Fei-Fei Li

Quotes are word for word from the source.

• Supporting Educators

  “What I worry about are teachers and some parents, because I think our society today, and especially Silicon Valley, are not doing them a service. We're forgetting about them.”

  Our noteShe expresses concern that society is neglecting the needs of teachers and parents in the AI era.

• Contrarian focus

  “we took a departure from everybody else who are really focusing only on algorithm and said that we need data.”

  Our noteShe explains why her lab shifted focus from algorithms to large-scale data collection.

• Identifying inflection points

  “we know in the research community when something this drastic happens, it means a inflection point.”

  Our noteFei-Fei Li was discussing the massive drop in error rates during the 2012 ImageNet competition.

• Limits of human memory

  “retaining the patterns of 1,000 object classes, even if some classes you're familiar is not that easy.”

  Our noteShe was explaining why even smart graduate students make errors when labeling large datasets.

• The AI recipe

  “the recipe hasn't changed, now we have an even more powerful neural network algorithm called Transformer, but we have even more data on the internet”

  Our noteShe was explaining how the combination of algorithms, data, and compute power drives AI progress.

• AI versus human learning

  “But I do want to point out that departure between today's AI algorithm that is learned with a humongous amount of data versus how humans have evolved.”

  Our noteFei-Fei Li was discussing how children learn from very few examples compared to AI.

• AI combinatorial creativity

  “Because AI has learned so many things, it can combine information in highly creative way.”

  Our noteAcknowledging that AI possesses a specific type of creativity through pattern synthesis.

• Learning for agency

  “By learning, one feels more in control. By learning, you're less scared of trying, and by learning, you retain that agency and that dignity”

  Our noteExplaining why non-technical people should still learn about AI technology.

• Math of Motivation

  “So you could build this in the mathematics. So certain things, whether you call it motivation, or in machine learning world, we call them objective functions, you can build certain things into math.”

  Our noteDescribing how human traits like motivation are translated into machine learning objectives.

• Avoiding Anthropomorphism

  “as a human, if you anthropomorphize it too much, you might call it urgency or motivation, but the truth is this is just a different kind of objective for the algorithm.”

  Our noteClarifying that AI 'behaviors' are actually just mathematical objectives.