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Stanford University let's do a quick round of introductions all right
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I guess I'll start I'm Ewan Ashley I'm a cardiologist in the School of Medicine I have the honor
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and privilege of leading the biomedical data science initiative
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I usually say I was born a geek
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but trained as a cardiologist I did medical school in Scotland
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you could probably tell from my accent I did some statistics during a PhD in Oxford
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and moved to Stanford about 12 years ago, and so from when I first visited, this was clearly the place I wanted to eventually call home,
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and the combination of bringing the sort of engineering and computer science
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and theoretical tools
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that we have developed in a place like this to the application of medicine is the thing that excites me the most, so looking forward to the discussion today.
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So I'm Vijay Pandey.
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I'm the leader of the Theory Initiative at the School of Humanities and Sciences.
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I'm actually in a couple of different departments.
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I'm in chemistry but also with courtesy appointments in computer science and structural biology.
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I'm also director of the program in biophysics.
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My research is at the intersection of a lot of different areas, but especially we use a lot of data science methods, especially for understanding fundamental biophysics and also for coming up with new small molecule therapeutics.
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My name is Hector Garcia Molina.
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I'm in the computer science and electrical engineering departments.
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I was talked into being the director of the Stanford Data Science Initiative.
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My colleagues twisted my arm, which is, it has a lot of engineering folks, but there are also other people in statistics and other departments,
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and we have a lot of already existing collaborations with other groups on campus.
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And I should point out that the executive director, the guy who really does everything is Steve Eglash, who's sitting right here.
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And if I get a really hard question, I'm just going to ask Steve to answer it.
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I'm John Hennessy.
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I'm actually a computer, I have the same appointment Hector has, double E and CS appointment.
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I'm a computer scientist.
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I came here to figure out what the difference between a data scientist and a computer scientist was, which I expect to learn today.
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But anyway, obviously I have an interest in this.
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I think it's an important initiative and I think it has many of the interdisciplinary roots, I think, that we've tried to hone and improve here in the university.
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It also, I think one of the other things that's attractive perspective is
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it has deep roots on everything from theory to application
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and I think that promises a lot of opportunity for the university where we have faculty across that entire spectrum.
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Perfect.
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Okay, great.
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Thank you.
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So, the first set of questions is more about understanding what
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data science is just to make sure we're all on the same page.
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So, let me ask by just asking maybe each of you to pick one application area for data science, be it, you know, healthcare, care, climate, whatever, one application area,
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and explain kind of how data science is going to revolutionize that field, and we can take it in turn.
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All right.
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Okay, I'll go first.
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So one of the things
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that I have been focused on in my own group over the last few years is a tremendous technological advance, which is the change in the cost of sequencing human genome.
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You may be aware
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that it cost $3 billion to fund the Human Genome Project about a decade ago
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and that over the course of the last 10 years
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that cost has dropped down to $1 ,000
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so a million fold drop in the cost of sequencing in genome and that has had an already an absolutely transformative
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role to play in changing medicine to give you an example of
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that we had a little baby admitted to the neonatal intensive
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care unit at the children's hospital a few months ago who had not a mystery syndrome
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but something we understood well long qt syndrome this was associated with cardiac arrest
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and the little baby had multiple cardiac arrests on day one of life.
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We know the genetic basis of that condition, but at the moment, with the standard approach, a genetic test would take three months.
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It would look at five genes, so very small data, and it would give an answer that would help the physicians then do what we're increasingly calling precision medicine.
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We'll probably come to that phrase later.
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With the new technology, we can do a whole genome sequence which reduces six billion data points in 24 hours, analyze it using algorithms that used to take days
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that now take hours and put the result back into the hands of the treating physicians in days.
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So if I was to think of one example
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that could encapsulate what we can do and what data science could bring to medicine, we've looked at one gene at a time, we've looked at you go in and you have a lab sample and maybe 10 reagents come back to you.
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But now we have technology that can deliver 6 billion results back to you.
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And there's a huge signal to noise issue there.
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And so the sorts of things
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that colleagues in engineering have been dealing with for many years suddenly become extremely relevant to things
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that are happening in the medical school.
