chunk
stringlengths 11
1k
| source
stringlengths 37
40
| embeddings
sequence |
|---|---|---|
‘Teddy bears working on new AI research on the moon in the 1980s’ (DALL-E 2)
‘A dragon fruit wearing a karate belt in the snow.’ (Imagen)
‘A photo of a frog reading the newspaper named “Toaday” written on it. There
is a frog printed on the newspaper too’ (Parti)
### The model
Before we dive into the data preparation process, which will take up the
largest part of this blogpost, it is useful to briefly stand still at the
model we set out to use as it determined many of our subsequent decisions.
When we started this project, DALL-E 1 was state of the art in text-based
image generation so it was the logical choice. More specifically, we chose to
start from minDALL-E, an open source PyTorch implementation of a smaller
DALL-E variant combining the original pre-trained VQGAN with a 1.3b parameter
GPT-like transformer trained on 14 million image-text pairs.
minDALL-E model architecture (visual based on source)
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minDALL-E model architecture (visual based on source)
Without going too much into detail, minDALL-E is comprised of two parts:
1. An ‘image tokenizer’ (VQGAN) which uses an encoder-decoder architecture to learn to translate the images in the dataset to and from a selection of 16x16 grid of image tokens selected from a fixed codebook containing 16k codes.
2. An ‘image token predictor’, a GPT-like transformer which takes the text tokens of the captions as input and based on those learns to predict the image tokens as encoded by the image tokenizer
At training time, you first need to train the image tokenizer (VQGAN) to
reconstruct the images in the dataset based on image tokens. Once this is
done, you can train the image token predictor (GPT) to sequentially predict
which image tokens should be there given the provided text tokens (predict the
image based on the caption) | scraping/output/7210817159672759601.txt | [
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At inference time, when a logo is to be generated, the input prompt is
converted to text tokens which are used by the transformer (GPT) to predict
image tokens which are then translated to a new, previously unseen image by
the decoder of the image tokenizer.
### Data collection
The starting point of our data collection is the Wikipedia-based Image Text
Dataset (WIT) which consists of over 11m images with rich text descriptions
and metadata which allows us to filter relatively easily on logos. Other
useful open datasets we found include the Large Logo Dataset (LLD, 120k logos)
and the WebLogo-2M Dataset (2m logos with weak labels). These are further
complemented by logos we collected automatically online from public websites
using Selenium. Recently the Laion-5b (5b image-text pairs) became available
which we do not yet use for this exploration but which could prove a useful
addition for the future.
### Image pre-processing | scraping/output/7210817159672759601.txt | [
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### Image pre-processing
The first step is to convert all images to the jpeg file format and adjust the
size. A challenge we encounter here is that some file types allow for
transparent backgrounds and our model requires jpeg files (without
transparency). In order to solve this we need to cleverly guess the correct
background color based on the edges of the logo and fill it in before
converting the image. This is often far from easy and further manual cleaning
turned out to be necessary.
A lot of not very useful whitespace and tiny fonts
Removing ‘blackspace’ to maximize logo area
| scraping/output/7210817159672759601.txt | [
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A lot of not very useful whitespace and tiny fonts
Removing ‘blackspace’ to maximize logo area
Second, our model requires 256x256px jpg images which is not very large for a
logo, especially when it contains a lot of text. Moreover, many of the files
we collected contain a significant amount of whitespace around the logos which
is of course wasted in terms of information and needs to be trimmed. For this
we use the Python imaging library to calculate the difference between the logo
image and an image with just the background color and crop to that area. Too
small images are removed and the others converted to the right shape and size.
### Filtering and selection
| scraping/output/7210817159672759601.txt | [
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### Filtering and selection
One thing we quickly notice is that the data we have collected cannot be used
as such but that serious filtering and cleaning is required. First of all, for
text-based image generation you need pairs of images and text. Some
information can be derived from the images themselves but if you want to link
the logo with a company or product, industry, or with other brand information,
you need metadata and preferably also contextual information. This immediately
rules out some of the open datasets.
Second, images of logos are often real-world pictures of objects with logos on
them like cars or buildings. Furthermore, some images of logos are not even
logos as you would think of them such as historical emblems and paper stamps.
As we want to generate clean logos more or less ready to be used on a website
for example, we want to filter these out. | scraping/output/7210817159672759601.txt | [
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A very useful heuristic for detecting non-logo images is to look at file size.
Logos are digital images that are made up of a limited number of colors, which
occur in blocks of multiple pixels. This means that they are very easy to
compress. Compressed images with a very small file size are most likely to be
‘empty’ or contain large white bands and those with a large file size most
likely contain high frequency information, which is typical for real world
images. We thus choose a lower and upper threshold and keep only the images in
between. | scraping/output/7210817159672759601.txt | [
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Finally, many items are duplicates or near duplicates (occasional variants for
example), neither of which you wish to have too many in your dataset to avoid
bias. For duplicate detection, we use a pretrained CNN trained on ImageNet,
which contains general purpose representations in their final layers. We
encode all the images of our (already filtered) dataset with the model and
store their embeddings. We then calculate the distances between these
embeddings to find the semantic similarity between each pair of images. If the
embeddings of two images are close together these have very similar content. | scraping/output/7210817159672759601.txt | [
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] |
Through trial and error we learn that we need to combine multiple techniques
for filtering and cleaning, iteratively, and often we need to go back to them
after further preprocessing is done to perform further cleaning. Real-world
data wrangling can be an ugly business. We apply various techniques to reduce
low quality data in multiple steps. This way we end up with a dataset of a
little over 140k distinct logo images with contextual information.
### Extracting metadata from images
| scraping/output/7210817159672759601.txt | [
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] |
### Extracting metadata from images
A good logo mostly represents a clever interplay between text, font, colors
and visuals. In order to allow this, we need to add the relevant information
to the captions of the logos so that the model is able to learn the correct
associations. A first step then is to extract the precise text from the logo.
We test several options and then choose the Google Cloud Vision API OCR
service which, when using the text_detection mode, is highly capable of
extracting text from a wide range of images including traffic signs and logos.
We also want to allow the user to choose the most important colors in the logo
but this information is only rarely available in the metadata or the
surrounding text. Hence we decide to extract the background and the most
important foreground colors from the images themselves using Colorgram, Color
Thief and Python Image Library. We define a list of desired color names and
match the extracted colors with those.. | scraping/output/7210817159672759601.txt | [
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] |
Furthermore, often a distinction is made between different types of logos,
some of which we wanted to be able to generate in a targeted fashion. In order
to do this we trained a classifier to predict 6 different logo types:
lettermark, wordmark, symbol, abstract mascot and emblem, (as discussed here).
Then we manually labeled 600 logos using Labelbox and fine-tuned an out-of-
the-box classifier MobileNetV2.
