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"Machine learning" used to be a safe haven. You could flee there to escape the Terminators and brain-on-a-chip graphics. Business PR deliberately killed that. They wanted their ML algorithms to be refered to as AI, so they could fully ride the hype train.

AI used to be a tight quirky community. Having the brain as inspiration led to all sorts of anthropomorphizing. This was ok. Researchers understood what was meant with "learning", "intelligence", "to perceive" in the context of AI. Nowadays, it is almost irresponsible to do this, not because you'll confuse your co-researchers, but because popular tech articles will write about chatbots inventing their own language and having to be shutdown.

Still, as a business research lab, it is good to get your name out there, so all the wrong incentives are there: Careful researchers avoid anthropomorphizing, and lose their source of inspiration -- you can not be careful with difficult unsolved problems, you need to be a little crazy and "out there". Meanwhile, profit-seeking business engineers and their PR departments, obfuscate their progress and basic techniques, all to get that juicy article with "an AI taught itself to X and you won't believe what happened next".

The researchers actually busy solving the hard problems of vision, natural language understanding, and common sense, do not have time to write books about how AI is not yet general. Nobody from the research community ever claimed that, nobody came forward to claim they've solved these decade-old problems. It is people selling books railing against the popular reporting of AI. Boring, self-serving, and predictive, and you do not need to fit a curve to see that.

All this quarreling about definitions and Venn diagrams and well-known limitations is dust in the wind. Go figure out what to call it on your Powerpoint presentation by yourself, and quit bothering the community.

This seems contradictory:

> AI can't predict social outcomes

> In most cases, manual scoring rules are just as accurate

So manual scoring rules don't work either for predicting social outcomes? There is some magic sauce that humans use for prediction that we haven't cracked yet? Nothing can predict social outcome?

AI is perfectly capable of predicting social outcomes, and only in very few cases are manual scoring rules as accurate as black box AI. The ethical concern is not about accuracy, but about our sensibilities when it comes to protected classes. The author cherry picked examples where simpler approaches also worked, but says nothing of practical feasability or increase in variance. Try actually doing face recognition or spam detection with manual rules.

Face recognition being way more accurate is just as much an ethical concern as a gun that is way more accurate. It all depends on who you point it at. Accurate face recognition at the border helps save lives as much as equipping the police with more accurate hand guns.

The talk of AGI is misguided. Everybody can see that the economy will be increasingly automated with narrow AI. Just because "big data" was a hype word, does not mean companies haven't been monitizing their big data (and were thus right to collect it).

We can predict probabilities about the future. The author is attacking these systems for not being 100% sure. Predictive policing is automated resource management. Militaries have been doing this for decades. It has its drawbacks, but also benefits (wiser usage of tax money, protecting low-income neighborhoods from falling in the hands of gangs).

The author also claims that algorithms automatically turn away people at the border for posting or liking or being connected to terrorist propaganda. But these systems just give a score and a human border guard makes the (more informed) decision.

A system not being 100% accurate is not an ethical concern, as long as we not treat those systems as 100% accurate and give proper recourse.

Just a spelling check can and does weed out poor candidates. Why does HR want to automate? Because they get 1000+ resumes for a single position. The manual glance they give them pale in comparison to what an automated system can do.

What is more likely? That these HR systems show promise? Or that the VC market has completely lost it (despite working with software and automation for decades, and have AI experts on staff) and is pumping billions into tealeaf reading, because now its called "AI"?

If you cheat the system by adding "Cambridge" or "Oxford" in white letters to your CV, is that ethical? Why not add it to your education section in black letters? Would you hire a good potential candidate, if you knew they acted like 90s search engine spammers? Maybe a candidate from Oxford or Cambridge really deserves to be on the top of the pile, or is it now unethical to look at education when hiring?

This presentation likes to mix ethics with technical success. Just say that a HR system is unethical, without calling it bogus with zero proof other than "some AI experts agree that this is impossible".

Yes, there is a lot of snake oil AI, and this will only increase. But these systems can and do work. I am sure there are AI experts building these systems right now.

The US economy (to which RenTech contributes with profits made on foreign markets) is a national security interest. Working to be very rich and then donating a large chunk of money to promote science - and maths education, also advances technological knowledge. Those depricated CRUD apps I wrote a few years back served neither.

We'd all like our opponent Poker players to play with their cards open, but if they did, they'd never win. Have to accept that you don't get to see the cards of winning players (which includes military technology until declassified).

I find this "Financial trading does not do any good for society" to be rather simplistic, romantic, and envious. The carpenter who turns wood into a chair is said to be producing value, but the investor who turned uncertainty into a profitable hardwood trade is not.

