On March 12, I attended the inaugural Global Grand Challenges Summit at the IET, formed by the Royal Academy of Engineering in partnership with the National Academies of the US and China. The event served to establish a platform for leading engineers, scientists and others to discuss today’s great challenges.
While similar events have often focused on more traditional forms of engineering such as building bridges, water defence and engine design, this event was an exception which for me marked a paradigm shift – there was remarkable emphasis on computer science and biology. What was once traditional in engineering is now completely different.
On the first day, Dr Craig Venter gave a plenary address, founder of the J. Craig Venter Institute and the scientist who led the first sequencing of the human genome. His talk described a vision of the future where synthetic viruses created to combat pandemics can be coded and distributed to the world online. Digital security already plays a big part of our lives, but with the advent of 3D printers and the dawn of the era of synthetic biology, cryptography will play a key part in protecting lives across the world, whether it be in the delivery mechanism (the network) or the device (physical or biological).
The summit’s panels, chaired by Jim Al-Khalili, discussed the importance of raising awareness and educating not only the general public, but also the next generation of kids on the engineering which surrounds us, and online learning platforms are starting to emerge to address this. Craig Venter explained that ‘disruptive change is needed, and synthetic life will be part of that change’, further joking that living in California, ‘we already have many synthetic humans walking around’. Hinting at his lab coming close to publishing more work on producing synthetic life, his serious and mildly humoured account of work in the field emphasised the impact it will have to our daily lives.
For me, it would appear the engineering community appears to have undergone two interesting transitions in the years I’ve been attending events. The first is that there is now more of an extreme appreciation for and emphasis on the scientific, mathematical and computing advances that have paved the way for improving the quality of life of both developed and developing countries. The second, however, is the tendency to emphasise educating the next, younger generation to join the field and solve our problems. I can’t help but admit despite being 21, I felt as if I were too old to make a difference, with the way such urgency on the next generation was portrayed. Though I understand the sentiments, I believe we need to lead by example, by not only educating the next generation, but demonstrating great work and science ourselves to show where that education leads. I felt an air of defeatism in the way some of these points were made, but I suspect such provocation may have been deliberate to get us motivated to keep doing what we do.
Communicating science well plays a big part in not just inspiring the next generation of researchers, but also in public opinion of our work. I learnt that over 50% of the world’s genetically modified food resided in developing countries – they rely on them in order to solve local sustainability problems for their crops. If we can effectively describe the science going on, people can get the full story and make fully informed conclusions regarding such controversial topics.
We had a surprise guest appearance by will.i.am, and Bill Gates appeared via video link to encourage the event ‘getting the best minds together to think about how to lift our living conditions’. I live tweeted throughout the event under the hashtag #GGCS2013 and found the summit extremely rewarding, motivating and an inspiration to continue to put in my best efforts in directing my work towards solving grand challenges and improving our quality of life.
I was particularly keen on the analogy of the cell and genetic data to ‘software that writes its own hardware’, vaguely linking to Turing’s Universal Machine. I’ve had Turing machines on my mind quite a lot these days, thinking about the shortcomings of the Von Neumann architecture. I find it interesting that many computer scientists, myself included, have directed their efforts to problems in biology or medicine. Turing’s paper on The Chemical Basis of Morphogenesis is possibly the first example of this – he pioneered the fundamentals of what it means to compute algorithms, and started applying this to mathematically explain how different biological shapes form. In the process of doing so, his reaction-diffusion ideas can also be seen as an early discussion of Chaos Theory. The fundamental which links all these fields together is clearly the maths.
I’m still in the process of getting my research on muscle interactions published, it’s come a very long way since my dissertation, which was Highly Commended in the Undergraduate Awards 2012. I’ve been working with my supervisors from the National Heart & Lung Institute to get our paper into a second draft and will be meeting with my Computing supervisor at Imperial sometime next month to discuss submission to the journal we’re going for.
Besides that, I’ve been keeping myself busy and became a Fellow of the Royal Institution of Great Britain. I’ve gone to a fair amount of their events now, and they’ve been consistently inspiring and intellectually stimulating. I also signed up to a few edX online courses, notably Quantum Mechanics and Quantum Computation from Berkley, and Introduction To Biology from MIT. I’ve been averaging 100% on my weekly assignments for QM (with a lot of hard work) and I’m looking forward to completing the course in a few weeks. My progress in the biology work hasn’t been as good, however, but I’m still on course to pass above 60% there.
I think it’s important to keep up with fields outside one’s area of expertise in order to retain the ability to craft cross-disciplinary solutions to big problems. There are so many avenues to explore and not enough scientists to research them, and one can think outside the box much clearer when initially starting outside its boundaries. We’re still scratching the surface with what we can do with computer science, and perhaps such models as Turing’s can be better realised in full with biological hardware, or perhaps it’ll be something else. Why should software and hardware be different at all?
One of the themes of the Grand Challenges Summit provides the thought-provoking sentiment that despite the incredible advancements undertaken to get where we are today, the computer revolution hasn’t begun yet, all we do know is that science will be 100% of the solution.