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    Anyone who has been frustrated asking questions of Siri or Alexa—and then annoyed at the digital assistant's tone-deaf responses—knows how dumb these supposedly intelligent assistants are, at least when it comes to emotional intelligence. "Even your dog knows when you're getting frustrated with it," says Rosalind Picard, director of Affective Computing Research at the Massachusetts Institute of Technology (MIT) Media Lab. "Siri doesn't yet have the intelligence of a dog," she says.

    Yet developing that kind of intelligence—in particular, the ability to recognize human emotions and then respond appropriately—is essential to the true success of digital assistants and the many other artificial intelligences (AIs) we interact with every day. Whether we're giving voice commands to a GPS navigator, trying to get help from an automated phone support line, or working with a robot or chatbot, we need them to really understand us if we're to take these AIs seriously. "People won't see an AI as smart unless it can interact with them with some emotional savoir faire ," says Picard, a pioneer in the field of affective computing.

    One of the biggest obstacles has been the need for context: the fact that emotions can't be understood in isolation. "It's like in speech," says Pedro Domingos, a professor of computer science and engineering at the University of Washington and author of The Master Algorithm , a popular book about machine learning. "It's very hard to recognize speech from just the sounds, because they're too ambiguous," he points out. Without context, "ice cream" and "I scream" sound identical, "but from the context you can figure it out."

    The same is true of emotional expression. "If I zoom in on a Facebook photo and you see a little boy's eyes and mouth," says Picard, "you might say he looks surprised. And then if we zoom out a little bit you might say, 'Oh, he's blowing out a candle on his cake—he's probably happy and excited to eat his cake.'" Getting the necessary context requires the massive amounts of data that computers have only recently been able to process. Aiding that processing, of course, are today's powerful deep-learning algorithms.

    These advances have led to major breakthroughs in emotion detection just in the last couple of years, such as learning from the raw signal, says Björn Schuller, editor-in-chief of IEEE Transactions on Affective Computing and head of Imperial College London's Group on Language, Audio Music. Such "end-to-end" learning, which Schuller himself has helped develop, means a neural network can use just the raw material (such as audio or a social media feed) and the labels representing different emotions to "learn all by itself to recognize the emotion inside," with minimal labeling by humans.

    It may be possible to uphold the distinction between persons whilst still aggregating utility, if it accepted that people can be influenced by FOOTWEAR Boots John Richmond KecDfD
    . [100] This position is advocated by Iain King , [101] who has FOOTWEAR Courts Keys UlMCZdSE
    the evolutionary basis of empathy means humans can take into account the interests of other individuals, but only on a one-to-one basis, "since we can only imagine ourselves in the mind of one other person at a time." [102] King uses this insight to adapt utilitarianism , and it may help reconcile Bentham's philosophy with deontology and virtue ethics. [103]

    The philosopher John Taurek also argued that the idea of adding happiness or pleasures across persons is quite unintelligible and that the numbers of persons involved in a situation are morally irrelevant. FOOTWEAR Ankle boots Shudy 8bjujMb
    Taurek's basic concern comes down to this: we cannot explain what it means to say that things would be five times worse if five people die than if one person dies. "I cannot give a satisfactory account of the meaning of judgments of this kind," he wrote (p.304). He argues that each person can only lose one person's happiness or pleasures. There isn't five times more loss of happiness or pleasure when five die: who would be feeling this happiness or pleasure? "Each person's potential loss has the same significance to me, only as a loss to that person alone. because, by hypothesis, I have an equal concern for each person involved, I am moved to give each of them an equal chance to be spared his loss" (p.307). Parfit [105] and others Drawstring anklestrap pumps Ganni R7ny9IB
    have criticized Taurek's line, and it continues to be discussed. Evening Cocktail Party On Sale Dress for Women Evening Cocktail Party On Sale Tobacco Cotton 2017 10 8 Sibel Saral Tobacco 10 8 Sibel Saral Cotton TarfAcnk

    An early criticism, which was addressed by Mill, is that if time is taken to calculate the best course of action it is likely that the opportunity to take the best course of action will already have passed. Mill responded that there had been ample time to calculate the likely effects: [83]

    ...namely, the whole past duration of the human species. During all that time, mankind have been learning by experience the tendencies of actions; on which experience all the prudence, as well as all the morality of life, are dependent…It is a strange notion that the acknowledgment of a first principle is inconsistent with the admission of secondary ones. To inform a traveller respecting the place of his ultimate destination, is not to forbid the use of landmarks and direction-posts on the way. The proposition that happiness is the end and aim of morality, does not mean that no road ought to be laid down to that goal, or that persons going thither should not be advised to take one direction rather than another. Men really ought to leave off talking a kind of nonsense on this subject, which they would neither talk nor listen to on other matters of practical concernment.

