Invention of the Day: Hypodermic Syringe

I'm reading a wonderful book by Roger Bridman - 1,000 Inventions and Discoveries. It documents an incredible range of human ingenuity from thousands years ago to our days. For example, here's an invention that we take for granted today: hypodermic syringe.

Remarkably, it was invented by two people in different countries. As the book says, "[in 1853] In Scotland, physician Alexander Wood invented the hollow needle and adapted Pravaz’s device to go with it, forming the first hypodermic syringe." That is, the invention cannot be attributed to each of them separately because a new system — the syringe — provides functionality beyond the sum of its parts. A well-defined interface between the parts, the cylinder and the needle respectively, enabled rapid innovation in manufacturing technologies and use. For example, here's how hollow needles are produced today.

From an innovation timing perspective, we need to be aware that the business success of the new injection technology was determined by a major invention that came about much later.

By the late 1800s hypodermic syringes were widely available, though there were few injectable drugs (less than 2% of drugs in 1905). Insulin was discovered in 1921. This drug had to be injected into the bloodstream, so it created a new market for manufacturers of hypodermic needles and drugs.

Overall, the invention of the hypodermic syringe illustrates a number of important principles for pragmatic creativity:
- a new combination of parts has to produce a new system effect;
- no new science is necessary for making a technology breakthrough;
- a well-defined interface between parts enables rapid innovation on both sides, e.g. the cylinder and the needle;
- the success of the invention comes from a new use, which may require a new science, e.g. liquid penicillin;
- the combination of new parts (cylinder + needle) and use (liquid drug) form Dominant Design and Use patterns that remain stable for decades, if not centuries.

Psychology of Creativity: Art, Science and Technology

Until the middle of the 20th century, creativity was generally considered as a psychological attribute of an artist and, sometimes, of a scientist. Then in the late 1950s, J.P. Gulliford applied the idea of creative thinking to technological inventions.

Nowadays, engineers and scientists are expected to be creative. A recent paper shows how creativity in science/tech and art relate to personality traits, the so-called Big Five:

The Big Five personality dimension Openness/Intellect is the trait most closely associated with creativity and creative achievement.

We confirmed the hypothesis that whereas Openness predicts creative achievement in the arts, Intellect predicts creative achievement in the sciences. Inclusion of performance measures of general cognitive ability and divergent thinking indicated that the relation of Intellect to scientific creativity may be due at least in part to these abilities. Lastly, we found that Extraversion additionally predicted creative achievement in the arts, independently of Openness. Results are discussed in the context of dual-process theory.

A related paper outlined the overall relationship between the Big Five, by grouping them into two complementary categories - Stability and Plasticity.

Remarkably, the brain uses a broad range of cognitive strategies to pursue goals within a social context. Given a chance, we can exercise our creative options through technological innovation.

As a side remark, from an innovation theory perspective, the brain and society solve the stability-plasticity dilemma by using both traits, e.g. through the separation in space and time.

Apple needs a new product

Steve Jobs used iPod's success to launch the iPhone. If Apple doesn't create a breakthrough product within the next two years, the company could be in trouble.

So far, the iWatch doesn't even register on the trendline.

Lunch Talk: Artificial Intelligence 65 years ago

Claude Shannon demonstrate an electro-mechanical mouse that navigates a labyrinth, computes and remembers the optimal path. (Bell Labs, 1950s)

tags: innovation, science, technology, lunchtalk, BUS239

Stanford CSP BUS 152, Innovation Timing, Session 2 Quiz 1

Background

In a 2017 Hype Cycle-related article, Gartner, an American Research and Advisory firm, put Deep Learning and Machine Learning at the top of the Hype Cycle.

In a follow-up article about Artificial Intelligence, Gartner mentioned 5 AI myths, including #1 "Buy an AI to solve your problems." The article says, "Enterprises don’t need an “AI.” They need business results in which AI technologies may play a role."

An independent investigation by Stats.com seems to confirm the conclusion. The publication considered the track record of IBM's Watson for Cancer, an AI system deployed in many hospitals around the world, and found that "the supercomputer isn’t living up to the lofty expectations IBM created for it." Furthermore,

Perhaps the most stunning overreach is in the company’s claim that Watson for Oncology, through artificial intelligence, can sift through reams of data to generate new insights and identify, as an IBM sales rep put it, “even new approaches” to cancer care. STAT found that the system doesn’t create new knowledge and is artificially intelligent only in the most rudimentary sense of the term.

IBM denies the assertions and says that the technology is on track "to offer guidance about treatment for 12 cancers that account for 80 percent of the world’s cases" by the end of the year.

Questions

1) Do you agree with the Gartner's assessment that AI in general, and Deep Learning and Machine Learning in particular are overhyped? Explain your opinion and provide supporting evidence.

2) In your opinion, which technology and business areas will benefit the most from rapid adoption of various forms of AI? Which forms of AI will play the most significant role? Explain briefly.

Lunch Talk: Quantum Computing

a discussion of why now is the right time to be thinking about this new technology and some of the recent developments that have been made, laying the groundwork for the future of this computing model.