Decision-making in a future of Big Data and AI | Christina Orphanidou | TEDxUniversityofNicosia

00:15:56
https://www.youtube.com/watch?v=ZT8uSJojmqw

Resumo

TLDRThe talk discusses decision-making, highlighting the transition from instinctive human choices to data-driven decisions facilitated by big data and artificial intelligence (AI). The speaker humorously contrasts their ideal productive morning with reality, illustrating how people constantly make decisions, often based on impulses. Using examples like a Cambridge study predicting personality traits via Facebook Likes, they demonstrate how AI can surpass human judgment. AI applications such as personalized recommendations from Amazon and Netflix show its impact on daily decisions. In healthcare, AI's predictive power can prevent crises. The speaker urges audience adaptation to AI, advising trust in data-driven decision-making, despite humans' impulsive nature, for better integration into the AI era.

Conclusões

  • ⏰ Decision-making is constant, affecting even small actions like responding to a phone notification.
  • 💡 Big data and AI are transforming how decisions are made, from intuitive to data-driven approaches.
  • 📊 Cambridge study showed AI predicts personality traits using Facebook Likes with high accuracy.
  • 🛍️ Companies like Amazon use AI to personalize shopping experiences based on user data.
  • 🎥 Netflix employs AI to recommend shows and movies, simplifying choices for users.
  • 🚑 AI in healthcare can predict critical events, potentially saving lives through early intervention.
  • 🔗 For AI to function effectively, people need to share their personal data and trust AI algorithms.
  • 🤔 Humans are naturally impulsive decision-makers, a trait inherited from ancestors for survival.
  • 📈 To benefit from AI, it's crucial to adapt, learn, and decide which decisions AI should make for you.
  • 🧠 Success with AI involves understanding and controlling how its decisions impact your life.

Linha do tempo

  • 00:00:00 - 00:05:00

    The speaker begins by describing their morning routine, emphasizing the choices and decision-making involved. They highlight that like everyone else, they are constantly making decisions, even subconsciously, about small things like scratching one's nose or responding to notifications. The speaker introduces the notion that most decisions are impulsive or based on mood, and suggests that this will fundamentally change with technological advancements and big data, shifting decisions towards a more data-driven process.

  • 00:05:00 - 00:10:00

    Research from the University of Cambridge demonstrated that digital records, such as Facebook Likes, could predict personal traits like personality and intelligence with high accuracy, outperforming even family and friends' assessments. Big data allows computers to make decisions based on statistical analysis, creating a form of artificial intelligence that can surpass human judgment in accuracy. Companies like Amazon and Netflix use this technology to influence consumer choices, suggesting that big data is already integrated into decision-making processes.

  • 00:10:00 - 00:15:56

    The speaker discusses how AI and big data can impact personal routines and even life-saving decisions in healthcare by predicting health crises. While technology advances rapidly, human nature remains impulsive and biased due to evolutionary survival instincts. The speaker advocates for embracing AI and suggests that success involves sharing personal data and trusting AI over impulse. They encourage vigilance and education to discern when to trust AI decisions, asserting individual control over whether to follow machine recommendations.

Mapa mental

Mind Map

Perguntas frequentes

  • What was the speaker's humorous anecdote about their morning routine?

    The speaker humorously described an ideal morning routine focused on productivity but contrasted it with their actual, more disorganized routine.

  • How are big data and AI changing decision-making?

    Big data and AI are shifting decision-making from being impulsive and intuitive to being data-driven and evidence-based.

  • What example did the speaker provide involving Facebook Likes?

    The speaker referenced a Cambridge study that used Facebook Likes to successfully predict people's personality traits.

  • What are some current applications of AI in decision-making?

    AI is used by companies like Amazon and Netflix to provide personalized recommendations based on user data.

  • What potential impact can AI have on healthcare?

    AI can predict health crises early on, such as in a project where AI anticipated potential hospital patient issues based on big data.

  • What does the speaker suggest about human adaptation to AI-driven decision-making?

    Humans should learn to trust AI and adapt to data-driven decisions, even when they conflict with instincts.

  • What is the significance of big data in creating AI?

    Having large datasets allows for statistically valid conclusions, supporting the development of effective AI systems.

  • Why are humans traditionally impulsive decision-makers?

    Historically, humans needed to make quick decisions with limited information for survival, such as reacting to a potential threat.

  • What is the speaker's advice for embracing AI?

    The speaker suggests educating oneself about AI decision-making and deciding when to trust machines.

