2016 Election Thoughts: Part 2/2

This post has absolutely nothing to do with science and is just some of my thoughts on the recent US Presidential election. I started writing up my thoughts and I realized it was easiest to organize my thoughts by things I would like to say to Anti-Trump vs Trump voters. In reality, both posts are relevant to either side, but it was a convenient way to cleanly separate my points. Since I respect everyone’s right to a private vote, I’m writing these thoughts as open letters to both sides.



Dear Trump Voters Who Love Me,

I cried.

I’m scared and I cried.

I need you to understand that. This fear of Trump has not gotten better since the election. In fact, it took me until Friday November 11th at 8PM PT for the full implications of the election of President Trump to set in. I finally truly understood what this election meant to me.

I need you to know that when I fully understood what this election meant to me, I cried. Uncontrollable sobbing. It hit me while walking down the hallway towards my apartment. I held it together long enough to go inside, sit down in the dark, and sob uncontrollably by myself. I cried because I was scared. I cried because of innocence lost, both my own and my future children’s. I cried because I didn’t do enough to prevent me from crying. I cried for being naive and stupid and taking this long to truly see the world. I cried for not figuring it out in time to communicate my viewpoint with Trump voters. I cried because I was crying. I cried out of despair and frustration because I realized my future children, at a much younger age, would feel a much worse pain. I cried because I had entered the Dark Forest.

I need you to know that I will remember that cry for a long time. I cry rarely enough that I am pretty sure I can name ever event since my teenage years. This is something I won’t forget anytime soon.

And I realized, that more than anything else, I need you to understand why I cried. I need you to understand why President Donald J. Trump can never be just another politician to me. I need you to realize that you have unleashed a political weapon on me that scares the shit out of me. I need you to understand why this just became a defining point in my life. I need you to understand that I have entered the Dark Forest and what it means for me.

First what is the Dark Forest. I am stealing this from a science fiction series, the Three Body Problem. While the book focuses on interactions between alien civilizations, I think it also a useful analogy for politics today since both sides seem to be alien to each other. The Dark Forest translated to democracy is this:

Axiom 1: A voter’s goal is to survive
Axiom 2: Resources are finite
Axiom 3: Voters and politicians have limited communication
Axiom 4: Strangers have limited communication

Consequence 1, The Light Forest: The combination of axiom 1 and 2 mean that we are all hunters in a forest, competing for resources. This by itself is a perfectly fine world and democracy. Yes we are competing with each other, but since we have plenty of light, we can stay safe. We don’t need to worry that we will mistake each other for the animals we are hunting.

Consequence 2, Chains of Suspicion: The combinations of axiom 3 and 4 lead to Chains of Suspicion. The extreme distances between strangers creates an insurmountable ‘Chain of Suspicion’ where the two strangers cannot communicate fast enough to relieve mistrust, making conflict inevitable.

Consequence 3, The Dark Forest: The Chains of Suspicion cast a dark shadow over the Forest, turning it dark. In the Dark Forest, other hunters become threats. I no longer know if the noise I hear in the dark is an animal or another hunter. I also know that the other hunter has the same problem. I know that this other hunter may shoot me, either by accident, out of fear, or worse, on purpose. Therefore, I can only guarantee my safety if I shoot first and ask questions later.

I need you to realize that politicians words matter. Trump and I will never talk in person. I will never be able to truly get to know Trump. That means, that when Trump says or tweets authoritarian or racist things, I will never know his true intent. It means that Trump and I have an insurmountable Chain of Suspicion.

Looking back, Trump and I have had this Chain of Suspicion for a long time. This Chain did not directly drive me into the Dark Forest of distrust largely because of you. I love and trust you. I know that we may have political differences, but I am confident we can work them out. But you and I are not the issue. You and I are not strangers.

What drove me into the Dark Forest is that Chains of Suspicion multiply like a virus. In the Dark Forest, Trump’s words matter because they are him broadcasting his potential future actions. Maybe Trump’s threats are just a bluff. Maybe those words won’t lead to actions. But I need you to understand, there are others that scare me to my core and I am afraid that Trump has given them more power. Trump has reinforced their terrible ideas and made them seem slightly more normal.

I need you to know that Trump is not a standard politician to me. Trump successfully won election despite doing two things that I thought individually would be disqualifying in modern society:

  1. disregard for democracy
  2. explicit racism

I need you to understand that when Trump combined those two together, he crossed a line that should never be crossed in a functioning democracy. Trump crossed the safety tape separating democracy and fascism. Trump himself has NOT taken us to fascism. But I am afraid he made fascism seem just a little more mainstream to extremists.

