Market-pull innovation is driven by customer needs. Demand for a solution to a problem triggers its development. For example, the digital camera was invented because customers grew impatient waiting for film to be developed, and expressed desire to be able to view their photos instantaneously. The philosophy behind a market-pull innovation strategy is encapsulated in the familiar adage, “necessity is the mother of invention.” Problem-driven biomimicry, comprising the following five iterative steps, can support market-pull innovation:
This blog is based on this paper: “Crowds vs Swarms, a Comparison of Intelligence” by Louis Rosenberg, David Baltaxe, and Niccolo Pescetelli.
Recently, I went for a conference organized by Daniel Palmer and Marc Kirschenbaum of John Carroll University on Blended Intelligence. I thought it appropriate to talk about one of the talks. How do we get intelligence from a crowd of people, surveys, interviews? How does nature get intelligence from its beings? Authors claim nature does not aggregate independent samples but works on a closed real-time loop with continuous feedback. Hence, can we have a human swarm similar to a flock of birds or a school of fish and does it result in better intelligence? That is exactly what the authors put to test with their software UNU. UNU works by having a group of knowledgable individuals about a specific topic to come together virtually and decided on an answer for a given question. Each user has a magnet which he/she can use to pull the puck toward their desired answer.
What of the results? Check this article on how it predicted the Kentucky Derby, or read their paper on its prediction for the 2016 Super Bowl; a human swarm of 20 people outperformed (68% correctly) a crowd of 469 football fans (47% correctly). If this doesn’t impress you, well the swarm outperformed 98% of independent individuals in the study. Now, could this be a reason to pool our intelligence in order to tackle more challenging questions facing us in the future? Could this help in finding solutions to climate change that is affecting us more every day.
Do you want to try it? All you need is to sign up, verify your email, and you’ll be in your way to create you first UNU human swarm, or you can just enter one of their open UNUs. Finally check out their tutorial: https://youtu.be/TkAoRUHs5F0
If you remember, the Biomimicry Fellows helped to organize the very first TEDxUniversityofAkron Salon event with a Biomimicry theme at the Akron Art Museum back in April this year. Continue reading
In Cats’ Paws and Catapults (1998), Steven Vogel compares the mechanics of nature and human technology. He acknowledges the crucial differences between these two “schools of design,” but still draws attention to a list of similar factors shaping and constraining both innovation processes. For instance, he mentions incremental progress as being a common feature:
“If the brain were so simple we could understand it, we would be so simple we couldn’t.” Lyall Watson
Summer time! For me it means working on bio-inspired algorithms, one in particular I’ve been spending some time on is Artificial Neural Networking (ANN). This had me asking my sister (who is working on her PhD in neuroscience) about how synapses, pathways, etc. work. This post will be on how ANN was inspired and some of the materials I found interesting on it. Let’s start with the obsession with neural network and why it matters? Machines do complicated mathematical calculations in a matter of seconds, yet they have difficulty performing some easy tasks such as recognizing faces, understanding and speaking in local languages, passing theTurning test. OK, let’s compare machines to our brain: A single transistor in your home computer is quite fast; only limited by speed of light and the physical distance to propagate a signal. A signal(Ions) in the neuron, on the other hand, propagates on a fraction of the speed (Flake, 1999). This begs the question, which is better? A good comparison can be found here. One main fact is that our brain makes use of a massive parallelism; it’s this massive interaction between axons and dendrites that contribute to how our brain works. Many argue that the comparison to computers is not very useful as they work differently from each other. Can we make a digital reconstruction of human brain? I follow Blue Brain project for this. Hence, as you can guess ANN algorithm is a simple imitation of how our neurons work. It works by feed forward and back propagations to learn patterns. Originally proposed as McCulloch-Putts neuron in the 1940s and 1980s by invention of Hopfield-Tank feedback neuron network. The 1960s had an good optimistic start on neural networks with the work of Frank Rosenblatt’s perceptron (a pattern classification device). However, by 1969 there was a decline in this research and publication of Perceptrons by Marvin Minsky and Seymour Papert caused it to almost die off. Minsky and Papert showed how a single perceptron was insufficient with any learning algorithm by giving it mathematical proofs. It took a while and many independent works till the value of Neural Networking came to light again. One main contribution is the two-volume book titled Parallel Distributed Processing by James L. McClelland and David E. Rumelhart and their collaborators. In this work, they changed the proposed unit step function proposed to a smooth sigmoid function and added a backward error signal propagation using weights of some hidden neurons called back propagation (Flake, 1999). Reading through chapter 20 of Parallel Distributed Processing written by F. Crick and C. Asanuma, I read about physiology and anatomy of the cerebral cortex. It shows different neural profiles.
It talks about different layers in the cortex such as the superficial, upper, middle, and deep layer, axons, synapses, neurotransmitters. The more I read, the more I come to appreciate the complexity of our brain and wonder about the simplicity of Artificial Neural Network algorithms, and can’t help but feel amazed by what Blue Brain Project is aiming to do.
Like a house-cat exploring its environment, lets dive into narrow unexplored places…
Flake, G. W. The computational beauty of nature, 1999
McClelland, J. L. Rumelhart, D. E. Parallel distributed processing, Volume 2. Psychological and biological models, 1989
Hi GermiNature readers,
I was hoping that by now I would be able to share the videos from our TEDxUniversityofAkron Salon event (April 5th, 2016) with you. However, the videos are not ready yet. So I’m going to tell you a little bit about my research crowdfunding experience that happened about the same time. Continue reading
As the last couple of my blog posts have given good examples of research projects and works that fuse art, design, and science, I thought it would be worth dedicating a short post to explain the similarities between the worlds, reveal what research could look like when merging them, and give a bio-inspired example.
Biomimicry is a tool/discipline that can be used in many fields ranging from industrial design, architecture, engineering, math, and even computer science. Being from a graphic design background and practicing digital painting, I find myself struggling to find exactly where biomimicry fits within the digital aesthetics realm. Can a designer/artist practice digital arts in a biomimetic way, or are the digital arts just a good tool to perform and carry out biomimetic thinking within a digital space? Surely when you are 3D modeling a biomimetic building or product on your computer, you are aiding in the biomimetic design process, but the 3D modeling process itself isn’t the thing that is biomimetic, is it? Biomimicry, in root words terms, is the act of mimicking life. How literally should we take this? Is virtual reality a sort of biomimicry because it does just that; mimics life? Maybe it’s just a useful tool to aid in the design process. These are some of the things I hope to figure out in my studies, but I’m finding as I dig deeper that when approaching biomimicry with a digital aesthetics lens, that it’s not just about the design process and appearance, but also about how using digital tools can help learn or experience something in the natural world. It is possible that, like art, digital aesthetics is particularly useful to inspire, evoke emotions, and increase understanding using the natural world as a muse. Continue reading
Addressing CRISPR and other genomic design techniques in class has made me wonder about the future of bioscience integration. Continue reading