Increasing the speed and diversity of invention using artificial intelligence

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Humankind have been innovating for thousands of years – and we’ve come a long way on the back of these inventions. From primitive tools, to the wheel, to the printing press and the internet, there is no doubt that the act of invention has worked. And based on the rate of patent applications, it is accelerating.

What is meant by invention? At its most basic, it is a technical innovation that can be protected by patents.  The patent is important to verify the quality of the invention.  At a more complex level, invention is something that is novel, surprising and valuable. An example of an invention would be a phone levitator that addresses the (admittedly first world) problem of picking up smartphones from their wireless charging units: a proximity sensor detects a hand, power is applied from the battery to the wireless charging coil creating an electromagnetic field which repels against the electromagnetic field generated by the charging matt and levitates the phone into the hand of the user.  On the element of surprise, Julian notes that it is wise for inventors to not be too surprising. Other stakeholders can find it difficult to perceive the value of something that is too unfamiliar.

So why is it when you Google “Inventors” and “Musicians” that you get such different results? Both professions have been in existence for centuries; they both use the same fundamental soft skill. Creativity is as much a quality needed by an inventor as it is for a musician, or an author, or artist.  But for the former the images are of historic figures in black and white, the latter are in technicolour and far more modern.  Does Google’s response to the search term suggest that society regards “inventors” as a thing of the past?

Potentially society has it right. It is surprising but true that, despite some of the world’s most creative figures working in technological innovation, the process of invention hasn’t changed in hundreds of years. Yet if the world is based on the results of inventiveness, the process needs to be the best it can be.

The state of human invention is inconsistent with the world in which we live today. Society is accelerating. There has been a vast increase in the amount of information available to people and the amount of data collected. Convergence between industries is increasingly common. Sectors that only a decade ago would have been incompatible are now finding potential collaboration points. An example of this is the ability for autonomous vehicles to interact with a user’s fitness tracker, assess how close to their goals they are for the day, and drop the user off that distance from their destination to encourage exercise.

As the world grew more complex, humans had to become more specialised in order to handle this complexity efficiently. But such a move means that humans are now less able to invent at points of convergence. We need support.  The latest advancements in artificial intelligence and machine learning are extremely capable of processing the vast increase in data available and identifying new business models, trends and technologies that can be taken from one sector and applied to another. Furthermore, they are able to do this at speed. One of the ingredients of “invention” is novelty, and there are no prizes for second to the market.

The creation of the CT Scanner epitomises this sad but realistic fact. Two inventors on two different continents created very similar products within weeks of each other. While McCormack shared the Nobel Prize in Physiology or Medicine with his competitor, Hounsfield, it was the latter who received the patent and its ensuing revenues. He had finished his invention just slightly before McCormack. Speed is very much of the essence in invention.

The story above supports the idea that inventions are not necessarily a product of genius. Rather they are the result of particular environmental factors meeting an individual with the relevant education, skills and creativity, at the right time. If this is the case, then anyone can be an inventor if they are fed information that is relevant to them.

This reiterates the need for artificial intelligence and machine learning technologies to support innovation.  Iprova is doing three things with their software. The first is address humankind’s inability to process a huge range of information at speed. To achieve this breadth, their “Disruption Platform” tracks the internet, scanning for new advancements and assessing whether such-and-such an advancement could be applied to such-and-such a sector. The second is to work at speed. Based on the information delivered by the scans, the “Invention Platform” can suggest concepts within 24 hours. The third is agility. Based on the concept output, the time taken for the team to deliver an invention to a customer can take as little as two weeks. Incremental innovation in the same market with the same technology is not the focus of Iprova’s solution. Rather it can successfully address architectural, radical or disruptive innovations.

In conclusion, the burden of knowledge and inevitability of invention - which gives rise to the invention diversity and timing problems - are a threat to owning IP for new “convergent” products and services.  Emerging tools based on artificial intelligence and machine learning can address this threat, but they will redefine the role of engineers. The idea of inventors as individual “knowledge tanks” will become less prominent. The most sought-after individuals will have a broad, multidisciplinary knowledge base and a strong fundamental knowledge of science and technology.  A shift is imminent that will move the invention advantage away from human talent towards access to the best information.

Interested? Check out CW Membership packages for unlimited access to CW SIG events.

Many thanks to Julian from Iprova for sharing his thoughts with the CW community, and to the Artificial Intelligence SIG Champions for delivering another excellent Byte-Size event:

• Laurent Brisedoux; Senior Manager, Amazon

• Phil Claridge; Founder, Mandrel Systems

• Richard Dearden; Senior Director and Advanced Analytics Project Leader, AstraZeneca

• Gunter Haberkorn; Senior Manager Product & Technology, Magna International

• Dr Vaiva Kalnikaite; CEO, Dovetailed

• Dr Vicky Schneider; Senior Scientific Programme Manager, Amazon

• Peter Whale; Founder, Vision Formers