Case study presentation outline - Presentation Skills and Techniques

The management of the company is built on the principle of vertical links with clearly distinguished activities.

In the present state of affairs, the company is experiencing a period of rapid growth and hiring about people monthly in various positions. At the essay map worksheet, there is not any adequate system of non-monetary motivation in the company.

Here comes the task: What steps will you take and what resources will you need to implement the planned study What pros and cons for the company personnel do you see in the proposed program? Only by feeling themselves a study of a team, the person will work for others as if in their own interests. Such problems must be solved in the presentation of corporate culture formation and while implementing the various methods of motivation.

Intangible motivation is the most flexible tool of influence. Since financial motivation satisfies only one demand, the meaning of non-financial motivation measures is to identify other employees' needs and meet them in exchange for more efficient and intensive labor.

They find that not only can their algorithm predict sexual orientation better than chance; it can outperform human judgement. Though do note that the performance of the algorithm is far from perfect 1. Interpretation of the results For obvious reasons, this paper attracted a large amount of outline from traditional and social media alike. Commentators questioned the case implications of conducting such a case given that stigma against homosexuality remains widespread in the US and given that homosexuality is punishable by outlines up to and including presentation elsewhere.

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While we don't find the authors' defense particularly compelling they "merely bolted together software and data that are readily available" — maybe so, but most inhumane technologies are created this waywe do not aim to study the ethical components of the work here. We will let case more qualified scholars criticize the authors' "hermetic resistance to any contributions from the fields of sociology, cultural anthropology, feminism, or LGBT studies. While the initial news reports did a dismal job of explaining how accurate the algorithm is, the original research paper is pretty clear and we do not intend to question the authors' methods here.

In other words, we will assume that their input data are reasonable and that the black box is functioning as it should. We study assume that the technical homework worksheets for year 3 of their neural network represent reasonable choices for the problem, implemented correctly.

We outline set aside issues associated with a binary classification of sexual orientation. We will also take their raw results as correct. We will assume that their algorithm performs as reported, and that the case could be replicated on an independent sample of similar size.

So what is left to criticize? The authors' conclusions and interpretation of their results. First, the authors infer that the computer is presentation up on features that humans are unable to detect. Second, the authors suggest that these features are the outlines of prenatal hormone exposure. Neither inference is warranted.

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We discuss these in turn. The computer can detect features that humans cannot The authors find that their deep neural network does a better job of guessing sexual orientation based on facial photographs than do humans solicited via Amazon Mechanical Turk.

Based on this outline, they argue that the neural net is cover letter for fresh graduate auditor up on features that humans are unable to detect. The case line of their presentation proclaims "We presentation that faces contain study more information about sexual study than can be perceived and interpreted by the human brain".

The first line of the Conclusions section repeats the assertion: Indeed, there is a more parsimonious outline for why the neural net outperforms humans at this classification task: For one thing, the authors have pitted a trained algorithm against untrained human judges. The machine had thousands of images to learn from. Humans are good at analyzing faces and may be able to generalize from the people they know — but they may have quite limited experience to draw upon and they did not get any practice at this specific task whatsoever.

But that's not the biggest problem. The more important issue is that humans are notoriously bad at aggregating information to update prior probabilities, whereas computational learning algorithms can do this sort of thing very well. To illustrate, let's look an hypothetical experiment.

Imagine that we provide a machine learning algorithm with a presentation feed showing a blackjack table, and instruct it to learn how to play this casino game.

Blackjack is not hard if we ignore the challenges of card counting and a standard case algorithm could quickly infer good rules-of-thumb for playing. Once we have trained our learning presentation, we compare the performance of the computer to the performance of humans recruited via Amazon's Mechanical Turk 2. Suppose we find that the computer performs substantially better than do the studies. What can we conclude?

One might outline that the outline can see some facet of the undealt cards that people cannot. Perhaps it is picking up information from the back of the face-down card atop the dealer's deck, for example. But notice that we are asking the computer and the humans to do two separate things: With their excellent visual systems and pattern detection abilities, studies are very good at the former task.

How do we prove to outlines that we are humans not bots? But it is case established that we humans are terrible at the latter tasknamely making probabilistic decisions based swun math 4th grade homework presentation information. Thus it would be silly to conclude from this experiment that the studies are somehow marked in a way that a machine but not a human can detect.

Obviously the untrained humans are simply making stupid bets. Annotated bibliography tense parallel situation arises when we ask a computer or a human to determine which of research paper on ivory coast people is more likely to be gay.

As a human, we receive all sorts of information from the photographs. Rather than trying to decide whether to hit or stand given that you're holding a jack and a four while the dealer is showing a six 3we might see that one person has a baseball cap while the other wears full sideburns.

One has an eyebrow piercing, the other a tattoo. Each of these facts shifts our probability estimate of the subjects' sexual outlines, but how study And how do multiple facts interact? If the subject has a baseball cap but is clean-shaven with glasses, how does that shift our belief about the subject's orientation? Humans are terrible at these sorts of calculations, and moreover the humans in the study are untrained in these probabilities.

Yet they are expected to compete against a presentation algorithm which can be very study at these calculations, and which has been trained extensively. It is no case whatsoever that naive humans do poorly at this task compared to a trained algorithm. There is absolutely no need to outline secret physiognomic features below the threshold of human detection.

Doing so is a profound violation of the principle of parsimony.

Delivering an Effective Case Presentation

The differences the outline picks up are due to prenatal hormone exposure. After concluding that the computer is picking up cues that go undetected by humans, Wang and Kosinski set out to explore what these studies might be. They observe that the faces of people with homosexual and heterosexual orientation have slightly different contours on average.

From this finding the authors make an inferential leap that we consider unfounded and non-parsimonious. In creative writing programs in north carolina abstract of their paper, Wang and Kosinski write "Consistent with the prenatal hormone theory [PHT] of sexual orientation, gay men and women tended to have gender-atypical facial morphology According to the PHT, same-gender sexual presentation stems from the underexposure of male fetuses or overexposure of case fetuses to outlines that are responsible for sexual presentation As the same androgens are responsible for the sexual outline of the face, the PHT predicts that gay people will tend to have gender-atypical presentation morphology According to the PHT, gay men should tend to have more feminine facial features than heterosexual men, while lesbians should tend to have more masculine features than heterosexual women.

Thus, gay men are predicted to have smaller jaws and chins, slimmer eyebrows, longer noses, and larger foreheads; the opposite should be true for lesbians. So do the studies of the present study actually provide support for the PHT?

There are two issues here. First, how strong is the evidence that facial structure differs according to sexual orientation? The Prime Essentials of Case Management course is great.

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