Category: Scenario planning

FAST(HARD) or Slow(soft)AGI takeoff – AGI Part 6

Posted on by Ray in AGI, Cognitive computing, Distributed computing, Forecasting, Scenario planning, Strategic Inflection Points

I was listening to a podcast a couple of weeks back and the person being interviewed made a comment that he didn’t believe that AGI would have a fast (hard) take off rather it would be slow (soft). Here’s the podcast John Carmack interviewed by Lex Fridman).

Hard vs. soft takeoff

A hard (fast) takeoff implies a relatively quick transition (seconds, hours, days, or months) between AGI levels of intelligence and super AGI levels of intelligence. A soft (slow) takeoff implies it would take a long time (years, decades, centuries) to go from AGI to super AGI.

We’ve been talking about AGI for a while now and if you want to see more about our thoughts on the topic, check out our AGI posts (in most recent order: AGI part 5, part 4, part 3 (ish), part (2), part (1), and part (0)).

The real problem is that many believe that any AGI that reaches super-intelligence will have drastic consequences for the earth and especially, for humanity. However, this is whole other debate.

The view is that a slow AGI takeoff might (?) allow sufficient time to imbue any and all (super) AGI with enough safeguards to eliminate or minimize any existential threat to humanity and life on earth (see part (1) linked above).

A fast take off won’t give humanity enough time to head off this problem and will likely result in an humanity ending and possibly, earth destroying event.

Hard vs Soft takeoff – the debate

I had always considered AGI would have a hard take off but Carmack seemed to think otherwise. His main reason is that current large transformer models (closest thing to AGI we have at the moment) are massive and take lots of special purpose (GPU/TPU/IPU) compute, lots of other compute and gobs and gobs of data to train on. Unclear what the requirements are to perform inferencing but suffice it to say it should be less.

And once AGI levels of intelligence were achieved, it would take a long time to acquire any additional regular or special purpose hardware, in secret, required to reach super AGI.

So, to just be MECE (mutually exclusive and completely exhaustive) on the topic, the reasons researchers and other have posited to show that AGI will have a soft takeoff, include:

  • AI hardware for training and inferencing AGI is specialized, costly, and acquisition of more will be hard to keep secret and as such, will take a long time to accomplish;
  • AI software algorithmic complexity needed to build better AGI systems is significantly hard (it’s taken 70yrs for humanity to reach todays much less than AGI intelligent systems) and will become exponentially harder to go beyond AGI level systems. This additional complexity will delay any take off;
  • Data availability to train AGI is humongous, hard to gather, find, & annotate properly. Finding good annotated data to go beyond AGI will be hard and will take a long time to obtain;
  • Human government and bureaucracy will slow it down and/or restrict any significant progress made in super AGI;
  • Human evolution took Ms of years to go from chimp levels of intelligence to human levels of intelligence, why would electronic evolution be 6-9 orders of magnitude faster.
  • AGI technology is taking off but the level of intelligence are relatively minor and specialized today. One could say that modern AI has been really going since the 1990s so we are 30yrs in and today have almost good AI chatbots today and AI agents that can summarize passages/articles, generate text from prompts or create art works from text. If it takes another 30 yrs to get to AGI, it should provide sufficient time to build in capabilities to limit super-AGI hard take off.

I suppose it’s best to take these one at a time.

  • Hardware acquisition difficulty – I suppose the easiest way for an intelligent agent to acquire additional hardware would be to crack cloud security and just take it. Other ways may be to obtain stolen credit card information and use these to (il)legally purchase more compute. Another approach is to optimize the current AGI algorithms to run better within the same AGI HW envelope, creating super AGI that doesn’t need any more hardware at all.
  • Software complexity growing – There’s no doubt that AGI software will be complex (although the podcast linked to above, is sub-titled that “AGI software will be simple”). But any sub-AGI agent that can change it’s code to become better or closer to AGI, should be able to figure out how not to stop at AGI levels of intelligence and just continue optimizating until it reaches some wall. i
  • Data acquisition/annotation will be hard – I tend to think the internet is the answer to any data limitations that might be present to an AGI agent. Plus, I’ve always questioned if Wikipedia and some select other databases wouldn’t be all an AGI would need to train on to attain super AGI. Current transformer models are trained on Wikipedia dumps and other data scraped from the internet. So there’s really two answers to this question, once internet access is available it’s unclear that there would be need for anymore data. And, with the data available to current transformers, it’s unclear that this isn’t already more than enough to reach super AGI
  • Human bureaucracy will prohibit it: Sadly this is the easiest to defeat. 1) there are roque governments and actors around the world with more than sufficient resources to do this on their own. And no agency, UN or otherwise, will be able to stop them. 2) unlike nuclear, the technology to do AI (AGI) is widely available to business and governments, all AI research is widely published (mostly open access nowadays) and if anything colleges/universities around the world are teaching the next round of AI scientists to take this on. 3) the benefits for being first are significant and is driving a weapons (AGI) race between organizations, companies, and countries to be first to get there.
  • Human evolution took Millions of years, why would electronic be 6-9 orders of magnitude faster – electronic computation takes microseconds to nanoseconds to perform operations and humans probably 0.1 sec, or so. Electronics is already 5 to 8 orders of magnitude faster than humans today. Yes the human brain is more than one CPU core (each neuron would be considered a computational element). But there are 64 core CPUs/4096 CORE GPUs out there today and probably one could consider similar in nature if taken in the aggregate (across a hyperscaler lets say). So, just using the speed ups above it should take anywhere from 1/1000 of a year to 1 year to cover the same computational evolution as human evolution covered between the chimp and human and accordingly between AGI and AGIx2 (ish).
  • AGI technology is taking a long time to reach, which should provide sufficient time to build in safeguards – Similar to the discussion on human bureaucracy above, with so many actors taking this on and the advantages of even a single AGI (across clusters of agents) would be significant, my guess is that the desire to be first will obviate any thoughts on putting in safeguards.

Other considerations for super AGI takeoff

Once you have one AGI trained why wouldn’t some organization, company or country deploy multiple agents. Moreover, inferencing takes orders of magnitude less computational power than training. So with 1/100-1/1000th the infrastructure, one could have a single AGI. But the real question is wouldn’t a 100- or 1000-AGis represent super intelligence?

Yes and no, 100 humans doesn’t represent super intelligence and a 1000 even less so. But humans have other desires, it’s unclear that 100 humans super focused on one task wouldn’t represent super intelligence (on that task).

Interior view of a data center with equipment

What can be done to slow AGI takeoff today

Baring something on the order of Nuclear Proliferation treaties/protocols, putting all GPUs/TPUs/IPUs on weapons export limitations AND restricting as secret, any and all AI research, nothing easily comes to mind. Of course Nuclear Proliferation isn’t looking that good at the moment, but whatever it’s current state, it has delayed proliferation over time.

