chunk stringlengths 11 1k | source stringlengths 37 40 | embeddings list |
|---|---|---|
An example showing Detic is capable to work with a social media feed. In the
upper left corner we have a picture taken from the instagram from
VerhulstMarie, followed by the objects detected by a YOLOv3 model [3]. While
you can already see that such a model recognizes a variety of objects, the
Detic model in the bottom... | scraping/output/6475038328432950049.txt | [
0.048808641731739044,
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#### (b) The power of combining detection data and image data
So now you know that Detic is able to detect a broad range of object classes
due to the fact that the Imagenet dataset has up to 21K classes. But the use
of image classification data comes with another benefit: getting more fine-
grained class detections wi... | scraping/output/6475038328432950049.txt | [
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The model is able to distinguish a labrador from a husky
#### (c) Zero-shot classification
The capabilities of the model don’t stop there. As already explained, the
usage of CLIP allows us to work with custom vocabularies containing classes of
which the model has never seen an image before. On the example on the left... | scraping/output/6475038328432950049.txt | [
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Even though the model has never seen images of “hoverboard” or “bust”, it can
still detect these objects because CLIP has seen them and knows their
embeddings
## (d) Object detection in videos
We have seen the model perform on different images, but we also applied it to
some videos, where it achieves similar results.... | scraping/output/6475038328432950049.txt | [
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Detic applied to a video from dualipa instagram
## Conclusion
Detection models are usually trained on specific data for a certain use-case,
but Detic has a very broad utility field. It is the first known model with
such a large vocabulary of object classes. It can be fine-tuned for more fine-
grained detection by onl... | scraping/output/6475038328432950049.txt | [
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## References
[1]Zhou, X., Girdhar, R., Joulin, A., Krähenbühl, P., & Misra, I. (2021).
Detecting Twenty-thousand Classes using Image-level Supervision. ArXiv
Preprint ArXiv:2201.02605.
[2]Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S.,
Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, ... | scraping/output/6475038328432950049.txt | [
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Join our newslet... | scraping/output/2401150086897253870.txt | [
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The following article is an abbreviated version of the article "Hoe staat het
met het voorstel voor de AI Act" by Agoria. Read the original article by
Agoria here (in Dutch)
### What about the A... | scraping/output/4712455591175985452.txt | [
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### The AI Act in a nutshell
The AI Act is the first European-level regulation specifically aimed at
artificial intelligence. The Commission realises that not all AI systems need
to be extensively regulated and therefore takes a 'risk-based approach' to the
legal framework. This means that the (potential) ris... | scraping/output/4712455591175985452.txt | [
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### Current evolutions within the European council
###
Among the current proposed adjustments by the European council are a rewriting
of the definition of AI. A few other topics were updated, such as for example:
The types of risks of an AI system as well as risk management were re... | scraping/output/4712455591175985452.txt | [
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The articles concerning how and for how long to keep documentation and logs
were also updated (Art. 11-14, 16-18, 20). Article 53 and 54 describe how
sandboxes should contribute to a list of objectives as well as their financial
conditions and various regulations around coordination and participation in
sandboxes. L... | scraping/output/4712455591175985452.txt | [
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0.03584586828947067,
-0.037331238... |
Foundation Models
Corporate
Corporate
People
People
Structured Data
Structured Data
Chat GPT
Chat GPT
Sustainability
Sustainability
Voice & Sound
Voice & Sound
Front-End Development
Front-End Development
Data Protection & Security
Data Protection & Security
Responsible/ Ethical AI
Responsible/ Ethical... | scraping/output/4712455591175985452.txt | [
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Copyright 202... | scraping/output/4712455591175985452.txt | [
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Structured Data
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... | scraping/output/478919339682848376.txt | [
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Reference: https://www.mckinsey.com/business-functions/mckinsey-d... | scraping/output/478919339682848376.txt | [
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#### What do we like?
As an AI company, we like to keep the infrastructure as simple and as managed
as possible. So we prefer to use serverless cloud data lake/data warehouse
services, as recommended in the report. This enables affordable scalability
and flexibility with no or minimal infrastructure maintenance. Schem... | scraping/output/478919339682848376.txt | [
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Organizing a data lake into multiple layers with a raw layer, preferably
immutable, and curated layers managed by the domain is an excellent approach.
