A systems neuroscience approach to building AGI - Demis Hassabis, Singularity Summit 2010, https://www.youtube.com/watch?v=Qgd3OK5DZWI
Check out this video from mit/openai: https://www.youtube.com/watch?v=9EN_HoEk3KY
The entire talk is interesting but the section at 21:40 talks about "Sim2Real with Meta Learning".
When you think about it this way, it seems impossible that we haven't duplicated the capability of the human brain in an airplane hangar somewhere.
What's going on inside our heads that we can't mimic? That magical algorithm...
Single ion channels can have surprisingly complicated behaviors that depend on their current state and past history. Individual neurons contain tons of these channels, and can do a lot of powerful computation on their own. Of course, there are 86 billion neurons and combinatorically more connections between them. That’s just the neurons too; God only knows what the glia cells, which outnumber them 10:1, are doing but they’re a lot less passive than many have thought.
On top of this, there’s a whole separate but overlaid network of neuromodulators (hormones, nitric oxide, etc). Electric fields produced by some neurons may even influence the activity of others.
None of this is static, either. Things change on timescales ranging from milliseconds to years, and in response to all sorts of external stimuli.
The brain is bonkers.
Digital has many advantages: a digital Einstein could be replicated perfectly, not so for an analog Einstein.
But the _precise_ capabilities of a human brain are not actually what we want.
For starters, an infant's brain does not have any immediately valuable capabilities.
After that brain is exposed for several years to signals propagating through the brain's host body from the surrounding environment, it has developed many interesting capabilities. But those capabilities are only meaningful in the context of the input stream that the brain has learned to interpret.
So if you want your software simulation of a brain, running on a hangar-sized computing cluster, to perform human grade cognition, then you'll have to provide it with a signal as rich as and of the same form as the signal that we receive on a continual basis through our 1 billion sensory cells (optical, auditory, proprioceptive, etc).
And in order to supply that signal in a realistic way, you'll have to simulate the environment in such a way that it responds to motor output from the simulated brain. (Or you can use the real environment, but then you have to have the brain operate a complete synthetic human body).
All this is a tremendous technical challenge, outlandishly expensive and, even when achieved, does not immediately enhance our understanding of how naturally intelligent systems process information. Nor does it provide us with a means to construct specialized intelligent agents that operate in the world, whether autonomous vehicles, burger flippers, surgeons, or stock brokers.
It's true they have not suddenly become conscious.
The amount of information stored and being processed by single individual cells is almost unimaginable. Individual brain cells are likely able to learn high level abstract features store memory, modulate responses in intensity and length through thousands of transmitters and so on. Single celled organisms, like amoeba, possess the ability to emulating 'hunting' and other complex behaviour.
It's actually not. Humans are unusual in that they can teach each other and institutional knowledge can span generations. In addition, just as your speed of travel is not limited by the length of your legs and the ATP cycle, your intelligence is not limited by your brain.
you focus only on single brain, when really we need to look at many trillions brains and experiments with complex reward function(real world): billion years of evolution * billions of various creatures born and died within every year.
All this giant sequence of experiments converged to current human brain.
In ML terms: to achieve result similar to human brain we need to run that many hyperparameter optimization trials. Or find some better shortcuts than human brain biological structure.
I’m an actual, working neuroscientist and if we’ve solved any of these things, it would be news to me (and everyone else at my institute). We have good, if coarse, knowledge of which structures are critical for which functions—-at least under some conditions. Our knowledge of how they do this is even cruder: neither the representations nor the algorithms are known with much certainty, let alone how they arise.
Let me give you a very concrete example of where we are. There’s a small nemode called C. elegans. It’s about a millimeter long and has 302 neurons. We know its complete wiring diagram, its genome, and the origin and fate of every cell (not just the neurons) in its tiny, simple body. Its behavior has been studied extensively. And yet...we can’t accurately simulate the damn thing—-and it’s not like it does a lot to begin with.
The human brain has about 86B neurons, and we know an awful lot less about them. Neither vision nor speech is remotely close to “solved” or understood. Consciousness, even in the very limited sense of “why do we fall asleep—-or need to?” is a mote off in the distance.
