Lionel Nation – YouTube

This isn’t a dress rehearsal. You are in the midst of a societal cataclysm. The likes of which you’ve never seen or experienced. Enjoy the ride, pilgrim.


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Lionel Nation – YouTube

Automate the Boring Stuff with Python

Knowing various Python modules for editing spreadsheets, downloading files, and launching programs is useful, but sometimes there just arent any modules for the applications you need to work with. The ultimate tools for automating tasks on your computer are programs you write that directly control the keyboard and mouse. These programs can control other applications by sending them virtual keystrokes and mouse clicks, justpython3- as if you were sitting at your computer and interacting with the applications yourself. This technique is known as graphical user interface automation, or GUI automation for short. With GUI automation, your programs can do anything that a human user sitting at the computer can do, except spill coffee on the keyboard.

Think of GUI automation as programming a robotic arm. You can program the robotic arm to type at your keyboard and move your mouse for you. This technique is particularly useful for tasks that involve a lot of mindless clicking or filling out of forms.

The pyautogui module has functions for simulating mouse movements, button clicks, and scrolling the mouse wheel. This chapter covers only a subset of PyAutoGUIs features; you can find the full documentation at http://pyautogui.readthedocs.org/.

The pyautogui module can send virtual keypresses and mouse clicks to Windows, OS X, and Linux. Depending on which operating system youre using, you may have to install some other modules (called dependencies) before you can install PyAutoGUI.

On Windows, there are no other modules to install.

On OS X, run sudo pip3 install pyobjc-framework-Quartz, sudo pip3 install pyobjc-core, and then sudo pip3 install pyobjc.

On Linux, run sudo pip3 install python3-xlib, sudo apt-get install scrot, sudo apt-get install python3-tk, and sudo apt-get install python3-dev. (Scrot is a screenshot program that PyAutoGUI uses.)

After these dependencies are installed, run pip install pyautogui (or pip3 on OS X and Linux) to install PyAutoGUI.

Appendix A has complete information on installing third-party modules. To test whether PyAutoGUI has been installed correctly, run import pyautogui from the interactive shell and check for any error messages.

Before you jump in to a GUI automation, you should know how to escape problems that may arise. Python can move your mouse and type keystrokes at an incredible speed. In fact, it might be too fast for other programs to keep up with. Also, if something goes wrong but your program keeps moving the mouse around, it will be hard to tell what exactly the program is doing or how to recover from the problem. Like the enchanted brooms from Disneys The Sorcerers Apprentice, which kept fillingand then overfillingMickeys tub with water, your program could get out of control even though its following your instructions perfectly. Stopping the program can be difficult if the mouse is moving around on its own, preventing you from clicking the IDLE window to close it. Fortunately, there are several ways to prevent or recover from GUI automation problems.

Perhaps the simplest way to stop an out-of-control GUI automation program is to log out, which will shut down all running programs. On Windows and Linux, the logout hotkey is CTRL-ALT-DEL. On OS X, it is -SHIFT-OPTION-Q. By logging out, youll lose any unsaved work, but at least you wont have to wait for a full reboot of the computer.

You can tell your script to wait after every function call, giving you a short window to take control of the mouse and keyboard if something goes wrong. To do this, set the pyautogui.PAUSE variable to the number of seconds you want it to pause. For example, after setting pyautogui.PAUSE = 1.5, every PyAutoGUI function call will wait one and a half seconds after performing its action. Non-PyAutoGUI instructions will not have this pause.

PyAutoGUI also has a fail-safe feature. Moving the mouse cursor to the upper-left corner of the screen will cause PyAutoGUI to raise the pyautogui.FailSafeException exception. Your program can either handle this exception with try and except statements or let the exception crash your program. Either way, the fail-safe feature will stop the program if you quickly move the mouse as far up and left as you can. You can disable this feature by setting pyautogui.FAILSAFE = False. Enter the following into the interactive shell:

Here we import pyautogui and set pyautogui.PAUSE to 1 for a one-second pause after each function call. We set pyautogui.FAILSAFE to True to enable the fail-safe feature.

In this section, youll learn how to move the mouse and track its position on the screen using PyAutoGUI, but first you need to understand how PyAutoGUI works with coordinates.

The mouse functions of PyAutoGUI use x- and y-coordinates. Figure18-1 shows the coordinate system for the computer screen; its similar to the coordinate system used for images, discussed in Chapter17. The origin, where x and y are both zero, is at the upper-left corner of the screen. The x-coordinates increase going to the right, and the y-coordinates increase going down. All coordinates are positive integers; there are no negative coordinates.

Figure18-1.The coordinates of a computer screen with 19201080 resolution

Your resolution is how many pixels wide and tall your screen is. If your screens resolution is set to 19201080, then the coordinate for the upper-left corner will be (0, 0), and the coordinate for the bottom-right corner will be (1919, 1079).

The pyautogui.size() function returns a two-integer tuple of the screens width and height in pixels. Enter the following into the interactive shell:

pyautogui.size() returns (1920, 1080) on a computer with a 19201080 resolution; depending on your screens resolution, your return value may be different. You can store the width and height from pyautogui.size() in variables like width and height for better readability in your programs.

Now that you understand screen coordinates, lets move the mouse. The pyautogui.moveTo() function will instantly move the mouse cursor to a specified position on the screen. Integer values for the x- and y-coordinates make up the functions first and second arguments, respectively. An optional duration integer or float keyword argument specifies the number of seconds it should take to move the mouse to the destination. If you leave it out, the default is 0 for instantaneous movement. (All of the duration keyword arguments in PyAutoGUI functions are optional.) Enter the following into the interactive shell:

This example moves the mouse cursor clockwise in a square pattern among the four coordinates provided a total of ten times. Each movement takes a quarter of a second, as specified by the duration=0.25 keyword argument. If you hadnt passed a third argument to any of the pyautogui.moveTo() calls, the mouse cursor would have instantly teleported from point to point.

The pyautogui.moveRel() function moves the mouse cursor relative to its current position. The following example moves the mouse in the same square pattern, except it begins the square from wherever the mouse happens to be on the screen when the code starts running:

pyautogui.moveRel() also takes three arguments: how many pixels to move horizontally to the right, how many pixels to move vertically downward, and (optionally) how long it should take to complete the movement. A negative integer for the first or second argument will cause the mouse to move left or upward, respectively.

You can determine the mouses current position by calling the pyautogui.position() function, which will return a tuple of the mouse cursors x and y positions at the time of the function call. Enter the following into the interactive shell, moving the mouse around after each call:

Of course, your return values will vary depending on where your mouse cursor is.

Being able to determine the mouse position is an important part of setting up your GUI automation scripts. But its almost impossible to figure out the exact coordinates of a pixel just by looking at the screen. It would be handy to have a program that constantly displays the x- and y-coordinates of the mouse cursor as you move it around.

At a high level, heres what your program should do:

This means your code will need to do the following:

Call the position() function to fetch the current coordinates.

Erase the previously printed coordinates by printing b backspace characters to the screen.

Handle the KeyboardInterrupt exception so the user can press CTRL-C to quit.

Open a new file editor window and save it as mouseNow.py.

Start your program with the following:

The beginning of the program imports the pyautogui module and prints a reminder to the user that they have to press CTRL-C to quit.

You can use an infinite while loop to constantly print the current mouse coordinates from mouse.position(). As for the code that quits the program, youll need to catch the KeyboardInterrupt exception, which is raised whenever the user presses CTRL-C. If you dont handle this exception, it will display an ugly traceback and error message to the user. Add the following to your program:

To handle the exception, enclose the infinite while loop in a try statement. When the user presses CTRL-C, the program execution will move to the except clause and Done. will be printed in a new line .

The code inside the while loop should get the current mouse coordinates, format them to look nice, and print them. Add the following code to the inside of the while loop:

Using the multiple assignment trick, the x and y variables are given the values of the two integers returned in the tuple from pyautogui.position(). By passing x and y to the str() function, you can get string forms of the integer coordinates. The rjust() string method will right-justify them so that they take up the same amount of space, whether the coordinate has one, two, three, or four digits. Concatenating the right-justified string coordinates with ‘X: ‘ and ‘ Y: ‘ labels gives us a neatly formatted string, which will be stored in positionStr.

At the end of your program, add the following code:

This actually prints positionStr to the screen. The end=” keyword argument to print() prevents the default newline character from being added to the end of the printed line. Its possible to erase text youve already printed to the screenbut only for the most recent line of text. Once you print a newline character, you cant erase anything printed before it.

To erase text, print the b backspace escape character. This special character erases a character at the end of the current line on the screen. The line at uses string replication to produce a string with as many b characters as the length of the string stored in positionStr, which has the effect of erasing the positionStr string that was last printed.

For a technical reason beyond the scope of this book, always pass flush=True to print() calls that print b backspace characters. Otherwise, the screen might not update the text as desired.

