Author Archives: Editors
Why is there zero gravity in space? By Michelle Thaller
Michelle Thaller: Joshua, you ask a really great question, “Why is there no gravity in space?” I bet you’ve seen pictures of the astronauts up in the space station and they’re floating around as if there’s no gravity at all. This is actually a really interesting misconception about what’s going on with the astronauts. And it gets to one of my favorite questions in all of astronomy, and that is: what is an orbit? What’s happening when astronauts are orbiting the Earth? And why does it appear like there’s no gravity in space?
So, to talk about why this is kind of a misconception, the astronauts are about 200 miles above the surface of the Earth in the space station; the space station orbits around us about once every 90 minutes. It’s not that they’re so far away from the Earth that there’s no gravity—in fact, if you built a skyscraper that was 200 miles tall and you were up on one of those top floors you might weigh a tiny little bit less, but you probably weigh at least about 80 percent what you normally would. You’re really not that much farther away from the Earth. So it’s not that they’re weightless because they’re in space and far away from the Earth, they’re actually close enough to the Earth to feel the gravitational pull of the Earth itself. So what’s going on? Why are they floating around?
Well, this is what an orbit really is. We have to get spacecraft going very, very fast to put them into orbit. That’s why we put them on rockets. Rockets launch spacecraft and get them going very, very fast, and if you’re in orbit where the space station is, you’re traveling at about 17,000 miles an hour.
Now, what happens to those astronauts is that they’re going very, very fast but they’re actually freely falling towards the Earth. The Earth has gravity, and that gravity is pulling them down just the same as what would happen as if you dropped a ball. If you dropped a ball it would fall to the floor. The same thing is happening to the astronauts; they are falling towards the surface of the Earth.
But here’s a cool thing: they’re going so fast they keep missing the Earth as they fall. And that’s the definition of an orbit.
Now think about it this way: I talked about dropping a ball and the ball just falls straight down; what would happen if I put the ball in a little cannon and shot it out? The ball would start to drop towards the Earth the minute it left the cannon, but the cannon has given it some velocity, and so the ball might go a hundred yards, right? It might actually go a hundred yards before it fell because the cannon gave it that velocity.
Now, let’s take an even bigger cannon, let’s take a huge cannon that can shoot things many, many miles. So you shoot the ball out, the ball is still falling freely towards the Earth all the time, but now it goes farther because you’ve given it more velocity. And maybe with a really big cannon, you can shoot a ball a hundred miles.
What about with a rocket? With a rocket, you could get something going so fast, up to 17,000 miles an hour, that as it fell freely towards Earth, Earth would keep curving away under it and it would keep missing it.
That’s what an orbit is, and that’s why you need a rocket to get into orbit. You need to get yourself going so fast that, as you fall back towards Earth, you keep missing it.
So the astronauts are not weightless because there’s no gravity in space; the astronauts are falling the same way a skydiver is falling freely through space, it’s just that they’re going so fast they keep missing the Earth.
They are all the time falling towards the Earth going fast enough that they keep missing it.
Everything that’s in orbit around anything else is doing the same thing. Right now you and I are falling freely towards the sun, but the Earth is moving, actually at about 30,000 miles an hour, and we keep missing the sun as we go around it, and that means we’re in orbit around the sun.
The moon is in orbit around the Earth. It’s falling towards the Earth, it’s just traveling so fast it keeps missing it. You’ve asked an excellent question. Everything in space that is orbiting is freely falling under gravity, it’s just going too fast to ever hit anything.
“Post-America World & Post-America, America” By F. Sheikh
After WWII, two new super powers emerged, United States of America and Soviet Union. America took a central stage in world politics as well as in world economy. Britain and other colonial powers were forced out of their colonial lands. American expanded its influence in many of those countries. America’s Marshal plan for war ridden Europe expanded its influence in Western Europe. Soviet Union took Eastern Europe under its fold and started to compete with America for its own influence in other countries. This gave us cold war, arms race, proxy wars and ideological war around the globe. America supported even dictators and monarch, especially in the Middle East, for its influence and economic interests.
America became a military giant and at the same time an economic powerhouse attracting talent from around the globe. Soviet Union tried to match America’s military might and dominance, but it sorely lacked economic development and disintegrated in 1991, leaving one super power standing-United States of America.
Despite America’s record of supporting favorite dictators, toppling unfriendly regimes and brutal ideological wars, it was still seen in the world as a symbol of human aspirations of liberty, success, and individual prosperity. Overall America projected itself as a force of good, democratic values, and morals; and it considered itself guardian of those values in the world and at home.
