Category: Technology

Important but surprising facts regarding Technology

Implications of the 23AndMe Bankruptcy

This post is not a super fact post but it contains some other important information. 23AndMe, the large personal genomics and biotechnology company just went into bankruptcy. This has implications for its 15 million customers including me and my wife. In fact, it is advised that you delete your data from their website, and I will tell you how to do that.

About 23AndMe

23AndMe, founded in 2006 provides a direct-to-consumer genetic testing service in which customers provide a saliva sample that is analyzed to generate reports relating to the customer’s ancestry, genetic predispositions, inherited health conditions and other health related topics. Who doesn’t want to know something about their ancestry going back possibly thousands of years? Who doesn’t want to know about genetic timebombs in their DNA? I took the test, and my wife took the test, our daughter took the test, other family members took the test, and it was fun and very interesting, and a good conversation starter. For example, I found out that I am practically a Neanderthal, well maybe not exactly.

Geneticist sequencing human genome Asset id: 2479929725 by FOTOGRIN

What we did not think about is that 23AndMe represented a significant privacy risk. This is data that can be misused in various ways. You can be discriminated against based on this data, you can be denied employment, insurance companies can use it to deny you health insurance, you can be subject to surreptitious testing without your consent. Not to mention familial complications, such as infidelity, and people finding out who their real parents are, and relatives were. In the wrong hands this data is dangerous.

In October 2023 hackers stole 7 million people’s data. Stolen information included people’s names, addresses and genetic data and was sold online. This made the economic difficulties the company was in even worse. Yesterday the company filed for Chapter 11 bankruptcy and their founder and CEO resigned. Now people are rightfully worried about their data.

Ancestry from 23AndMe

However, the information we got from our genetic tests was interesting and fun. I found out that my ancestry was 99.8% Northwestern European, 85.3% Scandinavian/Sweden/Norway, 14.4% Finnish, 0.1% other Northwestern European, and 0.2% Siberian. Not surprising since my family have lived in northern Sweden and northern Finland since at least 1628 according to the ancestry records. Other people in my family were a lot more mixed than that. I can add that I also thought it was fun to on occasion find second cousins or third cousins whose existence I was unaware of.

I also found out that I had strong Neanderthal ancestry. The report says I have more Neanderthal variants than 99% of customers. On the 23AndMe website there was a forum, or club for people with strong Neanderthal ancestry, so I joined. However, some people were taking it a bit too seriously and after I while I did feel comfortable in the Neanderthal club, so I left.

Reconstruction of a Neanderthal by Natural History Museum. Werner Ustorf, CC BY-SA 2.0 <https://creativecommons.org/licenses/by-sa/2.0&gt;, via Wikimedia Commons

Fun Facts from 23AndMe

I was happy to find that I did not seem to have any hereditary predisposition for any illnesses among the ones they listed. Well, I have the typical predisposition for type II diabetes. I was happy to see that I am not predisposed to get Alzheimer’s, which I was worried about, since I have a couple of relatives with that condition.

The most fun and perhaps least important aspect of the genetic testing was the non-health related predispositions. For example, regarding “ice cream flavor preference” my genes says that I am “more likely to prefer vanilla over chocolate ice cream”. My wife got the opposite, and this is correct. I love vanilla, she loves chocolate. I am less likely than average to be afraid of heights and less likely to be motion sick.

Our eye colors, finger and toe lengths, propensity for dandruff, cheek dimples, hair texture and thickness, earwax type, freckles, bunions, the DNA analysis got it all right. By the way I am good at smelling asparagus, just like my DNA test says. The one thing that my DNA test got wrong was that the most likely time for me to wake up in the morning is 6:53AM. The DNA test got my wife’s wake up time correct, but I am not waking up at 6:53AM.

Deleting your data from 23AndMe

OK this is a lot of fun and maybe useful, but the big question is do we want this information in the wrong hands. I’ve mentioned a few ways in which this data can be misused but there may be many more ways this data can be misused that I have not thought about, that no one has yet thought about. Therefore, I deleted all our data from 23AndMe today. If you are a member of 23AndMe I suggest you do the same. Below I am giving you the instructions for how to delete your data from 23AndMe.

  • Log into your 23andMe account. You may need to reset your password.
  • Go to your profile and locate the little menu up on the far top right. Select Settings.
  • Scroll to the “23andMe Data” section at the bottom of the page and click View (button). If you want to download your data, select what you want to download. I downloaded the “reports summary”, which is a pdf file. I also downloaded ancestry composition raw data, which is a large CSV file compressed into a zip file. Finally, I downloaded family tree data, which is in json format.
  • Scroll to the “Delete Data” section and click Permanently Delete Data. This is a Red button at the bottom.
  • Confirm your request: You’ll receive an email from 23andM. Click the link/button in the email to confirm.

Important Note : I am back from my ski vacation, and I once again respond to comments posting and visiting other people’s blogs.

To see the Super Facts click here

EV Cars Indeed Emit Less Carbon Pollution

Super fact 29: EV Cars emit less pollution than Internal Combustion Engine Cars, even considering manufacturing, disposal and EV Cars being charged by dirty grids.

