
AI and Economic Productivity: Expect Evolution, Not Revolution - bookofjoe
https://spectrum.ieee.org/computing/software/ai-and-economic-productivity-expect-evolution-not-revolution
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blazespin
There is a very weird movement right now underestimating the drastic power of
ML/AI. Take facial recognition for example (there are many many others, but
this is very illustrative). The possibilities of revolutionizing society with
facial recognition is legion. Imagine having a camera attached to your jacket
which is hooked up to your phone and immediately gives a compete profile of
everyone you meet. This tech is possible today. It probably won’t work with
total strangers, but will with people you are likely to meet given your social
circles. The power it gives governments as well for monitoring and policing is
very dramatic as well.

Just look at Hong Kong’s recent law outlawing masks.

NLP (GPT-2/BERT) is also extremely powerful and is evolving at a frightful
pace. Just google news over the last month. I read a lot of generated text and
non generated text and more and more I really can’t tell the difference. Yes,
it doesn’t work in isolation, but human guided NLP is stunning in what it can
do. Self-attention applied to many other areas is huge as well, such as
Software engineering.

Drones is another revolutionary technology which has already completely
upended how war is executed.

There are reasons why leading technology companies such as google, Microsoft,
Amazon are dealing with employee blow back around the military and government.
They recognize the possibilities here and are uncomfortable with with
potential contributions to the disruptive downsides of what can be done by
“smarter” algorithms (or whatever you want to call them).

Software truly is eating the world.

~~~
notahacker
It's less a 'very weird movement' and more the average person pushing back
against the AI hyperbole movement. And in this case less to do with claims of
social impact and more the tendency of marketing collateral and forecasts to
attribute operational improvements involving capex on new hardware and new
processes for humans entirely to 'AI' because somewhere along the line someone
did some statistical modelling with a computer which may even have involved an
ML process.

> Take facial recognition for example (there are many many others, but this is
> very illustrative). The possibilities of revolutionizing society with facial
> recognition is legion. Imagine having a camera attached to your jacket which
> is hooked up to your phone and immediately gives a compete profile of
> everyone you meet. This tech is possible today. It probably won’t work with
> total strangers, but will with people you are likely to meet given your
> social circles.

I mean, I can namesearch the internet for people I meet via my social circles
without [admittedly much improved] facial recognition AI: the innovation's all
in the hardware, culture of broadcasting one's life via social media and
search tech. Hell, I could and did check out people's more basic social
profiles online two decades ago when Kurzweil was predicting that by 2020 AI
would have pushed life expectancy over 100, eliminated financial crises,
economic deprivation and road accidents and simulated human personalities so
convincingly that human-robot relationships were a normal thing. The facial
recognition is neat, to the degree that it's accurate, but probably less
revolutionary for social profiling than the cultural shift towards putting
more information online.

Similarly, AI startups are far from the only types of business where employees
have ethical qualms about selling to the military and certain areas of law
enforcement, and one of the biggest concerns employees have is what happens
when bodies take action based on vastly overestimating the AI's conclusions...

~~~
erikerikson
Not just the average person but also the experts.

------
PeterStuer
To borrow some words of David Graeber, the author might be looking for
productivity gains while the real world is mostly busy _frying_ _fish_ (a
reference to self-referential and mutual-imposed busy work). Any efficiency
gains in those areas would result in nil or even negative real productivity
gains.

Then there is the whole 'attention economy' we have shifted the majority of
our economic endeavors to. Just like some examples in the article, 'efficiency
gains' in that space are just a red-queen race in a zero-sum game.

This is not to say AI will not have major impacts on those whose livelihoods
are swept up in its evolutionary or revolutionary (isn't that mostly just a
difference in timescale) jaws. And even AI might open up new pathways of
improvement and genuine innovations for mankind, and at the very least it is
exiting and intellectually stimulation to advance. But to look at 'economic
gains' might not be the right paradigm. That might just be like trying to
improve the health of a morbidly obese cat by feeding it more.

~~~
dredmorbius
By "frying fish", are you referring to Baumol's Cost Disease (the cost of
nonautomatable services rises as levels of automation and general wages
increase), or a self-elected pursuit of manual work for one's own immediate
benefit, not for compensation?

~~~
legulere
Neither, Graeber defines bullshit jobs something along the lines of that that
the employee knows that they don’t do anything meaningful. For instance being
an underling of a manager so he can feel more powerful having more people
under them without really having a task.

~~~
dredmorbius
I'm not following.

Either the concept, or how the terminology you're using arises.

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nopinsight
An area where AI can make a major impact is improving the efficiency of human
experts in the developing world and underprivileged communities even in
developed nations.

There is a huge lack of doctors, nurses, architects, etc in many of those
areas. In some countries, a doctor may need to see 100+ patients a day as a
matter of course. We can imagine how the quality of care suffers as a result.

As examples, AI, including NLP and computer vision technologies, can
complement doctors and nurses by:

\- performing initial querying of symptoms,

\- regular pre-screening for diseases at minimal costs (thus reducing costs
that incur with delayed diagnosis and treatment),

\- automatic checking for medicine side effects or, in future, recommend
suitable medicine for particular patients given diagnosis and test results,

\- etc

If deployed properly, the technologies will improve care, reduce healthcare
worker’s overload, and enable greater access from rural/poor areas.

The same applies to several other expertise that is not lacking in well-off
regions.