One major reason words speak louder than actions is that there are certain words that can’t be unsaid. Trump proclaimed in a nationally televised debate that he may not accept the outcome of the election if he does not win. I need you to really think about the future consequences of that. You need to understand what those words mean  to me and my insurmountable Chain of Suspicion with Trump.

Imagine this scenario that scares the shit out of me and needs to scare you too. Trump in 4 years, as the sitting President (maybe with a Republican House and Senate) says in a presidential debate that he may not accept the outcome of the election if he doesn’t win.

What am I suppose to believe if Trump wins again by a small margin like this year? Should I believe that the election was fair? Or should I worry that Trump used his power as president to ensure his own victory?

If you don’t understand this fear, and why the MERE POSSIBILITY of this fear itself should scare you too, please reconsider. Learn more about history. You need to understand the Dark Forest that I am in now. Talk to me until you understand my fear. A democracy CANNOT survive long if even a small percentage of voters fear the integrity of future votes. I have this fear. This fear leads to a Dark Forest where democracy will struggle.

This fear needs to be extinguished now because when it combines with my next issue, I am afraid it leads to an even Darker Forest were democracy is guaranteed to die. Trump has created an insurmountable racial Chain of Suspicion with me. Trump has engaged a variety of terrible racial rhetoric but there are two things that especially stick with me. The first is Trump’s attack on Judge Curiel which even Paul Ryan called “the textbook definition of a racist comment.”

I need you to know that since I have a Chain of Suspicion with Trump, I cannot avoid taking that attack personally. Trump attacked Judge Curiel for his Mexican heritage despite being born in the United States. Judge Curiel is clearly not American enough for Trump. It doesn’t matter that Tina has Chinese heritage. I need you to know that I see an attack on one minority as an attack on all. I need you to know that I see it as an attack against Tina and our future kids. Will they be American enough for Trump? I just don’t know.

But I really need you to the final realization that made me break down crying and pushed me deep into the Dark Forest. I had managed to forget about Trump’s strange relationship with David Duke (KKK member), see here for details. Trump’s refusal to disavow David Duke in 2016 despite doing so in 2000 scares me. I realized I truly don’t understand Trump.

What drove me to tears was that I realized, even if Trump made an innocent mistake, the damage is done. Trump broadcast a message to David Duke and other racists that can never be unsaid. Trump (unintentionally or intentionally) screamed to them: I can win the presidency despite authoritarian and racist rhetoric. It is not Trump I am scared of. It is the dark hunters he just empowered. I had no illusions that racial extremists did not exist, but now, due to Chains of Suspicion, I am no longer optimistic that their numbers are small.

I need you to realize that this is when I personally entered the Dark Forest. I was walking back from my car to the apartment when I walked past a large group of white men. I unconsciously started doing some math, trying to calculate what are the odds that they voted for Trump and specifically voted for Trump because of his racial rhetoric. Before I could finish the math, I realized I was deciding if I was safe around them and started tearing up. This is when I cried uncontrollably. This is when I realized that I had been naive and living in a false world. I thought I was realistic and understood the darkness that existed in the world. But I was living in a Light Forest that was only a product of many factors including but not limited to me being: male, white, upper middle class, well-educated, etc. I truly saw the Dark Forest.

I cried because I got the tiniest possible sliver of understanding of what it truly means to be a minority and I couldn’t handle the truth. As a minority, they live in the Dark Forest. They have heard and felt the racism. They know that not everyone can be trusted. They know that people can attack them when least expected and they must be suspicious. But I cried because its worse: minorities live in the Dark Forest but have a permanent spotlight on them. They are emitting light into this darkness. They don’t blend in. They always stand out in this vast darkness. That means they are always a target for those that hunt minorities.

I cried because I realized that I live in a Dark Forest and that Tina and our future children will always have a spotlight on them. I cried because the tiny glimpse of the darkness scared me. I cried because I realized that my future children will learn the nature of the Dark Forest at an age that is much too young. I cried because I know the Dark Forest my children will live in is worse than the one I am in. I cried because I am scared of hunters like David Duke. I cried because President Trump doesn’t seem to understand that his words empower these hunters. I cried because I was too stupid to put this all into words sooner. I cried because I don’t know how to protect Tina and our future children. I cried because my natural response to that helplessness was to lash out at others in the same way they want to attack Tina. And I had one final burst of tears when I realized the deep irony that David Duke had just made me into an inverse of himself and made me racist against random white people. I laughed, probably like a maniac, because I realized that after that, I am so far lost in the Dark Forest of distrust that I had managed to become the type of hunter that probably scares David Duke the most.

But most of all, I need you to understand that I love you and look forward to working with you to end the Dark Forest of distrust. I am sorry for not communicating better with you. I don’t know why you voted for Trump. Maybe you are already in the Dark Forest of distrust. Maybe you hated Hillary and had an insurmountable Chain of Suspicion with her. Maybe you thought Trump was a standard Republican candidate.

