College Requires a 115 IQ (Min)

Source: AlFinNextLevel website, Jun 2016

Most young people simply do not have the IQ to take a rigorous four year degree that will provide a reasonable return on investment.

There is no magic point at which a genuine college-level education becomes an option, but anything below an IQ of 110 is problematic. If you want to do well, you should have an IQ of 115 or higher. Put another way, it makes sense for only about 15% of the population, 25% if one stretches it, to get a college education. And yet more than 45% of recent high school graduates enroll in four-year colleges. Adjust that percentage to account for high-school dropouts, and more than 40% of all persons in their late teens are trying to go to a four-year college — enough people to absorb everyone down through an IQ of 104.__ Charles Murray quoted in


Ed Boyden: How to Think

Source: MIT Technology Review, Nov 2007

1. Synthesize new ideas constantly. Never read passively. Annotate, model, think, and synthesize while you read, even when you’re reading what you conceive to be introductory stuff. That way, you will always aim towards understanding things at a resolution fine enough for you to be creative.

2. Learn how to learn (rapidly). One of the most important talents for the 21st century is the ability to learn almost anything instantly, so cultivate this talent. Be able to rapidly prototype ideas. Know how your brain works. (I often need a 20-minute power nap after loading a lot into my brain, followed by half a cup of coffee. Knowing how my brain operates enables me to use it well.)

3. Work backward from your goal. Or else you may never get there. If you work forward, you may invent something profound–or you might not. If you work backward, then you have at least directed your efforts at something important to you.

4. Always have a long-term plan. Even if you change it every day. The act of making the plan alone is worth it. And even if you revise it often, you’re guaranteed to be learning something.

5. Make contingency maps. Draw all the things you need to do on a big piece of paper, and find out which things depend on other things. Then, find the things that are not dependent on anything but have the most dependents, and finish them first.

6. Collaborate.

7. Make your mistakes quickly. You may mess things up on the first try, but do it fast, and then move on. Document what led to the error so that you learn what to recognize, and then move on. Get the mistakes out of the way. As Shakespeare put it, “Our doubts are traitors, and make us lose the good we oft might win, by fearing to attempt.”

8. As you develop skills, write up best-practices protocols. That way, when you return to something you’ve done, you can make it routine. Instinctualize conscious control.

9. Document everything obsessively. If you don’t record it, it may never have an impact on the world. Much of creativity is learning how to see things properly. Most profound scientific discoveries are surprises. But if you don’t document and digest every observation and learn to trust your eyes, then you will not know when you have seen a surprise.

10. Keep it simple. If it looks like something hard to engineer, it probably is. If you can spend two days thinking of ways to make it 10 times simpler, do it. It will work better, be more reliable, and have a bigger impact on the world. And learn, if only to know what has failed before. Remember the old saying, “Six months in the lab can save an afternoon in the library.”

Logarithmic Time Planning

Two practical notes. The first is in the arena of time management. I really like what I call logarithmic time planning, in which events that are close at hand are scheduled with finer resolution than events that are far off. For example, things that happen tomorrow should be scheduled down to the minute, things that happen next week should be scheduled down to the hour, and things that happen next year should be scheduled down to the day. Why do all calendar programs force you to pick the exact minute something happens when you are trying to schedule it a year out? I just use a word processor to schedule all my events, tasks, and commitments, with resolution fading away the farther I look into the future. (It would be nice, though, to have a software tool that would gently help you make the schedule higher-resolution as time passes…)

Conversation Summaries

The second practical note: I find it really useful to write and draw while talking with someone, composing conversation summaries on pieces of paper or pages of notepads. I often use plenty of color annotation to highlight salient points. At the end of the conversation, I digitally photograph the piece of paper so that I capture the entire flow of the conversation and the thoughts that emerged. The person I’ve conversed with usually gets to keep the original piece of paper, and the digital photograph is uploaded to my computer for keyword tagging and archiving. This way I can call up all the images, sketches, ideas, references, and action items from a brief note that I took during a five-minute meeting at a coffee shop years ago–at a touch, on my laptop. With 10-megapixel cameras costing just over $100, you can easily capture a dozen full pages in a single shot, in just a second.

Older Sperm, Younger Eggs

Source: Alfinnextlevel website, Jun 2017

It turns out that older men chasing younger women contributes to human longevity and the survival of the species, according to new findings by researchers at Stanford and the University of California-Santa Barbara.

Women in their twenties have a good chance of becoming pregnant as a result of a relatively greater number of eggs in their ovaries. Additionally, a larger percentage of those eggs are normal genetically. Since a woman is born with all of the eggs that they will have in their lifetime, the older she gets the fewer eggs are left.

In addition, as women age the percentage of genetically normal eggs remaining decreases. This is why women have a decreasing fertility rate, increased miscarriage rate and increased chance of birth defects like Down syndrome as they age

Map of Science – Access to Scientific Papers

Source: Singularity Weblog, Feb 2016

Perhaps 5% of Published Scientific Papers are Accessed …

… the mind’s building blocks for constructing complex thoughts … are not word-based.

