Unreasonable Possibilities

Unreasonable Possibilities

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Scientific talent and long view funding. Cleantech is very capital intensive, and although there was a period of too much exuberance in the area, now it is met with investor disinterest. Organizations like Breakthrough Energy Ventures are trying to address this. There are many areas in which large breakthroughs must be attempted: From fusion to geothermal where linear cost for deeper drilling instead of exponential cost per foot when drilling at depth is the key breakthrough needed , storage, new materials and manufacturing, building materials, new agriculture and food.

Reinventing Consumer services from retailing, entertainment to elder care to delivery. Mobile, AI, Internet, communications, social networking, voice and image technology, sensor and cameras, data, mass personalized manufacturing. The supply chain is being reinvented, starting from 1 hour deliveries, a virtual pantries within minutes from many homes, all the way to completely re-invented grocery stores.

Wallet share is changing as well from physical products to technology-enabled experiences, often community experiences. Lastly, robotics are changing how we interact from food delivery to Amazon Echo. I recently asked a simple question: Is majoring in liberal arts a mistake for students?

The problem, I argued, is that the current liberal arts education does not teach critical thinking and scientific progress in the way that it should or in the way that STEM does. With technology and new tools, accessibility and equality in education will change, no matter what style or subject of education you want. AI tutors will not only allow for more affordable or free accessibility 24x7, but they will personalize education for each person. Perhaps, because of this personalization, the very notion of majors such as STEM or Liberal Arts will change altogether.

Reinventing Business, Cyber, defense, governmental services. Without too much elaboration, it is worth point out that business services, resource uses, and products have been changing and will change even more. No business, be it fintech, consumer goods design and production, industrial products design, drug research or manufacturing, materials design, manufacturing, spare parts, sales AI agents, or customer support agents, will remain untouched.

Technology will have an impact all of these, though my focus here is on things an individual entrepreneur can drive, not on governmental or regulatory driven change though those are often necessary followers. Space and cyber will be often entrepreneurially driven, although the latter will have many state actors.

On cyber services I refer you to AI; Scary for the right Reasons but suffice it to say that massive entrepreneurial opportunities in defensive and offensive cyber tools and services will exist. Entrepreneurs will need to push further innovation across these areas for true innovation to happen.

For instance, while you may think Uber is a mess, I think of Uber having started the chance in our notion of transportation and started with a limo service at the very high end, and Airbnb started with rooms in Philadelphia in during the Democratic National Convention and brokering rooms. That seed of an idea ends up being way more important than Hilton Hotels after almost a hundred of years. Because Silicon Valley runs so many experiments and people love to write about failures, the hubris, the messes, the trivial, the fraudulent or self-aggrandizing claims, be it Uber, Theranos, Juicero, Soylent, make better headlines.

These same factors are critical to get the self delusional attempts at grandiose or evolutionary innovation. If these entrepreneurs had normal expectations, they would not attempt the things they do. And their failures and diversion into societally good or bad business is a necessary side effect. Most businesses are improbable and fail but the few 1 in ?

He wanted a couple of million dollars to run an experiment. Could he do this? Silicon Valley is much more about the mindset and a culture than it is about a place. Here to much experience can be a handicap. If we have ten attempts each at many different areas covered in this essay we will really change the world.

Machines and systems can do medicine better than humans.

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Or that most jobs will be replaced or fusion energy is possible in my lifetime or that AI can make work an option for most people who will work if they want to work, but not need to work because we will have sufficient abundance. The disruption will be temporarily painful to some as being disrupted is never fun.

New technologies also rock the world by reallocating power and wealth and accentuating inequality. Fortunately capitalism is by permission of democracy and the electorate will have the ability to rectify the inequalities.

Unreasonable Possibilities

All these social factors will become urgent and critical to enable this transformation through democracy and the people who will be impacted. Sign in Get started. Reinventing Societal Infrastructure with Technology: I am not imagining here completely new axis that will surely happen like broad quantum computing, fusion energy, or molecular assembly. There are many axes of innovation that are opening up so the next decade or two look promising.

There are some I expect but seem too speculative even for me. To make it possible, you need new tools, a visionary and persistent founder, and evangelizing market participants. For instance, automotive companies had little appetite for electric vehicles. Nevertheless, Elon Musk had both the vision and the determination to create electric cars and gain adoption; that that allowed him to disrupt the automotive industry by building a better, more efficient car while paving the way for an autonomous future, as some would say.

