Chuyện sinh viên cao học sắp ra trường

Thời gian này là lúc tất cả bọn sinh viên sắp ra trường sốt sắng đi xin việc hoặc quyết định xem có nên học lên tiếp không. Mình cũng không phải ngoại lệ. Ở một đất nước lạ, không có ai nâng đỡ; hậu thuẫn tài chính không có, không quen biết ai, tất cả mọi thứ đều phải tự cố gắng – một mình. Mình cũng tự nhận thức bản thân không phải là đứa quá thông minh hay có gì đó hơn người – nếu không muốn nói thẳng ra là tương đối nhạt nhòa; vì vậy nên cố gắng không phải là một lựa chọn mà là một thái độ bắt buộc phải có để tồn tại.

Hồi nhỏ nhà mình tương đối khá giả. Mình suốt ngày ở nhà đọc sách, nghịch ngợm cái nọ cái kia, không phải lo nghĩ gì. Cho đến năm thứ 3 Đại học, gia cảnh sa sút trông thấy, mình bắt đầu phải lao vào đi làm thêm, tự học hành, hoặc có đồng nào thì để lại cho bố mẹ. Sự kiện này cũng giúp mình lớn lên rất nhiều và nhận ra nhiều thứ mà trước kia mình không hề để ý. Cơm áo gạo tiền. Những nỗi âu lo của bố mẹ. Những thứ nhỏ nhất từ chuyện đi chợ một ngày mất bao nhiêu tiền cho đến việc cho em Bống đi học. Lúc đó gần như mình đã từ bỏ hy vọng đi du học (đó là hè năm 2013, mình đã được nhận vào một công ty bên Singapore và chuẩn bị đi làm dài hạn). May mà mình được chính phủ cấp học bổng cộng với cho vay học phí và phí sinh hoạt nên gánh nặng tài chính đã giảm nhẹ đi rất nhiều, mặc dù trong đầu luôn tâm niệm đi thế này thì cực kỳ vất vả nhưng vẫn phải đi. Đúng như thế thật, lúc sang vừa đi học cả tuần vừa đi làm, nói chung chẳng có thời gian làm gì cho bản thân. Thi thoảng lắm hai vợ chồng 1 năm về VN 1-2 lần thăm bố mẹ và nghỉ ngơi được độ chục ngày. Vất vả nhất có lẽ là đợt thực tập tại IBM và ôn thi cuối kỳ đã vắt kiệt sức lực của mình (mình sụt 4 kg trong 1 tháng, mà tạng người mình béo và khó sụt cân). Riêng việc di chuyển từ Commonwealth Drive tới Tampines đã mất tới 1 tiếng 35 phút mỗi lượt. Mặc dù đúng là làm cho IBM là một công việc mơ ước của rất nhiều người, nhưng có lẽ không phải với mình. Môi trường làm việc chậm chạp, ít thử thách, không thân thiện, chồng chéo, ít thứ mới (hoặc có thể chỉ tại IBM Singapore mới vậy). Mình đã quyết định dứt bỏ mặc dù biết rằng làm vậy bản thân mình rất thiệt thòi. Tuy vậy mình vẫn ổn. Continue reading

