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On a separate sheet of paper please answer these questions (or fill in the blanks)
1. AI is intelligence exhibited by machines. It doesn’t mean that ____________ .
2. What does it mean for a computer to be intelligent?
3. People can do more than solve problems: we are aware, sentient, sapient, and conscious. What does it mean for someone to be sapient?
4. What does it mean for someone to be sentient?
5. Some AI problems have successfully been solved. People are so used to them that we often don’t even call them AI. You may already be using some AIs in your own life. List & clearly describe two examples of these AIs.
6. There is a field of computer science called “philosophy of artificial intelligence.” List 2 of the questions that field is working to answer.
7. How are strong and weak AIs different?
8. In our resource, look at the 2 articles we link to “How to Help Self-Driving Cars Make Ethical Decisions”, or “What Will It Take to Build a Virtuous AI?” Summarize the first article, in your own words, in 3 well-written paragraphs. Summarize the second article for homework.
I. Write a sophisticated Scratch computer program, on your own, not using someone else’s code. You must first come see me with your idea, and then present quick updates, showing your progress.
Checkpoint 1 See me with your specific idea, by 5/30/17. 10 points.
Checkpoint 2: Show me the code you have each day in class. You need to be clearly explain how your code works. Your code should have many comment sections. By the time that finals come around, your program must be complete. If done well you can earn up to an additional 90 points.
II. Write a 4 page paper on one of the following topics.
No cover page. Upper left of the 1st page will have your name, my name/class, date and a title. Use 12 point Arial or Times New Roman font, double spaced, 1″ margins. You may add small diagrams and pictures, but they don’t count towards the length of your paper. MLA Works Cited is an additional page. You must use at least four sources of information, which must be cited in MLA format.
For these topics, most Wikipedia articles are acceptable sources, however, you may not use Wikipedia for more than 2 of your sources, and you must first show me the specific , so I can make sure that it’s Ok.
A) Computers don’t actually think. So how do they know what to do with the code we write? What goes on under the hood, so to speak? I’ve prepared many sources that you can use: How-a-computer-interprets-instructions
B) the development of computers and software: Choose 1 of these systems: the classic IBM-PC, Apple II, Apple Macintosh, Commodore Vic-20, or Commodore 64.
C) the development and programming of second generation classic video games. Choose 1 or 2 of these systems: Odyssey, Atari 2600 (aka Atari VCS), Magnavox Odyssey 2, Mattel Intellivision, Vectrex, and Colecovision. What kind of hardware was in these computers? How did they work? How were they programmed? In what language were they programmed? What was the software capable of?
D) the development and programming of third generation classic video games for neo-classic video games. Choose 1 or 2 of these systems: Sega Master System (aka the SMS), Nintendo (aka the NES or Famicon), Atari 7800. What kind of hardware was in these computers? How did they work? How were they programmed? In what language were they programmed? What was the software capable of?
E) the development and programming of fifth generation classic video games for neo-classic video games. Choose 1 or 2 of these systems: Sega Saturn, Sony Playstation (PSX 1), Nintendo 64. What kind of hardware was in these computers? How did they work? How were they programmed? In what language were they programmed? What was the software capable of?
Teaching coding:3 Steps to Becoming a Coding Teacher, By Grant Smith
2. Prepare Yourself and Your Classroom
Notice how I included resources above for adults to learn coding. That means you! I recommend that you first review your selected curriculum and then move on to the more complicated stuff. I highly recommend the Intro to CS and Intro to Programming courses on Udacity. You should also prepare for your class by answering the following questions:
- What are your learning expectations for the students? (Check out these learning outcomes for the Khan Academy course as an example.)
- Are your students learning computational thinking, computer science, or computer programming? (There is a difference. Check out Harvard research on computational thinking.)
- What’s your classroom layout? (See my post for ideas.)
- Will your students work at their own pace or at your pace?
- Will students work through a curriculum, or will it be project based?
- How will students collaborate?
- How will students share their work with you, their peers, and the world?
- How will student accounts be managed? Will you create them? Do you need parent or administrator permission?
- Why should your students learn to code? (Students are more excited to learn when you are excited to teach. Check out the Top Ten Reasons to Code.)
- How will you assess your students? (This PDF details some research on assessing computational thinking.)