There are macro trends, too. The same is happening with AI eating the world, as well as computational design, blockchain and 3D-printing. Some of the tools and axes of innovation defined below will end up being transformative, most likely this will be the case of AI, others will just be rapid facilitators. Older technologies, like mobile and Internet, will keep turbocharging these newer innovations. Where could a single entrepreneur driven by passion and a vision enter the market with simple products and drive to much larger scale over twenty years or so? Media had not felt the push of Facebook, Youtube, Twitter.

Amazon was very nascent and not yet starting to reinvent retail. India was still trying to scale only landlines! Going further back through my career, in the idea of a computer in every home was considered absurd, grandma using email in was thought ridiculous, and the Internet was a crazy idea and never going to be an important public network in Transportation and related city services. Health, disease diagnosis and management, drug discovery. Manufacturing, Construction, Buildings, building efficiency and cities. Consumer consumption items, services, education, durable goods.

Driver technology tools that are plausible and visible today and are feeding on each other and on other research include:.

Social connectivity and networking; distributed access. Increasing research breakthroughs in all areas. Other new still fermenting ideas I have surely missed or underestimated open for candidate suggestions. AI will , inevitably, change the structure of our society. The rate of change of new AI capability, the building block for changing businesses and human activity, is very rapidly expanding.

Fundamentally, we can now or soon will be able to achieve human-like and occasionally super-human performance on tasks that were, just a few years ago, regarded as completely out of reach for machines. Probably the greatest example is computer vision. It was stagnant for decades, but has made so much progress in the last years we can now have computers classify images and videos with super human performance, provided we have enough training data in the domain, be it face recognition or reading MRI images.

Just thinking about machines with the capability to understand vision and recognizing voice by itself, will fundamentally change how we think of work in general and what our interface with machines will look like in the future. That being said, there is likely an even greater progress possible near term. Currently, the best performing AI systems require huge amounts of data to train to human-like performance. However, work is underway to reduce this burden in various domains; often all is needed are humans being to feed just a few examples to guide the neural nets.

This will enable us to apply AI to domains where little data exists or the data is hard to get for structural or legal reasons, greatly widening the applicability of AI in all business and societal processes. Model-building of the world as humans do is another dimension of innovation, For example, an AI may be able to predict how much force would knock over a glass of water. AIs are also learning fast from the world of simulations and games.

We are now on the cusp of having AI generate images in a domain, at high resolution, and high quality that mimic art or any desired input distribution. The same now goes for music where we can create AI that can mimic and improvise play in any style. Nonetheless, it looks increasingly doubtful that even that claim is irrefutable. One day, we might see a short film,generated solely by an AI or a top ten music hit that never had a human composer or a new art style generated by AI that appeals to humans.

One last comment that should make us quite hopeful about the accessibility of these technologies is that most of the fundamental breakthroughs have been out in the open, published, and discussed publicly. Yes, AI talent is hard to come by today. At the same time, it is also one of the most popular areas of study these days and sooner or later this challenge is going to be overcome. Coupled with high-quality frameworks for AI research and deployment now being freely available as open source, rapid progress on both research and applications is at hand.

Technology will reallocate where and how people spend time and resources. We will have great abundance, growing productivity, and GDP but with increasing income disparity. Further, changes will be slow, almost imperceptible in terms of employment the first five or ten years, and take decades before going exponential in actual number of jobs impacted. But by the time the first 5 percent of jobs are impacted, the future will be inevitable. The renewed interest in robotics is, to a large extent, similar to the renewed interest in AI. For a long time we had robots that were amazingly durable, amazingly precise, but fundamentally simply examples of good mechanical engineering and careful motor control.

This was enough to solve manufacturing tasks in very structured environments where all parts have defined positions and the manufacturing line does not change rapidly. A core example here is the chassis production of cars which has little human involvement today. But no robot could replace a human in the sorting of eggs by size and grade, only human assembly line workers could do that.

They were mostly programmed machines, but not rapidly and broadly learning machines. The new path in robotics involves robots that can make decisions in a largely unstructured environment. Probably the most discussed example of this today are self-driving cars that have to make decisions in the real world and not in a defined, pre-planned environment.

