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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 left corner captures many more, while using the same
detection threshold! In the bottom right corner, you can see the result when
using such a custom-made vocabulary, using only food and people
Another example to highlight the strength and flexibility of the model on
social media content, the model even detects the socket on the background of
the image. Image taken from therock instagram
#### (b) The power of combining detection data and image data | scraping/output/6475038328432950049.txt | [
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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 without the requirement of adding more (fine-grained)
labels to the detection dataset. Because, while an object detection dataset
such as LVIS contains a “dog” class, the labels do not specify the exact
species of dog. So for the detector to distinguish between different sorts of
dogs, you would be required to manually add or change data of these fine-
grained categories. But now, adding image labeled data of different species —
as contained in ImageNet — suffices, allowing the model to detect and name the
dogs by their correct species as seen in the example.
The model is able to distinguish a labrador from a husky
#### (c) Zero-shot classification | 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,
the model looked for the class “hoverboard”, which is not in any of the
training datasets, but because the embedding of the word lies closely to that
of the word “skateboard”, it is still able to retrieve this object that it has
never seen before! The same goes with the word “bust” in the image on the
right : CLIP enables the model to again correctly identify this unfamiliar
object.
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 | 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. To showcase the possibilities
of Detic on moving images, we made a simple tool that extracts the frames from
a video, applies detection (for which again the vocabulary can be custom-made)
on these separate frames and finally chains them back together. It is also
possible to save the detected objects in each frame to a text file, allowing
easier analysis of the whole video.
Detic applied to a video from dualipa instagram
## Conclusion | 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 only adding more image labeled classes, and by using CLIP
embeddings the model also performs in zero-shot detection tasks. There is of
course a lot to explore with these sorts of models. One limitation we found is
when using a custom vocabulary containing descriptions such as “a person
sitting on a bench”. The model often fails to correctly detect the whole
context, but rather only selects a single word from the sentence. This is
probably due to the fact that Detic extracts image labels from captions using
a naive text-match, and is mostly trained on singular words.
## References | 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, G., & Sutskever, I.
(2021). Learning Transferable Visual Models From Natural Language Supervision.
CoRR, abs/2103.00020. https://arxiv.org/abs/2103.00020
[3]Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental Improvement. CoRR,
abs/1804.02767. http://arxiv.org/abs/1804.02767
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Front-End Development | scraping/output/6475038328432950049.txt | [
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PressRandstad verhoogt verkoopeffectiviteit met ML6 | Computable
July 14, 2023
# Randstad verhoogt verkoopeffectiviteit met ML6 | Computable
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Oops! Something went wrong while submitting the form. | scraping/output/2401150086897253870.txt | [
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May 12, 2022
# EU AI Act : what you need to know
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Share this post | scraping/output/4712455591175985452.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 AI act proposal?
On April 21, 2021, the European Commission proposed the ‘AI Act’, a proposal
for a regulation for Artificial Intelligence. Today, almost a year after the
publication of this proposal, there are a number of evolutions in Parliament
and the Council. The AI Act is currently expected to come into effect in early
2025.
### The AI Act in a nutshell
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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) risk of an AI application
determines which rules will apply. The proposal lists 4 categories, each with
an associated set of rules and requirements: “low and minimal risk”, “limited
risk”, “high risk” and “unacceptable risk”.
The proposal is currently in the “trilogue phase”, with informal meetings
between representatives of the European Parliament, the European Commission
and the Council. All elements of the proposal are re-examined since it has
provoked many divergent reactions. When the trilogue is ended, the three
bodies will bring their proposals together to finalise the AI Act. Afterwards
a transition period of presumably 24 months will follow.
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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 rewritten
(Art. 9). In terms of data management, potential biases in data sets are now
defined as “that could affect the health and safety of individuals or lead to
discrimination”. As datasets are rarely error-free or complete, the term “as
good as possible” was added to soften the provisions. Lastly, in the absence
of training of an AI system, the provisions only now apply to datasets used
for testing (Art. 10).