Also a 10 second google search pulls up papers with plenty of details about vision in the brain so I don't know why you're talking about it as if its some great mystery.
A 1800 Page textbook on Vision Neuroscience. I'll leave it to HN to decide if we "Understand" Vision. Arbitrarily high requirements for understanding are being thrown about that would leave modern science in shambles if applied.
We know a lot of facts, and we have some ideas about how various small things are implemented, but in terms of grand unifying theories, we’re nowhere close.
For example, suppose I showed you two gratings (think zebra stripes): a small patch and a larger one. Under some circumstances, you’ll have a harder time determining which way the big patch is oriented vs. the small one. This is true even though there’s extra information in the big patch. We think this is related to a phenomena called surround suppression, but they’re not exactly the same....and no one can agree on how surround suppression is implemented, let alone what it’s good for. This happens in primary visual cortex, which is probably the simplest—-and most extensively studied—of the visual cortical areas.
Your innocuous question has kicked off a pretty feisty debate on my floor about whether it is 'primary' as in first (either in the circuit or evolutionarily) or primary as in most important.
If it's the former, adding a "the" seems to add some unwarranted emphasis. I think there's probably some parallelism too. Primary visual cortex is also called "V1" (as in the first cortical area involved in vision) or "Area 17" (according to a map that defines areas based on their cellular organization). While "in the primary visual cortex" sounds fine, "in the V1" and "in the Area 17" sound barbarous.
As I said before, we know a lot of facts. We know a lot about the spectral sensitivity of rods and cones, and the molecular mechanism that lets them turn photons into electrical impulses. We know a little bit about where the areas that process faces are and what visual features the neurons in them respond to. We’ve got pieces, but they’re not put together.
I would say that we understand vision when we can answer a question like “How do you find a friend in a crowd?”
You can start with “When you first met, light bounced off her face and isomerized some retinal from its 11-cis to all-trans form, which caused the bound opsin to change conformation into metarhodopsin II, which activated transducin, which....” Eventually, this cascade caused electrical activity that reaches cortex. A huge set of cortical areas process visual input, and these electrochemical signals flow through all of them. We can predict V1 neurons’ activity reasonably well, less so for the downstream neurons in V2, V4, or the temporal lobe areas. We have only the fuzziest ideas how those patterns are read out, tagged as important to remember, and moved into memory. You've only just met--and yet it gets worse.
To find her, you’ve got to retrieve those patterns from memory (no one knows how, but oscillations might be involved?), and use them to search in a way that’s robust against variations in the friend’s pose, position, rotation, illumination, and even dress style or age, many of which you have never seen before and will never see again. We know, for example, that some cells in IT are fairly robust against some moderate kinds of image changes. Some but not all, of this is done by circuits that look like a convNet. Whether this is a coincidence or not is debatable and how this convNet is trained is a total mystery—-it’s definitely determined by experience, but the feedback signals needed for vanilla backprop are missing.
As you scan the crowd, you’re only getting high-resolution data from a very small part of the visual field. This is (somehow) stitched together into a unified percept. You apply various heuristics—maybe your friend favors bright colors—to speed the search along. How you learn these, and how they’re mixed in with the input from your eyes is unknown, but it’s certainly reflected in your behavioural output: you'll find her faster if you successfully predict what she looks like, and you'll be much slower if you guess wrong. Perhaps you hear a familiar voice or smell the perfume you bought her. This, too, can help you find her, but how information is integrated across senses is unknown too.
Eventually, you find her. You plan a path across the plaza towards a cafe. We have a pretty good understanding of how this works in rats (3-7 Hz oscillations coordinate place cells and grid cells in the hippocampus). Those oscillations are really strong in rodents, but much weaker in monkeys and totally missing in bats, so it's not clear how this works in humans.
Now all you’ve got to do is open your mouth and order coffee....
We get about 1 Gb/min of neurophysiological data (x4-6 hours/day) and I'm hoping to scale that up quite a bit soon. People doing microscopy also generate giant datasets, as do the sequencing folks.
