AI (Artificial Intelligence) + ML (Machine Learning)

This lesson marks this class’s foray into the world of the up-to-the-minute and the near future. Bots are really just a subset of AI 1, but they’re also getting a lot of attention at the moment, so there they are in the title of the lesson.

Let’s get started!

Required readings:

An excellent overview of the current level of excitement about AI. Also does some nice contextualization of the current boom in interest in AI as compared to previous tech booms.

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.

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.)

A nice overview of why everyone’s so excited about bots, in particular, among the sea of excitement about AI in general. Here’s a great summary quote:

The type of bots getting the most coverage use conversational UI and interface with an existing messaging application. They help the user of the app find information or get something done in a seamless, automated way through text-based commands (think typing out: “Hey, Pizza Hut bot! Send me a large pepperoni pizza” and the pizza shows up at your door 30 minutes later).

The concept of conversational UI is very important (see: Apple’s Siri, Amazon’s Alexa, Google Now, Microsoft’s Cortana, etc.)

(PS If you liked this piece but want another pass at the topic, try this one by Recode.)

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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