The Wonderful and Terrifying Implications of Computers that Can Learn (TED Talks)

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excruciating – extremely painful/boring

excruciating pain/problems/noise

Now, programming, for those of you here that haven’t done it yourself, requires laying out in excruciating detail every single step that you want the computer to doing order to achieve your goal.


In 1956, he wanted to get this computer to be able to beat him at checkers.

vantage point — a position from which you can see things well (пункт наблюдения).

So from this vantage point, I was able to find out a lot about what machine learning can do in the past, can do today, and what it could do in the future.

obscure — not clearly expressed, or not easy to understand

In this obscure competition from Germany called the German Traffic Sign Recognition Benchmark,deep learning had learned to recognize traffic signs like this one.

to crunch (numbers) = to do a lot of calculations

In 2012, Google announced that they had a deep learning algorithm watch YouTube videos and crunched the data on 16,000 computers for a month.

sentiment = a belief or an attitude

As you can see here, this Stanford-based system showing the red dot at the top has figured out that this sentence is expressing negative sentiment.

to start from scratch = to start from the very beginning

So these images are entirely unlabeled, so I have to start from scratch.

iteration = the process of repeating a mathematical or computer operation, starting with the result of the previous operation each time

So this process takes about four or five iterations.

to be off the chart = at a very high level

Oh, computers are still pretty dumb.» Right? But in five years’ time, computers will be off this chart.



  1. Did you learn anything new from this talk? What are the examples of computers being able to learn?
  2. What are the possible positive and negative implications of computers being “off the chart”?
  3. Are you optimistic or pessimistic about the role of computers in the future?