  • What role does personal data play in AI effectiveness?

    AI systems require access to personal data to make accurate and beneficial decisions for individuals.

Ver mais resumos de vídeos

Obtenha acesso instantâneo a resumos gratuitos de vídeos do YouTube com tecnologia de IA!
Legendas
en
Rolagem automática:
  • 00:00:13
    so this morning my alarm went off at
  • 00:00:16
    6:00 a.m. as soon as it ran I got up
  • 00:00:20
    really quickly I chose to go for a quick
  • 00:00:22
    run I decided on a healthy breakfast of
  • 00:00:25
    yogurt and fruit while listening to
  • 00:00:27
    classical music I chose to spend some
  • 00:00:30
    quality time with my children because I
  • 00:00:33
    would not see that much today I got
  • 00:00:36
    dressed with the outfit I had laid out
  • 00:00:38
    the night before got in the car and
  • 00:00:41
    decided to drive here while listening to
  • 00:00:44
    a mindfulness podcast to calm myself for
  • 00:00:47
    giving this talk I'm kidding of course
  • 00:00:51
    once the alarm went off in the unholy
  • 00:00:54
    hour of 6:00 a.m. I press the snooze
  • 00:00:57
    button until was no longer possible I
  • 00:01:00
    dragged myself out of bed in a foul mood
  • 00:01:03
    and then I texted my friend to complain
  • 00:01:06
    about it why do I have to do this
  • 00:01:08
    you know I debated then for quite a long
  • 00:01:11
    time was whether I should leave my hair
  • 00:01:14
    down or put it up I got ready very
  • 00:01:17
    quickly and then I decided to ask my
  • 00:01:20
    husband to drive her here so that I
  • 00:01:22
    could spend the ride looking at my notes
  • 00:01:24
    calling my mom and all these things so
  • 00:01:27
    what does this story tell us first that
  • 00:01:31
    I need to get a bit more organized
  • 00:01:33
    second that I like the rest of you make
  • 00:01:37
    decisions all the time I have been
  • 00:01:41
    talking for about two minutes now and
  • 00:01:43
    probably you think you have not made any
  • 00:01:45
    decisions since I started talking but
  • 00:01:47
    actually you have whether to scratch
  • 00:01:50
    your nose whether to respond to that
  • 00:01:53
    notification on your phone you have also
  • 00:01:56
    already probably decided whether my talk
  • 00:01:59
    will be interesting and whether you will
  • 00:02:00
    commit to it or whether you will start
  • 00:02:03
    playing with your phone talking to the
  • 00:02:05
    person next to you whom I hope you know
  • 00:02:08
    or just keep looking at the time until
  • 00:02:11
    the coffee break all day every day
  • 00:02:15
    whether you realize it or not you make a
  • 00:02:19
    lot of decisions our decision-making is
  • 00:02:23
    largely based on
  • 00:02:24
    things we have learned an experience in
  • 00:02:26
    the past on associations we have made in
  • 00:02:30
    our brains and sometimes they are even
  • 00:02:33
    based on the pros and cons balancing
  • 00:02:36
    them looking at alternatives calculating
  • 00:02:39
    probabilities and making a decision that
  • 00:02:42
    will maximize benefit in the long term
  • 00:02:44
    not so often though right
  • 00:02:47
    most often our decisions are based on
  • 00:02:49
    our impulses what we feel our mood our
  • 00:02:53
    biases sometimes even how much we have
  • 00:02:56
    had to drink right remember that text we
  • 00:03:00
    all have that text so the way we make
  • 00:03:04
    decisions it's about to fundamentally
  • 00:03:07
    change as we enter this new era of
  • 00:03:10
    technological advancements the era of
  • 00:03:13
    digital the era of big data one big
  • 00:03:16
    change that is about to come away is
  • 00:03:19
    around the way we humans will be making
  • 00:03:22
    decisions both small and big ones in the
  • 00:03:27
    era of big data decision-making will
  • 00:03:31
    move away from impulsive intuitive and
  • 00:03:34
    sometimes guided by drinking to
  • 00:03:37
    decisions based on data and evidence and
  • 00:03:40
    our partners for making all these
  • 00:03:42
    decisions will be intelligent machines
  • 00:03:45
    so firstly what is this big data I keep
  • 00:03:48
    talking about why is everyone talking
  • 00:03:51
    about it and how is it linked to
  • 00:03:53
    decision making big data describes large
  • 00:03:57
    and diverse sets of information that
  • 00:04:00