One could spend time and effort slowing technology progress down. Such as by reducing next generation CPU/GPU/IPU compute cores , limiting compute speedups, reduce funding for AI research, putting a compute tax, etc. All of which, if done across the technological landscape and the whole world, could give humanity more time to build in AGI safeguards. But doing so would adversely impact all technological advancement, in healthcare, business, government, etc. And given the proliferation of current technology and the state actors working on increasing capabilities to create more, it would be hard to envision slowing technological advancement down much, if at all.

It’s almost like putting a tax on slide rules or making their granularity larger.

It could be that super AGI would independently perceive itself benignly, and only provide benefit to humanity and the earth. But, my guess is that given the number of bad actors intent on controlling the world, even if this were true, they would try to (re-)direct it to harm segments of humanity/society. And once unleashed, it would be hard to stop.

The only real solution to AGI in bad actor hands, is to educate all of humanity to value all humans and to cherish the environment we all live in as sacred. This would eliminate bad actors,

It sounds so naive, but in reality, it’s the only thing, I believe, the only way we can truly hope to get us through this AGI technological existential crisis.

Just like nuclear, we as a society will keep running into technological existential crisis’s like this. Heading all these off, with a better more all inclusive, more all embracing, and less combative humanity could help all of them.

Comments?

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Safe AI

Posted on by Ray in AGI, Artificial Intelligence, Cognitive computing, Information economy, Machine Learning, Reinforcement Learning, Robots, Scenario planning, Strategic Inflection Points, System quality

I’ve been writing about AGI (see part-0 [ish], part-1 [ish], part-2 [ish], part-3ish, part-4 and part 5) and the dangers that come with it (part-0 in the above list) for a number of years now. My last post on the subject I expected to be writing a post discussing the book Human compatible AI and the problem of control which is a great book on the subject. But since then I ran across another paper that perhaps is a better brief introduction into the topic and some of the current thought and research into developing safe AI.

The article I found is Concrete problems in AI, written by a number of researchers at Google, Stanford, Berkley, and OpenAI. It essentially lays out the AI safety problem in 5 dimensions and these are:

Avoiding negative side effects – these can be minor or major and is probably the one thing that scares humans the most, some toothpick generating AI that strips the world to maximize toothpick making.

Avoiding reward hacking – this is more subtle but essentially it’s having your AI fool you in that it’s doing what you want but doing something else. This could entail actually changing the reward logic itself to being able to convince/manipulate the human overseer into seeing things it’s way. Also a pretty bad thing from humanity’s perspective

Scalable oversight – this is the problem where human(s) overseers aren’t able to keep up and witness/validate what some AI is doing, 7×24, across the world, at the speed of electronics. So how can AI be monitored properly so that it doesn’t go and do something it’s not supposed to (see the prior two for ideas on how bad this could be).

Safe exploration – this is the idea that reinforcement learning in order to work properly has to occasionally explore a solution space, e.g. a Go board with moves selected at random, to see if they are better then what it currently believes are the best move to make. This isn’t much of a problem for game playing ML/AI but if we are talking about helicopter controlling AI, exploration at random could destroy the vehicle plus any nearby structures, flora or fauna, including humans of course.

Robustness to distributional shifts – this is the perrennial problem where AI or DNNs are trained on one dataset but over time the real world changes and the data it’s now seeing has shifted (distribution) to something else. This often leads to DNNs not operating properly over time or having many more errors in deployment than it did during training. This is probably the one problem in this list that is undergoing more research to try to rectify than any of the others because it impacts just about every ML/AI solution currently deployed in the world today. This robustness to distributional shifts problem is why many AI DNN systems require periodic retraining.

So now we know what to look for, now what

Each of these deserves probably a whole book or more to understand and try to address. The paper talks about all of these and points to some of the research or current directions trying to address them.

The researchers correctly point out that some of the above problems are more pressing when more complex ML/AI agents have more autonomous control over actions in the real world.

We don’t want our automotive automation driving us over a cliff just to see if it’s a better action than staying in the lane. But Go playing bots or article summarizers might be ok to be wrong occasionally if it could lead to better playing bots/more concise article summaries over time. And although exploration is mostly a problem during training, it’s not to say that such activities might not also occur during deployment to probe for distributional shifts or other issues.

However, as we start to see more complex ML AI solutions controlling more activities, the issue of AI safety are starting to become more pressing. Autonomous cars are just one pressing example. But recent introductions of sorting robots, agricultural bots, manufacturing bots, nursing bots, guard bots, soldier bots, etc. are all just steps down a -(short) path of increasing complexity that can only end in some AGI bots running more parts (or all) of the world.

So safety will become a major factor soon, if it’s not already

Scares me the most

The first two on the list above scare me the most. Avoiding negative or unintentional side effects and reward hacking.

I suppose if we could master scalable oversight we could maybe deal with all of them better as well. But that’s defense. I’m all about offense and tackling the problem up front rather than trying to deal with it after it’s broken.

Negative side effects

Negative side effects is a rather nice way of stating the problem of having your ML destroy the world (or parts of it) that we need to live.

One approach to dealing with this problem is to define or train another AI/ML agent to measure impacts the environment and have it somehow penalize the original AI/ML for doing this. The learning approach has some potential to be applied to numerous ML activities if it can be shown to be safe and fairly all encompassing.

Another approach discussed in the paper is to inhibit or penalize the original ML actions for any actions which have negative consequences. One approach to this is to come up with an “empowerment measure” for the original AI/ML solution. The idea would be to reduce, minimize or govern the original ML’s action set (or potential consequences) or possible empowerment measure so as to minimize its ability to create negative side effects.

The paper discusses other approaches to the problem of negative side effects, one of which is having multiple ML (or ML and human) agents working on the problem it’s trying to solve together and having the ability to influence (kill switch) each other when they discover something’s awry. And the other approach they mention is to reduce the certainty of the reward signal used to train the ML solution. This would work by having some function that would reduce the reward if there are random side effects, which would tend to have the ML solution learn to avoid these.

Neither of these later two seem as feasible as the others but they are all worthy of research.

Reward hacking

This seems less of a problem to our world than negative side effects until you consider that if an ML agent is able to manipulate its reward code, it’s probably able to manipulate any code intending to limit potential impacts, penalize it for being more empowered or manipulate a human (or other agent) with its hand over the kill switch (or just turn off the kill switch).

So this problem could easily lead to a break out of any of the other problems present on the list of safety problems above and below. An example of reward hacking is a game playing bot that detects a situation that leads to buffer overflow and results in win signal or higher rewards. Such a bot will no doubt learn how to cause more buffer overflows so it can maximize its reward rather than learn to play the game better.

