How to Live Forever

Catalyst Volume 7

Despite common debate over its desirability, immortality has been an object of fascination for humans since the beginnings of recorded history. Why do we die in the first place? Religious and philosophical explanations abound. Evolution also provides important insights, and we are just beginning to understand the detailed molecular underpinnings of aging. With knowledge, of course, comes application. Whether or not you seek immortality, the technology for significant life extension may become available in our lifetimes.¹ Eventually, with these new advances, every year we live will add more than a year to our lifespans; this is the point when we become immortal.

Immortality is a more complex concept than many realize. In the dictionary, it is defined simply as “the ability to live forever.”² What if you lived forever as an extremely old man or woman, physically and mentally weak and handicapped? This is not often the image associated with eternal life. Immortality, then, would be desirable only if it came hand-in-hand with another important concept: eternal youth. Another notion of immortality is embodied by Superman or the mythic Greek hero Achilles: invulnerability. By definition, however, invulnerability is not an essential feature of immortality. The real obstacles to immortality are not freak accidents or acts of violence but aging. Aging causes the loss of both youth and immunity to disease. In fact, no one dies purely from the aging process; death is caused by one of many age-related complications.

Eliminating aging is thus synonymous with achieving immortality. The first step in this direction, of course, is to understand the aging process and how it leads to disease. The best answer to this question has been offered by Cambridge biogerontologist Aubrey de Grey. De Grey argues that aging is the accumulation of damage as a result of the normal, essential biological processes of metabolism.² This damage accumulates over the course of our lifetimes and, once it passes a critical threshold, leads to pathological symptoms. The field of biogerontology mainly focuses on understanding the processes of metabolism in the hopes of preventing accumulation of damage. Geriatrics is a related specialty that focuses on mitigating the symptoms of age-related disease. De Grey points to the enormous complexity of understanding either process and offers an alternative: identifying and directly dealing with the damage.³

What types of damage does this entail? To begin, it is essential to understand that the body is a collection of billions of cells. The health of these cells directly translates to the wellbeing of our bodies. Aging is caused by deterioration of our cells, which typically destroy and recycle substances to prevent accumulation of damage over time. De Grey believes there are seven categories of damage that lead to aging. Two are mutations of DNA, the molecule that stores our genetic information. Two are accumulations of molecules that our cells have lost the ability to destroy. One is an accumulation of crosslinks between our cells, causing our tissues to become constrained and brittle. Another is the loss of irreplaceable cells, such as those in our heart or brain. The final classification is an accumulation of death-resistant cells that cause damage to our bodies. De Grey has proposed Strategies for Engineered Negligible Senescence (SENS) for repairing each source of damage. Some of these strategies, such as stem cell therapy, are theoretical and unproven; others, like gene therapy, are modeled after pharmaceuticals that have already gone through clinical trials. De Grey’s SENS are innovative and radical by the standards of the medical and scientific community, causing many to question their viability.

The first SENS therapies will not be perfect. They will eliminate enough damage to keep us below the threshold of developing age-related diseases for a few extra decades, but they will leave even more stubborn forms of damage behind. A few decades, however, is a long time for modern science. By the time our bodies start to show signs of aging, more effective therapies will be available. This process would continue indefinitely.

The fundamental weakness of SENS is that it is based on keeping an imperfectly understood biological organism functioning long after it was ever designed to be. The alternative is to switch out of our flesh and blood homes and into new territory: electronics. For our bodies, this seems relatively straightforward; while fully functioning humanoid robots are far from perfect, it is not a great leap to assume that they will be as capable, if not far more powerful than human bodies in the future-certainly by the time SENS would begin to wear out. Transferring our minds to an electronic medium offers far more considerable challenges. Amazingly, progress in this direction is already under way. Many scientists believe that the first step is to create a map of the synaptic circuits that connect the neurons in our brain.⁴ Uploading this map into a computer, along with a model of how neurons function, would theoretically recreate our consciousness inside a computer. The process of mapping and simulating has already started with programs such as the Blue Brain Project and Obama’s BRAIN initiative. In particular, the former has already succeeded in modeling an important circuit that occurs repeatedly in the mouse brain.⁵

Transferring our minds to computers would mean that any damage that occurred could be reliably fixed, making us truly immortal. Interestingly, the switch would also fulfill many other ambitions. Our mental processes would be significantly faster. We would be able to upload our minds into an immense information cloud, powerful robots, or interstellar cruise vessels. We would be able to fundamentally alter the architecture of our minds, eliminating archaic evolutionary vestiges (such as our propensity toward violence) and endowing ourselves with perfect memories and vast intelligences. We would be able to store and reload previous versions of ourselves. We would be able to create unlimited copies of ourselves, bringing us as close as possible to invulnerability as we may ever get.⁶

While you may have never seriously considered the idea that you might be able to live forever, theoretically it possible; technologies for radical life extension are currently in development. Whether such advancements reach the market in our lifetimes is in large part dependent on the level of public support for key research. Although the hope of living forever comes with the risk of disappointment, keep in mind that efforts toward achieving immortality will increase, if not your lifespan, that of your children and future generations.

