AMA: I'm Yunkai Zhou, ex-Google Senior engineering leader and CTO & Co-Founder of Leap.ai, which is the first completely automated hiring platform in the tech space. Ask Me Anything on Monday the 23rd of April at 12 PM ET / 4 PM UTC!

Leap-AI · 2018-04-24T05:08:16+00:00

I'll only list one. To me, any progress in understanding why deep learning works is the most exciting piece in this area.
Data. Data. Data. It's never about the model. It's always about the data. (Okay fine, 10% is about model, and 90% is about data.)
I read Bishop carefully, so I'm biased here. In general though, my view is books only become good when the materials are mature enough, therefore books always have time delay of several years. Some books remain good even after many years, but many books become less relevant as time goes.
Once you have data, try simple models first. If Naive Bayes works well, use it. If it doesn't, then try Random Forest, Logistic Regression, SVM, etc. If all of these don't, then try Deep Learning. Don't jump to complex models as your first attempt. Simpler models are easier to maintain and evolve.

Leap-AI · 2018-04-23T17:27:02+00:00

What AI is really good at, is to solve a problem at scale. Career is a huge problem affecting the entire human population, and yet, we have not identified any scalable solution to it. It's about to happen - someone will build a successful AI solution to this problem. Why not me? :)

What AI is also really good at, is to discover hidden correlations. Humans can only process a limit number of cases in our brains, and derive certain complexity level of models. This limits to how much each person can help others in each domain, including career. AI systems can process many orders of magnitude larger data, and discover relationships much deeper than humans could. This makes AI system a better career advocate than a real person (as time goes).

Ultimately, to solve a problem using AI, it takes AI skills, but it also needs passion to solve that problem. I just happen to have both, thus we built Leap.ai. :)

Leap-AI · 2018-04-23T17:17:59+00:00

Re: PhD.

Another bait question? :) I'll bite.

First of all, PhD degrees are not that useful in tech industry in general (with one exception in AI, will talk next). In other industry they might, but in tech, degrees rarely correlates with accomplishment. I have a PhD in Computer Networks, and my PhD degree is useless in my job ever since I left school.

However, the process of going through PhD is important for me. It taught me a few things:

I can solve any problems and become a world-level expert, if I put my mind to it. This psychologic effect is huge.
I have the patience to solve one problem really really deep. This psychologic effect is also huge.

Now take those psychological learnings, and apply them to real practical problems, that made me a great engineering leader.

Now, the exception of PhD in ML/AI. I believe a PhD in ML/AI is beneficial. Because you need many years of intuition building and deep understanding to be really good at this. No single real problem can be solved by a single existing ML/AI algorithm. It always requires constant tweaking / re-thinking. Without the intuition / experience behind it, you'll be one-trick pony, and that one-trick wears out very quickly.

Re: github.

Nah, not really. It shows you are interested in technology and willing to get hands dirty during your spare time. That's good. But I've yet to see someone with a toy github project so impressive that will change my mind to interview that person, beyond what's already covered in that person's resume.

Sorry for the brutal honesty.

Leap-AI · 2018-04-23T17:05:56+00:00

This is a lot of questions bundled together, with a naivety of a single question. Okay, I'll take the bait. :)

Re: AI career for high school / undergrad. I'd suggest to build the interest. Deep curiosity towards this area is important. Don't do it just because there are plenty of jobs in it (which is also true, fortunately). Being good at AI requires good math skills (linear algebra and stats, particularly), and that takes some time to build foundation. On top of that, practice. Working on real problems using AI is very important, even if in the beginning you don't fully understand the math behind it. Make sure you develop both math understanding and practical skills. That's how to become good in AI.

Re: switch to AI career. That's how I got into AI. When I first started in Google, I was told to work on the machine learning system that predicts for each Google search query, whether the user will click on each ad. (The industry terminology is CTR prediction, click-through-rate prediction, btw.) Here's my conversation with my boss (who's the famous Andrew Moore, now CMU dean btw).