All this perception started to change after America’s fierce and revengeful response to brutal 9/11 attack. In anger and short-sightedness, it trampled on the very same values and ideals, both at home and abroad, which it wore as a badge of honor and national identity. It adopted torture forbidden by Geneva Convention, opened Rendition Torture Centers abroad, locked up prisoners, including some innocents, for years at Guantanamo Bay without Habeas corpus, abused prisoners at Abu Gharib and in Afghanistan, and violated civil liberties at home with Patriot Act. Cities were bombed to stone ages with thousands of innocent civilians killed in Afghanistan and Iraq. America eroded its leadership in the world as guardian and leader of moral, liberal, and democratic values.
President Obama tried to reverse this erosion by half measures, but it has taken its roots. Anti-terrorism evolved into an industry requiring full attention and resources at the expense of other priorities. United States forced other countries to adopt similar anti-terrorist measures and allocate essential resources. Tyrants and oppressive regimes further strengthen their grip on power by utilizing anti-terrorist excuses. Meanwhile globalization created extreme inequality that went unnoticed and unattended by the mainstream political parties. Eroded values and inequality, a fertile ground for populous parties and demagogue leaders, gave us Donald Trump and populous leaders in other countries.
Donald Trump destroyed, within a short time, last vestiges of America’s leadership and moral standing in the world and is isolating itself from rest of the world. We are still superior economic and military power, but we are no longer the leader. It is a post-America world unfolding in front of us. Western Europe is adrift, while China is flexing its newfound economic muscle. New technology, including social media, has provided new tools to adversaries that can sow the seeds of discontent and factionalism without much investment. America lost its leadership at a crucial moment when it was needed the most to create strong global norms and rules to prevent the abuses of new technology which has the potential of great benefits but at the same time potential of unleashing destructive forces. This leadership was critical when the global and domestic infrastructure in every sphere of life depends on new technology.
When you are a world leader, you are under pressure to perform better in every aspect, both abroad and at home. The world holds you at a higher standard. When the leadership standing is eroded, performance deteriorates. It is a vicious cycle. When your own expectations are low, and others also expect low from you, it is hard to improve performance. In a recent article in Washington Post, Ben Guarino writes that some top researchers from ivy league institutions are moving to China for lack of funding and visa restrictions. China is offering them all the resources needed and better monetary benefits. China is attracting talent from the rest of the world also that was our hallmark and lifeline.
America itself is a post-America country now. American ideals, values and patriotism take a back seat to personal and political party interests. Our political leaders are no different from third world country leaders who do not hesitate to undermine established national institutions of democracy for personal interests. Our business corporations are richer than country, but no longer feel obligation to the country that provided them resources to get rich.
Can it be reversed? Off course, but first we must restore our ideals, morals and stop the slide. It will get harder with every passing day, especially with incompetent and shortsighted leadership.
What’s the difference between A.I., machine learning, and robotics?
Artificial intelligence is everywhere. On your screens, in your pockets and one day may even be walking to a home near you. The headlines tend to group together this vast and diverse field into one subject. Robots emerging from the labs, algorithms playing ancient games and winning, AI and its promises are becoming a part of our everyday lives. While all of these instances have some relationship to AI, this is not a monolithic field, but one that has many separate and distinct disciplines.
A lot of the times we use the term Artificial intelligence as an all-encompassing umbrella term that covers everything. That’s not exactly the case. A.I., machine learning, deep learning, and robotics are all fascinating and separate topics. They all serve as an integral piece of the greater future of our tech. Many of these categories tend to overlap and complement one another.
The broader AI field of study is an extensive place where you have a lot to study and choose from. Understanding the difference between these four areas are foundational to getting a grasp and seeing the whole picture of the field.
Artificial intelligence
At the root of AI technology is the ability for machines to be able to perform tasks characteristic of human intelligence. These types of things include planning, pattern recognizing, understanding natural language, learning and solving problems.
There are two main types of AI: general and narrow. Our current technological capabilities fall under the latter. Narrow AI exhibits a sliver of some kind of intelligence – be it reminiscent of an animal or a human. This machine’s expertise is as the name would suggest, is narrow in scope. Usually, this type of AI will only be able to do one thing extremely well, like recognize images or search through databases at lightning speed.
General intelligence would be able to perform everything equally or better than humans can. This is the goal of many AI researchers, but it is a ways down the road.