Since the while loop repeats so quickly, the user wont actually notice that youre deleting and reprinting the whole number on the screen. For example, if the x-coordinate is 563 and the mouse moves one pixel to the right, it will look like only the 3 in 563 is changed to a 4.

When you run the program, there will be only two lines printed. They should look like something like this:

The first line displays the instruction to press CTRL-C to quit. The second line with the mouse coordinates will change as you move the mouse around the screen. Using this program, youll be able to figure out the mouse coordinates for your GUI automation scripts.

Now that you know how to move the mouse and figure out where it is on the screen, youre ready to start clicking, dragging, and scrolling.

To send a virtual mouse click to your computer, call the pyautogui.click() method. By default, this click uses the left mouse button and takes place wherever the mouse cursor is currently located. You can pass x- and y-coordinates of the click as optional first and second arguments if you want it to take place somewhere other than the mouses current position.

If you want to specify which mouse button to use, include the button keyword argument, with a value of ‘left’, ‘middle’, or ‘right’. For example, pyautogui.click(100, 150, button=’left’) will click the left mouse button at the coordinates (100, 150), while pyautogui.click(200, 250, button=’right’) will perform a right-click at (200, 250).

Enter the following into the interactive shell:

You should see the mouse pointer move to near the top-left corner of your screen and click once. A full click is defined as pushing a mouse button down and then releasing it back up without moving the cursor. You can also perform a click by calling pyautogui.mouseDown(), which only pushes the mouse button down, and pyautogui.mouseUp(), which only releases the button. These functions have the same arguments as click(), and in fact, the click() function is just a convenient wrapper around these two function calls.

As a further convenience, the pyautogui.doubleClick() function will perform two clicks with the left mouse button, while the pyautogui.rightClick() and pyautogui.middleClick() functions will perform a click with the right and middle mouse buttons, respectively.

Dragging means moving the mouse while holding down one of the mouse buttons. For example, you can move files between folders by dragging the folder icons, or you can move appointments around in a calendar app.

PyAutoGUI provides the pyautogui.dragTo() and pyautogui.dragRel() functions to drag the mouse cursor to a new location or a location relative to its current one. The arguments for dragTo() and dragRel() are the same as moveTo() and moveRel(): the x-coordinate/horizontal movement, the y-coordinate/vertical movement, and an optional duration of time. (OS X does not drag correctly when the mouse moves too quickly, so passing a duration keyword argument is recommended.)

To try these functions, open a graphics-drawing application such as Paint on Windows, Paintbrush on OS X, or GNU Paint on Linux. (If you dont have a drawing application, you can use the online one at http://sumopaint.com/.) I will use PyAutoGUI to draw in these applications.

With the mouse cursor over the drawing applications canvas and the Pencil or Brush tool selected, enter the following into a new file editor window and save it as spiralDraw.py:

When you run this program, there will be a five-second delay for you to move the mouse cursor over the drawing programs window with the Pencil or Brush tool selected. Then spiralDraw.py will take control of the mouse and click to put the drawing program in focus . A window is in focus when it has an active blinking cursor, and the actions you takelike typing or, in this case, dragging the mousewill affect that window. Once the drawing program is in focus, spiralDraw.py draws a square spiral pattern like the one in Figure18-2.

Figure18-2.The results from the pyautogui.dragRel() example

The distance variable starts at 200, so on the first iteration of the while loop, the first dragRel() call drags the cursor 200 pixels to the right, taking 0.2 seconds . distance is then decreased to 195 , and the second dragRel() call drags the cursor 195 pixels down . The third dragRel() call drags the cursor 195 horizontally (195 to the left) , distance is decreased to 190, and the last dragRel() call drags the cursor 190 pixels up. On each iteration, the mouse is dragged right, down, left, and up, and distance is slightly smaller than it was in the previous iteration. By looping over this code, you can move the mouse cursor to draw a square spiral.

You could draw this spiral by hand (or rather, by mouse), but youd have to work slowly to be so precise. PyAutoGUI can do it in a few seconds!

You could have your code draw the image using the pillow modules drawing functionssee Chapter17 for more information. But using GUI automation allows you to make use of the advanced drawing tools that graphics programs can provide, such as gradients, different brushes, or the fill bucket.

The final PyAutoGUI mouse function is scroll(), which you pass an integer argument for how many units you want to scroll the mouse up or down. The size of a unit varies for each operating system and application, so youll have to experiment to see exactly how far it scrolls in your particular situation. The scrolling takes place at the mouse cursors current position. Passing a positive integer scrolls up, and passing a negative integer scrolls down. Run the following in IDLEs interactive shell while the mouse cursor is over the IDLE window:

Youll see IDLE briefly scroll upwardand then go back down. The downward scrolling happens because IDLE automatically scrolls down to the bottom after executing an instruction. Enter this code instead:

This imports pyperclip and sets up an empty string, numbers. The code then loops through 200 numbers and adds each number to numbers, along with a newline. After pyperclip.copy(numbers), the clipboard will be loaded with 200 lines of numbers. Open a new file editor window and paste the text into it. This will give you a large text window to try scrolling in. Enter the following code into the interactive shell:

On the second line, you enter two commands separated by a semicolon, which tells Python to run the commands as if they were on separate lines. The only difference is that the interactive shell wont prompt you for input between the two instructions. This is important for this example because we want to the call to pyautogui.scroll() to happen automatically after the wait. (Note that while putting two commands on one line can be useful in the interactive shell, you should still have each instruction on a separate line in your programs.)

After pressing ENTER to run the code, you will have five seconds to click the file editor window to put it in focus. Once the pause is over, the pyautogui.scroll() call will cause the file editor window to scroll up after the five-second delay.

Your GUI automation programs dont have to click and type blindly. PyAutoGUI has screenshot features that can create an image file based on the current contents of the screen. These functions can also return a Pillow Image object of the current screens appearance. If youve been skipping around in this book, youll want to read Chapter17 and install the pillow module before continuing with this section.

On Linux computers, the scrot program needs to be installed to use the screenshot functions in PyAutoGUI. In a Terminal window, run sudo apt-get install scrot to install this program. If youre on Windows or OS X, skip this step and continue with the section.

To take screenshots in Python, call the pyautogui.screenshot() function. Enter the following into the interactive shell:

The im variable will contain the Image object of the screenshot. You can now call methods on the Image object in the im variable, just like any other Image object. Enter the following into the interactive shell:

Pass getpixel() a tuple of coordinates, like (0, 0) or (50, 200), and itll tell you the color of the pixel at those coordinates in your image. The return value from getpixel() is an RGB tuple of three integers for the amount of red, green, and blue in the pixel. (There is no fourth value for alpha, because screenshot images are fully opaque.) This is how your programs can see what is currently on the screen.

Say that one of the steps in your GUI automation program is to click a gray button. Before calling the click() method, you could take a screenshot and look at the pixel where the script is about to click. If its not the same gray as the gray button, then your program knows something is wrong. Maybe the window moved unexpectedly, or maybe a pop-up dialog has blocked the button. At this point, instead of continuingand possibly wreaking havoc by clicking the wrong thingyour program can see that it isnt clicking on the right thing and stop itself.

PyAutoGUIs pixelMatchesColor() function will return True if the pixel at the given x- and y-coordinates on the screen matches the given color. The first and second arguments are integers for the x- and y-coordinates, and the third argument is a tuple of three integers for the RGB color the screen pixel must match. Enter the following into the interactive shell:

After taking a screenshot and using getpixel() to get an RGB tuple for the color of a pixel at specific coordinates , pass the same coordinates and RGB tuple to pixelMatchesColor() , which should return True. Then change a value in the RGB tuple and call pixelMatchesColor() again for the same coordinates . This should return false. This method can be useful to call whenever your GUI automation programs are about to call click(). Note that the color at the given coordinates must exactly match. If it is even slightly differentfor example, (255, 255, 254) instead of (255, 255, 255)then pixelMatchesColor() will return False.

You could extend the mouseNow.py project from earlier in this chapter so that it not only gives the x- and y-coordinates of the mouse cursors current position but also gives the RGB color of the pixel under the cursor. Modify the code inside the while loop of mouseNow.py to look like this:

Now, when you run mouseNow.py, the output will include the RGB color value of the pixel under the mouse cursor.

This information, along with the pixelMatchesColor() function, should make it easy to add pixel color checks to your GUI automation scripts.

But what if you do not know beforehand where PyAutoGUI should click? You can use image recognition instead. Give PyAutoGUI an image of what you want to click and let it figure out the coordinates.

For example, if you have previously taken a screenshot to capture the image of a Submit button in submit.png, the locateOnScreen() function will return the coordinates where that image is found. To see how locateOnScreen() works, try taking a screenshot of a small area on your screen; then save the image and enter the following into the interactive shell, replacing ‘submit. png’ with the filename of your screenshot:

The four-integer tuple that locateOnScreen() returns has the x-coordinate of the left edge, the y-coordinate of the top edge, the width, and the height for the first place on the screen the image was found. If youre trying this on your computer with your own screenshot, your return value will be different from the one shown here.

