cognitive’ jobs. As labor becomes a less important factor in production as compared to owning intellectual capital, a majority of citizens may find the value of their labor insufficient to pay for a socially acceptable standard of living. These changes will require a political, rather than a purely economic, response concerning what kind of social safety nets should be in place to protect people from large, structural shifts in the economy.

This also involves adapting workers to the new economy. The workers of today, and even more the workers of tomorrow, will need to be able to pick up and move to where the jobs are. They should engage in lifelong learning and upskilling whenever possible to make themselves as attractive as possible to future employers. Flexibility must be their byword. Of course, education and flexibility are good things; they can make us resilient in the face of the creative destruction of a churning free economy. But ‘workers’ are not just workers; they are not just individuals free and detached and able to go wherever and do whatever the market demands. They are also members of families — children and parents and siblings and so on — and members of communities, with the web of connections and ties those memberships imply. And maximizing flexibility can be detrimental to those kinds of relationships, relationships that are necessary for human flourishing.

In the short run, education, re-training, and inventing new goods and services may mitigate these effects. Longer term, the current social safety net may need to evolve into better social services for everyone, such as healthcare and education, or a guaranteed basic income. Technology conversions and AI may be thought of as a radically different mechanism of wealth creation in which everyone should be entitled to a portion of the world’s produced treasure. We must rethink our education system and how we prepare our young minds for the workforce. There will have to be a greater emphasis on skills such as creativity, analytical thinking, and abstraction as opposed to rote memorization, shallow learning. We need to teach students skills that align with mega-disruptive trends. To incentivize lifelong learning, as that is the only way future workers can stay relevant. Stop producing blind followers and instead develop more leaders, innovators, and creators.

In the next fifteen years, it is likely that human teachers will be assisted by AI technologies with better human interaction, both in the classroom and in the home. More general and more sophisticated virtual reality scenarios in which students can immerse themselves in subjects from all disciplines will be developed. AI techniques will increasingly blur the line between formal, classroom education and self-paced, individual learning. Adaptive learning systems, for example, are going to become a core part of the teaching process in higher education because of the pressures to contain cost while serving a larger number of students and moving students through school more quickly.

Organizations face the challenges to build adaptive capabilities and be especially aware of the challenges they are likely to encounter in developing adaptive capabilities. Classical approaches to managing scale—delegation and specialization—can be highly efficient under stable conditions, but the hierarchical structures they produce are too rigid for the rapid learning and change required in turbulent environments.

A narrow focus on leanness, too, can impede adaptability. Under pressure from competition and capital markets, some large companies have squeezed out not only inefficiency but also the diversity and variation needed to adapt to rapid change. What’s more, once adaptive capabilities in highly structured and specialized organizations have atrophied, they can be challenging to re-create.

In addition, the cultures of special large organizations—often internally oriented and with an intolerance of failure, an obsession with efficiency, and a bias toward consensus and obedience—can be ill suited to adaptability. And their management paradigms die hard, especially when they have been the basis for historical success, are integrated into training programs, and offer the comforting illusion that a company can perfectly foresee and control its destiny.

Modular organizational units provide flexibility in a changing environment. Standardized (plug-and-play) interfaces enable the organization to recombine parts and rapidly introduce variations in products and processes at low cost and risk, as well as rapid shifts in resource allocation. A free flow of knowledge and power enabled by decentralized decision-making allows operations to detect and respond quickly to changes. This attribute is often reinforced by weak or competing power structures and a culture of constructive conflict and dissenting opinions.  Adaptive organizations favor simple universal principles over strict rules for determining how individuals and teams should interact and how decisions should be made. Adaptive values encourage the organization to pursue economics favorable to experimentation rather than focus on avoiding failure, which is seen as a necessary part of experimentation. Adaptive values also promote productive dissidence, cognitive diversity, and an external orientation in order to allow a faster and more accurate response to a changing environment. And because adaptive organizations rely on individual creativity and initiative, they articulate a credible common purpose that transcends financial goals and mobilizes employees.

