Physicist, Startup Founder, Blogger, Dad

Monday, November 20, 2017

Saturday, November 18, 2017

Robot Overlords and the Academy

In a previous post Half of all jobs (> $60k/y) coding related? I wrote
In the future there will be two kinds of jobs. Workers will either

Tell computers what to do    
Be told by computers what to do
I've been pushing Michigan State University to offer a coding bootcamp experience to all undergraduates who want it: e.g., Codecademy.com. The goal isn't to turn non-STEM majors into software developers, but to give all interested students exposure to an increasingly important and central aspect of the modern world.

I even invited the CodeNow CEO to campus to help push the idea. We're still working on it at the university -- painfully SLOWLY, if you ask me. But this fall I learned my kids are taking a class based on Codecademy at their middle school! Go figure.

(Image via 1, 2)

Wednesday, November 15, 2017

Behold, the Super Cow

Hmm... how do they compute the Net Merit and GTPI? (But, but, what about all of that missing heritability?)

See also

Applied genomics: the genetic "super cow"

Genomic prediction: no bull.

Attention climate virtue signalers: more efficient cows produce less methane per liter of milk! Drink milk from genetically engineered cows :-)

Friday, November 10, 2017


I'm in Mountain View to give a talk at 23andMe. Their latest funding round was $250M on a (reported) valuation of $1.5B. If I just add up the Crunchbase numbers it looks like almost half a billion invested at this point...

Slides: Genomic Prediction of Complex Traits
Abstract: We apply methods from Compressed Sensing (L1-penalized regression; Donoho-Tanner phase transition with noise) to the UKBB dataset of 500k SNP genotypes. We construct genomic predictors for several complex traits. Our height predictor captures nearly all of the predicted SNP heritability for this trait -- thereby resolving the missing heritability problem. Actual heights of most individuals in validation tests are within a few cm of predicted heights. I also discuss application of these methods to polygenic disease risk: sparsity estimates (of the number of causal loci), combined with phase transition scaling analysis, allow estimates of the amount of case | control data required to construct good predictors.
Here's how people + robots handle your spit sample to produce a SNP genotype:

Wednesday, November 08, 2017

Pocket AI from Beijing and Smartphones

I need to replace my old iPhone 6, and, predictably, this led me down the rabbit hole of learning about mobile phones, the mobile industry, and even mobile technologies. Some quick remarks: from the least to most expensive phones, Chinese companies are now competitive with industry leaders like Samsung and Apple. The Chinese market is hyper-competitive: small innovative startups (Oppo, OnePlus, etc.) compete with medium sized entities (e.g., Xiaomi, only recently a small startup itself) and giants like Huawei and Lenovo (Motorola). To gauge the landscape, watch phone reviews by Indian techies (or this guy in Germany), who tend to be very focused on cost performance and have access to handsets not sold in the US.

Here's a short video about OnePlus which also explains a bit about the Shenzhen hardware ecosystem:

Huawei's Kirin 970 chipset includes a dedicated "Neural Processor Unit" (NPU), optimized for the matrix operations used in machine learning. An NPU allows the phone to execute ML code for tasks such as image and voice recognition, language translation, etc. without relying on cloud connectivity. At the moment it is mostly a marketing gimmick, but one can imagine in a few years (perhaps earlier!) the NPU could be as important to the phone experience as the GPU.

Here's a review of the Mate 10 Pro, Huawei's $1k flagship phone, with a brief demo of some of the AI features:

The NPU appears to be based on technology licensed from a small Beijing startup, Cambricon. The founder is an alumnus of the Special Class for Gifted Young, University of Science and Technology of China. I've reviewed many Physics PhD applications from 19 year old graduates of this program. There is an SV bidding war over chip designers in this area, ever since the advent of Google's proprietary TPU (and software package Tensorflow), which accounts for most of its computation at data centers around the world.

Here's a quick demo of text recognition and machine translation from Chinese to English:

Some marketing video about the AI processor:

From cat recognition to Her or Joi? How long? I was recently offered the opportunity to be a beta tester for a startup that is building a smartphone AI assistant. I was intrigued but didn't want to give them access to all of my information...

PS One of the reasons I am leaving iOS for Android is that Google Assistant is getting very good, whereas in my experience Siri is terrible!

