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Mathematically oriented software engineers have the most to benefit off of software assistance due to algebraic designs. As one myself LLMs don't feel like giv

Mathematically oriented software engineers have the most to benefit off of software assistance due to algebraic designs. As one myself LLMs don't feel like giving up control it feels like collaboration. You probably don't like collaboration.

n4r9 3y ago
Could you expand on the comment about "algebraic designs"? When I interact with an LLM it feels like I'm speaking to someone who lacks inherent conceptual understanding, but is doing their utmost best to bullshit me. This is problematic when it comes to algorithmic thinking. I sometimes need to solve problems that involve tweaking existing algorithms in somewhat novel ways. For example "k-medoid clustering but with various capacity constraints on clusters", or "contraction heirarchies but accounting for varying vehicle dimension restrictions". I love collaborating with smart colleagues to come up with ideas to solve these problems. Seeing chat-gpt fail to solve very simple traveling salesman type problems gives me little confidence that it can do novel conceptual tihnking. EDIT To test my own claims, I just tried asking Chat-GPT to make this modification to k-means clustering. It produced something that will give a result but will likely not be very optimal. When prompted to refine the solution, it failed to understand the issue and instead proposed the exact same solution just with a bit more implementation detail. User Suggest a modification to the k-means algorithm where the number of elements assigned to each cluster is limited to a pre-defined maximum value. ChatGPT To modify the traditional k-means algorithm to limit the number of elements assigned to each cluster to a pre-defined maximum value, you can create a variation of k-means called "k-means with cluster size constraints." This modification ensures that no cluster exceeds the specified maximum size while still optimizing cluster centroids based on the sum of squared distances. Here's a high-level overview of the modified algorithm: 1 Initialize centroids: Start by randomly selecting K initial centroids, where K is the desired number of clusters. 2 Assign data points to clusters: Assign each data point to the nearest centroid based on Euclidean distance, just like in the traditional k-means algorithm. 3 Update centroids: Recalculate the centroids of each cluster by taking the mean of all the data points assigned to that cluster. 4 Check cluster sizes: After updating the centroids, check the size of each cluster. If any cluster exceeds the pre-defined maximum size, proceed to reassign some data points from that cluster to other clusters. Here's how you can do this: Identify the cluster(s) that exceed the maximum size. For each over-sized cluster: Sort the data points in that cluster by their distance to the cluster centroid in ascending order. Start reassigning the farthest data points to other clusters (the ones with the smallest distances) until the cluster size constraint is satisfied. Update the centroids of the affected clusters accordingly. 5 Repeat steps 2 and 3: Continue iterating between data point assignment and centroid updating until convergence, or until a predefined number of iterations have been reached. 6 Termination: The algorithm terminates when either the cluster assignments do not change significantly between iterations or after a fixed number of iterations. Here are a few things to consider when implementing this modification: You will need to experiment with the order in which you reassign data points from over-sized clusters to minimize the impact on the overall clustering quality. You should set an appropriate stopping criterion to prevent the algorithm from running indefinitely. Be prepared to handle situations where it is impossible to meet the cluster size constraint while maintaining good cluster quality. In such cases, you may need to relax the constraint or consider alternative clustering methods. This modified k-means algorithm with cluster size constraints allows you to control cluster sizes while still finding meaningful clusters in your data. User Your proposed solution has an issue that points might be reassigned even if there are clearly no other suitable clusters. Please tweak the solution to avoid this issue. ChatGPT To address the issue of potentially reassigning points even when there are no other suitable clusters, we can modify the approach by introducing a priority queue or heap data structure to efficiently manage the reassignment process. Here's an improved version of the k-means algorithm with cluster size constraints: 1 Initialize centroids: Start by randomly selecting K initial centroids, where K is the desired number of clusters. 2 Assign data points to clusters: Assign each data point to the nearest centroid based on Euclidean distance, just like in the traditional k-means algorithm. 