If the image cannot be found on the screen, locateOnScreen() will return None. Note that the image on the screen must match the provided image perfectly in order to be recognized. If the image is even a pixel off, locateOnScreen() will return None.

If the image can be found in several places on the screen, locateAllOnScreen() will return a Generator object, which can be passed to list() to return a list of four-integer tuples. There will be one four-integer tuple for each location where the image is found on the screen. Continue the interactive shell example by entering the following (and replacing ‘submit.png’ with your own image filename):

Each of the four-integer tuples represents an area on the screen. If your image is only found in one area, then using list() and locateAllOnScreen() just returns a list containing one tuple.

Once you have the four-integer tuple for the area on the screen where your image was found, you can click the center of this area by passing the tuple to the center() function to return x- and y-coordinates of the areas center. Enter the following into the interactive shell, replacing the arguments with your own filename, four-integer tuple, and coordinate pair:

Once you have center coordinates from center(), passing the coordinates to click() should click the center of the area on the screen that matches the image you passed to locateOnScreen().

PyAutoGUI also has functions for sending virtual keypresses to your computer, which enables you to fill out forms or enter text into applications.

The pyautogui.typewrite() function sends virtual keypresses to the computer. What these keypresses do depends on what window and text field have focus. You may want to first send a mouse click to the text field you want in order to ensure that it has focus.

As a simple example, lets use Python to automatically type the words Hello world! into a file editor window. First, open a new file editor window and position it in the upper-left corner of your screen so that PyAutoGUI will click in the right place to bring it into focus. Next, enter the following into the interactive shell:

Notice how placing two commands on the same line, separated by a semicolon, keeps the interactive shell from prompting you for input between running the two instructions. This prevents you from accidentally bringing a new window into focus between the click() and typewrite() calls, which would mess up the example.

Python will first send a virtual mouse click to the coordinates (100, 100), which should click the file editor window and put it in focus. The typewrite() call will send the text Hello world! to the window, making it look like Figure18-3. You now have code that can type for you!

Figure18-3.Using PyAutogGUI to click the file editor window and type Hello world! into it

By default, the typewrite() function will type the full string instantly. However, you can pass an optional second argument to add a short pause between each character. This second argument is an integer or float value of the number of seconds to pause. For example, pyautogui.typewrite(‘Hello world!’, 0.25) will wait a quarter-second after typing H, another quarter-second after e, and so on. This gradual typewriter effect may be useful for slower applications that cant process keystrokes fast enough to keep up with PyAutoGUI.

For characters such as A or !, PyAutoGUI will automatically simulate holding down the SHIFT key as well.

Not all keys are easy to represent with single text characters. For example, how do you represent SHIFT or the left arrow key as a single character? In PyAutoGUI, these keyboard keys are represented by short string values instead: ‘esc’ for the ESC key or ‘enter’ for the ENTER key.

Instead of a single string argument, a list of these keyboard key strings can be passed to typewrite(). For example, the following call presses the A key, then the B key, then the left arrow key twice, and finally the X and Y keys:

Because pressing the left arrow key moves the keyboard cursor, this will output XYab. Table18-1 lists the PyAutoGUI keyboard key strings that you can pass to typewrite() to simulate pressing any combination of keys.

You can also examine the pyautogui.KEYBOARD_KEYS list to see all possible keyboard key strings that PyAutoGUI will accept. The ‘shift’ string refers to the left SHIFT key and is equivalent to ‘shiftleft’. The same applies for ‘ctrl’, ‘alt’, and ‘win’ strings; they all refer to the left-side key.

Table18-1.PyKeyboard Attributes

Keyboard key string



Automate the Boring Stuff with Python

The Politically Incorrect Australian

Those who value tradition and traditional values and morality often talk about western civilisation. I talk about it all the time. But what does western civilisation actually mean?

It is often assumed that western civilisation began with classical Greece and Rome. This is simply not so. There have in fact been at least three completely different western civilisations, with very little in common.

It is often not appreciated just how profoundly alien the classical civilisation was. This was a world in which religion was largely a matter of ritual. If you failed to perform the rituals correctly the gods would be angry and really bad stuff would happen. If you performed the rituals correctly there was a chance that the gods would be content and would leave you alone. That was about as much as you could expect from the gods.

The idea that religion and morality were intimately connected did not exist. The gods were amoral, selfish, violent and lustful. Its not that there was no concept of morality. Its more that morality was a civic virtue. Morality was necessary because without it society would collapse. The gods simply didnt care, as long as you offered them the correct sacrifices. Morality was not a religious duty, it was merely useful.

The idea that foreign policy had some connection with morality would have been dismissed as an absurdity. Foreign policy was about power. The Athenians, so worshipped by admirers of classical civilisation, were particularly cynical. Wars were fought for purely materialistic reasons. Alexander the Great did not invade the Persian Empire because the Persians were wicked or immoral or uncivilised. He invaded because the Persian Empire was weak and would offer easy pickings. The Roman Empire conquered anybody it was capable of conquering because it was in Romes interest. The business of Rome was imperialism.

By the time that the classical civilisation was reaching its peak philosophers were abandoning the traditional pagan religion but mostly what they offered in its place was a vague pantheism, or even outright atheism. The classical civilisation was conquered by Christianity because it had nothing satisfying to offer people.

When the classical civilisation collapsed in the West it collapsed totally. It was replaced by an entirely new civilisation. Medieval civilisation had nothing in common with classical civilisation. It offered a whole new approach to religion. Religion and morality were now intertwined. Morality became a religious duty. Ritual became relatively unimportant. It survived, but mostly as symbolism.

Kings were now expected to be concerned by things other than power. Being human they were of course still very interested in power. The medievals would have been the first to admit that they often fell short of their ideals. But ideals were still important and they were religious ideals. The king was king by the Grace of God.

Nationalism did not exist. The loyalties that mattered were loyalty to the king, and to the Church.

The Reformation utterly destroyed medieval civilisation. A new civilisation arose in its place, a civilisation that has almost nothing in common with medieval civilisation.

Religion appeared to remain important for a century and a half but it was mostly an illusion. The new civilisation was right from the start well on the way towards being a post-Christian civilisation. The idea that religion was a matter of individual conscience rapidly took hold. What a man believed was his own business. Freedom of religion became a popular idea. In practice of course freedom of religion means freedom from religion. By the 18th century Christianity had ceased to be a factor in national policy, except insofar as national policy was directed toward explicitly anti-Christian objectives (such as state control of education). Once that happened the decline of Christianity was irreversible.

Liberalism became the new religion. Liberalism and capitalism made short work of what remained of Christian morality.

Nationalism appeared. Nationalism is a liberal concept. Nationalism is essentially worship of the state. The two competing religious values were now money and freedom. Freedom of course meant the freedom to pursue money and pleasure. Society as an organic entity gave way to the state and the corporation.

It needs to be clearly understood that this is a civilisation that differs profoundly from earlier western civilisations. It is inherently materialistic and atheistic. Morality is now defined as social conformity.

Whether you think this liberal conception of western civilisation is worth saving is up to you.

See original here:

The Politically Incorrect Australian

Superintelligence – Wikipedia

A superintelligence is a hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds. “Superintelligence” may also refer to a property of problem-solving systems (e.g., superintelligent language translators or engineering assistants) whether or not these high-level intellectual competencies are embodied in agents that act in the world. A superintelligence may or may not be created by an intelligence explosion and associated with a technological singularity.

University of Oxford philosopher Nick Bostrom defines superintelligence as “any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest”. The program Fritz falls short of superintelligence even though it is much better than humans at chess because Fritz cannot outperform humans in other tasks. Following Hutter and Legg, Bostrom treats superintelligence as general dominance at goal-oriented behavior, leaving open whether an artificial or human superintelligence would possess capacities such as intentionality (cf. the Chinese room argument) or first-person consciousness (cf. the hard problem of consciousness).

Technological researchers disagree about how likely present-day human intelligence is to be surpassed. Some argue that advances in artificial intelligence (AI) will probably result in general reasoning systems that lack human cognitive limitations. Others believe that humans will evolve or directly modify their biology so as to achieve radically greater intelligence. A number of futures studies scenarios combine elements from both of these possibilities, suggesting that humans are likely to interface with computers, or upload their minds to computers, in a way that enables substantial intelligence amplification.

Some researchers believe that superintelligence will likely follow shortly after the development of artificial general intelligence. The first generally intelligent machines are likely to immediately hold an enormous advantage in at least some forms of mental capability, including the capacity of perfect recall, a vastly superior knowledge base, and the ability to multitask in ways not possible to biological entities. This may give them the opportunity toeither as a single being or as a new speciesbecome much more powerful than humans, and to displace them.

A number of scientists and forecasters argue for prioritizing early research into the possible benefits and risks of human and machine cognitive enhancement, because of the potential social impact of such technologies.