Starting from the curated layer, it’s easy to create new fit for purpose “data
products”, in data mesh terms (or data marts for the Kimball generation). A
good example... | scraping/output/478919339682848376.txt | [
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The move away from pre-integrated commercial solutions in favour of a well-
picked tech stack is exactly what we do for customers. It’s essential to only
invest in technologies that are crucial and unique to the growth strategy of
your business.
A mix of SaaS, commercial and plenty of open-source, backed by a large
c... | scraping/output/478919339682848376.txt | [
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### From batch to real-time processing
It makes absolute sense to ingest the data in real-time if:
* your applications are based on a microservices design with CQRS on top of Apache Kafka or similar services such as Apache Pulsar
* you have several business requirements that combine data from multiple domains in ... | scraping/output/478919339682848376.txt | [
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In the majority of cases, we have seen that the business value for real-time
processing in the data warehouse context is limited.
* A lot of data-driven decision making is not real-time. In some cases, we’ve seen daily usage but often insights are used on a weekly, monthly or quarterly basis.
* From a technical po... | scraping/output/478919339682848376.txt | [
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There are also alternatives to get to real-time insights:
* In operational applications, real-time reporting is often integrated on top of the application database to offer full transactional consistency and input capabilities.
* Inference with ML models tends to be tightly integrated into the operation applicatio... | scraping/output/478919339682848376.txt | [
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### From point to point to decoupled data access
McKinsey recommends decoupling using APIs managed by an API gateway.
This is a proven approach to decouple micro-services or provide data, with a
well-defined application interface and schema to one or more internal or
external applications. A modern API gateway, such... | scraping/output/478919339682848376.txt | [
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APIs are however not recommended for large scale data exchange.
Large scale data processing frameworks and data science tooling are more
efficient with a direct connection to the cloud data warehouse or data files
in distributed storage.
Modern drivers and file formats, for example, Apache Parquet or the BigQuery
s... | scraping/output/478919339682848376.txt | [
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We advise spending enough time to analyse the required integrations and take
pragmatic design decisions.
It’s perfectly acceptable to process data in the data lake and export a subset
of the data to ElasticSearch or an in-memory data store. Alerts can be
published on a message or task queue. Integration with internal... | scraping/output/478919339682848376.txt | [
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### Conclusion
We are looking forward to an update of the report this year because the data
landscape for data and ML keeps evolving at a fast pace.
We’d love to hear your point of view. Do not hesitate to reach out in case you
have any questions or suggestions.
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See our Cookie Policy to read more abo... | scraping/output/-7543089521676933654.txt | [
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b... | scraping/output/-7543089521676933654.txt | [
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Vendors Youtube, Google
Accept... | scraping/output/-7543089521676933654.txt | [
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As we’re growing fast and we continue to make more impact, we’re hiring AI
Client Partners. In this role, you will have an immediate impact on our
international business.
What about you? You get the opportunity to lead full sales cycles! You will
identify new business opportunities with artificial intelligence, develo... | scraping/output/-7543089521676933654.txt | [
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* Identify new business opportunities: gain a deep understanding of your vertical and develop relations with partners, the local ecosystem and business networks to discover new business opportunities and generate leads
* Develop client relationships and retain them: engage with prospects and clients to understand the... | scraping/output/-7543089521676933654.txt | [
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* Work with our marketing and lead generation team to set up brand awareness and lead generation campaigns for your vertical
* Work with our Advisory & Delivery unit and with our Cloud Alliance Managers to develop new offerings and services to bring to the market to better respond to client needs | scraping/output/-7543089521676933654.txt | [
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## Why you?
* A Master’s degree in a business or other related domain
* Three or more years of experience in consulting
* Passionate & with understanding of technology, data and AI
* Love to create real value for clients
* A consultative selling mindset and experience with exploring needs from clients ... | scraping/output/-7543089521676933654.txt | [
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* Analytical and great problem solving skills
* Love taking ownership to further accelerate businesses with AI
##
Why ML6?
Looking for a dynamic environment where you can create real business impact
with AI? Look no further. Don’t expect a big corporate organisation with pre-
defined jobroles and plans for the... | scraping/output/-7543089521676933654.txt | [
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Department
Business Development unit 🤝
Locations
Office Amsterdam 📍, Office Berlin 📍, Office Ghent 📍
Employment type
Full-time
## Why others choose ML6 🤝
* ### Create intelligence with a lasting positive impact
As a leader in AI, we work hard to ensure that AI is designed, developed and
d... | scraping/output/-7543089521676933654.txt | [
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* ### We achieve more together
Expect to build lasting relationships that extend beyond the office walls.