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I think the example I would give is also in the healthcare area, but from a different perspective.
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What I think a lot about is how we design small molecule drugs.
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Right now, the procedure is very empirical.
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If you think about it, when we do other things, like we design a bridge, like the bridge, the Bay Bridge, we don't do that the way we design drugs.
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If we do it that way, we would have some idea for the bridge.
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We'd think it works, and then we'd first send mice over the bridge to see if they survive.
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And then if the mice survive, we would take people who really want to go to San Francisco and we'd put them over the bridge first.
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So we would never do that, right?
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And so it's interesting to think, why can't we engineer small molecules in an analogous way?
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And there's many different answers to this.
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This is an extremely difficult problem.
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But I think the opportunity now is that we have a huge data sets, and so huge that you can't have a single person wrap their head around them.
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And I think that the promise of data science is
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that we'll be able to algorithmically gain insights and insights that are really non -obvious.
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Victor?
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Okay, well, just to be contrarian, I'm gonna mention old applications like finance, commerce, manufacturing, where they're not as trendy,
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but they're still very important.
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And there's a lot of great things that can happen to produce products and services cheaper, more efficiently, and safer again,
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because we have a lot more data about the process of manufacturing or about the customers and their needs and their desires.
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So it's a combination of more data and better ways to do things.
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So more data and better algorithms.
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And better algorithms.
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Yeah, yeah.
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And better services that we can provide.
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Yeah, makes sense.
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So I think one thing that is going to be possible now is the work that's going on on deep learning.
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And really, the funny thing about deep learning is deep learning will give computers what we would call common sense.
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It's really the ability to devise relationship.
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And that's happening now because we've learned enough about machine learning and we have enough data.
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You want to learn about the world?
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Go read the internet.
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Read the internet, put down all the relationship you get about the internet, and then build a system that can reason about them.
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So you need both large amounts of data and large amounts of compute.
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And we're going to spend a lot of money doing doing things
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which people could do easily right now for smaller amounts of money, but in ten years they'll be a lot cheaper and that will be liberating.
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For me, for my area, the application of data science that's really exciting is the application actually to computer security.
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So as you might realize, attackers are constantly doing new things and for us it's not new data, it's actually, it's not large amounts of data and new algorithms,
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it's actually the fact that there's new types of data that are being collected in order to detect attacks.
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Just as examples, LinkedIn, for example, is constantly being bombarded with fake accounts.
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So how do you detect fake accounts in the massive sea of accounts that they have?
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That's all based on data science techniques.
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But is this really a new field or is it just like John says?
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Is this just more computer science and more statistics?
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Is there something fundamentally new happening here?
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Are there kind of new techniques that are being developed, new ideas that are being developed? or is it just more of, it's just a new name to an old concept?
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Yeah, please, Hector.
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Can I take a crack at that?
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Definitely, data science is new in the same sense that Columbus discovered a new continent 500 years ago,
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where there happened to be a lot of people living there already.
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So, it's sort of similar.
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There's a lot of data -related work that's been going on for the past years, But, what's really new is the discovery
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that a group of influential people have discovered
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that there's a lot of value and power in data and mining it and analyzing it, and that's what's exciting.
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Now, I'm not implying that everything has been done, right?
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Even after Columbus discovered America, there were still a lot of good things that happened in America.
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There was South America. Did you?
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There was South America, for example.
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Well, I meant America in the broad sense.
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Broad sense, America.
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The Americas.
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Now, Hector, I want to point out that Columbus' discovery did not go so well for the people
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I hope you're not going to do the same thing in data science.
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Why not?
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Why not?
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Yeah, it's a good question.
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I think this is a good question because Jeff Ullman, my colleague in computer science, once said, anything that has to call itself a science ain't one.
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And I think there's some truth to that.
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You know, the question will be, take computer science's discipline, right?
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Over time, yeah, surely its foundations are in lots of other disciplines, but over time there became a core body of knowledge that distinguished what computer scientists know, right?
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And it's about algorithms and complexity and computability and autonoma, and there's a core body of knowledge.
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My guess is the same thing will happen here.
