NO HAY VUELTA ATRAS – Los Encierros Seran Permanentes || Klaus Schwab [Foro Economico Mundial]

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LA AUTOSUPERACIÓN – (Nietzsche) – Filosofía MOTIVACIONAL para el CRECIMIENTO y la VOLUNTAD de PODER

LA AUTOSUPERACIÓN - (Nietzsche) - Filosofía MOTIVACIONAL para el CRECIMIENTO y la VOLUNTAD de PODER

La filosofía motivacional de Friedrich Nietzsche nos vuelve a sorprender con un concepto revelador: la voluntad de poder. En su libro, el filósofo alemán profundiza en la importancia del crecimiento a la hora de alcanzar el máximo potencial como seres humanos. Y es que superar las resistencias que la vida nos planta es requisito indispensable para crecer internamente. ¿Estás preparado para conocer cuál es la clave de la autosuperación del hombre?
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Al igual que los estoicos y taoístas pensaban que el mundo se regía por una fuerza superior que era responsable de todo movimiento cósmico – a la cual se referían, respectivamente, como la Naturaleza o el Tao – Nietzsche afirmaba que el universo era una manifestación de otra fuerza subyacente a la que denominó voluntad de poder. Dicha esencia básica del cosmos se traduce, como bien indica el propio término, como un deseo insaciable en aras de manifestar poder. Así pues, comprender en qué consiste dicha voluntad de poder es la clave para saber cómo el filósofo alemán considera que el hombre debería vivir su vida, esto es, nos sumergiremos de lleno en el plano ético y moral del ser humano.

El poder nietzscheano no debe ser comprendido como el dominio, control o sometimiento de una persona sobre otra, esto es, el juego no va de sujeción y subordinación, ni de la facultad de dar órdenes a terceros; tampoco se refiere a la potestad en su sentido amplio, es decir, a la capacidad o habilidad para desempeñar una tarea. Para el pensador alemán, el poder va mucho más allá de ser un opulento empresario, un célebre general de guerra
o adulado emperador. Atento: voluntad de poder es sinónimo de voluntad de crecimiento.
Esta idea es expresada por primera vez en Voluntad de Poder: “los seres vivos hacen todo lo que pueden no con el fin de preservar su existencia, sino de cambiar a mejor (o lo que es lo mismo: crecer). Desde el momento en el que uno crece, se vuelve más poderoso”.

El crecimiento es poder y, como el poder consta como la fuerza superior y elemental de todo ente viviente, crecer nos permite superarnos a nosotros mismos. Al proceso de vencer a las resistencias inherentes al cometido que nos hemos propuesto se le denomina autosuperación. A fin de cuentas, lo más esencial en la vida de un hombre es cuánto ha crecido y cuántas limitaciones ha logrado superar, porque este factor es el que determina su nivel de poder y, en última instancia, su valía como ser humano. El autor reflejó esta idea en sus escritos: “no todas las personas son iguales. Es el individuo poderoso, el que se dedica a la superación constante de sí mismo, el que más vale. Lo que determina tu rango no es de dónde vienes ni lo que posees, sino la cantidad de poder que has acumulado a lo largo de tu existencia; el resto es sólo pura cobardía”.

What Is A Coaching Philosophy & How To Create Your Own

What Is A Coaching Philosophy & How To Create Your Own

What’s your winning coaching philosophy? Join us as we clarify what is a coaching philosophy, why you need one, coaching philosophy examples, and how you can get started in creating your own.

KEY HIGHLIGHTS:
0:00 What Is A Coaching Philosophy?
2:43 4 Reasons Why You Need One
4:35 How To Create Your Own
7:02 3 Coaching Philosophy Examples
8:33 How To Use A Coaching Philosophy

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#Coaching #CoachingPhilosophy #Methodology

Take Ownership of Your Future Self

Executive Summary

Your personality, skills, likes, and dislikes change over time — but that change isn’t out of your control. What can you do to become the version of yourself that you most want to be? Start by acknowledging the differences between your past, current, and future selves. Next, imagine your desired future self: Set goals that are as clear and specific as possible to maximize your chances of achieving them. Finally, develop (and re-develop) an identity narrative consistent with the person you want to become — and share that story with others! Your identity drives your behavior, which over time creates your personality. So start acting like the best version of yourself, and you will become that person.

Hardi Saputra/Getty Images

In his TED Talk “The Psychology of Your Future Self,” Harvard psychologist Dr. Daniel Gilbert explains a bias that almost all of us have: We tend to think that the person we are today is the person we will always be.

Most people, when asked if they are the same person they were 10 years ago, will say no — but we have a much harder time seeing potential for change in the future. Gilbert and others refer to this as the “end of history illusion.” Despite awareness that our past self is clearly different than our present self, we tend to think that who we are right now is the “real” and “finished” version of ourselves, and our future self will be basically the same as who we are today. Gilbert puts it simply: “Human beings are works in progress that mistakenly think they’re finished.”

Your personality, skills, likes, and dislikes change over time — whether you’re intentional about that change or not. A recently published study that spanned more than 60 years found that the personalities of nearly all participants were completely different than they had been 60 years prior.

