Would you eat a lab-grown steak?

18

October

2018

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How AI is making the food industry a newly vulnerable market

JUST is a biotech company turning the agricultural industry upside down, as it is proposing meat and dairy alternatives as tasty as the real thing, with the help of machine learning. JUST uses the database of the molecular properties of thousands of plants and computes how the chemical interactions between these molecules would imitate the texture, the taste and color of animal-derived food. It has for instance, discovered that the protein of mung beans has similar properties to scrambled eggs. JUST already sells mayonnaise, egg replacement and cookie dough entirely plant based. The replication is so perfect that corporations/incumbents in the egg industry have tried to take down the company, feeling directly threaten.

Replicating solid products like steak is harder, so JUST is taking one step further, shocking and shaking the food industry: growing meat in the lab by duplicating protein cells feeding them with vegetable-based nutrients identified by AI. The idea of plucking a feather from a chicken to collect a cell and duplicate it until it transforms into a fully lab-grown chicken breast and then transform it into nuggets is going to drive away more than one consumer. This revulsion for artificial imitation of lifeform even has a name: the uncanny valley effect which also applies in robotics, when we feel that something is quite off when seeing humanoids.

Yet, JUST is releasing lab-grown meat in US supermarkets by the end of 2018, has the support of PETA and the US government. How come? Because the environmental costs and animal cruelty are real issues that current industrial farming have a hard time finding answers to. Animal farming is alone responsible of 60% of food-related greenhouse emissions and a third of the annual global freshwater footprint (Nuwer, 2016) (Gerbens-Leenes, Mekonnen and Hoekstra, 2013). And with the advantage of being 30% cheaper than other meat products, JUST ambitions to introduce lab-grown meat in countries that are truly in need where people are touched by nutritional problems, war and famine. Do you think we should give lab-grown meat a try?

Click here to see examples of the uncanny valley effect (#4 and #10 are absolutely terrifying):

https://www.strangerdimensions.com/2013/11/25/10-creepy-examples-uncanny-valley/

humanoid

 

Click here to watch JUST CEO Josh Terick talk about his company:

References:

Gerbens-Leenes, P., Mekonnen, M. and Hoekstra, A. (2013). The water footprint of poultry, pork and beef: A comparative study in different countries and production systems. Water Resources and Industry, 1-2, pp.25-36.

Nuwer, R. (2016). What would happen if the world suddenly went vegetarian?. [online] Bbc.com. Available at: http://www.bbc.com/future/story/20160926-what-would-happen-if-the-world-suddenly-went-vegetarian [Accessed 18 Oct. 2018].

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Thinking fast and slow, takeaways in management & IT

28

September

2018

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Take some time to answer the following problem:

A bat and a ball cost $ 1.10.
The bat cost one dollar more than the ball.
How much does the ball costs?

Now, did you know that 50% of students from Ivy League schools like Stanford or MIT failed to give the correct answer? Students from less selective universities – peasants we are – fail by 80%. This is one of Kahneman’s tests in his book “Thinking fast and slow” unveiling how strongly we are influenced by biases. This demonstrates how limited are our cognitive abilities, leading to poor decision-making.

We trust too much in our “system 1” that is needed to operate automatically in our daily lives (e.g.: let’s trust our intuition and say this ball costs 10 cents) and make too little use of “system 2” that processes any activity requiring concentration (e.g.: no wait, actually this ball costs 20 cents).
The problem is, “system 2” is absolutely exhausting and we can only sustain in mental effort for a limited time. That is not the case for machines, as they can exercise mentally for an unlimited period of time and are unbiased: they simply make better research for decision-making than us.

Mr. Telang has shown us during lecture that machines gave better parole decisions than humans because they had relentlessly, impartially reviewed all the facts that were given before giving an approval. On the other hand, Kahneman recalls a quite disturbing experiment where human judges happily approved parole requests when they just had food but reject them in bulk when they were hungry. Yet, human biases are useful: overconfident optimism encourages people to take risks and be unexpectedly rewarded.

Thus, in business when we elaborate a strategy plan or assess job candidates, we should let machines do preliminary research as they are best qualified to do so but review the research ourselves as we are best qualified to spot abnormality with our intuition and to take more innovative, risky-yet-rewarding decisions. Kahneman also helps to understand how we work, encouraging managers to think twice before rushing into decisions based on first impressions.

reference:
Kahneman, D. (2011). Thinking, fast and slow. 1st ed. New York City: Farrar, Straus and Giroux.

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