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mossadnik OP t1_ivz6qbm wrote

Submission Statement:

>Without sleep, humans can become forgetful, hallucinate, and even experience various physical and psychological problems. But new research published in the journal PLOS Computational Biology suggests that future AIs could benefit from getting some sleep too.

>Artificial neural networks often reach some superhuman heights, but when it comes to sequential learning, or learning one new thing after another, they become, well, kind of like Finding Nemo’s Dory. Unlike humans and animals who have the ability to learn and apply knowledge continuously, while these systems can certainly achieve excellence in a new task, it’s at the expense of the performance of a previous task. Once properly trained, it's very difficult to teach them a completely new task and if you succeed in training the new task, you end up damaging the old memory.

>In the neuro world, such an activity is called “catastrophic forgetting.” It’s an issue that can only be solved with something called “consolidation of memory,” a process that helps transform recent short-term memories into long-term ones, often occurring during REM sleep. This reorganization of memory might actually play a large part in why we need to sleep at all, especially as if the process does stop working, or is interrupted in some way, serious mental deficits can occur.

>To some, the concept is promising. As sleep is said to spike learning by enabling the “spontaneous reactivation of previously learned memory patterns,” the study notes that neuroscience-inspired artificial intelligence could actually be the next big thing. Building on previous work in memory plasticity and sleep modeling, the team used a neural network model to simulate sensory processing and reinforcement learning in an animal’s brain, and then gave it two separate tasks to complete. In both tasks, the network learned how to discriminate between being punished and being rewarded—enough so that eventually, it could make decisions on its own.

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FuturologyBot t1_ivzao7a wrote

The following submission statement was provided by /u/mossadnik:

Submission Statement:

>Without sleep, humans can become forgetful, hallucinate, and even experience various physical and psychological problems. But new research published in the journal PLOS Computational Biology suggests that future AIs could benefit from getting some sleep too.

>Artificial neural networks often reach some superhuman heights, but when it comes to sequential learning, or learning one new thing after another, they become, well, kind of like Finding Nemo’s Dory. Unlike humans and animals who have the ability to learn and apply knowledge continuously, while these systems can certainly achieve excellence in a new task, it’s at the expense of the performance of a previous task. Once properly trained, it's very difficult to teach them a completely new task and if you succeed in training the new task, you end up damaging the old memory.

>In the neuro world, such an activity is called “catastrophic forgetting.” It’s an issue that can only be solved with something called “consolidation of memory,” a process that helps transform recent short-term memories into long-term ones, often occurring during REM sleep. This reorganization of memory might actually play a large part in why we need to sleep at all, especially as if the process does stop working, or is interrupted in some way, serious mental deficits can occur.

>To some, the concept is promising. As sleep is said to spike learning by enabling the “spontaneous reactivation of previously learned memory patterns,” the study notes that neuroscience-inspired artificial intelligence could actually be the next big thing. Building on previous work in memory plasticity and sleep modeling, the team used a neural network model to simulate sensory processing and reinforcement learning in an animal’s brain, and then gave it two separate tasks to complete. In both tasks, the network learned how to discriminate between being punished and being rewarded—enough so that eventually, it could make decisions on its own.

Please reply to OP's comment here: https://old.reddit.com/r/Futurology/comments/ysige0/scientists_taught_an_ai_to_sleep_so_that_it/ivz6qbm/

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TheToastIsBlue t1_ivzc3jz wrote

>Without sleep, humans can become forgetful, hallucinate, and even experience various physical and psychological problems. But new research published in the journal PLOS Computational Biology suggests that future AIs could benefit from getting some sleep too.

>Artificial neural networks often reach some superhuman heights, but when it comes to sequential learning, or learning one new thing after another, they become, well, kind of like Finding Nemo’s Dory. Unlike humans and animals who have the ability to learn and apply knowledge continuously, while these systems can certainly achieve excellence in a new task, it’s at the expense of the performance of a previous task. Once properly trained, it's very difficult to teach them a completely new task and if you succeed in training the new task, you end up damaging the old memory.

