This feels a lot scarier to me since as a ML engineer, I rarely see applications where we can reach human level performance easily but the idea of making existing top performers 2-5x more effective feels much more viable.
The idea of 80% of people in relatively high-skill jobs who are used to a relatively high income level becoming redundant is a pretty scary one.
...Even if this vision occurs, it's not obvious how soon it would occur. For example, will we need stable-value cryptocurrencies before any of this can occur? Will anything significant occur in the next two years, or will it be five? OR more?
We are a team of 3. There is no way this would have worked 10+ years ago. Maybe in another 10 we will be 1...
This actually reminds me of Palantir's focus, which is to develop tools to aid analysts.
I posted the other day about machine learning and radiology. It's the type of job that squarely fits your definition, although things move much more solely in medicine.
It seems risky to lose the human understanding and interpretation of the images. Human augmentation is probably better?
Already people make assumptions because of not understanding constraints in how the underlying tech works (like how FMRI is monitoring changes in blood flow not neural activity directly).
They have been, de facto, replaced by computers, even if the job role still exists.
It's easier to see this if you break down the work not into images but into human-image-minutes. If there are 100 minutes of work, but you can assign the computer 50 of them which it can complete in seconds, the same number of human-image-minutes can be completed as before, but the computer has now taken them away from a human.
Most of the automation work I do in financial services is a 90% automation. This leaves room for more analytical tasks where people can free up time to drive more business decisions and insights.
I work on a team of actuaries in a more data science-y role. Believe me, I had the same impression initially: "Can't we automate some of this stuff?". But I've since come to see their value.
How many actuaries does a major insurer employ? Would they like to halve that number?
https://www.theguardian.com/technology/2017/jan/05/japanese-...
(I don't know about the legalities, but the laws requiring companies to have a actuary sign off on stuff probably can be satisfied by having on part time actuary shared between multiple companies sign off on computer generated analysis.)
http://www.inc.com/kevin-j-ryan/artificial-intelligence-repl...
EX of some research from 2008: http://www.seas.upenn.edu/~cse400/CSE400_2007_2008/DavdaMitt...
You see real world movement within seconds of the release of some public data. Which is far to soon for humans to read much of anything.
Maybe it's not too important yet because HFT bots only need to game the slower mass of "dumb (reactionary) money"
[1] http://www.theatlantic.com/sponsored/etrade-social-stocks/th...
Is there a difference? If a person does 10x as much you need 1/10th as many of them.
AI everywhere is as naive as software everywhere. It costs a lot of money to develop and maintain software professionally. It's uneconomical to staff an IT team to replace every routine job there is with software.
It will be the same with AI. If you apply enough bright people's mind for long enough, we can possibly automate certain niche jobs, but that's going to be completely uneconomical. You need to pay these AI specialists, and retain them so that you can adapt your algos as the problem moves, and there needs to be more than one in case one goes or is ill. You can easily spend more that the salaries you are going to spare.
Other problems are I believe too vast and open ended for AI to come up with anything useful. What company will be successful in 5 years? What is the right level of renumeration for the management of that company? Where is innovation going next? You really think that AI is anywhere near answering that kind of questions?
AI will shine in similar areas where software shines. Problems that are simple enough to be understood by computer scientists (and that narrows down this vast world quite a bit), and that are broad enough to justify the economics of making high investments to build data, or where there is already ample of existing and relevant data. Driving a car. Cleaning or building a house. Replicating certain hand movements. Things like that. I doubt an expert on a narrow and specialised domain has much to fear from AI.
The thing is, it doesn't have to be an all-or-nothing proposition. If you can write software to make a team of ten traders 10% more productive, it may be possible to do the same job with only 9 traders.
Over time, those productivity gains compound and you have literally cut in half the number of people on your trading floor.
Derivatives trading has grown as cash trading has shrunk. And the banks haven't figured out how to automate that business yet.
Other areas may be more complex, but complexity isn't as big a barrier as it may seem.
Also, those areas are probably not as complex as people think they are. I'm reminded of the AI that started making breakthroughs in oncology by looking at the cells around a tumour, despite the scientific consensus that there was nothing to be learned from the cells around a tumor.
No it isn't. If I read an article about the Volkswagen emissions scandal, what table do I look in to figure out how much consumers will care, and how aggressive various governments will be with their punishment? When Disney buys the rights to Star Wars, where should I look to see if it's worth what they paid for it? Should I rely on data from 30 year old movies, or look at merchandise sales or something? If I start seeing a lot of articles about climate change and the dangers of fossil fuels, should I sell my stock in Exxon?