Change is inevitable, but it’s not out of your control. Below, we provide three strategies to help you become your desired future self.

Step 1: Distinguish Your Former, Current, and Future Selves

As a rule, people tend to place extreme emphasis on their present selves. We tend to cling to our current identities and speak in incredibly definitive terms about who we are now, i.e., “I’m an introvert,” “I’m not good with people,” etc. These labels leave little wiggle room for change and growth, creating what Harvard psychologist Dr. Ellen Langer calls “mindlessness.”

When you assume a label about yourself, you stop seeing alternatives. As Langer explains, “If something is presented as an accepted truth, alternative ways of thinking do not even come up for consideration … [for example] when people are depressed they tend to believe they are depressed all the time. Mindful attention to variability shows this is not the case.”

The truth is, you’re not the same person you were in the past. You don’t do things the same way you once did. You no longer want what you once wanted. Instead of labeling yourself and focusing on who you are today, recognize how much you’ve grown and changed from your former self.

As entrepreneurial coach Dan Sullivan explains, you should “measure the gain, not the gap.” You can train yourself to see even short-term growth by measuring progress on a weekly, monthly, or quarterly basis. Just ask yourself: What wins have I had in the past 90 days? Once you start to distinguish between your current and former selves, it becomes possible to view your future self as a different person as well.

Step 2: Imagine Your Desired Future Self

“Imagination is more important than knowledge. For knowledge is limited to all we now know and understand, while imagination embraces the entire world, and all there ever will be to know and understand.”

―Albert Einstein

It’s much easier to default to the present than to imagine a different future. But if you don’t take the time to imagine who you want to be, then you’ll reactively become whatever life drives you towards. Research has shown that shaping your future self requires “deliberate practice,” or the ability to develop yourself towards a specific goal. You can’t effectively grow without a direction to that growth; you need a clear goal to shape the process.

For example, when I decided I wanted to become a professional writer, the idea alone wasn’t enough. I had to turn my idea into a measurable outcome — getting a six-figure book deal with one of the Big Five publishers in New York — and then I could reverse-engineer a process for reaching that goal. Having a clear goal enabled me to ask useful questions to the right people.

In addition, research shows that both motivation and hope stem from the combination of a clear, desired outcome, the belief that you can succeed, and a path to get there. The burgeoning field of positive psychology has flipped many old assumptions, finding that humans are not driven solely by their pasts, but rather are actually drawn forward by their own views of the future — a concept psychologists refer to as “prospection.”

Put simply, your behavior in the present is largely shaped by your view of your own future. If your future is clear, exciting, and something you believe you can create, then your behavior in the present will reflect that.

So, who is your future self? Only you have that answer to that question. As Dr. Gilbert explains, the first step is imagining your future self. Your future self is not someone you discover, but someone you decide to be.

One way to begin that imaginative process is through journaling. Start by asking yourself: What are one to three things I could do today to make progress toward my future self? Any action you take will likely be outside your comfort zone, since your current comfort zone is determined by your current personality. But if you push through that initial discomfort, you’ll become more psychologically flexible, and over time, grow into the person you want to be.

Step 3: Change Your Identity Narrative

Identity is far more powerful than personality. Identity drives behaviors, which over time, become personality. Your personality — the sum of your consistent attitudes and behaviors — is merely a byproduct of identity.

Your identity narrative is the story you tell about yourself: past, present, and future. If your identity is rooted in your past and present alone, that fixed mindset can make personality feel permanent. But if you focus on envisioning your future self, instead of fixating on your current self, it becomes possible to change your identity narrative.

This isn’t just something you should think about internally. Tell those around you who you want to be! It’s not about “faking it until you make it,” but rather honestly and humbly acknowledging that your future self is in fact a different person than who you are today. You’re not your future self yet, but that’s where you’re going.

Of course, this takes courage. It’s much easier to just say, “This is who I am.” Publicly saying “This is who I want to be” is risky, since you’re not guaranteed to succeed. But it’s also the only way to be intentional about who you become.

Telling people who you want to be is incredibly powerful because it will compel you to make your behavior consistent with your new story. If your identity narrative is rooted in the past, your past will determine your behavior. But if you intentionally decide who your future self will be — and find the courage to share that vision with others — it becomes possible to actively transform into that desired future self.

Dr. Carol Dweck has spoken about the importance of being defined not by the present, but by who you want to be. We are all in a constant state of becoming. So, let your desired future self be the thing predicting your current behavior — not your past.

Your behavior signals back to you the type of person you think you are, solidifying your identity and eventually becoming your personality. It is your behavior that creates your personality, not the other way around.

So who do you want to be? Start telling people.

Start acting like your future self, rather than your former self. Embrace uncertainty and change. Embrace learning and failure. Never be defined by “now.” Engage in deliberate practice so that over time, you’ll grow into your own ever-evolving story. Take action, and invest in building your future identity.

This is how you become the version of you that you most want to be.