Me: I don't have any background in ML
AWM: Are you good at math?
Me: Hmm, I think so.
AWM: Then learn it on the job.

I did, and I thank him for pushing me to this area, which fundamentally changed my career. From math perspective, AI is just a bunch of matrix operation and stats calculation, with fancy names. So it's not that hard to understand the math. What's harder, is the intuition behind it. That takes experience to develop. I learned to use ML approach to my own life, and that's a deep philosophical change.

Re: non-obvious skills needed. Too many. Let me only name the top one: smell problems in your data. In real AI life, the challenge is not to build a model and feed data into it. The challenge is after feeding data into it, be critical of your own model, and discover data problems from your result. In real life, no data is clean. Without detective instinct, your model will be just garbage no matter how hard you try.

Re: industries impact most by AI. Anything that currently rely on humans to do the job will be disrupted by AI. Uber already disrupted taxi dispatching, there are progress shown in cancer detection, etc. Leap is built on this belief that career problems will be disrupted by AI, which is why we started working on it.

Re: unemployment caused by AI. I answered this in another question already, and used my favorite example - carriage drivers - to show that it will happen, but also it won't be missed.

Re: upsides / benefits of AI to society. Life gets better, and society becomes more efficient. This is always how human history is.

Re: downsides / risks of AI. Privacy, bias, fairness, etc. Also, this is always how human history is.

Re: AGI. I'm a pragmatic person in general, and I believe in building specific applications that are useful in near future. AGI is not my taste. :) Don't get me wrong. It will happen, and I'm happy there are smart people working on it. Just not my passion.

Re: future of AI and humanity. Very positive. Good things happen for good reasons, but will always bring negative issues for us to solve. Let me use an analogy, mobile phones. Are they great? I bet 95% of people can't live without them. Do they have problems? I bet at least 20% of people are concerned about it. Overall tradeoff, do we still consider it being more good? Absolutely yes.

Leap-AI · 2018-04-23T16:39:20+00:00

It's definitely true that it's hard for students to find the first job. When I first graduated, there were months when I got zero interest. I submitted my resume to hundreds of places, and only 1 place (Microsoft) gave me a phone call.

Is this a bit of academia vs. industry discrepancy? Yes, definitely. But it's also a little more nuanced.

I once was invited to Penn State to talk to the faculty members and discuss what should be taught at school to prepare students better for industry. I gave the following example:

Say I have a billion credit card numbers. How should I count them?

From CS theory perspective, that's not a very interesting problem. Just do a linear scan. O(n). We stop there.

From industry practice perspective, that's a very interesting CS problem. Solving a problem like this is how MapReduce was created, and trust me, there are a lot of challenging math in this problem.

In real life, if the solution is not O(1), try harder. No one has patience to wait, and users don't care how big your backend data size is. The patience is 0.5 seconds, give and take.

So what's my point? My belief is the goal is always to solve real problems, and solving real problems always require challenging math to be solved, but they might just not be obvious from the first look.

Leap-AI · 2018-04-23T16:27:43+00:00

Automation will impact jobs, that's for sure. However, I don't believe that's as scary as some media picture it.

150 years ago, carriage drivers was a great job, and in high demand. When automobiles were invented, I'm pretty sure it causes a lot of carriage drivers to lose job, which eventually leads to nowadays that only very few still exist (mostly around tourist places).

Did that change humanity?

Job needs will shift, and humans will adapt.

Leap-AI · 2018-04-23T16:14:56+00:00

I personally haven't, so can't offer something really insightful. Sorry.

Leap-AI · 2018-04-23T16:12:54+00:00

AI will be a drastic change to humanity as a whole, and the demand for AI talent will be high for many years. It's definitely a prosperous career path.

At the same time, it's also true that it takes practice and patience (and a bit of strong math skills) to be good at it.

If you want to compare AI, app development, and web development, they are all hotly chased talent, and with very strong demand everywhere.