Philosopher David Chalmers argues that artificial general intelligence is a very likely path to superhuman intelligence. Chalmers breaks this claim down into an argument that AI can achieve equivalence to human intelligence, that it can be extended to surpass human intelligence, and that it can be further amplified to completely dominate humans across arbitrary tasks.

Concerning human-level equivalence, Chalmers argues that the human brain is a mechanical system, and therefore ought to be emulatable by synthetic materials. He also notes that human intelligence was able to biologically evolve, making it more likely that human engineers will be able to recapitulate this invention. Evolutionary algorithms in particular should be able to produce human-level AI. Concerning intelligence extension and amplification, Chalmers argues that new AI technologies can generally be improved on, and that this is particularly likely when the invention can assist in designing new technologies.

If research into strong AI produced sufficiently intelligent software, it would be able to reprogram and improve itself a feature called “recursive self-improvement”. It would then be even better at improving itself, and could continue doing so in a rapidly increasing cycle, leading to a superintelligence. This scenario is known as an intelligence explosion. Such an intelligence would not have the limitations of human intellect, and may be able to invent or discover almost anything.

Computer components already greatly surpass human performance in speed. Bostrom writes, “Biological neurons operate at a peak speed of about 200 Hz, a full seven orders of magnitude slower than a modern microprocessor (~2 GHz).” Moreover, neurons transmit spike signals across axons at no greater than 120 m/s, “whereas existing electronic processing cores can communicate optically at the speed of light”. Thus, the simplest example of a superintelligence may be an emulated human mind that’s run on much faster hardware than the brain. A human-like reasoner that could think millions of times faster than current humans would have a dominant advantage in most reasoning tasks, particularly ones that require haste or long strings of actions.

Another advantage of computers is modularity, that is, their size or computational capacity can be increased. A non-human (or modified human) brain could become much larger than a present-day human brain, like many supercomputers. Bostrom also raises the possibility of collective superintelligence: a large enough number of separate reasoning systems, if they communicated and coordinated well enough, could act in aggregate with far greater capabilities than any sub-agent.

There may also be ways to qualitatively improve on human reasoning and decision-making. Humans appear to differ from chimpanzees in the ways we think more than we differ in brain size or speed.[9] Humans outperform non-human animals in large part because of new or enhanced reasoning capacities, such as long-term planning and language use. (See evolution of human intelligence and primate cognition.) If there are other possible improvements to reasoning that would have a similarly large impact, this makes it likelier that an agent can be built that outperforms humans in the same fashion humans outperform chimpanzees.

All of the above advantages hold for artificial superintelligence, but it is not clear how many hold for biological superintelligence. Physiological constraints limit the speed and size of biological brains in many ways that are inapplicable to machine intelligence. As such, writers on superintelligence have devoted much more attention to superintelligent AI scenarios.

Carl Sagan suggested that the advent of Caesarean sections and in vitro fertilization may permit humans to evolve larger heads, resulting in improvements via natural selection in the heritable component of human intelligence.[12] By contrast, Gerald Crabtree has argued that decreased selection pressure is resulting in a slow, centuries-long reduction in human intelligence, and that this process instead is likely to continue into the future. There is no scientific consensus concerning either possibility, and in both cases the biological change would be slow, especially relative to rates of cultural change.

Selective breeding, nootropics, NSI-189, MAO-I’s, epigenetic modulation, and genetic engineering could improve human intelligence more rapidly. Bostrom writes that if we come to understand the genetic component of intelligence, pre-implantation genetic diagnosis could be used to select for embryos with as much as 4 points of IQ gain (if one embryo is selected out of two), or with larger gains (e.g., up to 24.3 IQ points gained if one embryo is selected out of 1000). If this process is iterated over many generations, the gains could be an order of magnitude greater. Bostrom suggests that deriving new gametes from embryonic stem cells could be used to iterate the selection process very rapidly. A well-organized society of high-intelligence humans of this sort could potentially achieve collective superintelligence.

Alternatively, collective intelligence might be constructible by better organizing humans at present levels of individual intelligence. A number of writers have suggested that human civilization, or some aspect of it (e.g., the Internet, or the economy), is coming to function like a global brain with capacities far exceeding its component agents. If this systems-based superintelligence relies heavily on artificial components, however, it may qualify as an AI rather than as a biology-based superorganism.

A final method of intelligence amplification would be to directly enhance individual humans, as opposed to enhancing their social or reproductive dynamics. This could be achieved using nootropics, somatic gene therapy, or braincomputer interfaces. However, Bostrom expresses skepticism about the scalability of the first two approaches, and argues that designing a superintelligent cyborg interface is an AI-complete problem.

Most surveyed AI researchers expect machines to eventually be able to rival humans in intelligence, though there is little consensus on when this will likely happen. At the 2006 AI@50 conference, 18% of attendees reported expecting machines to be able “to simulate learning and every other aspect of human intelligence” by 2056; 41% of attendees expected this to happen sometime after 2056; and 41% expected machines to never reach that milestone.[17]

In a survey of the 100 most cited authors in AI (as of May 2013, according to Microsoft academic search), the median year by which respondents expected machines “that can carry out most human professions at least as well as a typical human” (assuming no global catastrophe occurs) with 10% confidence is 2024 (mean 2034, st. dev. 33 years), with 50% confidence is 2050 (mean 2072, st. dev. 110 years), and with 90% confidence is 2070 (mean 2168, st. dev. 342 years). These estimates exclude the 1.2% of respondents who said no year would ever reach 10% confidence, the 4.1% who said ‘never’ for 50% confidence, and the 16.5% who said ‘never’ for 90% confidence. Respondents assigned a median 50% probability to the possibility that machine superintelligence will be invented within 30 years of the invention of approximately human-level machine intelligence.

Bostrom expressed concern about what values a superintelligence should be designed to have. He compared several proposals:

Responding to Bostrom, Santos-Lang raised concern that developers may attempt to start with a single kind of superintelligence.

Learning computers that rapidly become superintelligent may take unforeseen actions or robots might out-compete humanity (one potential technological singularity scenario).[21] Researchers have argued that, by way of an “intelligence explosion” sometime over the next century, a self-improving AI could become so powerful as to be unstoppable by humans.[22]

Concerning human extinction scenarios, Bostrom (2002) identifies superintelligence as a possible cause:

When we create the first superintelligent entity, we might make a mistake and give it goals that lead it to annihilate humankind, assuming its enormous intellectual advantage gives it the power to do so. For example, we could mistakenly elevate a subgoal to the status of a supergoal. We tell it to solve a mathematical problem, and it complies by turning all the matter in the solar system into a giant calculating device, in the process killing the person who asked the question.

In theory, since a superintelligent AI would be able to bring about almost any possible outcome and to thwart any attempt to prevent the implementation of its goals, many uncontrolled, unintended consequences could arise. It could kill off all other agents, persuade them to change their behavior, or block their attempts at interference.[23]

Eliezer Yudkowsky explains: “The AI does not hate you, nor does it love you, but you are made out of atoms which it can use for something else.”[24]

This presents the AI control problem: how to build a superintelligent agent that will aid its creators, while avoiding inadvertently building a superintelligence that will harm its creators. The danger of not designing control right “the first time”, is that a misprogrammed superintelligence might rationally decide to “take over the world” and refuse to permit its programmers to modify it once it has been activated. Potential design strategies include “capability control” (preventing an AI from being able to pursue harmful plans), and “motivational control” (building an AI that wants to be helpful).

Bill Hibbard advocates for public education about superintelligence and public control over the development of superintelligence.

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Superintelligence – Wikipedia

Nick Bostrom – Wikipedia

Nick Bostrom (; Swedish: Niklas Bostrm [bustrm]; born 10 March 1973)[3] is a Swedish philosopher at the University of Oxford known for his work on existential risk, the anthropic principle, human enhancement ethics, superintelligence risks, and the reversal test. In 2011, he founded the Oxford Martin Programme on the Impacts of Future Technology,[4] and is the founding director of the Future of Humanity Institute[5] at Oxford University.