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There'll be a core body of knowledge.
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It will build on statistics and mathematics and other disciplines,
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computer science, but it will be increasingly distinct as our applications frontier moves forward over time.
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Also, a lot of times we define these disciplines by the curriculum that we teach, Because, you know, you can be in a computer science department
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or a biology department or a math department and do whatever research.
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But often it's that discipline that we want to teach the younger generation, which is a key aspect of it.
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I think the absolute scale is also larger.
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I mean, I think there's no debating that.. compared to 10 years ago, the amount of data in the world, the amount of data that is being passed through the pipes on an hourly basis is greater.
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And so that can, one, lead to, I think, learning that wasn't possible with smaller data.
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But I think it can lead to opportunity in extending current disciplines.
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So it's certainly not replacing anything, I think, as John said.
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It really builds on those, brings them together.
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And I think, for me, it's almost like I don't really care at some level whether it's new or not.
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What is clearly new is the availability of data and the problem as to what we're going to do with it.
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What are the techniques that make the field its own unique distinctive field from, you know, just machine learning or just databases or things we've had in the past?
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Are there specific things that folks here can learn about that make them data scientists?
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Sometimes it's not necessarily going to be something truly unique.
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In making an analogy to other things like biophysics, biophysics is a mixture of physics and biology in other areas.
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And it's maybe more about sort of how we do that mixture, what the cocktail is like.
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Because, you know, different cocktails can have similar ingredients, but different quantities of each.
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I think lots of people will want to own certain techniques and will want to have them under their wing.
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And I think that that's okay.
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I think that, again, for me, it's the application that counts, rather than exactly which box we put it in.
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And so, for sure, I think the most common answer to your question is machine learning, deep learning, and applying that to extremely large data.
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That's probably the closest to the thing that's new that's sort of associated with data science.
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But really, isn't that just an extension of statistics?
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I think probably, you know, many levels it is.
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I mean, I would say there's a very broad range of tools that come from multiple toolboxes, but I would.
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Yeah, I think that's right.
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Yeah, we're using a lot of existing tools, but the process of applying these tools to new domains, new applications will improve the tools over time.
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So I think Dan's point's well taken.
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I'd say right now, saying you're a data scientist
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or you're working on data science is a way of defining a collection of disciplines
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and knowledge that you have from different disciplines.
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It's not yet distinctive in that way, and it may not be, because I think you can see statistics
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and computer science kind of embracing these techniques and developing them further as a natural evolution of their own disciplines.
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So basically, we don't quite know yet what makes a data scientist a data scientist, but We know what we see.
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But we intend to discover.
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We know what we see.
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We're going to learn.
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That's part of the problem.
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That's part of the challenge, I guess.
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But it does sound like if you want to become a data scientist, you need to learn, you need to know machine learning, you need to know statistics.
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Those are the general areas that you must know in order to call yourself a data scientist.
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Is there agreement on that?
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Yeah.
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Yes?
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Yeah.
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And something about computation.
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Always helpful.
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So the question is, what is the role of universities in this new world?
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I mean, how do we get the data that is proprietary, has privacy regulations around it, The companies are just not going to share the data with us.
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So how are we going to do data science in academia if we don't have the core data?
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I mean, I actually would challenge the assumption that the company is the only ones with data.
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I think Ewan can speak very much to genomic data, which there's huge of it.
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And actually, we have a whole hospital there full of data.
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I think astronomy is very data rich.
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I think not all fields are data rich, but I think there's many, many fields just within academia that are themselves data rich.
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Yeah, I mean, I think if you take the hospital for a moment, I mean the 500 beds, many of them are telemetry beds which mean that we have real -time signals coming from the heart rate,
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from the ECG tracing, electrical tracings of the heart.
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We have every day on probably too many patients
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but we have lab tests that are coming in streams
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and for the most part that data is reviewed on a daily basis, kind of reviewed somewhat in a time series way manually on a screen at least,
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no longer on paper and then kind of dismissed.
