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I believe that this process of upgrading begins with changing our definition of what it means to “be smart.” To date, many of us have achieved success by being “smarter” than other people as measured by grades and test scores, beginning inour early days in school. The smart people were those that received the highest scores by making the fewest mistakes.

AI will change that because there is no way any human being can outsmart, for example, IBM’s Watson, at least without augmentation. Smart machines can process, store, and recall information faster and betterthan we humans. Additionally, AI can pattern-match faster and produce a wider array of alternatives than we can. AI can even learn faster. In an age of smart machines, our old definition of what makes a person smart doesn’t make sense.

What is needed is a new definition of being smart, one that promotes higher levels of human thinking and emotional engagement. The new smart will be determined not by what or how you know but by the quality of your thinking, listening, relating, collaborating, and learning. Quantity is replaced by quality. And that shift will enable us to focus on the hard work of taking our cognitive and emotional skills to a much higher level.

We will spend more time training to be open-minded and learning to update our beliefs in response to new data. We will practice adjusting after our mistakes, and we will invest more in the skills traditionally associated with emotional intelligence. The new smart will be about trying to overcome the two big inhibitors of critical thinking and team collaboration: our ego and our fears. Doing so will make it easier to perceive reality as it is, rather than as we wish it to be. In short, we will embrace humility. That is how we humans will add value in a world of smart technology.

Ed Hess is Professor of Business Administration and Batten Executive-in-Residence at the Darden Graduate School of Business and co-author of Humility Is the New Smart: Rethinking Human Excellence in the Smart Machine Age (Berrett-Koehler, 2017).

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Qui êtes vous pour décidée de la façon dont l'esprit , l'intelligence, la pensée humaine doit évoluer .Si vous amener l'être humain a raisonner demain , en fonction de vos intelligence artificielle , alors il ne s'agit plus d'évolution mais pluto de conditionnement de l'espèce humaine .Et désolé pour vous mais il n'y a que les animaux sans capacité cognitive qui sont conditionné

Levi, a successful entrepreneur, is an illustrative example. For most of his career, he saw himself as an effective leader and strong communicator. But after a 360 process, he discovered that his team didn’t share his opinion not only were their ratings consistently much lower than his self-ratings, the lowest score they gave him was for the competency of communication.

Levi embarked on a process to better understand this feedback and came to the informed conclusion that he might never be genuinely personable, no matter how hard he tried. But instead of stopping there (which may have been tempting), he knew he needed to come clean to his team. He called a company meeting where he admitted that he wasn’t the most likable or communicative leader. Next, he explained to his team that some behaviors were a personal shortcoming and certainly no indication that he didn’t care about or value them. Finally, he asked for their help and understanding as he worked to navigate this weakness.

To Levi’s great surprise, his employees were instantly more understanding. Over time, they even began to (lovingly) joke about some of his communication blunders. Levi saw that as a signal that they were more on his side, even if he couldn’t be the perfect leader. Sometimes the best response to critical feedback is to admit our flaws — first to ourselves, and then to others — while setting expectations for how we are likely to behave. When we let go of the things we cannot change, it frees up the energy to focus on changing the things we can.

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, PhD, is an organizational psychologist, researcher, and bestselling author. She is the principal of The Eurich Group, a boutique executive development firm that helps companies—from start-ups to the Fortune 100—succeed by improving the effectiveness of their leaders and teams. Her newest book, delves into the connection between self-awareness and success in the workplace.

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When someone give a negative feedback to us , those words revolve in mind always. How to overcome or get rid of those words. Please revert as soon as possible.

Figure 2. Meta-analysis of RCTs evaluating effects of increasing PUFA consumption in place of SFA and occurrence of CHD events.

Table 1. RCTs testing the effect on CHD events of increasing PUFA consumption in place of SFA.

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Weighted by the inverse-variance of each trial, the mean increase in PUFA consumption in the intervention group, compared to the control group, was 9.9%E, corresponding to a risk reduction for each 5%E greater PUFA consumption of 10% (RR = 0.90, 95% CI 0.83–0.97). Weighted by the inverse-variance of each trial, the mean decrease in blood total cholesterol (TC) levels in the intervention group, compared to the control group, was 0.76 mmol/l (29 mg/dl), corresponding to an observed risk reduction of 24% for each 1 mmol/l reduction in TC (RR = 0.76, 95% CI = 0.62–0.93).

The median duration of all trials was 4.25 years. Among the four trials with duration <4.25 years, the pooled RR was 0.91 (95% CI 0.76–1.10). Among the four trials with duration ≥4.25 years, the pooled RR was 0.73 (95% CI 0.61–0.87). In the four trials that evaluated exclusively or predominantly primary prevention populations, the pooled RR was 0.76 (95% CI 0.55–1.04). In the four trials that evaluated secondary prevention populations, the pooled RR was 0.84 (95% CI 0.72–0.98). For the six trials with a quality score of 2, the pooled RR was 0.78 (95% CI 0.66–0.91); for the two trials with a quality score of 3, the pooled RR was 0.91 (95% CI 0.63–1.31). Evaluating each of these potential sources of variation together in a metaregression model, study duration ( p  = 0.016), but not primary versus secondary prevention ( p  = 0.71) nor quality score ( p  = 0.78), was identified as a significant independent determinant of the extent of risk reduction. For each additional year of study duration, PUFA consumption lowered the relative risk of CHD events by an additional 9.2% in the intervention group (95% CI 1.7%–16.8%), compared with the control group. In secondary analyses restricted to CHD mortality alone (855 events, including 312 events from the full mortality report of one trial Balenciaga Velvet BB Heels I6PACn1kH
), the pooled RR was 0.80 (95% CI 0.65–0.98). Evaluating total mortality due to all causes (2,472 events), the pooled RR was 0.98 (95% CI 0.89–1.08).

The overall pooled result for CHD events was not substantially altered in post-hoc secondary analyses based on specific study design characteristics. For example, excluding the three reports (two trials) with open enrollment, the overall pooled RR was 0.83 (95% CI 0.72–0.95, p  = 0.006). Excluding the Finnish mental hospital trial (two reports) that used a cluster-randomization design, the overall pooled RR was 0.87 (95% CI 0.76–1.00, p  = 0.05). Only two trials were double-blind; restricting to these two studies, the pooled RR was 0.91 (95% CI 0.63–1.31), with wide confidence intervals indicative of limited statistical power. Restricting to the four reports that provided meals (i.e., that were feeding trials), the pooled RR was 0.76 (95% CI 0.55–1.04, p  = 0.08). Restricting to the four trials that provided mainly dietary advice, the pooled RR was 0.84 (95% CI 0.72–0.98, p  = 0.03). None of these subgroup analyses were significantly different from the main pooled result, as demonstrated by the 95% CIs in each subgroup analysis including the value of the main pooled RR estimate of 0.81.

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