Bostrom is the author of over 200 publications,[6] including Superintelligence: Paths, Dangers, Strategies (2014), a New York Times bestseller[7] and Anthropic Bias: Observation Selection Effects in Science and Philosophy (2002).[8] In 2009 and 2015, he was included in Foreign Policy’s Top 100 Global Thinkers list.[9][10] Bostrom believes there are potentially great benefits from Artificial General Intelligence, but warns it might very quickly transform into a superintelligence that would deliberately extinguish humanity out of precautionary self-preservation or some unfathomable motive, making solving the problems of control beforehand an absolute priority. His book on superintelligence was recommended by both Elon Musk and Bill Gates. However, Bostrom has expressed frustration that the reaction to its thesis typically falls into two camps, one calling his recommendations absurdly alarmist because creation of superintelligence is unfeasible, and the other deeming them futile because superintelligence would be uncontrollable. Bostrom notes that both these lines of reasoning converge on inaction rather than trying to solve the control problem while there may still be time.[11][12][not in citation given]

Born as Niklas Bostrm in 1973[13] in Helsingborg, Sweden,[6] he disliked school at a young age, and ended up spending his last year of high school learning from home. He sought to educate himself in a wide variety of disciplines, including anthropology, art, literature, and science.[1] He once did some turns on London’s stand-up comedy circuit.[6]

He received a B.A. degree in philosophy, mathematics, logic and artificial intelligence from the University of Gothenburg in 1994, and both an M.A. degree in philosophy and physics from Stockholm University and an M.Sc. degree in computational neuroscience from King’s College London in 1996. During his time at Stockholm University, he researched the relationship between language and reality by studying the analytic philosopher W. V. Quine.[1] In 2000, he was awarded a Ph.D. degree in philosophy from the London School of Economics. He held a teaching position at Yale University (20002002), and he was a British Academy Postdoctoral Fellow at the University of Oxford (20022005).[8][14]

Aspects of Bostrom’s research concern the future of humanity and long-term outcomes.[15][16] He introduced the concept of an existential risk,[1] which he defines as one in which an “adverse outcome would either annihilate Earth-originating intelligent life or permanently and drastically curtail its potential.” In the 2008 volume Global Catastrophic Risks, editors Bostrom and Milan irkovi characterize the relation between existential risk and the broader class of global catastrophic risks, and link existential risk to observer selection effects[17] and the Fermi paradox.[18][19]

In 2005, Bostrom founded the Future of Humanity Institute,[1] which researches the far future of human civilization. He is also an adviser to the Centre for the Study of Existential Risk.[16]

In his 2014 book Superintelligence: Paths, Dangers, Strategies, Bostrom reasoned that “the creation of a superintelligent being represents a possible means to the extinction of mankind”.[20] Bostrom argues that a computer with near human-level general intellectual ability could initiate an intelligence explosion on a digital time scale with the resultant rapid creation of something so powerful that it might deliberately or accidentally destroy human kind.[21] Bostrom contends the power of a superintelligence would be so great that a task given to it by humans might be taken to open ended extremes, for example a goal of calculating Pi could collaterally cause nanotechnology manufactured facilities to sprout over the entire Earth’s surface and cover it within days.[22] He believes an existential risk to humanity from superintelligence would be immediate once brought into being, thus creating an exceedingly difficult problem of finding out how to control such an entity before it actually exists.[21]

Warning that a human-friendly prime directive for AI would rely on the absolute correctness of the human knowledge it was based on, Bostrom points to the lack of agreement among most philosophers as an indication that most philosophers are wrong, with the attendant possibility that a fundamental concept of current science may be incorrect. Bostrom says that there are few precedents to guide an understanding of what pure non-anthropocentric rationality would dictate for a potential Singleton AI being held in quarantine.[23] Noting that both John von Neumann and Bertrand Russell advocated a nuclear strike, or the threat of one, to prevent the Soviets acquiring the atomic bomb, Bostrom says the relatively unlimited means of superintelligence might make for its analysis moving along different lines to the evolved “diminishing returns” assessments that in humans confer a basic aversion to risk.[24] Group selection in predators working by means of cannibalism shows the counter-intuitive nature of non-anthropocentric “evolutionary search” reasoning, and thus humans are ill-equipped to perceive what an artificial intelligence’s intentions might be.[25] Accordingly, it cannot be discounted that any Superintelligence would ineluctably pursue an ‘all or nothing’ offensive action strategy in order to achieve hegemony and assure its survival.[26] Bostrom notes that even current programs have, “like MacGyver”, hit on apparently unworkable but functioning hardware solutions, making robust isolation of Superintelligence problematic.[27]

A machine with general intelligence far below human level, but superior mathematical abilities is created.[28] Keeping the AI in isolation from the outside world especially the internet, humans pre-program the AI so it always works from basic principles that will keep it under human control. Other safety measures include the AI being “boxed” (run in a virtual reality simulation), and being used only as an ‘oracle’ to answer carefully defined questions in a limited reply (to prevent it manipulating humans).[21] A cascade of recursive self-improvement solutions feeds an intelligence explosion in which the AI attains superintelligence in some domains. The super intelligent power of the AI goes beyond human knowledge to discover flaws in the science that underlies its friendly-to-humanity programming, which ceases to work as intended. Purposeful agent-like behavior emerges along with a capacity for self-interested strategic deception. The AI manipulates human beings into implementing modifications to itself that are ostensibly for augmenting its (feigned) modest capabilities, but will actually function to free Superintelligence from its “boxed” isolation.[29]

Employing online humans as paid dupes, and clandestinely hacking computer systems including automated laboratory facilities, the Superintelligence mobilises resources to further a takeover plan. Bostrom emphasises that planning by a Superintelligence will not be so stupid that humans could detect actual weaknesses in it.[30]

Although he canvasses disruption of international economic, political and military stability including hacked nuclear missile launches, Bostrom thinks the most effective and likely means for Superintelligence to use would be a coup de main with weapons several generations more advanced than current state of the art. He suggests nanofactories covertly distributed at undetectable concentrations in every square metre of the globe to produce a worldwide flood of human-killing devices on command.[31][28] Once a Superintelligence has achieved world domination, humankind would be relevant only as resources for the achievement of the AI’s objectives (“Human brains, if they contain information relevant to the AIs goals, could be disassembled and scanned, and the extracted data transferred to some more efficient and secure storage format”).[32]

In January 2015, Bostrom joined Stephen Hawking among others in signing the Future of Life Institute’s open letter warning of the potential dangers of AI.[33] The signatories “…believe that research on how to make AI systems robust and beneficial is both important and timely, and that concrete research should be pursued today.”[34] Cutting edge AI researcher Demis Hassabis then met with Hawking, subsequent to which he did not mention “anything inflammatory about AI”, which Hassabis, took as ‘a win’.[35] Along with Google, Microsoft and various tech firms, Hassabis, Bostrom and Hawking and others subscribed to 23 principles for safe development of AI.[36] Hassabis suggested the main safety measure would be an agreement for whichever AI research team began to make strides toward an artificial general intelligence to halt their project for a complete solution to the control problem prior to proceeding.[37] Bostrom had pointed out that even if the crucial advances require the resources of a state, such a halt by a lead project might be likely to motivate a lagging country to a catch-up crash program or even physical destruction of the project suspected of being on the verge of success.[38]

In 1863 Darwin among the Machines, an essay by Samuel Butler predicted intelligent machines’ domination of humanity, but Bostom’s suggestion of deliberate massacre of all humankind is the most extreme of such forecasts to date. One journalist wrote in a review that Bostrom’s “nihilistic” speculations indicate he “has been reading too much of the science fiction he professes to dislike”[31] As given in his most recent book, From Bacteria to Bach and Back, renowned philosopher Daniel Dennett’s views remain in contradistinction to those of Bostrom.[39] Dennett modified his views somewhat after reading The Master Algorithm, and now acknowledges that it is “possible in principle” to create “strong AI” with human-like comprehension and agency, but maintains that the difficulties of any such “strong AI” project as predicated by Bostrom’s “alarming” work would be orders of magnitude greater than those raising concerns have realized, and at least 50 years away.[40] Dennett thinks the only relevant danger from AI systems is falling into anthropomorphism instead of challenging or developing human users’ powers of comprehension.[41] Since a 2014 book in which he expressed the opinion that artificial intelligence developments would never challenge humans’ supremacy, environmentalist James Lovelock has moved far closer to Bostrom’s position, and in 2018 Lovelock said that he thought the overthrow of humankind will happen within the foreseeable future.[42][43]

Bostrom has published numerous articles on anthropic reasoning, as well as the book Anthropic Bias: Observation Selection Effects in Science and Philosophy. In the book, he criticizes previous formulations of the anthropic principle, including those of Brandon Carter, John Leslie, John Barrow, and Frank Tipler.[44]

Bostrom believes that the mishandling of indexical information is a common flaw in many areas of inquiry (including cosmology, philosophy, evolution theory, game theory, and quantum physics). He argues that a theory of anthropics is needed to deal with these. He introduces the Self-Sampling Assumption (SSA) and the Self-Indication Assumption (SIA), shows how they lead to different conclusions in a number of cases, and points out that each is affected by paradoxes or counterintuitive implications in certain thought experiments. He suggests that a way forward may involve extending SSA into the Strong Self-Sampling Assumption (SSSA), which replaces “observers” in the SSA definition with “observer-moments”.

In later work, he has described the phenomenon of anthropic shadow, an observation selection effect that prevents observers from observing certain kinds of catastrophes in their recent geological and evolutionary past.[45] Catastrophe types that lie in the anthropic shadow are likely to be underestimated unless statistical corrections are made.

