numinary1

Being old as dirt, my observation is that potential tech revolutions take ten years after the initial exuberance to be realized broadly, or three to five years to fizzle. Of those that fizzle, some were bad ideas and some were good ideas replaced by better ideas. FWIW

Still upset about Wikipedia too?

This is amazing. Great format. It would be cool to link original recordings too, but merely nice-to-have, not essential. I love being able to listen to the solos unaccompanied.

Large companies’ CEOs need to be wealthy in order to be in the club with others whose influence might matter. Wealth == Power.

Kapton insulation was not shown to be the root cause of the crash of Swissair 111, nor was it entirely ruled out. https://www.tsb.gc.ca/eng/rapports-reports/aviation/1998/a98...

Right, excuses carry no weight. Got it. Agreed! But fuck football coaches and sports analogies for business. Sports is a zero-sum game. Business sometimes is,sometimes isn’t. Depends on agility, creativity, and the market you’re in. If your boss quotes football coaches just quit on the spot (unless of course you are in sales).

Yes, this!

Democracy is ostensibly majority rule (don't nitpick). The majority of people are poor. Therefore, democracy is the rule of the poor (Aristotle). We do not live in a democracy.

Sometimes what you need is change. I am sixty-five years old. I was a programmer for fifteen years, started a consulting company, then a software business. Was acqui-hired. Became VP Product Dev over 27 locations, 1200 developers, hundreds of products, then semi-retired for four years and worked on digital audio production. Got bored, did another startup, wrote code. Sold it and became CTO of a product division. Later worked for an investment group, became CTO for one of their acquisitions for almost ten years. Six years ago went back to technical work on my "reverse career plan" (junior programmer by the time I'm 75 ;-) ). Wrote js/react analytic front-end apps, Python back-end analytics, got into machine learning, co-authored a book about it, got some big data consulting gigs, learned Hadoop/Spark. Currently hands-on dev for Spark and ML apps.

For me, changes were the key. Burnout melts away. But you can burn out in any role. If you have the skills to do tech work, try to keep them alive. Coming back to hands on tech work is incredibly refreshing. So is leaving it behind for awhile when you've reached the burn-out point.

It's a thing, isn't it? I mean, as words and phrases go, it at least means something, like not just front-end, back-end, or client-side view-model reducer mapping specialist. AI is much close to fully content-free.

What's up with the phrase, "Still programming?" Would anyone think it odd for a 60-year-old physician to still be doctoring, or a 60-year-old lawyer to still practice law. Or for that matter for a 60-year-old artist or craftsperson to "still" pursue their craft.

Corporate culture embraces the notion of management as a profession. I think programming would benefit greatly from more of a tradecraft model, where leadership is provided by the master practitioner rather than the professional manager. In the alternate universe that's how we do it. The bottom line productivity boost is awesome. I don't know if it scales, but I don't care to scale.

-- 63-year-old full-stack web and machine learning programmer...living the dream

This discussion misses an important element, the skill of the interviewer. It is unsurprising that unskilled interviewers' assessments are poor predictors of future performance. It would be interesting to measure the accuracy of interviewers who have had years of experience interviewing, hiring, and managing people.

Here's how I think it works. Skilled interviewers are biased toward rejecting candidates based on any negative impression. Structured interviewing has the same effect. It's the precision versus recall tradeoff. For this use case only precision matters. Extremely low recall is fine.

Also, in the GPA prediction example, the interviewer is penalized for predicting a low GPA for a person who performed well. But in hiring, there is no penalty for failing to hire someone who would have performed adequately.

(Yes, I understand there is an implicit assumption in my argument that candidates are not in short supply, but that's usually true, certainly at Google)

The Triumph of Politics by David Stockman - totally changed the way I understand the operation of government.

All the time

Never ever take any kind of test before talking to a person, preferably the hiring manager. Not even for fun or practice.

If you're seeking work: If you want to be in demand, be the machine learning person for __________ , electric energy revenue protection, or healthcare payer fraud detection, investing, or supply chain. Pick a specialty.

If you're hiring: Get the above out of your pathetic small minds and start hiring the smartest people you can find. Look for successes in any industry. Your business isn't that unique. The best people can learn it much faster than you did.