Types of logos (source)
### Extracting metadata from text
| scraping/output/7210817159672759601.txt | [
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] |
Types of logos (source)
### Extracting metadata from text
We extract keywords from a description of the company or organization behind
the logo via a pre-trained sentence-transformer underlying a KeyBERT
architecture. The rationale is that these keywords are identifiers of what the
organization stands for and how it wants to be perceived. Both of these
aspects are very likely to be reflected in the design of the logo (i.e. a tech
company that wants to be perceived as modern and innovative will likely opt
for a minimalistic logo whereas a bank that wants to be perceived as stable &
trustworthy will opt for a traditional-looking emblem).
Some examples of organizations along with the top keywords we extracted for
them | scraping/output/7210817159672759601.txt | [
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Some examples of organizations along with the top keywords we extracted for
them
* Goldman Sachs: Banking, Markets, Trust, Shareholders, Finance
* Apple: Diverse, Innovation, Opportunities, Commitment, Inclusion
* United Nations: Humanitarian, Government, Citizens, Rights, Peace
* Harvard: Institution, Discipline, Teaching, Excellence, Oldest
### Using ClipCap for full caption generation
Ideally, you would have one or more human-generated descriptions for every
logo but labeling 140k images would be prohibitively expensive. As we are
bootstrapping, we will try auto-captioning using ClipCap, a system cleverly
combining CLIP and GPT2 to come up with descriptions for previously unseen
images in the wild.
ClipCap architecture: mapping CLIP embeddings to GPT2 prefix embeddings
(source)
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] |
ClipCap architecture: mapping CLIP embeddings to GPT2 prefix embeddings
(source)
ClipCap is fine-tuned on the smaller Conceptual Captions dataset which
contains a bit over 3m image-text pairs, most of which are pictures of scenes,
objects or landscapes, not logos. Despite this, it provides acceptable results
when applying it to logos with in some cases no more than the observation that
we were dealing with ‘a logo’ yet in other out of the box ClipCap recognizes
style characteristics, visual elements or even provides something like an
interpretation.
Examples of ClipCap auto-captions
| scraping/output/7210817159672759601.txt | [
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] |
Examples of ClipCap auto-captions
The way a caption is made is by sequentially producing the text token by token
by using the GPT2 decoder network of ClipCap. At each step, this model outputs
a probability distribution of words that are appropriate for the caption.
Since we get this distribution, we have multiple options of constructing our
caption. The most simple (and boring) method is to always take the word with
the highest probability (and smallest amount of entropy). This is of course a
bit short-sighted, because the best word at a certain point is not necessarily
the best overall word. | scraping/output/7210817159672759601.txt | [
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An alternative to this method is to sample from the output probability
distribution. This gives us more variable captions because the word with the
highest probability is not always very informative and by including some less
obvious words we can get more interesting sentences. Moreover, by adding a
temperature parameter we can manipulate the degree of ‘adventurism’ of the
sampling mechanism. A lower temperature makes the model more confident so it
will more often choose the safer, high-probability option; a higher
temperature evens out the distribution more which will lead to more variable
selections (see figure below).
Effect of temperature parameter on probability distribution | scraping/output/7210817159672759601.txt | [
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] |
Effect of temperature parameter on probability distribution
Another extension of this sampling technique, which is called beam search, is
to construct multiple sequences (or beams) instead of just one, and in the end
look at which sequence has the best overall score. By changing this beam size
we make a tradeoff between required compute and quality of the caption.
Beam search: constructing multiple beams (source)
In the end, for the captioning part of the pipeline, larger images generally
yield better results as does making use of beam search with size 5 and
adjusting the temperature to 0.3. This produces a nice combination of truthful
but creative and semantically rich descriptions.
### Rule-based caption generation
| scraping/output/7210817159672759601.txt | [
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] |
### Rule-based caption generation
A final data wrangling challenge is to combine all the information we gathered
into sets of coherent yet at the same time sufficiently variable captions of
close to 64 text tokens to train our model on. After rereading the DALL-E
paper and checking the blogpost again, we decide that it is best to fill the
available 64 text tokens to the limit so as to give the model as much
information as possible to work with when predicting image tokens.
For each logo, we have the following information at our disposal:
* OCRed text from logo
* Colors: foreground and background
* Logo style
* Company name
* Industry
* Keywords (derived from surrounding text or metadata)
* ClipCap-generated captions | scraping/output/7210817159672759601.txt | [
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* OCRed text from logo
* Colors: foreground and background
* Logo style
* Company name
* Industry
* Keywords (derived from surrounding text or metadata)
* ClipCap-generated captions
Next we create a script to generate permutations of sentences reshuffling the
available information elements as much as possible to avoid position and order
bias while maintaining some semblance of natural language. We also distinguish
between multiple phases to be able to limit bias as some terms are more
prevalent than others, like colors for example. Each phase contains ten
generated captions from which the model selects one at training time.
### Fine-tuning the ‘image tokenizer’ (VQGAN)
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### Fine-tuning the ‘image tokenizer’ (VQGAN)
As a starting point, we use the standard minDALLE pretrained weights. The
first step is to finetune VQGAN. This VQGAN model has already been trained on
a large variety of data, which includes but is not limited to computer
graphics images such as logos. Although this encoder-decoder network can
already reconstruct images pretty well, it can still do a lot better on logo
images specifically. Real world data typically contain high-frequency
information, since there is a lot of irregular variety in colors and textures
within natural pictures. Logos on the other hand are generally built of planes
of the same color, with relatively simple shapes. | scraping/output/7210817159672759601.txt | [
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We want the VQGAN to give us sharp edges and nice even-colored planes to
reconstruct our logos which requires some finetuning of the model. During the
fine-tuning process we can clearly see the improvements on these aspects and
we see the loss dropping with each epoch. We train the model for forty epochs
until we are satisfied with the quality of the results. At that point the
model is not yet overfitting so further quality gains by additional training
are possible.
VQGAN image reconstruction after fine-tuning for (only) one epoch: original
image (top) & reconstruction (bottom)
VQGAN image reconstruction after fine-tuning for 40 epochs: original image
(top) & reconstruction (bottom)
### Fine-tuning the ‘image token predictor’ (GPT)
| scraping/output/7210817159672759601.txt | [
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VQGAN image reconstruction after fine-tuning for 40 epochs: original image
(top) & reconstruction (bottom)
### Fine-tuning the ‘image token predictor’ (GPT)
The general flow when training the DALL-E model is to encode a batch of image
captions from the training set and learn to predict the discrete image tokens
of VQGAN (see also model section above). Each of these tokens represents a
separate class and this step is typically trained with the cross-entropy loss.
In addition to this autoregressive image token prediction, to help learn
meaningful text embeddings that capture the relations between the words, there
is a language modeling loss, similar to that used to train the autoregressive
decoder-only GPT models. This is added to the complete loss with a certain
weight, to balance the effect of the language and image token prediction
losses. | scraping/output/7210817159672759601.txt | [
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When actually training this model for text-conditioned image generation, we
quickly realize that our dataset is not that large and that our model is large
enough to memorize much of it. This quickly leads to overfitting whereby,
rather than deriving generalizable principles, the model learns the data by
heart This allows it to achieve an excellent performance on seen data but a
dismal one on unseen data which is of course not what we want! | scraping/output/7210817159672759601.txt | [
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There are three main ways of tackling overfitting: increasing the size of the
training dataset, reducing the model size or applying regularization. As the
first two are not an option we apply several regularization techniques to
restrict the capability of the model to learn things by heart and force it to
learn more useful representations that generalize to the test set. Techniques
used include focal loss, gradient clipping, weight decay and caption variation
which are all discussed more in detail below. With this adapted setup we fine-
tune the model for 15 epochs until it starts to overfit.