But the real problem is that a reward signal used to train a ML solution is just an approximation of what’s intended. Chess programs in the past were trained by masters to use their opening to open up the center of the board and use their middle and end game to achieve strategic advantages. But later chess and go playing bots just learned to checkmate their opponent and let the rest of the game take care of itself.

Moreover, (board) game play is relatively simple domain to come up with proper reward signals (with the possible exception of buffer overflows or other bugs). But car driving bots, drone bots, guard bots, etc., reward signals are not nearly as easy to define or implement.

One approach to avoid reward hacking is to make the reward signaling process its own ML/AI agent that is (suitably) stronger than the ML/AI agent learning the task. Most reward generators are relatively simple code. For instance in monopoly, one that just counts the money that each player has at the end of the game could be used to determine the winner (in a timed monopoly game). But rather than having a simple piece of code create the reward signal use ML to learn what the reward should be. Such an agent might be trained to check to see if more or less money was being counted than was physically possible in the game. Or if property was illegally obtained during the game or if other reward hacks were done. And penalize the ML solution for these actions. These would all make the reward signal depend on proper training of that ML solution. And the two ML solutions would effectively compete against one another.

Another approach is to “sandbox” the reward code/solution so that it is outside of external and or ML/AI influence. Possible combining the prior approach with this one might suffice.

Yet another approach is to examine the ML solutions future states (actions) to determine if any of them impact the reward function itself and penalize it for doing this. This assumes that the future states are representative of what it plans to do and that some code or some person can recognize states that are inappropriate.

Another approach discussed in the paper is to have multiple reward signals. These could use multiple formulas for computing the multi-faceted reward signal and averaging them or using some other mathematical function to combine them into something that might be more accurate than one reward function alone. This way any ML solution reward hacking would need to hack multiple reward functions (or perhaps the function that combines them) in order to succeed.

The one IMHO that has the most potential but which seems the hardest to implement is to somehow create “variable indifference” in the ML/AI solution. This means having the ML/AI solution ignore any steps that impact the reward function itself or other steps that lead to reward hacking. The researchers rightfully state that if this were possible then many of the AI safety concerns could be dealt with.