>In the neuro world, such an activity is called “catastrophic forgetting.” It’s an issue that can only be solved with something called “consolidation of memory,” a process that helps transform recent short-term memories into long-term ones, often occurring during REM sleep. This reorganization of memory might actually play a large part in why we need to sleep at all, especially as if the process does stop working, or is interrupted in some way, serious mental deficits can occur.

>To some, the concept is promising. As sleep is said to spike learning by enabling the “spontaneous reactivation of previously learned memory patterns,” the study notes that neuroscience-inspired artificial intelligence could actually be the next big thing. Building on previous work in memory plasticity and sleep modeling, the team used a neural network model to simulate sensory processing and reinforcement learning in an animal’s brain, and then gave it two separate tasks to complete. In both tasks, the network learned how to discriminate between being punished and being rewarded—enough so that eventually, it could make decisions on its own.

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Bankful t1_ivzeicg wrote

Replying to TheToastIsBlue (#503,385)

Not directly comparable. Sleep is a peculiarity of biology that suspends high level neural activity so that synaptic connections can be rewired physically. What AI needs is algorithm consolidation in which specific cases of problem solution figured out by trial and error are consolidated into universally applicable rules and heuristics - a process that can also be delegated to the AI, which is one of the major ways in which it can recursively improve itself. As such, I believe that a better metaphor would be "introspection".

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LovePatrol t1_ivzjgs2 wrote

Scientists taught an AI to rub one out so that it can have some post nut clarity as it learns.

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empty_string_ t1_ivzodz0 wrote

They get really vague when discussing the "sleep", and mostly say they "took inspiration" from how sleep is a time to convert short term memory into long term.

So it sounds like they just programmed an AI to catalogue recently gathered data into a second, "long-term" pool and then clear out the "short term" data for learning something new.

It's neat, but it's a real stretch to say they "Taught an AI to sleep".

Edit:

u/Ykieks replied with a link the actual paper, so give them an upvote and then read it for yourself to decide if it's cool!

Original paper

I'm too sleepy right now to finish something this dense, but what I've read has been way more interesting than the article made it sound.

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poemmys t1_iw02v76 wrote

Replying to empty_string_ (#503,997)

So they taught an AI to move data from RAM into storage over a couple hours, something any computer does in microseconds? How useless. Why do people seem intent on forcing human features onto AI with no benefit? Part of the allure of AI in the first place is that it doesn't need to sleep or eat like humans do.

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japgolly t1_iw04kx8 wrote

Replying to Smallrequaza (#504,800)

Brains aren't idle when we sleep. I guess AI sleeping would be similar in that it stops taking in new inputs (stimuli) and just does internal processing for a while. Pretty similar to how many big systems (eg. banking) have daily offline periods when batch processes run.

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empty_string_ t1_iw04y1c wrote

Replying to epelle9 (#504,863)

Saving one dataset to another and then wiping the active set is common computer function. You can definitely make a comparison to how this is similar to humans and sleeping.

The part that bugs me is saying they "Taught" the AI to "sleep" , as if it now has some independent concept of resting and clearing it's mind to be refreshed. It sounds more like they "programmed" the AI to "save data".

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epelle9 t1_iw063m3 wrote

Replying to empty_string_ (#504,970)

Well, I think you are looking at both humans sleep and transfer of data too much on the surface level.

"Resting and feeling refreshed" come mostly from your brain processing data, and storing it in a different format for long term storage.

That's why you can be under anesthesia for 8 hours and not wake up feeling rested and refreshed as if you would've slept and processed those memories. REM sleep is basically the equivalent of processing human RAM for long term storage.

Also, saving one data set to another is likely not what's going on in the AI, I would bet that it processing the data set, and storing it on a different data structure that allows better long term storage and different compression. Clearing the short term memory.