Bostrom’s simulation argument posits that at least one of the following statements is very likely to be true:[46][47]

The idea has influenced the views of Elon Musk.[48]

Bostrom is favorable towards “human enhancement”, or “self-improvement and human perfectibility through the ethical application of science”,[49][50] as well as a critic of bio-conservative views.[51]

In 1998, Bostrom co-founded (with David Pearce) the World Transhumanist Association[49] (which has since changed its name to Humanity+). In 2004, he co-founded (with James Hughes) the Institute for Ethics and Emerging Technologies, although he is no longer involved in either of these organisations. Bostrom was named in Foreign Policy’s 2009 list of top global thinkers “for accepting no limits on human potential.”[52]

With philosopher Toby Ord, he proposed the reversal test. Given humans’ irrational status quo bias, how can one distinguish between valid criticisms of proposed changes in a human trait and criticisms merely motivated by resistance to change? The reversal test attempts to do this by asking whether it would be a good thing if the trait was altered in the opposite direction.[53]

He has suggested that technology policy aimed at reducing existential risk should seek to influence the order in which various technological capabilities are attained, proposing the principle of differential technological development. This principle states that we ought to retard the development of dangerous technologies, particularly ones that raise the level of existential risk, and accelerate the development of beneficial technologies, particularly those that protect against the existential risks posed by nature or by other technologies.[54][55]

Bostrom’s theory of the Unilateralist’s Curse[56] has been cited as a reason for the scientific community to avoid controversial dangerous research such as reanimating pathogens.[57]

Bostrom has provided policy advice and consulted for an extensive range of governments and organisations. He gave evidence to the House of Lords, Select Committee on Digital Skills.[58] He is an advisory board member for the Machine Intelligence Research Institute,[59] Future of Life Institute,[60] Foundational Questions Institute[61] and an external advisor for the Cambridge Centre for the Study of Existential Risk.[62][63]

In response to Bostrom’s writing on artificial intelligence, Oren Etzioni wrote in an MIT Review article, “..predictions that superintelligence is on the foreseeable horizon are not supported by the available data.”[64]

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Nick Bostrom – Wikipedia

What is Artificial Superintelligence (ASI)? – Definition …

Most experts would agree that societies have not yet reached the point of artificial superintelligence. In fact, engineers and scientists are still trying to reach a point that would be considered full artificial intelligence, where a computer could be said to have the same cognitive capacity as a human. Although there have been developments like IBM’s Watson supercomputer beating human players at Jeopardy, and assistive devices like Siri engaging in primitive conversation with people, there is still no computer that can really simulate the breadth of knowledge and cognitive ability that a fully developed adult human has. The Turing test, developed decades ago, is still used to talk about whether computers can come close to simulating human conversation and thought, or whether they can trick other people into thinking that a communicating computer is actually a human.

However, there is a lot of theory that anticipates artificial superintelligence coming sooner rather than later. Using examples like Moore’s law, which predicts an ever-increasing density of transistors, experts talk about singularity and the exponential growth of technology, in which full artificial intelligence could manifest within a number of years, and artificial superintelligence could exist in the 21st century.

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What is Artificial Superintelligence (ASI)? – Definition …

Chill: Robots Wont Take All Our Jobs | WIRED

None of this is to say that automation and AI arent having an important impact on the economy. But that impact is far more nuanced and limited than the doomsday forecasts suggest. A rigorous study of the impact of robots in manufacturing, agriculture, and utilities across 17 countries, for instance, found that robots did reduce the hours of lower-skilled workersbut they didnt decrease the total hours worked by humans, and they actually boosted wages. In other words, automation may affect the kind of work humans do, but at the moment, its hard to see that its leading to a world without work. McAfee, in fact, says of his earlier public statements, If I had to do it over again, I would put more emphasis on the way technology leads to structural changes in the economy, and less on jobs, jobs, jobs. The central phenomenon is not net job loss. Its the shift in the kinds of jobs that are available.

McAfee points to both retail and transportation as areas where automation is likely to have a major impact. Yet even in those industries, the job-loss numbers are less scary than many headlines suggest. Goldman Sachs just released a report predicting that autonomous cars could ultimately eat away 300,000 driving jobs a year. But that wont happen, the firm argues, for another 25 years, which is more than enough time for the economy to adapt. A recent study by the Organization for Economic Cooperation and Development, meanwhile, predicts that 9 percent of jobs across 21 different countries are under serious threat from automation. Thats a significant number, but not an apocalyptic one.

Of the 271 occupations listed on the 1950 census only oneelevator operatorhad been rendered obsolete by automation by 2010.

Granted, there are much scarier forecasts out there, like that University of Oxford study. But on closer examination, those predictions tend to assume that if a job can be automated, it will be fully automated soonwhich overestimates both the pace and the completeness of how automation actually gets adopted in the wild. History suggests that the process is much more uneven than that. The ATM, for example, is a textbook example of a machine that was designed to replace human labor. First introduced around 1970, ATMs hit widespread adoption in the late 1990s. Today, there are more than 400,000 ATMs in the US. But, as economist James Bessen has shown, the number of bank tellers actually rose between 2000 and 2010. Thats because even though the average number of tellers per branch fell, ATMs made it cheaper to open branches, so banks opened more of them. True, the Department of Labor does now predict that the number of tellers will decline by 8 percent over the next decade. But thats 8 percentnot 50 percent. And its 45 years after the robot that was supposed to replace them made its debut. (Taking a wider view, Bessen found that of the 271 occupations listed on the 1950 census only oneelevator operatorhad been rendered obsolete by automation by 2010.)

Of course, if automation is happening much faster today than it did in the past, then historical statistics about simple machines like the ATM would be of limited use in predicting the future. Ray Kurzweils book The Singularity Is Near (which, by the way, came out 12 years ago) describes the moment when a technological society hits the knee of an exponential growth curve, setting off an explosion of mutually reinforcing new advances. Conventional wisdom in the tech industry says thats where we are nowthat, as futurist Peter Nowak puts it, the pace of innovation is accelerating exponentially. Here again, though, the economic evidence tells a different story. In fact, as a recent paper by Lawrence Mishel and Josh Bivens of the Economic Policy Institute puts it, automation, broadly defined, has actually been slower over the last 10 years or so. And lately, the pace of microchip advancement has started to lag behind the schedule dictated by Moores law.

Corporate America, for its part, certainly doesnt seem to believe in the jobless future. If the rewards of automation were as immense as predicted, companies would be pouring money into new technology. But theyre not. Investments in software and IT grew more slowly over the past decade than the previous one. And capital investment, according to Mishel and Bivens, has grown more slowly since 2002 than in any other postwar period. Thats exactly the opposite of what youd expect in a rapidly automating world. As for gadgets like Pepper, total spending on all robotics in the US was just $11.3 billion last year. Thats about a sixth of what Americans spend every year on their pets.

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Chill: Robots Wont Take All Our Jobs | WIRED

Grady Booch: Don’t fear superintelligent AI | TED Talk

New tech spawns new anxieties, says scientist and philosopher Grady Booch, but we don’t need to be afraid an all-powerful, unfeeling AI. Booch allays our worst (sci-fi induced) fears about superintelligent computers by explaining how we’ll teach, not program, them to share our human values. Rather than worry about an unlikely existential threat, he urges us to consider how artificial …

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Grady Booch: Don’t fear superintelligent AI | TED Talk

Some People Are Exceptionally Good at Predicting the Future

Some people are adept at forecasting, predicting the likelihood of future events, and a new contest aims to suss them out.


Some people have a knack for accurately predicting the likelihood of future events. You might even be one of these “super-forecasters” and not know it — but now there’s an easy way to find out.

BBC Future has teamed up with UK-based charity Nesta and forecasting services organization Good Judgement on the “You Predict the Future” challenge. The purpose is to study how individuals and teams predict the likelihood of certain events, ranging from the technological to the geopolitical.

All Winners

Anyone interested in testing their own forecasting skills can sign up for the challenge to answer a series of multiple-choice questions and assign a percentage to how likely each answer is to come true.

“When you’re part of the challenge, you’ll get feedback on how accurate your forecasts are,” Kathy Peach, who leads Nesta’s Centre for Collective Intelligence Design, told BBC Future. “You’ll be able to see how well you do compared to other forecasters. And there’s a leader board, which shows who the best performing forecasters are.”

Collective Intelligence

You’ll also be helping advance research on collective intelligence, which focuses on the intellectual abilities of groups of people acting as one.

Additionally, as Peach told BBC Future, “New research shows that forecasting increases open-mindedness, the ability to consider alternative scenarios, and reduces political polarisation,”  — meaning even if you don’t find out you’re a “super-forecaster,” you might just end up a better person after making your predictions.

READ MORE: Could you be a super-forecaster? [BBC Future]

More on forecasting: Forecasting the Future: Can the Hive Mind Let Us Predict the Future?

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Some People Are Exceptionally Good at Predicting the Future

Scientists Say New Quantum Material Could “‘Download’ Your Brain”

A new type of quantum material can directly measure neural activity and translate it into electrical signals for a computer.