The Current State of Machine Intelligence

I spent the last three months learning about every artificial intelligence, machine learning, or data related startup I could find — my current list has 2,529 of them to be exact. Yes, I should find better things to do with my evenings and weekends but until then…
Why do this?
A few years ago, investors and startups were chasing “big data” (I helped put together a landscape on that industry). Now we’re seeing a similar explosion of companies calling themselves artificial intelligence, machine learning, or somesuch — collectively I call these “machine intelligence” (I’ll get into the definitions in a second). Our fund,Bloomberg Beta, which is focused on the future of work, has been investing in these approaches. I created this landscape to start to put startups into context. I’m a thesis-oriented investor and it’s much easier to identify crowded areas and see white space once the landscape has some sort of taxonomy.
What is “machine intelligence,” anyway?
I mean “machine intelligence” as a unifying term for what others call machine learning and artificial intelligence. (Some others have used the term before, without quite describing it or understanding how laden this field has been with debates over descriptions.)
I would have preferred to avoid a different label but when I tried either “artificial intelligence” or “machine learning” both proved to too narrow: when I called it “artificial intelligence” too many people were distracted by whether certain companies were “true AI,” and when I called it “machine learning,” many thought I wasn’t doing justice to the more “AI-esque” like the various flavors of deep learning. People have immediately grasped “machine intelligence” so here we are. ☺
Computers are learning to think, read, and write. They’re also picking up human sensory function, with the ability to see and hear (arguably to touch, taste, and smell, though those have been of a lesser focus). Machine intelligence technologies cut across a vast array of problem types (from classification and clustering to natural language processing and computer vision) and methods (from support vector machines to deep belief networks). All of these technologies are reflected on this landscape.
What this landscape doesn’t include, however important, is “big data” technologies. Some have used this term interchangeably with machine learning and artificial intelligence, but I want to focus on the intelligence methods rather than data, storage, and computation pieces of the puzzle for this landscape (though of course data technologies enable machine intelligence).
Which companies are on the landscape?
I considered thousands of companies, so while the chart is crowded it’s still a small subset of the overall ecosystem. “Admissions rates” to the chart were fairly in line with those of Yale or Harvard, and perhaps equally arbitrary. ☺
I tried to pick companies that used machine intelligence methods as a defining part of their technology. Many of these companies clearly belong in multiple areas but for the sake of simplicity I tried to keep companies in their primary area and categorized them by the language they use to describe themselves (instead of quibbling over whether a company used “NLP” accurately in its self-description).
If you want to get a sense for innovations at the heart of machine intelligence, focus on the core technologies layer. Some of these companies have APIs that power other applications, some sell their platforms directly into enterprise, some are at the stage of cryptic demos, and some are so stealthy that all we have is a few sentences to describe them.
The most exciting part for me was seeing how much is happening the the application space. These companies separated nicely into those that reinvent the enterprise, industries, and ourselves.
If I were looking to build a company right now, I’d use this landscape to help figure out what core and supporting technologies I could package into a novel industry application. Everyone likes solving the sexy problems but there are an incredible amount of ‘unsexy’ industry use cases that have massive market opportunities and powerful enabling technologies that are begging to be used for creative applications (e.g., Watson Developer Cloud, AlchemyAPI).
Reflections on the landscape:
We’ve seen a few great articles recently outlining why machine intelligence is experiencing a resurgence, documenting the enabling factors of this resurgence. (Kevin Kelly, for example chalks it up to cheap parallel computing, large datasets, and better algorithms.) I focused on understanding the ecosystem on a company-by-company level and drawing implications from that.
Yes, it’s true, machine intelligence is transforming the enterprise, industries and humans alike.
On a high level it’s easy to understand why machine intelligence is important, but it wasn’t until I laid out what many of these companies are actually doing that I started to grok how much it is already transforming everything around us. As Kevin Kelly more provocatively put it, “the business plans of the next 10,000 startups are easy to forecast: Take X and add AI”. In many cases you don’t even need the X — machine intelligence will certainly transform existing industries, but will also likely create entirely new ones.
Machine intelligence is enabling applications we already expect like automated assistants (Siri), adorable robots (Jibo), and identifying people in images (like the highly effective but unfortunately named DeepFace). However, it’s also doing the unexpected: protecting children from sex trafficking, reducing the chemical content in the lettuce we eat, helping us buy shoes online that fit our feet precisely, anddestroying 80’s classic video games.
Many companies will be acquired.
I was surprised to find that over 10% of the eligible (non-public) companies on the slide have been acquired. It was in stark contrast to big data landscape we created, which had very few acquisitions at the time.No jaw will drop when I reveal that Google is the number one acquirer, though there were more than 15 different acquirers just for the companies on this chart. My guess is that by the end of 2015 almost another 10% will be acquired. For thoughts on which specific ones will get snapped up in the next year you’ll have to twist my arm…
Big companies have a disproportionate advantage, especially those that build consumer products.
The giants in search (Google, Baidu), social networks (Facebook, LinkedIn, Pinterest), content (Netflix, Yahoo!), mobile (Apple) and e-commerce (Amazon) are in an incredible position. They have massive datasets and constant consumer interactions that enable tight feedback loops for their algorithms (and these factors combine to create powerful network effects) — and they have the most to gain from the low hanging fruit that machine intelligence bears.
Best-in-class personalization and recommendation algorithms have enabled these companies’ success (it’s both impressive and disconcerting that Facebook recommends you add the person you had a crush on in college and Netflix tees up that perfect guilty pleasure sitcom). Now they are all competing in a new battlefield: the move to mobile. Winning mobile will require lots of machine intelligence: state of the art natural language interfaces (like Apple’s Siri), visual search (like Amazon’s “FireFly”), and dynamic question answering technology that tells you the answer instead of providing a menu of links (all of the search companies are wrestling with this).Large enterprise companies (IBM and Microsoft) have also made incredible strides in the field, though they don’t have the same human-facing requirements so are focusing their attention more on knowledge representation tasks on large industry datasets, like IBM Watson’s application to assist doctors with diagnoses.