But there are other, equally broad implications on the horizon. A company struggling with automation due to dealing with soft materials and rapidly changing product mixes right now faces large costs of automation. However, the next generation robots might change this by being able to learn new tasks rapidly on little data and with no programming.

The main driver for this is two-fold. The other boils down to the fact we can now interpret vision and 3D data by learning from examples instead of having to hand-code the rules. Reinforcement learning, learning from simulation, and understanding how to reduce the training samples required are the core elements of modern robotics. Adding general learning including concepts, concept hierarchies physics, and more will happen. Those robots are a very different breed from the old and the trade-off space will be vastly different.

Formerly, we got precision from adding tighter motor control or heavier arms.. This new class of robots have cheaper, lighter arms and still get the precision back by relying on visual servoing. In essence, it means the vision system is able to correct the robotic arm as it gets close to the object we wish to manipulate. A robot arm capable of doing human tasks should not weigh any more than a human arm does and then scale sub-linearly from there. This makes this next generation of robots cheaper, able to handle very flexible tasks, and quick to deal with environments that have been thought as impossible in robotics before.

There will be many contributions to robotics, but AI learning systems will be a big factor. From a societal and economic perspective this enables a completely new way of thinking about production lines. Proximity to the end-customer, thus, becomes more important than the availability of cheap labor for menial tasks in unstructured environments or the need for scale.

This is especially true when combined with new technologies like 3D-printing. Custom, personalized, and local may become economically better in areas like producing jeans, sofas and beds or many types of fresh food. It consists of a family of technologies that can manufacture polymer parts to high-density metal parts.

Even composites are being 3D-printed. The current beachheads for those technologies have been largely in design and prototyping environments. This means shortening the design cycle as we can almost instantly have a prototype part; it has actually already become a standard feature for many industries. This is, however, changing and with robotics. We are seeing more and more production parts made by additive manufacturing. Using these techniques to create performance critical parts that are not manufacturable with traditional methods is already becoming commonplace.

Examples here include turbine parts, rocket engine components, and implants. This acts as the key catalyst to move the industry from using the technology for prototyping to a manufacturing regime. We climb down the cost curve as an ever greater number of parts that used to be hard to customize, not buildable at all, or consisted of multiple assemblies, can now be built with these machines.

We are now tackling some of the fundamental limitations of the technology, such as cost per part, materials we can use, removal or avoidance of necessary support structures to make this family of technologies even more widely applicable. These technologies, in turn, are also changing conventional wisdom like benefits of scale, locations, and schedules for manufacturing, supply chains, spare parts, or maintenance. Do we need to make shoes in China for US consumption or can they be 3D-printed locally and customized to each foot? Do we need to stock every spare part for a Boeing in every airport in the world?

Should it take six months to get a sofa manufactured in China only to see it does not fit in your small studio apartment? This has significant consequences for the way we think about complexity in our design. If complexity becomes in essence free, that is not tied to manufacturing steps, our possible design space explodes. In particular, if manufacturing complexity is not the bottleneck or cost factor anymore, designing structures will be the new bottleneck. Instead of designing by hand, we will likely create them by specifying the input loads and tasks fed into AI systems.

Optimization software will process the data to create structures looking a lot more organically than now and producible ONLY by additive manufacturing. The technologies of robotics, AI, 3D printing, will feed on each other making exponential change on products, materials, and supply chains. In design of objects we have long used computer tools from EDA tools for the electronics industry to CAD tools for physical objects and from simulations for verification of performance of the designed objects.

We are now moving into a regime where the actual act of designing a structure is now becoming part of the duty of our tools and humans act more as a trainer, judge and specifier of external conditions. And even those roles may change to just specifying goals given constraints and preferences. It has to fulfill various structural loads and remain as light as possible at the same time. We will in the coming decades let the algorithms decide the design to minimize weight and specify the external loads.

In no place is this more apparent than in the design of drug targets. Instead of running quantum simulations to understand the binding of molecules to targets, a slow and costly procedure, at systems in the future be able to learn from past binding data to automatically come up with novel designs that might be good candidates for a new drug. A general principle behind it being that even though we often understand the underlying physics of what we are trying to design, the exploration process is too costly to run by brute force exploration of the design space.

