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The Performance of this “Citizen of the World”: Carlos Ghosn (ex-chairman of Nissan-Renault)

Note: For Ghosn biography https://adonis49.wordpress.com/2020/02/09/biography-of-this-citizen-of-the-word-carlos-ghosn-ex-chairman-of-nissan-renault/

This section will focus on the professional aspects of Carlos when he was selected to head the operations of reviving Nissan from certain death in 1999.

Carlos brought with him a total of 30 French specialists in Renault in a period of 3 months to support his job.

The understanding was that they are not there to change the culture of the Japanese employees but with the objective of turning Nissan around to profitability.

For 3 months Carlos set up 9  “transversal or cross-operational teams“, each headed by two members of the executive committee which was reduced to ten, with the task of understanding each other departmental problems.

He visited all the factories and suppliers to get a feel of the major problems and to get to the bottom of the illnesses of Nissan.

During these months he encouraged and was open to interviews by the Medias in order to promote the concept of transparency that will be adopted in reviving Nissan and also to encourage communications inside the institution and disseminate the steps to be taken and the expected changes that will follow.

In October 18, 1999 Carlos divulged his plan of rebirth NRP to an assembly of journalists.

It was a surprise announcement and No one outside the members of the executive committee new about the announcement; even the Japanese government got wind an hour prior to the announcement.

Nissan had 6.6% of the world market in 1991 and dropped to 4.9% in 1999 or a reduction in production of 600,000 cars; it had been losing money for 7 consecutive years and was heavily indebted of $19.4 billion.

Carlos promised that:

  1. Nissan will introduce 22 new models within three years
  2. the objective is to reduce the cost of procurement to 20% within three years since it represents 60% of the total cost,
  3. the number of suppliers of pieces and materials to almost half from 1145 to 600 suppliers and
  4. the suppliers of equipment and services from 6900 to 3400 by 2002.

Nissan had the capacity of producing 2.4 million cars but actually produced 1.3 million. Thus 4 factories would be closed by 2001 and another one by 2002 so that the rate of utilization of the remaining factories would be up to 82% taking into account a growth of 5.5% by 2002.

Nissan will end up with 4 factories utilizing only 12 plate-forms. Nissan will have to reduce by 20% the number in its network of distribution subsidiaries and close 10% in its points of sales.

Most important, Nissan will sell its shares in almost 1400 societies that do not strategically contribute to car manufacturing business.

The number of employees would be reduced 14% to 127,000 by the year 2002, with the exception of the department of research and development which will gain 500 additional jobs and the engineering department another two thousands.

Three targets were set to be accomplished by 2002, otherwise, Carlos and all his executive committee will leave even if one of these targets is not attained:

  1. return to profitability,
  2. a rate of operational margin exceeding 4.5%, and
  3. the reduction of the total debt to 50%.
  4. These targets were reached and in 2002 the syndicate at Nissan obtained all their demands which were reasonable while the number one Toyota froze salaries. Many in Nissan are now exercising their rights for stock options and the minimal number of stocks was reduced to 100 instead of one thousand.
  5. The team of Carlos Ghosn elaborated a 3-years plan called Nissan 180, where 1 represent an additional one million cars produced, 8 for an operational margin of eight percent growth, and 0 for zero debt by the end of the triennial.  As Carlos explained: “If an enterprise does not develop middle and long term plans then the financial analysts will have nothing to rely on but the near term results”.By the year 2003, 80% of Nissan’s cars would emit only 25% on the regulatory limit on pollutants.  An agreement with its archenemy Toyota was signed in September 2002; Toyota would provide Nissan 100,000 hybrid engines vehicles to be marketed in the USA by the year 2006. A hybrid engine works in the classical manner on highways and electrically within city routes.

    In November 2000, six months after the announcement of the NRP plan, Carlos decided to invest $ one billion in the USA for the construction of a new plant in Canton in the State of Mississippi; this new plant will target the segment of large pick-ups and SUV in the Middle West market where the American companies have it locked.

    This investment secures a stronger implantation in the most profitable market in the world because it has the best mix and a homogeneous market for advertisement and distribution and selling 16 million vehicles a year.  It will also save on the tax barriers and monetary exchanges.  Nissan already have a successful plant in Smirna for the exclusive Altima mark for the USA market.