Wednesday, November 01, 2017

The Future is Here: Genomic Prediction in MIT Technology Review

MIT Technology Review reports on our startup Genomic Prediction. Some basic points worth clarifying:

1. GP's first product, announced at the annual ASRM (American Society of Reproductive Medicine) meeting this week, tests chromosomal abnormality. It is a less expensive but more accurate version of existing tests.

2. The polygenic product, to be launched in 2018, checks for hundreds of known single-gene ("Mendelian") disease risks, and will likely have some true polygenic predictive capabilities. This last part is the main emphasis of the story, but it is just one component of the overall product offering. The article elides a lot of challenging laboratory work on DNA amplification, etc.

3. GP will only deliver results requested by an IVF physician. It is not a DTC (Direct to Consumer) company.

4. All medical risk analysis proceeds from statistical data (analyzing groups of people) to produce recommendations concerning a specific individual.

5. I am on the Board of Directors of GP but am not an employee of the company.

MIT Technology Review

Eugenics 2.0: We’re at the Dawn of Choosing Embryos by Health, Height, and More

Will you be among the first to pick your kids’ IQ? As machine learning unlocks predictions from DNA databases, scientists say parents could have choices never before possible.

Nathan Treff was diagnosed with type 1 diabetes at 24. It’s a disease that runs in families, but it has complex causes. More than one gene is involved. And the environment plays a role too.

So you don’t know who will get it. Treff’s grandfather had it, and lost a leg. But Treff’s three young kids are fine, so far. He’s crossing his fingers they won’t develop it later.

Now Treff, an in vitro fertilization specialist, is working on a radical way to change the odds. Using a combination of computer models and DNA tests, the startup company he’s working with, Genomic Prediction, thinks it has a way of predicting which IVF embryos in a laboratory dish would be most likely to develop type 1 diabetes or other complex diseases. Armed with such statistical scorecards, doctors and parents could huddle and choose to avoid embryos with failing grades.

IVF clinics already test the DNA of embryos to spot rare diseases, like cystic fibrosis, caused by defects in a single gene. But these “preimplantation” tests are poised for a dramatic leap forward as it becomes possible to peer more deeply at an embryo’s genome and create broad statistical forecasts about the person it would become.

The advance is occurring, say scientists, thanks to a growing flood of genetic data collected from large population studies. ...

Spotting outliers

The company’s plans rely on a tidal wave of new knowledge showing how small genetic differences can add up to put one person, but not another, at high odds for diabetes, a neurotic personality, or a taller or shorter height. Already, such “polygenic risk scores” are used in direct-to-consumer gene tests, such as reports from 23andMe that tell customers their genetic chance of being overweight.

For adults, risk scores are little more than a novelty or a source of health advice they can ignore. But if the same information is generated about an embryo, it could lead to existential consequences: who will be born, and who stays in a laboratory freezer.

“I remind my partners, ‘You know, if my parents had this test, I wouldn’t be here,’” says Treff, a prize-winning expert on diagnostic technology who is the author of more than 90 scientific papers.

Genomic Prediction was founded this year and has raised funds from venture capitalists in Silicon Valley, though it declines to say who they are. Tellier, whose inspiration is the science fiction film Gattaca, says the company plans to offer reports to IVF doctors and parents identifying “outliers”—those embryos whose genetic scores put them at the wrong end of a statistical curve for disorders such as diabetes, late-life osteoporosis, schizophrenia, and dwarfism, depending on whether models for those problems prove accurate. ...

This week, Genomic Prediction manned a booth at the annual meeting of the American Society for Reproductive Medicine. That organization, which represents fertility doctors and scientists, has previously said it thinks testing embryos for late-life conditions, like Alzheimer’s, would be “ethically justified.” It cited, among other reasons, the “reproductive liberty” of parents.

... Hsu’s prediction is that “billionaires and Silicon Valley types” will be the early adopters of embryo selection technology, becoming among the first “to do IVF even though they don’t need IVF.” As they start producing fewer unhealthy children, and more exceptional ones, the rest of society could follow suit.

“I fully predict it will be possible,” says Hsu of selecting embryos with higher IQ scores. “But we’ve said that we as a company are not going to do it. It’s a difficult issue, like nuclear weapons or gene editing. There will be some future debate over whether this should be legal, or made illegal. Countries will have referendums on it.”

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