Faced with growing complexity and performance pressures in the work environment, individuals are increasingly seeking a more suitable balance and better boundaries between the requirements of work and private life.

At first glance there does not seem to be anything philosophically problematic about human enhancement. Activities such as physical fitness routines, wearing eyeglasses, taking music lessons and prayer are routinely utilized for the goal of enhancing human capacities.

Anthropomorphic interfaces are increasingly associated with AI raise novel privacy concerns. Social science research suggests people are hardwired to respond to anthropomorphic technology as though it were human Humans and AI systems have complementary abilities. People are likely to focus on tasks that machines cannot do as well, including complex reasoning and creative expression.

The advancement of technology and artificial intelligence, autonomous robots will raise philosophical and social cultural questions of law and governance that scholars are just beginning to grapple with. These questions are likely to have growing economic and perhaps political consequences in the years to come, no matter which of the three scenarios above you consider likeliest. While work has intrinsic value, most people work to be able to purchase goods and services they value. Because AI systems perform work that previously required human labor, they have the effect of lowering the cost of many goods and services, effectively making everyone richer. But as exemplified in current political debates, job loss is more salient to people—especially those directly affected—than diffuse economic gains, and AI unfortunately is often framed as a threat to jobs rather than a boon to living standards.

There is even fear in some quarters that advances in AI will be so rapid as to replace all human jobs—including those that are largely cognitive or involve judgment—within a single generation. This sudden scenario is highly unlikely, but AI will gradually invade almost all employment sectors, requiring a shift away from human labor that computers are able to take over.

The economic effects of AI on cognitive human jobs will be analogous to the effects of automation and robotics on humans in manufacturing jobs. Many middle-aged workers have lost well-paying factory jobs and the socio-economic status in family and society that traditionally went with such jobs. An even larger fraction of the total workforce may, in the long run, lose well-paying “cognitive” jobs. As labor becomes a less important factor in production as compared to owning intellectual capital, a majority of citizens may find the value of their labor insufficient to pay for a socially acceptable standard of living. These changes will require a political, rather than a purely economic, response concerning what kind of social safety nets should be in place to protect people from large, structural shifts in the economy. Absent mitigating policies, the beneficiaries of these shifts may be a small group at the upper stratum of the society.

In the short run, education, re-training, and inventing new goods and services may mitigate these effects. Longer term, the current social safety net may need to evolve into better social services for everyone, such as healthcare and education, or a guaranteed basic income. Indeed, countries such as Switzerland and Finland have actively considered such measures. AI may be thought of as a radically different mechanism of wealth creation in which everyone should be entitled to a portion of the world’s AI-produced treasure. It is not too soon for social debate on how the economic fruits of AI-technologies should be shared. As children in traditional societies support their aging parents, perhaps our artificially intelligent children should support us, the “parents” of their intelligence.

TO RETHINK, CONSIDER

Application design and policy decisions made in the near term are likely to have long-lasting influences on the nature and directions of such developments, making it important for technology conversions, researchers, developers, social scientists, and policymakers to balance the imperative to innovate with mechanisms to ensure that AI’s economic and social benefits are broadly shared across society. If society approaches these technologies primarily with fear and suspicion, missteps that slow AI’s development or drive it underground will result, impeding important work on ensuring the safety and reliability of AI technologies. On the other hand, if society approaches AI with a more open mind, the technologies emerging from the field could profoundly transform society for the better in the coming decades.

 

THREE BILLION YEARS OF R&D

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Life is the most extraordinary phenomenon in the known universe; but how did it come to be? Even in an age of cloning and artificial biology, the remarkable truth remains: nobody has ever made anything living entirely out of dead material. Life remains the only way to make life. Are we still missing a vital ingredient in its creation?