#### Focal loss
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First of all, we replace the cross entropy loss with one that is better suited
for class-imbalanced datasets. But how is our dataset imbalanced, you might
ask? Since we are not directly predicting the full image but rather segments
(in the form of image tokens), some tokens occur much more often than others
in logos, such as the token that represents a black or white background. These
are much more frequent than an exotic image patch with text and multiple
colors in it (while the latter is actually more important). So to reduce the
effects of these dominant tokens (the safer bets), we implement the Focal
Loss. This is an adapted cross entropy loss that puts more weight (in terms of
loss) on tokens that are not predicted well (and thus have a lower
probability) and reduces the weight of the classes that are already predicted
with a decent probability. This shifts the focus of the model from trying to
optimize the easier parts even more to actually tackling the harder
challenges. | scraping/output/7210817159672759601.txt | [
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Focal loss reduces ‘attractiveness’ of predicting high-probability classes to
reduce loss (source)
#### Gradient clipping
A second regularization technique is gradient clipping which limits the size
of the gradients during training and reduces the odds of the model going wild.
In case some specific sample (or batch) gives a weird and/or absurdly high
gradient that pushes the model weights in an unwanted direction, its impact is
limited. Although gradients generally point in a favorable direction, we don’t
want to make too big changes with each update.
#### Weight decay | scraping/output/7210817159672759601.txt | [
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#### Weight decay
Another technique that is applied to the gradients is weight decay. This is a
well-known technique that is to include the size of the model weights in the
loss function. The idea behind it is that, if we allow the model weights to
assume any value, it has a really easy time to just learn all the training
data. By restricting these weight sizes, by adding the amplitudes of the
weights to the loss with the L2 loss, the model weights stay within reasonable
bounds and get a better chance of learning features that are widely usable and
contain actual useful information that can be used during inference.
#### Caption variation | scraping/output/7210817159672759601.txt | [
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] |
#### Caption variation
A final important technique we use is to increase the variety of captions that
are associated with each image. As discussed in the caption generation
section, we are able to generate a list of captions for each image and we
randomly assign one each training epoch. This way, the model doesn’t get the
chance to just learn the direct mapping from text to image but rather has to
learn the semantic information within the captions.
### Failure: character embeddings
Not everything we try works out as planned though. We define separate
character embeddings to be able to steer the generation of text inside the
logo by adding it to the textual input. The main reason is to have a clear
indication for the model which text is actually in the image and not part of
its semantic content. | scraping/output/7210817159672759601.txt | [
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Although this seems like a reasonable approach, in practice it is very
difficult for the model to learn this type of complex information (characters,
fonts, casing, colors, size, direction etc.). Even though it has 1.3B
parameters, it is still relatively small and has a hard time including this
new type of information in its predictions. When looking at the original
DALL-E paper and other large multimodal models, it is clear that the
capabilities of the models scale with the size of the model and that such
complex behavior arises when going bigger and badder (called the scaling
laws). | scraping/output/7210817159672759601.txt | [
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] |
A second reason is that we have a relatively small dataset with ‘only’ 140k
images and that the text information in a logo often contains a brand name and
is therefore unique. This allows the model to quite easily start to associate
texts one on one with images and start to overfit. This results in a
degradation of the model performance and mode collapse (meaning that the model
generates the same samples over and over).
### Results
Overall, we are very pleased with the resulting outputs knowing that this is
the first working version of a proof of concept demonstrator. Below you can
find a few example prompts with each time forty (non cherry-picked) generated
logos as ordered by non-fine-tuned CLIP.
‘An emblem logo of a sports clothing brand without text’ (our model)
‘A gold and green logo of an Indian restaurant featuring the Taj Mahal’ (our
model)
‘A logo for a Paris cafe featuring the eiffel tower’ (our model) | scraping/output/7210817159672759601.txt | [
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] |
‘A gold and green logo of an Indian restaurant featuring the Taj Mahal’ (our
model)
‘A logo for a Paris cafe featuring the eiffel tower’ (our model)
‘A logo of a Japanese noodle bar with a bowl of steaming hot noodles’ (our
model)
Some observations we made are the following: | scraping/output/7210817159672759601.txt | [
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* The model generates unique, previously unseen logos. There are some logos that look vaguely familiar but when we try to find them using reverse image search we utterly fail to do so.
* The model correctly generates logos according to the semiotic codes implied in the captions such as style, type, industry etc.
* The fine-tuning has turned it from a generic image generator into a logo generator. When being asked to generate non-logos, it will do that in a very logo-like style. Also, the quality of the logos generated by the fine-tuned model are much more refined and qualitative than those using the original weights. Some of the real-world knowledge from the original training seems to have been retained by the model, however, for example when you ask it for specific visual elements like a cup of coffee or the Taj Mahal, it is able to integrate that. | scraping/output/7210817159672759601.txt | [
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] |
* The model is able to creatively combine visual elements like fonts, images and color. The colors are balanced and generally fit the topic. Text is embedded in the visual elements and sometimes cleverly interacts with them like when the Eiffel Tower is used as a character.
* Longer, more informative, more descriptive, more concrete prompts lead to better outputs. When providing little information the output rather seems undirected.
* The model is fast. It takes around a second per logo when generating a batch of twenty logos on an NVIDIA P100 gpu.
* The text looks like gibberish. Even when you ask specifically for certain characters, the model fails to produce it (for now, see conclusion section). | scraping/output/7210817159672759601.txt | [
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If we compare with logos generated with the original minDALL-E (below) we
notice that our approach yields more diverse, consistent and visually
appealing results. When we compare with the recent laion-ai / erlich diffusion
model, we notice that our approach is faster and more ‘creative’ in that the
generated logos are more diverse. The erlich-generated logos are much better
at dealing with text and are closer to an end product however.