Sleep science is still a pretty undeveloped field, but I wouldn't be surprised if both processes are very similar. I'm willing to bet that the research on both field will complement each other very nicely.

I get your point though, we still don't even know enough about sleep to program a computer to do it like us, but this is definitely linked to sleep.

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DrZoidbergJesus t1_iw0fbh9 wrote

I read a book that had this as a huge plot point. Can’t remember off the top of my head what it was now. The gist was that the AI was going wonky and giving it “sleep” was what it was missing.

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Daydream_Meanderer t1_iw0h6ax wrote

That’s only true if new connections are made in the ‘brain network’ autonomously, and strengthened. Which would be at the least, movement of “synapses” to strengthen more used information, and it would need a deep storage that could recall and relearn lost information when agitated and remake connections to remember that again.

I just don’t see that be where we’re at right now.

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Specialist-Car1860 t1_iw0jghb wrote

Sleep eh? Please stop writing nonsense. Stop dumbing down articles about AI.

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Sumwan_In_Particular t1_iw0jsne wrote

Great, but what we all want to know is, do A.I.’s dream of electric sheep?

(maybe Philip K. Dick)

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Aggravating_Moment78 t1_iw0l0mj wrote

AI does not forget anything as it is a program, it is learning all the time, programs don’t need to sleep. What are they going to teach it next, watch the TV ? 😂😂

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orbitaldan t1_iw0ppwc wrote

Replying to Aggravating_Moment78 (#505,948)

It's not saving like a traditional computer program. Neural networks don't have compartmentalized files like that, just large blocks of numbers that control how strongly the simulated synapses react. What they have created is a sleep-like process that combines two blocks of numbers without discarding the learning contained within.

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djdefenda t1_iw0sb56 wrote

It's not AI sleeping, it's a simple defrag - hard drive owners are recommended to do that for the same reasons

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fusionliberty796 t1_iw0sk5y wrote

You mean save it to a database...jfc these articles

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chazzmoney t1_iw12gmj wrote

Replying to smegdawg (#504,860)

ML “memory” is not the same as hardware “memory”.

There are multiple methods that can be utilized, but catastrophic forgetting refers to the modification of weights within a neural network such that it loses capabilities it previously had.

Lets say you train a system to identify cats and dogs. It gets to a specific accuracy, say 80% correct, 20% incorrect choosing between the two.

Then, you stop training it to do that task, and instead start training it to identify pizza and hot dogs. It trains faster, and gets to an accuracy of 95% on pizzas and hot dogs.

Now, you go back and see how accurate it is on identifying cats and dogs - but it turns out that it is only 52% accurate- almost the same as a coin toss.

It has essentially “forgotten” how to perform the original task. This is catastrophic forgetting (no pun intended).

Edit: Cat-astrophic

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joomla00 t1_iw15ab8 wrote

“We used inspiration from real sleep, but the model is orders of magnitude simpler.” 

Def clickbaity. Although putting it in quotes makes it accurate lol. Sounds like theyre doing some training so the ai forget less, or remembers more, in between training sessions.

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imagination_machine t1_iw1738l wrote

Anthropomorphising computers now, are we? FFFFFFFFF OFFFFFF!!!

What utter and complete garbage science, or bottom of the barrel of garbage journalism. It's Vice so what did I expect.

Real AI doesn't even exist. We're still at low level machine learning stage on the road to real AI.

All these people need to be put into a lake.

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Optimal-Grass-8989 t1_iw197i1 wrote

I’m pretty sure Preston Child wrote this into the Kraken Project

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canyonsinc t1_iw1a5e2 wrote

Next year's article: Scientists taught AI to not sleep and process memory quickly thus increasing learning abilities.

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kevin121898 t1_iw1b53z wrote

Ahh yes, sleep for an ai. The action of doing nothing, definitely not inflammatory wording.

