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10 votes

Difference between machine learning and artificial intelligence

The subject areas Artifical Intelligence and Machine Learning (plus Data Science) are loosely defined, such that it is hard to make strict statements about how they relate. In the general case, it ...
Neil Slater's user avatar
9 votes

Difference between machine learning and artificial intelligence

Machine learning in layman terms is an algorithm that allows machines to identify patterns in data and then develop a model which can be used to predict unseen data. Artificial Intelligence is the ...
Ajay Sant's user avatar
  • 276
7 votes

What are some good books on Machine Learning and AI like Krugman, Wells and Graddy's "Essentials of Economics"

The two books that come into my mind are: Artificial Intelligence: A Modern Approach The Deep Learning Book They both start from the basics and escalate while moving on. Also thanks for your ...
6 votes

Does OpenAI and ChatGPT use Scikit Learn?

Based on the limited amount of code in OpenAI's GitHub, one of the primary packages is PyTorch. There is a much smaller amount of scikit-learn code. Since OpenAI has not released any code for ChatGPT, ...
Brian Spiering's user avatar
5 votes

What is the relationship between AI and data science?

Though neither are well defined, as commonly used they are somewhat orthogonal concepts. In my opinion, AI has a fairly narrow definition - it is about optimization through actions. AI is about ...
5 votes

Difference between machine learning and artificial intelligence

Deep Learning is a subset of Machine Learning which is a subset of Artificial Intelligence. Machine learning is a particular approach for AI but not the only one. Symbolic Logic, Bayersian Statistics ...
Dhruv Mahajan's user avatar
4 votes
Accepted

What does scaling a gradient do?

Author of the paper here - I missed that this is apparently not a TensorFlow function, it's equivalent to Sonnet's scale_gradient, or the following function: ...
Mononofu's user avatar
  • 156
4 votes
Accepted

How was the token library constructed for ChatGPT / other GPT systems?

ChatGPT uses byte-pair encoding (BPE) as tokenization strategy. This approach was first proposed in the scientific article Neural Machine Translation of Rare Words with Subword Units. BPE uses subword-...
noe's user avatar
  • 27k
3 votes

Data science which is not part of AI?

I would not concern yourself too much about any structuring of knowledge that declares that one subject is categorised as one thing or another. These structures are often wrong and knowledge in ...
Neil Slater's user avatar
3 votes

Policy Gradient with continuous action space

Yes, that is possible. It can be done in the following way: We assume that the action distribution is guassian, i.e. that we need to learn the parameters $\theta$ of $\mathcal{N}(a|\mu_\theta,\sigma_\...
hh32's user avatar
  • 2,762
3 votes

What are some good books on Machine Learning and AI like Krugman, Wells and Graddy's "Essentials of Economics"

What do you want to learn in AI and Machine learning? Artificial Intelligence covers many practical applications, so your question might be a bit vague here. I will suggest you books on Machine ...
3 votes
Accepted

Using SMOTE for Synthetic Data generation to improve performance on unbalanced data

First of all, you have to split your data set into train/test splits before doing any over/under sampling. If you do any strategy based on your approaches, and then split data you will bias your model ...
Victor Oliveira's user avatar
3 votes

Difference between machine learning and artificial intelligence

ML, by Tom M. Mitchell: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, ...
3 votes

Difference between machine learning and artificial intelligence

A good example of AI, but not machine learning is evolutionary computation. Here instead of learning from experience (as in Tom M. Mitchell's definition) we have genotype changing in each generation ...
DmytroSytro's user avatar
3 votes
Accepted

Significance of Object-Oriented Programming (OOP) in Data Science

It actually depends on the role you get as a Data Scientist. If you have to write production-quality code at a large software company, then you need to be knowing the basics of Object-Oriented ...
Shayan Shafiq's user avatar
3 votes

How to grade an interaction that a user had with a post with an AI based on big data?