    basically keep growing because as you
  • 00:04:04
    very well know every aspect of our lives
  • 00:04:06
    has now been digitalized the way we work
  • 00:04:09
    they will communicate the way we
  • 00:04:11
    socialize even the way we fall in love
  • 00:04:13
    has been digitalized the word is now
  • 00:04:16
    full of data all activity that will
  • 00:04:19
    perform can now be locked and saved and
  • 00:04:22
    as a result the word now is packed with
  • 00:04:25
    an inconceivable amount of digital
  • 00:04:28
    records which we now call big data big
  • 00:04:32
    data is well big it's fast
  • 00:04:36
    and it comes in many different forms it
  • 00:04:38
    can be numbers it can be text can be
  • 00:04:41
    images that can be videos but what does
  • 00:04:44
    this have to do with decision making let
  • 00:04:47
    me share with you a story a bit more
  • 00:04:50
    than a decade ago two researchers at the
  • 00:04:53
    University of Cambridge in the UK
  • 00:04:55
    decided to investigate whether those
  • 00:04:57
    digital records of human behavior could
  • 00:05:00
    be used to predict someone's personality
  • 00:05:03
    what they wanted to see whether was
  • 00:05:05
    whether you could predict things like
  • 00:05:07
    openness and intelligence or even other
  • 00:05:11
    more intrusive things like sexual
  • 00:05:14
    orientation ethnic origin and political
  • 00:05:17
    views the digital records which they
  • 00:05:20
    would use would be simply people's
  • 00:05:22
    Facebook Likes couple of years later
  • 00:05:26
    they published a study where they showed
  • 00:05:29
    the results on data that they had
  • 00:05:31
    collected from 58,000 people who had
  • 00:05:34
    given them access to their Facebook
  • 00:05:36
    profiles and had also answered some
  • 00:05:38
    personality tests the results were
  • 00:05:41
    amazing they showed that on the basis of
  • 00:05:45
    only Facebook Likes the computer could
  • 00:05:47
    predict someone's skin colour with 95
  • 00:05:50
    percent accuracy and someone's gender
  • 00:05:53
    with 93 percent accuracy and what else
  • 00:05:57
    they did was that they showed what kind
  • 00:05:59
    of likes could be linked and could be
  • 00:06:02
    predictive of some personality traits
  • 00:06:04
    for example they showed that like that
  • 00:06:08
    were predictive of a high IQ where on
  • 00:06:11
    pages are just the lord of the rings'
  • 00:06:13
    science and kerry fries coincidentally
  • 00:06:18
    all things i like in contrast like that
  • 00:06:24
    could predict a low IQ where are you
  • 00:06:29
    worried
  • 00:06:31
    on pages related to makeup to
  • 00:06:34
    motorcycles and on I like being a Manny
  • 00:06:37
    groups I do like those even more
  • 00:06:43
    remarkably in a study the same
  • 00:06:46
    researchers published a couple of years
  • 00:06:47
    later what they did was that they asked
  • 00:06:50
    colleagues relatives and friends of the
  • 00:06:53
    participants to give their judgments of
  • 00:06:56
    the participants personalities and then
  • 00:06:58
    they compared them with the computers
  • 00:07:00
    judgments who do you think won
  • 00:07:02
    they showed remarkably that with only
  • 00:07:06
    ten likes the computer knew you better
  • 00:07:09
    and your colleague with only seventy
  • 00:07:12
    likes the computer you do better than
  • 00:07:14
    your friend with only a hundred and
  • 00:07:16
    fifty likes the computer you do better
  • 00:07:18
    than a relative and with 300 likes the
  • 00:07:22
    computer knew you better than your
  • 00:07:23
    partner the analysis they used were
  • 00:07:27
    really not that sophisticated and I hope
  • 00:07:30
    they're not watch yet they managed to
  • 00:07:33
    build a system that could make better
  • 00:07:35
    decisions than humans how did they
  • 00:07:38
    manage to do that
  • 00:07:39
    they managed it because they had big
  • 00:07:41
    data by having this big data set and
  • 00:07:44
    being able to analyze it they were able
  • 00:07:47
    to draw conclusions that were
  • 00:07:49
    statistically valid and to build an
  • 00:07:51
    intelligent decision making machine to
  • 00:07:54
    basically create artificial intelligence
  • 00:07:58
    the equation usually looks like this
  • 00:08:00
    data big data plus fancy or not so fancy
