Biological systems in nature have evolved for millions of years to adapt and survive the environment. Many features they developed can be inspirational and beneficial for solving technical problems in modern industries. This leads to a specific form of design-by-analogy called bio-inspired design (BID). Although BID as a design method has been proven beneficial, the gap between biology and engineering continuously hinders designers from effectively applying the method. Therefore, we explore the recent advance of artificial intelligence (AI) for a data-driven approach to bridge the gap. This paper proposes a generative design approach based on the generative pre-trained language model (PLM) to automatically retrieve and map biological analogy and generate BID in the form of natural language. The latest generative pre-trained transformer, namely GPT-3, is used as the base PLM. Three types of design concept generators are identified and fine-tuned from the PLM according to the looseness of the problem space representation. Machine evaluators are also fine-tuned to assess the mapping relevancy between the domains within the generated BID concepts. The approach is evaluated and then employed in a real-world project of designing light-weighted flying cars during its conceptual design phase The results show our approach can generate BID concepts with good performance.

Biomimicry, the science-slash-art of copying natural structures, is not a new idea. For decades, we have been trying to copy biological brains to make efficient computers, only slightly deterred by the fact that we don't know how biological intelligence works exactly. Armed with our best guesses, we developed models of the neuron and spiking neural networks based on the human brain, and we are now trying to develop these in silicon. Silicon imitations generally use simplified versions of the neuron, but they can still offer distinct advantages to edge applications that need fast, energy-efficient processing to make decisions. ABI Research reports that 4.6 billion sensors will ship in 2027, embedded in smart-home devices, robots, and appliances, up from 1.8 billion in 2021.

With the advancement of technology, Artificial Intelligence starts to live its golden age. We wake up everyday to new and exciting inventions that can be used for the benefit of living things. Throughout the history, human beings are influenced by the nature. We use nature to cope with the problems we encountered by mimicking it. A lot of tools and vehicles are inspired by animals and nature.

Biomimicry is a tool which can be used while seeking innovation. The concept is that nature has already solved many design problems through the process of evolution. Living things that are still extant have received bits and bytes of code in the form of genetic material and when this information interfaces with the environment, sustainable life forms emerge. Animals, plants, viruses, and bacteria adapt by engineering themselves over the billions of years that life has existed on Earth. The Biomimicry Institute provides numerous examples.

We are living in the midst of a profound technological restructuring of human society. The machines that once only frolicked in science fiction have begun to infiltrate our lives. Self-driving cars promise to transform our roads, and the first truly sophisticated robots have begun laboring in hospitals and construction sites and even Walmart. But behind the autonomous revolution is a mountain of problems. Well, challenges, if you want to be more optimistic.

In an example of biomimicry, cicada wings could inspire bacteria-resistent materials. Knowledge transfer across domains leads to significant breakthroughs in science and technology. For example, through biomimicry, innovators get inspiration from nature/biology to solve complex engineering problems. An exciting example of biomimicry is the recent creation of artificial materials that imitate the surface of cicada's wings and gecko's skin, which have antibacterial properties due to their physical structure. These type of materials could be used in hospitals for surfaces that get easily contaminated with bacteria and help drastically reduce the number of hospital infections, a leading cause of health complications during hospitalization.

When: Wed, May 10, 7:00pm – 8:30pm, 2017 The talk will provide a basic introduction to artificial intelligence focusing on how biology contributes to the field. In particular, it will dive into the field of artificial neural networks, discussing some of the basics and then moving onto new insights from biology and how it can help us build more intelligent machines. Speaker Tomás Maul is Head of the School of Computer Science at University of Nottingham Malaysia. The talk will provide a basic introduction to artificial intelligence focusing on how biology contributes to the field. In particular, it will dive into the field of artificial neural networks, discussing some of the basics and then moving onto new insights from biology and how it can help us build more intelligent machines.

Like death and taxes, the inevitable question I cannot escape when on the road is, "Will artificial intelligence (AI) spell human extinction?" We humans have a natural fear of the unknown, so the question is, well, natural. The sentiment comes largely from countless movies portraying robots (with self-awareness AI) turning on their human makers and emotionlessly eliminating them. We fear what we do not understand, and AI (or self-learning) algorithms are far from dinner table conversation for most people. So do we have anything to fear and, if so, when will we need to build that cabin in the Canadian Rockies?

Like death and taxes, the inevitable question I cannot escape when on the road is, "Will artificial intelligence (AI) spell human extinction?" We humans have a natural fear of the unknown, so the question is, well, natural. The sentiment comes largely from countless movies portraying robots (with self-awareness AI) turning on their human makers and emotionlessly eliminating them. We fear what we do not understand, and AI (or self-learning) algorithms are far from dinner table conversation for most people. So do we have anything to fear and, if so, when will we need to build that cabin in the Canadian Rockies?