As a university lecturer, one of the challenges I often face is uncertainties. When we do research, we face ethical issues and when we teach, we, too, have to deal with a myriad of moral issues, e.g. what scores to assign, and our lectures notes.
Like most professionals, our duties and responsibilities involve making decisions. The best we can manage lies in our moral principles and professional standards. When we conduct a human-centered study, we need to seek advice and recommendations from a specialized committee. There are procedures to follow and principles to adhere to.
Another meaning of hygiene is the mental one. We as teachers have to face challenges, too, and we need to produce academic works and perform our duties as a scholar, and as a good employee of an organization. Sometimes, we have to deal with mental conflicts, such as conflicts of interest. Hygiene helps us to move forward.
How do you make a decision? How do you choose which smartphone to buy or which guy to date or marry with?
Are you afraid that you will make the wrong decision?
Why?
The issue of noises is addressed in the book authored by three professors, and one of them won a Nobel prize. The language is accessible, even for me, an English teacher and a non-native speaker of the English language. I bought a paper book from a local store in Bangkok, Thailand, and I never regret paying for the copy.
It reminds me of one bon mot : "To err is human." We are living with noises, most of them are noises from within. The book does not mention the concept of hobgoblins directly, but I can sense it.
I found this book immensely thought-providing and erudite
The three authors are:
1. Daniel Kahneman, Professor of Psychology and Public Affairs, Princeton University
2. Olivier Sibony, Professor, HEC Paris
3. Cass Sunstein, Robert Walmsley University Professor, Harvard University)
What is noise?
Daniel Kahneman and Olivier Sibony, renowned experts in cognitive biases and decision making explain that noise is " unwanted variability". This noise as a metaphor sometimes clouds organizations’ judgments. The question is--- what can we do about it?
According to Daniel Kahneman:
Bias is the average error in judgments. If you look at many judgments, and errors in those judgments all follow in the same direction, that is bias.
By contrast, noise is the variability of error. If you look at many judgments, and the errors in those judgments follow in many different directions, that is noise.
An example is given by one of the authors, Olivier Sibony,:
Here’s a forecasting example to make it more concrete. Say we are planning how long it will take to redecorate our kitchen. We can expect that all of us will be too optimistic; all of us will underestimate the time it will take to finish the renovation. But even though we’re all talking about the same kitchen, none of us will have the exact same estimate of how long the project will take. The average error, whereby we underestimate the time, will be the bias in our forecast. The variability in those forecasts is the noise.
He adds:
Noise is the unwanted variability in professional judgments. The inclusion of “unwanted” in the definition is very important because sometimes variability in judgments is not a problem; sometimes it’s even desirable.
But not when it involves professional judgment. The obvious example would be a doctor’s diagnosis. If two doctors give you two different diagnoses, at least one of them must be wrong. That is a judgment where variability is not desirable. There is a correct answer, and you would want these two people to have the same answer. When you don’t have the same answer to something where you’d want the same answer, that’s noise.
Daniel Kahneman said:
The noise we’re talking about in the book is “system noise,” or unwanted variability within a system of judgments. A good example is a judicial system. Judges should be interchangeable. They should give the identical sentence in the identical case. When they don’t, that is system noise. We found the same dynamics in medicine, with underwriters in insurance, and in many other functions.
Decisions to give a loan to a person
For instance, if lending money to someone would put a strain on your own finances and make it difficult to keep up with your bill payments, it’s probably not the best move. On the other hand, if you have a sizable emergency fund, little or no debt, and you’re getting a steady paycheck, making a loan might not be as difficult to manage.
Aside from the financial implications, it’s also important to think about how likely you are to get the money back. If the friend or family member who’s asking for a loan is responsible for paying their bills and experiencing a one-time financial crisis, being paid back might not be an issue. If, on the other hand, you’re approached by someone with a history of being financially irresponsible, you could be taking a bigger risk by lending them money.
(Source: Investopedia)
Yes, there are noises, but what can we do to reduce those errors?
1. Use automatic systems
Artificial Intelligence can extract the data from various sources and classify them according to their type. AI can also compare it with the loan requirements and regulations and provide meaningful insights that can help the lender make more effective decisions on the borrower's creditworthiness.
Many financial institutions are turning to AI to reverse past discrimination in lending, and to foster a more inclusive economy.
Using algorithms or rules of some kind, or artificial intelligence (A.I.) , to replace human judgment. Wherever there is judgment there is noise, but the corollary of that is wherever you want to get rid of noise, you need to take away the human element of the judgment.
The beauty of algorithms is that they will do that. They will eliminate the noise. There will be no mood, no temperature, no difference between your judgments and my judgments. The machine will churn out the same judgments so long as the algorithm doesn’t change.
2. Practice Decision Hygiene
Decision hygiene is a set of specific procedures for reducing noise. We call it hygiene because it is a form of prevention, not a remedy to an identified problem. As with other forms of hygiene, it can be a little bit thankless. You never get a pat on the back saying, “Well done washing your hands today, the disease you did not catch is the flu.” Likewise, you will never know which bias or error you averted by applying decision hygiene. It just needs to become second nature.
Take preventive measures - in short
Wear a mask
Clean your hands
Keep a safe distance
3. Experts and Competent Individuals
Competence matters. Some people are going to be better than others at any judgment. In medicine, for instance, some diagnosticians are better than others. If you can pick the better people, that helps. The better people are going to be more accurate; they are going to be less biased but they’re also going to be less noisy. There is going to be a less random error in their judgments.