That certainly makes sense since the problem has a higher likelihood of stemming from the same root cause, but also speaks to how little is known.

So include the social graphs of people who are on antidepressant discussion boards and pick up on friends, family, socioeconomic features ...

I so want to use ML tools on medical treatment data, but it's so hard to come by. Have toyed with a couple of startup ideas based on the tendency of people who share a condition to establish ties. Crohn's disease, cutaneous lymphoma. But the groups are too small. Mood disorders on the other hand...

It seems to me that many of the drugs in this category are effective sometimes for some people. People I have known who were suffering acutely often cycled through several drugs, sometimes in combination, dosages etc. It appears to me to be mostly trial and error.

But for some people the results are miraculous. They solve real problems with few if any side effects. For others, they either don't work at all or have intolerable side effects.

The situation would be a whole lot better if we could predict likely efficacy and side effects in advance, especially cases where treatment is more likely to do harm that to help.

Interestingly, I've read a lot (not so much recently) on the subject and taken several SSRIs myself (with good short term experience, minor sides), but I learned a lot from the discussion here. So many people take these drugs — the data necessary to understand and perhaps to predict ... is out there. Not easy to get at, but really plentiful. Reading this made me think about the feasibility of mining message boards for first person accounts of SSRI experience.

@dilemma's suggestion is the right one. The best way to use powerpoint is DON'T USE POWERPOINT to create your presentation. For example, if you use PPT shapes and text objects on a slide, and somebody tries to view it on a different version of PPT (mac v. PC for example), the layout may change. Yesterday, I presented in a webinar. I sent my slides to the sponsor in PPT format. The webinar presentation software wanted 4:3 aspect slides and mine were 16:9 so they mangled the format.

Use another tool to create each slide. Photoshop if that's your thing. I use omnigraffle on my mac a lot. Each slide should consist of a single jpg or png.

So you don't have to learn anything about PPT, which is a worthless tool, but unfortunately ubiquitous. Make your content and use PPT only to share and present.

Exactly what I saw. Your expectation is correct.

When I was working on a recommender for television shows, I ran SVD on a large User/Item matrix to create a low rank approximation, essentially reducing thousands of user features (TV show preferences) to user vectors representing twenty or thirty abstract "features". Then I looked at the actual item preferences of users who expressed each feature at the greatest and least magnitude. The features, in some cases, mapped to recognizable constructs. There were distinct masculine and feminine features, several obvious Hispanic / Latino elements, and strong liberal versus conservative indicators. Others were less explainable using common labels.

It struck me at the time that the qualities that were expressed most strongly were the ones that ended up having names in our language. But there were others for which I would say to myself, there is something about this group (e.g. those with the greatest expressed value of F124) that I recognize, but can't quite put my finger on.

Of course, I was looking at people through a keyhole, their TV viewing preferences being the only information I had.

Also, I noticed that these "came into focus" most clearly at a certain level of compression (rank).

FWIW

Perhaps the best minds are thinking about the challenges of teaching middle school special education in Detroit or how to deliver basic healthcare in East Africa. Are the minds of marketers better? Really?

Use the opportunity to learn: (1) What it is that attracted the potential investor to your idea; (2) What they would require of you in order to make an investment. Investors can be a distraction, but when the time comes to raise money you need to understand what investors are looking for. And yes, the most likely outcome of such a meeting will certainly be "let's keep in touch," but the very worst time to get to know investors is when you are desperate for money.

Q: What is the difference between a methodologist and a terrorist? A: You can negotiate with a terrorist.

Methodologies are useful as tools used by developers to organize their work and make themselves more productive.

When methodologists enter the picture, the benefits are lost, because methodologists serve the methodology, not the developers' product.

All methodologies are eventually hijacked by methodologists. Some dev orgs are smart enough to avoid them or at least limit their influence. Mahmoud is clearly not working in one of them.

The arrogance of the subtext that machine intelligence is nothing like human intelligence and therefore inherently inferior and nothing to be afraid of is more disturbing than the prospect of artificial intelligence gone awry as it reminds us how far awry natural intelligence goes routinely.