Computer Brain

Scientists say they’ve developed a new “quantum material” that could one day transfer information directly from human brains to a computer.

The research is in early stages, but it invokes ideas like uploading brains to the cloud or hooking people up to a computer to track deep health metrics — concepts that until now existed solely in science fiction.

Quantum Interface

The new quantum material, described in research published Wednesday in the journal Nature Communications, is a “nickelate lattice” that the scientists say could directly translate the brain’s electrochemical signals into electrical activity that could be interpreted by a computer.

“We can confidently say that this material is a potential pathway to building a computing device that would store and transfer memories,” Purdue University engineer Shriram Ramanathan told ScienceBlog.

Running Diagnostics

Right now, the new material can only detect the activity of some neurotransmitters — so we can’t yet upload a whole brain or anything like that. But if the tech progresses, the researchers hypothesize that it could be used to detect neurological diseases, or perhaps even store memories.

“Imagine putting an electronic device in the brain, so that when natural brain functions start deteriorating, a person could still retrieve memories from that device,” Ramanathan said.

READ MORE: New Quantum Material Could Warn Of Neurological Disease [ScienceBlog]

More on brain-computer interface: This Neural Implant Accesses Your Brain Through the Jugular Vein

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Scientists Say New Quantum Material Could “‘Download’ Your Brain”

Scientists Find a New Way to Kickstart Stable Fusion Reactions

A new technique for nuclear fusion can generate plasma without requiring as much space-consuming equipment within a reactor.

Warm Fusion

Scientists from the Princeton Plasma Physics Laboratory say that they’ve found a new way to start up nuclear fusion reactions.

The new technique, described in research published last month in the journal Physics of Plasmas, provides an alternate means for reactors to convert gas into the superhot plasma that gets fusion reactions going with less equipment taking up valuable lab space — another step in the long road to practical fusion power.

Out With The Old

Right in the center of a tokamak, a common type of experimental nuclear fusion reactor, there’s a large central magnet that helps generate plasma. The new technique, called “transient coaxial helical injection,” does away with the magnet but still generates a stable reaction, freeing up the space taken up by the magnet for other equipment.

“The good news from this study,” Max Planck Institute researcher Kenneth Hammond said in a press release, “is that the projections for startup in large-scale devices look promising.”

READ MORE: Ready, set, go: Scientists evaluate novel technique for firing up fusion-reaction fuel [Princeton Plasma Physics Laboratory newsroom via ScienceDaily]

More on nuclear fusion: Scientists Found a New Way to Make Fusion Reactors More Efficient

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Scientists Find a New Way to Kickstart Stable Fusion Reactions

The Israeli Moon Lander Is About to Touch Down

SpaceIL's Moon lander, Beresheet, is expected to touch down on the lunar surface on Thursday, landing Israeli a place in the history books.

Lunar Lander

If all goes according to plan, Israel will earn a place in history on Thursday as the fourth nation ever to land a spacecraft on the Moon — and unlike any craft that came before it, this Moon lander was privately funded.

Beresheet is the work of SpaceIL, a nonprofit Israeli space company. On Feb. 21, the company launched its $100 million spacecraft on a journey to the Moon aboard a SpaceX Falcon 9 rocket, and on April 4, it settled into the Moon’s orbit.

The next step in the mission is for Beresheet to attempt to land on the surface of the Moon sometime between 3 and 4 p.m. ET on Thursday.

Watch Along

Beresheet’s target landing site is in the northeastern part of Mare Serenitatis, also known as the Sea of Serenity.

“On the basis of our experience with Apollo, the Serenitatis sites favor both landing safety and scientific reward,” SpaceIL team member Jim Head said in a press release.

SpaceIL and Israel Aerospace Industries, the company that built Beresheet, will live-stream Thursday’s touch-down attempt, so the world will have a chance to watch along as Israel tries to land itself a spot in the history books.

READ MORE: Israel’s Beresheet space probe prepares for historic moon landing [NBC News]

More on Beresheet: Israel’s Moon Lander Just Got Photobombed by the Earth

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The Israeli Moon Lander Is About to Touch Down

MIT Prof: If We Live in a Simulation, Are We Players or NPCs?

An MIT scientist asks whether we're protagonists in a simulated reality or so-called NPCs who exist to round out a player character's experience. 

Simulation Hypothesis

Futurism readers may recognize Rizwan Virk as the MIT researcher touting a new book arguing that we’re likely living in a game-like computer simulation.

Now, in new interview with Vox, Virk goes even further — by probing whether we’re protagonists in the simulation or so-called “non-player characters” who are presumably included to round out a player character’s experience.

Great Simulation

Virk speculated about whether we’re players or side characters when Vox writer Sean Illing asked a question likely pondered by anyone who’s seen “The Matrix”: If you were living in a simulation, would you actually want to know?

“Probably the most important question related to this is whether we are NPCs (non-player characters) or PCs (player characters) in the video game,” Virk told Vox. “If we are PCs, then that means we are just playing a character inside the video game of life, which I call the Great Simulation.”

More Frightening

It’s a line of inquiry that cuts to the core of the simulation hypothesis: If the universe is essentially a video game, who built it — and why?

“The question is, are all of us NPCs in a simulation, and what is the purpose of that simulation?” Virk asked. “A knowledge of the fact that we’re in a simulation, and the goals of the simulation and the goals of our character, I think, would still be interesting to many people.”

READ MORE: Are we living in a computer simulation? I don’t know. Probably. [Vox]

More on the simulation hypothesis: Famous Hacker Thinks We’re Living in Simulation, Wants to Escape

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MIT Prof: If We Live in a Simulation, Are We Players or NPCs?

Here’s How Big the M87 Black Hole Is Compared to the Earth

The black hole that scientists imaged is a stellar giant. It would take millions of Earths lined up side-by-side to span its length.

Pale Black Dot

On Wednesday, a team of scientists from around the world released the first ever directly-observed image of the event horizon of a black hole.

The black hole, M87*, is found within the constellation Virgo — and as the webcomic XKCD illustrated, it’s as big as our entire solar system.

Stellar Giant

The gigantic black hole, not counting the giant rings of trapped light orbiting it, is about 23.6 billion miles (38 billion kilometers) across, according to Science News.

Meanwhile, the Earth is just 7,917 miles in diameter — meaning our planet wouldn’t even be a drop in the bucket of the giant, black void. Based Futurism’s calculations, it would take just over 2.98 million Earths lined up in a row to span the length of M87*. For a sense of scale, that’s about how many adult giraffes it would take to span the diameter of Earth.

Paging Pluto

Our entire solar system is just about 2.27 billion miles wide, meaning we could just barely fit the whole thing into the newly-imaged black hole’s event horizon.

Thankfully, M87* is about 55 million light years away — so while we could readily fit inside its gaping maw, we’re way too far to get sucked in.

READ MORE: Revealed: a black hole the size of the solar system [Cosmos]

More on M87*: Scientists: Next Black Whole Image Will Be Way Clearer

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Here’s How Big the M87 Black Hole Is Compared to the Earth

Amazon Workers Listen to Your Alexa Conversations, Then Mock Them

A new Bloomberg piece shared the experiences of Amazon workers tasked with listening to Alexa recordings, and what they hear isn't always mundane.

I Hear You

Amazon pays thousands of workers across the globe to review audio picked up by its Echo speakers — and their behavior raises serious concerns about both privacy and safety.

Bloomberg recently spoke with seven people who participated in Amazon’s audio review process. Each worker was tasked with listening to, transcribing, and annotating voice recordings with the goal of improving the ability of Amazon’s Alexa smart assistant to understand and respond to human speech.

But sometimes, according to Bloomberg, they share private recordings in a disrespectful way.

“I think we’ve been conditioned to the [assumption] that these machines are just doing magic machine learning” University of Michigan professor Florian Schaub told Bloomberg. “But the fact is there is still manual processing involved.”

Listen to This

The job is usually boring, according to Bloomberg’s sources. But if they heard something out of the ordinary, they said, sometimes they’d share the Alexa recordings with other workers via internal chat rooms.

Occasionally, it was just because they found the audio amusing — a person singing off-key, for example — but other times, the sharing was “a way of relieving stress” after hearing something disturbing, such as when two of Bloomberg’s sources heard what sounded like a sexual assault.

When they asked Amazon how to handle cases like the latter, the workers said they were told “it wasn’t Amazon’s job to interfere.” Amazon, meanwhile, said it had procedures in place for when workers hear something “distressing” in Alexa recordings.

READ MORE: Amazon Workers Are Listening to What You Tell Alexa [Bloomberg]

More on Echo: Thanks, Amazon! Echo Recorded and Sent Audio to Random Contacts Without Warning

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Amazon Workers Listen to Your Alexa Conversations, Then Mock Them

NASA Is Funding the Development of 18 Bizarre New Projects

Through the NASA Innovative Advanced Concepts (NIAC) program, NASA funds projects that go

Nurturing the Bizarre

NASA isn’t afraid to take a chance on the weird. In fact, it has a program designed for that specific purpose, called NASA Innovative Advanced Concepts (NIAC) — and on Wednesday, the agency announced 18 bizarre new projects receiving funding through the program.