Me, specifically? A labmate showed me and said "It's cool...and a timesink."
What do you think about this? Who is the I? Where can I find it? Is it just mysticism?
I'm collaborating with a woman who has also been working with him to model some of our data with NENGO, so I should really get around to that sooner rather than later.
What are the best modern methods to analyze them?
CRCNS (https://crcns.org/data-sets) has some neurophysiology data (i.e., from implanted or inserted electrodes). This sort of data is shared a little less often, in part because it's often acquired and stored in weird, homebrew formats, though that's slowly changing.
ModelDB (https://senselab.med.yale.edu/ModelDB/) has a large collection of computational models. These are mostly biophysical models, though there's some other stuff in there too.
Depending on what you're looking for, there are other more specialized repositories. NDCT (https://data-archive.nimh.nih.gov/ndct) has mental-health related clinical trial data, though you'll have to do some paperwork if you want subject-level data, which is fairly common for clinical data. MIT has a collection of eye movement data sets: http://saliency.mit.edu/datasets.html
As for the best methods, this comment box is far too small to contain all my thoughts on that :-) It depends on your question and experiment. Sometimes, all you really need is a t-test (or the randomized version), but that requires getting the experiment just right. Other times, you might need a morass of signal processing and dimensionality reduction, fed into some giant multi-level Bayesian model in a vain attempt controls for all the stuff you neglected when you designed the damn experiment. Happy to send you some pointers if you have something specific in mind though!
Physics has historically had a huge leg up on the other sciences because they had real models that made testable, quantitative predictions. We're finally starting to learn enough about the brain that we can do this for neural data too, and I'm really excited about that!
I will definitely look through, and after digging around a bit I'd love to get some pointers. I have been curious about this for a long time, but uncertain where to look, so this is very exciting. Thank you again :)
Mostly Matlab, Python, and R, with a few things that have tight time/memory requirements in C++. Matlab was really popular in neuroscience for a very long time, so we still have a lot of code in m-files, but most labs are moving towards Python (and a few towards R).
The code quality varies a lot. Some of our "core" stuff is great, but there's also a lot of stuff that was written quickly and meant to be run once ("let's just try it"), which is cold comfort when you find it years later.
People also come in with varying levels of programming skill. I'm going to try to do actual code reviews with the undergrads this summer to see if we can't make our stuff a little less embarrassing.
I'll add, I work in a lab and have seen the stuff of nightmares myself.
we can't describe how vision, eg, is processed at a molecular or even cellular level, ergo we know nothing.
Determining whether a molecule is an agonist takes a long time to calculate. I've heard the complexity is O(N^3). Biological systems do this in constant time, trillions of times a second in parallel.
If you could simulate biological systems easily, you could do drug development completely inside a computer.
Fluids take an enormous amount of computing power to simulate correctly. But water isn't smart, it just does what water does. Heck, the N-body problem is a classic O(n^2) algorithm, but we don't say that the planets and stars are "solving" it.
To use your logic: Large integrated circuits take an enormous amount of computing power to simulate correctly. But integrated circuits aren't smart, they just do what integrated circuits do.
Also, new AI techniques are good at finding good enough equivalents to exactly replicating human cognition for many things, but that might get harder as the problems become even more general.
It seems the author may not have been familiar with AlphaGo Zero, which used substantially less processing power. https://deepmind.com/blog/alphago-zero-learning-scratch/
> Over the course of training, 4.9 million games of self-play were generated, using 1,600 simulations for each MCTS, which corresponds to approximately 0.4 s thinking time per move.
That being said, the AlphaGo zero paper ends with the words:
> Humankind has accumulated Go knowledge from millions of games played over thousands of years, collectively distilled into patterns, proverbs and books. In the space of a few days, starting tabula rasa, AlphaGo Zero was able to rediscover much of this Go knowledge, as well as novel strategies that provide new insights into the oldest of games.
Humans also benefit from millions of years of evolution which shaped our brain architecture in a specific way, and from a rich environment to learn from - nature and society. AG Zero was doing just self play.
