This document discusses various topics related to artificial intelligence and education including: - The potential limits of big data and its fit in education and learning. - Early visions of adaptive tutoring and computer-aided instruction from 1960 and 1980. - Providing thousands of educational videos and skills practices to help self-directed learning. - The challenge of accepting uncertainty when relying on messy real-world data inputs. - The computational complexity of human decision making compared to artificial systems. - Letting the problem or meaning determine the solution rather than technology itself.

1. artificial or assisted
intelligence?
a few stories about the most sophisticated
computing machinery in the world

2. sprout & co.
creating tools to support
everyday investigations and
help make science a cultural
activity

3. What do we expect the limits of
big data to look and feel like?
Where’s the [mis]fit of big data in
education & learning?

4. almost social
programming
environments

13. sourcing innovations for incorporation
helping people find others who can help
helping people solve their own problems

21. sourcing innovations for incorporation
helping people find others who can help
helping people solve their own problems

22. adaptive tutoring

23. “motion picture and televised
instruction can offer superior
teaching to a large number of
students [...] programmed self-
instructional methods provide the
individual student with a learning
sequence under his control. [...] A
question or problem is presented
[...] the student records his answer
[...] obtains a check on the
accuracy of his answer as soon as
he records it [...] the timing is
under the control of the student,
who proceeds at his own pace.”
Written in 1960

24. “In many schools today, the phrase
‘computer-aided instruction’
means making the computer teach
the child. One might say the
computer is being used to
program the child. In my vision,
the child programs the computer
and, in doing so, both acquires a
sense of mastery over a piece of
the most modern and powerful
technology and establishes an
intimate contact with some of the
deepest ideas from science, from
mathematics, and from the art of
intellectual model building.”
Written in 1980

25. With over 3,200 videos on
everything from arithmetic to
physics, finance, and history
and hundreds of skills to
practice, we're on a mission to
help you learn what you want,
when you want, at your own
pace.

27. Is a machine solving your problem?
Or is a person?

28. empowerme
nt
interconnection

38. empowerme
nt
interconnection

39. Convivial
empowerme
technologies
nt
interconnection

40. People need not only to obtain
things, they need above all the
freedom to make things among
which they can live, to give shape
to them according to their own
tastes, and to put them to use in
caring for and about others.

41. Q&A + 2 questions
What do we expect the limits of
big data to look and feel like?
Where’s the [mis]fit of big data in
education & learning?

42. fin

45. I'm visiting Woz and his daughter
Suzanne, who is in the hospital after
an emergency appendectomy, when
another visitor asks if a certain
friend has been told about the
surgery. Woz proudly whips out his
Magic Link to get her address and
number. Before the device can
retrieve the data, however, Suzanne
produces the number from an
address book in her handbag.

46. Fighting the data providers to give
you those signals in a convenient
form is a losing battle, so the key to
success is getting comfortable with
messy requirements and chaotic
inputs. As an engineer, this can feel
like a deal with the devil, as you
have to accept error and
uncertainty in your results. But the
alternative is no results at all. Pete Warden,
co-founder Jetpac

47. A mammalian neuron takes
about ten milliseconds to
respond to a stimulus. A
driver can respond to a visual
stimulus in a few hundred
milliseconds, and decide an
action, such as making a
turn. So the computational
depth of this behavior is only
a few tens of steps. We don’t
know how to make such a
machine, and we wouldn’t
Gerald Sussman,
know how to program it. Professor of CS @ MIT

48. "What is above all needed is to
let the meaning choose the
word, and not the other way
around. In prose, the worst
thing one can do with words is
surrender to them."

49. "What is above all needed is to let the
problem choose the technology, and not
the other way around. In innovation, the
worst thing one can do with technologies is
surrender to them."