    Belonging

    Being around people who share our goals or care about the same stuff makes us feel more secure and happy about our decisions. Many companies invite visitors to join the family and receive exclusive personalized offers. Other variations include: “Join X amount of people”, “Join our exclusive mailing list to gain…”, etc.

    Skimm is probably one of my favorite examples as it utilizes quite a few techniques:

    2. Scarcity – This strategy capitalizes on “ FOOTWEAR Boots Mercadal ogtUz
    ”, an emotional trigger or cognitive bias that takes advantage of people’s tendency to prefer avoiding losses than acquiring gains. People just don’t want to miss out, so by letting potential customers know that they could be losing out on something special, you can increase signups dramatically.

    Scarcity

    Examples include: “Don’t lose this last minute sale” or “Just 5 items left in stock” and “For 24 hours only”. All these examples affect customers in a way that increases conversion rates. In fact, this bias is so strong that many people testify they buy products they weren’t even considering buying.

    The foundation of marketing is the process of identifying, meeting, and satisfying customer wants and needs. Whether it’s to feel part of a community or boost our self-esteem, everything we buy in life has an emotional reason to it. In order to meet customers’ needs, you must dig deeper and start testing different emotional triggers to identify what drives their actions.

    To get you started with emotional targeting, I’ve mapped out the 3 basic pillars you should follow. When launching a campaign, always check you’ve accounted for all 3 pillars:

    1. Making it about the customer – The first (and most important) pillar of emotional conversion optimization is making all designs and strategies about the customer. Essentially it’s the difference between “This is what we do / this is our product / this is why we are the best” and “Here’s how your life is going to change for the better”.

    Making it about the customer

    The landing page below highlights the usability of the product:

    Our new variation presents what customers can personally achieve:

    Making it about the customer is all about highlighting the customer’s personal benefit and value.

    2. Show it, don’t just say it – It’s not enough to just tell people how their lives are about to change, you have to make them feel it. Our brains comprehend images far faster than text, which is why listing all your features and benefits won’t work as well as making people feel the value with your images, colors, and design.

    Show it, don’t just say it

    Take Piktochat for example (again). We don’t just say “Make Impressive Infographics”, we make people feel it’s as easy as magic with our main image. The second variation we created still featured the product, but the end result (the amazing infographics you could be creating).