Learning from past successful designs, be they molecules designed for a target, physical objects, or different layouts on a circuit board, allows us to meaningfully change the performance of these objects. We may soon see a new range of computationally-designed materials beyond copper, steel, and aluminium alloys for everything, from medical devices to body organs to your car and sofa. A decade ago computers learnt to beat humans at chess by brute force computation. Innovations in biotechnology might be grouped into three different levels: Our ability to measure biomolecules at continually higher resolution and in greater bandwidth is enabling steady improvements of our measurements of individual organisms like humans , but also groups of organisms from the microbiome of a human gut to the complex commensal relationships of organisms in a coral reef or in a patch of forest floor.

This amount of data acquisition these days is extremely complex and high-dimensional. Currently, only AI is able to create accurate predictive models and, thus, an efficient form of understanding the data. This, however, requires considerable advances in data storage and analytics. The third element is in the increasingly advanced and precise toolkit being developed for editing biology down to a single molecule. These capabilities will dramatically improve and become more diverse over time. George Church has likened studying the diversity and complexity of biology to an advanced alien civilization leaving all its technology in our backyard for us to analyze.

Biology has been able to create the machinery to very efficiently convert wide ranges of energy from one form to another,. It is able to harness that energy into vast abilities to transmute forms of matter. This alchemy of biology still produces the vast majority of materials of interest to humanity.

We are developing a deep control of the machinery of biology, which is just as crucial as the initial domestication of plants and animals thousands of years ago. Synthetic biology will impact chemicals and materials, energy, and human and animal editing, which will have great economic and societal implications. These and future capabilities will give us god-like powers with its benefits and danger over the next decade or two. Food products and pharmaceuticals are largely the result of biochemical processes. Basic components of our environment, like the oxygen we require to breathe, are the result of biological processes.

Changing these systems with tools like a shovel or hammer would be impossible. However, we are gaining the potential to have molecular level control of all living things, giving us powerful new ways of combating issues like food security or climate change. If human history has been a push to control of the world for human good, then we are at the start a major new type of development. This process started with the quest for control over environmental exposure by development of fire, buildings, and clothing; extending to gaining control over supply of food and materials by domestication of plants and animals, efficient agriculture, creating mining and mineral extraction and the industrial revolution; and then recently control over information and data through the development of language and literacy to modern methods of data transmission, storage, and analysis.

It will become systematic starting with fixing genetic defects in babies. The tools for editing DNA are only the first step in modifying the physiology of an adult living being. In order to do this, we have to target the cells and tissues we want to address specifically. Nevertheless, technological development in that direction is ongoing.

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We are also developing the ability to modify cells that can be introduced into the body with new genetics and designed molecular biochemistry or to modify and edit embryos prior to their implantation. We are learning to genetically modify a pig embryo to produce human compatible organs for transplantation, or 3D print organs.

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We can change our body composition, but we may also decide to edit ourselves from the very start. In terms of editing human embryos directly, we as a society need to determine how we want to use this technology; there are clearly many opportunities for improving health and wellness, and some large dangers as well.

One of the more promising avenues is to capture human knowledge in biomedicine, reconcile inconsistencies automatically, and be able to simulate at the molecular level every pathway and all omics in the body computationally. This could lead to true understanding of normal and deviations from normal, the usual definition of disease.

Drugs and their effects on a particular person could be modeled and dosages calculated. The possibilities for human or animal biology management are exciting though impossible to specifically predict if we develop this capability. Social networking has changed our access and rate in which we access communication and how we collaborate.

It has spurred new ideas and influenced the way we think about democracy. It has democratized information in a way that enhances education and new ideas. Twitter, as an example, has changed how we get news. It has influenced the end of regimes, and depending on the point of view, has had a positive or negative effect on politics. Social networking is a powerful tool that has allowed people to have a voice and connect globally.

Slack has brought social networking to industries and enterprises. It enabled more voices to collaborate and be heard, and helped making processes and businesses more efficient. Like anything powerful, social networks can have negative and positive implications on the world. They are undoubtedly a powerful tool to both spur innovative ideas and, influence them to get traction.

Scientific social networks accelerate communication and collaboration and increase the rate of progress or discussion. There are many ways in which industrial progress is leveraging these social tools. This tool is speeding the pace of change and innovation across key areas like education, health, and government. AI systems added to social networks and messaging will change them materially again. I am continuously optimistic this will be for the better. A marriage of the Internet and cryptography created the blockchain which has given rise to the distributed ledger, new payment systems, and cryptocurrencies like Bitcoin.