The article about corrective actions was also updated: When an AI system is
found to be non-compliant by users, the providers must work with those users
to investigate the issue (Art. 21). The intricacies of human oversight were
rephrased (Art. 29).
| 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. Lastly, new articles were added to describe how to test an AI
system in sandbox or real-world conditions, how to supervise it and obtain
consent of test subjects.
For a more complete and extensive list of the proposed adjustments, please
refer to the original article by Agoria here (in Dutch)
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Thank you! Your submission has been received! | scraping/output/478919339682848376.txt | [
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Reference: https://www.mckinsey.com/business-functions/mckinsey-digital/our-
insights/how-to-build-a-data-architecture-to-drive-innovation-today-and-
tomorrow
In June 2020 McKinsey published a report called “How to build a data
architecture to drive innovation — today and tomorrow”. The report is an
excellent view of modern future proof data architecture. In January 2021
McKinsey published “Breaking through data-architecture gridlock to scale AI”,
a new iteration that highlights the importance of data architecture for AI.
The article highlights the advances in data technology and the recommended
building blocks and methodologies. Based on our experience, we offer our view
on the elements put forward in the article. | 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. Schema-on read
is a game-changer for semi-structured data.
The focus on a domain-driven approach highlights that the end to end
responsibility for the data belongs within the domain that generates the data.
Check out my blog post with a summary of the data mesh principles to learn
more about shifting responsibilities to domains. | 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 is that a data set for an analytic tool often has a different
data model or even file format than the same data used for ML model training. | 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
community, is more efficient and cost-effective than hitting the limits of
traditional ETL software or (over)-engineering your own data processing
frameworks. We are glad we’ve seen this data architecture in several requests
for proposals.
It is a great blueprint. Yet, in our opinion, 2 shifts require a bit more
consideration.
### From batch to real-time processing
It makes absolute sense to ingest the data in real-time if: | 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 realtime and require real-time decision making
* your data is streaming in nature. For example, the internet of things, web analytics, …
In the majority of cases, we have seen that the business value for real-time
processing in the data warehouse context is limited. | 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 point of view, the tech stack behind a lot of applications is not yet event-based so plenty of refactoring is needed to enable it for every action.
* The costs of real-time processing are considerably higher and streaming data pipelines are more complex to develop compared to a (micro)-batch approach.
* In front-end applications, users often request controls to minimize or suppress the number of real-time alerts because it interrupts the flow of their work.
* Real-time management dashboards are underused because a limited number of employees have the time to analyse and act frequently during the day.
There are also alternatives to get to real-time insights: | 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 application using trained models exposed as REST APIs or the inference is moving even closer to the device using edge processing.
In our data pipeline designs, we aim to process the data at the right time.
As data volumes increase we increase data ingestion rate to avoid long-running
processes. In a modern job orchestrator, it’s easy to change the refresh rate.
Data processing frameworks, such as Apache Beam, require minimal changes to
switch to streaming or micro-batches in case it’s needed.
And don’t forget that AI, for example, advanced anomaly detection, offers
plenty of opportunities to trigger only relevant alerts.
### From point to point to decoupled data access | 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 as Kong or Apigee, offers an
additional security layer, observability and a documented user-friendly
interactive developer portal. | 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
storage API support high-speed, zero-copy data access using Apache Arrow. This
is more efficient than using a REST API with limited filter capabilities, a
limited number of rows for each request and slow JSON deserialization.
Furthermore, the majority of the data visualization tools prefer or only
support drivers that support SQL. | scraping/output/478919339682848376.txt | [
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-0.03449955955147743,
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0.02283378504216671,
0.03365853801369667,
0.029611701145768166,
0.04429486393928528
] |
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/external REST
API can be tricky if the API is not scalable enough to handle the number of
requests modern data processing frameworks generate.
In the report, we suggest moving the data catalog, mentioned in shift 5, to
this section.
The data catalog should be the main entry point for business users, data
scientists and application developers to discover the available data, check
data lineage and the options to access the data. If you like to learn more
about the pitfalls of microservices check this point of view.