How to Fight Discrimination in AI

Executive Summary

Ensuring that your AI algorithm doesn’t unintentionally discriminate against particular groups is a complex undertaking. What makes it so difficult in practice is that it is often extremely challenging to truly remove all proxies for protected classes. Determining what constitutes unintentional discrimination at a statistical level is also far from straightforward. So what should companies do to steer clear of employing discriminatory algorithms? They can start by looking to a host of legal and statistical precedents for measuring and ensuring algorithmic fairness.

Juan Moyano/Stocksy

Is your artificial intelligence fair?

Thanks to the increasing adoption of AI, this has become a question that data scientists and legal personnel now routinely confront. Despite the significant resources companies have spent on responsible AI efforts in recent years, organizations still struggle with the day-to-day task of understanding how to operationalize fairness in AI.

So what should companies do to steer clear of employing discriminatory algorithms? They can start by looking to a host of legal and statistical precedents for measuring and ensuring algorithmic fairness. In particular, existing legal standards that derive from U.S. laws such as the Equal Credit Opportunity Act, the Civil Rights Act, and the Fair Housing Act and guidance from the Equal Employment Opportunity Commission can help to mitigate many of the discriminatory challenges posed by AI.

At a high level, these standards are based on the distinction between intentional and unintentional discrimination, sometimes referred to as disparate treatment and disparate impact, respectively. Intentional discrimination is subject to the highest legal penalties and is something that all organizations adopting AI should obviously avoid. The best way to do so is by ensuring the AI is not exposed to inputs that can directly indicate protected class such as race or gender.

Avoiding unintentional discrimination, or disparate impact, however, is an altogether more complex undertaking. It occurs when a seemingly neutral variable (like the level of home ownership) acts as a proxy for a protected variable (like race). What makes avoiding disparate impact so difficult in practice is that it is often extremely challenging to truly remove all proxies for protected classes. In a society shaped by profound systemic inequities such as that of the United States, disparities can be so deeply embedded that it oftentimes requires painstaking work to fully separate what variables (if any) operate independently from protected attributes.

Indeed, because values like fairness are subjective in many ways — there are, for example, nearly two dozen conceptions of fairness, some of which are mutually exclusive — it’s sometimes not even clear what the most fair decision really is. In one study by Google AI researchers, the seemingly beneficial approach of giving disadvantaged groups easier access to loans had the unintended effect of reducing these groups’ credit scores overall. Easier access to loans actually increased the number of defaults within that group, thereby lowering their collective scores over time.

Determining what constitutes disparate impact at a statistical level is also far from straightforward. Historically, statisticians and regulators have used a variety of methods to detect its occurrence under existing legal standards. Statisticians have, for example, used a group fairness metric called the “80 percent rule” (it’s also known as the “adverse impact ratio”) as one central indicator of disparate impact. Originating in the employment context in the 1970s, the ratio consists of dividing the proportion of the selected group in the disadvantaged class by the proportion of selected members of the advantaged group. A ratio below 80% is generally considered to be evidence of discrimination. Other metrics, such as standardized mean difference or marginal effects analysis, have been used to detect unfair outcomes in AI as well.

All of which means that, in practice, when data scientists and lawyers are asked to ensure their AI is fair, they’re also being asked to select what “fairness” should mean in the context of each specific use case and how it should be measured. This can be an incredibly complex process, as a growing number of researchers in the machine learning community have noted in recent years.

Despite all these complexities, however, existing legal standards can provide a good baseline for organizations seeking to combat unfairness in their AI. These standards recognize the impracticality of a one-size-fits-all approach to measuring unfair outcomes. As a result, the question these standards ask is not simply “is disparate impact occurring?”. Instead, existing standards mandate what amounts to two essential requirements for regulated companies.

First, regulated companies must clearly document all the ways they’ve attempted to minimize — and therefore to measure — disparate impact in their models. They must, in other words, carefully monitor and document all their attempts to reduce algorithmic unfairness.

Second, regulated organizations must also generate clear, good faith justifications for using the models they eventually deploy. If fairer methods existed that would have also met these same objectives, liability can ensue.

Companies using AI can and should learn from many of these same processes and best practices to both identify and minimize cases when their AI is generating unfair outcomes. Clear standards for fairness testing that incorporate these two essential elements, along with clear documentation guidelines for how and when such testing should take place, will go a long way towards ensuring fairer and more-carefully-monitored outcomes for companies deploying AI. Companies can also draw from public guidance offered by experts such as BLDS’s Nicholas Schmidt and Bryce Stephens.

Are these existing legal standards perfect? Far from it. There is significant room for improvement, as regulators have in fact noted in recent months. (A notable exception is the Trump administration’s Department of Housing and Urban Development, which is currently attempting to roll back some of these standards.) Indeed, the U.S. Federal Trade Commission has indicated an increasing focus on fairness in AI in recent months, with one of its five commissioners publicly stating that it should expand its oversight of discriminatory AI.

New laws and guidance targeting fairness in AI, in other words, are clearly coming. If shaped correctly, they will be a welcome development when they arrive.

But until they come, it’s critical that companies build off of existing best practices to combat unfairness in their AI. If deployed thoughtfully, the technology can be a powerful force for good. But if used without care, it is all too easy for AI to entrench existing disparities and discriminate against already-disadvantaged groups. This is an outcome that both businesses and society at large cannot afford.