Not that humans are particularly good at this stuff, but I don't think computers will be as good or better for a long time.
Even if a computer can't figure out the stats you mention, maybe it doesn't matter? Maybe just reacting fast enough to what people in the market are doing is good enough? Maybe simple sentiment analysis on articles on Disney around the web is a good enough proxy for share price increase.
Not yet.
'Following, ALM, we refer to these procedural, rule-based activities to which computers are currently well-suited as "routine" tasks. By routine, we do not mean mundane (e.g., washing dishes) but rather sufficiently well understood that the task can be fully specified as a series of instructions to be executed by a machine (e.g., adding a column of numbers). Routine tasks are characteristic of many middle-skilled cognitive and manual jobs, such as bookkeeping, clerical work, repetitive production, and monitoring jobs. Because the core job tasks of these occupations follow precise, well-understood procedures, they can be (and increasingly are) codified in computer software and performed by machines.'
There doesn't need to be a single AI to create the entire app. An AI to model data structure, an AI to build an interface, an AI to consume external APIs, etc. We already mostly bolt libraries together to make software; it doesn't take a huge leap for those libraries to be generated by machine learning.
Building anything but the most complex apps will be trivial in 10 or 20 years time.
Ever try to build a web application in Assembly language or C? It would probably be pretty difficult. You don't have to do that anymore.
1 web developer can do more work today than 100 assembly developers could, from 50 years ago.
"The last people to be replaced are going to be software devs"
If your job is to write code so people can look at data and make decisions based on that data, you're gonna be the second to go.
Physicians are another great example. A lot of their job today seems to be about translating signs and symptoms and following a decision tree. I'm excited about the opportunities to displace current duties with today's nice to haves, such as physicians who have time to dig deeper into a person's well being than their symptoms that day.
Manufacturing and agricultural jobs have already been automated away, while data analysis jobs are only just now starting to feel the pinch - and then only in companies with very deep pockets working on a niche problem. 'First to go' has missed the mark by many years.
Depends on the type of data, how it's collected, how it's aggregated, etc.
I'm a market researcher so I look at data all day. Thing is - I look at both qualitative and quantitative data from a ton of different sources (financial filings, surveys, macreoconomic organizations, vendor briefings, engineer interviews, etc.).
I size the markets for embedded technologies that are automating away data-driven jobs. Most notably in the industrial sector, where years of near-zero industrial productivity growth have left a lot of manufacturing/oil&gas/utilities companies hungry for any way to bring costs down.
The newest technology right now is the "IoT Cloud Platform" which aggregates data from a bunch of industrial machines (directly from devices, or through IP enabled gateways), routes the data, and sends it to a cloud where it can be analyzed and monitored automatically to predict failure and prevent unplanned downtime. From a component standpoint its made up of a (1) piece of client software that sits on the machine or gateway, or a configured agentless client, (2) infrastructure VMs for load balancing/server provisioning, (3) host VMs to run an OS, middleware, and a runtime framework, and (4) applications that run on top of the host VM.
One of the more interesting demos of this tech is a Microsoft/GE joint project that used drones to take pictures of power lines, sent that data to the Azure IoT Suite, which then performed visual analysis to determine which power lines were damaged or deteriorating, saving the cost of sending humans out to climb up these structures. All the big companies - Amazon, Microsoft, IBM, SAP, Oracle - are trying to add more intelligent applications on top of these platforms, with machine learning, neural nets, and blockchain-based applications being some of the most advanced.
If your job follows a simple binary data check or logical chain - is this machine functioning within specs (if not, order replacement), did we hit the target price for this asset (if so execute x number of orders), does this piece of equipment look functional (if not, report to manager) - sure you should be worried about your job.
If you go one step up from the basic logical workflow, and enter the realm of data synthesis, or of handling data that requires skepticism/critical thinking, I think you have no reason to worry about an algorithm doing your job anytime soon.
The financial sector in general is getting some of its excess fat trimmed due to a period of increased regulation and low interest rates/returns. This is a good spin for these finance companies, rather than "we're laying off 2/3rds of our cash equities trading desk".
When people talk about the risk of automation stealing jobs, they tend to think it's the low skilled jobs that are most at risk. But of your job is to look at data and make descisions based on that data, you're gonna be the first to go. And the more money pinned to your work, the quicker you will be replaced.