~~~
solidasparagus
I've been hearing that technology is going to transform healthcare in the
developing world for the past two decades. While all of those things you
mention help, they aren't the big problems - logistics, finances, corruption,
stigma around being diagnosed with diseases, low density of clinics, low
education. These technologies will only help if you have trained healthcare
workers, equipped with the medicine they need and a population that is willing
and able to come into clinics.

~~~
nopinsight
AI-assisted telemedicine along with (non-doctor) healthcare workers who
regularly visit homes can help mitigate some of the problems you describe.

Healthcare workers can be almost an order of magnitude cheaper than doctors
and take much less time to train.

In a middle-income country I know very well, there is no lack of such workers,
but doctor shortage, esp specialists, is acute in many rural areas.

~~~
131012
The main problem with healthcare access is money. Unless you create the
socialist-bot-of-ai-love, tech won't fix this issue.

~~~
nopinsight
AI and telemedicine can help lower costs by improving the efficiency of some
logistics and the most expensive kinds of expertise.

------
bumby
> _Many of McKinsey’s estimates were made by extrapolating from claims made by
> various startups._

Nobody thought to pause and question whether startups had an incentive to
overstate their impact? Watch one episode of Shark Tank and you can see the
fault in this methodology

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buboard
It's quite possible that the biggest leaps will be in white-collar jobs , and
maybe not by the funded companies the author looked at.

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solidasparagus
I'm unimpressed with this analysis. It seems pretty shallow and based on not
very useful bad data.

First, 2035 is very far away. The first modern smartphone came out 12 years
ago. I bet in 2007 you could have analyzed the mobile startup space and come
to the same conclusion. Projecting what benefits a revolutionary technology
will bring us in the next 16 years by analyzing what startups exist right now
does not seem like an effective way to make predictions. Uber (arguably the
largest mobile-first company), came into existence only 10 years ago. The AI
startups that will shape the industry of 2035 probably don't even exist yet.

Then there is the lack of deep analysis of the three divisions in the current
wave of AI, each of which have very different trajectories. There is:

1\. Computer-world interaction. The work that focuses on giving computers new
ways to interact with the world - speech recognition/synthesis, natural
language understanding, computer vision, interpreting more specialized sensor
data such as lidar or 3d imaging.

2\. Data as an economy of scale. The work that focuses on using data to make
better spending and planning decisions by leveraging vast amounts of
historical data.

3\. Exploratory research. Speculative research towards autonomous agents and
AGI. Stuff like most reinforcement learning and other pure research that is
not ready for production.

Computer-world interaction is where most of the current set of startups seem
to live. You can build on top of publicly available data and then finetune it
for your needs with a relatively low amount of data, which means the barrier
to using AI is not insurmountable. Example successful products using this type
of AI - smart speakers, L2 autonomous driving (e.g. Autopilot), Pixel night
camera, medical diagnosis. This is still in the early stages of
commercialization, but the technology is improving at a ridiculous rate. This
is mostly still in the late R&D/early commercialization phase so I would not
expect many of the players in this space to be focused on profitability. What
matters for the long-term projections here is whether the technology is
advancing towards being viable for many real-world usecases within the next 5
years (IMO it is), whether the technology will be cost effective (less obvious
but it looks promising with advancements in hardware and more efficient
network designs) and whether it will be accessible to many companies (lots of
good work happening here between AutoML research and MLaaS companies).

Data as an economy of scale is where most of the near-term financial benefits
of AI are being demonstrated. The successes I've seen are primarily confined
to big companies since they have the data and resources needed to train and
benefit from these models. There are some startups in the space (maybe the
security startups mentioned in the article?), but I'm not sure exactly how
they would build useful AI models without the large amounts of historical data
that established companies have. It's possible startups in this space are
sometimes guilty of overselling their 'AI'. However, big companies are finding
a lot of success here - better logistics decisions, better fraud detection,
better content/ad recommendations. Some of the techniques have existed for
quite a while and we are only seeing benefits now because it has taken a long
time to build up the data and ML infrastructure to apply those techniques.
Other techniques such as graph neural nets are still in their early phases but
are showing great promise. This is completely missed in the analysis done by
the article because it only looked at startups.

Finally there is exploratory research. Some of this is quite promising - RL as
a technology is incredible, although RL as a useful tool for business still
appears to be a ways away. While any advancements here would be great for the
impact of AI, it doesn't really matter if none of them pan out - the current
set of proven techniques will still have a huge impact. 'Companies' in this
area are in many ways just research labs. I would not include them in my
analysis - DeepMind and OpenAI.

