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The actual best method I think is a 3 month probationary period

My workplace has a six month probationary period; I brought in a mandatory three month review after watching one group screw this up badly. At the end of the six months, the employee came in to work all happy as usual thinking everything was great, and was promptly fired.

The three month review is the point at which the employee is told that they're on track, or they're below standard. If on track, just keep going the same way and if they don't get taken aside for a specific chat in the following three months, told to assume that they're going to pass the probation period; we have definitely turned probation failures into probation successes via this mid-point review. It's also their opportunity to tell the company what the company is doing wrong; what the company is doing that will make them choose not to stay. This too has happened, and we have retained good employees by listening to them at the three month point and making changes.

If they're below standard, they're told what they need to improve and are offered help to improve, or they can just sack it now and walk (or, as happened once and once only so far, they're considered unrecoverable and we take a long hard look at how that person was hired).

The principle we subscribe to is that if the employee is surprised by the results of their probation period, that employee's team lead and by association "the company" has really screwed up. If an employee doesn't know how they're doing after six months on the job, something has gone very wrong.


Hmm... that aligns somewhat with my own thoughts on the actual cause of depression. I've spent a lot of time thinking about since I spent a significant portion of my life depressed, and I find the current approach to it in health care unsettling.

Allow me, if you will, to engage in some inexpert speculation. If you read the following, please keep in mind that I am just some idiot on the internet and not in any way qualified to give advice.

It seems to me that depression is not a disorder, disease, or abnormality, but a necessary and purposeful reaction of the mind and brain to certain stimuli. Of course this is not always the case, and the same symptoms can be triggered by other factors that affect our neurochemistry or mental function, but in a normally functioning mind and brain I think this is true. When examined in this context, what do we find?

Depression makes us apathetic, reluctant to act, and unconfident. A while back there was an article on HN spitballing that depression and mania were related to our mind's assessment of its own ability to predict outcomes. Overconfidence in its own predictive ability manifests as mania, and low confidence manifests as depression. This makes some sense. If you are confident in your predictions you are more likely to act on them, and if you are not you are less likely to. Given this, I submit that it's possible that what depression really is, much of the time, is a philosophical problem.

Philosophy is our model of reality, and we use that model to make predictions and decide how to act in the world to affect change. When that model is known to be broken, we lower our confidence in it and act less. Over time, as more and more of our model is revealed as flawed and our confidence in it continues to plummet, we enter a state of learned helplessness. Finding ourselves unable to predict the results of our actions, we are unable to determine how to effect the changes we desire in our lives, leading to interesting contradictions like being bored and at the same time unmotivated to do things we used to enjoy. We don't want to be in this state, but we lack the ability to see a path out of it, so we become frustrated, angry, and/or sad. It can eventually reach a point where the only path out of the suffering that we're confident in, is death.

In fact, this model-breaking occurs many times in our minds' development. As we grow up we form several different models of reality, all of which are inevitably revealed to be flawed. This is the reason you find children who believe they are hidden just because they can't see you (their model of reality doesn't include the concept of different perspectives), and why the terrible twos are so terrible (the young mind is dealing with its model of reality failing), for instance. With children, however, there are plenty of people around them operating with better models of reality to help them work out a new one. Societies can also be modeled this way, and if we look at the past we find that human cultures also go through a similar pattern of forming a stable model of reality, eventually finding it flawed, suffering through process of dealing with that, and ultimately resolving the crisis. I say resolving because, in actuality, there are two solutions to the problem of realizing your model is broken: forming a new, more accurate, one; or ignoring the information that contradicts it.

This is the important point, I think: When an individual's model of reality is broken, and society cannot guide them towards a more accurate one because society itself is still operating on the model that individual has determined to be flawed, then chronic depression is a likely result. Our current societal philosophy, the one our health care system is also based on, see's this individual's suffering not as a transition period in which they form a new model, but a severe disorder. To them, the rejection of the model is a form of insanity, and unclear thinking. This is why you sometimes see people tell a depressed person an obvious platitude in an attempt to cheer them up, only for it to further frustrate the depressed individual: they are aware that the platitude is part of a flawed model.

Further, the health care system is, like most of current western society, firmly implanted in empiricism. Science and measurement are the hammer, and everything else is a nail. Society as a whole forms its model of depression on measurements and manipulation of the neurochemical and behavioral aspects of depression, the social side effects, etc, but without regard for its greater reason for being. They are witchdoctors, sacrificing chickens to drive out the demons and bloodletting to balance the humors. Sometimes it works, because even a broken clock is right twice a day, but a lot of times it doesn't.