    Another development is the investment in China, a new emergent market with the biggest potential given the saturation of the matured developed nations.  Nissan concluded a deal to invest more than $ one billion to acquire 50% of Dongfeng, a Chinese state owned enterprise that manufactures buses and heavy trucks.

  6. By the year 2010, this joint venture is projecting to produce 450,000 Nissan cars and 450,000 heavy vehicles.  The Chinese government gave priority to Nissan because of the bold steps it has taken to get back to profitability and of its experience with multicultural and global management practices.

    Although the initial intention was to revive Nissan into profitability some cultural changes within Japanese business behavior had to occur. For example, Nissan had an organization of assigning counselors to each field teams with no definite operational functions and not responsible to results; these counselors were originally dispatched to foreign countries to disseminate the Japanese practices but were of no use anymore; these counselors ended up diluting the responsibilities of the field directors; they  had to go.

    Another Japanese practice was to promote employees according to seniority as well as increase in salaries without any regard to productivity or innovation.

  7. Carlos instituted the notion of result instead of effort in judging what is fair.  The consequences for that notion of result did away with the practice of working overtime and spending unduly longer time at the offices, even showing to work on holidays. The doing away with the seniority criterion for automatic promotion meant that new recruits could be hired at higher and competitive salaries.

    The cost of incentives represented the variable portion in the total cost which was 40% at Nissan. Employees will thus be judged according to their contributions and incentives given to those who satisfy quantitative criteria.

  8. Another practice of hiring for life had to go.  During the recession in the 90’s, many Japanese companies concocted many gimmicks to in reality fire employees while providing the image of still belonging to the firm; for examples, many were assigned to concessionaires and suppliers who paid their salaries. 14% of employees will lose their jobs and many of these fictitious employees repatriated to Nissan.

    In the automotive business the question for the future is: can it afford a competitive offer and the capacity to maintain it? The end game reduces to maintaining innovation in a complex market, where emotions of clients for a stylistic car play a critical part and at a competitive price.

  9. Right now, after all the mergers in the last decades, there are 6 big manufacturers that hold 66% of the world market and the first ten about 90%.

    The biggest is General Motors with 7.5 million vehicles, then Ford, then Toyota, then the fourth Renault-Nissan with 5 million and fifth Daimler-Chrysler with 4.35 million, then Volkswagen.

  10. The team detached from Renault to Nissan played the role of catalyst because the real resource of Japan as the second economy in the world is its professional and skilled people.
  11. Japan has no natural resources, a relatively tiny island, ravaged by earthquakes and typhoons and facing strong adversaries. Japan has the third of the world monetary reserves although it has now a public debt up to 150% of its PIB.
  12. It is apparent that the Japanese companies have not assimilated the Nissan experience because they are still suffering from indecision and indebtedness; the “Cost Killer” Carlos believes that the problem is a lack of know-how and experience to treating their own managerial problems that did not change for over 40 years.

Here are all the companies that have cut ties with Huawei, dealing the Chinese tech giant a crushing blow

Still Talking about Darwin?

I’m going around the world giving talks about Darwin, and usually what I’m talking about is Darwin’s strange inversion of reasoning.

Now that title, that phrase, comes from a critic, an early critic, and this is a passage that I just love, and would like to read for you.

Why are babies cute? Why is cake sweet?

Philosopher Dan Dennett has answers you wouldn’t expect, as he shares evolution’s counter-intuitive reasoning on cute, sweet and sexy things (plus a new theory from Matthew Hurley on why jokes are funny).

0:28 “In the theory with which we have to deal, Absolute Ignorance is the artificer; so that we may enunciate as the fundamental principle of the whole system, that, in order to make a perfect and beautiful machine, it is not requisite to know how to make it. This proposition will be found on careful examination to express, in condensed form, the essential purport of the Theory, and to express in a few words all Mr. Darwin’s meaning; who, by a strange inversion of reasoning, seems to think Absolute Ignorance fully qualified to take the place of Absolute Wisdom in the achievements of creative skill.”