I know you didn’t mean to scare me. But I need you to realize that Trump is not a standard candidate to me. I need you to realize that I can never personally trust Trump based on the words he has said. I need you to realize that I am especially scared of Trump and the people he might either intentionally or accidentally empower.

And I especially need you to realize that what I am actually more scared about is the fact that I am scared. The part of me that remembers the Light Forest thinks the fear is irrational. But the part of me that has seen the Dark Forest of distrust thinks the fear is rational and maybe that I am not scared enough. I see how the Chains of Distrust multiply. If even a few people share my distrust, it must be extinguished now before it grows too strong.

We have to break taboos. We need to talk about politics. We need to establish ground rules for the type of political discourse and political tactics that are allowed in America. We need to talk about race and discrimination. The only way to turn the Dark Forest into the Light Forest is to break Chains of Suspicion by better communication. We can’t wait four years to discuss these issues. We had a deep divide in this country before the election and Trump made the divide wider. We can only heal this distrust if we start soon.

And finally, I want you to know that I have made peace with this election. I want to sincerely thank you for voting for President Trump. I can now see the world clearer than before. My naivety was dangerous to Tina and our future children. I was complacent. I assumed my children would grow up in a Light Forest. I now realize that they cannot. But I will fight to make the Dark Forest just a little bit brighter. I will fight to extend the time that my children think they are only in a Light Forest. And I now realize the true depths of the Dark Forest, and that I can only fight it with you help. I look forward to working with you to bring Light to the Dark Forest.

With all the love in my heart,
Alex

PS. This is not the world’s weirdest baby announcement. These children I discuss are still in the future. But I still cried for the hypothetical children.

PSS. Dave Chappelle and SNL are very wise. I admit thinking I was more realistic about the US than the people in the skit, but I was just in a slightly different bubble than they were.

2016 Election Thoughts: Part 1/2

This post has absolutely nothing to do with science and is just some of my thoughts on the recent US Presidential election. I started writing up my thoughts and I realized it was easiest to organize my thoughts by things I would like to say to Anti-Trump vs Trump voters. In reality, both posts are relevant to either side, but it was a convenient way to cleanly separate my points. Since I respect everyone’s right to a private vote, I’m writing these thoughts as open letters to both sides.



Dear Anti-Trump Voters Who Love Me,

We fucked up.

Don’t get me wrong, I voted against Trump and you voted against Trump, but that doesn’t mean I don’t still have issues with both you and myself. We didn’t do enough. You can read my letter to Trump Voters to realize the pain I felt.

I have several central ideas and several additional points later.

1. Don’t Disrespect Democracy

We lost and we lost fair and square. I am 100% in support of electoral college reform for 2020 and beyond. I am 0% in any attempt to change it in 2016. Don’t sow seeds of doubt. Accept the results and move forward.

2. Think Long and Hard about WHY People Supported Trump

Spend a lot of time thinking about the chart in this article.  The automation and elimination of jobs is real and will only accelerate. The pain and despair are real. Trump addressed the anger and angst felt by people in these counties. These issues are not going away. I don’t claim to have an answer, but if you want to win over the hearts of Trump supporters, this is a great starting point. Also, despite being on a comedy website, this article also makes many serious points. Its time to win over Trump supporters not demonize them.

3. Words Matter: Stop Crying Wolf

A recent conversation with a wise office mate of mine involved us reminiscing about the good old days when with Mitt Romney we only had to worry about binders full of women and terrible renditions of Who Let the Dogs Out. Those were not real issues, but we cried wolf. Well, the real wolf just got elected and we blew all credibility too soon.

Trump must be opposed. But its time to reserve the harsh words for him and others who are truly racist, sexist, etc. Don’t use the same rhetoric on other Republicans. The false equivalence will continue to cause a credibility gap in the future.

4. Governance Reform Starts Now and MUST Continue When Democrats Win

The political system is broken and we were part of the problem. It doesn’t matter who did it first, last, or most. Both sides have abused weird technicalities in our process of government and that must stop.

I have ideas for more sweeping reforms, but for now, I will just focus on a few of the major problems I see.

A. President: Limit Executive Power
Executive power is like heroin. Might feel great while you are high and in charge but it sucks the rest of the time. We let Obama do too much. The withdrawal is going to suck majorly.

B. House of Representatives: Gerrymandering
The Republicans are going to win just over 52% of the two party vote but around 55% of the seats. Not all of that is due to gerrymandering, but at least part of it is. Check out the Texas districts. Both Democrats and Republicans should learn about California’s new redistricting commission. I can attest that the districts seem more reasonable and that the “jungle” primary is quite fun.