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there is no algorithm sitting in the background
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that understands like your fraud alert on your credit card you know what the normal boundaries for you are
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and probably there should be but actually they've got resources
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that we don't have at Google you could run a 10
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million core job it wouldn't be a big deal that's impossible to do on campus so I
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but I grew Dan I think our our data is a
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drop in the bucket we don't need data on 500 patients or even a year's worth of 500 patients.
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We need data on 10 million patients, or 20 million patients, if you really want to start working on the problem.
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And that's got to be a full set of data, not only genomic data.
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We need lots of medical data.
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And I think we're going to have to find a way, and I thought the secret was going to be, I thought you were on this panel, Dan, as the one who was going to help us break into their systems and send all the data out.
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I see.
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Data theft is a different panel.
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That's a whole other department.
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The issue is related to privacy, one of the topics new to you, because I don't think it's an issue of Stanford data versus other industry data.
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It's an issue of people are very paranoid these days for various reasons, and even the government.
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public data doesn't want to share it.
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So getting any type of data is hard.
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I mean, we've had some trouble in the past, although I'm sure that that's no longer going to be true, with our hospital trying to get any data out of there was hard.
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And there's a good reason for that.
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There's HIPAA and all sorts of constraints.
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One of the things we learned about the NSA Snowden thing is
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that just collecting the metadata in fact gave them a fairly good look into some people's lives.
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So how you anonymize it while maintaining the structure of the data in such a way
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that you can reason from it is going to be harder, I think.
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We have a lot of admissions data on campus.
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Can we use that for research?
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I think it is the intention that we have the education data and we're going to use that as a way to, and this I think is one of the big issues about moving to more online methods is
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that you can build the analytics and get more information about what students learn and have a shorter feedback loop.
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I think we definitely want to do that.
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So the university would be willing to share education data that is being collected with the researchers in the data science initiative?
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Sure, sure, sure.
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And even admissions data at some level, obviously.
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There are certain things you've got to keep anonymous, but at some level.
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That's great.
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That's good to hear.
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It's a fascinating data set.
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They wanted our help to study this data and tell them what to do, what they could do with this data.
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But at the end of the discussion, I asked, well, okay, so we're very interested.
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How do we get the data?
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Oh, I don't think we can give you the data.
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Anyone who's here who would like to come and work on it, please come and find me afterwards
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and I can point you to any number of data sets that need people to work on them.
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So I think absence of data is not our challenge, but there is still a role, I think, for synthetic data sets.
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I could just see, when you go to your doctor, I guess you are a doctor, but you must have a doctor.
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I can sort of tell him.
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Here, I have this pill for you.
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We've tested it on Tron, this simulated human whose simulation was written by an undergrad I have here at Stanford, and it works fine.
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Tron survived.
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And the students graduate and we don't understand how the simulation works, but we think it's okay.
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I just, I think the synthetic, I worry about models that are developed around synthetic data
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and the fact that you fit the algorithm to match the synthetic data.
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I worry about that.
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I also worry about, we've kind of moved away from this name big data.
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And I think one of the reasons we did is there's lots of big data out there, but lots of it isn't particularly complicated.
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It's simple data, it's not complex relationships.
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And when you think about synthetic data, how are you going to get synthetic data that has deep, complex relationships that are not artificial but, in fact, look like the real thing?
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I think, ultimately, we're all talking about the same thing.
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We're not going to simply put data sets together and then sit back and say, well, we feel really good now.
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We've got a great data set.
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The point of putting the data set together is to ask a question.
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So I suspect we don't disagree as much as maybe we sounded like we might have done.
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Yeah, I think essentially we led off with some examples of questions.
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But I think questions are very domain -specific.
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and the part that is common is maybe more methods.
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I could give another quick example just from the idea that it builds upon federated queries but moves towards federated statistics because, again, as we mentioned earlier,
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probably one hospital, I'm going to keep away from the domain, but one unit, it may not have all the data in one place
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that you want to analyze and there may be privacy
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and we'll get to this privacy or regulatory reasons that you can't actually bring that data together in one place.
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There's a lot of really challenging problems and just getting data that's usable.
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It's not just you get some file from somebody and you're all set to go.
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You have to organize the data.
