What to watch for
After completing this lesson, you’ll be able to:
- Describe the current state of artificial intelligence
- Relate the history of artificial intelligence
- Outline the various use cases for bots
“Artificial intelligence“, Wikipedia
(11,271 words / 57-65 minutes, but see below—many sections can be skimmed, so probably much shorter)
Lots of good stuff in the introductory summary paragraph here:
Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence(NI) displayed by humans and other animals. In computer science AI research is defined as the study of “intelligent agents“: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.
The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring “intelligence” are often removed from the definition, a phenomenon known as the AI effect, leading to the quip, “AI is whatever hasn’t been done yet.” For instance, optical character recognition is frequently excluded from “artificial intelligence”, having become a routine technology. Capabilities generally classified as AI as of 2017 include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go), autonomous cars, intelligent routing in content delivery network and military simulations.
Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an “AI winter“), followed by new approaches, success and renewed funding. For most of its history, AI research has been divided into subfields that often fail to communicate with each other. These sub-fields are based on technical considerations, such as particular goals (e.g. “robotics” or “machine learning”), the use of particular tools (“logic” or artificial neural networks), or deep philosophical differences. Subfields have also been based on social factors (particular institutions or the work of particular researchers).
The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects. General intelligence is among the field’s long-term goals. Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy and many others.
The field was founded on the claim that human intelligence “can be so precisely described that a machine can be made to simulate it”. This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence which are issues that have been explored by myth, fiction and philosophy since antiquity. Some people also consider AI to be a danger to humanity if it progresses unabatedly. Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.
In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science.
- Spend some time poking around the links in that paragraph. Of particular interest are the setbacks mentioned in the third paragraph. Thinking about people investigating AI with the computer technology of the 50s is really something else—such vision, they must have had, when the technology was so primitive.
- The “Approaches” section is pretty nerdy, even for this class—feel free to skim / skip. (But if you like this stuff, good news—AI is a booming career field, and if you learn it, you’ll have work for a long time! 1)
- The “Technology” section, while still pretty nerdy, is worth reading. Don’t feel that you need to understand the details of each tool, just the big picture idea behind each as best as you can.
- Be sure to follow the link for the Turing test. If you’re going to be a tech-savvy person, you need to know what it is, and you definitely need to know who Alan Turing is, too. 2
- The “Philosophy” section is full of so many interesting ideas. It’ll be unbelievably interesting (probably at turns delightful and horrific) to watch as more and more of the ideas here move from the theoretical to the practical in our lifetimes.
- There are, of course, great gobs of fiction / literature / film / games / etc. about AI, and rightly so.
- Spend a bit of time reading about Isaac Asimov’s Three Laws of Robotics, which have since been carried over from robots in particular to AI in general.
MarI/O, via Kottke
Kottke.org (where I first saw the video) is one of my absolute favorite blogs. If you at all like any of the stuff we’re talking about in this course, my guess is that you’d like regularly reading the site.
Anyway, the video:
Really watch the whole thing—it’s such a great explanation of what machine learning and neural networks are. Enjoy.
(PS Here’s the relevant Wikipedia page if you want to dig deeper into the concept of machine learning, which you probably should.)
But what is a Neural Network? | Deep learning, chapter 1 by 3Blue1Brown
Don’t be scared off by the math in this video—the math isn’t the point! Rather, this video provides an excellent overview of what actually happens when we say a machine (a computer) is learning. Take your time with this video—it’s worth it.
“The A to Z of AI” by Google
(~3,500 words / 18-25 minutes)
An excellent overview of the current state of play with AI and ML. Plus, things organized alphabetically are always fun!
“This may be your brain on Alexa, but Alexa isn’t ready for the average human brain” by John Weatherford
This is totally a story I wrote, but it really is pretty relevant to what we discussed. (Really!) Even if you don’t read my story, you should read the article it’s responding to. If you’re feeling really ambitious, you could even write your own response to the original story or to me on Medium!
“Artificial Intelligence Course Creates AI Teaching Assistant” by Jason Maderer
Really fascinating story about an AI TA, shared with me by Amy Waters. There’s also a TEDx talk with the teacher, too. And no, none of the TAs for this course are AIs (yet).
- Think about your daily technology use. Where might you be bumping into AI and machine learning that you might not have realized before this lesson?
- Does the way machine learning functions as described in the two videos make some sort of sense to you? What makes sense, and what doesn’t?
- What do you think about the notion that “As machines become increasingly capable, facilities once thought to require intelligence are removed from the definition. For example, optical character recognition is no longer perceived as an exemplar of “artificial intelligence” having become a routine technology.” What other technologies that we now view as commonplace might once have been viewed as AI?
- Pick one of the ethical dilemmas relating to AI from the Wikipedia entry and discuss.
- Do you regularly use Siri / Google Assistant / Amazon Alexa / Microsoft Cortana / etc.? Why or why not?
Words on / reading time for this page: 1,179 words / 6-7 minutes
Words in / reading time for required readings: 14,383 words / 74-86 minutes
Total words in / reading time for this lesson: 15,562 words / 80-93 minutes