- If you are building a product, it is your responsibility to define how much time you're willing to bet/spend on a feature, this is called the appetite
- Unlike a task duration estimate, an appetite is a fixed amount of time that is defined in order to define the boundaries of what the feature to develop should be vs an estimate which means that the task should be completed and we expect it to take X amount of time (variable)
- The appetite helps set the amount of time before a task is deemed without a proper scope
- Record initial task duration estimate
- Record task duration after its completion
- Make a high-level, gut feeling estimate of the duration of the whole project
- Lowest (fastest, optimistic), average (most likely), and highest (slowest, pessimistic) time duration estimates
- A final value is computed by taking (lowest + 4*average + highest)/6
- List the reasons why you think things may go right/wrong
- Heuristic estimation
- At a high level, you should be able to tell how much each portion of a project should have over the complete project
- Use historical data
- If you have underestimated in the past, you will keep underestimating if you do not adjust
- Break down any item that is larger than 10% of the project into smaller items
- Duration will depend on the skills of the person completing the task. An expert will generally take less time than a novice to accomplish the same task
- Being interrupted while working (difficulty tracking beginning and ending)
- Working on unrelated things at the same time (diluted productivity)
- Working on multiple tasks at the same time (task overlapping)
- Determining what is and what isn't part of a task (task isolation)
- Changes in task definition
- Estimating tasks for which we have no experience
- Easy to start/stop tracking a task (less than 15 seconds, otherwise it feels like an interruption)
The workstack is a very simple idea I had while working. It is based on the concept of a stack as the name clearly implies. As you work, you, like a computer, process things one at a time and as new things need to be done, you either throw them in a todo list (a queue), or you start doing them right away (you stack them).
The workstack is a way to record notes about what you work on. As you work on something, you can either work on them to completion, or be interrupted by the necessity of working on another task. In the first case, tasks are simply written one after the other with their begin and end time. In the second case, items are also indented, such that it is possible to observe when a task forced you to "switch context".
An example of this note taking format is as follow.
2018-05-18
Task 1 10:00-10:30
Task 2 10:35-10:50
Task 3 11:00-...
Task 4 11:05-11:15
Task 6 11:17-...
Task 7 11:20-...
Task 5 (not begun)
In this case, the person started working on tasks 1 and 2, then began working on task 3. As he began his work, he noticed that something else was necessary, which spawned task 4. While he was working on task 4, he observed something that could be done, but didn't have to be done right away, which spawned task 5. As he completed task 4, he returned to task 3, but noticed that something else also had to be done, which effectively spawned task 6. During task 6, something else also interrupted him, which forced him to work on task 7. In this case, it could have been a coworker asking you for help on something. Task 5 could be a coworker asking for help as soon as you're available, but not wanting to interrupt you.
Conceptually, you would want to always complete a stack of operations before moving to a new task. However, it is highly common in programming that a programmer will start going down such stack while working on code and then will not end up climbing back the stack, effectively not completing all he started working on.
This format thus allows a programmer (or anyone working on tasks that can spawn other tasks) to better track what they were doing and what they did and did not complete.
AI/AGI/ML - A short overview
History / Edit / PDF / EPUB / BIB / 3 min read (~580 words)After having spent about 200 workdays of my life getting familiar with the fields of AI/AGI/ML, I think it is a good moment for me to reflect back on my learning experience.
Of the three domains, AGI is the least well defined. It has to do with general intelligence, but our definition of intelligence is still up for debate. Furthermore, the different known approaches in the field have different opinions regarding what sort of agent an AGI system should be. Should it be a self-contained entity with its own goals and purpose? Or should it be a system that is exhibiting human capabilities while being the servant of a human being or a group of human beings?
Next is AI, which encompasses many different types of approach to try and solve high level goals such as deciding, planning, organizing,, understanding, etc. AI is about the discovery of strategies to solve problems without having to provide the whole solution. I think it is also where goals for the ML field have been defined.