C. Senate: Filibuster

Let’s all agree to just end the filibuster now. Just because the Republicans successfully used the filibuster to block a Supreme Court nominee for nearly a year does not mean that Democrats should turn around and do the same. It is time to end the filibuster and just let the majority of the Senate govern. This will really hurt in the short term. But it will be much better in the long term.

D. Electoral College Reform

Again, I am 100% in support of electoral college reform for 2020 and beyond. I am 0% in any attempt to change it in 2016.

Any argument in favor of the electoral college has to explain this fact for me: Hillary Clinton will probably win the popular vote by about 1% and lose the electoral college by 6.5%. That huge discrepancy goes against every principle of one person, one vote. Look back at past elections, the popular vote is way out of sync with the electoral college.

Love,
Alex

PS Points:

1. #TrumpIsOurPresident

While I understand the spirit of #NotOurPresident is that you disagree with Trump, no one gets to pretend that Trump isn’t truly our president. We are all responsible for Trump. I know I personally didn’t do enough to oppose him, since I honestly didn’t truly think he would win. But Trump did win and this is on everyone now.

2. Please Protest Peacefully
I am 1000% behind everyone’s right to protest. Just please don’t turn violent, that will only play into Trump’s hand and give his paranoid rants more legitimacy.

3. Stop Crashing Canada’s Immigration Website
Back to PS point 1, Trump is our president. Deal with it here. You don’t get to flee.

4. Stop Imagining Alternative Pasts
What if Bernie Sanders was the nominee? What if the third party vote was different? Etc, etc, etc. The election is done. Now don’t get me wrong, it is worth learning from mistakes. But learn from the past to make the future you dream of a reality, instead of only dreaming about the past.

5. California Doesn’t Get to Secede
Just stop, its stupid.

NSF GRFP 2016-2017

For a couple of years now, I have had a website with my thoughts on the National Science Foundation Graduate Research Fellowship (NSF GRFP) and examples of successful essays. The popularity of the site in the past few years has grown well beyond what I expected, so this year I’m going to use this blog to try out a few new things.

 

Questions from You

I end up getting lots of emails asking for advice. While sometimes the advice really does merit an individualized result, many of the questions are applicable to everyone. So in the interest of efficiently answering questions, here is my plan this year.

  1. Before asking me, make sure you’ve read my advice, checked out the NSF GRFP FAQ, skimmed GradCafe, read my FAQ (next section), and checked out the comments for this blog post.
  2. I will not answer any questions about eligibility due to gaps in graduate school because I am honestly clueless on it.
  3. If you feel comfortable asking the question publicly, post it by commenting below.
  4. If you want to ask me privately, send me an email (my full name at gmail.com, include NSF GRFP Question in subject line). I will try and answer you and also work with you on a public question/answer that I can include here.

 

FAQ

Here are some past questions I have been asked and/or questions I anticipate being asked this year.

  • My research is closely related to medicine. Am I still eligible?
    • I think the best test for this is to ask your advisor if they would apply to NSF or NIH for grants on this topic. If NSF you are definitely good, but if NIH, you will need to reframe the research to fit into NSF.
  • I am a first year graduate student. Should I apply this year or wait until my second year? (New issue this year since incoming graduate students can only apply once).
    • This is the toughest question for me since no one has had to make this choice yet. However, here is how I would personally decide. The important thing to remember is that undergrads, first year grads, and second year grads are each separately graded relative to their respective years. So you really need to decide how you currently rank relative to your peers versus how you will rank next year. If you did a bunch of undergrad research, have papers, etc, definitely apply as a first year. If you didn’t, it might payoff to wait, but only if your program lets you get right into research. If you will just be taking classes, I’m less confident your relative standing will improve. Good luck to everyone with this tough choice!

 

Requests for Essay Reading

Unfortunately, I now get more requests to read essays than I can reasonably accomplish. But I am still willing to read over a few and here is how I will decide on the essays to read.

  1. If you are in San Diego, and you think I am a better fit for you than the other local people on the experienced resource list,  send me an email with the subject NSF GRFP Experienced Resource List.
  2. If you are not in San Diego, first check out the experienced resource list and also ask around your school for other resources.
  3. If you can’t find anyone to read your essays, fill out this form. I will semi-randomly select essays to read.

What do I mean by semi-randomly? Well, in the interest of supporting the NSF GRFP’s goal of increasing the diversity of graduate school, I will give priority to undergrads who are without a local person on the experienced resource list and/or are from underrepresented groups. The NSF GRFP specifically “encourages women, members of underrepresented minority groups, persons with disabilities, and veterans to apply”, and I am willing to extremely loosely define minority group by race, ethnicity, sexual orientation, family socio-economic status, geography, colleges that traditionally send few students to graduate school, etc. The form is fill in the blank, so feel free to justify your inclusion in any other underrepresented group that I did not explicitly list.