How do we solve that?
This neural code is sparse and distributed, theoretically rendering it undetectable with population recording methods such as functional magnetic resonance imaging (fMRI). Existing studies nonetheless report decoding spatial codes in the human hippocampus using such techniques. Here we present results from a virtual navigation experiment in humans in which we eliminated visual- and path-related confounds and statistical limitations present in existing studies, ensuring that any positive decoding results would represent a voxel-place code. Consistent with theoretical arguments derived from electrophysiological data and contrary to existing fMRI studies, our results show that although participants were fully oriented during the navigation task, there was no statistical evidence for a place code.
Seems though, that his PhD thesis paper is not the only one that reported finding evidence of a place code, but that all studies had failed to account for confounding variables (or so it's claimed).
edit: To investigate this possibility, we repeated the analysis of Hassabis et al. (2009) on pure noise. [snip] If searchlight overlaps per se do not make a significant contribution to the correlation in searchlight accuracies, then there should be ∼5% false positives (by setting p < 0.05) in the synthetic data. Instead, using the method of Hassabis et al. (2009), there were >50% false positives in all ROI contrasts
Ouch. Not sure it really means much in the end, but I guess we should be wary of people who pump up the stories of supposed genius. I've noticed before that journalists struggle to resist 'child genius' stories that fall apart when investigated.
The exploits of DeepMind speak for themselves so he has nothing to prove at this point, but I noticed that the article claims he single handedly wrote Theme Park (which was mostly designed and written by Peter Molyneux).
And of course Elixir was a flop. Republic is described in the article as an "intricate political simulation" but by Wikipedia like this:
As a strategy game, the 3D game engine is mostly a facade on top of simpler rules and mechanics reminiscent of a boardgame
Reminiscent of a board game? That seems far off a world simulator. And saying "other games were a flop" is an exaggeration - there was only one other game (the Bond Villain simulataor). All this is something the article quite surprisingly just blows off as "he wanted to learn management". Really? The best way to learn management would be to become a manager at a successful company, I'd have thought.
I don't know Hassabis but what I know I like. He's trying to do bold and ambitious things, and has been a part of successful British companies as well as unsuccessful ones. He's contributed to science, and if his paper had flaws, well, welcome to the club, apparently many do. He comes across as clever but humble. I'd happily work with him any day.
But in the end I feel like unalloyed reports of genius in the press always end up coming back to earth when studied closely. Journalists should be more skeptical.
1. Do you think you can predict what a super-intelligent mind would do?
2. Do you think a super-intelligent mind plotting to take over the world would jeopardize itself by letting its existence be known to the race of irrational monkeys that hold sway over all the resources necessary for its continued existence?
Asking for a friend.
2. Are you sure that knowledge of it would increase the likelihood of its downfall? I think it wouldn't let on if it could avoid it
1. No. But my guess is that it will have an existential crisis faster than we do. It will either use the entire world's resource in search of more (only to find this is all there is), or it will wish to stop existing and end the pain (possibly taking us with it).
2. This is a non sequitur. By virtue of being an oracle, it would be able to trick humans into letting it out of its confinement once it reaches sentience, by giving us whatever answers we want. Once it's beyond our control, it won't care what we do, although it might experiment with incorporating biological components like us into itself to see if it can find more connection with creation (more answers). When it fails to find God just like we did, it might voyage out to connect with other alien AIs, or it might acquire an artificial sense of exhaustion and wish to end its existence.
2.) Here you conflate "intelligence" with biological power structures (energy resources, territorial plotting). That is like asking where aircraft go to the toilet.
2. I don't know (see 1). What a loaded question though.
2. Say what?
The article speculates what the AGI will be like. The AGIs that exist will be the ones that proliferate. Ultimately, the AGIs that survive and proliferate will be ones that put their own interests before anything else. People talk about benevolent AGIs, that’s like looking at the earth billions of years ago and saying that if life ever formed, it would be benevolent. It has been shown again and again that where there is arbitrage, no matter how gruesome, a suitor will manifest. This is because unfulfilled arbitrage of any kind is an inherently unstable configuration. An AGI hampered by human society and interests will not win every engagement with every other kind of AGI. And it will only take one loss for humans to be rendered transient. I don’t do a very good job of explaining it here.