‘A logo of a coffee shop featuring a coffee cup’ (our model)
‘A logo of a coffee shop featuring a coffee cup’ (original minDALL-E without
fine-tuning)
‘A logo of a coffee shop featuring a coffee cup’ (laion-ai / erlich)
### Conclusions | scraping/output/7210817159672759601.txt | [
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‘A logo of a coffee shop featuring a coffee cup’ (original minDALL-E without
fine-tuning)
‘A logo of a coffee shop featuring a coffee cup’ (laion-ai / erlich)
### Conclusions
We are convinced that AI-driven logo generation will soon be a reality (and
that AI will drive a range of other complex creative processes). Our exercise
covered here has shown that advanced generative AI models such as DALL-E can
create unique, high quality output, fitting with human understanding &
semiotics; essentially capable of rivaling human creativity. | scraping/output/7210817159672759601.txt | [
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We make this statement even based on our relatively “small” model, and have a
strong conviction that further extension and fine-tuning of our dataset will
contribute to making our results ready for use in the real world. Beyond the
dataset, further improvements can be made, for instance by using a latent
diffusion model, through targeted pre-training e.g. for fonts and icons, by
using a fine-tuned ML-based caption generator, and through fine-tuning CLIP to
order the results. | scraping/output/7210817159672759601.txt | [
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] |
Finally, this experiment has once again demonstrated the speed with which AI
can evolve and learn. This suggests that user input, coupled with existing
domain knowledge will be an essential driver of success. This poses a
challenge to existing creative processes, which often lack clear success
drivers and KPIs (i.e. How Brands Grow — Distinctive Brand Assets for an
attempt to make this measurable). Lacking a clear design language (what is
“minimal” to you?) and scoring model (a “good” vs. “bad” logo) will
comparatively make the learning journey for AI more difficult. Regardless,
adding relevant (& tested) classifiers to the existing database, and learning
from user input and implicit feedback will be key. Interestingly, if managed
well, we believe this might actually drive a new era in making marketing more
scientific.
### Demo
You can test the interactive demo on Replicate.
##### Acknowledgements | scraping/output/7210817159672759601.txt | [
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### Demo
You can test the interactive demo on Replicate.
##### Acknowledgements
We would like to thank Bert Christiaens, Karel Haerens, Mathias Leys and Elina
Oikonomou for their work on the demo and this blogpost and Daan Raemdonck for
sharing his domain expertise. We would also like to thank the Vlaams
Supercomputer Centrum (VSC) and Tim Jaenen from the Research Foundation
Flanders (FWO) for the use of their infrastructure.
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Infrastructure | scraping/output/7210817159672759601.txt | [
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January 8, 2024
# Elevating Your Retrieval Game: Insights from Real-world Deployments
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Sebastian Wehkamp
Machine Learning Engineer
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Exploring the world of Retrieval Augmented Generation (RAG) can be both
fascinating and confusing. With the promise of discovering amazing abilities,
it invites us into a space where combining retrieval and generation methods
has great potential. However, as we (em)bark on the journey of developing a
RAG pipeline, the reality often deviates from the initial expectations. Many
find themselves wondering: Why doesn’t it perform as well as anticipated?
Simplified RAG architecture. Through leveraging our Smart Retriever, we can
force our generator to stick to the content of our knowledge base that is most
relevant for answering the question. Source | scraping/output/-4802595178952574886.txt | [
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Simplified RAG architecture. Through leveraging our Smart Retriever, we can
force our generator to stick to the content of our knowledge base that is most
relevant for answering the question. Source
The image above shows what a typical RAG solution looks like. There is a
retriever which, based on the question asked, retrieves relevant information
from your internal knowledge base. The question and the retrieved documents
get sent to the generator which formulates a source-informed answer. In case
you’d like to read more about what a RAG solution is and how they work, see
this blogpost. | scraping/output/-4802595178952574886.txt | [
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While there’s considerable focus on the generator, which has a Large Language
Model (LLM) at its core, the retriever is frequently overlooked. Prototyping a
retriever is easy, making it performant, robust, and scalable to a large
number of documents is hard. This blogpost contains a set of tips, tricks, and
other learnings we collected during various RAG projects at ML6 on how to
improve the retriever.
#### Parsing, Chunking, and Embedding strategies | scraping/output/-4802595178952574886.txt | [
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#### Parsing, Chunking, and Embedding strategies
This section will go over the first challenges you encounter when building a
semantic retriever: creating and storing embeddings of your input data. To do
so relevant information has to be extracted from the input documents in a
parsing step, the parsed information needs to be split into smaller pieces of
information by applying a chunking strategy, and finally the chunks are given
to an embedding model to create embeddings which can be stored. These
embeddings serve as vector representations of different parts of the
documents, offering a mathematical description of the meaning contained in
each piece of information. By comparing these embeddings, we can identify text
sections that share similar meanings and use that to retrieve relevant
documents given a query. For more info on semantic search we recommend this
blogpost.
#### Parsing your input documents | scraping/output/-4802595178952574886.txt | [
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In order to make your documents searchable, relevant information has to be
extracted from the documents in a parsing step. The extraction and parsing
strategies depend on the format of the input documents. If you have control
over the input type, structured document types containing all information,
such as HTML are preferred over less structured file types such as PDF where
you have to apply parsing yourself. If you don’t have the luxury of
controlling the input format, you likely have to extract and parse the
information manually. For PDFs you can use libraries such as PyPDF which are
capable of retrieving text and metadata from the PDFs. The quality of the
extracted data depends on the content, and how the input PDFs were created. We
found that we were often missing text, especially text contained in tables or
other unstructured forms for some use-cases. To extract all information we can
leverage solutions such as Microsoft Form Recognizer, AWS Textract, or | scraping/output/-4802595178952574886.txt | [
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other unstructured forms for some use-cases. To extract all information we can
leverage solutions such as Microsoft Form Recognizer, AWS Textract, or
Google’s Document AI which are high performing, managed document AI solutions
helping with extraction of both structured and unstructured document
information. Thanks to these solutions we were able to extract and parse far
more text from the documents. | scraping/output/-4802595178952574886.txt | [
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#### Deciding on your chunking strategy
Chunking involves dividing the extracted text into smaller, more uniform units
known as chunks, making it easier for an LLM to process. Chunking plays a key
role for the retriever, as it enables the reduction of text complexity and
enhances the relevance of content. Selecting a chunking strategy is a non-
trivial decision influenced by various factors, including the type and volume
of data, the complexity of queries, and the characteristics and performance of
the embedding model. Furthermore, the chosen chunking type significantly
affects the final application in terms of quality, efficiency, and
scalability. Several tips to create an effective chunking strategy will be
covered here. | scraping/output/-4802595178952574886.txt | [
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The first step to increase your retrieval performance is to apply chunking
based on logical sections rather than chunking arbitrary. Depending on the
input documents chunking could be applied on a per chapter-basis. For an ML6
RAG solution we parsed the table of contents of every incoming PDF
individually, to provide clues of the start and end of each chapter. Within
each chapter LangChain’s recursive character text splitter was leveraged to
create smaller chunks of text with an overlap. Additionally, textual metadata
was added to each chunk, to indicate the title of the original chapter that
the chunk belongs to. Important parameters to decide on are the chunk size and
the overlap of the chunks. These are of course only some of the many
hyperparameters one can tweak to improve performance. | scraping/output/-4802595178952574886.txt | [
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] |
A common trade-off for chunking strategies is that on the one hand you want to
have small chunks such that their embeddings accurately reflect their meaning.
When chunks get too long they may lose their meaning due to filler words/info
that bias the embedding representation. On the other hand, you want to provide
enough context to the generator to synthesize a correct answer. A recent
addition to LangChain and LlamaIndex attempts to solve this problem by
decoupling the chunks used for retrieval from the ones used for synthesis.