If I understood correctly, you will eventually have, after some time, those user ratings right? So, assuming that you will have some labeled data (i.e. user ratings together with the features you say) ...
German C M's user avatar
  • 2,724
3 votes
Accepted

How to exercise Quality Assurance Engineering principles to Artificial Intelligence systems?

Without being sure if the approach makes sense but one could take the various steps of the lifecycle of an Artificial Intelligent system and thus attempt to see how as a Quality Assurance Engineer can ...
George Pligoropoulos's user avatar
3 votes

Where does AI/ML theories come into play when nowadays the AI libraries already so powerful?

First "linear algebra, statistics, complicated optimization" are not ML theories, they are mathematics toolkits that guides specific ML algorithms designs and improvement at operation level. ...
imadcat's user avatar
  • 276
2 votes

Difference between machine learning and artificial intelligence

As the great Tom Mitchell has said in his book "Machine Learning is the ability to learn without being explicitly programmed." Machine learning algorithms are widely employed and are encountered ...
surya rahul's user avatar
2 votes

Difference between machine learning and artificial intelligence

Let's take the total Turing test as an example. A computer is often said to be intelligent if it can pass the total Turing test. A computer passes the test if a human interrogator, after posing some ...
Bayequentist's user avatar
2 votes
Accepted

How large of a value should a weight have in a neural network?

One of the problems that can occur when training a neural network is known as the exploding gradient problem. A poorly initialised network could lead to a large increase in the norm of the gradient ...
Simon's user avatar
  • 209
2 votes

What are some good books on Machine Learning and AI like Krugman, Wells and Graddy's "Essentials of Economics"

AI and Machine Learning is a big field. If you want the broadest nontrivial introduction, you should check out: Machine Learning: A Probabilistic Perspective. It covers everything from classical ...
2 votes

How to handle differences between training and deploying of an RL agent

If I train an agent for taking actions for 15 mins during the training process, is it ok I make my agent take actions at every 5 min interval during deployment? It is impossible to say in general. ...
Neil Slater's user avatar
2 votes

Will the job of Data Science is going to be at risk?

I highly doubt that the job itself will going to be at risk. It is rather the other way around: Data science and machine learning will replace a lot of other jobs. In the end there, at least, always ...
iShazook's user avatar
2 votes

Significance of Object-Oriented Programming (OOP) in Data Science

Using python vs R is more of a personal choice, but most people I know in data science, including myself, use python. If you decide to take up python, almost all of the python ML libraries are written ...
jdsurya's user avatar
  • 387
2 votes
Accepted

Difference between $Q(s,a)$ ,$V^*(s)$ and $V^\pi(s)$ in Markov Decision Process?

Your confusion seems to come from mixing up between some policy $\pi$ and an optimal policy $\pi^*$. Your summary is generally correct, but missing these extra details. Let me try go through it again. ...
Kostya's user avatar
  • 171
2 votes
Accepted

what does one Shot learning mean? do they only need one image to train for some new class detection?

One-Shot Learning refers to the problem when you only have very few or a single sample for some classes in your training dataset. A common application is, for example, face recognition. Here you may ...
Jonathan's user avatar
  • 5,430
2 votes
Accepted

Convolutional neural network low performance

There are a couple of things I would suggest: Reshape the input data: It looks to me that you want to analyse a time series if IQ-values and each time series is 128 datapoints. In this case you ...
matthiaw91's user avatar
  • 1,545
2 votes

Convolutional Neural Network for Signal Modulation Classification

There is relatively little data for a deep learning solution - 220 total data points and 20 data points for each of the 11 labels. Increasing the amount of data would probably have the greatest impact ...
Brian Spiering's user avatar
2 votes

How to create AI voice generator for fantasy language?

Given the regularity in the language, I suggest you create an equivalence between it and the International Phonetic Alphabet (IPA) pronunciation system. Then, you can just convert your text to IPA and ...
noe's user avatar
  • 27k

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