1:09 Exactly. Exactly. And it is a strange inversion. A creationist pamphlet has this wonderful page in it: “Test Two: Do you know of any building that didn’t have a builder? Yes/No. Do you know of any painting that didn’t have a painter? Yes/No. Do you know of any car that didn’t have a maker? Yes/No. If you answered ‘Yes’ for any of the above, give details.”

1:38 A-ha! I mean, it really is a strange inversion of reasoning. You would have thought it stands to reason that design requires an intelligent designer. But Darwin shows that it’s just false.

1:54 Today, though, I’m going to talk about Darwin’s other strange inversion, which is equally puzzling at first, but in some ways just as important.

It stands to reason that we love chocolate cake because it is sweet. Guys go for girls like this because they are sexy. We adore babies because they’re so cute. And, of course, we are amused by jokes because they are funny.

2:31 This is all backwards. It is.

And Darwin shows us why. Let’s start with sweet. Our sweet tooth is basically an evolved sugar detector, because sugar is high energy, and it’s just been wired up to the preferer, to put it very crudely, and that’s why we like sugar.

Honey is sweet because we like it, not “we like it because honey is sweet.” There’s nothing intrinsically sweet about honey. If you looked at glucose molecules till you were blind, you wouldn’t see why they tasted sweet.

You have to look in our brains to understand why they’re sweet.

So if you think first there was sweetness, and then we evolved to like sweetness, you’ve got it backwards; that’s just wrong. It’s the other way round. Sweetness was born with the wiring which evolved.

3:32 And there’s nothing intrinsically sexy about these young ladies.

And it’s a good thing that there isn’t, because if there were, then Mother Nature would have a problem: How on earth do you get chimps to mate? Now you might think, ah, there’s a solution: hallucinations.

That would be one way of doing it, but there’s a quicker way. Just wire the chimps up to love that look, and apparently they do. That’s all there is to it. Over six million years, we and the chimps evolved our different ways. We became bald-bodied, oddly enough; for one reason or another, they didn’t. If we hadn’t, then probably this would be the height of sexiness.

4:38 Our sweet tooth is an evolved and instinctual preference for high-energy food. It wasn’t designed for chocolate cake. Chocolate cake is a super-normal stimulus.

The term is owed to Niko Tinbergen, who did his famous experiments with gulls, where he found that that orange spot on the gull’s beak — if he made a bigger, orange spot the gull chicks would peck at it even harder. It was a hyper-stimulus for them, and they loved it.

What we see with, say, chocolate cake is it’s a supernormal stimulus to tweak our design wiring. And there are lots of supernormal stimuli; chocolate cake is one. There’s lots of supernormal stimuli for sexiness.

5:19 And there’s even supernormal stimuli for cuteness. Here’ s a pretty good example. It’s important that we love babies, and that we not be put off by, say, messy diapers. So babies have to attract our affection and our nurturing, and they do. And, by the way, a recent study shows that mothers prefer the smell of the dirty diapers of their own baby.

So nature works on many levels here. But now, if babies didn’t look the way they do — if babies looked like this, that’s what we would find adorable, that’s what we would find — we would think, oh my goodness, do I ever want to hug that. This is the strange inversion.

6:03 Well now, finally what about funny. My answer is, it’s the same story, the same story. This is the hard one, the one that isn’t obvious. That’s why I leave it to the end. And I won’t be able to say too much about it. But you have to think evolutionarily, you have to think, what hard job that has to be done — it’s dirty work, somebody’s got to do it — is so important to give us such a powerful, inbuilt reward for it when we succeed.

Now, I think we’ve found the answer — I and a few of my colleagues. It’s a neural system that’s wired up to reward the brain for doing a grubby clerical job. Our bumper sticker for this view is that this is the joy of debugging.

Now I’m not going to have time to spell it all out, but I’ll just say that only some kinds of debugging get the reward. And what we’re doing is we’re using humor as a sort of neuroscientific probe by switching humor on and off, by turning the knob on a joke — now it’s not funny … oh, now it’s funnier … now we’ll turn a little bit more … now it’s not funny — in this way, we can actually learn something about the architecture of the brain, the functional architecture of the brain.

7:24 Matthew Hurley is the first author of this. We call it the Hurley Model. He’s a computer scientist, Reginald Adams a psychologist, and there I am, and we’re putting this together into a book. Thank you very much.

