Yes or no
Answers, anytime
contents
We have answers to your problems. You may be wondering where all these problems are coming from. Well, it's pretty simple actually.
Experts agree that we are bombarded with a increasing amount of information with each passing day. Praised by the industry, the “innovation” of email has caused only a small increase in corporate productivity. What is the cost of this invention? A sharp decline in personal happiness.
Research has determined that 93% of all emails contain at least one question mark. Furthermore, an average person receives 10-15 emails per day. Therefore, an average person receives 10–15 questions per day!
Added to any problems that arise during the day that could total over 20 problems. Since the number of problems is inversely proportional to happiness, these data paint a disturbing picture. What can we do? Indeed, the situation is bleak.
Reduced time spent in personal interaction adds to the problem.
Recent studies show that the average couple talks 4 minutes per day. Consider this: if your partner helps you solve only 50% of your problems, that still leaves only 4 minutes for 10 problems. In other words, you have 24 seconds to
- state your problem
- allow them to formulate and present a response
- offer a rebuttal, and
- present your thanks
- repeat 9 more times
Obviously, such tight timing cannot be practically achieved. Even under controlled conditions, laboratory studies show that volunteers engaged in such activity show symptoms of burnout after addressing only 14 problems, on average.
What's more, it's known that 90% of communication is done through body language. Since it's very difficult to answer complex questions with your body, we can assume that the actual amount of verbal time remaining to solve a problem is not 24 seconds but 2.4 seconds!
What can you do? The numbers conspire against you. Things are obviously not your fault. Email is here to stay and with a busy schedule you'll be doing even less talking in the days to come. How do you solve your problems and stay ahead? Simple - use our linear congruent problem solver.
We have answers because we have a lot of problems. The overwhelming number of problems has taught us how to generate germane answers quickly and efficiently.
The purpose of the yesorno service is to
- offer answers to problems, thereby
- increasing personal happiness
- lowering the fraction of time spent in problem-solving activities
- centralize problem-solving facilities
- address a variety of issues, not limited to queries of a geopolitical, economic, philosophical and scientific nature
- decrease the effect of consequences arising from incorrect answers by providing a simple iterative approach of obtaining additional answers
Getting the answer is crucial. Interpreting and acting accordingly can be a challenge in itself. Therefore we provide a comprehensive lookup table to offer an interpretation of our answer and help you assess the correct course of action.
When you receive an answer from yesorno, look it up in the second column of the table. The text in that row corresponds to different outcomes, depending on the true answer. Of course, you do not know the true answer (if you did, you wouldn't be using our service), you are well on your way to solving your problem with our answer.
| universal best course of action | ||||
|---|---|---|---|---|
| yes | no | maybe | ||
| our answer | yes | Our methods have correctly determined that affirmative action is the correct route. Hold your course and reap the rewards. | This is a rare opportunity to experience what most do not attempt. We encourage you to be adventurous, daring, and cavalier. | The universe does not currently support experimental verification of this course of action. In its paradigm, quantum mechanics uses superposition of states, interpreted as both yes and no states simultaneously, but such states are not observables. Although we provide the answer for you, you must wait until the universe becomes compatible with these kinds of predictions. |
| no | While others may be tempted to act, we urge caution and healthy skepticism. This is a good time to stand back and reconsider or take up a new hobby. | General truths in this case are negative and you are certain to avoid them. Others will fail where you will persevere. Not to act is an action. | ||
| maybe | Although to us the answer is clear, the time is not right to reveal it. Consider your current situation and act accordingly. See this as a chance to gain perspective on an old problem. | You naturally wish to act, motivated by the momentum of past successes. Take caution. Looking in the rear view mirror can often tell you where you are going (not only when going backwards). | We cannot fit the explanation of this combination here. For the time being, you are to consider the probability of this combination infinitely small | |
If you have received an answer from our yesorno service but have not formulated a question in your mind, the answer is lost.
Do not under any circumstance attempt to retroactively apply the answer to a problem which you have cognitively visualized after the answer is given.
Simply put, such action contravenes the accepted causality laws of relativistic information transfer.
We cannot accept any responsibility about the correlation between your problem, our answer and the universal best course of action in this instance.
Our linear congruent answer generation method is a quantitative approach and such estimates can be computed. We find that the accuracy level is on avearge 33% for spent answers.
However, in individual cases the accuracy ranges as high as 100%.
For reasons we don't quite understand ourselves, it seems that our answers are good for only 5 minutes after they are generated.
After this time, they become stale.
Never use a stale answer. Instead, consider getting a new answer — but make sure you first have a problem. Answers without problems cannot be retroactively applied.
Convolutional neural networks
Nature uses only the longest threads to weave her patterns, so that each small piece of her fabric reveals the organization of the entire tapestry. – Richard Feynman
Following up on our Neural network primer column, this month we explore a different kind of network architecture: a convolutional network.
The convolutional network replaces the hidden layer of a fully connected network (FCN) with one or more filters (a kind of neuron that looks at the input within a narrow window).
Even through convolutional networks have far fewer neurons that an FCN, they can perform substantially better for certain kinds of problems, such as sequence motif detection.
Derry, A., Krzywinski, M & Altman, N. (2023) Points of significance: Convolutional neural networks. Nature Methods 20:.
Background reading
Derry, A., Krzywinski, M. & Altman, N. (2023) Points of significance: Neural network primer. Nature Methods 20:165–167.
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nature Methods 13:541–542.
Neural network primer
Nature is often hidden, sometimes overcome, seldom extinguished. —Francis Bacon
In the first of a series of columns about neural networks, we introduce them with an intuitive approach that draws from our discussion about logistic regression.
Simple neural networks are just a chain of linear regressions. And, although neural network models can get very complicated, their essence can be understood in terms of relatively basic principles.
We show how neural network components (neurons) can be arranged in the network and discuss the ideas of hidden layers. Using a simple data set we show how even a 3-neuron neural network can already model relatively complicated data patterns.
Derry, A., Krzywinski, M & Altman, N. (2023) Points of significance: Neural network primer. Nature Methods 20:165–167.
Background reading
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nature Methods 13:541–542.
Cell Genomics cover
Our cover on the 11 January 2023 Cell Genomics issue depicts the process of determining the parent-of-origin using differential methylation of alleles at imprinted regions (iDMRs) is imagined as a circuit.
Designed in collaboration with with Carlos Urzua.
Akbari, V. et al. Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq (2023) Cell Genomics 3(1).
Browse my gallery of cover designs.