### Conclusion | 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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Computer vision | scraping/output/478919339682848376.txt | [
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# AI Client Partner
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At ML6, we design tailor-made AI solutions for our clients across industries
such as ASML, Randstad, VRT, GfK, and many more.
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. | 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, develop deep
client relationships and act as a trusted advisor. In short, if you are a
consultant with a business mindset that loves to translate needs to technical
solutions for your clients, you are the one we’re looking for!
You will: | 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 their needs and formulate solutions
* Co-create proposals, prepare commercial offers and reply together with our Advisory & Delivery team on tenders
* Act as a trusted advisor on key client accounts and manage and nurture the relationship: work with clients on a strategic level to identify how we can best support them in realizing their ambitions and accelerating their innovation roadmap
* Represent ML6 at local events and inspire (potential) customers, candidates and advocates with our content, ideas and insights as a speaker
* Work with our marketing and lead generation team to set up brand awareness and lead generation campaigns for your vertical | 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
* Skilled in defining complex solutions and positioning them
* Worked as a consultant on different projects
* Excellent verbal and written communication skills in English and Dutch (German for Germany) and comfortable speaking to C-level executives
* Goal oriented team player, as you will work together with several experts from different units
* Analytical and great problem solving skills
* Love taking ownership to further accelerate businesses with AI
##
Why ML6? | 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 coming years. Expect the unexpected.
Growth, change and impact. Expect to work for the most innovative and biggest
companies across countries. Expect exponential personal growth in your career.
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.
Scroll down to find out more about why others choose ML6! 👇
Department
Business Development unit 🤝
Locations
Office Amsterdam 📍, Office Berlin 📍, Office Ghent 📍
Employment type
Full-time
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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
deployed to create positive impact. We take our responsibility to build secure
and sustainable technology seriously and have integrated ethics within our way
of working. This shows in every client case and do good projects we do.
* ### Innovate and share knowledge
Learning is part of our DNA. Whether it's through our team's expertise,
creating spin-offs or the latest tech breakthrough, our culture will empower
you to deliver top-notch AI solutions to our customers. We organise chapter
conferences, guest lectures, meet ups, dedicate time to research, blogposts, a
personal training catalog and so much more.
* ### We achieve more together | 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 our interpersonal
connections, we love to see each other's pretty faces at the office.
* ### Fast growing and international
We're gearing up for our next growth milestone. Don’t expect a big corporate
organisation with pre-defined jobroles and plans for the coming years. Expect
the unexpected. Growth, change and impact. Expect to work for the most
innovative and biggest companies across countries. Expect exponential personal
growth in your career.
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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 ML6?
There is a reason why we don’t work (fully) remotely at ML6 and why we love
being present at our offices so often. At ML6, our culture is based on
interpersonal connections and our strength lies in our personal collaboration.
With our clients, and with each other. We work from home about 2 days a week.
Our offices are situated in Berlin, Amsterdam and Ghent.
* ### What to expect when joining ML6? | 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 biggest companies across countries.
We are all T-shaped consultants working on a shared goal.
* ### Am I a fit for the company?
We are an AI Consulting company on a mission. Guiding the AI revolution
towards positive impact. You can become part of this mission. We are always on
the look for business minded people with a consultative approach. We dedicate
great effort in building a caring culture full of learning opportunities that
accelerates us to accomplish our main purpose. You can be in the driver’s seat
of your career. You can be the next ML6 agent to create value for our
customers and company.
Are you up for the challenge? | 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
evaluate how you can contribute to our innovative projects by demonstrating
your skills & creativity. As you approach the finish line, we schedule a final
interview (physically) to reflect on your dream job at ML6.
* ### How is ML6 structured?
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? | 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. From
manufacturing, to healthcare or media and entertainment industries (and lots
more!).
You can find some of our client cases and testimonials here.
* ### I would love to meet up with someone from ML6
You can find all of our upcoming meet ups and events here. If you would like
to schedule a first get-to-know, we recommend you to apply officially via our
jobsite where we will follow up with you soon.
* ### What about the ethical aspect of AI?