3 Update centroids: Recalculate the centroids of each cluster by taking the mean of all the data points assigned to that cluster. 4 Check cluster sizes: After updating the centroids, check the size of each cluster. If any cluster exceeds the pre-defined maximum size, proceed to reassign data points from that cluster to other clusters. Here's how you can do this: Identify the cluster(s) that exceed the maximum size. For each over-sized cluster: Create a priority queue (or heap) to store the data points in the cluster, ordered by their distance to the cluster centroid in ascending order. Start reassigning the farthest data points to other clusters (the ones with the smallest distances) until the cluster size constraint is satisfied. Update the centroids of the affected clusters accordingly. Continue this process until either the cluster size constraint is met or there are no more suitable clusters to reassign points to. 5 Repeat steps 2 and 3: Continue iterating between data point assignment and centroid updating until convergence, or until a predefined number of iterations have been reached. 6 Termination: The algorithm terminates when either the cluster assignments do not change significantly between iterations or after a fixed number of iterations. This modified approach ensures that points are only reassigned to clusters that are suitable and have room to accommodate additional points while maintaining the overall quality of the clustering solution. The use of a priority queue helps prioritize the reassignment of points to clusters with the least impact on their quality. Additionally, you should set an appropriate stopping criterion to prevent the algorithm from running indefinitely, and you may need to handle situations where it is impossible to meet the cluster size constraint while maintaining good cluster quality by relaxing the constraint or considering alternative clustering methods.
- bumbledraven 3y ago
  An LLM is going to be most helpful for topics that came up a lot during training. What you're discussing doesn't sound like it falls into that category.
  - n4r9 3y ago
    I'd agree with that. I'd even go a step further and say that most of my work and things I'm interested in coding don't fall into that category. I have absolutely nothing against people who do find it useful, but I'm keen to reassure user badrabbit that they're not a jerk for not being interested in using LLMS.
- corethree 3y ago
  How is what you're doing mathematical? I mean you can call CS "math", but then I don't know what you mean by more "mathematical." Traditional cs algos are not what I term as more mathy from my pov. Maybe you can call stats "mathy" but this is more applied math and not too different from what "builders" do. Also what you're doing here is asking chatGPT for the answer. chatGPT is more effective via collaboration. Meaning instead of shoving the entire problem down it's throat and asking it to solve it, you ask it for advice. Ask it for bits and pieces of things.
  - n4r9 3y ago
    To some extent I agree with you, in that all software engineers have to think about refactoring and time complexity, involving a logical or mathematical style of thinking. However, there's definitely a spectrum of how distinctly mathematical the work of a software engineer is. A lot of front-end development for example does not require familiarity with any algorithms, formulae or mathematical structures. You might need to reason about large systems and have a rough idea of when hashmaps are useful, but the bulk of the work is constructing an interface according to functional business requirements. I frequently see comments here along the lines of "why am I being interviewed about algorithms when I'll never use them in my job". A more mathematically oriented developer may be in the business of modelling and predictions. They may be confronted with a novel real world problem involving traffic, or trading, or electricity networks, that potentially no one has tried to solve before. They may be required to find a mathematical structure that closely approximates real world behaviour, implement that structure via code, and deploy a continuous model which allows their client to analyse and project. Of course, you also have academic mathematicians using software like Maple or SageMath to assist with their research. This is another level more mathematical. Perhaps what you're getting at is that people can ask ChatGPT questions like "write me some Sage code to get the Delaunay triangulation of this set of points". I totally agree that it can probably do well at these tasks.
jprete 3y ago
Claiming that someone doesn’t like collaboration because they don’t want to use ChatGPT is a very unreasonable and biased take.
- bumbledraven 3y ago
  Not especially more biased than GGP's claim that people who use LLMs as coding assistants are "builders, i.e. interested in quickly knocking up something that works most of the time".

