Just curious - have any investors pulled out of an investment because one or more of the most senior / talented employees of a firm has left to found their own or to join a competitor? One employer I worked with had pretty much grown around the skills of a tech lead whose business they had bought out. If he had left, their growth would have been compromised. Saying that, I thought he was under-compensated - I am not sure what the compensation model should be to retain key talent, but I think part ownership of the fruits of one's labour seems logical. This is at least an incentive to stay as opposed to a disincentive to leave.
Just living a modern urbanised life seems to be enough to drive humanity towards apocalypse within a few decades. Is that the case? If so, then I expect humanity to behave as it always does - with both good and bad elements of behaviour on display, perhaps accentuated as pressures to survive mount. Cohesive action will be fragmented and many will suffer and some will prosper. Sounds pretty much like the way things work today. I would be interested to know of any models which avert disaster that do not rely on billions of people all acting to live quite differently to how they do today.
What are the tool available to interspecies negotiators - stick / carrot diplomacy? Unfortunately the risks for anyone in control of budget to find solutions to these types of problems make brute force solutions seem quite palatable. Finding a way to create orca free zones or inventing orca deterrents to scare them away from boats is the more likely course of action. Maybe there is something that can be learned from cases where rural populations in India and Africa coexist with elephants or other wildlife that is large, intelligent and potentially dangerous?
Inspirational to hear about people who defy the odds to live larger than life dreams. I wonder if they were less rare before the 1900s - maybe I just don't meet the types of people whom I would put in the same bracket as von Berlichingen, or if I do, maybe I am either unaware of or discount the courage and achievement of people I meet in day to day life.
It would be great if it was possible to make it easier to assess unscrupulous behaviour by medical professionals and to weed out bad actors whilst simultaneously screening out damaging but unjustified claims about them made by patients or detractors with an axe to grind.
I do have concerns about how easy it is for some specialists to create a gravy train for themselves by simply requiring regular 'assessment' visits for patients under their care or observation at what seems to be a ludicrous rate for five minutes of their time.
In the UK there is an ombudsman that people can report their concerns to, but what then? - How anonymous is the patient really and how significantly do they compromise their relationship with what is actually a small pool of people who all know each other and are subject to the pressures of their professional clique's members?
How can this be improved, because even if they are sure that they are being overcharged and poorly treated there is still an incentive for patients not to go up against the medical establishment?
Pricing transparency is one thing which can help, but if there is a departure from the 'expected' level, then what? Is the right thing to do simply for the patient to always assess the rate vs service and report anything which is an outlier without concern for potential downstream consequences from their specialists? Or does pragmatism prevail, even if it perpetuates poor behaviour by the medical professionals?
Interesting video here of one of the San people running down a Kudu which collapses from exhaustion after an 8 hour chase: https://www.youtube.com/watch?v=826HMLoiE_o. Hard to compare this to Sorokin because the hunter is running in veld, not on a road, and has far less access to refueling points. Also, if he fails to track the prey down on day one, he would probably have a go again on day two, maybe even day three. Interesting claims too, that as an upright runner which sweats from glands all over his body, and as a creature capable of carrying water, man may have had persistence advantages over creatures with less ability to cool themselves and which run on four legs - a less energy efficient mode of running according to Attenborough.
"Rather than being the elite heat-endurance athletes of the animal kingdom, humans are instead using their elite intellect to leverage everything they can from their moderate endurance capabilities, optimising their behaviours during a hunt to bridge the gap between their limited athleticism and that of their more physically capable prey. Our capacity for profuse sweating provides a subtle but essential boost to our endurance capabilities in hot environments. This is a slight but critical advantage that our ingenuity magnifies to achieve the seemingly impossible: the running down of a fleeter-footed quarry."
2020 "Are humans evolved specialists for running in the heat? Man vs. horse races provide empirical insights"
"Over the course of 20 years, only two of the ER hunts observed by Liebenberg were spontaneous. Eight others were prompted by Liebenberg so that they could be filmed for television documentaries."
p436 "The endurance running hypothesis and hunting and scavenging
in savanna-woodlands"
It makes sense that machine learning would be applied to personality types to correlate them against things like financial performance, level or field of education, personal health, etc. Here is one example of a data gathering exercise carried out by Myers-Briggs and Goldman Sachs:
https://www.themyersbriggs.com/en-US/Connect-with-us/Blog/20....
The FutureSelf AI seems to take this a step further, to assisting people who sign up to the service with suggestions (presumably exercises to develop specific mindsets, or adjust their psychological state?) that will help them progress to an overall happier state. As a tool, it is possibly of some use, depending on how much effort one puts into it - perhaps not too different to buying a course or a book on NLP. Those who get the benefit of it will no doubt swear by it and encourage others to adopt it, too - people are very keen on anything which can augment the level of their physical, mental or psychological performance.
I wonder if we could get AI to code biological outcomes using biomolecular objects (as in object oriented programming), and what level of computing technology / how comprehensive a database of biochemical reactions would be needed to do this. Could this be something that is achievable in 20 yrs, perhaps speeded up with the aid of quantum computing?
Disclaimer, not a biologist. Ex did a lot of work in this area though.
If I understand your query (it's hard to parse), then no, AI is nothing that would help. This is an insanely hard problem to understand let alone solve. You're asking for a cartesian of every possible interaction of every possible enzyme, protein, molecule, etc. which, if it were possible to do with existing tech, it would have been done already.