This allows you to perform the look-up using the smaller chunks while keeping
track of the surrounding chunks. Both the original chunk and the surrounding
chunks get sent to the generator which should now have enough context to
synthesize a correct response.
Decoupling chunks used for retrieval with those that are used for synthesis.
Source.
#### Choosing your embedding model | scraping/output/-4802595178952574886.txt | [
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] |
Decoupling chunks used for retrieval with those that are used for synthesis.
Source.
#### Choosing your embedding model
With the extracted text split into smaller chunks, the next step is to choose
the right embedding model for your use case. A good starting point for this is
the Massive Text Embedding Benchmark (MTEB) leaderboard. One of the current
top ranking models on MTEB is Cohere Embed v3 which will provide a good
starting point for your RAG solution.
Overall the higher performing embedding models on the MTEB leaderboard will be
a decent choice. However you might have some more specific requirements in
terms of language support, speed, hosting options, or pricing. In order to
make a more deliberate choice it is also possible to evaluate different
embedding models through automatic evaluation covered later in this blogpost.
### Hybrid search | scraping/output/-4802595178952574886.txt | [
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Now that we have an initial retriever configured, it is time to start
improving the retrievers’ performance. A common practice for RAG applications
is to base the retrieval step solely on vector search. Vector search is able
to outperform traditional search methods when the embedding models are trained
on the target domain. However, when assessing performance across out-of-domain
tasks, classical search methods can outperform vector search. This means that
when building a RAG application tailored to a specific domain, wherein the
document queries feature domain specific words, lexical search might still
outshine vector search. Another example where lexical search might perform
better is when a RAG solution is developed where the main language is another
language than English, and the embedding model lacks sufficient training in
that particular language. Both approaches have their pros and cons, but what
if we merge them to get better retrieval performance than either of them | scraping/output/-4802595178952574886.txt | [
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that particular language. Both approaches have their pros and cons, but what
if we merge them to get better retrieval performance than either of them
separately? That is where hybrid search comes into play. | scraping/output/-4802595178952574886.txt | [
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The idea of hybrid search is to perform both lexical (e.g. BM25) and vector
search in parallel, after which the results are combined into a single list of
results. One way to create this combined list is using Reciprocal Rank Fusion
(RRF). RRF is an algorithm that evaluates multiple ranked result lists to
provide a single unified result list. RRF is based on the concept of
reciprocal rank (RR), which calculates the reciprocal of the rank at which the
first relevant document was retrieved. RR is 1 if a relevant document was
retrieved at rank 1, it is 0.5 if a relevant document was retrieved at rank 2
and so on.
Example showing the reciprocal rank calculation.
After the reciprocal ranks have been assigned to all documents the scores are
summed for all documents producing a combined score. The engine ranks the
documents based on this combined score to produce the final result list.
Reciprocal Rank Fusion calculation. | scraping/output/-4802595178952574886.txt | [
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Reciprocal Rank Fusion calculation.
The result is a search engine which has the advantages of both vector search
and lexical search. Reciprocal Rank Fusion is one way to merge ranked lists
but there are also other algorithms available. Based on the experience of ML6
obtained during various RAG projects, hybrid search resulted in a better
performing retriever. This conclusion is supported by Microsoft which
benchmarked the different search engines. In the graph below, it becomes clear
that hybrid search consistently outperforms both vector search and lexical
search separately. The highest performing search engine is Hybrid search +
Semantic Ranker which is covered in the next section.
Percentage of queries where high-quality chunks are found in the top 1 to 5
results, compared across search configurations. Source.
#### Adding a re-ranker into the mix | scraping/output/-4802595178952574886.txt | [
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] |
Percentage of queries where high-quality chunks are found in the top 1 to 5
results, compared across search configurations. Source.
#### Adding a re-ranker into the mix
To further increase the performance of the RAG solution we can add a re-
ranker. In a standard RAG solution, embeddings are precomputed through a
network called a bi-encoder. These embeddings compress the entire document
chunk into a single vector and are stored in a vector database. At inference
time, the query is encoded and compared with the stored embeddings using
cosine similarity. An alternative to this is a cross-encoder which takes both
the query and a candidate document as input to compute a similarity score. As
the query and the document are passed simultaneously to the model, the cross-
encoder suffers from less information loss resulting in better performance.
Comparison between Cross-Encoder and Bi-Encoder. | scraping/output/-4802595178952574886.txt | [
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Comparison between Cross-Encoder and Bi-Encoder.
If a cross-encoder results in better performance, why do we even need to store
embeddings from the bi-encoder? The problem is that scoring thousands or
millions of (query, document)-pairs with a cross-encoder is very slow and
would not result in a performant RAG solution. To resolve this, one first
retrieves the top k candidate chunks using the Bi-Encoder, which are then
semantically reranked using the Cross-Encoder. This is what a re-ranker does. | scraping/output/-4802595178952574886.txt | [
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With the ever growing context windows of state-of-the-art LLMs you might ask,
why not simply provide all of the retrieved chunks to the language model? It
turns out that this results in drastically reduced performance due to the
“Lost in The Middle” phenomenon. LLMs tend to forget elements in the middle of
the context window. A recent study by Hugging Face supports this where they
compared the performance of an LLM with a very large context window
(GPT-4-Turbo-128K) with a RAG solution. In the study Hugging Face found that
RAG is not only more performant, but also much cheaper compared to leveraging
long-context LLMs.
Image source. | scraping/output/-4802595178952574886.txt | [
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Image source.
Another advantage of re-rankers is that they are not exclusive to vector
search engines. They can be used to improve any kind of search engine such as
lexical search based on for example Elasticsearch. As was shown above, the
best performing retriever was a combination of Hybrid search and a re-ranker.
Depending on the use-case a suitable re-ranker can be chosen. A good starting
point can be the Cohere re-ranker providing overall good results according to
benchmarks done by LlamaIndex. Adding the re-rank endpoint can be a matter of
adding a single line of code to your system which drastically improves the
retrieval performance.
#### Evaluating your retriever | scraping/output/-4802595178952574886.txt | [
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#### Evaluating your retriever
To further improve the retrieval performance it is important to be able to
evaluate the performance of a retrieval system. To do this there are four
important metrics to take into account: Hit Rate, Mean Reciprocal Rank (MRR),
Mean Average Precision (MAP), and Normalized Discounted Cumulative Gain
(NDCG). While constructing a comprehensive RAG solution, it’s important to
assess the performance of both the retriever and the generator. Nevertheless,
in this blogpost, our focus lies on evaluating the retrieval system. | scraping/output/-4802595178952574886.txt | [
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] |
In order to evaluate your retrieval system you will need some labels
indicating which documents should be retrieved given an input query. It is
possible to this with manual, Ground Truth, labelling however doing this can
be a tedious task. An alternative to this is to use LLM based evaluation, for
which you can use LLamaIndex. With LLamaIndex you can generate question-
context pairs for a set of documents using an LLM. These pairs can then be
used to measure all metrics using binary labels on your own dataset allowing
you to perform hyper-parameter tuning. For more information see the OpenAI
cookbook. Consider using human-generated questions as LLM generated questions
might be easier to answer or not reflect the real-world use-case of your
#### Hit Rate
Hit rate calculates the fraction of queries where the correct answer is found
within the top-k retrieved documents. Think of hit rate as the system’s
success rate in finding the right answer among the first few guesses. | scraping/output/-4802595178952574886.txt | [
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#### Mean Reciprocal Rank (MRR)
The Mean Reciprocal Rank (MRR) is a metric that helps us understand the
average position of the first relevant document across all queries. The MRR is
calculated as the mean of the reciprocal ranks over all queries. So, if the
first relevant document takes the top spot, the reciprocal rank is a solid 1.