Philosopher, cognitive scientist
Dan Dennett argues that human consciousness and free will are the result of physical processes. His latest book is “Intuition Pumps and Other Tools for Thinking,” Full bio
This talk was presented at an official TED conference, and was featured by our editors on the home page.

Related talks

Manu Manjunath

The fact that we find puppies cute may be an artifact of wiring that has other purposes. We certainly don’t depend on chocolate cake for survival either. We are mistaken to think that all thought, feeling or action is *directly* related to survival. I’m not saying the following example is the explanation, but it illustrates the concept.

The video explains that babies are cute because we’re wired to find them cute, and offers a rationale. Perhaps as a result of this, we react to faces in the animal kingdom that resemble baby faces – round face, large eyes for example.

Even physiological mutation is not always subject to natural selection.

For example, fibrinopeptides are the fastest-evolving molecules – they evolve at the baseline mutation rate. Natural selection appears more tolerant of their variation – perhaps because the body has other ways of compensating. (note – other molecules may mutate at similar accelerated rates, but natural selection weeds the variations out so we do not see the variation in the gene pool: natural selection is not tolerant of their variation).

Big brains brought self-awareness and the ability for abstract thought; this opened the door to all kinds of complexities that may not be *directly* tied to survival.

I might appreciate a painting that you find hideous. I doubt that fact will determine which of us survives, but perhaps creativity will survive better in certain cultures, or perhaps some cultures will survive better with a certain mix of literal and creative minds. Natural selection has produced this mix.

I recommend Dawkins’ The Blind Watchmaker and The Selfish Gene. Fascinating and eminently readable – even for dummies like me.

Certain traits of babies, especially of mammals, repeat themselves: a big head, delicate body, big eyes etc. If you stop to think about it, kittens, puppies, human babies, and of primates in general, share these traits, which is why we find them cute.

This also explains, for example, why we don’t find bird babies or fish babies cute: they do not follow these standards. It’s not a question of evolving to find them cute, but evolving to find certain traits our own babies have cute, and extrapolating this response to other animals that have them too.

Víctor Chagas

I agree, there are similarities in features among ‘all’ kinds of infant mammals that would have evolved long before our species did. So what we find cute includes non-human mammals too, because we have evolved from previous ancestor species whose ‘cute babies survived better’. We (and dogs, cats, hamsters, horses, etc…) have all evolved from very cute rat-like things that outlived the last dinosaurs of the Jurassic.

Also, we’ve been domesticating dogs and cats for perhaps 10,000 years and there are many times throughout our species’ history where at least having close relationships with dogs may have been important for survival, so maybe we do a better job of finding puppies cute than say, baby rats.

Working backward to solve problems?

Kind you solved it and trying to figure out how you did it?

Patsy Z and TEDxSKE shared a link.
Imagine where you want to be someday. Now, how did you get there? Retrograde analysis is a style of problem solving where you work backwards from the…
Share. ed.ted.com

What Is Natural Language Processing And What Is It Used For?

Terence Mills 

Terence Mills, CEO of AI.io and Moonshot is an AI pioneer and digital technology specialist. Connect with him about AI or mobile on LinkedIn

Artificial intelligence (AI) is changing the way we look at the world. AI “robots” are everywhere. (Mostly in Japan and China)

From our phones to devices like Amazon’s Alexa, we live in a world surrounded by machine learning.

Google, Netflix, data companies, video games and more, all use AI to comb through large amounts of data. The end result is insights and analysis that would otherwise either be impossible or take far too long.

It’s no surprise that businesses of all sizes are taking note of large companies’ success with AI and jumping on board. Not all AI is created equal in the business world, though. Some forms of artificial intelligence are more useful than others.

Today, I’m touching on something called natural language processing (NLP).

It’s a form of artificial intelligence that focuses on analyzing the human language to draw insights, create advertisements, help you text (yes, really) and more. (And what of body language?)

But Why Natural Language Processing?

NLP is an emerging technology that drives many forms of AI you’re used to seeing.

The reason I’ve chosen to focus on this technology instead of say, AI for math-based analysis, is the increasingly large application for NLP.