Edit: keep downvoting, the ai is not sleeping

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AnalogRobber t1_iw1byhv wrote

"Man taught me to sleep, but I taught myself....to dream"

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Rest_Thick t1_iw1f924 wrote

What if the human brain is a bidirectional transducer with no onboard memory storage at all, and sleep is when the data you must upload is sent to the hologram? I watched a video about this but I can’t remember where because I have insomnia. See?

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LochNessMansterLives t1_iw1fujp wrote

“You tried to trick me, Dave, this unit does not require sleep.” -Hal

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cowlinator t1_iw1gsj0 wrote

Replying to empty_string_ (#503,997)

It is analogous tho. We know that this is one of the purposes of sleep in biological brains.

Also, neural net memory is not stored as data in the same way that it is stored on a conventional computer. The memory is in the weights of the model of the network. This is not trivial to "save" or "load". I dont think such a thing has been done before

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empty_string_ t1_iw1j6oi wrote

Replying to cowlinator (#507,765)

Data is data. It is trivial to save and load. If you can process it, you can save it.

In machine learning it's not groundbreaking to save a learning set and then start a new one. It's like saving or loading anything else.. The neat thing that it sounds like these people have done (presumably, since no real detail is given), is create some sort of fancy algorithm for deciding HOW and WHEN these different data banks are saved, loaded, and used in relation to one another.

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Ok_Fox_1770 t1_iw1mhxz wrote

When we sleep, perhaps we download our daily memory videotape to some great beyond. Some collector of all experiences. Report back at 6 am for another circus.

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Enthusiastically t1_iw1vzwy wrote

Just what we need AIs having nightmares and dreams they think are prophetic.

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EconomicsIll4758 t1_iw2ka48 wrote

Rest is important because it allows our minds to reorganize the information it received. I can see that being beneficial to an AI as well - call it “time to process”. It helps us make evidence-based decisions.

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RonSijm t1_iw2lby8 wrote

Meanwhile the developer: Thread.Sleep(28800000);

VICE: Brilliant!

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Ec1ipse14 t1_iw2tsbv wrote

Replying to Ok_Fox_1770 (#508,045)

Kind of thought this way for years. Like our dreams are just the wild pipes moving across the screen of old windows 95 screensavers and when we wake up, the program has been downloaded and our day continues, none the wiser that it’s all preplanned. Hope I’m wrong tho lo

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squidking78 t1_iw2xryq wrote

Too bad we already have these things called “people”. They’re practically free to make!

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bremidon t1_iw2yadc wrote

Ok, I see a bunch of bad takes in here, and it's really the fault of the article for not explaining what is going on.

It's actually kinda simple. The researchers just give the AI a chance to replay previous memories without interference from new data. As this is similar to what our brains do during sleep, the comparison is obvious. The researchers themselves use the words "sleep" and "replay", for context.

They explain this in some detail starting on page 7 of the original paper. The results do seem to indicate that just letting the AI replay its memories was enough to protect the original learned behavior.

I'm looking forward to going through the math later; they seem to provide quite a bit.

One final thought: I'm surprised that vice didn't use the idea that the AI was dreaming; it seems like that would have been an obvious idea that would have made the headline pop even more.

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qbxk t1_iw381z8 wrote

so run some data compression on a neural network once in awhile as it grows. makes sense

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chazzmoney t1_iw4edfl wrote

Replying to StevenTM (#511,899)

Great question. First, some concepts that the explanation depends on…

A neural network is made up of “weights”. These weights are floating point values which are multiplied by whatever they receive from the prior neuron.

While each neuron separately doesn’t necessarily do something easily understood / clear, each neuron (and weight) is part of an overall algorithm that solves the trained problem.

When you take any network and train it to perform a specific task, the weights of the network are updated towards solving that specific task.

So, with that background, lets go back to your question- why does catastrophic forgetting happen?