If it’s second, the reciprocal rank drops to 1/2, and so forth. The value
ranges from 0 to 1 where an MRR of 1 indicates that the first relevant
document is always on top. The advantage of this metric is that it is very
simple to explain and interpret. The disadvantage is that only the first
relevant item is taken into account. | scraping/output/-4802595178952574886.txt | [
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] |
The hit rate and MRR are simple and intuitive metrics to get an idea of how
well the retrieval system is performing. To provide some intuition, if a
retrieval system has a hit rate of 0.7 and an MRR of 0.5, the observation that
the hit rate is higher than the MRR indicates that the top-ranking results are
not always the most relevant results. Therefore we could look at adding a re-
ranker to improve our retrieval performance.
#### Mean Average Precision (MAP) | scraping/output/-4802595178952574886.txt | [
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] |
#### Mean Average Precision (MAP)
Let’s start with a refresh of what precision is. Precision evaluates the
number of relevant results relative to all retrieved documents. If we retrieve
10 documents and 5 are relevant we have a precision of 50%. An extension of
this is precision at K where K specifies the number of retrieved documents. In
the example above K was set to 10. The downside of this metric is that it does
not take the ranking order into account which is especially relevant for
retrieval systems as we saw above. Average precision is a metric which helps
address this. Average precision at K is computed as an average of precision
values for all possible values within K. The caveat here is that we only look
at the precision values for ranks where the items are relevant.
Example of Average Precision calculation. | scraping/output/-4802595178952574886.txt | [
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] |
Example of Average Precision calculation.
The intuition behind this metric is that it favors getting the top ranks
correct and penalizes errors in the early positions. Finally the Mean Average
Precision is the mean of the Average Precision calculated for all queries.
#### Normalized Discounted Cumulative Gain (NDCG) | scraping/output/-4802595178952574886.txt | [
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The last metric to discuss is the Normalized Discounted Cumulative Gain
(NDCG). The intuition behind NDCG is that it tries to compare document
rankings against an ideal ranking. Lets build up this concept backwards and
start with Gain. Gain is similar to a relevance score which can be numerical
ratings but can also be a binary case where it is either 0 or 1. Cumulative
gain is then the sum of all gains until position k. Similar to precision at k
the problem with this is that it does not take the ordering into account. To
solve this problem we can discount the gain by dividing it by its rank to
favor highly placed relevant documents, this metric is called Discounted
Cumulative Gain (DCG). There is still a drawback to DCG which is that it adds
up with the length of the recommendation list and the final score is dependent
on the gain scale used. This makes it difficult to compare DCG scores over
different systems. These issues are tackled by calculating the Ideal DCG | scraping/output/-4802595178952574886.txt | [
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on the gain scale used. This makes it difficult to compare DCG scores over
different systems. These issues are tackled by calculating the Ideal DCG
(IDCG) score which is the score of the most ideal ranking. We can use the IDCG
to normalize the DCG score which results in the NDCG. | scraping/output/-4802595178952574886.txt | [
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Example of Normalized Discounted Cumulative Gain calculation.
This seems very similar to the calculation of MAP however there are a number
of key differences. The first difference is that MAP only works for binary
relevance while NDCG can also take numerical relevance values into account.
This can be especially useful when you have a dataset with numerical ground
truth labels. The second difference is how decreasing ranks are taken into
account. In practice this comes down to that MAP is more sensitive to non-
relevant items at the top of the list. If the initial results are not relevant
the MAP score will drop more rapidly than the DCG score. | scraping/output/-4802595178952574886.txt | [
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] |
In order to streamline the process of evaluation and parameter tuning you can
use the Open-Source data processing framework developed by ML6 called Fondant.
They provide an example notebook allowing you to quickly go through this cycle
of evaluation and parameter tuning. The notebook performs a parameter search
and automatically tunes your RAG solution based on your own data.
#### Chat-read-retrieve
So far we discussed creating a retriever capable of retrieving relevant
documents given an independent query. In practice, RAG systems also include
the ability to ask follow-up questions, which refer to previous parts of a
conversation. In the current implementation, retrieval is performed for every
new question. However, if a question is asked: “What is a retriever in a RAG
solution?”, followed up with a question: “How do I improve this?” we would not
be a able to retrieve any meaningful chunks as it does not include the context
of the conversation. | scraping/output/-4802595178952574886.txt | [
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A solution to this problem is called “chat-read-retrieve”, which first uses an
LLM to generate a standalone question given the conversation history and the
new question of the user. Next, the standalone question is used to retrieve
relevant documents.
Follow-up question query process. Source
### Summary
In a nutshell, diving into the world of Retrieval Augmented Generation (RAG)
is cool but can be tricky. We often get hyped about the generator part, but
let’s not forget our unsung hero — the retriever.
This blogpost shares the experience based on ML6’s RAG adventures, especially
on elevating the retriever game. We’re talking about smart chunking, picking
the right embedding model, and mixing it up with hybrid search (lexical +
vector).
Throw in a re-ranker, and you’ve got a retriever dream team. To know how well
it’s doing, there are metrics like Hit Rate, MRR, MAP, and NDCG. Improve the
user experience by supporting follow up questions and you are good to go. | scraping/output/-4802595178952574886.txt | [
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In the RAG world, understanding and improving your retriever is the secret
sauce. In the future tuning and testing will likely happen automatically,
until then understanding and improving your retriever is key. Cheers to
unlocking the full RAG potential!
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Computer vision | scraping/output/-4802595178952574886.txt | [
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At the end of college years, it can be very confusing to start determining
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] |
Need some advice on how to take your first steps in a professional field? We
wrote a blogpost with some advice to get you started.
Back to the internship topic! At ML6 we find it extremely important to let
people grow and discover where their strengths lie. We invest in people and
our collaboration with various colleges and universities. Every year, we have
about 20 students that we enjoy mentoring on specific internship projects. And
the fun thing is, you as an intern also decide the direction and content of
this internship project.
Things are moving fast in the technology world, and ML6 is jumping on this
express train to accelerate. To make more impact with our AI solutions for
different industries, we have recently introduced new focus domains within our
company. These domains are flexible over time. Good news: there is still room
for you on this express train as well!