This will be critical axes of innovation that will enable new businesses and paradigms, whether it be smart contracts to rethinking workflows, food traceability, medical records, and other mission critical data tools. Blockchain will be a new way to use technology to rethink complete industries like the financial system or being full transparency and tracing into supply chains.

When Haiti was hit by the hurricane, many or most of the records were lost. This could have been easily avoided. Blockchain could allow people, businesses, and governments to rethink how they are storing and using their data, such as documents, information, payments. Keep in mind there are dangers of misuse, volatility, diversion into fraud and illicit use as dangers. Old tools that still have impact…. When you take the cost structure of an Uber, multiply its usage by 5—10X in any given city, assume cars that are used ,—, miles per year amortized as a few cents per passenger mile serviced over its million mile designed life instead of 12, and as a result operating costs become much more important than capital costs.

Thus, electric cars become much more cost effective. Interest and maintenance costs decline because of scale and the cost of the driver disappears because of driverless technology. At the moment, the driver is the largest part of a Uber or taxi service and would approach a few cents per passenger mile. It becomes hard to see how owning a car makes sense except for a small fraction of the population. Cars will remain a thing for car enthusiasts. However, besides special use cases for the vast percentage of passenger miles cars, trains and public transportation will be reinvented.

We could have public transportation in smart cities, enabled by clever legislation, and offer point-to-point rides on demand. All that dramatically reduces cost for cities and citizens. Batteries and electricity would be the main cost per passenger mile. These pods, given the service time, will need to be electric, which incidentally lowers carbon per passenger mile for cities that are carbon sensitive about their electric supply. It also means higher reliability because of fewer moving parts. Pods will be be less prone to crashing. Hence, they will be lighter and cheaper, which will allow them to go much further on a kilowatt hour of electricity, reducing battery costs.

A light bicycle is 17 pounds. Would a four passenger pod that can be frequently recharged need more than a few hundred pounds to carry 1, pounds of four people? Electricity cost would be very small at 50— pounds of vehicle weight per person for each mile. One could summon specialty pods for wheelchairs or other specialty loads. For forward-looking cities, we may see these as anywhere to anywhere on demand public transportation for a few dollars or maybe near free! Parking lots and spaces could be replaced by parks or housing, or commuter lanes.

Commute distances may expand, housing may get cheaper, and environmental pollution decline. Cities could be redesigned to work differently, especially if one adds in improved communications technology. The number of cars could decline five fold or more. The need for natural resources like steel, rubber and plastics decline concomitantly. Automobiles as a large part of GDP could change dramatically. Even trains could become autonomous pods on roads or tracks, dispatched on demand, instead of being enormous beasts carrying , pound cabins that go empty much of the day that only make economic and climate sense when fully loaded and whose schedule is limited by when they can carry a breakeven number of passengers.

A key metric might be average pounds and costs of material required to carry a human. Ideally, we start with key arteries. If residents of a city get closer to their destination, they might even walk the last half mile, which could have beneficial influence on their health. Thus, it can ease housing shortages and improve housing affordability. Parking land would be freed up for parks and low-cost housing. The city without automobiles would be a different animal. To understand what drives cities check out A Physicist Solves the City! The pattern of adoption is not yet clear.

The rate of adoption will depend upon how the technology is targeted at social solutions. It might happen first on elder care communities, with free taxi service to avoid the disadvantages of having average age 70 drivers and enable everyone in age restricted communities to have more freedom. On the other hand, it may be an incentive to make affordable housing more prevalent by guaranteeing a certain commute time with free service in dedicated lanes from certain communities to work centers?

Or it may be used in order to relieve traffic congestion in cities like inner London; offering near point to point convenient and affordable service can render private transportation unnecessary. Another likely development is to relieve truck drivers of tedious jobs by letting driverless trucks ply the freeways and the drivers to take over when off the freeway? Adoption and social acceptance in my view will have a huge path dependence and a range of adoption options are available. Is it likely that technology could multiply doctors including many, if not most, specialties many-fold?

Perhaps it could even, invent a better doctor, making them always available everywhere, accessible, and affordable or near free like Google search? Maybe people should only provide the human element of medical care? There are probably a million doctors in the United States, give or take, but with AI systems, we could create ten or a few hundred million doctors worth of expertise and use human doctors only for what they love to do, which is interfacing with patients, making health more more personal, accessible, convenient, and less costly. The reality of medicine today is very different.