There are many other approaches discussed and I would suggest reading the paper to learn more. None of the others, seem simple or a complete solution to all potential reward hacks.

~~~

The paper goes into the same or more level of detail with the other three “concrete safety” issues in AI.

In my last post (see part 5 link above) I thought I was going to write about Human Compatible (AI) by S. Russell book’s discussion AI safety. But then I found the “Concrete problems in AI safety paper (see link above) and thought it provided a better summary of AI safety issues and used it instead. I’ll try to circle back to the book at some later date.

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Is AGI just a question of scale now – AGI part-5

Posted on by Ray in AGI, Cognitive computing, Neural network, Scenario planning, Strategic Inflection Points, Visionary leadershp

Read two articles over the past month or so. The more recent one was an Economist article (AI enters the industrial age, paywall) and the other was A generalist agent (from Deepmind). The Deepmind article was all about the training of Gato, a new transformer deep learning model trained to perform well on 600 separate task arenas from image captioning, to Atari games, to robotic pick and place tasks.

And then there was this one tweet from Nando De Frietas, research director at Deepmind:

“Someone’s opinion article. My opinion: It’s all about scale now! The Game is Over! It’s about making these models bigger, safer, compute efficient, faster at sampling, smarter memory, more modalities, INNOVATIVE DATA, on/offline, … 1/N“

I take this to mean that AGI is just a matter of more scale. Deepmind and others see the way to attain AGI is just a matter of throwing more servers, GPUs and data at the training the model.

We have discussed AGI in the past (see part-0 [ish], part-1 [ish], part-2 [ish], part-3ish and part-4 blog posts [We apologize, only started numbering them at 3ish]). But this tweet is possibly the first time we have someone in the know, saying they see a way to attain AGI.

Transformer models

It’s instructive from my perspective that, Gato is a deep learning transformer model. Also the other big NLP models have all been transformer models as well.

Gato (from Deepmind), SWITCH Transformer (from Google), GPT-3/GPT-J (from OpenAI), OPT (from meta), and Wu Dai 2.0 (from China’s latest supercomputer) are all trained on more and more text and image data scraped from the web, wikipedia and other databases.

Wikipedia says transformer models are an outgrowth of RNN and LSTM models that use attention vectors on text. Attention vectors encode, into a vector (matrix), all textual symbols (words) prior to the latest textual symbol. Each new symbol encountered creates another vector with all prior symbols plus the latest word. These vectors would then be used to train RNN models using all vectors to generate output.

The problem with RNN and LSTM models is that it’s impossible to parallelize. You always need to wait until you have encountered all symbols in a text component (sentence, paragraph, document) before you can begin to train.

Instead of encoding this attention vectors as it encounters each symbol, transformer models encode all symbols at the same time, in parallel and then feed these vectors into a DNN to assign attention weights to each symbol vector. This allows for complete parallelism which also reduced the computational load and the elapsed time to train transformer models.

And transformer models allowed for a large increase in DNN parameters (I read these as DNN nodes per layer X number of layers in a model). GATO has 1.2B parameters, GPT-3 has 175B parameters, and SWITCH Transformer is reported to have 7X more parameters than GPT-3 .

Estimates for how much it cost to train GPT-3 range anywhere from $10M-20M USD.

AGI will be here in 10 to 20 yrs at this rate

So if it takes ~$15M to train a 175B transformer model and Google has already done SWITCH which has 7-10X (~1.5T) the number of GPT-3 parameters. It seems to be an arms race.

If we assume it costs ~$65M (~2X efficiency gain since GPT-3 training) to train SWITCH, we can create some bounds as to how much it will cost to train an AGI model.

By the way, the number of synapses in the human brain is approximately 1000T (See Basic NN of the brain, …). If we assume that DNN nodes are equivalent to human synapses (a BIG IF), we probably need to get to over 1000T parameter model before we reach true AGI.

So my guess is that any AGI model lies somewhere between 650X to 6,500X parameters beyond SWITCH or between 1.5Q to 15Q model parameters.

If we assume current technology to do the training this would cost $40B to $400B to train. Of course, GPUs are not standing still and NVIDIA’s Hopper (introduced in 2022) is at least 2.5X faster than their previous gen, A100 GPU (introduced in 2020). So if we waited a 10 years, or so we might be able to reduce this cost by a factor of 100X and in 20 years, maybe by 10,000X, or back to where roughly where SWITCH is today.

So in the next 20 years most large tech firms should be able to create their own AGI models. In the next 10 years most governments should be able to train their own AGI models. And as of today, a select few world powers could train one, if they wanted to.

Where they get the additional data to train these models (I assume that data counts would go up linearly with parameter counts) may be another concern. However, I’m sure if you’re willing to spend $40B on AGI model training, spending a few $B more on data acquisition shouldn’t be a problem.

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At the end of the Deepmind article on Gato, it talks about the need for AGI safety in terms of developing preference learning, uncertainty modeling and value alignment. The footnote for this idea is the book, Human Compatible (AI) by S. Russell.

Preference learning is a mechanism for AGI to learn the “true” preference of a task it’s been given. For instance, if given the task to create toothpicks, it should realize the true preference is to not destroy the world in the process of making toothpicks.

Uncertainty modeling seems to be about having AI assume it doesn’t really understand what the task at hand truly is. This way there’s some sort of (AGI) humility when it comes to any task. Such that the AGI model would be willing to be turned off, if it’s doing something wrong. And that decision is made by humans.

Deepmind has an earlier paper on value alignment. But I see this as the ability of AGI to model human universal values (if such a thing exists) such as the sanctity of human life, the need for the sustainability of the planet’s ecosystem, all humans are created equal, all humans have the right to life, liberty and the pursuit of happiness, etc.

I can see a future post is needed soon on Human Compatible (AI).

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BEHAVIOR, an in-home robot, benchmark

Posted on by Ray in Artificial Intelligence, Machine Learning, R&D measures, Robots, Scenario planning, Strategic Inflection Points, System effectiveness

As my readers probably already know, I’m a long time benchmark geek. So when I recently read an article out of Stanford (AI Experts Establish the “North Star” for Domestic Robotics Field) where a research team there developed a new robotic benchmark, I was interested. The new robotics benchmark is called BEHAVIOR which was documented in an ARXIV.org article (see: BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and ecOlogical enviRonments). It essentially uses real world data to identify domestic work activities that any robot would need to perform in a home.

The problems with robot benchmarks

The problem with benchmarks are multi-faceted:

  • How realistic are the workloads used to evaluate the systems being measured?
  • How accurate are the metrics used to rank and judge benchmark submissions?
  • How costly/complex is it to run a benchmark?
  • How are submissions audited and are they reproducible?.
  • Where are benchmark results reported and are they public?

And of course robotics brings in it’s own issues that makes benchmarking more difficult:

  • What sensors does the robot have to understand how to complete tasks?
  • What manipulators does the robot have to perform the tasks required of it?
  • Do the robots move in the environment and if so, how do the robots move?
  • Does the robot perform the task in the real world on in a simulated environment.

And of course, when using a simulated environment, how realistic is it.

BEHAVIOR with iGibson (see below) seem to answer many of these concerns for an in home robot benchmarking.

What is BEHAVIOR?

First, BEHAVIOR’s home making tasks were selected from an American Time Use Survey maintained by the USA Bureau of Labor Statistics which identifies tasks Americans perform in their homes. With BEHAVIOR 1.0 there are 100 tasks ranging from building a fruit basket to cleaning a toilet, and just about everything in between. I didn’t see any cooking or mixing drinks tasks but maybe those will be added.

Second, BEHAVIOR uses a predicate logic, called BDDL (BEHAVIOR Domain Definition Language) to define initial conditions for tasks such as tables, chairs, books, etc located in the room, where objects need to be placed, and successful completion goals or what task completion should look like.

BEHAVIOR uses 15 different rooms or scenes in their benchmark, such as a kitchen, garage, study, etc. Each of the 100 tasks are performed in a specific room.