The talent’s in the New (AI)vy League.
In the last 20 years, most of the best minds in machine intelligence (especially the ‘hardcore AI’ types) worked in academia. They developed new machine intelligence methods, but there were few real world applications that could drive business value.
Now that real world applications of more complex machine intelligence methods like deep belief nets and hierarchical neural networks are starting to solve real world problems, we’re seeing academic talent move to corporate settings. Facebook recruited NYU professors Yann LeCun and Rob Fergus to their AI Lab, Google hired University of Toronto’s Geoffrey Hinton, Baidu wooed Andrew Ng. It’s important to note that they all still give back significantly to the academic community (one of LeCun’s lab mandates is to work on core research to give back to the community, Hinton spends half of his time teaching, Ng has made machine intelligence more accessible through Coursera) but it is clear that a lot of the intellectual horsepower is moving away from academia.
For aspiring minds in the space, these corporate labs not only offer lucrative salaries and access to the “godfathers” of the industry, but, the most important ingredient: data. These labs offer talent access to datasets they could never get otherwise (theImageNet dataset is fantastic, but can’t compare to what Facebook, Google, and Baidu have in house).
As a result, we’ll likely see corporations become the home of many of the most important innovations in machine intelligence and recruit many of the graduate students and postdocs that would have otherwise stayed in academia.
There will be a peace dividend.
Big companies have an inherent advantage and it’s likely that the ones who will win the machine intelligence race will be even more powerful than they are today. However, the good news for the rest of the world is that the core technology they develop will rapidly spill into other areas, both via departing talent and published research.
Similar to the big data revolution, which was sparked by the release of Google’s BigTable and BigQuery papers, we will see corporations release equally groundbreaking new technologies into the community. Those innovations will be adapted to new industries and use cases that the Googles of the world don’t have the DNA or desire to tackle.
Opportunities for entrepreneurs:
“My company does deep learning for X”
Few words will make you more popular in 2015. That is, if you can credibly say them.Deep learning is a particularly popular method in the machine intelligence field that has been getting a lot of attention. Google, Facebook, and Baidu have achieved excellent results with the method for vision and language based tasks and startups like Enlitic have shown promising results as well.
Yes, it will be an overused buzzword with excitement ahead of results and business models, but unlike the hundreds of companies that say they do “big data”, it’s much easier to cut to the chase in terms of verifying credibility here if you’re paying attention.The most exciting part about the deep learning method is that when applied with the appropriate levels of care and feeding, it can replace some of the intuition that comes from domain expertise with automatically-learned features. The hope is that, in many cases, it will allow us to fundamentally rethink what a best-in-class solution is.
As an investor who is curious about the quirkier applications of data and machine intelligence, I can’t wait to see what creative problems deep learning practitioners try to solve. I completely agree with Jeff Hawkins when he says a lot of the killer applications of these types of technologies will sneak up on us. I fully intend to keep an open mind.
“Acquihire as a business model”
People say that data scientists are unicorns in short supply. The talent crunch in machine intelligence will make it look like we had a glut of data scientists. In the data field, many people had industry experience over the past decade. Most hardcore machine intelligence work has only been in academia. We won’t be able to grow this talent overnight.
This shortage of talent is a boon for founders who actually understand machine intelligence. A lot of companies in the space will get seed funding because there are early signs that the acquihire price for a machine intelligence expert is north of 5x that of a normal technical acquihire (take, for example Deep Mind, where price per technical head was somewhere between $5–10M, if we choose to consider it in the acquihire category). I’ve had multiple friends ask me, only semi-jokingly, “Shivon, should I just round up all of my smartest friends in the AI world and call it a company?” To be honest, I’m not sure what to tell them. (At Bloomberg Beta, we’d rather back companies building for the long term, but that doesn’t mean this won’t be a lucrative strategy for many enterprising founders.)
A good demo is disproportionately valuable in machine intelligence
I remember watching Watson play Jeopardy. When it struggled at the beginning I felt really sad for it. When it started trouncing its competitors I remember cheering it on as if it were the Toronto Maple Leafs in the Stanley Cup finals (disclaimers: (1) I was an IBMer at the time so was biased towards my team (2) the Maple Leafs have not made the finals during my lifetime — yet — so that was purely a hypothetical).
Why do these awe-inspiring demos matter? The last wave of technology companies to IPO didn’t have demos that most of us would watch, so why should machine intelligence companies? The last wave of companies were very computer-like: database companies, enterprise applications, and the like. Sure, I’d like to see a 10x more performant database, but most people wouldn’t care. Machine intelligence wins and loses on demos because 1) the technology is very human, enough to inspire shock and awe, 2) business models tend to take a while to form, so they need more funding for longer period of time to get them there, 3) they are fantastic acquisition bait.Watson beat the world’s best humans at trivia, even if it thought Toronto was a US city. DeepMind blew people away by beating video games. Vicarious took on CAPTCHA. There are a few companies still in stealth that promise to impress beyond that, and I can’t wait to see if they get there.
Demo or not, I’d love to talk to anyone using machine intelligence to change the world. There’s no industry too unsexy, no problem too geeky. I’d love to be there to help so don’t be shy.I hope this landscape chart sparks a conversation. The goal to is make this a living document and I want to know if there are companies or categories missing. I welcome feedback and would like to put together a dynamic visualization where I can add more companies and dimensions to the data (methods used, data types, end users, investment to date, location, etc.) so that folks can interact with it to better explore the space.
Questions and comments: Please email me. Thank you to Andrew Paprocki, Aria Haghighi, Beau Cronin, Ben Lorica, Doug Fulop, David Andrzejewski, Eric Berlow, Eric Jonas, Gary Kazantsev, Gideon Mann, Greg Smithies, Heidi Skinner, Jack Clark, Jon Lehr, Kurt Keutzer, Lauren Barless, Pete Skomoroch, Pete Warden, Roger Magoulas, Sean Gourley, Stephen Purpura, Wes McKinney, Zach Bogue, the Quid team, and the Bloomberg Beta team for your ever-helpful perspectives!
Disclaimer: Bloomberg Beta is an investor in Adatao, Alation, Aviso, Context Relevant, Mavrx, Newsle, Orbital Insights, Pop Up Archive, and two others on the chart that are still undisclosed. We’re also investors in a few other machine intelligence companies that aren’t focusing on areas that were a fit for this landscape, so we left them off.
For the full resolution version of the landscape please click here.

