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A agency that I once worked for was asked to create a presentation of the future of trading for a well known trading platform. It was the typical fluff around v

• burkaman 10y ago
  Isn't this type of trading sort of unique in how easy it is to consume the data? Even in other investing areas, people who just "look at data and make decisions" cannot be easily replaced until computers can read and understand natural language and understand the state of the world. I can't immediately think of anything else this type of automation could apply to.
  • Retric 10y ago
    Computers are already making trades based on natural language articles. EX of some research from 2008: http://www.seas.upenn.edu/~cse400/CSE400_2007_2008/DavdaMittal/project.html 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.
    • nosuchthing 10y ago
      I'd have to wonder what type of safeguards vet against intentional false data released to poison the well of other HFT bots? Maybe it's not too important yet because HFT bots only need to game the slower mass of "dumb (reactionary) money"
      • adamiscool8 10y ago
        Not a whole lot. [1] [1] http://www.theatlantic.com/sponsored/etrade-social-stocks/the-day-social-media-schooled-wall-street/327/ http://www.theatlantic.com/sponsored/etrade-social-stocks/th...
    • burkaman 10y ago
      Absolutely, but isn't this type of software generally used to augment existing jobs, rather than replace them? The program can make basic inferences like "10 negative GE articles -> Sell GE stock", but you still need a human to make more general decisions about an industry as a whole, related companies, how long-term an issue is, etc. Basically, nobody's job is as simple as reading the news and deciding whether articles are positive or negative. In the case of this Goldman Sachs story, some people's jobs really were as simple as looking at the performance of equities and making decisions based on that alone.
      • zardo 10y ago
        >Absolutely, but isn't this type of software generally used to augment existing jobs, rather than replace them? Is there a difference? If a person does 10x as much you need 1/10th as many of them.
        • burkaman 10y ago
          But this is about responding faster, not responding more often. It helps you react to a story before everyone else, but there's only so many stories in a day.
        • Scea91 10y ago
          Or you can have 10× productivity.
          • zardo 10y ago
            You have that either way. The question is do you need 10x the output?
        • cm2187 10y ago
          These algorithms are only good at front running the reaction of the market, which is fairly predictable in the short term when a relevant piece of news is published. They are nowhere for making long term investment decisions. 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.
    • fauigerzigerk 10y ago
      Ah but in 2008 these news articles were still written by humans :-)
  • totalZero 10y ago
    Yes. Not to mention that spreads are so tight that the only real challenge for a cash trading desk is how to read the flow, and manage inventory, into events. Computers don't do a great job of interpreting doublespeak and insinuation, which is a substantial part of earnings calls and other releases of information. Derivatives trading has grown as cash trading has shrunk. And the banks haven't figured out how to automate that business yet.
  • tomelders 10y ago
    Not really. The world is data. Anything you need to know about the world is in a table somewhere, and the web has done a great job of making the planets data accessible to machines. 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.
    • taurath 10y ago
      Spoken like a true data scientist. Hope there's room for culture in there somewhere.
      • pdog 10y ago
        Sorry to burst your bubble, but culture is data too.
    • rand_r 10y ago
      The problem is understanding data. A human can learn a new word's meaning by reading its definition in a dictionary; a computer can't.
      • NikolaeVarius 10y ago
        It's a dictionary. I'm pretty sure a computer can look it up faster than a human can.
        • rand_r 10y ago
          Access to raw text is not the hard part.
      • eon1 10y ago
        Even so, that doesn't stop one human helping the computer to understand, and thus replace x more humans.
  • teachrdan 10y ago
    Another obvious example is actuaries and any number of other analysts in the insurance industry. According to Google[1], actuaries earn an average of just over $97k/year. While this may not strictly be an "investing area," it's a clear target for automation. 1. https://www.google.com/search?q=actuaries https://www.google.com/search?q=actuaries
    • grondilu 10y ago
      I don't know about actuaries, but I do remember there was an article lately about an insurance company in Japan that replaced hundreds of workers with IBM's Watson. https://www.theguardian.com/technology/2017/jan/05/japanese-company-replaces-office-workers-artificial-intelligence-ai-fukoku-mutual-life-insurance https://www.theguardian.com/technology/2017/jan/05/japanese-...
      • api_or_ipa 10y ago
        That's really interesting. In my circle of friends, most of us can't point to a single success of Watson, despite it's very publicized capabilities. This may only be a small company, but almost certainly larger insurance agencies & medical foundations must be on to this.
