Have you tried Named Entity Recognition (NER)? You could use NER that involves identifying and classifying named entities (such as people, organizations, and locations) in text. You could use this tool to extract the named entities from each paragraph and then filter them based on specific criteria, such as location or birth place. If you're feeling a bit ambitious, why not try semantic search?

One option is Stanford NER, which is a named entity recognition tool developed by Stanford University. It uses a CRF (conditional random field) model trained on a large dataset of named entities, and it's relatively fast and accurate. Stanford NER also has pre-trained models available for various languages, so you could use one of these models or train your own model on a custom dataset.
Another option is spaCy, which is a popular natural language processing (NLP) library for Python. spaCy includes a named entity recognition component that uses a convolutional neural network (CNN) to identify named entities in text. It's generally quite accurate and fast, and it has pre-trained models available for various languages. spaCy also provides tools for training custom models on your own dataset, if you have specific named entities that you'd like the model to recognize.
Finally, you might also consider using the Google Cloud Natural Language API, which is a cloud-based NER service provided by Google. The API uses a machine learning model to identify named entities in text, and it's generally quite accurate and fast. It has pre-trained models available for various languages, and it provides tools for training custom models on your own dataset.

Have you tried using tools such as TensorBoard, which is a visualization tool for TensorFlow that can be used to track the performance of your Keras models?
To use TensorBoard with Keras, you will need to install TensorFlow and modify your Keras code to write log files that TensorBoard can read. This can be done by using the TensorBoard callback provided by Keras, which writes log files to a specified directory that TensorBoard can use to visualize the results of your training runs.
Here is an example of how you might use the TensorBoard callback in your Keras code:

from tensorflow.keras.callbacks import TensorBoard
# Create a TensorBoard callback
tensorboard_callback = TensorBoard(log_dir='/path/to/logs')
# Compile and fit your Keras model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=10, callbacks=[tensorboard_callback])

After you have trained your model, you can start TensorBoard by running the tensorboard command in a terminal, specifying the directory where the log files are stored. TensorBoard will then start a web server that you can access in a web browser to visualize the results of your training runs.

Riffusion is an excellent way to be flexible with music production. An artist can go from heavy metal to pop. Not to mention, using stable diffusion and interpolation in latent space will create smooth transitions in the generated audio clips, which could help to create more coherent and pleasant-sounding music. I'm curious about one thing, though. What do you use for semantic analysis of the text inputs? Have you had issues with that?

Here is an example of a research paper that investigates the use of neural networks to discover classical algorithms:
"Neural Combinatorial Optimization with Reinforcement Learning" (Bello et al., 2016)
In this paper, the authors propose a method for using reinforcement learning to train a neural network to discover efficient algorithms for combinatorial optimization problems. The neural network is trained to solve a particular optimization problem by generating a sequence of actions, which are then executed by a virtual machine to find the optimal solution. The authors show that this approach can discover a variety of classical algorithms, such as the Knapsack algorithm and the Traveling Salesman algorithm, and can achieve similar or better performance compared to the original algorithms.

The best approach will depend on the nature of the data and the specific research question you are trying to answer. It may be helpful to try a few different approaches and see which one works best for your specific case.

For the PhD: It will allow you to delve deeply into the technical and theoretical aspects of machine learning and artificial intelligence, and may open up more opportunities for academic and research positions. It will also give you the chance to work on original research and make contributions to the field.

For the masters: It focuses on using data to solve real-world problems. It may be a good choice if you are more interested in applying machine learning and AI techniques to solve practical problems and want to start working in the field as soon as possible.

It may be helpful to research the job market in your area and see what types of positions are available and what qualifications they require. This can help you make a more informed decision about which degree program is right for you.

The application is limitless! Have you had a problem with inaccurate translations from complex prompts as demands for text-to-powerpoint grow? What are you doing for its semantics?

We never know. Imo LLM will grow exponentially. Today, they're data inefficient, but someday (definitely not tomorrow lol), more sentient than we can ever imagine.

PyTorch and Taichi are not really competitors. Both can be combined to make some tasks simpler. (That's why I said I was curious)

I agree. Using all of them are redundant. It would take too much space. It's better to compare performance on the validation set.

Same question here (just curious, though). PyTorch mainly targets ML applications, while Taichi targets general high-performance numerical computation. Thus, PyTorch is mostly always the go-to option.

I mean. It could be if it always has context on every domain area.

Better semantic search can help solve this problem as it allows us to augment that project with an external knowledge base. At Marqo (the startup I work for), we created a demo where GPT provides up-to-date news summarisation through the use of Marqo as a knowledge base:

https://medium.com/creator-fund/building-search-engines-that-think-like-humans-e019e6fb6389

This could be applied to op's project. You can visit Marqo: https://github.com/marqo-ai/marqo

Also, TFIDF is lexical/algorithmic search (aka keywords-based search). It's faster, but has a lower accuracy and relevance outputs than tensor-based search. On the other hand, Marqo, with tensor-based search (where you can get the vectors from SBERT for example), allows semantic search by understanding the meaning of the text, rather than the keywords. Thus, users can search with questions, related terms or with images, audio or videos directly (or any combination thereof), allowing a better user experience and better relevant search yields.

In your article, you mentioned that "The search is mainly based on a v0 semantic algorithm (using TfIdf model mainly).... So the usage was pretty slow and the models were heavy (not the best user experience)."

Quick question: Have you heard of tensor search? It uses 2 key algorithms: CLIP and SBERT, where every components of the tensor can be associated with specific parts of a document, image, or video. Not only can this improve search semantics, but it can provide other key information like localization and explainability, without using text as an intermediate representation.

You can look them up: https://github.com/marqo-ai/marqo
Website: https://www.marqo.ai