References

1. Kurzweil, R. The singularity is near: when humans transcend biology. Penguin Books: New York, 2006.
2. Oxford Dictionaries. http://www.oxforddictionaries.com/us/definition/american_english/immortality (accessed March 13, 2014).
3. De Grey, A. D. ; Rae, M. Ending aging: the rejuvenation breakthroughs that could reverse human aging in our lifetime. St. Martin’s Griffin: New York, 2008.
4. Morgan, J. L.; Lichtman, J. W.  Nature Methods 2013, 10, 494–500.
5. Requarth, T. http://www.nytimes.com/2013/03/19/science/bringing-a-virtual-brain-to-life.html (accessed March 13, 2014).
6. Hall, J. S. Nanofuture: What’s Next for Nanotechnology. Prometheus Books: New York, 2005.

Hands-free driving: A Roadmap to the Future

Catalyst Volume 7

The simple act of driving can be an unproductive, dangerous, and time consuming activity, one that can be solved through the installation of autonomous technology within vehicles. This technology is considered to be among the most crucial breakthroughs in human travel that is being developed today; it is believed to have the capacity to create an improved and efficient driving experience by limiting fuel consumption, decreasing traffic congestion, and reducing wasted time during road trips.

One of the driving forces behind the creation of autonomous vehicles is safety. Autonomous technology promises safer travel compared to human-operated vehicles, as the cars are equipped with laser and video detection systems to control the car's speed and steering mechanisms while avoiding obstacles in the roadway. This blend of autonomous technologies promises to make driving 99% safer while also allowing the travelers to focus on other activities.¹

These cars must detect and make rapid decisions to avoid objects in the roadway; the simple act of crossing an intersection requires the robotic cars to account for the inertias, right-of-way, and velocity of approaching vehicles.² A major problem facing autonomous vehicles is the idea of real-time communication. As humans correspond face-to-face, these autonomous cars need to interact in real-time, allowing the cars to work together safely. However, this type of communication is unpredictable and extremely hard to maintain.³ Autonomous technology presents near endless benefits to automobile commuters; however, this technology faces not only current mechanical and software problems but also major legal and social issues. This technology needs to be perfected in every way possible before being released into city streets. Through my review of the autonomous technology within these computer-driven cars, I will explore the type of technology that operates these cars, how it operates the vehicle, the benefits created from this technology, and any possible legal and social concerns that arise from their use.

Developing Technologies: Seeing, Thinking, Steering

The ability for autonomous cars to see and judge risks in the roadway is vital to safe operation of the vehicle. An outstanding prototype of autonomous technology was created in 2007 by the Stanford Racing Team. Their robotic car Stanley, which won the DARPA Grand Challenge, operated solely on a software system that processed and converted visual data into appropriate driving commands.⁴ This software system uses an onboard sensors including lasers, cameras, and radar instruments to gather outside information from the road, allowing the robotic vehicle to observe and judge the approaching roadway;⁴ these sensors are placed on top of the vehicle. The combination of lasers and cameras allows for increased detection of obstacles by allowing both short and long range detection, respectively.⁴ As the cameras receive the long range images, the lasers allow the vehicle to detect the dimension of approaching objects that could harm the vehicle. Detection of hazardous obstacles is one of the easier aspects of autonomous driving; split second decision-making based on the detection system is harder to accomplish. An autonomous vehicle must use the information from the detection systems to determine if the road surface is safe for driving. Measuring the dimensions of detected objects allows the car to determine if they are true obstacles, such as roadway debris, or non-obstacles, such as grass and gravel. The researchers who helped build Stanley stated that the robot had trouble determining the difference between tall grass and rocks, which poses obvious difficulties in application.⁴ In addition to obstacle recognition software, autonomous vehicles require extensive algorithms to accomplish and maintain velocity, steering, acceleration, and braking—functions all controlled by the same system of detection and decision making.

Dynamically Guided Routes

Route guidance is core to autonomous vehicle technology, which is not safe and effective without a computed path. The purpose of route guidance is to gather information from outside sources (e.g. other vehicles, fleet signals) and stored data to create the most efficient route. However, this technology is hindered by the limited amount of information that can be stored within the vehicle due to static map conditions.⁵ Static conditions are defined as the basic components of individual roadways, such as the length of the road, speed limit, and pre-existing intersection signals. Using static systems can result in unreliable and slower routes due to an inability to account for dynamic road situations; for example, these static routes can be highly ineffective once an accident occurs on the roadways.

Generating accurate routes while on the road is another computationally challenging problem for autonomous technology.⁵ Due to the mobile condition of autonomous vehicles, current onboard computational power cannot compute and translate both long algorithms and dynamic conditions at the same time. Researchers attempting to create an algorithm must balance quick execution and efficient route creation with low computational power.