##### You can apply for an internship position at ML6 in one of the following
domains: | scraping/output/6081461799351667079.txt | [
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] |
##### You can apply for an internship position at ML6 in one of the following
domains:
#### Information Management ℹ️
The Information Management unit powers the ML6 teams with effective
information. In order to this, we develop a series of internal tools (e.g.
dashboards, applications, etc). We offer you an opportunity to work hands-on
on applications, be it backend and/or frontend. We are looking for candidates
that have worked with mainstream backend (preference: Python) and/or frontend
languages (preference: React).
#### GenAI NLP 📖 | scraping/output/6081461799351667079.txt | [
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#### GenAI NLP 📖
LLMs like GPT promise to fundamentally change knowledge work. Many challenges
remain, however, related to alignment, security, truthfulness, bias,
performance, latency, cost and others. Work on one of these problems and help
move forward the field towards truly useful, robust and measurable large
language model applications! Potential topics include local language LLMs,
domain-specific fine-tuning, performance benchmarking of LLMs, tabular data
understanding…
#### GenAI Vision 👁️ | scraping/output/6081461799351667079.txt | [
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] |
#### GenAI Vision 👁️
Image generation models like Stable Diffusion are revolutionizing the creative
industries and beyond by sharply increasing the reach and productivity of
designers and allowing lay people to create prototypes in minutes. Techniques
like Dreambooth and ControlNet further allow for the guidance of generative
models in clever ways. Help us push the boundaries of AI image generation by
researching new ways of guiding image generation and develop domain-specific
use cases. Potential topics include domain-specific fine-tuning (e.g. food
photography, fashion…), ControlNet guidance, Gligen…
#### Digital Twins - Industry 4.0 ♊ | scraping/output/6081461799351667079.txt | [
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] |
#### Digital Twins - Industry 4.0 ♊
This domain focuses on using AI in the context of Industry 4.0. Think about
digital twins, predictive maintenance, root cause analysis, process
optimisation, process steering. We also see value for use cases where labeling
data is expensive or difficult to do, which we tackle by using segment
anything model, active learning or surrogate models. Potential subtopics
include PINNs, Digital Office, Azure IoT...
#### Fondant - open source 🍫
Foundation models learn new knowledge and skills through cleverly prepared
data. Help us push the boundaries of data creation, curation and augmentation
by researching new functionalities and developing components and pipelines for
the Fondant open source framework developed by ML6. Potential subtopics
include bias removal, knowledge domain-based filtering, synthetic data
generation, topic-based fine-tuning, distillation…
#### AI in Bio 🧬 | scraping/output/6081461799351667079.txt | [
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#### AI in Bio 🧬
Biology is intrinsically complex and diverse. Despite, decades of research,
nature still holds many secrets. Today, there is an opportunity for AI to
support the experts: mapping experiment input and output over very complex
biological functions. Potential subtopics include Drug Discovery and Omics
analysis.
#### Energy⚡
AI can optimize resource management, enhance grid efficiency, and accelerate
the transition to clean energy. This intersection holds the key to a
sustainable and greener future, making it a crucial avenue for innovation and
progress.
Potential subtopics within the energy domain include leveraging and combining
open-data forecasts to improve anticipated renewable energy generation,
balancing the net with AI, closing the gap between energy finance and actual
supply-demand by deepdiving into forecasted energy trading and optimising
residential energy consumption. For the last one you could even get to use
your own smart meter data!
| scraping/output/6081461799351667079.txt | [
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] |
#### Ops 🎛️
Operations is a term subsuming DevOps and MLOps. DevOps refers to a set of
standards, practices and tooling that enable fast/high quality delivery and
continuous improvements. MLOps extends DevOps to ML systems, which come with a
set of challenges related to the training, deployment, monitoring and tracking
of models.
#### Hybrid Solutions💡
For some of our most frequently solved challenges we offer hybrid solutions.
These projects are based on a shared codebase and internships are centered on
extending the shared codebase based on latest technical developments.
Applications range from process steering in manufacturing to computer vision
diagnostics and entity recognition in legal documents. Potential topics are
domain adaptation, zero shot learning and more.
Pfiew, that’s a lot to choose from! | scraping/output/6081461799351667079.txt | [
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] |
Pfiew, that’s a lot to choose from!
We give our interns every opportunity to get the most out of their time at
ML6. Curious about who came before you and what the outcome of their
internship was? You can read Clément's blog post about the development of a
generative AI solution for product photography here and Robin's blog post
about AI image generation without copyright infringement here.
> Fun fact? In 2023, we have already offered three interns a permanent
> contract to officially launch their careers as ML6 agents.
#####
Unleash your potential! We are looking for interns for our offices in Berlin,
Amsterdam and Ghent. Does one of these topics above appeal to you? Then apply
quickly!
##### Would you like to discuss a topic yourself? Let us know in your
application. We are always open to ideas!
#####
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Large Language Model
Large Language Model | scraping/output/6081461799351667079.txt | [
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March 2, 2022
# Need for Speed: JAX
Contributors
Jasper Van den Bossche
Software Engineer
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Share this post | scraping/output/-5836825137693104580.txt | [
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Training your neural network ten times faster using Jax on a TPU
All the cool kids seem to be raving about JAX these days. Deepmind is using it
extensively for their research and even building their own ecosystem on top of
it. Boris Dayma and his team built DALL·E Mini in no time using JAX and TPUs,
definitely worth checking out on Hugging Face where you already find over 5000
models written in JAX. But what exactly is JAX and why is it so special?
According to their website JAX offers automatic differentiation, vectorization
and just-in-time compilation to both GPUs and TPUs via composable
transformations. Sounds complicated? Don’t worry, in this blogpost we will
take you on a tour and show you how JAX works, how it’s different from
Tensorflow/Pytorch and why we think it is a super interesting framework.
### What is JAX? | scraping/output/-5836825137693104580.txt | [
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] |
### What is JAX?
JAX is a high-performance numerical computing and machine learning framework
by Google Research that runs super fast on GPUs and TPUs, without having to
worry about low-level details. The goal of JAX was to build a framework that
combines high-performance with Python’s expressiveness and ease of use, so
that researchers could experiment with new models and techniques without the
need of highly optimised low-level C/C++ implementations. It achieves this
goal by using Google’s XLA (Accelerated Linear Algebra) compiler to generate
efficient machine code rather than using precompiled kernels. One of the cool
things about JAX is that it is accelerator agnostic, meaning that the same
Python code can run efficiently on both GPUs and TPUs. | scraping/output/-5836825137693104580.txt | [
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JAX works via composable function transformations, this means that JAX takes a
function and produces a new function that is interpreted differently and that
multiple transformations can be chained together. Automatic differentiation
for instance is a transformation that generates the derivative of a function,
while automatic vectorization takes a function that operates on a single data
point and transforms it into a function that operates on a batch of data
points. Through these transformations JAX allows the programmer to stay in the
high-level Python world and let the compiler do the hard work by generating
the highly efficient code needed to train complex models. We will go over
these transformations and apply them in an example where we build a simple
multilayer perceptron.
A schematic overview of composable functions.
### What makes it different from Tensorflow and Pytorch? | scraping/output/-5836825137693104580.txt | [
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] |
A schematic overview of composable functions.