Current AI technology is responsible for a lot of amazing things. These algorithms help Amazon give you personalized recommendations and makes sure your Google searches are relevant to what you’re looking for. Mostly any technologically literate person uses this type of tech every day.
One of the main differentiators between AI and conventional programming is the fact that non-AI programs are carried out by a set of defined instructions. AI on the other hand learns without being explicitly programmed.
Here is when the confusion starts to take place. Often times – but not all the time – AI utilizes machine learning, which is a subset of the AI field. If we go a little deeper, we get deep learning, which is a way to implement machine learning from scratch.
Furthermore, when we think about robotics we tend to think that robots and AI are interchangeable terms. AI algorithms are usually only one part of a larger technological matrix of hardware, electronics and non-AI code inside of a robot.
Robot… or artificially intelligent robot?
Robotics is a branch of technology that concerns itself strictly with robots. A robot is a programmable machine that carries out a set of tasks autonomously in some way. They’re not computers nor are they strictly artificially intelligent.
Many experts cannot agree on what exactly constitutes a robot. But for our purposes, we’ll consider that it has a physical presence, is programmable and has some level of autonomy. Here are a few different examples of some robots we have today:
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Roomba (Vacuum Cleaning Robot)
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Automobile Assembly Line Arm
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Surgery Robots
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Atlas (Humanoid Robot)
Some of these robots, for example, the assembly line robot or surgery bot are explicitly programmed to do a job. They do not learn. Therefore we could not consider them artificially intelligent.
These are robots that are controlled by inbuilt AI programs. This is a recent development, as most industrial robots were only programmed to carry out repetitive tasks without thinking. Self-learning bots with machine learning logic inside of them would be considered AI. They need this in order to perform increasingly more complex tasks.

What’s the difference between Artificial Intelligence and Machine Learning?
At its foundation, machine learning is a subset and way of achieving true AI. It was a term coined by Arthur Samuel in 1959, where he stated: “The ability to learn without being explicitly programmed.”
The idea is to get the algorithm to learn or be trained to do something without being specifically hardcoded with a set of particular directions. It is the machine learning that paves way for artificial intelligence.
Arthur Samuel wanted to create a computer program that could enable his computer to beat him in checkers. Rather than create a detailed and long-winding program that could do it, he thought of a different idea. The algorithm that he created gave his computer the ability to learn as it played thousands of games against itself. This has been the crux of the idea ever since. By the early 1960s, this program was able to beat champions in the game.
Over the years, machine learning developed into a number of different methods. Those being:
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Supervised
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Semi-supervised
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Unsupervised
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Reinforcement
In a supervised setting, a computer program would be given labeled data and then be asked to assign a sorting parameter to them. This could be pictures of different animals and then it would guess and learn accordingly while it trained. Semi-supervised would only label a few of the images. After that, the computer program would have to use its algorithm to figure out the unlabeled images by using its past data.
Unsupervised machine learning doesn’t involve any preliminary labeled data. It would be thrown into the database and have to sort for itself different classes of animals. It could do this based on grouping similar objects together due to how they look and then creating rules on the similarities it finds along the way.
Reinforcement learning is a little bit different than all of these subsets of machine learning. A great example would be the game of Chess. It knows a set amount of rules and bases its progress on the end result of either winning or losing.

Deep learning
For an even deeper subset of machine learning comes deep learning. It’s tasked with far greater types of problems than just rudimentary sorting. It works in the realm of vasts amounts of data and comes to its conclusion with absolutely no previous knowledge.
If it was to differentiate between two different animals, it would distinguish them in a different way compared to regular machine learning. First, all pictures of the animals would be scanned, pixel by pixel. Once that was completed, it would then parse through the different edges and shapes, ranking them in a differential order to determine the difference.
Deep learning tends to require much more hardware power. These machines that run this are usually housed away in large data centers. Programs that use deep learning are essentially starting from scratch.
Of all the AI disciplines, deep learning is the most promising for one day creating a generalized artificial intelligence. Some current applications that deep learning has spurned have been the many chatbots we see today. Alexa, Siri and Microsoft’s Cortana can thank their brains because of this nifty tech.
A new cohesive approach
There have been many seismic shifts in the tech world this past century. From the computing age to the internet and to the world of mobile devices. These different categories of tech will pave the way for a new future. Or as Google CEO Sundar Pichai put it quite nicely:
“Over time, the computer itself—whatever its form factor—will be an intelligent assistant helping you through your day. We will move from mobile first to an A.I. first world.”
Artificial intelligence in all of its many forms combined together will take us on our next technological leap forward. Full Artcle