At ML6 we take our responsibility to build secure, reliable and sustainable
technology seriously and integrate ethics within our way of working. We have
our own Ethical Advisory board to discuss sensitive projects. | 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 ;-)
* ### What does a typical AI project look like?
This really depends on the scope of the project. A project can run for a
couple of months, to even years. Our ML6ies work on projects together with a
team of colleagues. This team can have different skills like data engineering,
software, machine learning and project management to deliver top notch service
to our clients. They sometimes work on different projects simultaneously or
combine project work with an internal project or research topic.
Contact Julie Plusquin Talent Partner – Talent & Culture 🫶
## Colleagues
Sophie Decock
Head of Business Development
## Join us
* Alliance Manager - Azure | scraping/output/-7543089521676933654.txt | [
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Contact Julie Plusquin Talent Partner – Talent & Culture 🫶
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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 to innovate and gain a competitive edge. Now more
than ever is the time to create intelligence with lasting positive impact. And
you can be a part of this!
1. We deliver end-to-end projects where we accompany organisations through the entire AI journey
2. We build bespoke hybrid ML solutions
3. We navigate and set up advisory projects
As ML6 highly values diversity, inclusion and transparent communication, we've
implemented a formal channel to report serious misconduct here.
Examples of serious misconduct (not limited to): safety of the individual
(aggression, discrimination, sexual harassment), data privacy issues, public
health, tax and fraud. | 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.
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Applicant tracking system by Teamtailor | scraping/output/-7543089521676933654.txt | [
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No items found.
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November 15, 2023
# Unlocking the Secrets of Life: AI Protein Models Demystified
Contributors
Medha Hegde
Machine Learning Engineer
Pieter Coussement
Data Engineer | Squad Lead
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Thank you! Your submission has been received! | 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 how artificial
intelligence is being implemented in the field of biology, specifically with
regard to proteins. We give a brief overview of what proteins are, their
characteristics and the applications of protein engineering. The potentials of
AI in this area are explored by giving an overview of current state-of-the-art
models that are involved in solving various protein-related problems.
### Introduction
#### What is a Protein? | 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 reactions such as hydrolysis, condensation
or hydroxylation. There are estimated to be around 75,000 different proteins
in the human body that keep it running.
#### Why do we care about Proteins?
Over the span of millions of years, nature has designed a complete toolbox of
proteins that drive different facets of life: conversion of sunlight into high
energy molecules, breaking down molecules for energy, building cell organelles
building blocks, etc. Through natural evolution, nature has optimised these
proteins to perform these tasks in the most efficient way. | 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 enzymes could break down plastic in a process that is
very much a natural process.
While evolution will, for some cases, ensure that these enzymes start to
emerge, the process could be sped up if we, as humans, could speak “protein”,
and design the necessary enzymes ourselves. Understanding and predicting the
interplay between sequence, 3D structure as well as function is crucial to be
able to build these enzymes with desired functions. The structure and the
function are both embedded in the base primary sequence of the protein.
Potential Applications of Protein Engineering (Images created using Adobe
Firely)
#### What are Proteins made of and how do they look? | 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 occurring amino acids. The composition and the order of
the linear string contains all necessary information for the 3D structure of
the protein and thus also its function. However, multiple levels of protein
organisation sit between the linear amino acids string and the 3D protein
structure. | 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 acids fully determines the formation of this secondary
structure. The subsequent folding of these components creates the overall 3D
shape of the protein, called the tertiary structure. The quaternary structure
is formed when multiple proteins start to organise in an ordered way, but this
does not occur with every protein. Starting from the tertiary structure,
proteins can have a biological function.
Protein Structure Overview (Source: Secondary Structure, Tertiary Structure) | 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 groups, allowing it to securely interact with the
molecules involved in the reaction, known as substrates. Consequently, the
active site comprises a limited set of catalytic amino acids that play a
crucial role in facilitating the reaction.
#### Protein Sequences, Structures and Functions
It is important to understand that the folding of proteins is a well regulated
process, and that the folding blueprint is fully embedded in the original
amino acid chain. Therefore, the linear amino acid chain is information
complete. | 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 challenging objective, and recent developments have focussed on
the intermediate step towards structure.