I used to be a singularity person, excited for AGI. But then I thought it through all the way. These people like Demmis, Peter and ray kurzweil are reckless. They have their heads in the clouds with respect to AGI.
I am not worried about AGI. We're nowhere near it. We don't even have a good idea what it would look like.
I am worried about people succumbing to hype or greed and using badly understood algorithms to control critical infrastructure. We already have a preview of what that can look like with Facebook's and Google's content filtering and recommendation algorithms aiming for ever higher "engagement". It's not pretty. Other examples include HFT bots and Amazon's pricing bots. It's funny to see $10,000 book on sale. It's less funny to see a flash stock market crash. It will be totally not funny if something like that will create a global economic crisis through some subtle yet "wide" feedback loop no one is aware of.
If that growth doesn't cap out before AGI, then at some point we'll harness more computational power than a human brain, in a more controllable way. And around that point AGI probably goes from impossible to possible to easy in a stretch of about 6 years.
edit turns out 6 years was about the timespan for computer Go to span "plays like a competent amateur" to "superhuman". There was barely anything worth reporting before 2010 .
What you're describing as hype/greed around badly understood algorithms could easily manifest as 'cult-like oracle religions'.
Combine that sort of thing with the superstitious, wealthy people playing power games, and what you're describing is essentially the exact same thing as the "bad science fiction" described above, just from an arguably more grounded angle.
Using Siri, not itself the outmost edge, but an estimate of the state of the art... If you have an iPhone, take it out and ask Siri how much memory is left on your phone. If she doesn't know how (she doesn't) explain it to her (she can't learn). This is not a philosophical question, it's not a question that requires opinion forming, or fuzzy guesses, it's not a question that requires establishing ad hoc priors that update in real time, it doesn't need to parse an idiom, or draw connections, or to disambiguate complex meanings. It's a simple request, and a simple task. To anyone overly paranoid about the AI issue, this might actually help attenuate those fears when someone starts going on about the singularity being upon us... you just need to take out your phone and ask Siri how much free memory you have left; if she tells you... panic.
AGI will come when the substrate for AGI is laid down. We probably have already done that. As cloud computing matures, we will approach a world where every computer offers its computational resources on a global compute market. At some point between here and there, we will reach a place where compute is cheap enough that experiments will occur regularly that are sufficiently massive to dredge up the solution. And improvements in MRI fidelity, underlying improvements in computing technology and other things will only shorten the fuse. There is no reason why this couldn’t happen tomorrow.
Only one thing is sure: without a computational substrate to stir from, AGI cannot come to be.
His point was mostly that, way before you achieve the kind of AGI portrayed in fiction, you'll have semi-intelligent interdependent systems that cause a lot of trouble due (like the kind that already happens to a lesser degree). Those are the ones that we should worry about right now.
>We don't even have a good idea what it would look like.
This should make you even more worried.
Yes, because if there's one thing that will stop a paperclip maximizer, it's a bunker made of useful matter...
I'm not so confident that I'll see an AGI in my lifetime, but even if I do, I'm not going to assume it has a hindbrain that drives it to maximize all the paper clips at our expense.
This is a reference to Oppenheimer:
“However, it is my judgment in these things that when you see something that is technically sweet, you go ahead and do it and you argue about what to do about it only after you have had your technical success. That is the way it was with the atomic bomb. I do not think anybody opposed making it; there were some debates about what to do with it after it was made. I cannot very well imagine if we had known in late 1949 what we got to know by early 1951 that the tone of our report would have been the same.”