Strange that I think that a sarcastic reply that goes a bit further in moral punishment would be indistinguishable from a real reply these days. Mercer was practically forced to resign due to the backlash that his democratic political views generated against RenTech, but you deem the company as beyond redemption. You speak as if commenters here are idolizing, while being black-and-white religious and faux-cult-collective yourself, prescribing what others, we, should or should not worship. Take that to the street corner. If you gather a large enough audience, I will tell you that you are the one negatively influencing the lives of others. By my and your definition, companies and institutions would then be forced to disable you, your business, and your platform. But first let's go after Mercer.

So, assuming nothing against the law, how would they do it legitly? I am guessing:

- Treat the markets as a complex dynamical system and use the tools from statistical physics such as the Gibbs Ensemble, to derive internal states from input and output.

- Treat the markets as an encryption algorithm and use the tools from cryptanalysis, such as differential cryptanalysis: Even when unable to decipher the full algorithm (total break), one may still derive details and a subset of system functionality.

- They were probably the first to heavily use Hidden Markov Models (see Baum–Welch algorithm and the IBM speech recognition recruitment) and keep on the frontline with new machine learning algorithms (their deep learning revolution would have started 10-15 years before industry).

- They'd have an extremely solid backtesting pipeline, where any new feature can be stress-tested for signal. Features could be very arcane (% of mentions of the currency on neighboring state television) and are constantly (re-)added and removed: concept drift and market competition would gradually weaken signals, but fresh signals are added to keep the performance.

- All features are fed into a single final model (which may be an ensemble of many different forecasting techniques as to lower the variance). This model is very dynamic year-by-year (with just a few long-term signal features).

- Finally, I suspect there are strategies that only become available when you have 1 billion under control. In a physics sense: That is a lot of energy / control theory experimentation budget. Normally, hedge funds would like to avoid feedback loops and their trades moving the markets, but I suspect there is a lot of money to be made when you can calculate in which direction the market would move when the system is deprived of - or infused with a jolt of energy. More hands-off: Buy for 1 billion in stock at market open, sell at market close. Buy signal will take a few hours to converge and result in a higher price, so you make a profit when you sell your portfolio to the very buyer's market you created, causing a drop in price to complete the loop.

- The extreme returns for 2007/2008 could be due to the increase in volatility of the crisis (you can make more money when there is a lot of action, and competitors suffer from human herd bias / hysteria), but also, in part, due to them being the first to effectively exploit signals in growing social media platforms and search engines. A few years later it was public knowledge that gauging frequency and sentiment on Twitter was once a valuable signal.

- The NSA/CIA type recruits would not work on industrial spying, but on cryptanalysis, (graph) data mining, OSINT, HUMINT, IMINT, and for the security of the firm (which probably runs a tighter security than the intelligence agencies of smaller countries).

The one who ordered the drone strike is responsible (but hardly held responsible). Easy to draw the parallel with ordering the deployment of anti-personel landmines: the one who ordered deployment is responsible (but may not have signed the treaty, and thus, is hardly held responsible).

Autonomy or explainability is often a red herring. Look at who gives the orders. It is unlikely to ever be the programmer, even if they made a grave mistake. We have a history for that with smart rocket systems.

Television incentivizes forgetable reality TV, the radio incentivizes meaningless poppy music, social media incentivizes bickering about the controversies of today.

But, nowadays, you can also use your TV to watch a French arthouse film, to go to Youtube and be recommended a Japanese jazz album from 1974, to join the conversation on Twitter and ask questions to leaders in their respective fields.

Now you can swim against the current: Force all these power - and money - hungry institutions to fundamentally change their tune. Or you can find one of the many new waves to surf. Life is good, science is good, progress is good. The choice, as a scientist, is up to you. Can't write one groundbreaking paper a year? Write two or three mediocre ones. No amount of foundational change is going to make you a groundbreaking scientist. And change the channel once in while: the world is only getting bigger and more connected.

In the Netherlands there is the NCSC (National Cyber Security Centre). They also scan the internet: It continuously monitors all (potentially) suspect sources on the internet. When it identifies a threat (such as a virus or an attack on a website), it alerts public authorities and organisations. and can act as a mediator: If you discover a security flaw in another government body (such as a municipality or province) or in an organisation with a vital function (such as an energy or telecoms company), please contact the body or organisation first. If you receive no response, please notify the National Cyber Security Centre, which will mediate between you and the body or organisation concerned. with anonimity garantuee: The government treats the notifications it receives confidentially. It will not share your personal details with third parties without your permission unless required to do so by law or a court order. while avoiding court cases for doing your civic duty: When you report the security flaw, check that you comply with the conditions described above. If you do so, the government will not attach any legal consequences to your notification.