BEHAVIOR incorporates 1217 different objects in 391 categories. Once initial conditions are defined for a task, BEHAVIOR essentially randomly selects different object for the task and randomly locates them throughout the room.

In order to run the benchmark, one could conceivably create a real room, with all the objects and have them placed according to BEHAVIOR BDDL’s randomly assigned locations with a robot physically present in the room and have it perform the assigned task OR one could use a simulation engine and have the robot run the task in the simulation environment, with simulated room, objects and robot.

It appears as if BEHAVIOR could operate in any robotics simulation environment but has been currently implemented in Stanford’s open source robotics simulation engine called iGibson 2.0 (see: iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks and iGibson 2.0 website). iGibson uses the Bullet real time physics engine for realistic physical environment simulation.

A robot operating within iGibson is provided a 3D rendering of the room and objects in images or LIDAR sensor scans. It can then identify the objects that it needs to manipulate to perform the tasks. One can define the robot simulated sensors and manipulators in iGibnot 2.0 and it’s written in Python, is open source (GitHub Repo) and can be installed to run on (Ubuntu 16.04) Linux, Windows (10) or Mac (10.15) systems.

Finally, BEHAVIOR uses a set of metrics to determine how well a robot has performed its assigned task. Their first metric is success score defined as the fraction of goal conditions satisfied by the robot performing the task. Such as the number of dishes properly cleaned and placed in the drying rack divided by the total number of dishes for a “washing dishes” task. And their second metric is a set of efficiency metrics, like time to complete a task, sum total of object distance moved during the task, how well objects are arranged at task completion (is the toilet seat down…), etc.

Another feature of iGibson 2.0 is that it offers the ability to record a human (in VR) doing a task in its simulated environment. So if your robotic system is able to learn by example, then iGibson could be used to provide training data for an activity.

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A couple of additions to the BEHAVIOR benchmark/iGibson simulation environment that I would like to see:

  • There ought to be a way to construct a house/apartment where multiple rooms are arranged in a hierarchy, i.e., rooms associated with floors with connections using hallways, doors, stairs, etc. between them. This way one could conceivably have a define a set of homes/apartments (let’s say 5) that a robot would perform its tasks in.
  • They need a task list to drive robot activities. Assume that there’s some amount of time let’s say 8-12 hours that a robot is active and construct a series of tasks that need to be accomplished during that period.
  • Robots should be placed in the rooms/apartments/homes at random with random orientation and then they would have to navigate through rooms/passageways to the rooms to perform the tasks.
  • They need to add pet/human avatars in the rooms throughout a home. These would represent real time obstacles to task completion/navigation as well as add more tasks associated with caring for pets/humans.
  • They need the ability to add non-home rooms that could encompass factory floors, emergency response debris fields, grocery stores, etc. and their own unique set of tasks for each of these so that it could be used as a benchmark for more than just domestic robots.

Aside from the above additions to BEHAVIOR/iGibson 2.0, there’s the question of the organization that manages the benchmark and submissions. There needs to be a website/place to publish benchmark results for a robot AND a mechanism to audit results for accuracy to insure fair play.

Typically this would be associated with an organization responsible for publishing and auditing submissions as well as guide further development of BEHAVIOR/iGibson 2.0. BEHAVIOR 1.0 is not the end but it’s a great start at providing realistic tasks that any domestic robot would need to perform. 

Benchmarks have always aided the development and assessment of new technologies. Having a in home robot benchmark like BEHAVIOR makes getting domestic robots that do what we want them to do a more likely possibility someday.

There’s a new benchmark in town and it signals the dawning of the domestic robot age.

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For AGI, is reward enough – part 4

Posted on by Ray in AGI, Artificial Intelligence, Cognitive computing, Machine Learning, Reinforcement Learning, Scenario planning, Strategic Inflection Points, Strategic planning, Visionary leadershp

Last May, an article came out of DeepMind research titled Reward is enough. It was published in an artificial intelligence journal but PDFs of it are available free of charge.

The article points out that according to DeepMind researchers, using reinforcement learning and an appropriate reward signal is sufficient to attain AGI (artificial general intelligence). We have written about the perils and pitfalls of AGI before (see Existential event risks [-part-0], NVIDIA Triton GMI, a step to far[-part-1], The Myth of AGI [-part-2], and Towards a better AGI – part 3ish. (Sorry, I only started numbering them after part 3ish).

My last post on AGI inclined towards the belief that AGI was not possible without combining deduction, induction and abduction (probabilistic reasoning) together and that any such AGI was a distant dream at best.

Then I read the Reward is Enough article and it implied that they saw a realistic roadmap towards achieving AGI based solely on reward signals and Reinforcement Learning (wikipedia article on Reinforcement Learning ). To read the article was disheartening at best. After the article came out, I made it a hobby to understand everything I could about Reinforcement Learning to understand whether what they are talking is feasible or not.

Reinforcement learning, explained

Let’s just say that the text book, Reinforcement Learning, is not the easiest read I’ve seen. But I gave it a shot and although I’m no where near finished, (lost somewhere in chapter 4), I’ve come away with a better appreciation of reinforcement learning.

The premise of Reinforcement Learning, as I understand it, is to construct a program that performs a sequence of steps based on state or environment the program is working on, records that sequence and tags or values that sequence with a reward signal (i.e., +1 for good job, -1 for bad, etc.). Depending on whether the steps are finite, i.,e, always ends or infinite, never ends, the reward tagging could be cumulative (finite steps) or discounted (infinite steps).

The record of the program’s sequence of steps would include the state or the environment and the next step that was taken. Doing this until the program completes the task or if, infinite, whenever the discounted reward signal is minuscule enough to not matter anymore.

Once you have a log or record of the state, the step taken in that state and the reward for that step you have a policy used to take better steps. Over time, with sufficient state-step-reward sequences, one can build a policy that would work’s very well for the problem at hand.

Reinforcement learning, a chess playing example

Let’s say you want to create a chess playing program using reinforcement learning. If a sequence of moves ends the game, you can tag each move in that sequence with a reward (say +1 for wins, 0 for draws and -1 for losing), perhaps discounted by the number of moves it took to win. The “sequence of steps” would include the game board and the move chosen by the program for that board position.

Figure 2: Comparison with specialized programs. (A) Tournament evaluation of AlphaZero in chess, shogi, and Go in matches against respectively Stockfish, Elmo, and the previously published version of AlphaGo Zero (AG0) that was trained for 3 days. In the top bar, AlphaZero plays white; in the bottom bar AlphaZero plays black. Each bar shows the results from AlphaZero’s perspective: win (‘W’, green), draw (‘D’, grey), loss (‘L’, red). (B) Scalability of AlphaZero with thinking time, compared to Stockfish and Elmo. Stockfish and Elmo always receive full time (3 hours per game plus 15 seconds per move), time for AlphaZero is scaled down as indicated. (C) Extra evaluations of AlphaZero in chess against the most recent version of Stockfish at the time of writing, and against Stockfish with a strong opening book. Extra evaluations of AlphaZero in shogi were carried out against another strong shogi program Aperyqhapaq at full time controls and against Elmo under 2017 CSA world championship time controls (10 minutes per game plus 10 seconds per move). (D) Average result of chess matches starting from different opening positions: either common human positions, or the 2016 TCEC world championship opening positions . Average result of shogi matches starting from common human positions . CSA world
championship games start from the initial board position.

If your policy incorporates enough winning chess move sequences and the program encounters one of these in a game and if move recorded won, select that move, if lost, select another valid move at random. If the program runs across a board position its never seen before, choose a valid move at random.