  • 00:08:05
    algorithms or months' equals artificial
  • 00:08:09
    intelligence the maps have been around
  • 00:08:12
    for a long time what we now have
  • 00:08:14
    available to us is big data and they are
  • 00:08:17
    already being used for effective our
  • 00:08:20
    decision-making Amazon is doing it
  • 00:08:23
    Amazon has been doing it for years they
  • 00:08:25
    have been shaping our shopping decisions
  • 00:08:27
    for years by giving us personalized
  • 00:08:30
    recommendations on products who might be
  • 00:08:33
    interested in buying using data of our
  • 00:08:36
    own past purchases and the past
  • 00:08:39
    purchases of people who are similar to
  • 00:08:41
    us Netflix is also doing it
  • 00:08:44
    for recommending a series or movies we
  • 00:08:46
    are likely to like both Amazon and
  • 00:08:49
    Netflix make these recommendations using
  • 00:08:52
    big data and artificial intelligence
  • 00:08:54
    methodologies making our everyday
  • 00:08:57
    decision-making much much easier so
  • 00:09:00
    christmas is coming up my 9 year old is
  • 00:09:03
    obsessed with Harry Potter so he's
  • 00:09:05
    getting a Harry Potter Lego set but
  • 00:09:08
    you're not supposed to tell so what else
  • 00:09:11
    can i buy him to go with it
  • 00:09:13
    do I need to worry about it no because
  • 00:09:16
    Amazon has filtered down all the choices
  • 00:09:19
    for me making that my decision-making
  • 00:09:21
    much much easier potential employers may
  • 00:09:26
    now be making decisions about us without
  • 00:09:29
    us knowing using our digital records if
  • 00:09:33
    Facebook Likes are so linked with
  • 00:09:35
    personality traits why not use digital
  • 00:09:38
    records like Facebook likes to see if I
  • 00:09:41
    will be resilient employee and a good
  • 00:09:44
    team player you could this photo come
  • 00:09:47
    back and bite me and yes I'm in the
  • 00:09:50
    haircut in the future
  • 00:09:54
    sometimes intelligent decisions maybe
  • 00:09:58
    even made before weaving knew that the
  • 00:10:00
    decision had to be made while I was
  • 00:10:03
    working as a researcher at the
  • 00:10:05
    University of Oxford part of my research
  • 00:10:07
    had to do with developing intelligent
  • 00:10:10
    tools to facilitate decision-making in
  • 00:10:13
    healthcare a project I was working on
  • 00:10:16
    had to do with use using a big data set
  • 00:10:18
    of health data from hospital patients in
  • 00:10:22
    order to build intelligent systems that
  • 00:10:24
    could predict whether something bad was
  • 00:10:27
    about to happen to someone while they
  • 00:10:29
    were in hospital how these systems
  • 00:10:31
    worked was by learning what combinations
  • 00:10:35
    of physiological characteristics could
  • 00:10:37
    signal future health crises and then
  • 00:10:40
    unalloyed was being generated now for
  • 00:10:43
    someone in hospital something bad
  • 00:10:45
    happening is not totally unexpected but
  • 00:10:48
    let's consider this scenario you're
  • 00:10:50
    peacefully sitting in your living room
  • 00:10:52
    watching a David Attenborough
  • 00:10:54
    documentary or Maria is today
  • 00:10:57
    you're watching the Kardashians so you
  • 00:11:00
    have been feeling a bit uneasy all day
  • 00:11:03
    but you are dismissing your symptoms
  • 00:11:05
    because you had a long day you don't
  • 00:11:08
    want to bother anyone you don't want to
  • 00:11:10
    make a fuss and suddenly you hear an
  • 00:11:12
    ambulance and there's a knock on the
  • 00:11:14
    door an intelligent system predicted
  • 00:11:17
    that you are about to have a heart
  • 00:11:19
    attack and they have come to take into
  • 00:11:21
    hospital making the right decision for
  • 00:11:24
    you
  • 00:11:25
    in those cases artificial intelligence
  • 00:11:28
    can literally proved to be a lifesaver
  • 00:11:31
    so as we enter this new era of digital
  • 00:11:35
    Big Data machine based decisions how
  • 00:11:39
    would my morning routine look like was
  • 00:11:41
    probably my sleep time in wake time
  • 00:11:43
    would have been optimized for me using
  • 00:11:46
    some fancy maths and the text message to
  • 00:11:49
    my friend could have been sent for me
  • 00:11:51
    using some prediction algorithm of what
  • 00:11:54