I’ll then take the prioritized list and make some random selection. The number of people I select this way will depend on the number of local people I end up advising, but I will definitely read at least 2 non-local applications.

 

Here is a my time-line for essay reading:

  • Sept 16th – Random drawing number 1
  • Sept 30th Extended to Oct 5th – Random drawing number 2 (I’ll include everyone again, so early birds get double the chances of being selected)
  • Oct 21st – Last day I will help people (sorry I’m traveling near the deadline)

 

 

Best Machine Learning Resources

Machine learning is a rapidly evolving field that is generating an intense interest from a wide audience. So how can you get started?

For now, I’m going to assume that you already have the basic programming (ie general introduction to programming and experience with matrices) and mathematical skills (calculus and some probability and linear algebra).

These are the best current books on machine learning:

These are some out of date books that still contain some useful sections (for example, Murphy several times refers you to Bishop or MacKay for more details).

Here is a list of other potential resources:

 

I3: International Institute for Intelligence

While I was previously discussing my opinion of Open AI, I mentioned that I would do something different if I was in charge. Here is my dream.

 

What OpenAI is Missing

Helping everyday people throughout the whole world.

OpenAI’s stated goal is:

OpenAI is a non-profit artificial intelligence research company. Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return.

In the short term, we’re building on recent advances in AI research and working towards the next set of breakthroughs.

However, based on their actions so far, this interview with Ilya Sutskever, and popular press articles, the main focus of OpenAI appears to be advanced research in an artificial intelligence by stressing open source, as well as thinking longterm about the impacts of letting advanced artificial intelligence systems control large aspects of our life. While I strongly support these goals, in reality, these will not benefit all of humanity. Instead, it only benefits those with either the necessary training (which is a minimum of a bachelors, but usually means a masters or PhD) or money (to hire top people, buy the required computing resources, etc) to take advantage of the advanced research. So this leaves out the developing world as well as the poor in developed countries, ie contrary to their stated goal, OpenAI is missing the vast majority of humanity.

While one can argue that by making OpenAI’s research open source, eventually it will trickle down and help a wider swath of humanity. However, the current trend suggests that large corporations are best poised to benefit the most from the next revolution (I mean, who is more likely to invent a self driving car, Google, or someone in a developing country?). Additionally, these innovations focus on first world problems (since these are the highest paying customers). And finally, each round of innovation ends up creating fewer and fewer jobs (so the number of unemployed in developed countries may expand). I firmly believe that unless there is a global educational effort (and probably an implementation of basic income), the benefits of AI will be directed towards a tiny sliver of the world’s population.

 

My Proposal: I3

Here I lay out my proposal for a new institute that would actually expand the benefits of recent and future advances in machine learning / artificial intelligence to a wider swath of humanity. I don’t claim that it would truly benefit all of humanity (again, see basic income), but it is a way for research advances to reach a larger proportion of it.

I propose a new education and research institute focused on artificial intelligence, machine learning, and computational neuroscience which I’ll call the International Institute for Intelligence. I like alliterations, and since I think it should focus on three types of intelligence, I especially like the idea of calling it I3 or I-Cubed for short.

Why these three research areas? Well, machine learning is currently revolutionizing how companies use data and is facilitating new technological advances everyday. Designing artificial intelligence systems on top of these machine learning algorithms seems like a realistic possibility in the near future. The less conventional choice is computational neuroscience. I think it is important to include for two reasons. First, the brain is the best example we have of an intelligent system, so until we actually design an artificial intelligence, it seems best to understand and mimic the best example (this is the philosophy of Deep Mind according to Demis Hassabis). Second, the US Brain Initiative  and similar international efforts are injecting significant resources into neuroscience, with the hopes of sparking a revolution similar in spirit and magnitude to the widespread effect the Human Genome Project had on biotechnology and genomics. So I figure we might as well prepare everyone for this future.

So what would be the actual purpose of I3? Sticking with the theme of threes, I propose three initiatives that I will list in my order of importance as well as some bonus points.