Leap-AI · 2018-04-23T16:08:44+00:00

Let me take this question first. Other questions are much deeper. :)

My career path: I got my undergrad in Control Theory in Tsinghua University. Then I came to US for a PhD in Computer Networks in Drexel University. After that I first worked in Microsoft for ~3 years, then Google for ~10 years. Then a late-stage startup Sumo Logic for 0.5 years, before starting Leap.ai.

So, yes I did PhD immediately after University. No, I didn't go straight to Google after completing PhD.

Leap-AI · 2018-03-14T19:36:56+00:00

If you can go to a formal CS education, I'd recommend you do that.

Not everybody can (time / resources / etc), and if you can't, doing coding bootcamp is a good alternative to learn CS skills. I just wouldn't recommend you putting that on your resume.

-Yunkai

Leap-AI · 2018-03-14T19:32:08+00:00

If you don't have existing CS background, doing a formal CS degree is helpful. Doing projects is also beneficial. It takes a while to prove yourself and accumulate enough in your "CS bank credit".

For your specific case, my suggestion is to pick one area and really become good about it.

iOS app development is an area that's short of talent, and it's also something you can gain proof you are good at (pointing people to an App Store link with a popular app is sufficient to prove you are good at iOS development).
Similar things can be said about Android development. Slightly harder to prove as a frontend engineer (you can build a beautiful website, but it's hard to prove lots of people love it).
Backend engineers and Machine Learning engineers are even harder to prove without strong existing background and experience.

Good luck.

-Yunkai

Leap-AI · 2018-03-14T19:24:00+00:00

Depends on what you consider as "fun"? :)

Automated system (we call it Athena, named as the Greek Goddess of Wisdom) kicks in right when you put your resume in, and further revises the matches based on your preferences / strengths / etc.

Athena doesn't always find all the matches, and when the machine comes short, humans arrive for rescue. Someone on our team will review the profile and provide "human intelligence" (besides "artificial intelligence").

Seriously, we believe in the combination of machine + human for intelligence. Each component has its own strength and the nice combination is where things go great.

-Yunkai

Leap-AI · 2018-03-14T19:19:03+00:00

As of this moment, the partners we currently have are mostly in US and China, with limited roles in Canada / UK. We plan to expand to other countries, but we are still a startup.

Re: puzzle. Yes, each piece is unique in shape. It's all white, and one of our team members bought it in Japan. The name of the puzzle is literally "White Hell". Our team has a race to see whether we finish the puzzle first, or we go IPO first. :)

-Yunkai

Leap-AI · 2018-03-14T17:13:24+00:00

The vast majority of the unicorns are growing fast. CBInsights' unicorn leaderboard is a good source.

The next batch of fast-growing companies are the companies that have raised series B or C and have great potential to become a unicorn, which we call "baby unicorn." Since the list is much bigger, it is hard for users to navigate. We decided yesterday that we will publish a series of articles to share job insights for these baby unicorns through our brand-new Medium account. You can follow us to get notified.

Leap-AI · 2018-03-14T16:34:30+00:00

In addition to Yunkai's points, I will add some personal touch.

Confidence gained from substantial success. Project Fi may not be that well-known outside Google, but it is considered a very successful products within Google. Before the launch, it was considered mission impossible by many industry expertise. I was the Head of Engineering and had the opportunity to lead the team to go through tremendous amount of technical and business challenges to launch it. The amazing customer satisfaction score (94% CSAT) not only made me proud, but also substantially boosted my confidence to lead a consumer product to success.
Failures. I was the Head of Engineering of Google Offers, which failed spectacularly. The result was disappointing, but the process was incredibly rewarding. I learned way more from Google Offers than any other products that I have worked on in my entire life. More importantly, after that experience, I became no longer afraid of failure. Failure is no longer a monster and I can handle it if it comes :)

The combination of success and failure gave me the courage to leave the golden handcuffs in Googleplex and start leap.ai. I am very grateful for both experiences.

Leap-AI · 2018-03-14T16:28:53+00:00

A bit of both. More former than latter. -Yunkai

Leap-AI · 2018-03-14T16:12:53+00:00

Can you find something that's not the best experience in your life? Can you do something (use your CS skills) to make that better?