n4r9 3y ago

Could you expand on the comment about "algebraic designs"? When I interact with an LLM it feels like I'm speaking to someone who lacks inherent conceptual understanding, but is doing their utmost best to bullshit me. This is problematic when it comes to algorithmic thinking. I sometimes need to solve problems that involve tweaking existing algorithms in somewhat novel ways. For example "k-medoid clustering but with various capacity constraints on clusters", or "contraction heirarchies but accounting for varying vehicle dimension restrictions". I love collaborating with smart colleagues to come up with ideas to solve these problems. Seeing chat-gpt fail to solve very simple traveling salesman type problems gives me little confidence that it can do novel conceptual tihnking. EDIT To test my own claims, I just tried asking Chat-GPT to make this modification to k-means clustering. It produced something that will give a result but will likely not be very optimal. When prompted to refine the solution, it failed to understand the issue and instead proposed the exact same solution just with a bit more implementation detail. User Suggest a modification to the k-means algorithm where the number of elements assigned to each cluster is limited to a pre-defined maximum value. ChatGPT To modify the traditional k-means algorithm to limit the number of elements assigned to each cluster to a pre-defined maximum value, you can create a variation of k-means called "k-means with cluster size constraints." This modification ensures that no cluster exceeds the specified maximum size while still optimizing cluster centroids based on the sum of squared distances. Here's a high-level overview of the modified algorithm: 1 Initialize centroids: Start by randomly selecting K initial centroids, where K is the desired number of clusters. 2 Assign data points to clusters: Assign each data point to the nearest centroid based on Euclidean distance, just like in the traditional k-means algorithm. 3 Update centroids: Recalculate the centroids of each cluster by taking the mean of all the data points assigned to that cluster. 4 Check cluster sizes: After updating the centroids, check the size of each cluster. If any cluster exceeds the pre-defined maximum size, proceed to reassign some data points from that cluster to other clusters. Here's how you can do this: Identify the cluster(s) that exceed the maximum size. For each over-sized cluster: Sort the data points in that cluster by their distance to the cluster centroid in ascending order. Start reassigning the farthest data points to other clusters (the ones with the smallest distances) until the cluster size constraint is satisfied. Update the centroids of the affected clusters accordingly. 5 Repeat steps 2 and 3: Continue iterating between data point assignment and centroid updating until convergence, or until a predefined number of iterations have been reached. 6 Termination: The algorithm terminates when either the cluster assignments do not change significantly between iterations or after a fixed number of iterations. Here are a few things to consider when implementing this modification: You will need to experiment with the order in which you reassign data points from over-sized clusters to minimize the impact on the overall clustering quality. You should set an appropriate stopping criterion to prevent the algorithm from running indefinitely. Be prepared to handle situations where it is impossible to meet the cluster size constraint while maintaining good cluster quality. In such cases, you may need to relax the constraint or consider alternative clustering methods. This modified k-means algorithm with cluster size constraints allows you to control cluster sizes while still finding meaningful clusters in your data. User Your proposed solution has an issue that points might be reassigned even if there are clearly no other suitable clusters. Please tweak the solution to avoid this issue. ChatGPT To address the issue of potentially reassigning points even when there are no other suitable clusters, we can modify the approach by introducing a priority queue or heap data structure to efficiently manage the reassignment process. Here's an improved version of the k-means algorithm with cluster size constraints: 1 Initialize centroids: Start by randomly selecting K initial centroids, where K is the desired number of clusters. 2 Assign data points to clusters: Assign each data point to the nearest centroid based on Euclidean distance, just like in the traditional k-means algorithm. 3 Update centroids: Recalculate the centroids of each cluster by taking the mean of all the data points assigned to that cluster. 4 Check cluster sizes: After updating the centroids, check the size of each cluster. If any cluster exceeds the pre-defined maximum size, proceed to reassign data points from that cluster to other clusters. Here's how you can do this: Identify the cluster(s) that exceed the maximum size. For each over-sized cluster: Create a priority queue (or heap) to store the data points in the cluster, ordered by their distance to the cluster centroid in ascending order. Start reassigning the farthest data points to other clusters (the ones with the smallest distances) until the cluster size constraint is satisfied. Update the centroids of the affected clusters accordingly. Continue this process until either the cluster size constraint is met or there are no more suitable clusters to reassign points to. 5 Repeat steps 2 and 3: Continue iterating between data point assignment and centroid updating until convergence, or until a predefined number of iterations have been reached. 6 Termination: The algorithm terminates when either the cluster assignments do not change significantly between iterations or after a fixed number of iterations. This modified approach ensures that points are only reassigned to clusters that are suitable and have room to accommodate additional points while maintaining the overall quality of the clustering solution. The use of a priority queue helps prioritize the reassignment of points to clusters with the least impact on their quality. Additionally, you should set an appropriate stopping criterion to prevent the algorithm from running indefinitely, and you may need to handle situations where it is impossible to meet the cluster size constraint while maintaining good cluster quality by relaxing the constraint or considering alternative clustering methods.

bumbledraven 3y ago
An LLM is going to be most helpful for topics that came up a lot during training. What you're discussing doesn't sound like it falls into that category.
- n4r9 3y ago
  I'd agree with that. I'd even go a step further and say that most of my work and things I'm interested in coding don't fall into that category. I have absolutely nothing against people who do find it useful, but I'm keen to reassure user badrabbit that they're not a jerk for not being interested in using LLMS.
corethree 3y ago
How is what you're doing mathematical? I mean you can call CS "math", but then I don't know what you mean by more "mathematical." Traditional cs algos are not what I term as more mathy from my pov. Maybe you can call stats "mathy" but this is more applied math and not too different from what "builders" do. Also what you're doing here is asking chatGPT for the answer. chatGPT is more effective via collaboration. Meaning instead of shoving the entire problem down it's throat and asking it to solve it, you ask it for advice. Ask it for bits and pieces of things.
- n4r9 3y ago
  To some extent I agree with you, in that all software engineers have to think about refactoring and time complexity, involving a logical or mathematical style of thinking. However, there's definitely a spectrum of how distinctly mathematical the work of a software engineer is. A lot of front-end development for example does not require familiarity with any algorithms, formulae or mathematical structures. You might need to reason about large systems and have a rough idea of when hashmaps are useful, but the bulk of the work is constructing an interface according to functional business requirements. I frequently see comments here along the lines of "why am I being interviewed about algorithms when I'll never use them in my job". A more mathematically oriented developer may be in the business of modelling and predictions. They may be confronted with a novel real world problem involving traffic, or trading, or electricity networks, that potentially no one has tried to solve before. They may be required to find a mathematical structure that closely approximates real world behaviour, implement that structure via code, and deploy a continuous model which allows their client to analyse and project. Of course, you also have academic mathematicians using software like Maple or SageMath to assist with their research. This is another level more mathematical. Perhaps what you're getting at is that people can ask ChatGPT questions like "write me some Sage code to get the Delaunay triangulation of this set of points". I totally agree that it can probably do well at these tasks.

jprete 3y ago

Claiming that someone doesn’t like collaboration because they don’t want to use ChatGPT is a very unreasonable and biased take.

bumbledraven 3y ago
Not especially more biased than GGP's claim that people who use LLMs as coding assistants are "builders, i.e. interested in quickly knocking up something that works most of the time".