EV Cars emitting less carbon pollution is a Super Fact

At least here in Texas it is quite common to hear people say that EV cars do not reduce emissions. After all EV cars use electricity from the dirty grid. It is also frequently implied that environmentalists and people who care about fossil fuel emissions do not understand that the electricity for EV cars typically comes from the dirty grid. However, the environmentalists I know do know that. In fact, they typically know more and have sometimes done the math. This is why I consider it a super fact. We know that it is true that EV Cars emit less carbon pollution. This is a fact that matters, it is not trivia, and yet this fact is frequently disputed, argued over, or surprising to people.

EV Cars are more efficient than Internal Combustion Engines

For starters, EV cars are much more efficient than Internal Combustion Engine cars, or ICE, and even a coal-fired power plant is less wasteful than a car engine. The net result is that the emissions caused by EVs via the electrical grid are significantly less per mile. The miles per gallon equivalent (MPGe) for electric vehicles (EVs) varies by state/grid and depends on the model of the car but in general it is much better than for an ICE . Replacing gasoline-powered cars with EVs saves energy, regardless of the energy source used to recharge the EVs. For an ICE 16-25% of the original energy goes to the wheels whereas for an EV 87-91% of the original energy goes to the wheels.

16-25% of original energy goes to the wheels. Data from FuelEconomy.gov, Image by Karin Kirk for Yale Connections.
87-91% of original energy goes to the wheels. Data from FuelEconomy.gov, Image by Karin Kirk for Yale Connections.

The Manufacture and Disposal of EV Cars

It takes more energy to manufacture an EV battery for an EV car than it does to produce a combustion engine. So, the production of an electric vehicle does emit more carbon than a petrol car. However, the lower emissions resulting from driving an EV means that an electric car quickly pays back that debt, so to speak. It is typically paid back within two years, according to Hannah Richie, the research director at Our World in Data. The statistics show that switching from an average ICE to an equally sized EV will save 1.2 tons of carbon emissions per person and year. That is a lot considering that the average carbon footprint per year is 4 tons worldwide and 14.4 tons per year for an American.

So, are electric vehicles definitely better for the climate than gas-powered cars? This article from MIT answers the question in the affirmative. The graph below includes construction of facilities, manufacturing of vehicle and battery, production of fuel, vehicle operation as well as disposal. It  is taken from this government website and this article also answers the question above in the affirmative. This is an article from the Department of Energy is stating the same thing.

Lifecycle greenhouse gas emissions comparison of average gasoline car and average EV.

Hannah Richie at Our World in Data also states that other environmental damages related to EVs such as mining for minerals are less than the damage from mining and extraction for fossil fuel cars, and she claims that the price of lithium-ion batteries has fallen by 98% over the last three decades. It should be noted that EVs are becoming increasingly common. According to Our World in Data in 2022, 88% of all cars sold in Norway were EVs and 54% of all cars in Sweden were EVs.

Photo by Kindel Media on Pexels.com

Other EV Myths

There are other EV myths that you may want to have debunked, such as Electric vehicle batteries are unreliable and need to be replaced every few years. In 2011 battery failures were common, 7.5%, but in 2023 battery failures were 0.1%. See this article for details and other myth debunking. A related post is my post on electrification.

I should add that there are some drawbacks with EVs such as the easy with which you can charge them, depending on your location. This post is not a promotion of EVs, and I do not drive an EV for various reasons. This post, like most posts in this blog, is about correcting misinformation and getting the facts correct.

To see the other Super Facts click here

GPS uses relativity for accuracy

Superfact 23: GPS uses relativity for accuracy. Global Positioning Systems or GPS uses Special Relativity and General Relativity to guide you to your destination. In fact, GPS systems would be rendered useless without the Theories of Relativity.

Stock Photo ID: 2502019165 by mayam_studio

Did you use Einstein’s Theories of Relativity to get to the grocery store today?

The theories of relativity may seem strange and impractical, something you only use for astrophysics, black holes, cosmology and extreme velocities. They feature strange concepts such as time dilation, the stretching and bending of space, events simultaneous to some are not to others, the universal constancy of the speed of light in vacuum, the energy and mass equivalency, etc. 

Therefore, it is a bit surprising that without the theories of relativity the GPS app on your phone would not be able to guide you to the grocery store. That’s why I call it a super fact that GPS uses relativity for accuracy.

Stock Illustration ID: 1372134458 by Boris Rabtsevich

GPS and Time Dilation

GPS is a satellite-based radio navigation system that provides location information and time anywhere on Earth. The fact that the information is provided by satellites that orbit earth at high speeds and high above earth’s surface makes General Relativity and Special Relativity necessary. 

The GPS system needs to calculate precisely the time it takes for signals to travel from the satellites to a receiver on Earth for it to work. GPS satellites travel at high speeds causing a large enough time dilation that has to be accounted for. In addition, they orbit earth high above earth’s surface where earth’s gravitational field is weaker than on earth’s surface. Clocks run faster in weaker gravitational fields due to gravitational time dilation, so you must correct that as well.