Thanks to our collaborative and diverse culture, we achieve the extraordinary.
From our ML6 agents to our ML6 Maffia, we’ve created a safe environment where
mutual trust and open feedback is cherished. To foster o... | scraping/output/-7543089521676933654.txt | [
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* ### Byte size Benefits
Last but not least, we provide an attractive salary package for your expertise
and an optimised benefits program where we stay true to our caring and
learning goals. In a nutshell, we've built a place where we truly love
working, we think you will too.
## FAQ
* ### Can I work remotely at ... | scraping/output/-7543089521676933654.txt | [
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* ### What to expect when joining ML6?
Don’t expect a big corporate organisation with pre-defined jobroles and a
defined plan for the coming years. Expect the unexpected. Growth, change and
impact. Learn from engaged experts with a like minded passion for technology.
Expect to work for the most innovative and bigges... | scraping/output/-7543089521676933654.txt | [
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Are you up for the challenge?
* ### What does the application process look like?
It depends on the role that you're applying for. For our technical roles, we
always start with a technical matching (ex. coding challenge) and get to know
interview. Every role at ML6 receives a presentation challenge where we will... | scraping/output/-7543089521676933654.txt | [
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We work in Units and with Unit Coaches. For more information about our
structure, we recommend you to watch this video.
* ### What are examples of projects that ML6 worked on?
At ML6, we boost and increase revenue growth for other companies, by
implementing AI solutions. We do this for a broad range of industries... | scraping/output/-7543089521676933654.txt | [
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* ### Why is ML6 called ML6?
ML stands for our main expertise, Machine Learning. Next to that, we don't
take ourselves always very seriously. Our people are 'ML6 agents', wink to
MI6. Seen the James Bond movies? Our meeting rooms for example are named after
them. After all, we can be a bit geeky at times ;-)
* ##... | scraping/output/-7543089521676933654.txt | [
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Contact Julie Plusquin Talent Partner – Talent & Culture 🫶
## Colleagues
Sophie Decock
Head of Business Development
## Join us
* Alliance Manager - Azure
Business Development unit 🤝 * Multiple locations
* AI Client Executive
Business Development unit 🤝 * Multiple locations
More jobs
Office Amsterdam �... | scraping/output/-7543089521676933654.txt | [
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* AI Client Executive
Business Development unit 🤝 * Multiple locations
More jobs
Office Amsterdam 📍 Office Berlin 📍 Office Ghent 📍
## About ML6
We guide the AI revolution towards positive impact. 🌐
Exciting developments are happening in the world of AI, offering unprecedented
opportunities for businesses ... | scraping/output/-7543089521676933654.txt | [
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Examples of serious misconduct (not limited to): safety of the individual
(aggression, discrimination, sexual harassment), data privacy issues, public
health, tax and fraud.
Join us
Business Development unit 🤝 * Multiple locations
# AI Client Partner
## Love taking ownership to further accelerate businesses with... | scraping/output/-7543089521676933654.txt | [
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No items found.
🚀 Unleash the Power of Large Language Models and Foundation Models - Read our
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References
... | scraping/output/300095212007731430.txt | [
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Data Engineer | Squad Lead
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This blogpost is aimed at those who want to understand... | scraping/output/300095212007731430.txt | [
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### Introduction
#### What is a Protein?
Proteins are the essential building blocks of life and are omnipresent. More
often than not, they play an essential role in the functioning of every living
thing. Proteins are large and complex molecules, and enzymes are a subgroup of
proteins that can speed-up chemical reacti... | scraping/output/300095212007731430.txt | [
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Today, we face certain problems that have arisen due to environmental
pollution or new diseases with increased life expectancies, for example. Very
often, enzymes, because of their natural way of working and composition, can
be at the core of the solution to these problems. For example, newly
developed, short-lived enz... | scraping/output/300095212007731430.txt | [
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Potential Applications of Protein Engineering (Images created using Adobe
Firely)