    • eru 10y ago
      Actuaries have quite a high regulatory wall protecting them from other human competitors. But probably not from computers. (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.)
      • go_prodev 10y ago
        I know in banking, regulators are satisfied with ML models making decisions about bank capitalisation, so long as the models are annually audited by an external auditor (eg. KPMG, EY)
      • 10y ago
  • eon1 10y ago
    Computers don't need to understand natural language, they just need a little guidance towards what the data means. You can train a neural network, or you can just write some solution-specific code. A lot of people are not that hard to replace.
  • walshemj 10y ago
    yeh you can't send a robot of an on site visit to look the management in the eye
  • AznHisoka 10y ago
    i agree 100%. i think the only reason traders can be replaced by machines is because these traders didnt perform better than monkeys shooting darts.
• tomjen3 10y ago
  No doubt. The last people to be replaced are going to be software devs (because when you can write a computer program with an AI you can make the AI write the AI and you have general AI), cleaning ladies and probably police investigators.
  • thavalai 10y ago
    "But of your job is to look at data and make descisions based on that data, you're gonna be the first to go." "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.
  • zardo 10y ago
    The world's oldest profession will be the world's last profession.
    • tortarga 10y ago
      Fishing?
      • Chos89 10y ago
        Picking fruit
    • secfirstmd 10y ago
      Not if those weird Japanese robot sex toys take off.
    • tomjen3 10y ago
      Nah a 3D environment with the right force feedback input would be both much more convenient, much cheaper and much more private.
  • onion2k 10y ago
    because when you can write a computer program with an AI you can make the AI write the AI and you have general AI 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.
• roymurdock 10y ago
  > If your job is to look at data and make decisions based on that data, you're gonna be the first to go 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".
  • 10y ago
• aabajian 10y ago
  >>But of your job is to look at data and make descisions based on that data, you're gonna be the first to go. 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.
  • TallGuyShort 10y ago
    It's also likely that human-in-the-loop will stay around, possibly indefinitely in medicine. A human-in-the-loop is a huge safety thing for medicine, but it defeats the whole point of HFT.
  • fossuser 10y ago
    My dad is a neuroradiologist so I've seen a decent amount of the reading work (which is one component of the job - though a big one). I also thought it'd be well suited to ML, but I suspect the future is more suggestions and flagging things in scans rather than entirely replacing the doctor (I'd be surprised if this isn't happening already in some capacity). 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).
    • abstractbeliefs 10y ago
      The thing is, when you have 4 radiologists reading 100 scans each, if flagging and highlighting and generally improving performance of these personnel leads to two of them being able to process 200 scans each, the other two are out of a job. 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.
      • 10y ago
• rezashirazian 10y ago
  I agree. Doctors should be very worried.
• Futurebot 10y ago
  Autor and Acemoglu wrote about this in their paper 'Skills, Tasks and Technologies': '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.'
• vacri 10y ago
  > But of your job is to look at data and make descisions based on that data, you're gonna be the first to go 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.
  • kilotaras 10y ago
    The difference is in marginal cost. Industrialization was a relatively gradual change. AI can make whole professions almost redundant basically overnight.
• gthippo 10y ago
  The terrifying thing is that you don't even need to reach human-level performance to cause structural unemployment. As a hypothetical example, if automation can make top-performers in your field 10x more efficient, that makes 90% of people in that role redundant. 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.
  • Gracana 10y ago
    Lots of high-earning people tied up in managing money is pretty inefficient for society, though. Maybe those people can switch to factory jobs and help reduce the cost of common things for the common person.
    • zepto 10y ago
      I can't tell if that's meant as sarcasm or not.
      • Gracana 10y ago
        It's kind of a joke. People getting rich off moving money around is kind of silly, and it would certainly be better if those jobs could be eliminated by machines.
        • grzm 10y ago
          Oh, don't worry. People will still get rich off of those transactions.
          • chrispeel 10y ago
            The vision of public blockchains (such as Ethereum) is to make the core of financial transactions automated and non-profit. While there may still be profit to be made on the margin (credit scores), the vision is that currency and equity trades will happen on decentralized markets that are automated and non-profit. ...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?
• debt 10y ago
  Software engineers think they're somehow immune to the cycle.
• Waterluvian 10y ago
  I always consider automation to first target jobs with "trivial decision making." I think trading fits into that, as you can pretty much distill it down to an algorithm. At least as far as I understand it. 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.