 

1. International PhD Education

The central goal is to similar program to ICTP (International Centre for Theoretical Physics) but with a different research emphasis. So what is ICTP? It was founded by Nobel Prize Winner Abdus Salam and it has several programs to promote research in developing countries, including:

  • Predoctoral program – students get a 1 year course to prep them for PhDs
  • Visiting PhD program – students in a developing nation PhD program get to spend a couple of months each year for 3 years at ICTP to participate in their research
  • Conferences
  • Regional offices (currently Sao Paolo, Brazil, but more in the planning)

So the idea is to implement a similar program but with the research emphasis now focused on machine learning, artificial intelligence, and computational neuroscience. While I think the main thing is to get the predoctoral program and visiting PhD program started, eventually it would be great to have 5 regional offices spread throughout the developing world. For example, I think one is needed in South America (Lima, Peru?), one in Africa (Nairobi, Kenya?), and 2 in Asia (India, and China, but not in a traditional technological center). And assuming I3 is based in the US (see my case for San Diego below), it would be great to have an affiliate office in Europe, maybe in Trieste next to ICTP.

One additional initiative that I think could be useful would be paying people to not leave their country and instead help them establish a research center at their local universities. This could also wait until later because it might be easiest to convince some of the future alumni of the predoctoral or visiting PhD programs to return/stay in their home country.

A second additional initiative would be to encourage professors from developed and developing countries to take their sabbatical at I3. This would provide a fresh stream of mentors and set up potential future collaborations. This is a blend of two programs at KITP (this and that).

 

2. US Primary School Education

The science pipeline analogy is overused, but I don’t have a better one yet. So currently, the researchers in I3 focused areas are predominately male, white or Asian, and middle to upper class. So not a very representative sample of the US (or world) population. Therefore, the best longterm solution is to get a more diverse set of students interested in the research at a young age.

Technically this should have a higher priority over the next initiative (US College Education), but since there are other non-profits interested in this (for example, CodeNow), maybe I3 does not need to be a leader in this and instead can play a supporting role.

 

3. US College Education

And again back to science pipeline analogy, if we are to have a more diverse set of researchers, we need to encourage a diverse set of undergrads to pursue relevant majors and continue on into graduate programs. This won’t be solved by any single program, but here are some potential ideas.

  • US underrepresented students could apply for the same 1 year program that is offered to international students.
  • Assist universities in establishing bridge programs that partner research universities with colleges that have significant minority populations. A great example of this is the Vanderbilt-Fisk Physics program.
  • US colleges would also benefit from the proposed sabbatical program offered to international researchers. I also like the KITP idea of extending it to undergraduate only institutes (especially those with large minority populations) as a way to get more undergrads interested in research.
  • Establish a complete set of free college curriculum for machine learning, artificial intelligence, and computational neuroscience. While there are many useful MOOCs on these topics, I still don’t think they beat an actual course.

 

Bonus #1 : Research

ICTP has proven that it is possible to further global educational goals and still succeed at research. I would argue that the people working at I3 should mainly be evaluated for tenure based on their mentorship and teaching of students. Research of course will play a role (otherwise it would be poor mentorship of future researchers), but I think there shouldn’t be huge pressure to bring in grants, high-profile publications, etc. But even without that emphasis, there is no way that a group of smart people with motivated students will not lead to great research.

 

Bonus #2: International Primary and College Education

This is longer term, but if there are successful programs in improving the US primary and college education, international regional offices, and PhD alumni who are in their home countries, it seems like there should be possible to leverage those connections into a global initiative to improve primary and college education.

 

Final Thoughts

So Elon Musk, Peter Thiel, and friends, if you have another billion you want to donate (or Open AI funds to redirect), here is my proposal. In reality, implementing all of my ideas would probably cost several billions, but once you got the center founded, I think that it would be easy to get tech companies, the US government, and even UNESCO to help provide funding.

My final point is that I think San Diego would be a perfect location. I know I’m biased since I live here now, but there a many legitimate reasons San Diego is great for this institute.

  1. UCSD already partners with outside research institutes (Salk, Scripps, etc)
  2. UCSD (and Salk, etc) are leaders in all of these research areas
  3. It is extremely easy to convince people to take a sabbatical in San Diego

While there are many other great potential locations, I strongly suggest that I3 is not in the Bay Area, Seattle, Boston, or New York City. These cities already have plenty of tech jobs, please spread the wealth to other parts of the US.

Anyways, I’ll keep dreaming that someday I’ll get to work at a place like the one I just described.

 

Deep Learning in Python

So maybe after reading some of my past posts, you are fired up to start programming a deep neural network in Python. How should you get started?

If you want to be able to run anything but the simplest neural networks on easy problems, you will find that since pure Python is an interpreted language, it is too slow. Does that mean we have to give up and write our own C++ code? Luckily GPUs and other programmers come to your rescue by offering between 5-100X speedup (I would estimate my average speedup at 10X, but it varies for specific tasks).