3. Get Support
Just because anyone can learn to code online doesn’t mean that’s the best way to do it. Code.org’s research found that “students who are learning with the support of their teacher in a classroom setting complete courses more than those learning on their own” (Teachers Matter). We all know that for teachers to be successful, we need support. So rally the troops!
- Find a champion for your coding crusade. The higher level the champion is, the easier it will be for you to gain access to resources and spread the word about your 21st-century class.
- Get the community involved. Host an Hour of Codecommunity event. Last year, the Avondale Elementary School District held an Hour of Code event where the students taught their parents how to program.
- Build your PLN. Follow people on your favorite social network and ask for help. Some great hashtags are#CSK8, #KidsCanCode, and #AllKidsCode.
- Present to your governing board. Show them how your curriculum aligns to CCSS and builds 21st-century skills.
Jump Into 21st-Century Learning!
If you’ve already had successful experiences coding in your class, share them in the comments section of this post or on your PLN. If not, you may be asking the following questions:
- Will you know the answer to every question that your students will have?
- Will you feel well rested, prepared, and in control at all times?
- Will every class run without a hitch?
Answers: 1) No. 2) You wish. 3) In your dreams!
Will it be worth it? You better believe it! Now go make it happen!
15+ Ways of Teaching Every Student to Code
(Even Without a Computer)
…While the Hour of Code is in December, Code.org hassuggested resources for educators, unplugged lessons (those not requiring computers), and tutorials to help you teach computer science to kids of all ages any time of the year….
- Scratch is a programming game that can be downloaded or used on the Web and is supported by MIT. They’ve got a powerful Hour of Code tutorial where students can program a holiday card in their web browser.
- Lightbot has a version on just about any platform and even has an online one-hour version. This puzzle game has a free version which lasts an hour but sells full versions on iTunes and Google Play. It teaches planning, testing, debugging, procedures, and loops.
- Kodu is another programming tool that can be easily used on a PC or XBOX to create a simple game. There’s also a math curriculum. This is one method that Pat Yongpradit, Code.org’s Director of Education, used in his computer science classroom. (I’ve used it as well.)
- Gamestar Mechanic offers a free version that you might want to use for your hour, but if you fall in love with it, the educational package allows teachers to track student progress, among other features. The company supports educators, and there’s also an Edmodo community that shares lesson plans and ideas for the tool, along withvideos and a must-see teacher’s guide.
- GameMaker is an option if you want to make games that can be played in any web browser. The resources aren’t as comprehensive and the community isn’t vibrant, but this one has been around for a while and might be fun for a more tech-savvy teacher.
- My Robot Friend is a highly-rated app according toCommon Sense Media. It costs $3.99, but no in-app purchases are required to go to higher levels.
- SpaceChem is an interesting mix of chemistry, reading, and programming for age 12 and up. As students read the 10,000-word novelette, they have to solve puzzles by assembling molecules. SpaceChem created a helpful guide for educators. This tool is available for download on Steam and installation on Windows, Mac, and Ubuntu. (Download a free demo.)
- CodeCombat is a multiplayer game that teaches coding. It’s free to play at the basic level, and students don’t have to sign up. This has the advantage that teachers don’t have to know computer science to empower learning in this programming. It’s recommended for age 9 and up. See theteacher guide for the information and standards covered in this game.
- Minecraft.edu is an option that lets you install and use Minecraft in the classroom. While this does require some purchase and setup, Minecraft seems to be gaining in popularity among educators as an in-house, 3D world-programming environment that kids love. Minecraft.edu has a Google group and best practices wiki. (My son took a course at Youth Digital that taught him Java to mod Minecraft — while pricey, it was a great course.)
- Do you want a board game for older children? Code Monkey Island is designed for children age 9 and up. This is a great addition to your game corner.
Tutorials Point originated from the idea that there exists a class of readers who respond better to online content and prefer to learn new skills at their own pace from the comforts of their drawing rooms. The journey commenced with a single tutorial on HTML in 2006 and elated by the response it generated, we worked our way to adding fresh tutorials to our repository which now proudly flaunts a wealth of tutorials and allied articles on topics ranging from programming languages to web designing to academics and much more.