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You have to understand what does it mean when you have a field there, how an instrument was used to measure this, and how do I combine this reading in this field with another reading that's related
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but was done with a different instrument or measured in a different way, for instance.
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So there's a lot of challenges.
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How do I represent the accuracy in the data itself?
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And generally, how do we adapt the curriculum to deal with teaching data science and data science skills.
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I mean, I have one idea towards this.
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One of the things
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that I have always been really impressed about about the new CS curriculum is
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that CS can be done in a very project -oriented way.
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If you think about like 106A, students can see the value of computer science very quickly because they're actually doing it.
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And you can imagine having data science -like projects in other disciplines where the project has to do with analyzing the data.
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And maybe they don't take the data themselves, and that would be very onerous and almost may be impossible in some cases, but I can imagine that there could be applications like that.
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And I think my personal prediction is that education will start to look more
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and more project -based as I think it has a greater impact on the students
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and also becomes just more exciting for them.
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Yeah, we've had successes here with some classes where at the beginning of the class the instructor has put together 20, 30 data sets and then the students can pick one that interests them.
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Often they can even go and talk to the person who generated
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that data set and then they can do an interesting project with it.
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So I think that same idea could be used in many disciplines.
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So you see this as, I mean, there are many faculty here are teaching classes
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that could adapt to that model where there would be more data sets available
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and the courses would actually focus on how to analyze those data sets?
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Possibly.
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I mean, I'm not sure, since I'm not an expert, I don't know how far we can go to data mine Shakespeare or something like that, but it's...
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It can be done.
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There's actually a professor here in the English department who's been analyzing old manuscripts.
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I don't know, I think Shakespeare.
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Yeah, right.
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Well, I think then in the medical school, I think we have a slightly different challenge in that we're generally training people not to be data scientists, we're training them to be doctors,
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but yet data science will be such an important part of medicine going forward
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that we need them to be much more quantitatively aware than they currently are.
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And so there's been a number of movements towards thinking about how to improve
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or increase the amount of exposure to those topics
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that our medical students have because they're going to need to understand the theory behind it
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if not necessarily have to be able to actually apply the command line and all the things that they might...
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And understand what they can trust and what they can't.
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If you think about core data scientists, as opposed to...
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We're going to have lots of students who learn about data science techniques in the context of whatever they're doing, biology, chemistry, engineering.
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But I think we also probably want to think about a core educational program
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that trains people who are deeper in the fundamental techniques
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and applying those techniques and maybe not so focused on the applications in any single area.
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And that's a good model for how that might be done. So, here I have to remind everybody of this example of a father who went
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and complained to Target about his teenage daughter getting bombarded with ads for cribs and for diapers,
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only to find out that Target figured out that his teenage daughter was pregnant before she told him.
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So, yeah, that was quite interesting.
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True story.
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True story.
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True story.
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Absolutely true story.
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So, the question is, how did Target actually do this?
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And it turns out what they did is actually, well, they had data scientists who were actively working on their data,
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where they had mothers sign up for their baby registry, and then they started analyzing what do pregnant mothers,
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soon -to -be mothers, what do they buy?
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and it turns out like in their second trimester they tend to buy unscented lotion
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and in their first 20 weeks they load up on supplements like calcium
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and magnesium and quickly they correlated that to you know the fact
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that someone is expecting just based on their purchase patterns
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so that's an example of you know kind of crossing the border of ethics and data
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and I guess that's what you're getting at is it is
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there a limit should there be limits to what data is being collected
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and generally what do we do about the ethical question.
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And should we teach them the difference between correlation and causation?
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There you go.
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There you go.
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Very important.
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Very important.
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If you have the data, you can study it.
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And if you discover patterns like this, as long as it's not referring to a specific teenage girl, I don't see an issue.
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It's the specifics of when it leaks that this specific girl is pregnant, that's when you have problems.
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So then, Hector, let's put it in.
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So now Now you discover this connection,
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you publish it, and the next thing that happens is Target then deploys that algorithm using your discovery,
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or somebody else deploys it, and they start then bombarding the advertisements.
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Now they've used your insight to create a problem.