There are two main Python packages, Theano and TensorFlow, that are designed to let you write Python code that can either run on a CPU or a GPU. In essence, they are each their own mini-language with the following changes from standard Python:

  • Tensors (generalizations of matrices) are the primary variable type and treated as abstract mathematical objects (don’t need to specify actual values immediately).
  • Computational graphs are utilized to organize operations on the tensors.
  • When one wants to actually evaluate the graph on some data, it is stored in a shared variable that when possible gets sent to the GPU. This data is then processed by the graph (in place of the original tensor placeholders).
  • Automatic differentiation (ie it understands derivatives symbolically).
  • Built in numerical optimizations.

So to get started you will want to install either Theano (pip install theano), TensorFlow (details here), or both. I personally have only used Theano, but if Google keeps up the developmental progress of TensorFlow, I may end up switching to it.

At the end of the day, that means that if one wants to actually implement neural networks in Theano or TensorFlow, you essentially will learn another language. However, people have built various libraries that are abstractions on top of these mini-languages. Lasagne is one example that basically organizes Theano code so that you have to interact less with Theano, but you will still need to understand Theano. I initially started with Theano and Lasagne, but I am now a convert to Keras.

Instead, I advocate for Keras (pip install keras) for two major reasons:

  1.  High level abstraction. You can write standard Python code and get a deep neural network up and running very quickly.
  2. Back-end agnostic. Keras can run on either Theano or TensorFlow.

So it seems like a slam dunk right? Unfortunately life is never that simple, instead there are two catches:

  1. Mediocre documentation (using Numpy as a gold standard, or even comparing to Lasagne). You can get the standard things up and running based on theirs docs. But if you want to do anything advanced, you will find yourself looking into their source code on GitHub, which has some hidden, but useful, comments.
  2. Back-end agnostic. This means if you do want to introduce a modification to the back-end, and you want it to always work in Keras, you need to implement it in both Theano and TensorFlow. In practice this isn’t too bad since Keras has done a good job of implementing low-end operations.

Fortunately, the pros definitely outweigh the cons for Keras and I highly endorse it. Here are a few tips I have learned from my experience with Keras:

  • Become familiar with the Keras documentation.
  • I recommend only using the functional API which allows you to implement more complicated networks. The sequential API allows you to write simple models in fewer lines of code, but you lose flexibility (for example, you can’t access intermediate layers) and the code won’t generalize to complex models. So just embrace the functional API.
  • Explore the examples (here and here).
  • Check out the Keras GitHub.
  • Names for layers are optional keywords, but definitely use them! It will significantly help you when you are debugging.

Now start coding your own deep neural networks!

Basic Income

Recently the Swiss voted no on their referendum to implement basic income. Personally, I think that we should strongly consider implementing a basic income in the United States. At the minimum, I think that we deserve a national conversation on poverty that should include a serious discussion of the pros and cons of basic income. Therefore, I got really pissed off by this recent piece in the New York Times (or this, etc).

The author dismisses basic income out of hand for two major reasons:

  1. Cost (proposal of $10,000 to everyone over 21 for a total of $3 trillion)
  2. Negative effect on the poor (through government cuts due to the cost of implementing the above basic income)

I’ll go through the details below, but some rudimentary math shows that basic income could be paid for in the United States by tax increases that would not be a burden on the poor (or even most of the middle class). Thus, no government programs would need to be cut.

The purpose of this exercise is not to propose a foolproof implementation of basic income. Instead, I want to show that dismissing basic income due to cost is incorrect. If you want to debate basic income, the real issue is how our employment-centered economy would be changed by altering people’s motivation to work.

Here is a the conclusion of the calculations, please read on for all the details. I estimate that $11,500 (ie the US poverty line) could be paid to every non-Social Security receiving adult and $5750 (half the adult payment) to every child by adding a new flat tax on income (adjusted gross income) of 26.6% (see the appendix for alternative proposals that include a lower flat tax). This means that any individual that makes less than $49,500 would get MORE money from the government under this simple plan. Therefore, around 70% of non-Social Security US adults would get more money from the government.

 

Simple Calculation

Estimate Cost

I will start off by calculating the cost of basic income. First, how many people do we need to cover? I am going to ignore the 65 million that are on Social Security. My reasoning is that Social Security is almost a basic income (or could be with a few reforms) and that it is financially secure if we eliminate the cap ($118,500) on the payroll tax but do not increase benefits (see here and here for details).

Looking at the US census facts, there are 74 million children under 18, leaving 183 million US adults not on Social Security. I’m propose paying a half-adult benefit for children, so that means adult benefits will be paid to an effective population of 220 million.

Therefore, if each individual approximately gets the US poverty line, ($11,500), this would result in a total cost of $2.53 trillion.

Estimate Flat Tax

So how could we pay for this? The simplest possible mechanism would be a new flat tax on personal income.