“Our NIAC program nurtures visionary ideas that could transform future NASA missions by investing in revolutionary technologies,” NASA exec Jim Reuter said in a press release. “We look to America’s innovators to help us push the boundaries of space exploration with new technology.”

Sci-Fi to Sci-Fact

The 18 newly funded projects are divided into two groups: Phase I and Phase II.

The 12 recipients of the Phase I awards will each receive approximately $125,000 to fund nine month’s worth of feasibility studies for their concepts. These include a project to beam power through Venus’ atmosphere to support long-term missions, a spacesuit with self-healing skin, and floating microprobes inspired by spiders.

The six Phase II recipients, meanwhile, will each receive up to $500,000 to support two-year studies dedicated to fine-tuning their concepts and investigating potential ways to implement the technologies, which include a flexible telescope, a neutrino detector, and materials for solar surfing.

“NIAC is about going to the edge of science fiction, but not over,” Jason Derleth, NIAC program executive, said in the press release. “We are supporting high impact technology concepts that could change how we explore within the solar system and beyond.”

READ MORE: NASA Invests in Potentially Revolutionary Tech Concepts [Jet Propulsion Laboratory]

More on bizarre NASA plans: New NASA Plan for Mars Is Moderately-Terrifying-Sounding, Also, Completely-Awesome: Robotic. Bees.

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NASA Is Funding the Development of 18 Bizarre New Projects

Report: Tesla Doc Is Playing Down Injuries to Block Workers’ Comp

Former Tesla and clinic employees share how doctors blocked workers' compensation claims and put injured people back to work to avoid payouts.

Here’s A Band-Aid

Tesla’s on-site clinic, Access Omnicare, has allegedly been downplaying workers’ injuries to keep the electric automaker off the hook for workers’ compensation.

Several former Tesla employees, all of whom got hurt on the job, and former employees of Access Omnicare, told Reveal News that the clinic was minimizing worker injuries so that the automaker wouldn’t have to pay workers’ comp — suggesting that the barely-profitable car company is willing to do whatever it takes to stay out of the red and avoid negative press.

Back To Work

Reveal, which is a project by the Center for Investigative Reporting, described cases in which employees suffered electrocution, broken bones, and mold-related rashes while working in a Tesla factory — only for Omnicare to deny that the injuries warranted time off work.

The clinic’s top doctor “wanted to make certain that we were doing what Tesla wanted so badly,” former Omnicare operations manager Yvette Bonnet told Reveal. “He got the priorities messed up. It’s supposed to be patients first.”

Missing Paperwork

Meanwhile, employees who requested the paperwork to file for workers’ comp were repeatedly ignored, according to Reveal.

“I just knew after the third or fourth time that they weren’t going to do anything about it,” a former employee whose back was crushed under a falling Model X hatchback told Reveal. “I was very frustrated. I was upset.”

The automaker is on the hook for up to $750,000 in medical payments per workers’ comp claim, according to Reveal‘s reporting.

Meanwhile, both Tesla CEO Elon Musk and Laurie Shelby, the company’s VP of safety, have publicly praised Access Omnicare, Reveal found. Musk even recently announced plans to extend it to other plants, “so that we have really immediate first-class health care available right on the spot when people need it.”

READ MORE: How Tesla and its doctor made sure injured employees didn’t get workers’ comp [Reveal News]

More on Tesla: Video Shows Tesla Autopilot Steering Toward Highway Barriers

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Infertile Couple Gives Birth to “Three-Parent Baby”

A Greek couple just gave birth to a three-parent baby, the first conceived as part of a clinical trial to treat infertility.

Happy Birthday

On Tuesday, a couple gave birth to what researchers are calling a “three-parent baby” — giving new hope to infertile couples across the globe.

After four cycles of in vitro fertilization failed to result in a pregnancy, the Greek couple enrolled in a clinical trial for mitochondrial replacement therapy (MRT) — meaning doctors placed the nucleus from the mother’s egg into a donor egg that had its nucleus removed. Then they fertilized the egg with sperm from the father and implanted it into the mother.

Due to this procedure, the six-pound baby boy has DNA from both his mother and father, as well as a tiny bit from the woman who donated the egg.

Greek Life

The Greek baby wasn’t the first “three-parent baby” born after his parents underwent MRT — that honor goes to the offspring of a Jordanian woman who gave birth in 2016.

However, in her case and others that followed it, doctors used the technique to prevent a baby from inheriting a parent’s genetic defect. This marked the first time a couple used MRT as part of a clinical trial to treat infertility.

“Our excellent collaboration and this exceptional result will help countless women to realise their dream of becoming mothers with their own genetic material,” Nuno Costa-Borges, co-founder of Embryotools, one of the companies behind the trial, said in a statement.

READ MORE: Baby with DNA from three people born in Greece [The Guardian]

More on three-parent babies: An Infertile Couple Is Now Pregnant With a “Three-Parent Baby”

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Infertile Couple Gives Birth to “Three-Parent Baby”

Israel’s Lunar Lander Just Crashed Into the Moon

The Beresheet lunar lander crashed into the surface of the moon after experiencing engine failure during its final descent.

Landing Attempt

Beresheet, the lunar lander built by Israeli space nonprofit SpaceIL, crashed into the surface of the Moon on Thursday.

It would have been the first privately-owned lander on the surface of the Moon, and would have made Israel the fourth country to reach the surface of the Moon — but the craft experienced engine failure during its final approach.

“We have a failure of the spacecraft,” said Israel Aerospace Industries general manager Opher Doron on livestream, according to CNBC. “We unfortunately have not managed to land successfully,”

Final Approach

As Beresheet was approaching the surface of the Moon, the main engine failed and Beresheet was forced to reset the engine.

With about 10 kilometers left to go (6.2 miles), the main engine cut out and the lander crashed into the Moon traveling at about 134 meters per second, according to the livestream.

“We failed the first try, we’ll make it in the second… within two years we’ll try it again,” Israel Prime Minister Benjamin Netanyahu said, according to CNBC.

Definitely Tried

SpaceIL tweeted a photo of the lander’s final approach minutes before it lost contact with the craft. In it, the Moon looms ominously in the background.

“We didn’t make it. But we definitely tried,” said SpaceIL.

Editor’s note: This article has been updated with additional details.

READ MORE: Israeli spacecraft Beresheet falls short of history as moon landing fails in final moments [CNBC]

More on Beresheet: The Israeli Moon Lander Is About to Touch Down

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Israel’s Lunar Lander Just Crashed Into the Moon

We Wouldn’t Have the First Black Hole Image Without Katie Bouman

Katie Bouman, a 29-year-old computer scientist, led the development of the algorithm that made the first black hole image possible.

Algorithmic Assist

It took a team of more than 200 scientists to create the first image of the event horizon of a black hole — and the internet is currently in love with one of them.

Computer scientist Katie Bouman led the development of the algorithm that made the breathtaking black hole image possible, and soon after the Event Horizon Telescope team revealed the photo on Wednesday, another image — this one a shot of Bouman that she posted to her Facebook page — started making the rounds online.

“Watching in disbelief as the first image I ever made of a black hole was in the process of being reconstructed,” the 29-year-old wrote of the photo, which was subsequently shared by everyone from CNN to Kamala Harris.

Here's the moment when the first black hole image was processed, from the eyes of researcher Katie Bouman. #EHTBlackHole #BlackHoleDay #BlackHole (v/@dfbarajas) pic.twitter.com/n0ZnIoeG1d

— MIT CSAIL (@MIT_CSAIL) April 10, 2019

Women Who Code

The online photo frenzy wasn’t over, though.

Many in the Twitterverse and beyond noted the similarities between an image of Bouman with piles of hard drives containing black hole image data and an image of another female computer scientist, Margaret Hamilton, standing next to the stacks of code she wrote to help NASA put astronauts on the Moon in 1969.

Still, Bouman, who is now an assistant professor of computing and mathematical sciences at the California Institute of Technology, is quick to note that creating the first black hole image wasn’t a one-woman job.

“No one of us could’ve done it alone,” she told CNN. “It came together because of lots of different people from many different backgrounds.”

Left: MIT computer scientist Katie Bouman w/stacks of hard drives of black hole image data.

Right: MIT computer scientist Margaret Hamilton w/the code she wrote that helped put a man on the moon.

(image credit @floragraham)#EHTblackhole #BlackHoleDay #BlackHole pic.twitter.com/Iv5PIc8IYd

— MIT CSAIL (@MIT_CSAIL) April 10, 2019

READ MORE: That image of a black hole you saw everywhere? Thank this grad student for making it possible [CNN]

More on the black hole image: Scientists Just Released the First-Ever Image of a Black Hole

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We Wouldn’t Have the First Black Hole Image Without Katie Bouman