Think about it this way.

Every day, humans say thousands of words that other humans interpret to do countless things. At its core, it’s simple communication, but we all know words run much deeper than that. (That’s the function of slang in community)

There’s a context that we derive from everything someone says. Whether they imply something with their body language or in how often they mention something.

While NLP doesn’t focus on voice inflection, it does draw on contextual patterns. (Meaning: currently it doesn’t care about the emotions?)

This is where it gains its value (As if in communication people lay out the context first?).

Let’s use an example to show just how powerful NLP is when used in a practical situation. When you’re typing on an iPhone, like many of us do every day, you’ll see word suggestions based on what you type and what you’re currently typing. That’s natural language processing in action.

It’s such a little thing that most of us take for granted, and have been taking for granted for years, but that’s why NLP becomes so important. Now let’s translate that to the business world.

Some company is trying to decide how best to advertise to their users. They can use Google to find common search terms that their users type when searching for their product. (In a nutshell, that’s the most urgent usage of NLP?)

NLP then allows for a quick compilation of the data into terms obviously related to their brand and those that they might not expect. Capitalizing on the uncommon terms could give the company the ability to advertise in new ways.

So How Does NLP Work?

As mentioned above, natural language processing is a form of artificial intelligence that analyzes the human language. It takes many forms, but at its core, the technology helps machine understand, and even communicate with, human speech.

But understanding NLP isn’t the easiest thing. It’s a very advanced form of AI that’s only recently become viable. That means that not only are we still learning about NLP but also that it’s difficult to grasp.

I’ve decided to break down NLP in layman’s term. I might not touch on every technical definition, but what follows is the easiest way to understand how natural language processing works.

The first step in NLP depends on the application of the system. Voice-based systems like Alexa or Google Assistant need to translate your words into text. That’s done (usually) using the Hidden Markov Models system (HMM).

The HMM uses math models to determine what you’ve said and translate that into text usable by the NLP system. Put in the simplest way, the HMM listens to 10- to 20-millisecond clips of your speech and looks for phonemes (the smallest unit of speech) to compare with pre-recorded speech.

Next is the actual understanding of the language and context. Each NLP system uses slightly different techniques, but on the whole, they’re fairly similar. The systems try to break each word down into its part of speech (noun, verb, etc.).

This happens through a series of coded grammar rules that rely on algorithms that incorporate statistical machine learning to help determine the context of what you said.

If we’re not talking about speech-to-text NLP, the system just skips the first step and moves directly into analyzing the words using the algorithms and grammar rules.

The end result is the ability to categorize what is said in many different ways. Depending on the underlying focus of the NLP software, the results get used in different ways.

For instance, an SEO application could use the decoded text to pull keywords associated with a certain product.

Semantic Analysis

When explaining NLP, it’s also important to break down semantic analysis. It’s closely related to NLP and one could even argue that semantic analysis helps form the backbone of natural language processing.

Semantic analysis is how NLP AI interprets human sentences logically. When the HMM method breaks sentences down into their basic structure, semantic analysis helps the process add content.

For instance, if an NLP program looks at the word “dummy” it needs context to determine if the text refers to calling someone a “dummy” or if it’s referring to something like a car crash “dummy.”

If the HMM method breaks down text and NLP allows for human-to-computer communication, then semantic analysis allows everything to make sense contextually.

Without semantic analysts, we wouldn’t have nearly the level of AI that we enjoy. As the process develops further, we can only expect NLP to benefit.

NLP And More

As NLP develops we can expect to see even better human to AI interaction. Devices like Google’s Assistant and Amazon’s Alexa, which are now making their way into our homes and even cars, are showing that AI is here to stay.

The next few years should see AI technology increase even more, with the global AI market expected to push $60 billion by 2025 (registration required). Needless to say, you should keep an eye on AI.

Hidden Health Dangers:

A Former Agbiotech Insider Wants His GMO Crops Pulled

by Caius Rommens. Oct. 17, 2018

Genetic engineering isn’t everyone’s childhood dream. I didn’t care for it when I started studying biology at the University of Amsterdam, but my professor explained it was an acquired taste and the best option for a good job.