An additional problem arises from dynamic roadways. Dynamic roads are defined as streets that are always changing due to traffic jams, accidents, and construction.⁵ In his article on route guidance, Yanyan Chen stated that a good route is one that, although possibly not the fastest, is both reliable and acceptable to the driver’s needs. As a solution, Chen and his team created the Risk-Averse A' Algorithm (Figure 1). This algorithm suggests a risk-averse strategy that pre-computes factors that affect traffic (such as weather and time of day), accounts for dynamic traffic flow and accidents, and computes a low-risk and reliable route. The Risk-Averse A' Algorithm is widely accepted in the field of autonomous research as the most efficient form of computing reliable and adaptive directions. In fact, Stanley used this algorithm in the DARPA Challenge.⁴

Navigating Intersections Proves Difficult

The task of navigating an autonomous car through an intersection is not simple. The vehicles must be able to use algorithms to derive not only the distance from the car to the intersection but also its current inertia. Simultaneously, this information must be constantly compared with that of other vehicles. The two main challenges in crossing an intersection are establishing reliable communication with other vehicles as well as the dynamic, convoluted environment of intersections. For autonomous navigation to be possible, vehicles must communicate with each other to determine which car has right of way. When approaching an intersection, each car should propagate signals to the other vehicles, a failsafe in case oncoming cars are not detected by the visual and laser system (Figure 2). In theory, autonomous vehicles will discharge signals containing position and velocity information. At an intersection, approaching cars can detect and process this information to determine the appropriate mechanical move.

The dynamic environment of an intersection creates a whole new series of problems with the introduction of unknown variables. An autonomous system must be able to adapt, sense, and make decisions in short periods of time. The proposed ideas on how to navigate intersections use a decentralized navigation function, a method that has no need for long-range communication between vehicles. It enables cars to navigate independently while maintaining network connectivity and an overall goal. This function allows the car to account for dynamic traffic and improves the use of algorithms.²

Robotic Communication

The problem of real-time coordination between vehicles is a major obstacle that must be overcome for this technology to function safely on city streets and highways. Without reliable and fast communication, autonomous vehicles cannot navigate intersections, conserve energy, drive in safe formations, or create efficient routes. However, communication through wireless networks is not always reliable. Dr. Mélanie Bouroche from Trinity College, Dublin, stated that a “vehicle intending to cross an un-signaled junction needs to communicate in an area wide-enough to ensure that other vehicles … will receive its messages.”³ Figure 3 illustrates how the cars should disperse signals to communicate with other vehicles.

In the article “Real-Time Coordination of Autonomous Vehicles,” Bouroche, Hughes, and Cahill found a solution to this communication issue by creating a space-elastic communication model. A coordination model for autonomous cars allowed autonomous vehicles to adapt their behavior depending on the state of communication, ensuring safety constraints were never violated.³

Conclusion

Autonomous technology should improve daily travel by decreasing fuel consumption, traffic congestion, and accidents. The construction of new highways and streets to accommodate this technology would modernize and improve the efficiency of cities. Daily life could be enhanced, as driving time could be spent more productively. Autonomous technology can greatly improve everyday vehicular travel—but only if it is correctly implemented into society. Many problems still remain in the realization of autonomous vehicles: detection systems must be improved to effectively identify and avoid obstacles, algorithms need to be refined to quickly compute dynamic routes, and communication between vehicles needs to be drastically improved in order to avoid accidents. The legal and societal issues must also be addressed: will all vehicular travel be converted to automated travel? If so, will all citizens be forced to use technology that controls their movement? If not, will separate highways and roads be built? Who will fund this new creation of streets and roads? Who will ultimately control and maintain such a system? Autonomous technology has the potential to vastly improve travel, but it can introduce system vulnerabilities and malfunction. Self-directed vehicles must be thoroughly researched and tested before the technology can be implemented on city, state, and national streets.  

References

1. Hayes, B. Am. Sci. 2011, 99, 362-366.
2. Fankhauser, B. et al. CIS 2011: IEEE 5th International Conference, Qingdao, China, Sept 17-19, 2011; pp.392-397.
3. Bouroche, M. et al. IEEE Conference on Intelligent Transportation Systems, 2006, 1232-1239.
4. Thrun, S. et al. J. Field Robot. 2006, 23, 661-692.
5. Chen, Y. et al. J. Intell. Transport. S. 2010, 14, 188–196.
6. Bergenhem, C. et al. Sartre, 2008, 1-12.
7. Dahlkamp, H. et al. In Proceedings of the Robotics Science and Systems Conference, 2006, 1-7.
8. Elliott, C. et al. In The Royal Academy of Engineering, 2009, 1-19.
9. Douglas, G. W. Unmanned Systems, 1995, 13, 3.
10. Laugier, C. et al. In Proceedings of the IFAC Symposium on Intelligent Autonomous Vehicles. 2001, 10-18.
11. Wright, A. Comm. ACM 2011, 54, 16-18.