hongloumeng

I agree that a real name policy would help remove the bots and anonymous hate speech. But there is plenty of non-anonymous hate speech that still make Twitter toxic. Those people would have an easier time harassing other people if they knew real names.

The ad-revenue model relies on attention, and attention thrives on memes that inspire outrage, contempt, and fear. The solution is a freemium subscription model. But I don't think he'll do that. I admire the guy's accomplishments like everyone else but we all know he's got a bit of an ego. He'll not put a paywall in front of his Twitter army.

I agree that a real name policy would help remove the bots and anonymous hate speech. But there is plenty of non-anonymous hate speech that still make Twitter toxic.

The ad-revenue model relies on attention, and attention thrives on memes that inspire outrage, contempt, and fear. The solution is a freemium subscription model. But I don't think he'll do that. I admire the guy's accomplishments like everyone else but we all know he's got a bit of an ego. He'll not put a paywall in front of his Twitter army.

Not too long ago in China, consumers preferred duck eggs with red tinted yolks. Market vendors would crack off the top half of an egg and display its yolk, and consumers thought the redder the better.

So, of course, farmers started feeding a carcinogenic dye called Sudan Red directly to the ducks.

* http://www.china.org.cn/english/health/189567.htm

I'm a professor of machine learning and I also work for a large tech company. I've worked for startups in the past. My PhD is in stats.

Large tech companies hire economists. Go to Linkedin and search "economist Amazon" or "economist Microsoft." Find some early or mid career people. Then you can look at roles and titles they took after school and extrapolate from there.

Make sure you can talk a good game about causal inference. As an economist, you ought to know about econometrics-flavored causal inference ideas like instrumental variables, propensity scores, regression discontinuity, etc.

And, though you don't want "computer science based" roles, make sure you have the skillset to pass a data scientist interview. I suggest online exercises to prepare for those skills rather than course for credit at your school. I like Datacamp.

Small tech companies hire data scientists. They don't care if your degree is in physics, economics, or math, so long as you can do what's needed. When an early stage tech company is hiring for a data science role, they care more about the technical skill set and the ability to learn than exactly how good they are at whatever domain they come from. This is because early stage startups tend to have more fluid role definitions because much work needs to be done. Most of that work is technical.

Some people want to own houses they can afford and don't hate. Some people want to be present and flexible for their children. Deal with it.

All these comments about a story about an undergrad with a chip on his shoulder analyzing data he PULLED OFF OF SOCIAL MEDIA.

I suppose, despite the provenance of the data, that causal inferences taken on this data are valid because (1) he seems to be on the spectrum, (2) did well in physics, and (3) did "various types of statistical analysis".

If they use data science to make content decisions, and algorithms to recommend content, is that not a tech company?

I guess. But we've ramped up uncertainty, people are afraid, and people who have been considering coming to the US will now go elsewhere.

This account seems exaggerated. Forced late-term abortions in China were certainly a real thing, and the value of the life of a stranger is on par with that of livestock. However, guns are rarely seen in everyday life in China. This is especially the case with policemen and low-level enforcers. The exception are soldiers on duty, and security for transporting currency. Guns are kept in armories, brought out by specially trained "armed police".

As a company, in an industry where IP and secrets are so important, what is their motivation for open sourcing it?

I hate when people act as though some of these opinions are just a different point of view on the correct way to fold a shirt. Sam Altman may "strongly disagree with" with certain ideas; other people feel "strongly feel existentially threatened by" these ideas. In other words these ideas have painful consequences on people's lives. People who vote according to fact-immune beliefs that black people are thugs, Mexicans are rapists, and brown people are terrorists are directly attacking the safety and security of many Americans (who are not Sam Altman).

"Digital Nomad" is not the keyword you are looking for. The Digital Nomad narrative targets individuals who are trying to escape the prototypical rat race, so most of the focus is on what can be done with a small set of technical skills, like setting up a Wordpress website and building landing pages, email marketing, sourcing from Alibaba, building web courses, Amazon stores, Shopify and Teespring, etc. You sound like you are interested in small tech startup teams that are location independent. Try Rob Walling's podcast Startups for the Rest of Us. And, for a more general and non-technical but still informative view, try TropicalMBA.com. If you are more interested in joining a traditional startups that lets you remote work, try remotebase.io/companies

I don't think it is quite a long con. Johnny might seem to be exploiting her my Western norms of social relationships. But if he has turned her into a patron of sorts, it is still out in the open, and he might sincerely care for her. After all, it would probably seem less weird if their genders were reversed.

I'm a machine learning and have done work with Bayesian methods for modeling dynamic systems. I've been considering jumping into a synthetic biology company in industry. I've been searching for statistics and machine learning problems that need solving in synthetic biology. I suspect that there are two problems where a statistician or machine learning expert could contribute. The first is building data-driven models of metabolic pathways. The second is implementing an active learning approach to organism design -- basically building a robot that iteratively conducts experiments that maximize information while minimizing cost. But I also suspect that synthetic biology companies like yourself and Zymergen are more concerned with scaling up your business in the short term, and that implementing machine learning or computational biology-types of processes is a long term "optimization" task, not important to the core business in the near term. I'm afraid of making the jump if this type of work isn't important to the organization. Can you please comment? Cheers.