#### What are Proteins made of and how do they look?
Proteins are composed of ten to multiple thousand building blocks, linearly
chained together to form a string. These building blocks are amino acids and
there are 20 naturally occurri... | scraping/output/300095212007731430.txt | [
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The primary structure, as described above, refers to the linear sequence of
amino acids and is one-dimensional. Parts of this chain regularly fold or
arrange themselves in a predefined way to form components, such as an alpha
coil or a flat beta sheet, which is known as the secondary structure. The
order of the amino a... | scraping/output/300095212007731430.txt | [
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Protein Structure Overview (Source: Secondary Structure, Tertiary Structure)
The 3D structure determines the chemical reactions that the enzyme can
perform. Every enzyme possesses a specialized active site where catalytic
reactions occur. This portion of the enzyme is characterized by its unique
shape and functional g... | scraping/output/300095212007731430.txt | [
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The holy grail of protein design is jumping from sequences to function, and
reverse, from function to sequence. Based on the sequence, we could understand
what the protein does and how it behaves. But more importantly, we could
obtain a protein sequence that fulfils a specific, desired function. However,
this is a very... | scraping/output/300095212007731430.txt | [
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Protein Design Interactions
The primary structure of a protein (“Sequence” in the image above), i.e, the
linear chain of amino acids, determines its native state (“Structure” in the
image). This folding process by which the protein reaches its final unique
form is not fully understood and is known as the “protein fold... | scraping/output/300095212007731430.txt | [
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This primary structure is observed by a process called protein sequencing,
referring to the amino acid sequence that makes up the protein. The tertiary
structure of a protein is measured by experimental methods which are
expensive, time consuming and applicable to all proteins; only ~170k 3D
protein structures have bee... | scraping/output/300095212007731430.txt | [
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#### Where does AI come in?
Since physical measurement of every protein structure is not feasible with the
current state of equipment, computational methods have been used to attempt to
predict the structure instead. The final structure of a protein is a function
of its amino acid sequence, so this function can be mod... | scraping/output/300095212007731430.txt | [
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Notably, in 2020, Google’s DeepMind used a model called AlphaFold to achieve
breakthrough results and they claimed the protein folding problem to be
“solved”. There have been many, many other deep learning models since then
that work on protein folding as well as on other protein-related areas of
research that we will ... | scraping/output/300095212007731430.txt | [
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Recent Timeline of Protein-related Models
#### Protein Language Models
Model Code linked in model names, # Parameters (M/B = M/Billion) indicates the
size of the model
We first start with Protein Language Models (PLMs) since they are used to
represent protein sequences in the form of embeddings. Embeddings are
mathe... | scraping/output/300095212007731430.txt | [
0.04463363438844681,
-0.03569912537932396,
-0.013870187103748322,
-0.06184544414281845,
0.09213777631521225,
-0.04750220105051994,
0.08092178404331207,
0.07170072942972183,
0.08612823486328125,
-0.02584145776927471,
0.049069710075855255,
-0.03988731652498245,
0.02649860642850399,
-0.029490... |
Large Language Models (LLMs) are able to model natural language structure and
grammar simply by training on large amounts of text data. They have been shown
to be very useful for tasks such as text generation and translation, with
bigger and bigger models being released over time with improved capabilities
and applicat... | scraping/output/300095212007731430.txt | [
0.048432134091854095,
-0.034864459186792374,
-0.06174958124756813,
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0.07899495214223862,
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0.07155603915452957,
0.01167647447437048,
0.06195373460650444,
-0.045206863433122635,
0.03773980215191841,
-0.002335081109777093,
0.062339965254068375,
-0.0... |
##### ProtTrans
In this 2020 paper, 6 LLM architectures (T5, Electra, BERT, Albert,
Transformer-XL and XLNet) were pretrained on raw protein sequences and were
shown to be able to capture features of amino acids, protein structure,
domains and function. The models are available here and can be used to extract
features... | scraping/output/300095212007731430.txt | [
0.062453560531139374,
-0.04614700376987457,
-0.07460784167051315,
0.01852964051067829,