If one were to assume that this assessment is accurate, then reason we get depressed is so that our mind is motivated to take a step back and build a more accurate model of reality. The thing to do, then, is to help the sufferer realize why they are suffering. There's nothing wrong with them, they don't have a chemical imbalance of the humors, they aren't bad people for feeling the way they do or for not having faith in what society tells them is true. They have in fact taken a step toward growth, and nearly all growth comes at the cost of suffering. They need to look hard at where reality has shone the light on their flawed conception of it, reason through the problems, and build a more accurate replacement, and we may not be equipped to help them.


To be honest, this isn't the best list, it's a bit too blog heavy. I've started reading up on ML only recently but here are my recommendations. Note that I haven't went through all of them in entirety but they all seem useful. Note that a lot of them overlap to a large degree and that this list is more of a "choose your own adventure" than "you have to read all of these".

Reqs:

* Metacademy (http://metacademy.org) If you just want to check out what ML is about this is the best site.

* Better Explained (https://betterexplained.com/) if you need to brush up on some of the math

* Introduction to Probability (https://smile.amazon.com/Introduction-Probability-Chapman-St...)

* Stanford EE263: Introduction to Linear Dynamical Systems (http://ee263.stanford.edu/)

Beginner:

* Andrew Ng's class (http://cs229.stanford.edu)

* Python Machine Learning (https://smile.amazon.com/Python-Machine-Learning-Sebastian-R...)

* An Introduction to Statistical Learning (https://smile.amazon.com/Introduction-Statistical-Learning-A...)

Intermediate:

* Pattern Recognition and Machine Learning (https://smile.amazon.com/Pattern-Recognition-Learning-Inform...)

* Machine Learning: A Probabilistic Perspective (https://smile.amazon.com/Machine-Learning-Probabilistic-Pers...)

* All of Statistics: A Concise Course in Statistical Inference (https://smile.amazon.com/gp/product/0387402721/)

* Elements of Statistical Learning: Data Mining, Inference, and Prediction (https://smile.amazon.com/gp/product/0387848576(

* Stanford CS131 Computer vision (http://vision.stanford.edu/teaching/cs131_fall1617/)

* Stanford CS231n Convolutional Neural Networks for Visual Recognition (http://cs231n.github.io/)

* Convex Optimization (https://smile.amazon.com/Convex-Optimization-Stephen-Boyd/dp...)

* Deep Learning (http://www.deeplearningbook.org/ or https://smile.amazon.com/Deep-Learning-Adaptive-Computation-...)

* Neural Networks and Deep Learning (http://neuralnetworksanddeeplearning.com/)

Advanced:

* Probabilistic Graphical Models: Principles and Techniques (https://smile.amazon.com/Probabilistic-Graphical-Models-Prin...)

I have also found that looking into probabilistic programming is helpful too. These resources are pretty good:

* The Design and Implementation of Probabilistic Programming Languages (http://dippl.org)

* Practical Probabilistic Programming (https://smile.amazon.com/Practical-Probabilistic-Programming...)

The currently most popular ML frameworks are scikit-learn, Tensorflow, Theano and Keras.


After reading the first Soylent post, I felt inspired to try and come up with a recipe for a "nutritionally complete" soup. I used an online tool that calculates the total nutrients for a recipe and came up with this:

3 potatoes 1 onion 500 grams of wild alaskan salmon 1/2 cup of mushrooms 3.5 tbsp of olive oil 30 grams of sunflower seeds 1 tbsp of dried parsley 2 tbsp of ground thyme 50 grams of parmesan cheese 3 cloves of garlic 20 grams of sesame seeds 1 medium oyster (from a can) 1 tbsp of ground mace 1 tsp of cod liver oil

To cook it I just added everything to boiling water in order of cooking time, starting with the potatoes and onions and ending with the salmon.

I tried making it last night and ate it for dinner and breakfast, and it was delicious! I also feel amazing. I guess I should track the effects of the recipe on quantified-mind.com. :-)

I was actually surprised by how hard it was to fit all of the daily nutrient requirements into a recipe with about 2000-2500 calories (while also avoiding nutrient overdoses). It would be great if someone would create a website for "nutritionally complete" recipes, especially recipes that are cheap and easy to make with a good blender or crockpot.


Startups: never have so many understood so little about the statistics of variance present in the outcomes of small samples.