Protein Design Interactions | 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 folding problem”
(green arrow). The reverse of this process is known as “inverse-folding” (red
arrow). Protein Function, i.e., the biological process the protein performs is
determined by its 3D structure, which in turn is dependent on the primary
structure. As seen in the image above, these direct and indirect connections
between the three, are functional processes that could be modelled. | 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 been so determined while about 250 million proteins
have been sequenced. Methods to model this process of protein folding would
help us understand the elementary units of life and facilitate faster and more
sophisticated drug exploration.
#### Where does AI come in? | 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 modelled using such
prediction methods. This is where artificial intelligence comes in. Using deep
learning methods, the structure of a protein has been shown to be successfully
predictable with more accuracy than any other prediction methods. | 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 discuss further. In the upcoming sections, recent models
that have shown promising results will be described. They cover the tasks of
Protein Language Modelling, Structure Prediction, Inverse Folding, Function
Prediction and Protein Design.
### Protein Models: The Standouts
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 | scraping/output/300095212007731430.txt | [
0.08285170793533325,
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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
mathematical vector representations of protein sequences that contain
information about the structure and function of the protein itself (see this
paper for more information). These embeddings can then be used in the
subsequently described Structure, Sequence and Function Prediction models. | scraping/output/300095212007731430.txt | [
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] |
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 applications. PLMs aim to do the same and learn the evolutionary patterns
and principles that guide the functioning of proteins by training on large
amounts of protein sequence data. Protein sequences could be considered the
“words” in the language of biology. We give an overview of the ProtTrans,
ProteinBERT, ProGEN2 and the ProtGPT2 models.
##### ProtTrans | scraping/output/300095212007731430.txt | [
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] |
##### 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, fine-tune models, predict secondary structures and sequence
generation.
##### ProteinBERT
ProteinBERT was released in 2021, a model that uses the classic BERT
architecture and was pretrained on 106 Million sequences on two tasks:
bidirectional language modeling and GO (Gene Ontology) annotation of sequences
and thus took as input protein sequences and GO labels. Despite its smaller
size, ProteinBERT exhibits comparable and sometimes exceeding performance of
larger models such as ProtT5.
##### ProGen2 | scraping/output/300095212007731430.txt | [
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] |
##### 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 variants (the largest one is mentioned in the table above), and
is able to capture the distribution of observed proteins and generate new
protein sequences. These generated sequences resemble existing ones but may
not actually exist in nature. This allows for protein engineering and creating
protein structures that perform specific functions. The model is freely
available and has been shown (using AlphaFold) to generate sequences that fold
into well-formed structures.
##### ProtGPT2 | scraping/output/300095212007731430.txt | [
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] |
##### 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,
while still exhibiting characteristics that closely resemble those found in
nature.
##### ESM-2
This family of model was released along with ESMFold (detailed in later
section) which is a structure prediction model. It is an encoder-only
transformer model, and the largest variant is the largest protein language
model currently available. This enabled it to outperform other PLMs on
structure prediction benchmarks. It was trained on 65 million unique protein
sequences.
#### Structure Prediction
Model Code linked in model names, # Parameters (M/B = M/Billion) indicates the
size of the model | scraping/output/300095212007731430.txt | [
0.07460641115903854,
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] |
#### 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 have built upon the work of
AlphaFold to predict structures using different methods. Here we explore
AlphaFold, RosettaFold, OmegaFold and ESMFold.
##### AlphaFold | scraping/output/300095212007731430.txt | [
0.06289725750684738,
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] |
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 information of related proteins to be able to predict the
3D-coordinates of the final structure using a transformer-based architecture.
It is trained on publicly available datasets such as the Protein Data Bank and
UniProt, while also incorporating structures predicted with high confidence
back into the model to make use of unlabelled sequences. The model was
publicly released and they also released all the predictions made by the model
to create the AlphaFold Database of the 3D structures of almost every protein
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 | scraping/output/300095212007731430.txt | [
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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 | [
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] |
##### 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 it is
also able to model protein complexes. It comes close to AlphaFold’s
performance on many benchmarks. They both rely on Multiple-Sequence Alignments
(MSAs) which leverage similar sequences or homologues and hence do not perform
as well on sequences that do not have MSAs.