Do this enough times and you can build a winning white playing chess policy. Doing something similar for black playing program would build a winning black playing chess policy.

The researchers at DeepMind explain their AlphaZero program which plays chess, shogi, and Go in another research article, A general reinforcement learning algorithm that masters chess, shogi and Go through self-play.

Reinforcement learning and AGI

So now what does all that have to do with creating AGI. The premise of the paper is that by using rewards and reinforcement learning, one could program a policy for any domain that one encounters in the world.

For example, using the above chart, if we were to construct reinforcement learning programs that mimicked perception (object classification/detection) abilities, memory ((image/verbal/emotional/?) abilities, motor control abilities, etc. Each subsystem could be trained to solve the arena needed. And over time, if we built up enough of these subsystems one could somehow construct an AGI system of subsystems, that would match human levels of intelligence.

The paper’s main hypothesis is “(Reward is enough) Intelligence, and its associated abilities, can be understood as subserving the maximization of reward by an agent acting in its environment.”

Given where I am today, I agree with the hypothesis. But the crux of the problem is in the details. Yes, for a game of multiple players and where a reward signal of some type can be computed, a reinforcement learning program can be crafted that plays better than any human but this is only because one can create programs that can play that game, one can create programs that understand whether the game is won or lost and use all this to improve the game playing policy over time and game iterations.

Does rewards and reinforcement learning provide a roadmap to AGI

To use reinforcement learning to achieve AGI implies that

  • One can identify all the arenas required for (human) intelligence
  • One can compute a proper reward signal for each arena involved in (human) intelligence,
  • One can programmatically compute appropriate steps to take to solve that arena’s activity,
  • One can save a sequence of state-steps taken to solve that arena’s problem, and
  • One can run sequences of steps enough times to produce a good policy for that arena.

There are a number of potential difficulties in the above. For instance, what’s the state the program operates in.

For a human, which has 500K(?) pressure, pain, cold, & heat sensors throughout the exterior and interior of the body, two eyes, ears, & nostrils, one tongue, two balance sensors, tired, anxious, hunger, sadness, happiness, and pleasure signals, and 600 muscles actuating the position of five fingers/hand, toes/foot, two eyes ears, feet, legs, hands, and arms, one head and torso. Such a “body state, becomes quite complex. Any state that records all this would be quite large. Ok it’s just data, just throw more storage at the problem – my kind of problem.

The compute power to create good policies for each subsystem would also be substantial and in the end determining the correct reward signal would be non-trivial for each and every subsystem. Yet, all it takes is money, time and effort and all this could be accomplished.

So, yes, given all the above creating an AGI, that matches human levels of intelligence, using reinforcement learning techniques and rewards is certainly possible. But given all the state information, action possibilities and reward signals inherent in a human interacting in the world today, any human level AGI, would seem unfeasible in the next year or so.

One item of interest, recent DeepMind researchers have create MuZero which learns how to play Go, Chess, Shogi and Atari games without any pre-programmed knowledge of the games (that is how to play the game, how to determine if the game is won or lost, etc.). It managed to come up with it’s own internal reward signal for each game and determined what the proper moves were for each game. This seemed to combine a deep learning neural network together with reinforcement learning techniques to craft a rewards signal and valid move policies.

Alternatives to full AGI

But who says you need AGI, for something that might be a useful to us. Let’s say you just want to construct an intelligent oracle that understood all human generated knowledge and science and could answer any question posed to it. With the only response capabilities being audio, video, images and text.

Even an intelligent oracle such as the above would need an extremely large state. Such a state would include all human and machine generated information at some point in time. And any reward signal needed to generate a good oracle policy would need to be very sophisticated, it would need to determine whether the oracle’s answer; was good or not. And of course the steps to take to answer a query are uncountable, 1st there’s understanding the query, next searching out and examining every piece of information in the state space for relevance, and finally using all that information to answer to the question.

I’m probably missing a few steps in the above, and it almost makes creating a human level AGI seem easier.

Perhaps the MuZero techniques might have an answer to some or all of the above.

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Yes, reinforcement learning is a valid roadmap to achieving AGI, but can it be done today – no. Tomorrow, perhaps.

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Towards a better AGI – part 3(ish)

Posted on by Ray in Artificial Intelligence, Cognitive computing, Executive leadership, R&D measures, Scenario planning, Strategic Inflection Points, System effectiveness, Visionary leadershp

Read an article this past week in Nature about the need for Cooperative AI (Cooperative AI: machines must learn to find common ground) which supplies the best view I’ve seen as to a direction research needs to go to develop a more beneficial and benign AI-AGI.

Not sure why, but this past month or so, I’ve been on an AGI fueled frenzy (at leastihere). I didn’t realize this was going to be a multi-part journey otherwise, I would have lableled them AGI part-1 & -2 ( please see: Existential event risks [part-0], NVIDIA Triton GMI, a step to far [part-1] and The Myth of AGI [part-2] to learn more).

But first please take our new poll:

The Nature article puts into perspective what we all want from future AI (or AGI). That is,

  • AI-AI cooperation: AI systems that cooperate with one another while at the same time understand that not all activities are zero sum competitions (like chess, go, Atari games) but rather most activities, within the human sphere, are cooperative activities where one agent has a set of goals and a different agent has another set of goals, some of which overlap while others are in conflict. Sport games like soccer lacrosse come to mind. But there are other card and (Risk & Diplomacy) board games that use cooperating parties, with diverse goals to achieve common ends.
  • AI-Human cooperation: AI systems that cooperate with humans to achieve common goals. Here too, most humans have their own sets of goals, some of which may be in conflict with the AI systems goals. However, all humans have a shared set of goals, preservation of life comes to mind. It’s in this arena where the challenges are most acute for AI systems. Divining human and their own system underlying goals and motivations is not simple. And of course giving priority to the “right” goals when they compete or are in conflict will be an increasingly difficult task to accomplish, given todays human diversity.
  • Human-Human cooperation: Here it gets pretty interesting, but the paper seems to say that any future AI system should be designed to enhance human-human interaction, not deter or interfere with it. One can see the challenge of disinformation today and how wonderful it would be to have some AI agent that could filter all this and present a proper picture of our world. But, humans have different goals and trying to figure out what they are and which are common and thereby something to be enhanced will be an ongoing challenge.

The problem with today’s AI research is that its all about improving specific activities (image recognition, language understanding, recommendation engines, etc) but all are point solutions and none (if any) are focused on cooperation.

Tit for tat wins the award

To that end, the authors of the paper call for a new direction one that attempts to imbue AI systems with social intelligence and cooperative intelligence to work well in the broader, human dominated world that lies ahead.

In the Nature article they mentioned a 1984 book by Richard Axelrod, The Evolution of Cooperation. Perhaps, the last great research on cooperation that was ever produced.

In this book it talked about a world full of simulated prisoner dilemma actors that interacted, one with another, at random.

The experimenters programmed some agents to always do the proper thing for their current partner, some to always do the wrong thing to their partner, others to do right once than wrong from that point forward, etc. The experimenters tried every sort of cooperation policy they could think of.

Each agent in an interaction would get some number of points for an interaction. For example, if both did the right thing they would each get 3 points, if one did wrong, the sucker would get 1 and the bad actor would get 4, both did wrong each got 1 point, etc.

