Artificial Intelligence and Human Stupidity

There is no question that the continuing development of artificial intelligence (AI) technology will help humanity solve some difficult problems, with its ability to see patterns in complex data and arrive at novel solutions that any human mind would easily miss. But there is also no question that AI will be weaponized by those with ill intent to spread misinformation, destroy our privacy, and cleverly manipulate people to do their bidding. And it will enable a lot of us to do even less deep thinking and reflection then we do already.

Furthermore, if a significant fraction of the population won’t trust or believe humans who are experts in their field, why would they trust or believe AI insights guided by those same experts? Opportunists of questionable integrity and moral character always have been able to manipulate and distract the willfully or wantonly ignorant, and AI will make their job that much easier.

If we humans are to survive and thrive, we must address and solve fundamental human problems. I am not at all convinced that AI is going to help with that.


How is AI going to reduce, over several generations, human population from its current 8 billion to a more sustainable 1 billion by lowering the birth rate uniformly world wide? Those of us alive today are currently burning through the Earth’s physical and natural resources at an unprecedented rate and degrading the Earth’s climate and ecology, all because there are about 7 billion too many people alive today.


How is AI going to rid the world of nuclear weapons and other weapons of mass destruction? How is AI going to transform the world’s armies into agencies that provide humanitarian assistance, keep the peace, and enforce sensible international laws? How is AI going to transform the endless trillions the nations of the world spend on defense and warfare and redirect that vast sum of money to the betterment of humankind?

Fear and Hopelessness

How is AI going to rid the world of firearms and other weapons of self destruction?


How is AI going to ensure that the wisest, most knowledgeable, thoughtful, adaptable, intelligent, and compassionate people are our chosen leaders?

Pathological Behaviors

Will AI help us to understand why some people are hateful, or narcissistic, or violent, while most of us tend to be loving, altruistic, and peaceful—no matter what life throws at us? Will AI help us to truly and humanely rehabilitate those who begin exhibiting dangerous and antisocial behaviors?

Knowledge and Faith

Will AI help us to find a way for all religious believers to peacefully coexist with nonbelievers?

Or, will AI be just another technological distraction?

Welcome to the Zooniverse!

We live in a society where science is little more than a “spectator sport” for most of us who have an interest in it.  Data collection and original research often require substantial investments of time and money, as well as a long-term commitment.  Those of us who are already working full time and, in spite of that, have little discretionary income, often find “participatory science” out of reach, no matter how great our enthusiasm or aptitude.

As today’s scientific instruments increasingly generate enormous quantities of data, the people who “do science” for a living are too few in number to analyze all that data.  Fortunately, this is one area where “citizen scientists” can help.

There are a number of interesting scientific projects that lend themselves well to “crowd sourcing”, and Zooniverse is a portal to many of them.

Here are the currently active Zooniverse projects in the disciplines of astronomy and physics.

Backyard Worlds: Planet 9
Discover new brown dwarfs and possibly a new solar system planet by scrutinizing images from the Wide-field Infrared Survey Telescope (WISE).

Comet Hunters
Discover new comets previously misidentified as asteroids by analyzing deep images taken by the Subaru 8.2-meter telescope in Hawaii.

Disk Detective
Help search for stars with undiscovered disks of dust around them.  These stars show us where to look for planetary systems and how they form.

Exoplanet Explorers
Discover transiting exoplanet candidates in Kepler’s K2 data.

Galaxy Zoo   Galaxy Zoo: 3D
Classify galaxies, many of which have never been studied before, and look for unusual features.

Gravity Spy
Identify and characterize “glitches” in LIGO data to make it easier to identify gravitational wave events.

Higgs Hunters
Help search for unknown exotic particles in data from the Large Hadron Collider (LHC), the world’s largest and most powerful particle collider.

Milky Way Project
Classify images from two infrared space telescopes: the Spitzer Space Telescope (SST) and the Wide-field Infrared Survey Telescope (WISE).

Planet Four
Identify and measure features on the surface of Mars.

Planet Hunters
Discover transiting exoplanet candidates in data from the Kepler spacecraft.

Radio Galaxy Zoo
Search radio images of galaxies for evidence of jets caused by matter falling into supermassive black holes.

Radio Meteor Zoo
Identify meteors through the reflection of radio waves from their ionization trails.

Solar Stormwatch II
Characterize solar storms and their interaction with the solar wind through the analysis of images from NASA’s twin Solar Terrestrial Relations Observatory (STEREO) spacecraft.

Supernova Hunters
Scrutinize the most recent images collected by the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) in Hawaii in comparison to reference images to discover new supernovae that can then be immediately followed by ground-based and space-based telescopes.

All of these projects utilize “machine learning” computer algorithms such as neural networks and random forests (artificial intelligence, or AI) to some extent, and in fact citizen scientist participants help “train” these algorithms so they do a better job of finding or classifying or whatever.  For a great introduction to this subject, see “Machines Learning Astronomy” by Sky & Telescope news editor Monica Young in the December 2017 issue, pp. 20-27.

As machine learning algorithms get better and better, they may no longer need citizen scientists to train them.

In the meantime, have fun and contribute to science!