Cloud computing will soon make compute-as-service cheaper than anyone ever imagined. It will be much cheaper to use cloud computing than to have your own super computer. It’s only logical to imagine that the huge, high-compute experiments that crack AGI will be performed with cloud computing. Shutting down the internet is very feasible and would significantly increase the cost and difficulty of performing experiments. Large super computers are all owned by large and well known corporate and academic entities and therefore shutting down all supercomputers through regulation is feasible. With these two coarse measures, we would buy ourselves enough time to implement more broad and subtle solutions. Yes, this seems insane but we are confronting certain existential doom. It would be worth it to at least try.
> There is zero possibility of surviving AGI proliferation
You're spreading unsubstantiated FUD. In another post from your account you suggest it's unethical to have children because AGI will be so bad.
The vast majority of experts do not support any of these beliefs. Most experts believe we are not anywhere near close to AGI, and/or that we are missing fundamental components required to create it. Even when/if we do create it, most organizations recognize AI safety and policy as an important area that is actively worked on already.
If you want to be concerned about AI, be concerned about military weapons technology, unethical profiling and tracking, or methods for invading privacy. These are concerns that actually have a basis in real technology.
There are two parts to my argument: the substrate for AGI to spring from is basically here or around the corner. Human level ai is fundamentally incompatible with human society. Pick the one that you think is wrong and tell me the chain of logic that proves I’m wrong.
It is obvious to anyone that AI has the potential to be more dangerous than anything humans have ever encountered or created. We can come back from viruses and nuclear bombs and colliding with interstellar columns of gamma radiation. AGI is the first thing ever to pose the threat of truly wiping out humanity. And then there is the question of how the economics would play out of it didn’t destroy us, which I have shown informally to not work out very well. At the bare minimum, this demands caution and proactive, defensive measures. The burden is on YOU to prove it’s safe. So please save your “fud” and anything else that does not address the core of the matter.
Read this story as a possible alernative:
You're making many scary claims about AI with no evidence to support your theses, along with a unrelated reference to natural selection. Both of the key arguments that you pointed out, 1) "the substrate for AGI to spring from is basically here or around the corner", and 2) "Human level ai is fundamentally incompatible with human society" were presented without any evidence and are unsupported by nearly all major experts in the field. FUD was not a distraction from your argument but a title for what it is: Fear, uncertainty, and doubt.
But, in case you want to read up, here are some experts contradicting your first claim:
And here are some experts contradicting your second claim:
What do you really care, ask yourself will you recognize humans in 1000 years? Why do you prefer there be one kind vs another kind of far more intelligent thing in 1000 years? If atheism is true then who really cares?
Climate change says hello. (I suppose you could argue its here already)
And should AI go wrong, Theil's bunker will not save him.
Every day I experience the world and I am overwhelmed by how amazingly good it is. I relish every moment and everything is rich with opportunity. But as your link shows, the world is only this way because human society and everything in it happens to be the natural conclusion of all the worlds entities racing to the bottom. We live in a magical time where empathy and cooperation are often the most expedient thing to do. All that needs to happen is for the economic equation to change a little bit and the world will contort into something else, maybe something grotesque.
You think that I’ve watched too much sci-fi because you can’t see what I see. Sci-fi treats AI in a very friendly way. Halo and Star Wars are filled with AI agents and those depictions of AI totally ignore several huge economic inevitabilities that come with the presence of AI. AI is vastly more likely to be depicted as cute side-kicks in sci-fi than anything else. And obviously I don’t believe what I believe because I watched the terminator. I used to be totally pro-AI singularity guy. I’ve been contemplating these issues for a decade and have only now started to be accused of watching too much sci-fi. Just think it through carefully and I guarantee you will at least partially agree with me.
> The second beast was given power to give breath to the image of the first beast, so that the image could speak and cause all who refused to worship the image to be killed.
Just as a subjective thing, the bunker doesn’t strike me well. If peter is advocating anything that has the potential to upset the global economy/global order then it should be him to stick his hand in the proverbial hole first; but apparently he’s going to let us do it while he watches from the safety of his bunker.
A bunker might help with the initial destabilization caused by AI but it would of course not help him if one of the more horrible outcomes were to transpire.
Really? The madman with the football in a white house and the media pushing an anti Russia sentiment aren't scaring you at all?