0.07434002310037613,
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0.07348214089870453,
0.02198837138712406,
0.04996943473815918,
-0.06202353164553642,
0.04087818041443825,
0.004042235668748617,
0.026033876463770866,
-0.035489... |
##### ProGen2
This 2022 PLM from Salesforce is a Transformer-based model trained on billions
of protein sequences to predict the next token in the sequence
autoregressively. Its predecessor, ProGen, was the first decoder-only model
trained specifically for protein sequence design. The model comes in 4
different size v... | scraping/output/300095212007731430.txt | [
0.03440989926457405,
-0.026548760011792183,
-0.03947209566831589,
-0.0481630302965641,
0.05860435962677002,
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0.04695367068052292,
0.01956922933459282,
0.02680017612874508,
-0.04897966608405113,
0.05208031088113785,
0.0039689503610134125,
0.03984742611646652,
-0.055658... |
##### ProtGPT2
Also released in 2022, ProtGPT2 is similarly capable of modeling protein
sequences using an autoregressive GPT2-like Transformer architecture. It is a
smaller model that’s been trained on 50 million sequences. It is capable of
producing proteins within uncharted areas of the natural protein landscape,
w... | scraping/output/300095212007731430.txt | [
0.07460641115903854,
-0.031199948862195015,
-0.037728480994701385,
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0.05453961715102196,
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0.06606922298669815,
0.024328608065843582,
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-0.051904115825891495,
0.01882467232644558,
-0.002402783138677478,
0.06471672654151917,
-0.... |
#### Structure Prediction
Model Code linked in model names, # Parameters (M/B = M/Billion) indicates the
size of the model
Models attempting to “solve” the protein folding problem as described above
are involved in predicting the structure of a protein from its amino acid
sequence. There have been many models that ha... | scraping/output/300095212007731430.txt | [
0.06289725750684738,
-0.0454503670334816,
-0.05439474433660507,
-0.031758155673742294,
0.07167085260152817,
-0.05345136299729347,
0.06227591261267662,
0.07993966341018677,
0.07809371501207352,
-0.0345296747982502,
0.030828511342406273,
-0.04619229957461357,
0.05624016001820564,
-0.01867125... |
As mentioned above, DeepMind’s 2020 AlphaFold model is a deep-learning
architecture that predicts with high accuracy the 3D structure of a protein
based on its amino acid sequence. The 3D structure is modelled as a graph and
the prediction itself is modelled as a graph inference problem. It leverages
evolutionary infor... | scraping/output/300095212007731430.txt | [
0.07670940458774567,
-0.04421665519475937,
-0.0420544259250164,
-0.014560045674443245,
0.07634586095809937,
-0.031878113746643066,
0.10195087641477585,
0.08220510929822922,
0.04663477838039398,
-0.0501825213432312,
0.045442961156368256,
-0.007339163683354855,
0.06185534968972206,
-0.048775... |
sequenced to date. At the time of its release, it became the state-of-the-art
for protein structure prediction from amino acid sequences, with particularly
good predictions for sequences with homologues. | scraping/output/300095212007731430.txt | [
0.04312307387590408,
-0.07223951816558838,
-0.061237700283527374,
-0.04271126911044121,
0.06790099292993546,
-0.05895896255970001,
0.0394250862300396,
0.034365084022283554,
0.11164046078920364,
-0.021555321291089058,
0.045837994664907455,
0.015360744670033455,
0.0533071830868721,
-0.026306... |
##### RoseTTAFold
In 2021, a model named RoseTTAFold that similarly predicts protein structures
was released from the Baker Lab. It differs from AlphaFold in that it is a
“three-track” network as it simultaneously looks at the primary and tertiary
structures and the 2D distance map during training and prediction, and ... | scraping/output/300095212007731430.txt | [
0.07020178437232971,
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0.044369738548994064,
-0.025492215529084206,
0.06399918347597122,
-0.0284... |
##### OmegaFold
OmegaFold uses a large pretrained protein language model (OmegaPLM) to predict
tertiary structure using an alignment-free methodology, i.e., without the need
for MSAs. It is able to make predictions based on only a single protein
sequence. Similar to how language models like GPT-4 are able to learn lan... | scraping/output/300095212007731430.txt | [
0.06366334855556488,
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0.07898231595754623,
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-0.046286121010780334,
0.00789433903992176,
-0.024734536185860634,
0.07471952587366104,
-0.02166... |
##### ESMFold
In 2022, Meta AI unveiled their ESMFold protein structure prediction model
that also makes use of a large (the largest, in fact) protein language model,
ESM-2. As in OmegaFold, the model does not require MSAs and outperforms
AlphaFold and RoseTTAFold on single sequences. The largest model in their
ensemb... | scraping/output/300095212007731430.txt | [
0.062300633639097214,
-0.007844066247344017,
-0.035166990011930466,