Programming Lego NXT robots
In developing AIs (artificial intelligences) there’s no guarantee that they will think like we do. We need to ask:
What possible type of minds could people have?
What possible type of minds could AIs have?
We’ll illustrate possible minds on (at least) a 2D (two dimensional) chart.
Let’s start with interpreting 1D, 2D and 3D graphs; then we’ll show how to graph possible minds.
1. What is intelligence?
“The whole of cognitive or intellectual abilities required to obtain knowledge, and to use that knowledge in a good way to solve problems that have a well described goal and structure.”
Resing, W., & Drenth, P. (2007). Intelligence: knowing and measuring. Amsterdam: Publisher Nieuwezijds
also see What is intelligence and IQ?
2. What is the Wechsler IQ scale?
A simplistic test to represent intelligence with a single number.
|IQ Range (“deviation IQ”)||IQ Classification|
|130 and above||Very Superior|
|69 and below||Extremely Low|
3. Is the Wechsler IQ scale 1D, 2D, or 3D?
A 1D (one dimensional) graph is used when there is only one variable.
Thermometer / Wechsler scale / Speedometer
4. In history class we sometimes plot political beliefs on a 1D scale.
What is being plotted on this axis?
5. However, not all positions can be accurately shown on a 2D graph.
We need at least 2 different dimensions. On this chart, what are axes being plotted?
6. Why is it better for some subjects to use 2D plotting instead of 1D?
7. How would we represent something that needs 3 different variables?
With a 3D plot.
On this chart, what are the 3 different dimensions (axes) being plotted?
8. For minds we would need more than 1D to represent ideas.
So this chart is insufficient.
We don’t really have just one intelligence dimension (“dumb-to-smart”)
We have many, such as:
ability to think and reason logically, problem solving
ability to have empathy, understand the emotional state of other people)
ability to understand one’s own emotional state/sentience
The Universe of Minds, on a 2D graph
By Roman V. Yampolskiy
What is a mind? No universal definition exists… Higher order animals are believed to have one as well and maybe lower level animals and plants or even all life forms.
We believe that an artificially intelligent agent such as a robot or a program running on a computer will constitute a mind….
The set of human minds (about 7 billion of them currently available and about 100 billion ever existed) is very homogeneous both in terms of hardware (embodiment in a human body) and software (brain design and knowledge).
The small differences between human minds are trivial in the context of the full infinite spectrum of possible mind designs. Human minds represent only a small constant size subset of the great mind landscape. Same could be said about the sets of other earthly minds such as dog minds, or bug minds or male minds or in general the set of all animal minds…
Yudkowsky describes the map of mind design space as follows:
“In one corner, a tiny little circle contains all humans; within a larger tiny circle containing all biological life; and all the rest of the huge map is the space of minds-in-general. The entire map floats in a still vaster space, the space of optimization processes. Natural selection creates complex functional machinery without mindfulness; evolution lies inside the space of optimization processes but outside the circle of minds”
Figure 1 illustrates one possible mapping inspired by this description.
Yudkowsky describes the map of mind design space as follows:
“In one corner, a tiny little circle contains all humans; within a larger tiny circle containing all biological life; and all the rest of the huge map is the space of minds-in-general. The entire map floats in a still vaster space, the space of optimization processes”
(Yudkowsky 2008, 311).
Ivan Havel writes:
All conceivable cases of intelligence (of people, machines, whatever) are represented by points in a certain abstract multidimensional “super space” that I will call the intelligence space (shortly IS).
Imagine that a specific coordinate axis in IS is assigned to any conceivable particular ability, whether human, machine, shared, or unknown (all axes having one common origin). If the ability is measurable the assigned axis is endowed with a corresponding scale. Hypothetically, we can also assign scalar axes to abilities, for which only relations like “weaker-stronger,” “better-worse,” “less-more” etc. are meaningful; finally, abilities that may be only present or absent may be assigned with “axes” of two (logical) values (yes-no).
Let us assume that all coordinate axes are oriented in such a way that greater distance from the common origin always corresponds to larger extent, higher grade, or at least to the presence of the corresponding ability. … (Havel 2013, 13)
What do we see here?