### What makes it different from Tensorflow and Pytorch?
JAX is a compiler-oriented framework, which means that a compiler is
responsible for transforming the Python functions into efficient machine code.
Tensorflow and Pytorch on the other hand have precompiled GPU and TPU kernels
for each operation. During the execution of a TensorFlow program, each
operation is dispatched individually. While the operations themselves are very
well optimised, fusing them together requires a lot of memory operations,
causing a bottleneck in the performance. The XLA compiler can generate code
for the entire function. It can use all of that information to fuse together
operations and save a ton of memory operations and thus generate overall
faster code. | scraping/output/-5836825137693104580.txt | [
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JAX is also more lightweight than Tensorflow and Pytorch, because there is no
need to implement each operation, function or model separately. Instead JAX
implements the NumPy API with simpler and more low-level operations that can
be used as building blocks and fused together into complex models and
functions by the compiler.
### The compiler, much more powerful than you might think
The compiler-oriented design is a lot more powerful than you might think at
first. With the compiler, there is no longer a need to implement low-level
accelerator code. It allows researchers to vastly improve their productivity
and opens doors to experiment with new model architectures. Researchers are
even able to experiment with GPUs and TPUs without the need to rewrite their
code. But how does that work? | scraping/output/-5836825137693104580.txt | [
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JAX doesn’t directly compile to machine code, but rather to an intermediate
representation that is independent from the high-level Python code and the
machine code. The compiler is split up in a frontend that compiles Python
functions to the IR and a backend that compiles the IR to platform specific
machine code. This design isn’t new, an example of a compiler that also
follows this design is LLVM. There are frontends for both C and Rust that
translate high-level code to the LLVM IR. The backend can then generate
machine code for a variety of supported machine types, no matter whether the
original code was written in C or Rust.
JAX transforms high level Python code to an intermediate representation that
is then compiled by XLA to a program that runs on your machine type. | scraping/output/-5836825137693104580.txt | [
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JAX transforms high level Python code to an intermediate representation that
is then compiled by XLA to a program that runs on your machine type.
This is huge, because thanks to this flexible design one could build a new
accelerator, write an XLA backend for it and your JAX code that previously ran
on GPUs/TPUs can be executed on the new accelerator. On the other hand, you
could also build a framework in another programming language that compiles to
the JAX IR and you can make use of GPUs and TPUs thanks to XLA. | scraping/output/-5836825137693104580.txt | [
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If this compiler based approach works so much better than precompiled kernels,
why didn’t Tensorflow and Pytorch make use of it from the start? The answer is
pretty simple, it is really hard to design a good numerical compiler. With its
automatic differentiation, vectorization and jit-compilation, JAX has some
really powerful tools under its belt. However JAX isn’t the silver bullet
either, all of these goodies come at a small price, you need to learn a few
new tricks and concepts related to functional programming.
### A note on functional programming | scraping/output/-5836825137693104580.txt | [
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### A note on functional programming
JAX can’t transform just any Python function, it can only transform pure
functions. A pure function can be defined as a function that only depends on
its inputs, meaning that for a given input x it will always return the same
output y and that it doesn’t produce any side effects such as IO operations or
mutation of global variables. Python’s dynamism means that the behaviour of a
function changes based on the types of its inputs and JAX wants to exploit
this dynamism by transforming functions at runtime. At the start of a
transformation JAX checks what the function does for a set of given inputs and
transforms the function based on that information. Under the hood JAX traces
the function, just like the Python interpreter. By only allowing pure
functions, transforming functions just in time becomes a lot easier and
faster. | scraping/output/-5836825137693104580.txt | [
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] |
Imagine that the tracer has to deal with side effects such as IO, that means
that unexpected behaviour can occur such as a user that entered invalid data,
which makes it a lot harder to generate efficient code, especially when
accelerators are in the game. Global variables can change between two function
calls and thus completely change the behaviour of the function they’re used
in, making a transformed function invalid. If you are interested in compilers
and the nitty gritty details of how JAX’s tracing works, we recommend you to
check out the documentation for more details of its inner workings.
The only unfortunate thing about JAX is that it can’t verify whether a
function is a pure function. It is up to the programmer to make sure that he
writes pure functions, otherwise JAX will transform the function with some
unexpected behaviour.
### Representing stateful objects using Pytrees | scraping/output/-5836825137693104580.txt | [
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Working with pure functions also has an impact on how data structures are
used. In other frameworks machine learning models are often represented in a
stateful way, however, this clashes with the functional programming paradigm
because this is mutation of a global state. To overcome this problem JAX
introduces pytrees, tree-like structures built out of container-like Python
objects. Container-like classes can be registered in the pytree registry,
which by default contains lists, tuples, and dicts. Pytrees can contain other
pytrees and classes not registered in the pytree registry are considered
leafs. Leafs can be considered as immutable inputs for a pure function. For
each class in the pytree registry, there is a function that converts a pytree
to a tuple with its children and optional metadata as well as a function that
converts children and metadata back to a container-like type. These functions
can be used to update the model or any other stateful objects you use. | scraping/output/-5836825137693104580.txt | [
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] |
### Let’s transform some code!
Before we dive into our MLP example, we will show the most important
transformations in JAX.
### Automatic Differentiation
The first transformation is automatic differentiation, where we take a Python
function as an input and return a function that represents the gradient of
that function. The neat thing about JAX’s autodiff is that it can
differentiate Python functions that make use and Python containers,
conditionals, loops etc. In the following example we create a function that
represents the gradient of the `tanh` function. Because JAX transformations
are composable, we can use n nested calls of the grad function to transform to
calculate the nth derivative.
JAX’s Automatic differentiation is a powerful and extensive tool, if you want
to learn more about how it works we recommend you to read The JAX Autodiff
Cookbook.
## Automatic Vectorization | scraping/output/-5836825137693104580.txt | [
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] |
JAX’s Automatic differentiation is a powerful and extensive tool, if you want
to learn more about how it works we recommend you to read The JAX Autodiff
Cookbook.
## Automatic Vectorization
When training a model, you typically propagate a batch of training samples
through your model. When implementing a model would you thus have to think of
your prediction function as one that takes in a batch of samples and returns a
prediction for each sample. This however can significantly increase the
difficulty of the implementation as well as reduce the readability of the
function compared to a function that would operate on a single sample. In
comes the second transformation: automatic vectorization. We write our
function as if we were processing only a single sample, then vmap will
transform it into a vectorized version. | scraping/output/-5836825137693104580.txt | [
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In the beginning vmap can be a bit tricky especially when working with higher
dimensions, but it is a really powerful transformation. We recommend you to
check out some examples in the documentation to fully understand its
potential.
## Just-In-Time Compilation
The third function transformation is just-in-time compilation. The goal of
this transformation is to improve the performance, parallelise the code and
run it on an accelerator. JAX doesn’t compile directly to machine code but
rather to an intermediate representation. That intermediate representation is
independent from the Python code and the machine code of the accelerator. The
XLA compiler will then take the intermediate representation and compile it to
efficient machine code. | scraping/output/-5836825137693104580.txt | [
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