-0.01822100393474102,
0.08272170275449753,
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0.0625901073217392,
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-0.06695123016834259,
0.017970064654946327,
-0.03427698090672493,
0.06902710348367691,
-0.0449... |
#### Sequence Prediction
Model Code linked in model names, # Parameters (M/B = M/Billion) indicates the
size of the model
The reverse process of protein folding, termed as inverse folding, starts at a
specific target protein structure and searches for the protein sequence/s that
folds into that structure. A solution ... | scraping/output/300095212007731430.txt | [
0.0729600265622139,
-0.048359282314777374,
-0.010823985561728477,
-0.04214473068714142,
0.07344525307416916,
-0.03662468492984772,
0.049243491142988205,
0.02646961808204651,
0.07055395841598511,
-0.038888897746801376,
0.016384180635213852,
-0.0030058196280151606,
0.07743461430072784,
-0.02... |
##### ESM-IF1
In 2022, the ESM-IF1 model was shown to be able to predict protein sequences
from the 3D coordinates of the protein’s tertiary structure. Since the size of
the existing sequence-structure database was very small, only 16k structures,
they augmented this data by adding 12 million predicted structures usin... | scraping/output/300095212007731430.txt | [
0.08971353620290756,
-0.031750086694955826,
-0.03551448881626129,
-0.010819912888109684,
0.06698119640350342,
-0.023258600383996964,
0.06947505474090576,
0.043180957436561584,
0.06809045374393463,
-0.044007543474435806,
0.03514634445309639,
0.010217297822237015,
0.07001803070306778,
-0.050... |
##### ProteinMPNN
Also in 2022, again from the Baker Lab, ProteinMPNN was shown to be able to
model the inverse folding process by training an autoregressive model on
experimentally determined structures. The model follows an encoder-decoder
structure where the inputs to the encoder are the distances between the
eleme... | scraping/output/300095212007731430.txt | [
0.10096683353185654,
-0.04356950893998146,
-0.017869098111987114,
-0.03628843277692795,
0.0775170549750328,
-0.040330760180950165,
0.07330934703350067,
0.04082563519477844,
0.06496797502040863,
-0.02596052549779415,
0.02661265805363655,
0.0021283181849867105,
0.0731336921453476,
-0.0511096... |
##### MIF-ST
Released this year in 2023, the MIF-ST (Masked Inverse Folding-Sequence
Transfer) model leverages a structured GNN-based masked-language model. The
outputs from this masked-language model trained only on protein sequences are
inputted to this MIF-ST model to be pretrained conditionally on structures.
Here... | scraping/output/300095212007731430.txt | [
0.0984349399805069,
-0.04842730611562729,
-0.0194208063185215,
-0.0291207917034626,
0.04958133399486542,
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0.06803570687770844,
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0.049731895327568054,
-0.04209281876683235,
0.004336214158684015,
-0.005316738970577717,
0.06252903491258621,
-0.0613188... |
#### Function Prediction
Model Code linked in model names
Protein function refers to the biological process it performs. This process is
largely determined by its tertiary structure which in turn is determined by
the primary sequence of amino acids. Being able to know the function that a
particular protein sequence h... | scraping/output/300095212007731430.txt | [
0.037326786667108536,
-0.038301147520542145,
-0.0467987023293972,
-0.021881956607103348,
0.08194493502378464,
0.00740205030888319,
0.0693567618727684,
0.07048474997282028,
0.10814963281154633,
-0.04132410138845444,
0.004814709071069956,
-0.01644417829811573,
0.03846257925033569,
-0.0078266... |
Source: Structure, Function
##### DeepGO
Released in 2018, DeepGO introduced an approach to forecast protein
functionalities by leveraging protein sequences. It employed deep neural
networks to acquire insights from both sequence data and protein-protein
interaction (PPI) network data, subsequently organizing them hi... | scraping/output/300095212007731430.txt | [
0.0439540259540081,
-0.048504844307899475,
-0.025460638105869293,
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0.08055420964956284,
-0.02052403800189495,
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-0.04817351698875427,
0.025822965428233147,
-0.008108765818178654,
0.03600289300084114,
-0.035... |
##### DeepFRI
This 2019 model predicts protein function as represented by both the GO class
and the EC number using protein structure and features extracted from protein
sequences. For this, an LSTM protein language model is used to obtain residue-
level features from the sequences. A GCN (Graph Convolutional Network)... | scraping/output/300095212007731430.txt | [
0.039120495319366455,
-0.05523630976676941,
-0.020205089822411537,
0.009537075646221638,
0.08921880275011063,
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-0.03975532948970795,
0.013887662440538406,
-0.04257817938923836,
0.05761036276817322,
-0.048742... |
##### GAT-GO
The GAT-GO model is similar to the DeepFRI model but it uses a GAT (Graph
Attention network), a type of GNN that uses self-attention, instead of a GCN.