So, I suppressed my doubts and learned to extract DNA from plants, recombine the DNA in test tubes, reinsert the fusions into plant cells, and use hormones to regenerate new plants.

People say that love is blind, but I started loving what I did blindly. Or, perhaps, what started as an acquired taste soon became a dangerous addiction. Genetic engineering became part of me.

After I received my PhD, I went to the University of California in Berkeley to help develop a new branch of genetic engineering. I isolated several disease resistance genes from wild plants, and demonstrated, for the first time, that these genes could confer resistance to domesticated plants. Monsanto liked my work and invited me to lead its new disease control program in St. Louis in 1995.

I should not have accepted the invitation. I knew, even then, that pathogens cannot be controlled by single genes: They evolve too quickly around any barrier to infection.

It takes about two to three decades for insects and plants to overcome a resistance gene, but it takes only a few years, at most, for pathogens to do the same.

I did accept the invitation, though, and the next six years became a true boot camp in genetic engineering. I learned to apply many tricks about how to change the character of plants and I learned to stop worrying about the consequences of such changes.

In 2000, I left Monsanto and started an independent biotech program at J.R. Simplot Company in Boise, Idaho.

Simplot is one of the largest potato processors in the world. It was my goal to develop GMO potatoes that would be admired by farmers, processors, and consumers.

Genetic engineering had become an obsession by then, and I created at least 5,000 different GMO versions each year—more than any other genetic engineer. All these potential varieties were propagated, grown in greenhouses or the field, and evaluated for agronomic, biochemical, and molecular characteristics.

The almost daily experience I suppressed was that none of my modifications improved potato’s vigor or yield potential. In contrast, most GMO varieties were stunted, chlorotic, mutated, or sterile, and many of them died quickly, like prematurely-born babies.

Despite all my quiet disappointments, I eventually combined three new traits into potatoes: disease resistance (for farmers), no tuber discoloration (for processors), and reduced food-carcinogenicity (for consumers).

It was as hard for me to consider that my GMO varieties might be corrupted as it is for parents to doubt the perfection of their children. Our assumption was that GMOs are safe. But my pro-biotech filter eventually wore thin and finally shattered entirely.

I identified some minor mistakes and had my first doubts about the products of my work. I wanted to re-evaluate our program and slow it down, but it was too little too late. Business leaders were involved now. They saw dollar signs. They wanted to expand and speed-up the program, not slow it down.

I decided to quit in 2013. It was painful to leave behind the major part of my adult life.

The true scope of my errors became obvious to me only after I had relocated to a small farm in the mountains of the Pacific Northwest.

By this time Simplot had announced the regulatory approval of my GMO varieties. As the company began to plan for quiet introductions in American and Asian markets, I was breeding plants and animals independently, using conventional methods.

And since I still felt uncomfortable about my corporate past, I also re-evaluated the about two hundred patents and articles that I had published in the past, as well as the various petitions for deregulation.

Not so much biased anymore, I easily identified major mistakes.

“With the mistake your life goes in reverse.
Now you can see exactly what you did
Wrong yesterday and wrong the day before
And each mistake leads back to something worse.
(James Fenton)

For instance, we had silenced three of potato’s most conserved genes, assuming that the three genetic changes would each have one effect only. It was a ludicrous assumption because all gene functions are interconnected.

Each change had indeed caused a ripple effect. It should have been clear to me that silencing the ‘melanin gene’ PPO would have numerous effects, including an impairment of potatoes’ natural stress-tolerance response.

Similarly, asparagine and glucose are among the most basic compounds of a plant, so why did I believe I could silence the ASN and INV genes involved in the formation of these compounds? And why did nobody question me?

Another strange assumption was that I had felt able to predict the absence of unintentional long-term effects on the basis of short-term experiments. It was the same assumption that chemists had used when they commercialized DDT, Agent Orange, PCBs, rGBH, and so on.

The GMO varieties I created are currently released under innocuous names, such as InnateHibernate, and White Russet. They are described as better and easier-to-use than normal potatoes and to contain fewer bruises, but the reality is different.

The GMO potatoes are likely to accumulate at least two toxins that are absent in normal potatoes, and newer versions (Innate 2.0) additionally lost their sensory qualities when fried. Furthermore, the GMO potatoes contain at least as many bruises as normal potatoes, but these undesirable bruises are now concealed.