    I would be likely to want to tell my
  • 00:11:56
    friends my outfit would have been chosen
  • 00:11:59
    for me using a personalized algorithm
  • 00:12:02
    and the music I would listen to in the
  • 00:12:05
    car while coming here would have been
  • 00:12:07
    selected from me based on my
  • 00:12:09
    physiological signals as detected by my
  • 00:12:13
    car seat great huh actually the machine
  • 00:12:16
    could even give this talk for me in the
  • 00:12:18
    future but are we ready for all this are
  • 00:12:22
    we ready for data-driven and
  • 00:12:24
    evidence-based decision-making the
  • 00:12:27
    answer is not really because we humans
  • 00:12:30
    are not really built to be data-driven
  • 00:12:33
    decision makers technology is changing
  • 00:12:37
    our lives in a very fast pace but our
  • 00:12:39
    DNA has not really yet caught up
  • 00:12:42
    let's consider this scenario our
  • 00:12:45
    ancestors and sitting in the white
  • 00:12:47
    around the fire and they suddenly hear a
  • 00:12:51
    strange sound what could it be is it a
  • 00:12:54
    lion preparing for his evening meal
  • 00:12:58
    meaning us or is it some juicy prey that
  • 00:13:02
    could feed our family for a few days had
  • 00:13:05
    that been a world driven by artificial
  • 00:13:08
    intelligence our ancestors would have
  • 00:13:10
    pulled
  • 00:13:11
    their smartphones and would have checked
  • 00:13:13
    what the alcohol said that would have
  • 00:13:15
    made the decision according to the
  • 00:13:17
    algorithm with great accuracy in actual
  • 00:13:22
    fact our decisions had to make the
  • 00:13:24
    decision had to be made quickly with
  • 00:13:26
    little or no information at hand who do
  • 00:13:30
    you think survived the ones who stood up
  • 00:13:32
    and impulsively started running or the
  • 00:13:35
    ones who sticked around to make a
  • 00:13:37
    probabilistic assessment of the
  • 00:13:39
    situation let's have a show of hands who
  • 00:13:42
    thinks the impulsive ones who run away
  • 00:13:44
    survived okay who thinks the analysts
  • 00:13:50
    survived you're just trying to ruin my
  • 00:13:54
    point so most of you have common sense
  • 00:13:59
    and you've realized that it was the
  • 00:14:03
    impulsive ones who survived and that is
  • 00:14:06
    why we the descendants have evolved to
  • 00:14:09
    be largely impulsive decision makers
  • 00:14:11
    this impulsiveness along with many
  • 00:14:14
    biases we are carrying have plagued
  • 00:14:17
    human judgment and decision-making for
  • 00:14:20
    thousands of years as someone who works
  • 00:14:24
    on developing artificial intelligence
  • 00:14:27
    and on helping people and organizations
  • 00:14:29
    use it I can tell you for certain that
  • 00:14:32
    while those systems are not perfect in
  • 00:14:35
    many many situations machine based
  • 00:14:38
    decisions I excellent and they are
  • 00:14:40
    better than human decisions embracing
  • 00:14:43
    artificial intelligence can definitely
  • 00:14:46
    make our lives better if you want to
  • 00:14:49
    adapt to this new world and to benefit
  • 00:14:52
    from artificial intelligence in my
  • 00:14:54
    opinion there is two things you need to
  • 00:14:57
    accept the first is that for artificial
  • 00:15:00
    intelligence to work for you you have to
  • 00:15:03
    accept to expose yourself by sharing
  • 00:15:06
    your own personal data and secondly that
  • 00:15:09
    you will have to learn to trust
  • 00:15:12
    artificial intelligence even when it
  • 00:15:14
    goes against your own instincts and
  • 00:15:17
    impulses the recipe for success is not
  • 00:15:21
    allowing yourself to be passively swept
  • 00:15:24
    by this big day dine
  • 00:15:26
    artificial-intelligence wave with no
  • 00:15:29
    control of the situation
  • 00:15:30
    stay vigilant and educate yourselves
  • 00:15:33
    about which decisions you can trust at
  • 00:15:36
    the end of the day the decision about
  • 00:15:39
    whether you should follow the machines
  • 00:15:42
    decision should be yours and yours only
  • 00:15:47
    [Applause]
  • 00:15:54
    [Applause]
Etiquetas
  • decision-making
  • big data
  • artificial intelligence
  • impulsiveness
  • Facebook Likes
  • personalization
  • healthcare AI
  • human adaptation
  • AI ethics
  • data trust