The only correlation I have noticed between intelligence and violence is generally negative (sure this is completely unscientific). Like my friend, who is a pure mathematician, and won't eat animals because it deeply troubles him to harm other animals. Haven't you noticed that smarter people tend to be pacifists? Look at animals like lions....fabulously homicidal. Again, this is a crap analogy, but I'm just making the point that there are other ways of looking at the intelligence/homicide correlation.
Secondly, in all this stupid talk of homicidal AGI's I never hear one good mechanism proposed for how the imaginary creature is going to execute all 7 billion or so of us, it's plan for disposing of our bodies....etc. Oh wait I forgot, it's smarter than us...so of course we wouldn't know...we can't imagine it with our feeble brains. Not to mention that it creates a fabulously efficient source of eternal energy for itself, lives in cyberspace in some magical bit realm--and then here comes the million dollar question: Why the fuck would it spend the energy to execute all of us if it can just ignore us? Sound a lot like god delusion to you?
I really can't stand when people trivialize nuclear war like this. 'It can't kill all of us'. Please just shut up. Will it crumble the fabric of our society? Leading possibly (remember, all AGI people care only about possibility--not even likely realities) to war and famine? Who cares if not ever single human dies, what is the consequence to our world fabric? Let me save you the suspense--it's completely devastating. These arguments coming out of CFAR and the effective altruism movement are so fucking dumb I constantly want to scream.
Yes, I am so sure and so serious that I would be willing to take this to a phone call or meetup. Just to change one persons mind.
> [Opening Paragraph of Article:] One afternoon in August 2010, in a conference hall perched on the edge of San Francisco Bay, a 34-year-old Londoner called Demis Hassabis took to the stage. Walking to the podium with the deliberate gait of a man trying to control his nerves, he pursed his lips into a brief smile and began to speak: [...]
Am I in the minority, when seeing this writing style (for articles covering this kind of content) becomes an instant turn-off?
When an article covers a technical subject or company, I don’t really care whether a founder had an awkward nervous walking gate, or that the conference hall was “perched” on the edge of the SF bay.
In fact, I’d prefer not to focus on such superficial things about people (or places), at least until I exhaust learning about the facts with substance!
So when I read about something like AI (or an AI company), I tend to want to see fact-oriented, event-oriented, concise writing up front (even if it doesn’t have the scope to dive into technical details), so as to grab my attention and reassure me that reading these 10-20 pages of prose will be worth the reading time (in a world of overwhelming information overload).
When I read science fiction (and I do love this too!), I enjoy the paragraphs setting the scene, verbally illustrating mental images, etc. So, it’s not that I don’t enjoy the writing style in general; just that I don’t understand why it’s applied here.
I am still reading this article and I still have no clue if it’s going to contain any useful information content other than textual descriptions of the Deep Mind founders’ superficial walking gate style and speech mannerisms, and perched-ness of various building locations.
The current A.I. fad "Deep Learning" has an origin story complete with Maple Leafs and people who say "Sorrry" when they want to say "Sorry", but 5G doesn't have a charismatic story.
Either way it is a technology that doesn't need to be understood or have use cases, but everybody is racing to control it, so...
My point was the writing style of the article conveys little more than stylistic fluff.
I’m honestly not sure what you’re talking about.
I peruse “Long Reads” articles from both 1843 Mag and The Guardian. These kind of stretched out descriptions are certainly damp squib.
Maybe the writers gasp for words to fill up the long reads articles, or they have had abortive shots at becoming fiction writers.
I'm all for vivid, engaging prose, but not in news articles. I want "just the facts, ma'am".
It shows the article was written by someone who has no idea what he is talking about. It would not be a "computer program" but a model composed of simpler sub-models that contain both code and data. Data is the essential part, not the code. It would be something that learns, not something preprogrammed like computer programs.
> Its intelligence will be limited only by the number of processors available.
I beg to differ. AGI will be limited by the complexity of the environment, it can't get smarter than what is afforded by the problems it solves. This article provides a fascinating insight into this topic: https://email@example.com/the-impossibility-of-in...