The total US personal income in 2014 was $14.7 trillion. However, not all of that is taxable income (standard deductions, mortgage interest deduction, etc), so the actual taxable personal income is the adjusted gross income (AGI). Using some old numbers on AGI, I estimate that the total US AGI was $9.5 trillion in 2014.

Since the cost is $2.53 trillion, and US AGI is $9.5 trillion, that gives a flat tax rate of 26.6%.

Estimate Break Even Point

For a single individual, the standard deduction is $6300 (this amount of income is not taxed). It would take a taxable income of $43,180 to have a flat tax burden equal to the new basic income. Combine that with the standard deduction, and rounding a bit, leads to the conclusion that anyone making under $49,500 would gain money from the basic income/flat tax proposal.

 

Conclusion

Please don’t dismiss basic income purely out of cost. As the estimates above show, one could introduce basic income and pay for it with a new tax in a manner that preserves all other government programs.

I think there are two major reasons to embrace basic income:

  1. Fairness
  2. Needed security due to potential changes in employment

Maybe the fairness argument doesn’t fit with everyone’s political leanings, but I think the future of employment is strong motivation. Each new technological revolution seems to require fewer and fewer workers (compare Ford’s workforce vs Google’s). Since I don’t see that trend reversing and machine learning / artificial intelligence should actually accelerate it, I think we need to be proactive and provide a floor for people before we have large unemployment.

However, I recognize that basic income is a very controversial idea. That is why I am interested in seeing experimental implementations of it. While this experiment is nice, it really is too small to truly learn anything from. Instead, I would love to see a national trial. Why not start at a very small number, and slowly increase it over time? That would allow us to adapt to the changing culture (ie potentially NOT work centric) and make sure that there are no adverse incentives. For example, my dumb proposal of a half benefit to children needs to be more carefully monitored to ensure that people do not have children just for the sake of getting their share of the benefit.

No matter what you think, the debate isn’t going away. So we might as well start examining it now.

 


Appendix: Other Possible Tax Plans

Here I outline my own personal preferences for tax reforms (in addition to a flat tax) that could be used to pay for basic income. Note that all dollar amounts are per year.

Tax Reforms Within Current System

All numbers listed below are the estimated cost per year of the various deductions.

These are some tax reforms that many economists support:

I think this program is superseded by the introduction of a basic income:

And here are some additional reforms I support:

  • Tax capital gains and dividends as regular income ($85 billion)
  • Limit deductions for the wealthy ($25 billion)
  • Variety of corporate tax reforms ($40 billion) (I don’t understand depreciation so only the others on the list)

This comes to a total reform of $335 billion.

Note: I could have included food stamps or unemployment benefits in the superseded cost savings, but I’m going to assume that the benefits get reformed, but the money still is diverted towards health and employment initiatives respectively.

VAT

I would propose adding a federal value added tax as is common in Europe (see here for pros/cons). Bloomberg estimates that a VAT of 10% on a broad base of items would raise $750 billion per year. For ease of collection, this should be accompanied by a local/state replacement of the standard sales tax (which generates around $500 billion per year, or effectively a 6.66% VAT). I propose a 15% VAT (similar to European rates) that is split evenly between local/state governments and the federal government. This would generate $560 billion additional federal revenue (as a bonus the states get an additional $60 billion).

Financial Transaction Tax

This would impose a small fee on all financial transactions. If we implemented a 0.05% transaction fee (ie 50 cents on every $1000), this would raise an additional $90 billion.

Estate / Transfer Taxes

Currently $1.2 trillion is inherited per year, but estate taxes only bring in $8 billion in revenue. I suggest an estate / transfer tax reform to collect more revenue from this. I would structure it in a progressive manner (ie increasing with wealth), but I again just want to estimate the necessary average rate. If an average rate of 25% was applied to estates, this would lead to an additional $290 billion.

Personal Income Flat Tax

In the end, we still need $1.255 trillion in new revenue. And since the US AGI is $9.5 trillion (it would be slightly higher after the above reforms, but I will ignore that), that implies that we would need to implement a 13.2% flat tax to raise $1.255 trillion.

Summary

If one does a similar flat tax break even point, for people below $93,400 would receive more in basic income than in the flat tax. However, this is misleading since I no easy way to estimate the increase taxes due to the VAT. A worse-case scenario would be that people spend their complete income every year on VAT taxable items. Since there is currently an effective VAT of 6.66%, this is a VAT increase of 8.33%. So a break-even point for the combined flat tax / VAT rate (21.55%) would be $59,600. All the other taxes are much more complicated so I have no easy estimate for them.

The main point of this detailed appendix is that one could replace the flat income tax with a diverse set of taxes that again would not be an unfair burden on the poor or middle class. Additionally, the tax base would be diversified and less prone to swings in the economy.