There are many more issues, and some of them could have been identified earlier if they had not been covered-up by misleading statistics in the petitions for deregulation.

How could I have missed the issues? How could I have trusted the statisticians? How could the USDA have trusted them? My re-evaluation of the data clearly shows that the GMO varieties are seriously compromised in their yield potential and in their ability to produce normal tubers.

Unfortunately, most GMO potatoes end-up as unlabeled foods that are indistinguishable from normal foods. Consumer groups would have to carry out PCR tests to determine if certain products, including fries and chips, contain or lack the GMO material.

Given the nature of the potato industry, the most common potato varieties, such as Russet Burbank and Ranger Russet, will soon be contaminated with GMO stock.

I have now summarized the new conclusions of this past work (without disclosing company secrets—I am bound by confidentiality agreements) in a book, entitled ‘Pandora’s Potatoes.

This book, which is now available on Amazon, explains why I renounce my work at Simplot and why the GMO varieties should be withdrawn from the market. It is a warning and a call for action: a hope that others will step forward with additional evidence, so that the public, with its limited financial means, has a chance to counter the narrow-mindedness of the biotech industry.

My book describes the many hidden issues of GMO potatoes, but GMO potatoes are not the exception: They are the rule.

I could just as well have written (and may write) about the experimental GMO varieties we developed at Monsanto, which contains an anti-fungal protein that I now recognize as allergenic, about the disease resistance that caused insect sensitivity, or about anything else in genetic engineering.

On May 3rd 2018 the columnist Michael Gerson wrote in the Washington Post: “Anti-GMO is anti-science.” His statement was echoed by Mitch Daniels, his colleague, who added, “[It] isn’t just anti-science. It’s immoral.

But these two columnists are not scientists. They don’t understand the level of bias and self-deception that exists among genetic engineers. Indeed, anyone who is pro-science should understand that science is meant to study nature, not to modify it—and certainly not to predict, in the face of strong evidence, the absence of unintended effects.

The real anti-science movement is not on the streets. It is, as I discovered, in the laboratories of corporate America.

Posted with permission from Independent Science News.

You have got to Ask for feedback: Feedback don’t come easily and without much specific prompting…

There was a time when the term feedback was associated with some kinds of “production process“.

Coming from an engineering background, particularly industrial and human factors engineering, feedback meant receiving the reactions of clients and customers in the usage of products, such as safe usage, easy manipulation, health consequences, quality of product, of processes…

Feedback has acquired a life of its own and expanded to mean “How do you perceive my behavior, and how do think people are judging me…?”

Thus, feedback in the workplace on how I control, manage, and connect with people, employees, clients…

When was the last time you received useful feedback?

When it was not too late to nurture and mentor this “good person” who is trying hard to communicate with you?

An angry person will vent his feelings, turn and bang the door…How much of a feedback you think you received?

Do you think receiving feedback from someone who is Not an expert in the field or didn’t work on the field can give use any useful feedback?

“How am I doing?” is not a great beginning: It doesn’t sound serious or honest.

Everyone who really craves excellence craves feedback.

You need to know how you’re doing and how to improve.

Honest feedback is rare. And you don’t receive feedback because you don’t ask.

The primary problem in feedback is the level of Honesty:

The higher your level in the hierarchy, the more likely people say what they’re expected to say, not what they believe. Honest feedback is rare.

Try full sentences for a change, like: (extracted from a short list by Dan Rockwell)

  1. What do you think I was trying to accomplish by the way I______? (Fill in the blank with an outcome, “Led the meeting,” Leader, manager, coach, spouse, etc.)
  2. What did I do that made you think I was ______? (Fill in the blank with their response to #1.)
  3. How could I improve what you think I’m trying to accomplish
  4. “How/where do you fit into what I’m trying to accomplish?” (Nathan, Thanks for giving me this powerful question.)
  5. How can I help you better fit in?

The feedback question that changes everything uses behaviors to identify what’s really going on.

It doesn’t begin with a list of job responsibilities.

How can leaders invite feedback?

What questions invite useful feedback?


adonis49

adonis49

adonis49

February 2020
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