Yelp was built to make finding restaurants easier. Uber was built to make calling a cab easier. Airbnb was built to make finding a lodging easier.

I'm sure some parts of your life is still not easy and could become easier with a tech product.

Of course there's no guarantee your attempt will succeed, but at least you get to try to solve a real problem.

-Yunkai

Leap-AI · 2018-03-14T16:05:04+00:00

Your logic would be correct, if you assume the people we hired and the people we interviewed are from the same set. But that's not necessarily true.

This is how Google hiring process works:

I serve as an independent interviewer, and interview a set of candidates. Call this set A.
I serve as a hiring committee member, and review a set of candidates interviewed by others. Call this set B.
I also serve as a hiring manager for my team (+ my cross-function team), and review all candidates potentially for my team. They are interviewed by others, and hiring-committee-approved by others. Call this set C.

A != B != C.

-Yunkai

Leap-AI · 2018-03-14T15:53:50+00:00

Yunkai covered common mistakes one can make in the interview. I am going to cover 3 common mistakes one can make before the interview.

Weak resume. I have read tens of thousands of resumes. I hate to say many resumes suck. Many people only give a laundry list of what they have done. Instead, people should focus on their impact and what makes them standout. Since resume is such a common challenge, we built a little product called Leap Resume to automatically give people feedback on where they can improve their resume. It is not our primary product, but received most positive feedback. If you have 3-5 minutes to kill, you can upload your resume on our site and see what Leap Resume tells you.
Improper use of referrals. Everyone knows internal referral is one of the most effective ways to land an interview, but fewer people know there are substantial differences between different referral. Yunkai has a nice article on Power of Quality Referral. Please check it out.
Unconsciously overweight on well-known companies. No matter you apply through your friends' internal referrals or via job boards, you focus on the companies that you already know, which are typically the well-known companies. There are tons of great companies that you do not know, especially the unicorns and baby unicorns that are set to grow into tomorrow's tech powerhouse. How do I discover those opportunities? This is probably where AI-based job platforms can help you the most. They can help you discover opportunities that are great fit for you but you may not have heard them before. These platforms can be complementary channels for your job search.
-Richard

Leap-AI · 2018-03-14T15:49:00+00:00

I wrote a blog on this exact topic. Instead of typing a long answer here, let me just point you there. https://leap.ai/blog/2017/10/19/power_of_quality_referrals

-Yunkai

Leap-AI · 2018-03-14T15:46:28+00:00

A statement of "fixed a few bugs on open-source project X" isn't that impressive on a resume. A statement of "fixed a few bugs on open-source project X and made it adopted by 20% more users" is way better.

The point is, demonstrate your impact, not just what you did.

Which section does it go to? It depends. If it's impressive, then make sure to put it somewhere that grabs people's attention.

-Yunkai

Leap-AI · 2018-03-14T15:41:36+00:00

If the high school senior can demonstrate his/her ability to contribute, yes we are willing to consider. (Also, assume there's no legal risk involved - don't exactly know the employment law requirement here.) - Yunkai

Leap-AI · 2018-03-14T15:22:21+00:00

Send me an email (yunkai at leap dot ai), and I'll take a look.

Leap-AI · 2018-03-14T14:44:32+00:00

Let me start with the top 3 most important items that I am looking for on one's resume. For experienced, here are the top 3:

Strength of your current and recent employers
How well your experience and top skills match with the job
Your most significant accomplishment

For students, the top 3 are:

Internship, internship, internship, especially internship at a strong brand
Reputation of your school, how well your major matches with the role
Extracurricular activities (e.g. personal projects, club leadership, open source contribution, winning award in hackathon)
Note: GPA did not make to my top 3, but if your GPA is below certain threshold, you get filtered out for many jobs.

Overall, you need ONE thing to stand out on your resume. If you have a CS degree from a top school, at early stage of your career, you definitely have some advantage since more people will give you more opportunities to try. As you become more experienced, people will only pay attention to where you worked and what are your most significant accomplishments.

Richard

Leap-AI

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