What are the best study methods?*

My examination result has been released today. Not so good in compared to the previous semester, in which I have mostly A. This time, I get mostly B+, which is quite disappointing. So I found out some good tips by myself as a reminder. Hope this semester my grade will have more A. Studying in graduate school is tough isn’t it?

Followed by this question on Quora, and answered by Ahmad Ali

“This is my research on how to study over two years. I succeeded to get a distinction in 8000 students from many colleges. I did not spend more than two months in my college.

There are two important studies worth sharing before I describe my study method. The first study is about memory graph and the second one is aboutconcentration span.

The Human Memory Graph
This study reveals to us that when you read something, your memory of what you read or heard is almost alive.  If we represent this with graph it is horizontally at 100%, and it slowly declines over time. When you review it after one day, memory connections are strengthened. Now its declination is very slow as compared to without review.  This speed decreases with every review of the thing you want to remember. It is explained in the graph below.

Instead of memorizing, try setting a review plan without any tension and be relaxed. Read with concentration, and then leave it. Read again in the evening, then again the next day, and then again the next week. Test yourself on the 15th day, and then review after one month. You’ll notice that your memory, of what you heard, read, or listened, will not decline so easy now plus you remember most of it including subtle things related or within the material. Continue reading

Away from here

“The path is long, it’s cold and wet
Desire paths will lead you quicker here than the rest, and you can hope
for a life that is calm

But come in time
You’re gonna pick up one
that feels a little hard

The wind lays heavy, It weighs in stone, my instinct tells me I should walk this path alone
And you can hope for a life that is calm
But come in time, you’re gonna pick up one that feels a little hard”


I’ve been back after a 12 days vacation in my hometown – Hanoi, Vietnam. It can be considered as one of the most beautiful days of my life.