People like to speak of 10x productivity, non-stop work and geniuses - but the reality is much less interesting. A large number of small teams working on many different problems will by definition have a great variance in outcomes just by random extraneous factors (also known as the law of small numbers and insensitivity to sample size).

> A certain town is served by two hospitals. In the larger hospital about 45 babies are born each day, and in the smaller hospital about 15 babies are born each day. As you know, about 50% of all babies are boys. However, the exact percentage varies from day to day. Sometimes it may be higher than 50%, sometimes lower.

For a period of 1 year, each hospital recorded the days on which more than 60% of the babies born were boys. Which hospital do you think recorded more such days?

1) The larger hospital

2) The smaller hospital

3) About the same (that is, within 5% of each other)

56% of subjects chose option 3, and 22% of subjects respectively chose options 1 or 2. However, according to sampling theory the larger hospital is much more likely to report a sex ratio close to 50% on a given day than the smaller hospital.

Relative neglect of sample size were obtained in a different study of statistically sophisticated psychologists

-- http://en.wikipedia.org/wiki/Insensitivity_to_sample_size

> A deviation of 10% or more from the population proportion is much more likely when the sample size is small. Kahneman and Tversky concluded that "the notion that sampling variance decreases in proportion to sample size is apparently not part of man's repertoire of intuitions. For anyone who would wish to view man as a reasonable intuitive statistician such results are discouraging."

-- http://www.decisionresearch.org/pdf/dr36.pdf

Taking lessons as gospel from these "10x" events is by definition foolhardy and merely an extension of the bullshit pushed by the entire "Good To Great" Jim Collins business book industry.

It's like taking lessons from survivors of the Titanic on how to survive the sinking of a ship. It's quite simple - be a young female child with a life vest and rich parents (or in startup land - a young upper-middle class male living in California during a venture bubble, a cyclical investment in the Valley with a convergence of secondary technologies, above average intelligence and a college degree from a reputable university).

I have a personal rule with any kind of advice or explanation coming out of anyone working in a "soft" industry - if it's vague - it's bullshit. All of the advice given at these events are bullshit by this definition. So are many other things - and yeah it doesn't preclude me from spouting it. Or using the advice at my discretion.

But honestly - startup founders literally have no idea why things take off and they have no idea why they win. That's why they have to keep pivoting - it increases their luck surface area and their ability to gain traction - after which they simply must hold on tight while surfing the wave.

YouTube was a dating site - didn't work - pivot - video traction - venture up - ride.

PayPal was a Palm Pilot app - didn't work - pivot - traction - venture up - ride.

Google sold corporate search - didn't work - pivot - copy PPC from Overture - lever up - traction hits - ride.

Instagram - started with a location checking HTML5 app 2 years too early - pivot - copy PicPlz and Hipstamatic - hit traction - lever up - ride.

Angry Birds - fail at hitting nearly every game in the past decade - pivot - take a shot at the iPhone - hits traction - lever up - ride.

Of the startups that didn't pivot - they either skipped the pivot thanks to previous side projects/companies or already had traction - and all they had to do was lever up and ride.

I'm going to make this clear - there is absolutely, positively nothing wrong with this - not at all - it is merely reality and not particularly unfair.

People stating pointless platitudes that success is due to things like "Be 10x more productive", "Commitment" and "People, product, and philosophy" are simply wasting their breath, other people's time and confusing what actually happens. These things may or not be either actionable, predictive or sufficient for success.

Here's my list of startup advice:

Be alive. Be male. Be young. Don't have health issues. Be born in America or move there. Enter the cycle after a recession. Speak English. Enter a growing/new field where the level of competition is low and so is the sophistication of your competition. Surf cost trends down from expensive to mass consumer markets. Work bottom up - on small things. Be of above average intelligence. Have family support. Have a college degree.

Oh and most importantly of all: Get fucking lucky.

The hindsight/survivorship biases in combination with faulty causality and the narrative fallacy will completely hose your thinking - so be careful.

More interesting stuff:

http://en.wikipedia.org/wiki/List_of_biases_in_judgment_and_...

http://en.wikipedia.org/wiki/Black_swan_theory

http://en.wikipedia.org/wiki/List_of_fallacies

http://en.wikipedia.org/wiki/List_of_memory_biases

http://www.econ.yale.edu/~shiller/behfin/2000-05/rabin.pdf

Disclaimer: Biases rule your thoughts and mine - this post is also subject to both bullshit and biases (mostly bullshit - I do love that word). Think for yourself.


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