If you ignore relativity, you will accumulate a discrepancy of six miles in one day.  You are not going to find the grocery store that way, unless you use the old-fashioned method of reading a map. In a sense, if your GPS device finds the grocery store for you, you have proven Einstein right.

To see the other Super Facts click here

The Nobel Prize in Physics and Neural Networks

“The Nobel Prize in Physics and Neural Networks” is not a super-fact but just what I consider interesting information

The Nobel Prizes are in the process of being announced. The Nobel Prize in Physiology or Medicine, Chemistry, Physics and Literature have been announced and the Nobel Prize in Peace will be coming up at any minute. The Nobel Prize in Economics will be announced October 14.

The Nobel Prize in Peace tends to get the most attention but personally I focus more on the Nobel Prizes in the sciences. That may be because of my biases, but those prizes also tend to be more clearcut and rarely politized. Nobel Prize in Peace is announced and given in Oslo, Norway, and all the other prizes are announced and given in Stockholm, Sweden.

Nobel Prize In Physics

What I wanted to talk about here is the Nobel Prize in Physics given to John J. Hopfield and Geoffrey J. Hinton. They made a number of important discoveries in the field of Artificial Intelligence, more specifically neural networks. This is really computer science, not physics. However, they used tools and models from physics to create their networks and algorithms, which is why the Nobel committee deemed it fit to give them the Nobel Prize in Physics.

Perhaps we need another Nobel Prize for computer science. It is also of interest to me because I’ve created and used various Neural Networks myself. It was not part of my research or part of my job, so I am not an expert. For all of you who are interested in ChatGPT, it consists of a so-called deep learning neural network (multiple hidden layers) containing 176 billion neurons. By the way that is more than the 100 billion neurons in the human brain. But OK, they aren’t real neurons.

So, what is an artificial neural network?

A simple old-style 1950’s Neural Network (my drawing)

The first neural networks created by Frank Rosenblatt in 1957 looked like the one above. You had input neurons and output neurons connected via weights that you adjusted using an algorithm. In the case above you have three inputs (2, 0, 3) and these inputs are multiplied by the weights to the outputs.
3 X 0.2 +0 + 2 X -0.25 = 0.1 and 3 X 0.4 + 0 + 2 X 0.1 = 1.4 and then each output node has a threshold function yielding outputs 0 and 1.

To train the network you create a set of inputs and the output that you want for each input. You pick some random weights and then you can calculate the total error you get, and you use the error to calculate a new set of weights. You do this over and over until you get the output you want for the different inputs. The amazing thing is that now the neural network will often also give you the desired output for an input that you have not used in the training. Unfortunately, these neural networks weren’t very good, and they often failed and could not even be trained.

In 1985/1986, Geoffrey Hinton, David Rumelhart and Ronald J. Williams presented an algorithm applied to a neural network featuring a hidden layer that was very successful. It was effective and guaranteed to learn patterns that were possible to learn. It set off a revolution in Neural Networks. The next year, in 1987, when I was a college student, I used that algorithm on a neural network featuring a hidden layer to do simple OCR (optical character recognition).

Note that a computer reading an image with a letter is very different from someone typing it on a keyboard. In the case of the image, you must use OCR, a complicated and smart algorithm for the computer to know which letter it is.

A multiple layer neural network with one hidden layer. This set-up and the associated backpropagation algorithm set off the neural network revolution. My drawing.

In the network above you use the errors in a similar fashion to the above to adjust the weights to get the output you want, but the algorithm, the backpropagation algorithm is very successful.

Below I am showing two 10 X 10 pixel images containing the letter F. The neural network I created had 100 inputs, one for each pixel, a hidden layer and then output neurons corresponding to each letter I wanted to read. I think I used about 10 or 20 versions of each letter during training, by which I mean running the algorithm to adjust the weights to minimize the error until it is almost gone.

Now if I used an image with a letter that I had never used before, the neural network typically got it right even though the image was new. Note, my experiment took place in 1987. OCR has come a long way since then.

Two examples of the letter F in a 10 X 10 image. You can use these images (100 input neurons) to train a neural network to recognize the letters F.

At first, it was believed that adding more than one hidden layer did not add much. That was until it was discovered that by applying the backpropagation algorithm differently to different layers created a better / smarter neural network and so at the beginning of this century the deep learning neural network was born (or just deep learning AI). Our Nobel Prize winner Geoffrey J. Hinton was a pioneer in deep learning neural networks.

My drawing of a deep learning neural network (deep learning AI). There are three hidden layers.

I should mention that there are many styles of neural networks, not just the ones I’ve shown here. Below is a network called a Hopfield network (it was certainly not the only thing he discovered).

In a Hopfield network all neurons are input, and output neurons and they are all connected to each other.

For your information, ChatGPT-3.5 is a deep learning neural network like the one in my colorful picture above, but instead of 3 hidden layers it has 96 hidden layers in its neural network and instead of 19 neurons it has a total of 176 billion neurons. Congratulations to John J. Hopfield and Geoffrey J. Hinton.

To see the Super Facts click here