Source: The Next Big Future, Jun 2017

This latest research led by CMU’s Marcel Just builds on the pioneering use of machine learning algorithms with brain imaging technology to “mind read.” The findings indicate that the mind’s building blocks for constructing complex thoughts are formed by the brain’s various sub-systems and are not word-based. Published in Human Brain Mapping and funded by the Intelligence Advanced Research Projects Activity (IARPA), the study offers new evidence that the neural dimensions of concept representation are universal across people and languages.

“One of the big advances of the human brain was the ability to combine individual concepts into complex thoughts, to think not just of ‘bananas,’ but ‘I like to eat bananas in evening with my friends,’” said Just, the D.O. Hebb University Professor of Psychology in the Dietrich College of Humanities and Social Sciences. “We have finally developed a way to see thoughts of that complexity in the fMRI signal. The discovery of this correspondence between thoughts and brain activation patterns tells us what the thoughts are built of.”

He added, “A next step might be to decode the general type of topic a person is thinking about, such as geology or skateboarding. We are on the way to making a map of all the types of knowledge in the brain.”

Related Resource: Alfinnextlevel, Jun 2017

The Carnegie Mellon University research is not at all revolutionary or particularly advanced — and it rests upon a number of tenuous assumptions. Nevertheless, it is suggestive. Rather than proving or disproving any particular hypothesis, this type of research is useful for suggesting newer iterations of studies and techniques.

The suggestion that the mind’s building blocks for complex thoughts are not word-based is insightful, but also quite obvious. It may take many years and decades, however, for mainstream society to catch up with that basic and seminal insight — if it ever does.


Declining Productivity

Source: MIT Technology Review, Apr 2016

Between 1920 and 1970, American total factor productivity grew by 1.89 percent a year, according to Gordon. From 1970 to 1994 it crept along at 0.57 percent. Then things get really interesting. From 1994 to 2004 it jumped back to 1.03 percent. This was the great boost from information technology—specifically, computers combined with the Internet—and the ensuing improvements in how we work. But the IT revolution was short-lived, argues Gordon. Today’s smartphones and social media? He is not overly impressed. Indeed, from 2004 to 2014, total factor productivity fell back to 0.4 percent. 

Related Resource: MIT Technology Review, Jun 2016

According to Chad Syverson, an economist at the University of Chicago Booth School of Business, U.S. productivity grew at a mere 1.3 percent per year from 2005 to 2015, far less than the 2.8 percent annual growth rate during the decade earlier. Syverson calculates that had the slowdown not occurred, the gross domestic product would have been $2.7 trillion higher by 2015—about $8,400 for every American.

Michael Mandel, an economist at the Progressive Policy Institute in Washington, D.C., says the productivity slowdown is occurring in what he calls the physical industries, including manufacturing and health care. Such industries, which he estimates make up 80 percent of the national economy, account for only 35 percent of investments in information technology and their productivity reflects that, growing at only 0.9 percent annually. Meanwhile, productivity is growing by 2.8 percent a year in what Mandel calls digital industries, which include finance and business services.

Mapping the Brain’s 11 dimensions

Source: Wired, Jun 2017

Instead of a meaningless pile of data, she saw in Markram’s results an obvious place to apply her abstract math goggles. “Topology is really the mathematics of connectivity in some sense,” she says. “It’s particularly good at taking local information and integrating it to see what global structures emerge.”

For the last two years she’s been converting Blue Brain’s virtual network of connected neurons and translating them into geometric shapes that can then be analyzed systematically. Two connected neurons look like a line segment. Three look like a flat, filled-in triangle. Four look like a solid pyramid. More connections are represented by higher dimensional shapes—and while our brains can’t imagine them, mathematics can describe them.

Using this framework, Hess and her collaborators took the complex structure of the digital brain slice and mapped it across as many as 11 dimensions. It allowed them to take random-looking waves of firing neurons and, according to Hess, watch a highly coordinated pattern emerge. “There’s a drive toward a greater and greater degree of organization as the wave of activity moves through the rat brain,” she says. “At first it’s just pairs, just the edges light up. Then they coordinate more and more, building increasingly complex structures before it all collapses.”

Nanoporous materials are super useful for all sorts of industries—from gas separation to chemical storage to medicine. And the performance of these materials depends on the shape of their pores, something that’s really difficult to quantify. So when scientists are looking for new materials to do certain jobs, they rely almost entirely on visual inspection of the more than 3 million nanoporous materials out there. Hess used algebraic topology to quantify the similarity of pore structures instead, assigning a sort of geometric fingerprint to each one. It’s a computational method chemical engineers can now use to find exactly what they need without having to stare into a microscope for days on end.

Markram is as on-brand now as ever. His signature style is to present ideas too speculative for most scientists to countenance and then find ways to test them despite (and often in spite of) the haters. His latest hypothesis: that those patterns of increasingly complex neuronal structures represent ever richer and more interesting responses to stimuli. He thinks it’s how people learn. Maybe even where they store memories. To find out, Argonne National Laboratory outside of Chicago, Illinois gave him 100 million core hours on their super-super computer to run a year-long simulation to see how those patterns change and evolve over time. At the end of 2017 Markram will pass off that mountain of data to Hess. Then it will be up to the math to decide.