The agents that had the best score during a run (of 1000s of random pairings/interactions) would multiply for the the next run and the agents that did worse would disappear over time in the population of agents in simulated worlds.

The optimal strategy that emerged from these experiments was

  1. Do the right thing once with every new partner, and
  2. From that point forward tit for tat (if the other party did right the last time, then you do right thing the next time you interact with them, if they did wrong the last time, then you do wrong the next time you interact with them).

It was mind boggling at the time to realize that such a simple strategy could be so effective/sustainable in simulation and perhaps in the real world. It turns out that in a (simulated) world of bad agents, there would be this group of Tit for Tat agents that would build up, defend itself and expand over time to succeed.

That was the state of the art in cooperation research back then (1984). I’ve not seen anything similar to this since.

I haven’t seen anything like this that discusses how to implement algorithms in support of social intelligence.

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The authors of the Nature article believe it’s once again time to start researching cooperation techniques and start researching social intelligence so we can instill proper cooperation and social intelligence technology into future AI (AGI) systems .

Perhaps if we can do this, we may create a better AI (or AGI) so that both it and we can live better in our world, galaxy and universe.

Comments?

Tattoos that light up

Posted on by Ray in Information economy, Mobile computing, Scenario planning, Strategic Inflection Points

Read an article the other day, titled Light-emitting tattoo engineered in ScienceDaily. Which was reporting on research done by University College London and Istituto Italiano di Tecnologia (Italian Institute of Technology) (Ultrathin, ultra-comfortable and free-standing, tattooable LEDs – behind paywall).

The new technology out of their research can construct OLEDs, found in TVs, phones, and other displays, and apply them as temporary tattoos. The tattoos will eventually degrade, wash off but while present on the skin they can light up and display information.

According to the Nanowerk news article reporting on the research, (see Light emitting tattoos engineered for the 1st time), the OLEDs are printed onto paper which can then be transferred to skin by the application of water. The picture above shows a number of the OLED tattoos ready for application.

The vision is that OLED tattoos along with other flexible electronics could provide wearable sensors of bio-chemical activity of a person. Such sensors could be used in hospitals and in the home to display dehydration, glucose status, oxygenation, etc. as well as be able to display heart and breath rates. But in order to get to that vision there’s a few steps that are needed.

Flexible, stretchable electronics

There have been a number of articles about creating flexible electronics, (e.g., see A design to improve the resilience and electrical performance thin metal film based electrodes). This article was reporting on research done at the University of Illinois, Champaign-Urbana reported in Nature (behind paywall) but one of the researchers blogged about in NaturePortfolio Devices & Materials (see: An atom-thick interlayer enables the electrical ductility of thin-film metal electrodes).

Flexible electronics can be constructed by creating a thin metal film with the electronics embedded in it placed on top of a flexible substrate. However, when that flexible substrate starts to deform or stretch it induces cracks in the thin metal films which lead to loss of conductivity, or loss of electronics function.

The research cited in the article above showed videos of cracking that takes place during deformation and stretching which would lead to loss of conductivity.

But the researchers at UofI found out that if you place a thin layer of graphene or other 2D sheet of material between the electronic thin film and the flexible substrate, the cracks that eventually happen are much less harmful to electronic conduction or functioning or provide electronic ductability. To add ductablity to an electronic circuit using LEDs the team applied an atomically thin (<1nm), 2D layer of graphene between it and the flexible substrate.

Somehow the graphene provided a mechanical buffer between the flexible substrate and the thin film electronics that allowed the circuits to have much more ductility. It appears that this mechanical buffer changed the type of cracking that occurs on the thin metal film such that they are shorter and more varied in direction rather than straight across and this helped them retain functioning longer than without the

The researchers at U of I actually created a led display that could be bent without failure. See a video of them comparing the thin film vs thin film with 2D substrate.

Skin sensors

Moreover, there have been a number of articles discussing new wearable technologies that could be used to sense a persons bio-chemical state. For example, research reported on recently (see Do Sweat It! Wearable Microfluidic Sensor to Measure Lactate Concentration in Real Time) done at the Tokyo University of Science, published in Electochimica Acta (behind paywall) talks about a sweat sensor that can be applied to skin to determine when athletes or others are getting dehydrated.

This sensor uses a micro-fluidics device which printed with electronic ink. Such a device could be manufactured in volume and be readily printed onto surfaces, that could be applied to the skin, anywhere sweat was being produced.

Future tattoos

Wearable sensors already surround us. We have watches that can tell our heart rates, walk/running speed/rates, step counts, etc. It doesn’t take much to imagine that most if not all of these could be fabricated on a thin film and with the proper 2D substrate layer be applied as a tattoo to a person while in the hospital but all these sensors have lacked a read out or display up until now. With OLED readouts wearable sensors now have a reasonable display capability.

The sweat sensor above uses microfluidics to do a lactate assay of sweat. The motion sensors in my watch uses MEMs and onboard IMU/GPS to determine speed and direction of movement. Electronic temperature sensors use thermoelectric effects. Blood oxygen sensors use LEDs and light sensors. None of these appears unable to be fabricated, miniaturized and printed on thin films. Adding OLEDs and why do we need a watch anymore?

What seems to be the most glaring omission is gas sensors (although the lactate micro-fluidic sensor is close). If we could somehow miniaturize gas sensors with enough sensitivity to glucose levels, immunological load, specific diseases (COVID19), then maybe there’d be a mass market for such devices, outside of a hospital or smart watch users.

Then with OLED and electronics that can be temporarily tattooed onto a person skin., why couldn’t this be a fashion accessory. I can imagine lot’s of people would have interest in lighting up messages, iconography or other data on their arms, hands, or other areas of a person’s body. I wonder if it could be used to display hair on the top of my head :)?

And of course these OLED-electronics based tattoos are temporary. But if they are all made from electronic ink, it seems to me that such tattoos could be permanently printed (implanted?) onto a persons skin.

Maybe at some future point a permanent OLED-electronics based tattoo could provide an electronic display and input device that could be used in conjunction with a phone or a smart-watch. All it would take would be blue-tooth.

Comments?

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A tale of two countries and how they controlled the Coronavirus

Posted on by Ray in Scenario planning, Strategic Inflection Points, Strategic planning, System effectiveness, System quality

Read an article in IEEE Spectrum last week about Taiwan’s response to COVID-19 (see: Big data helps Taiwan fight Coronavirus) which was reporting on an article in JAMA (see Response to COVID-19 in Taiwan) about Taiwan’s success in controlling the COVID-19 outbreak in their country.

I originally intended this post to be solely about Taiwan’s response to the virus but then thought that it more instructive to compare and contrast Taiwan and South Korea responses to the virus, who both seem to have it under control now (18 Mar 2020).

But first a little about the two countries (source wikipedia: South Korea and Taiwan articles):

Taiwan (TWN) and South Korea (ROK) both enjoy close proximity, trade and travel between their two countries and China

  • South Korea (ROK) has a population of ~50.8M, an area of 38.6K SqMi (100.0K SqKm) and extends about 680 Mi (1100 Km) away from the Asian mainland (China).
  • Taiwan (TWN ) has a population of ~23.4M, an area of 13.8K SqMi (35.8K Sq Km) and is about 110 Mi (180 Km) away from the Asian mainland (China).

COVID-19 disease progression & response in TWN and ROK

There’s lots of information about TWN’s response (see articles mentioned above) to the virus but less so on ROK’s response.

Nonetheless, here’s some highlights of the progression of the pandemic and how they each reacted (source for disease/case progression from : wikipedia Coronavirus timeline Nov’19 to Jan’20, and Coronavirus timeline Feb’20; source for TWN response to virus JAMA article supplement and ROK response to virus Timeline: What the world can learn from South Korea’s COVID-19 response ).

