The future depends on what you do today.
― Mahatma Gandhi
The future is already here – it’s just not evenly distributed.
― William Gibson
When did the future switch from being a promise to being a threat?
― Chuck Palahniuk
It has been a long time since I wrote a list post, and it seemed a good time to do one. They’re always really popular online, and it’s a good way to survey something. Looking forward into the future is always a nice thing when you need to be cheered up. There are lots of important things to do, and lots of massive opportunities. Maybe if we can muster our courage and vision we can solve some important problems and make a better world. I will cover science in general, and hedge the conversation toward computational science, cause that’s what I do and know the most about.
Mediocrity will never do. You are capable of something better.
― Gordon B. Hinckley
Here is the list:
- Fixing the research environment and encouraging risk taking, innovation and tolerance for failure
- Additive manufactoring
- Exascale computing
- Nontraditional computing paradigms
- Big data
- Reproducibility of results
- Algorithmic breakthroughs
- The upcoming robotic revolution (driverless cars)
- Cyber-security and cyber-privacy
- Fixing the research environment and encouraging risk taking, innovation and tolerance for failure. I put this first because it impacts everything else so deeply. There are many wonderful things that the future holds for all of us, but the overall research environment is holding us back from the future we could be having. The environment for conducting good, innovative game changing research is terrible, and needs serious attention. We live in a time where all risk is shunned and any failure is punished. As a result innovation is crippled before it has a chance to breathe. The truth is that it is a symptom of a host larger societal issues revolving around our collective governance and capacity for change and progress.Somehow we have gotten the idea that research can be managed like a construction project, and such management is a mark of quality. Science absolutely needs great management, but the current brand of scheduled breakthroughs, milestones and micromanagement is choking the science away. We have lost the capacity to recognize that current management is only good for leeching money out of the economy for personal enrichment, and terrible for the organizations being managed whether it’s a business, laboratory or university. These current fads are oozing their way into every crevice of research including higher education where so much research happens. The result is a headlong march toward mediocrity and the destruction of the most fertile sources of innovation in the society. We are living off the basic research results of 30-50 years past, and creating an environment that will assure a less prosperous future. This plague is the biggest problem to solve but is truly reflective of a broader cultural milieu and may simply need to run its disastrous course.Over the weekend I read about the difference between first- and second-level thinking. First-level thinking looks for the obvious and superficial as a way of examining problems, issues and potential solutions. It is dealing with things in an obvious and completely intellectually unengaged manner. Let’s just say that science today is governed by first-level thinking, and it’s a very bad thing. This is contrasted with second-level thinking, which teases problems apart, analyzes them, and looks beyond the obvious and superficial. It is the source of innovation, serendipity and inspiration. Second-level thinking is the realm of expertise and depth of thought, and we all should know that in today’s World the expert is shunned and reviled as being dangerous. We will all suffer the ill-effects of devaluing expert judgment and thought as applied to our very real problems.
- CRISPR What can I really say here, this technology is huge, enormous and an absolute game changer. When I learned about it the first time it literally stopped me in my tracks and I said “holy shit this could change everything!” If you don’t know CRISPR is the first easy to use and flexibly programmable method for manipulating the genetic code of living beings as well as short-circuiting the rules of natural selection. Just like nuclear energy CRISPR could be a massive force for good or evil. It has the potential to change the rules of how we deal with a host of diseases and plagues upon mankind. It also has the capacity to produce weapons of mass destruction and unleash carnage upon the world. We must use far more wisdom than we typically show in wielding its power. How we do this will shape the coming decades in ways we can scarcely imagine. It also emerges in the current era where great ideas are allowed to whither and die. It seems reasonable to say that we don’t know how to wield the very discoveries we make, and CRISPR seems like the epitome of this.
- Additive manufacturing In engineering circles this is a massive opportunity for innovation and a challenge to a host of existing practices and knowledge. It will both impact and draw upon other issues from this list in how it plays out. It is often associated with the term 3-D printing, where we can produce full three dimensional objects in a process free of classic manufacturing processes like molds and production lines. The promise is to break free of the tyranny of traditional manufacturing approaches, limitations and design for small lots of designer, custom parts. Making the entire process work well enough for customers to rely upon it and have faith in the manufacturing quality and process is a huge aspect of the challenges. This is especially true for high performance parts where the requirements on the quality are very high. The other end of the problem is the opportunity to break free of traditional issues in design and open up the possibility of truly innovative approaches to optimality. Additional problems are associated with the quality and character of the material used in the design since its use in the creation of the part is substantially different than traditional manufactured part’s materials. Many of these challenges will be partially attacked using modeling & simulation drawing upon cutting edge computing platforms.
- Exascale computing The push for more powerful computers for conducting societally important work is as misguided as it is a big deal. I’ve written so much recently about this I find little need to say more. It is an epitome of the first item on my list, as a wrongly managed, risk intolerant solution to a real issue, which will end up doing more harm than good in the long run. It is truly the victory of first-level thinking over the deeper and more powerful second-level thinking we need. Perhaps I’m being a bit haughty in my contention that what I’ve laid out in my blog constitutes second-level thinking about high performance computing, but I stand by it, and the summary that the first-level thinking governing our computing efforts today constitutes hopelessly superficial first-level thought. Really solving problems and winning at scientific computing requires a sea change toward applying the fruits of in-depth thinking about how to succeed at using computing as a means for societal good including the conduct of science.