Human minds – what we humans have, from a day old baby, to a child in 3rd grade, to an adult businesswoman, to the greatest geniuses the world has ever seen, like Albert Einstein and Isaac Newton. We’re all represented in the pink circle, in the image above. The left side of the circle are the least smart people, the right side represent the smartest people. The vertical axis might represent sapience, sentience, or some other aspect of intelligence.
Transhuman minds – This larger salmon-colored region represents the possible minds of humans who have chosen to expand their brains. In theory, humans could use genetic engineering, or cybernetics, or both, to expand our intellectual powers.
Transhumanism is”the intellectual and cultural movement that affirms the possibility and desirability of fundamentally improving the human condition through applied reason, especially by using technology to eliminate aging and greatly enhance human intellectual, physical, and psychological capacities” – Nick Bostrum, 1999.
Posthuman minds – If humans continue to push their biology and minds past the transhuman state, the result would be a being that no longer looks or thinks like a human being at all.
Freepy AIs are any type of artificial intelligence that human beings might be able to make; although they may produce results we can understand, we can’t understand the way that they think. They are not only smarter than us, they think differently than we do.
Bipping AI’s are a kind of artificial intelligence so advanced that humans couldn’t even possibly design them. They might be designed by other AIs, or by transhumans, or posthumans. They are amazingly intelligent, but utterly nonhuman. It might not even be possible to have a conversation with them, since their view of reality and their way of thinking about the world is so different from our own.
Gloopy AI’s are a kind of artificial intelligence so advanced that humans couldn’t even possibly design them, but not necessarily smarter than us. They would have a capacity to think, but perhaps at a lesser organized level. It might not even be possible to have a conversation with them, since their view of reality and their way of thinking about the world is so different from our own.
All computation and physical action requires the physical resources of space, time, matter, and free energy. Almost any goal can be better accomplished by having more of these resources. In maximizing their expected utilities, systems will therefore feel a pressure to acquire more of these resources and to use them as efficiently as possible. Resources can be obtained in positive ways such as exploration, discovery, and trade. Or through negative means such as theft, murder, coercion, and fraud. Unfortunately the pressure to acquire resources does not take account of the negative externalities imposed on others. Without explicit goals to the contrary, AIs are likely to behave like human sociopaths in their pursuit of resources. Human societies have created legal systems which enforce property rights and human rights. These structures channel the acquisition drive into positive directions but must be continually monitored for continued efficacy.
Non-human intelligences here on Earth
What in god’s name was this octopus trying to do? Maybe that’s the wrong question. There’s no question that octopi are smart — they can puzzle their way through surprisingly complex tasks — but they’re also not a lot like humans.
There’s only a limited extent that we can empathize with animals — and there’s a good chance that we’ll get it wrong. (consider, for example, “What is it like to be a bat?” By Thomas Nagel)
Octopi, though. Octopi are particularly difficult, and I don’t know if “volition” is really the right model to describe what this animal is trying to do.
Most of an octopus’ neurons are in its arms. The rest are in a donut-shaped brain that surrounds its digestive tract. Vision and hearing are handled centrally, but proprioception, smell, touch, and taste are mostly delegated to the nerve cords in the arms.
Which means that, subjectively, an octopus is probably something like an unruly parliament of snakes ruled by a dog.
If you’ve ever gotten a chance to interact with an octopus in person, you’ll find that it really doesn’t have much control over the details of what its tentacles do. Run your finger over the sensory surface, and its suckers will cup your fingers and the end will curl around it. Only afterward — when the octopus actually looks at what you’re doing — does the octopus seem to get a grip on what its tentacle is gripping.
This octopus is crawling out of its tank. But it probably doesn’t have a great idea about where the tips of its tentacles are, and — because it can’t see what its arms are doing — probably doesn’t yet know that it’s trying to make a break for freedom.
CD.L2-07 Describe what distinguishes humans from machines, focusing on human intelligence
versus machine intelligence and ways we can communicate.
CD.L2-08 Describe ways in which computers use models of intelligent behavior (e.g., robot motion,
speech and language understanding, and computer vision).
CD.L3A-01 Describe the unique features of computers embedded in mobile devices and vehicles
(e.g., cell phones, automobiles, airplanes).
CD.L3A-10 Describe the major applications of artificial intelligence and robotics.
Common Core ELA. WHST.6-8.1 Write arguments focused on discipline-specific content.