ML (AI) is, at least right now, fancy pattern matching. Nothing more.
Further, Quantum computers can only run certain classes of programs, at least for now. Also not an expert there but if these two fields have been married in any way it's certainly not been done with any amount of clarity.
Hopefully that's a somewhat sufficient, serious answer. The question itself is very.... uh, r/futurism, if we're being honest. You can't just throw AI and Quantum at hard problems expecting them to just somehow solve them.
> ML (AI) is, at least right now, fancy pattern matching. Nothing more.
I mean, every problem can be boiled down to some sort of 'fancy pattern matching', the question is really how fancy/sophisticated the solver and how large the problem space the problem. I'm not sure why AI couldn't be helpful here even if the convergence of the solver/problem space are still many years off.
That's basically equivalent to saying, by the church-turring thesis computers can solve any solvable problem, therefore it can probably solve the problem at hand.
Which is technically true, but as a pragmatic matter doesn't really tell us much about if, when, or how the problem will be solved.
Exactly - I’m not claiming a specific timeframe for AI to be helpful in this area - just pointing out that the claim that it is, ‘just fancy pattern matching’ isn’t limiting to its utility and that in theory it should be able to contribute here.
Thanks for your response. I did chemistry and physics and uni (almost 30 years ago now) and I remember how complex some of the computer modelling that was done at the time was (even for very simple things - I think we looked at a model of what happened when a proton and a hydrogen atom came into close proximity).
Since then things have advanced hugely - both in biochem and in computing - and I was curious to see what might have been done. Also, hard science is fundamentally pattern recognition, isn't it: it requires that given the same inputs, the same output is consistently delivered.
Biology is not a serial process though. Everything is interacting with everything all at once. Some of those processes take exponential time complexity to simulate in computers, though deep learning is getting us better approximations of those processes in a shorter amount of time. The point being, biological systems don't have the certainty and exactness to program them like a computer. Everything does works out roughly at the macro scale.
Buzzword soup aside I think we all understand what they are asking and it is an interesting question. Will we be able to model (through any computation via any computing means) biological processes at a deeper level to accurately determine outcomes someday in the future?
I mean, if you divorce it from the buzzwords like that, the question becomes trivial:
* will we at some point in the future be able to model biological processes on a computer better (even if only slightly) than we currently can, at some point in the future? Obviously yes
* will we fully solve biological systems so that we can model them in their entirety with 100% accuracy? Not in this lifetime and probably not in the next generation.
The question when phrased this way is basically asking (depending on interpretation) either: will we make any progress ever? or will we make all the progress?
Seems pretty straightforward to me: 1) programming objects have properties and methods; 2) within cells it is probably possible to have analagous entities (perhaps various types such as molecules, organelles, etc) which have defined properties and predictable behaviours; 3) could we soon have a computer and a sufficiently comprehensive database of these objects and their behaviours for an AI to start correlating how they are combined and how they would need to act to produce a cellular effect (e.g. regenerate a damaged cell); 4) could this be speeded up with the advent of quantum computing?
Less flippantly: biological processes don't behave similarly to a big network of discrete objects with specific traits (methods and properties in OOP parlance). The domain of biology is composed of lots of molecules that combine to form bigger molecules that in turn get classified into hormones and proteins and amino acids and other organic compounds, and these all interact in super complex ways that are very difficult to model. For example, protein folding is a big area of research that is attempting to model the behaviors of just one set of molecules [1], and it is proving to be a really difficult problem to solve despite throwing enormous amounts of computing power at it [2].
And, we don't even know what we don't know yet in broader biological terms. It's not like we have a pretty good model for biology at macroscopic scales and we're just working out details -- this isn't civil engineering. The details that we're still missing matter a lot in how biological systems behave.
Quantum computing likewise is not a magic pill that will suddenly make all of this easier. Quantum computing is good at solving certain kinds of problems a little bit faster, but expectations for quantum computing have so far greatly outpaced its actual development.
As a side note, "systems thinking" in programmers often leads down dark dead-end alleys full of misunderstandings and wrong questions. Modern science is pretty darn advanced, and today's PhD candidates are introduced to programming as part of their education. It's usually safe to assume that if an advancement in a given field were possible through rudimentary programming, then someone would be working on it; programmers who are curious about specific fields should first start at the basics in those fields and put the time in to become familiar with them. That process will eventually lead to the right questions to ask in those fields.
Thanks for your response - I was curious if AI and tech might be able to bridge from a suitably detailed statistical picture to (at least some) cases of underlying deterministic behaviour, perhaps in a way (or ways) that might surprise us.
That's pretty much what AlphaFold has been doing for protein folding. It has been more successful than any other approach so far, but it hasn't yet "solved" protein folding, despite what some marketing materials and naive reporting has suggested. Last I heard, it was around 60% accurate when compared to experiments.
It does now seem like protein folding is within reach of being solvable, and that will be really cool and likely help advance our understanding of this part of biology, and possibly develop some new treatments for some diseases.
There will still be many more biological processes left to solve, however.
Interesting - could really help some people who do not have a fixed address. Great to see that an employment services firm, Reed in Partnership, is one of the partners who will be used to validate the candidate's authenticity - it can be a struggle for someone without a fixed address to get a bank account and it is often easier to initially get part time employment than it is to get a bank account or a lease in your own name. Lessors want a bank account, and banks want proof of a place of residence. However, where does the employer deposit the salary? I know this is a problem - I was in this precise position twenty years ago.