I have a, umm, bad characteristic that makes me want to quit and run away whenever I feel not okay, even to the point I could book an one-way ticket to go back to my country immediately, leave all the good education, the fanciness, the desired job and high salary here to be a normal person with a small job like everybody else and a decent income (which I did once last year, after just a month going to a business trip alone) just to stay near the people I love and care so much about, plus, my little puppy is in bad shape. I do have ambition, but in the meantime I need more than just “ambition”.

Right now, something tells me that there’re things I must do, and I have to be patient.

I am aware that I’m a lucky person. However, because I’m greedy, good things must become better things, and I want to have it all until I’m satisfied, though I know it will never happen.

I miss them so much. I miss my everything.


Chập tối, tôi bắt xe buýt đi về nhà. Xe số 51. Tôi đi từ gần khu City Hall về đến Commonwealth Avenue. Trời chưa tối hẳn nhưng đường phố đã lên đèn, xe cộ đi băng băng, vượt qua không biết bao nhiêu cây cối, con đường càng đi càng tối, đèn càng sáng, hai bên màu xanh rì. Ngước nhìn đồng hồ, tôi mới giật mình nhớ ra là đã muộn.

Screen Shot 2014-11-10 at 1.38.25 am

Nguồn: 500px

Ở Singapore thật kỳ lạ. Bạn có cảm giác an toàn, bình yên kiểu không ai làm hại mình, không ai đụng đến mình, không ai để ý việc mình làm; đi uống nước một mình ngắm đường phố chẳng may muốn đi vệ sinh, bạn có thể thoải mái để điện thoại trên bàn lúc sau quay lại vẫn không sao. Nhưng cùng một lúc lại cảm thấy cô đơn đến nghẹt thở.

Đây chắc chắn không phải lần đầu tiên tôi cảm thấy vậy. Nhưng có lẽ là lần đầu tiên tôi viết về nơi này. Continue reading

Stairway to heaven

Khi bạn nhận ra rằng bạn buồn đến nỗi không thể viết ra được một từ một chữ một câu nào để miêu tả về nó. Mọi ngôn từ đều bất lực.


Bạn nhận ra rằng điều duy nhất bạn có thể làm lúc này là co mình lại và nghe nhạc.


“And as we wind on down the road
Our shadows taller than our soul”


Sad cat

[Reblogged] Academic Careers (a.k.a. “Man looks up in the tree”)

From Prof. Hung Q. Ngo’s blog, University of Buffalo.

Hello everyone. Thanks very much for attending my talk. I’m honored to be invited and humbled to be sandwiched between these two hugely successful gentlemen. I actually had no idea what this talk is supposed to be about until roughly a week ago when Tường sent me an email with the following excerpt

Dear anh Hung, 
Blah blah blah ...
In the current agenda, the title of your part in the plenary session is "academic careers". 
This is really a broad topic under the "All the way home" session name.

That is why you will hear something vaguely in the realm of “academic careers,” about which I have no authority whatsoever.

Why in the world did I agree to go give a talk when I didn’t even know the title, and even after knowing the title I am less than qualified to give it? Here’s why:

  • Amount I was paid: $400
  • The round-trip Amtrak ticket from Buffalo to Troy: $128
  • A chance to network with the future of Vietnam’s science and technology: priceless
    (As you can see, I watched too much TV.)

Alright, as I have already spent some of those $272 (exclusively on Starbucks coffees, of course) it is now time to cook up something. I have never given a non-technical talk before in my life. All of my technical talks have the following outline:

  • Here’s an optimization problem with a wonderful real-world motivation
  • Here’s how I modified it to become a version I can solve (which is a world away from the motivation)
  • Here’s how I solved it.
  • Future works (which are even further away from the real-world motivation)

Remember, the trick to get by in graduate school is, if you can’t solve a problem, modify it! If your advisor has not taught you this trick then you should change advisor. So I will stick with what I am comfortable with and follow the outline. There are three points I want to get across

  1. Academic career as an optimization problem
  2. Because of (1), beware of the opportunity costs
  3. Don’t think about an academic career as an optimization problem

Continue reading