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  • I'm long winded and not very good at Reading, which makes my current career of reading and summarizing thousands of documents on odd choice on teaching a combination of students and supercomputers to speed read.

  • Well, that's an insanity that I never expected to embrace, let alone make up a word to describe it.

  • The word contextual.

  • Combining the quantitative power of modern machine learning with the contextual savvy of human experts to summarize research.

  • My goal is to rid the world off information overload and to help you with your personal information.

  • Deluge.

  • As a finance researcher, I'm inundated with documents, financial reports, economic analysis, Southside research and academic papers.

  • Hundreds of documents received online every day, millions available online.

  • I'm inundated with information with barely enough time to read the headlines, let alone sit down and dig deep for inspiration.

  • And as I talked to researchers all over the world, I realize that we are all in the same boat.

  • Sure, talk to them at a crowded cocktail party and they'll tell you about an interesting article they read in the Wall Street Journal.

  • Or they'll say something like, You know, I read an interesting paper by Professor Mark Greenblatt in The Journal of Finance, and they sound so educated as they tell you this.

  • But talk to them in private, as I've done countless times on.

  • The disturbing truth emerges that research that they paid good money to receive their straight from their inbox into the trash without even being opened, and that grand plan for getting inspiration from academic research.

  • Pick a paper at random from the nearest journal and read the abstract.

  • Now, if you want to cool cocktail party trick.

  • Remember the names of the author's So you sound intelligent when you bring it up, let me try Mark Grin Blatz Did I do?

  • It isn't deception of epic proportions, and I'm just as guilty as anyone because I'm a researcher and I don't read research.

  • Reading research is hard work and, frankly, overwhelming.

  • Now.

  • When I was a college studying for my doctorate, my professors would get me to read all the time papers by people like Mark Greenblatt, and I think their hope was that if I kept reading all the time, they wouldn't have to answer difficult questions like, What's up with this data or when am I going to graduate?

  • well, reading as much as I did, I learned a couple of simple tricks that helped me along the way.

  • I looked at the paper on Dhe, read both the abstracting conclusion on, decided whether the paper was worth reading.

  • Now if it waas I looked at the meat of the paper on dhe.

  • Look to see if the key conclusions held true.

  • And then if the paper was truly awesome, I would read and re read it again in greater and greater detail.

  • Well, I finally read enough research to be allowed to play with data on graduates, but college never taught me the secret of keeping my head above water with research.

  • So five years ago, I set out on a crazy journey to read as much research as I possibly could.

  • I hope that I might find personal inspiration along the way or the very least, offer my colleagues research summaries, Cliff notes for finance.

  • Tunney's I set out to read 600 papers over two months with my boss and an intern on it.

  • Waas torture, academic paper.

  • Torture is that point of exhaustion where you can't even stand the sight of a paper, let alone 100 more.

  • And not only was this hard work, we were barely even scratching the surface.

  • S s r n the leading source of economic papers receives over 60,000 newpapers every single year.

  • Now, most people are gonna do the maths to figure out how much work it would take to read all of that.

  • But I did 45,000 hours of work.

  • Now that's more than five times the number of hours in a year.

  • So nobody is gonna be able to do this alone.

  • Realizing this, I recruited all of my co workers to help me and as they started to realize what was in store for them, Well, let's just say there's a sudden increase in unannounced vacations and jury duty.

  • Herding co workers is harder than herding cats.

  • So I turned to the only resource that I knew I could rely on interns, current PhD students working part time, too.

  • Read for me.

  • Now, this sounds like an incredibly evil plan.

  • Give the interns the job that nobody else wants, but the uniquely motivated.

  • They're already in the same hell That's my professors.

  • Put me through.

  • So why not get paid to redress that you're gonna read anyway.

  • And besides reading for a couple of hours each week, helping to develop skill in the same way that an athlete tones the body after a couple of weeks, an intern can re better research than a full time researcher.

  • But even if I had an army of researchers, I still wouldn't be doing anything to address that deluge of information that comes into my inbox every single day.

  • It's handled that I thought about the quantum contextual, the power of machines I actually remember back to when I was a college.

  • I moonlighted for Google, acting as a human interpreter for the search engine.

  • No, you will know how Google is grated answering questions even when you phrase them with Lee.

  • Well, that's because they've used interpreters like myself to train their algorithms over and over again.

  • And now I'm doing the same sort of thing with economic students and economics questions.

  • Here's how it works.

  • I ask a researcher to think about a question that a document answers, for example, on a paragraph on global earnings answers.

  • Well, a question like What are the prospects for global earnings?

  • They highlighted paragraph on the question, and then a series of algorithms breaks it down on links it all together, creating a rich tapestry of insights, which then adds two with new insights based on new documents on regurgitates.

  • All of it in a second when prompted.

  • Kind of like Syria on your phone.

  • But for economics questions, Siri, what are the prospects for global earnings this year?

  • I'm sorry, I don't understand your question.

  • Why don't you go ask the interns?

  • It has its limitations.

  • Computers are great for things like figuring out the topic or the general sentiment or tone, but they struggle with things like Is this research novel?

  • Or do we believe is key conclusions For that, you need experts.

  • You need the PhD students, so I use a combination of the two when I need speed.

  • I rely heavily on machines, deep insights.

  • I unleash the PhDs.

  • The combination of the two works well together.

  • They train, summarize and prioritize for each other, and they're both fast and flexible.

  • As my interns have seen their work become leveraged buy machines that come to embrace contextual as Obama for a combined efforts.

  • For me, life in a contextual world is better organized.

  • Sure, I haven't inbox full of research, but now I know what's in there, What's important on where to look for answers, and because I'm no longer lying to myself about what I'm reading, I'm less likely to be caught off guard.

  • How we handle information overload is changing in ways that we're only beginning to grasp.

  • For example, doctors would be better able to prioritize their patient updates and keep on top of important medical advances.

  • Politicians and rock stars ableto understand what their constituents or fans are saying, and they have rich co worker.

  • It doesn't have to worry about emptying their inbox every day.

  • They can just focus on getting that work done because having a human in the mix, keeping tabs on things, we can worry a little bit less that a computer is overlooking something important.

  • And with all the time that we have for inspiration, we can focus on things that are of relevance to us.

  • For example, I just read that analysts are more likely to be overly optimistic about highly risky stocks and that this overoptimism drives up prices in stocks.

  • This has important investments, implications on my interns and computer highlights of this to me.

  • This is brand new research that hasn't even been published yet.

  • And yet I'm reading it right now.

  • But even though it hasn't been published, I suspect that it will do soon.

  • Probably even in the Journal of Finance.

  • Because I can honestly tell you that I just read a great article written by Professor Mark Brim Blatz.

  • Thank you.

I'm long winded and not very good at Reading, which makes my current career of reading and summarizing thousands of documents on odd choice on teaching a combination of students and supercomputers to speed read.

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閱讀的未來。這是個快速的過程|斯蒂芬-勞倫斯|TED研究所 (The future of reading. It’s fast | Stephen Lawrence | TED Institute)

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    林宜悉 發佈於 2021 年 01 月 14 日
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