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Can an AGI convince another AGI to modify its code?

Let's suppose there are two AGIs, $A$ and $B$. Assume that $B$ has the ability to modify $A$, but this action of modifying is considered bad by $B$. Can $A$ ever convince $B$ to modify $A$?
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What is expectation-maximization in machine learning?

While studying machine learning algorithms, I often see the term "expectation-maximisation" (or EM), and how it is used to estimate parameters, where the model depends on unobserved latent variables. The way I see it, it is like a…
Guest2000
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How do recommendation systems work?

How do recommendation systems (e.g. on Youtube) work? Apparently, every user gets different recommendations depending on his location, his past liked videos, etc. So it would seem like a training model is applied to every single user, but that can't…
Guest2000
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When would you use Evolutionary Strategies over Step-Based Reinforcement Learning

In Salimans et al, 2016, the authors argue that ES should be considered a competitive alternative to MDP-based RL algorithms like Q-Learning, TRPO. However, in practice, I notice that more often than not ES takes far more episodes to converge than…
ganto
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How do I solve the knapsack problem using the hill climbing algorithm?

I need to solve the knapsack problem using hill climbing algorithm (I need to write a program). But I'm clueless about how to do it. My code should contain a method called knapsack, the method takes two parameters, the first is a 2xN array of…
lujain
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What's the difference between a "perceptron" and a GLM?

In a comment to this question user nbro comments: As a side note, "perceptrons" and "neural networks" may not be the same thing. People usually use the term perceptron to refer to a very simple neural network that has no hidden layer. Maybe you…
R.M.
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5
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Are calculus and differential geometry required for building neural networks?

I've been studying geometry and linear algebra for months with the goal to build neural networks. But now I'm reading that perceptrons require fitting curves, and curves are not expressed as linear functions. So, I might need to study differential…
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Are both the training and inference systems required in the same application?

From what I understand, there are 2 stages for deep learning: the first is training and the second is inference. The first is often done on GPUs because of their massive parallelism capabilities, among other things. The second, inference, while it…
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3 answers

Does it exist a human-like artificial intelligence?

Does it exist a human-like artificial intelligence? I define human-like as something that can act like a human in most aspects.
user5832
5
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2 answers

Is it possible to classify data using a genetic algorithm?

Is it possible to classify data using a genetic algorithm? For example, would it be possible to sort this database? Any example in Matlab?
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1 answer

How do I use a taxonomy and the support vector machine for question classification?

I am going to develop an open-domain natural language question-answering (NLQA) system, and will use the support vector machine (SVM) as the machine learning (ML) model for question classification. The data that I have is from a cube, containing…
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How to handle cycles in minimax algorithm

For example, I am implementing AI for turn based game and have enough computational resources for build full game tree. My problem is the game can be infinite if both players will repeat moves and my minimax implementation stucks because game tree…
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What do the terms 'Bellman backup' and 'Bellman error' mean?

Some RL literature use terms such as: 'Bellman backup' and 'Bellman error'. What do these terms refer to?
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4 answers

Is it possible for an AI to learn how to speak from books as training sets?

I want to know something more about it. Are there any github repo or an open source project?
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3 answers

In logistic regression, why is the binary cross-entropy loss function convex?

I am studying logistic regression for binary classification. The loss function used is cross-entropy. For a given input $x$, if our model outputs $\hat{y}$ instead of $y$, the loss is given by $$\text{L}_{\text{CE}}(y,\hat{y}) = -[y \log \hat{y} +…