In the future, almost certainly data science and AI will provide much better diagnosis, monitoring, and follow-up than most human doctors, as per my paper in It will do much better prescription, whether prescribing a drug or a procedure. We will have real science behind medicine as doctors today learn mostly from constantly improving iterative practice. Every AI agent will be updated with the latest research and up-to-date with every speciality, instead of being knowledgeable in just their own vertical specialty. Medicine is much better than it has ever been, so we have to acknowledge every aspect of medicine has improved over the last ten years, thirty years, hundred years.

That, however, does not mean it cannot be even better. AI will enable less errors in doctor diagnosis and surgery, better drug discovery, and personalized prescriptions. We will be able to measure many more variables thousands or millions per sample or more and make decisions based on complexity no human doctor could master. It seems silly there is one dosage prescription for aspirin or opioids for seven billion people on the planet.

Drug discovery and surgery will change primarily to computational techniques, opening up more possibilities. Procedures like surgery and anesthesia could get roboticized, either with a human assisting robot or the other way around. Should you have bypass surgery or a stent? Most of that medicine is based on debatable evidence. According to the American Heart Association, t here is class A evidence for only about 11 percent of their cardiac-related recommendations, meaning evidence from multiple randomized trials or meta-analyses exists to support the diagnosis. Whereas 45 percent are based on recommendations founded on expert opinion level C evidence, the worst kind.

And great quality will only be possible to achieve because of very low marginal costs, as has happened in so many other computationally based services. Better and faster patient outcomes, lesser healthcare costs, and more accessibility to all people are most likely to happen simultaneously. Technology has generally increased healthcare costs, but hopefully, it will be different this time as marginal cost will be much lower, as opposed to those of proton beam accelerators. Much has been discussed about precision medicine. Unfortunately, however, it is mostly focused on genomic data.

Such approach would be more personalized than precision medicine. One of the fundamental limitations in medical practice has been the propensity to collect data that people can interpret. What is promising to me is the number of fundamental technologies being attempted to collect large AI scale data from everything, from an MRI image to a blood sample to the lowly ECG.

All this would be much better for the patient. Imaging is also ripe for reinvention, using computational capabilities and consumerized components. Looking into the body should be routine, radiation-free and inexpensive. In my views,very little about medicine needs to stay the same. We have to get away from the idea that we use symptoms to diagnose disease. Data science should diagnose disease and monitor progress or recommended dosage of a medication and therapy to best treatment. We should get away from the notion of one prescription for all seven billion people on the planet based mostly on their weight.

As I described on Quora last year, one should take a million people, measure hundreds, if not thousands of variables, their blood and their microbiome and their physiology, every week, for a year. This can not only contribute to preventive health. It can and will combat diseases and cancer by very early diagnosis. Plus, layer this into gene editing techniques and microbiome research. There is much to be done around personalization and targeted medicine.

We have already seen entrepreneurs working on it in bits and pieces. As I only half jokingly told the Stanford Medical School audience about it three years ago: Here are the facts: We can get enough information to recommend whether you really, are at high risk and should get a colonoscopy This analysis will get better and, eventually lead to generalized early cancer detection.

Disease will be detected early. Right now, most people with heart disease learn about their disease from a heart attack, not twenty years earlier when it started. When this is no longer the norm, we will move closer to healthcare from the sickcare we have today. We should have real science behind medicine. Medicine is much better than it has ever been, so we have to acknowledge every aspect of medicine has improved over the last ten, thirty or hundred years.

It is criminal that they cannot, especially if medication exists. If our frame is negative, the reality we create in the present moment is negative. If our frame is positive, the reality we create is positive. Through humorous examples and stories of his own journey, Mike Jones will teach you how to break through from prod The conversations we have with ourselves become the frame of reference through which we view the events in our lives.

Through humorous examples and stories of his own journey, Mike Jones will teach you how to break through from producing reasonable, predictable outcomes to producing new powerful, positive, unreasonable possibilities in every aspect of your life! Paperback , pages. To see what your friends thought of this book, please sign up. To ask other readers questions about Unreasonable Possibilities , please sign up. Be the first to ask a question about Unreasonable Possibilities.

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