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Yeah, but that's going to happen.
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Whatever algorithm I develop, it can be used by bad guys to do bad things, so I shouldn't be doing my research?
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This is a common issue in technology, from atom bombs to anything else.
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I mean, I think the challenge especially is that if Hector didn't do it, maybe 10 or 20 years later, someone else would do it.
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Of course, somebody else would do it.
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In the digital world, increasingly, you have to do something really contrived to,
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if you really want ultimate privacy.
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It's the honest.
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I mean, Dan's right.
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There's a scale issue here and a pervasiveness issue that's very different.
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I mean, well, look at credit card data.
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Everybody, you know, does everybody use credit cards?
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Yes.
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And imagine that data being crunched not just by the credit card form, but then sold, because, of course,
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it is sold, and used in lots of different ways to determine buying techniques and things, people, I mean, it's, and this is true in all the stores now.
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You know, the new way to try a new product out is you pick a few stores, and you go watch who buys it, and you watch information about who buys it, and they use a credit card.
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Now you deduce from that what kind of people might buy
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that product and you decide what stores should have it and how much space it should get.
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That's why when I go shopping, I wear a brown paper bag on my head.
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You do.
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And you use cash?
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I use cash.
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He uses Bitcoin.
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Some kind of regulation is going to be necessary.
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I think trying to shut down the research side of the agenda doesn't work.
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I mean, it basically doesn't work.
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So you're going to have to figure out what companies' regulations are about security and privacy.
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And the problem with that is that that involves government.
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And government moves about 10 times slower than technology moves.
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So it's gonna be very hard to develop that.
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And you just have to look at what's happened in copyright law and how far out of date it is.
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Not, you know, 50 years out of date, probably something like that, right?
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And so we're gonna have that problem as a society.
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This is a societal issue, it's gonna have to get.
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Okay, I guess we have to leave some topics for next time.
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I did wanna talk about the role of libraries and can libraries help with data collection and data retention?
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But I guess those are things we'll have to talk about next time.
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I think they'll have to is the answer.
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They'll have to.
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They'll have to adapt, including the Stanford Library.
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Yes?
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It would be really nice if the Stanford Library...
389
We don't have the most old -fashioned library in the world.
390
Exactly.
391
We're ahead of that curve in terms of some.
392
Oh, yeah.
393
Actually, I did want to advertise.
394
There's something called...
395
Maybe you guys want to talk about the Stanford Digital Repository.
396
I think they don't think libraries about books.
397
Libraries about data.
398
Okay.
399
Well, thank you, everybody.
400
Thank you.
401
Thank you.
402
For more, please visit us.
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Từ vựng và ghi chú luyện nói cho bài học này
Video này có 402 câu và 5232 từ để luyện shadowing. Phần lời nói dài 25:43. Người nói nói nhanh, khoảng 203 từ mỗi phút, nên sẽ có nhiều chỗ nối âm và nuốt âm. 86% số từ nằm trong 3.000 từ tiếng Anh thông dụng nhất; phần còn lại nên xem trước khi luyện.
Từ vựng quan trọng trong video
15 từ đáng học trong video, kèm phiên âm và nghĩa:
| Từ | Phiên âm | Nghĩa |
|---|---|---|
| discipline danh từ | /ˈdɪsəplɪn/ | kỉ luật |
| algorithm danh từ | /ˈælɡəɹɪðm̩/ | thuật toán |
| scientist danh từ | /ˈsaɪəntəst/ | nhà khoa học, khoa học gia |
| analyze động từ | /ˈæn.əˌlaɪz/ | phân tích |
| privacy danh từ | /ˈpɹaɪ.və.si/ | sự riêng tư |
| biology danh từ | /baɪˈɑ.lə.d͡ʒi/ | sinh học, sinh vật học |
| genome danh từ | /ˈd͡ʒiː.noʊm/ | bộ gen |
| topic danh từ | /ˈtɑpɪk/ | chủ đề |
| discovery danh từ | /dɪˈskʌv.ɹi/ | khám phá |
| discover động từ | /dɪˈskʌvə/ | phát hiện, phát kiến |
| domain danh từ | /ˌdəʊˈmeɪ̯n/ | đất đai, tài sản |
| insight danh từ | /ˈɪnsaɪ̯t/ | sự sáng suốt |
| curriculum danh từ | /kəˈɹɪk.jə.ləm/ | chương trình giảng dạy |
| colleague danh từ | /ˈkɒliːɡ/ | đồng nghiệp |
| molecule danh từ | /ˈmɑ.lɪ.kjul/ | phân tử |
Cụm động từ bạn sẽ nghe
| Từ | Nghĩa |
|---|---|
| shut down động từ | tắt nguồn |
Phát âm cần chú ý
Người nói dùng 108 dạng rút gọn, ví dụ I'm, don't, we're. Hãy nói theo dạng ngắn đúng như bạn nghe.