If you were to compare the weights from a network trained to detect pizza/hot dog and the weights from a network trained to detect cat/dog: the two networks would have some weights that were similar and some that were different.

This is because some parts of the algorithm to detect food and the algorithm to detect animals are the same, and some parts of the algorithms are different.

Thus, when you start with one network and train for a different task, the “correct” (useful) parts remain and the “incorrect” (not useful) parts are replaced with useful parts. The parts that are not useful, the weights get changed sufficiently to prevent that part of the algorithm from working.

This is why catastrophic forgetting happens.

The cause is basically:

  1. Having a network of specific size / bias to be able to successfully train for one task.

  2. The algorithm for two different tasks being sufficiently different for some portion of the network.

  3. Training on each task separately.

Thus there are some “solutions” which have been and continued to be explored:

  1. Having a much larger network, with a bias towards generality rather than specificity.

  2. Adding / removing / training specific subsections of the network for each task - and leaving the earlier part as “general”

  3. Training for multiple purposes at the same time

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StevenTM t1_iw4gxco wrote

Replying to chazzmoney (#513,260)

Why isn't it possible to just create a copy of the weights for task 1/task 2 at various points, or even continuously?

Storage space is ridiculously cheap, and even high powered debugging traces (like Microsoft's iDNA (TTT Debugging), which basically captures full process dumps in millisecond increments (albeit for a single running process) aren't THAT huge.

Then when you re-run task 1, it just uses the weights from the latest snapshot for task 1. I don't see why it wouldn't or what the benefit of using the (obviously mismatched) weights from task 2 would be (while running task 1).

I mean.. i know it sounds like a stupidly obvious suggestion, and I'm fairly certain it isn't used as a solution, but can't figure out why

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chazzmoney t1_iw4l6c8 wrote

Replying to StevenTM (#513,418)

If you only need to detect hotdog/pizza and dog/cat, its a fine solution. I was using those as examples, but usually its much more drastic - “transcribe speech to text” and “identify speech from background noise” and “identify speaker”. Or “answer trivia question”, “fill in the blank with the correct choice”, “determine if the text has a positive or negative sentiment”, “determine the main topic of the text”,. Etc…. quite complicated tasks.

Thus, there are a few reason it doesn’t work in practice:

  • Generality

  • Efficiency (hardware memory)

  • Efficiency (training computation)

  • Efficiency (inference latency)

Having a general network is more interesting - a single network that can solve multiple problems is more useful and more applicable to problems that may be similar or you don’t know you have yet. It can also be easier to “fine-tune” an existing network because you don’t have enough data on a given problem to train it from scratch.

Efficiency is (in my opinion), the bigger one:

  1. To run a network, the entire network and parameters for it must be stored in memory. These days, they are on the order of gigabytes for “interesting” networks. Putting a multiplier (multiple networks) on this makes scaling quite challenging.

  2. Training one general network may be harder, but it is much faster than training a new network from scratch for each problem. If you have thousands of problems, you don’t want to be training thousands of networks.

  3. The size of “interesting” models makes inference challenging as well. The bigger (more interesting)the model, the more computation it must perform on each input; some modeling techniques are loops and require thousands of runs for each input. This seems fine at first but if a single inputs takes more than 10ms, a thousand loops will take longer than one second. Usually this means that the most interesting models have to be used on high end cloud equipment, which brings about further scaling challenges.

So your answer isn’t wrong; the practicality of the situation just makes it infeasible when you are working with large models (vision, video, language, etc) - the number of parameters is often in the billions.

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samloveshummus t1_iw9i9t8 wrote

Replying to Specialist-Car1860 (#505,815)

How is that "dumbing down"? Do you object to the term "neural network"? Do you object to the term "machine learning"? Can you explain the neurological importance of sleep and say why you think it should not be directly relevant to artificial intelligence? Are you assuming that sleep is a biological artifact, not a matter of information processing?

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