One hundred and fifty years ago, paleontologist Thomas Henry Huxley asserted that humankind would eventually take the processes of evolution into our own hands. Within a few decades of his proclamation, a cadre of equally brilliant scientists including Werner Heisenberg, David Bohm, and Max Planck began to unravel the mysterious properties of quantum mechanics. These two theories — evolutionary and quantum dynamics — can each be considered among the most important discoveries of all time. Taken together, they have changed almost everything about the way we understand reality. However, in spite of the popularity of interdisciplinary research and unifying theories over the last hundred years (despite, even, quantum physicist Erwin Schröedinger’s 1944 book, What Is Life?), it was only recently that the relationship between these two vastly important domains was even considered. Now, a new kind of science, called quantum biology is emerging — and it could change everything we know, again. The interplay between Quantum Mechanics and Biology, which have long been regarded as unrelated disciplines, the former describing nature at the inanimate microscopic level, while the latter describing the living species. The fundamental nature of quantum mechanics in unifying the two fields, however, is coming to the forefront.

A new perspective on how evolution works and alter our understanding of our world’s fundamental dynamics. Revealing that the missing ingredient is quantum mechanics; the phenomena that lie at the heart of this most mysterious of sciences. What does this all mean? Not only do quantum phenomena occur in living systems, but the basic processes of life we take for granted rely on the transfer of information backward in time. Everything in nature – animate and inanimate – is composed of finer pieces, parts so small that they obey ordinary laws of physics, but follow quantum mechanics. Introducing superposition, coherence, resonance, tunneling and entanglement — and life unfolds into the pure magic.

Quantum Biology is complex and counter intuitive. But it is magic, life is so magical because it cheats.

I ‘am fascinated by the concepts developed in quantum information science to explore quantum dynamics in biological systems. The existence of quantum coherence in certain biological processes has been identified in a number of experiments. The role of quantum coherence and quantum entanglement has been carefully investigated, which suggests that quantum effect is important in these processes although in a more complicated way. There are known quantum effects within biological systems. And is it even possible in theory for cognitive processes, or neuroanatomical functioning, to be affected by events at the quantum entanglement? Quantum entanglement is defined as a relation between two or more micro-particles (photons, electrons, …) in which the quantum state of one is entangled with the quantum state of the other. When observation of the first part of the pair brings about alteration of the quantum state in that particle, quantum theory entails that the state of the second particle will change as well.

Biological phenomena involve molecules (such as proteins) that are composed of typically hundreds of thousands of atoms and have, therefore, been considered too complex to be tackled by physicists using similar quantum mechanical approaches. However, evidence of quantum phenomena occurring in living organisms suggests that quantum mechanics does play a role in biological systems:

  • Photosynthesis uses quantum transport between chlorophyll molecules
  • Olfaction and smell recognition is based on molecular vibrations using the quantum mechanism of electron tunnelling
  • Magnetic sensing in birds allowing them to orientate is based on properties of electronic spin in birds’ retinas
  • The induction of a state of general anaesthesia in the fruit fly is accompanied by changes in electron spin, suggesting that electron current inside neurons may play a role in nervous system function
  • All biological electron transfer is based on electron and/or nuclear tunneling.

The research challenges that quantum biology is facing span from fundamental issues related to the interplay between the energy scales of the different phenomena and the thermal noise emanating from the biological environment, to the (theoretical) understanding of the observed phenomena and technological bottlenecks related to measurement techniques. It is difficult to apply currently available (nano)measurement methods to in vivo measurements.

The development of new techniques to probe and excite quantum phenomena in biomatter reflecting the in vivo situation would truly represent a breakthrough. The applications of quantum biology in the longer term are potentially enormous and could impact upon a large number of technologies including sensing, health, environment and information technologies. Model systems will serve as a proof of concept to test the understanding of the physics and chemistry of quantum biological phenomena and lead the way to working devices such as bioinspired photovoltaic cells, and chemical, magnetic and biological sensors.