- Âm “th”: algorithm /ˈælɡəɹɪðm̩/, synthetic /sɪnˈθɛtɪk/, mathematics /mæθ(.ə)ˈmæt.ɪks/, ethics /ˈɛθ.ɪks/, theft /θɛft/
- Âm “sh” và “zh”: initiative /ɪˈnɪʃ.ə.tɪv/, physician /fəˈzɪʃən/, simulation /ˌsɪm.jəˈleɪ.ʃn̩/, extension /ɪkˈstɛnʃən/, exposure /ɪkˈspəʊ.ʒə(ɹ)/
- Từ dài — đặt trọng âm cho đúng: initiative /ɪˈnɪʃ.ə.tɪv/, biology /baɪˈɑ.lə.d͡ʒi/, curriculum /kəˈɹɪk.jə.ləm/, necessarily /ˌnɛs.əˈsɛɹ.ə.li/, electrical /ɪˈlɛktɹɪkəl/
Cách luyện với video này
- Nghe hết video một lần, chưa cần nói, và ghi lại những từ bạn chưa biết.
- Bắt đầu ở tốc độ 0,75×, nói đuổi từng câu, rồi quay lại tốc độ bình thường khi đã quen.
- Ghi âm giọng mình rồi so với bản gốc, chú ý các từ như discipline, algorithm, scientist.
Ngữ pháp trong video
Những cấu trúc người nói dùng nhiều nhất, kèm đúng cụm từ trong video:
| Cấu trúc | Trong video |
|---|---|
| Thì hiện tại hoàn thành tiếp diễn have/has been + -ing — việc bắt đầu từ trước và vẫn đang tiếp tục | have been dealing · that's been going · who's been analyzing |
| Thì hiện tại hoàn thành have/has + quá khứ phân từ — việc đã xảy ra nhưng còn liên quan đến hiện tại | have developed · we've tried · have been |
| Câu bị động be + quá khứ phân từ — nhấn vào việc xảy ra, không phải người làm | was born · was talked · was associated |
| Mệnh đề quan hệ who / which + mệnh đề — thêm thông tin về người hoặc vật | arm, which is · guy who really · advance, which is |
Phương Pháp Shadowing Là Gì?
Shadowing là kỹ thuật học ngôn ngữ có cơ sở khoa học, ban đầu được phát triển cho chương trình đào tạo phiên dịch viên chuyên nghiệp và được phổ biến rộng rãi bởi nhà đa ngôn ngữ học Dr. Alexander Arguelles. Nguyên lý cốt lõi đơn giản nhưng cực kỳ hiệu quả: bạn nghe tiếng Anh của người bản xứ và lặp lại to ngay lập tức — như một "cái bóng" (shadow) đuổi theo người nói với độ trễ chỉ 1–2 giây. Khác với luyện ngữ pháp hay học từ vựng bị động, Shadowing buộc não bộ và cơ miệng phải đồng thời xử lý và tái tạo ngôn ngữ thực tế. Các nghiên cứu khoa học xác nhận phương pháp này cải thiện đáng kể phát âm, ngữ điệu, nhịp điệu, nối âm, kỹ năng nghe và độ lưu loát khi nói — đặc biệt hiệu quả cho người luyện IELTS Speaking và muốn giao tiếp tiếng Anh tự nhiên như người bản ngữ.