- Nontraditional computing paradigms We stand at the brink of a deep change in computing by one way or another. We will either see the end of the Moore’s law (actually its done, at all scales already), which has powered computing into a central role societally whether it is business or science. The only way the power of computing will continue to grow is through a systematic change in the principles by which computers are built. There are two potential routes being explored both being rather questionable in their capability to deliver the sort of power necessary to succeed. The most commonly discussed route is quantum computing, which promises incredible (almost limitless) power for a very limited set of applications. It also features rather difficult to impossible to manage hardware among problems limiting its transition to reality. The second approach is neuro-morphic, or brain-inspired computing, which may be more tangible and possible than quantum, but a longer shot at being a truly game changing technology. The jury is out on both technology paths, and we may just have to live with the end of Moore’s law for a long time.
- Big data The Internet brought computing to the masses, and mobile computing brought computing to everyone in every aspect of our lives. Along with this ubiquity of computing came a wealth of data on virtually every aspect of everyone’s lives. This data is enormous and wildly varied in its structure teaming with possibility for uses of all stripes, good and bad. Big data is the route toward wealth beyond measure, and the embodiment of the Orwellian Big Brother we should all fear. Taming big data is the combination of computing, algorithms, statistics and business all rolled into one. It is one of the places where scientific computing is actually alive with holistic energy driving innovation all the way from models of data (reality), algorithms for taming the data and hardware to handle to load. New sensors and measurement devices are only adding to the wealth as the Internet of things moves forward. In science, medicine and engineering new instruments and sensors are flooding the World with huge data sets that must be navigated, understood and utilized. The potential for discovery and progress is immense as is the challenge of grappling with the magnitude of the problem.
- Reproducibility of results The trust of science and expertise is seemingly at an all-time low. Part of this is the cause of the information (and misinformation) deluge we live in. It is feeding on and fed by the lack of trust in expertise within society. As such there has been some substantial focus on being able to reproduce the results of research. Some fields of study are having veritable crises driven by the failures of studies to be reproducible. In other cases the stakes are high enough that the public is genuinely worried about the issue. Such a common situation is a drug trial, which have massive stakes for anyone who might be treated with or need to be treated by a drug. Other areas science such as computation have fallen under the same suspicion, but may have the capacity to provide greater substance and faith in the reproducibility of their work. Nonetheless, this is literally a devil is in the details area and getting all the details right that contributes to research finding is really hard. The less oft spoken subtext to this discussion is the general societal lack of faith in science that is driving this issue. A more troubling thought regarding how replicable research actually comes from considering how uncommon replication actually is. It is uncommon to see actual replication, and difficult to fund or prioritize such work. Seeing how commonly such replication fails under these circumstances only heightens the sense of the magnitude of this problem.
- Algorithmic breakthroughs One way of accelerating the progress in computers and the work they do is to focus on innovations in algorithms. Instead of relying on computational hardware to increase our throughput we rely on innovation in how we use those computers or implement our methods on those computers. Over time improvements in methods and algorithms have outpaced improvements in hardware. Recently this bit of wisdom has been lost to the sort of first-level thinking so common today. In big data we see needs for algorithm development overcoming the small-minded focus people rely upon. In scientific computing the benefits and potential is there for breakthroughs, but the vision and will to put effort into this is lacking. So I’m going to hedge toward the optimistic and hope that we see through the errors in our thinking and put faith in algorithms to unleash their power on our problems in the very near future!
- The upcoming robotic revolution (driverless cars) The fact is that robots are among us already, but their scope and presence is going to grow. Part of the key issue with the robots is the lack of brainpower to really replace the human decision-making in tasks. Computing power, and the ubiquity of the Internet in all its coupled glory is making problems like this tractable. It would seem that driverless-robot cars are solving this problem in one huge area of human activity. Multiple huge entities are working this problem and by all accounts making enormous progress. The standard for the robot cars would seem to be very much higher than humans, and the system is biased against this sort of risk. Nonetheless, it would seem we are very close to seeing driverless cars on a road near you in the not too very distant future. If we can see the use of robot cars on our roads with all the attendant complexity, risks and issues associated with driving it is only a matter of time before robots begin to take their place in many other activities.
- Cyber-security and cyber-privacy The advent of computing at such an enormous societal scale particularly with mobile computing penetrating every aspect of our lives is the twin security-privacy dilemma. On the one hand, we are potentially victimized by cyber-criminals as more and more commerce and finance takes place online driving a demand for security. The government-police-military-intelligence apparatus also sees the potential for incredible security issues and possible avenues through the virtual records being created. At the same time the ability to have privacy or be anonymous is shrinking away. People have the desire to not have every detail of their lives exposed to the authorities (employers, neighbors, parents, children, spouses,…) meaning that cyber-privacy will become a big issue too. This will lead to immense technical-legal-social problems and conflict over how to balance the needs-demands-desires for security and privacy. How we deal with these issues will shape our society in huge ways over the coming years.
The fantastic advances in the field of electronic communication constitute a greater danger to the privacy of the individual.