4. Prefer Relative Judgement to the Absolute one
This is a recommendation by Olivier Sibony:
If you replace an absolute scale with a relative scale, you can eliminate a very big chunk of the noise. Think of performance evaluations again. Saying that someone is a “two” or a “four” on a performance-rating grid—even when you have the definition of what those ratings mean—remains fairly subjective, because what “an outstanding performer” or “a great relationship skill” means to you is not necessarily the same thing that it means to me. But if you ask, “Are Julia’s relationship skills better than those of Claudia?” that’s a question I can answer if I know both Julia and Claudia. And my answers are probably going to be very similar to yours. Relative judgments tend to be less noisy than absolute ones.
Microteaching is a technique aiming to prepare teacher candidates for the real classroom setting.
Micro-teaching is a teacher training technique.
Why this technique?
Doing so is to get constructive feedback from peers and/or students about what has worked and what improvements can be made to their teaching technique.
Steps
Microteaching is a teacher training technique for learning teaching skills. It employs real teaching situation for developing skills and helps to get deeper knowledge regarding the art of teaching. This Stanford technique involved the steps of “plan, teach, observe, re-plan, re-teach and re-observe.”
Seq2seq, developed by Google for use in machine translation, is a family of machine learning approaches used for language processing. Applications include language translation, image captioning, conversational models, and text summarization.
Seq2seq turns one sequence into another sequence (sequence transformation). It does so by use of a recurrent neural network (RNN) or more often LSTM or GRU to avoid the problem of vanishing gradient.
Meena is a chatbot, developed by a team of Google programmers and researchers.
Most commercial chatbots deployed by large organizations are designed for narrow uses, such as Dialogflow chatbots. These narrow-function chatbots are called closed-domain chatbots. "Meena" is not, it is an example of an open-domain chatbot -- one designed to converse on any topic that can function as a "friend," advisor, and even a tutor. This new development may affect most information and knowledge-based industries, including foreign language education, especially, English as a global language.
An open-domain chatbot needs the knowledge and capabilities of thousands of closed-domain chatbots combined.
In Towards a Conversational Agent that Can Chat About…Anything, Daniel Adiwardana, Senior Research Engineer, and Thang Luong, Senior Research Scientist, Google Research, Brain Team explain why Meena is a clever conversation agent.
- Meena is a 2.6 billion parameter end-to-end trained neural conversational model.
- Hence, Meena can conduct conversations that are more sensible and specific than existing state-of-the-art chatbots.
- Researchers fed Meena 341 gigabytes of social media conversation from public social media posts.
What is outstanding about the agent is the ability to deliver utterances that are sensible and specific. Their creators have proposed a new index called 'SSA', and Meena scored higher than any other chatbots.
SSA is a new human evaluation metric that we propose for open-domain chatbots, called Sensibleness and Specificity Average (SSA). The metric is designed to capture basic, but important attributes for human conversation.
Mike Elgan, a columnist at Computer World notes that:
Meena scored 79 percent on the SSA. That's lower than the average human score of 86 percent, but much higher than the highest score of the previous Loebner Prize chatbot champion, Mitsuku, which scored 56 percengt. (You can chat with Mitsuku here.) In other words, Meena is theoretically closer to the ability to converse to humans than to the second best chatbot. Google researchers claim that human-level SSA is "within reach."
A recent AI
technology has offered a big promise, that is, to create a chatbot that is
versatile and is capable to handle a variety of conversational topics. Adiwardana
and Luong (2020) describe a Google chatbot by the name of Meena. Meena is hailed
as being “a chatbot that is not specialized but can still chat about virtually
anything a user wants.” In fact, one of the applications cited is the
development of chatbots to improve foreign language practice. Below is an
example of the human-Meena interaction.
Figure: An
excerpt of a chat between Meena and a person
I would like to end this short note with these quotes:
One of the most astonishing feats performed in the research is that Meena invented a joke.
and
At some point in the future -- it could be 25 years from now, or just five years, most of our interaction with computers and the internet will happen through spoken conversation.
(Mike Elgan)
References
Adiwardana, D. et al (2020). Towards a Human-like Open-Domain Chatbot. Available online at https://arxiv.org/abs/2001.09977
Mike Elgan (3 Feb 2020). "Meet Meena: Why you'll want to hire this Google chatbot" From https://www.computerworld.com/article/3541292/meet-meena-why-youll-want-to-hire-this-google-chatbot.html
The issue of noises is addressed in the book authored by three professors, and one of them won a Nobel prize. The language is accessible, even for me, an English teacher and a non-native speaker of the English language.
I bought a paper book from a local store in Bangkok, Thailand, and I never regret paying for the copy.
It reminds me of one bon mot : "To err is human." We are living with noises, most of them are noises from within. The book does not mention the concept of hobgoblins directly, but I can sense it.
There are many sentences that I like and I would like to share with you.
"Wherever there is judgement, there is noise --- and more of it than you think" (p. 12)
I found this book immensely thought-providing and erudite
The three authors are:
1. Daniel Kahneman, Professor of Psychology and Public Affairs, Princeton University
2. Olivier Sibony, Professor, HEC Paris
3. Cass Sunstein, Robert Walmsley University Professor, Harvard University)
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