Additionally, instead of the LSTM language model, the pretrained large protein
language model ESM1 is used to extract features. The GAT-GO model is shown ... | scraping/output/300095212007731430.txt | [
0.03885361924767494,
-0.030886972323060036,
-0.03471008315682411,
0.007718225941061974,
0.08055610209703445,
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0.0933958888053894,
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-0.025786127895116806,
0.028814349323511124,
-0.03673304617404938,
0.07836258411407471,
-0.037... |
##### SPROF-GO
Released in 2022, the SPROF-GO is a sequence based, MSA-free protein function
prediction model. It predicts the GO classification directly from the protein
sequence. The architecture consists of a pretrained T5 protein language model,
the embedding matrix of which is fed to two Multi layer Perceptrons (... | scraping/output/300095212007731430.txt | [
0.046884577721357346,
-0.0242623258382082,
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0.005226420238614082,
0.05854854732751846,
-0.02169720269739628,
0.09434554725885391,
0.04735138267278671,
0.03659200668334961,
-0.017936449497938156,
0.011954282410442829,
0.0035039312206208706,
0.02489851415157318,
-0.02188... |
##### ProtNLM
This natural language processing model was developed in 2022 by Google
Research in partnership with EMBL’s European Bioinformatics Institute (EMBL-
EBI). With a different approach to describing protein function, the ProtNLM
model uses a Transformer architecture to accurately predict a natural language
de... | scraping/output/300095212007731430.txt | [
0.07469445466995239,
-0.03162390738725662,
-0.06627500057220459,
-0.03142604976892471,
0.07555197924375534,
-0.007520793937146664,
0.06706569343805313,
0.07723276317119598,
0.07328501343727112,
-0.040328897535800934,
-0.01931081712245941,
-0.008529048413038254,
0.04023044556379318,
-0.0182... |
##### RFDiffusion
De novo protein design aims to design novel proteins with a specific target
function or structure. The RFDiffusion model uses a DDPM diffusion model,
inspired by image generation models like DALL-E, along with RoseTTAFold, to
perform protein design and generate new, diverse protein structures. The
pr... | scraping/output/300095212007731430.txt | [
0.08186230808496475,
-0.02580425515770912,
-0.0582832396030426,
-0.04840497300028801,
0.10123582184314728,
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0.08035201579332352,
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-0.052162930369377136,
0.016093263402581215,
-0.011827312409877777,
0.06542801856994629,
0.01421... |
##### ProT-VAE
ProT-VAE is a deep generative model that is able to generate diverse protein
sequences from specific families with high functionality. The model’s
architecture sandwiches a Variational Autoencoder model in between ProtT5
encoder and decoder blocks. The inputs to the model during training are
unaligned p... | scraping/output/300095212007731430.txt | [
0.06176730990409851,
-0.04983384534716606,
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-0.04254937544465065,
0.007817849516868591,
0.02119312807917595,
0.053036388009786606,
-0.02503... |
#### Conclusion
We can see that the past few years have seen a major burst in protein-related
AI research and model publications. The potentials for applications in the
fields of drug design, antibody engineering and design, vaccine development,
disease biomarker identification and personalised medicine (to name a few... | scraping/output/300095212007731430.txt | [
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0.010509863495826721,
0.05953288450837135,
-0.026383... |
##### References
* Protein Folding Problem Explanation
* Overview of Protein Models
* Podcast with David Baker about Protein Design
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Accolade Wines
# Accolade Wines is saving 1 million litres of wine per year with AI
## Impact
ML6 helped Accolade Wines to implement a ML process to capture real time
insights during the manufacturing process and p... | scraping/output/-6457410234367962254.txt | [
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... |
## Challenge
Accolade Wines with an impressively low margin of waste, still had ambitions
to reduce wine loss even further but had no way of tracking live wine flow
during the bottling process. Therefore, they decided to seek a strategic
partner in machine learning to investigate how a self learning and adaptive
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The solution was built on Google Cloud to iterate quickly and scale up the
solutions to multiple lines and/or countries.
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By
## Results
Beverage packing businesses typically quote a wine process loss of2%. Accolade
wines had already driven the average loss down significantly below this level
by using standard process improvement techniques. However the extra process
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... | scraping/output/-6672370828350515768.txt | [
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A company can only be as successful as the success of its people. Everything
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