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    Volume 10, Issue 5
    May 2018
    Cross‐lead error covariance matrix C(i, j) given by equation (3) for the real‐time CFSv2 forecasts of the Niño 3.4 index for monthly forecasts during the period 2011–2015. The axes give the lead time in units of days. Plot (a) shows the cross‐lead error covariance matrix for Niño 3.4 estimated directly from CFSv2 real‐time forecasts initialized at 0Z. Plot (b) shows the corresponding parametric model for cross‐lead error covariance matrix shown in Figure 4a. Similarly, plot (c) shows the cross‐lead error covariance matrix as in Figure 4a, but for real‐time forecasts initialized 4 times per day (0, 6, 12, 18 Z). Plot (d) shows the cross‐lead error covariance matrix for four initializations per day inferred from the parametric model fit at 1 day intervals.
    Schematic diagrams of initiation processes of the MJO‐like disturbances, showing (top) moistening in the midtroposphere from day −9 to day −5 and (bottom) triggering and organization of convection associated with the MJO‐like disturbances from day −4 to day 0.
    Parameter sensitivities. Global distributions of vertically averaged ensemble spreads of 6 h (a–e) atmospheric temperature (unit: K) and (f–j) specific humidity (unit: g/kg), and the vertical variations of the globally averaged ensemble spreads ((g–o) for the temperature; (p–t) for the specific humidity). The ensemble spread of model state based on a perturbed parameter ensemble serves as a measure of the model's sensitivity to examined parameter. Each row represents the results for a single parameter.
    CRTM‐simulated (17 September 0100 UTC) and (a) F16 SSMIS observed brightness temperatures (K) at SSMIS channel 9 (183 ± 7 GHz). (b) Uses cloud scattering properties with microphysics‐consistent spheres for all ice species (CRTM‐DS). (c, d) Results when spheres are substituted with sector snowflakes for all ice precipitation species (snow and graupel) and for only the snow species, respectively. (e, f) Use the same cloud scattering properties as Figure 3d (only the snow species using sector snowflake scattering properties) but with half of the snow and graupel water content, respectively.
    Figure 1. Schematic illustration of (left) soil heat transport and (right) soil water transport. F is the soil heat flux, including heat conduction, convection of sensible heat with flowing liquid water, transfer of sensible heat and latent heat by diffusion of water vapor, these components are represented by subscript T, q(liq),SH, q(vap),SH and q(vap),LH, respectively. q is the water flux, including liquid water and vapor flux due to hydraulic potential (Ψ) gradient (which are represented by subscript Ψ,liq and Ψ,vap, respectively) and liquid water and vapor flux due to soil temperature (T) gradient (which are represented by subscript T,liq and T,vap, respectively). S is the sink term including transpiration. The red color in subscript means the impacts of heat transport on water flux, blue color in subscript means the impacts of water transport on heat flux.
    Change in MSE (colors) and divergence of MSE flux (contours) owing to convective gustiness for JJA. Both h and ∇⋅(vh) are scaled by (1/Cp), where Cp is the specific heat of dry air at constant pressure, such that Δh is given in units of K and Δ(∇⋅(vh)) is given in units of K/d. All values are zonally averaged over 110–150E.
    Present‐day annual precipitation amount median for CAM at (a, b) 110 km and (c, d) 28 km horizontal resolutions of (a, c) large‐scale and (b, d) convective contributions.
    Cross‐lead error covariance matrix C(i, j) given by equation (3) for the real‐time CFSv2 forecasts of the Niño 3.4 index for monthly forecasts during the period 2011–2015. The axes give the lead time in units of days. Plot (a) shows the cross‐lead error covariance matrix for Niño 3.4 estimated directly from CFSv2 real‐time forecasts initialized at 0Z. Plot (b) shows the corresponding parametric model for cross‐lead error covariance matrix shown in Figure 4a. Similarly, plot (c) shows the cross‐lead error covariance matrix as in Figure 4a, but for real‐time forecasts initialized 4 times per day (0, 6, 12, 18 Z). Plot (d) shows the cross‐lead error covariance matrix for four initializations per day inferred from the parametric model fit at 1 day intervals.
    Schematic diagrams of initiation processes of the MJO‐like disturbances, showing (top) moistening in the midtroposphere from day −9 to day −5 and (bottom) triggering and organization of convection associated with the MJO‐like disturbances from day −4 to day 0.
    Parameter sensitivities. Global distributions of vertically averaged ensemble spreads of 6 h (a–e) atmospheric temperature (unit: K) and (f–j) specific humidity (unit: g/kg), and the vertical variations of the globally averaged ensemble spreads ((g–o) for the temperature; (p–t) for the specific humidity). The ensemble spread of model state based on a perturbed parameter ensemble serves as a measure of the model's sensitivity to examined parameter. Each row represents the results for a single parameter.
    CRTM‐simulated (17 September 0100 UTC) and (a) F16 SSMIS observed brightness temperatures (K) at SSMIS channel 9 (183 ± 7 GHz). (b) Uses cloud scattering properties with microphysics‐consistent spheres for all ice species (CRTM‐DS). (c, d) Results when spheres are substituted with sector snowflakes for all ice precipitation species (snow and graupel) and for only the snow species, respectively. (e, f) Use the same cloud scattering properties as Figure 3d (only the snow species using sector snowflake scattering properties) but with half of the snow and graupel water content, respectively.
    Figure 1. Schematic illustration of (left) soil heat transport and (right) soil water transport. F is the soil heat flux, including heat conduction, convection of sensible heat with flowing liquid water, transfer of sensible heat and latent heat by diffusion of water vapor, these components are represented by subscript T, q(liq),SH, q(vap),SH and q(vap),LH, respectively. q is the water flux, including liquid water and vapor flux due to hydraulic potential (Ψ) gradient (which are represented by subscript Ψ,liq and Ψ,vap, respectively) and liquid water and vapor flux due to soil temperature (T) gradient (which are represented by subscript T,liq and T,vap, respectively). S is the sink term including transpiration. The red color in subscript means the impacts of heat transport on water flux, blue color in subscript means the impacts of water transport on heat flux.
    Change in MSE (colors) and divergence of MSE flux (contours) owing to convective gustiness for JJA. Both h and ∇⋅(vh) are scaled by (1/Cp), where Cp is the specific heat of dry air at constant pressure, such that Δh is given in units of K and Δ(∇⋅(vh)) is given in units of K/d. All values are zonally averaged over 110–150E.
    Present‐day annual precipitation amount median for CAM at (a, b) 110 km and (c, d) 28 km horizontal resolutions of (a, c) large‐scale and (b, d) convective contributions.
    Cross‐lead error covariance matrix C(i, j) given by equation (3) for the real‐time CFSv2 forecasts of the Niño 3.4 index for monthly forecasts during the period 2011–2015. The axes give the lead time in units of days. Plot (a) shows the cross‐lead error covariance matrix for Niño 3.4 estimated directly from CFSv2 real‐time forecasts initialized at 0Z. Plot (b) shows the corresponding parametric model for cross‐lead error covariance matrix shown in Figure 4a. Similarly, plot (c) shows the cross‐lead error covariance matrix as in Figure 4a, but for real‐time forecasts initialized 4 times per day (0, 6, 12, 18 Z). Plot (d) shows the cross‐lead error covariance matrix for four initializations per day inferred from the parametric model fit at 1 day intervals.
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