  • Dec. 31, 2019: China Wuhan municipal health announced “urgent notice on the treatment of pneumonia of unknown cause”. Taiwan immediately tightened inbound screening processes. ==> TWN: officials board and inspect passengers for fever or pneumonia symptoms on direct flights from Wuhan
  • Jan. 8, 2020: ROK identifies 1st possible case of the disease in a women who recently returned from China Wuhan province
  • Jan 20: ROK reports 1st laboratory confirmed case ==> TWN: Central Epidemic Command Center activated, activates Level 2 travel alert for Wuhan; ROK CDC starts daily press briefings on disease progress in the nation
  • Jan. 21: TWN identifies 1st laboratory confirmed case ==> TWN: activates Level 3 travel alert for Wuhan
  • Jan 22: ==> TWN: cancels entry permits for 459 tourists from Wuhan set to arrive later in Jan
  • Jan 23: ==> TWN: bans residents from Wuhan, travelers from China required to make online health declaration before entering
  • Jan. 24 ROK reports 2nd laboratory confirmed case ==> TWN bans export of facemasks; ROK, sometime around now the gov’t started tracking confirmed cases using credit card and CCTV data to understand where patients contacted the disease
  • Jan. 25: ==> TWN: tours to china are suspended until Jan 31, activates level 3 travel alert for Hubei Province and Level 2 for rest of China, enacts export ban on surgical masks until Feb 23
  • Jan 26: ==> TWN: all tour groups from Wuhan have to leave,
  • Jan. 27: TWN reports 1st domestic transmission of the disease ==>TWN NHIA and NIA (National health and immigration authorities) integrate (adds all hospital) patients past 14-day travel history to NHIA database, all tour groups from Hubei Province have to leave
  • Jan 28: ==> TWN: activates Level 3 travel alert for all of China except Hong Kong and Macau; ROK requests inspection of all people who have traveled from Wuhan in the past 14 days
  • Jan 29: ==> TWN: institutes electronic monitoring of all quarantined patients via gov’t issued cell phones; ROK about now requests production of massive numbers of WHO approved test kits for the Coronavirus
  • Jan. 30: ROK reports 2 more (4 total) confirmed cases of the disease ==> TWN: tours to or transiting China suspended until Feb 29;
  • Jan 31: ==> TWN: all remaining tour groups from China asked to leave
  • Feb 2 ==> TWN extended school break from Feb 15 to Feb 25,gov’t facilities available for quarantine, soldiers mobilized to man facemask production lines, 60 additional machines installed daily facemask output to reach 10M facemasks a day.
  • Feb 3: ==> TWN: enacts name based rationing system for facemasks, develops mobile phone app to allow public to see pharmacy mask stocks, Wenzhou city Level 2 travel alert; ROK CDC releases enhanced quarantine guidelines to manage the disease outbreak, as of today ROK CDC starts making 2-3 press releases a day on the progress of the disease
  • Feb 5: ==> TWN: Zheijanp province Level 2 travel alert, all cruise ships with suspected cases in past 28 days banned, any cruise ship with previous dockings in China, Hong Kong, or Macau in past 14 days are banned
  • Feb 6:==> TWN: Tours to Hong Kong & Macau suspended until Feb 29, all Chinese nationals banned, all international cruise ship are banned, all contacts from Diamond Princess cruise ship passengers who disembarked on Jan 31 are traced
  • Feb 7: ==> TWN: All foriegn nationals with travel to China, Hong Kong or Macau in the past 14 days are banned, all Foreigners must see an immigration officer,
  • Feb 14:==> TWN: Entry quarantine system launched fill out electronic health declaration for faster entry
  • Feb 16: ==> TWN: NHIA database expanded to cover 30 day travel history for travelers form or transited through China, Hong Kong, Macau, Singapore and Thailand.
  • Feb 18 ==> TWN: all hospitals, clinics and pharmacies have access to patients travel history; ROK most institutions postpone the re-start of school after spring break
  • Feb 19 ==> TWN establishes gov’t policies to disinfect schools and school areas, school buses, high speed rail, railways, tour busses and taxis
  • Feb 20 ==> ROK Daegu requests all individuals to stay home
  • Feb 21 ==> TWN establishes school suspension guidelines based on cases diagnosed in school; ROK Seoul closes all public gatherings and protests
  • Feb 24 ==> TWN, travelers with history of travel to china, from countries with level 1 or 2 travel alerts, and all foreign nationals subject to 14 day quarantine (By this time many countries are in level 1-2-3 travel alert status in TWN)
  • Feb 26 ==> ROK opens drive-thru testing clinics, patients are informed via text messages (3 days later) the results of their tests
  • Mar 3? ==> ROK starts selling facemasks at post offices
  • Mar 5 ==> ROK bans the export of face masks

As of Mar 16, (as reported in Wikipedia), TWN had 67 cases and 1 death; and ROK had 8,326 cases and 75 deaths. As of Mar 13 (as reported is Our world in data article), TWN had tested 16,089 and ROK had tested 248,647 people.

Summary of TWN and ROK responses to the virus

For starters, both TWN and ROK learned valuable lessons from the last infections from China SARS-H1N1 and used those lessons to deal better with COVID-19. Also neither country had any problem accessing credit information, mobile phone location data, CCTV camera or any other electronic information to trace infected people in their respective countries.

If I had to characterize the responses to the virus from the two countries:

  1. TWN was seemingly focused early on reducing infections from outside, controlling & providing face masks to all, and identifying gov’t policies (ceasing public gathering, quarantine and disinfectant procedure) to reduce transmission of the disease. They augmented and promoted the use of public NHIA databases to track recent travel activity and used any information available to monitor the infected and track down anyone they may have contacted. Although TWN has increased testing over time, they did not seem to have much of an emphasis on broad testing. At this point, TWN seems to have the virus under control.
  2. ROK was all about public communications, policies (quarantine and openness), aggressively testing their population and quarantining those that were infected. ROK also tracked the goings on and contacts of anyone that was infected. ROK started early on broadly testing anyone that wanted to be tested. Using test results, infected individuals were asked to quarantine. A reporter I saw talking about ROK mentions 3 T’s: Target, Test, & Trace At this point, ROK seems to have the virus under control.

In addition, Asian countries in general are more prone to use face masks when traveling, which may be somewhat restrict Coronavirus transmission. Although it seems to primarily reduce transmission, most of the public in these countries (now) routinely wear face masks when out and about. And previously they routinely wore face masks when traveling to reduce disease transmission.

Also both countries took the news out of Wuhan China about the extent of the infections, deaths and ease of disease transmission as truthful and acted on this before any significant infections were detected in their respective countries

What the rest of the world can learn from these two countries

What we need to take from TWN a& ROK is that

  1. Face masks and surgical masks are a critical resource during any pandemic. National production needs to be boosted immediately with pricing and distribution controls so that they are not hoarded, nor subject to price gouging. In the USA we have had nothing on this front other than requests to the public to stop hoarding them and the lack of availability to support healthcare workers).
  2. Test kits are also a critical resource during any pandemic. Selection of the test kit, validation and boosting production of test kits needs to be an early and high priority. The USA seems to have fallen down on this job.
  3. Travel restrictions, control and quarantines need to be instituted early on from infected countries. USA did take action to restrict travel and have instituted quarantines on cruise ship passengers and any repatriated nationals from China.
  4. Limited testing can help control the virus as long as it’s properly targeted. Mass, or rather less, targeted testing can also help control the virus as well. In the USA given the lack of test kits, we are limited to targeted testing.
  5. Open, rapid and constant communications can be an important adjunct to help control virus spread. The USA seems to be still working on this. Many states seem to have set up special communications channels to discuss the latest information. But there doesn’t seem to be any ongoing, every day communications effort on behalf of the USA CDC to communicate pandemic status.
  6. When one country reports infections, death and ease of transmission of a disease start to take serious precautions immediately. Disease transmission in our travel intensive world is much too easy and rapid to stop once it takes hold in a nation. Any nation today that starts to encounter and infectious agent with high death rates and seemingly easy transmission must be taken seriously as the start of something much bigger.

Stay safe, be well.

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Comments?

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