Finally, ML is about using statistical approaches to solve problems. These problems have to be formalized and specify their input/output in the shape of data, may it be as text, audio, video, or simply vectors of numbers. Most of the recent and exciting development that has happened in ML is due to deep learning, which is the ability to develop deep neural networks that can learn to accomplish certain tasks without writing any explicit logic. This logic would be discovered by the network itself, with some help of the developer which would define a composition of modules that would process the data in order to create associations between the input and outputs.
When I initially started my "adventure", I was interested in AGI. I wanted to know how a machine would be able to exhibit human behavior without having to tell it too much about it. I wanted to learn how you could teach a machine as you would teach a child. The AGI field itself does not currently have any curriculum as to what you should learn about, nor a reference book you can refer yourself or others to (when asked "What should I first read if I want to learn AGI?"). One can be interested in anthropomorphic aspects such as memory, intelligence, perception/senses, processing and so on, while others may be more interested about a computational oriented approach, such as the algorithms, the implementations, the computational complexity of diverse approaches and so on. I think it makes AGI a very interesting topic/field, however it lacks the definiteness of the other fields. One can see this by reading the various iterations of the book "Artificial General Intelligence", which is a collection of articles by various researchers that is generally published every year in relation to its conference.
When I started learning about AGI, I decided I would explore various venues and determine the approaches that matched the most my own interests and which looked the most promising. I looked at different venues such as cognitive science, set theory, logic, universal artificial intelligence, biology, and philosophy amongst others.
Along the road, AGI research has challenged many of my beliefs as well as how I see life. For those two reasons alone, I'm grateful I have been able to dedicate some of my time on it.
One thing I've been wondering about lately is when should one write blog articles (short articles ranging from 250 to 1000 words) or articles (1000+ words) which would be updated (in)frequently but not be as easily consumable.
The main reason I've preferred articles over blog articles is that it makes it possible to keep articles "alive", while once a blog article has been published, it will not evolve over time. By the time it is posted, it is effectively dead and has very little reason to change.
Being a programmer, I've always enjoyed seeing software project evolve over the years. They start small and grow big and complex over time. I like the satisfaction of seeing something grow step by step, and because of that, I've liked the idea of writing about specific topics and seeing these topics and articles change over time.
The problem with articles however is that it is difficult for visitors to consume. Some articles might grow large and complex. Furthermore, it is difficult for visitors to notice any changes from the last time they've come onto the blog.
Another thing is that articles allow the author to "refactor" what he has written, or rewrite/rephrase/reword, such that the article itself improves over time. With a blog article, it is still possible to do so, but the expectation is that a visitor that has already seen the blog article will not re-read it. And here's the important difference between the two: I expect blog articles to be consumed once, but articles to be consumed multiple times. This expectation is likely wrong. The only person that is likely to consume the same article over and over is its author. Visitors may come to the blog many times, but they are unlikely to expect articles to change over time. An extreme example of this idea would be Wikipedia. People will consult the page of the topic they are interested in, but once they've read it, they aren't likely to come back to it again. The only reason they might do so is if they contribute to it.
What this means is that it is still acceptable to improve both blog articles and articles. The difference lies in how we expect our audience to consume each ones. Articles are likely to be seen as references while blog posts are likely to be perceived as an ephemeral observation. One should expect both to be consumed at best once by a visitor, and that the visitor will not come again to see if it has changed.
Thus, in the end, what matters is what we want to communicate with our audience. Do I want to tell you about a thought (a blog article) or do I want to tell you about some information that is likely to evolve over time (an article).
In the case of my machine learning and artificial general intelligence research, what makes sense is to communicate discoveries or shifts in approaches through blog articles while using articles as a location for thoughts to be worked on.
- Have a question
- Define the field that would best answer this question
- Search online
- Search for articles and books on the topic
- Determine the likelihood that this field has already been researched