##### OmegaFold | scraping/output/300095212007731430.txt | [
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##### 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 language
structure and form just by processing large amounts of text data, the protein
language models learn analogous structural information by training on large
amounts of protein sequences (the sentences in natural language). Unlike in
natural language, the protein structure involves the 3D world, and hence
geometric intuition is incorporated using a vector geometry transformer in the
architecture. It matches the performance of AlphaFold and RoseTTAFold on the
CASP and CAMEO datasets while outperforming them both on single sequences.
Because it doesn’t rely on MSAs or known structures, it is about 10 times
faster than them.
##### ESMFold | scraping/output/300095212007731430.txt | [
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-0.033348798751831055,
0.03257180377840996,
0.016090651974081993,
-0.05337212234735489,
0.05154216289520264,
0.03200990706682205,
-0.04996332898736,
0.022623879835009575,
-0.024714400991797447,
-0.029784424230456352,
-0.01998644694685936,
0.06946182250976562,
-0.10174014419317245,
-0.10554339736700058,
0.02012781985104084,
0.05899328738451004,
0.05276307463645935,
0.07573894411325455
] |
##### 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
ensemble of models is about 150x the size of Alphafold, reporting a 60x
inference speed increase over the previous models for shorter sequences. Owing
to this speed increase, a large metagenomic database called the ESM
Metagenomic Atlas was created that reveals structures at the scale of hundreds
of millions of proteins.
#### Sequence Prediction
Model Code linked in model names, # Parameters (M/B = M/Billion) indicates the
size of the model | scraping/output/300095212007731430.txt | [
0.062300633639097214,
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0.03361186385154724,
0.0414578840136528,
0.04678158834576607,
0.06883402168750763
] |
#### 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 to this problem would aid in the de novo
design of proteins: designing new protein sequences that fold into a specific
structure to perform a desired biological function. For example, we could
design proteins that have a certain structure to enhance T cells such that
they are able to better fight cancer using inverse folding models². Similar to
the protein folding problem, several AI models are able to model this reverse
process to generate protein sequences conditionally. Here we focus on the ESM-
IF1, ProteinMPNN and MIF-ST models.
##### ESM-IF1 | scraping/output/300095212007731430.txt | [
0.0729600265622139,
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] |
##### 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 using
AlphaFold. The problem was modelled as a seq2seq task between amino acid
sequences and structures by maximising the conditional probability of a
sequence given the structural coordinates. A generic Transformer was used for
this task, together with a GVP-GNN (Geometric Vector Perceptron-Graph Neural
Network) for geometric feature extraction.
##### ProteinMPNN | scraping/output/300095212007731430.txt | [
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] |
##### 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
elements that form the protein structure for it to produce graph node and edge
features. The decoder then uses those features to generate amino acids
iteratively. They evaluated the generated sequences by predicting the
structure and comparing it to the original structure. Of significance is that
the predicted sequences were also experimentally evaluated on the tasks of
protein monomer design, nanocage design and function design and were shown to
be robust and accurate. Several “failed” designs were successfully recovered
by ProteinMPNN.
##### MIF-ST | scraping/output/300095212007731430.txt | [
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] |
##### 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, inverse folding is only used as a pretraining task to be able to perform
well on downstream tasks such as creating functional homologues by inverse
folding a protein’s structure and then sampling the sequence space. It has
also been shown to be able to predict effects of mutations.
#### Function Prediction
Model Code linked in model names | scraping/output/300095212007731430.txt | [
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] |
#### 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 has would be very helpful in understanding the
behaviour of biological systems. Protein function is generally expressed by a
classification system such as the Gene Ontology (GO) that classifies proteins
based on their function and their intracellular location, and the EC (Enzyme
Commission) number that classifies enzymes based on the chemical reactions
they catalyze. Below we take a look at the DeepGO, SPROF-GO, DeepFRI, GAT-GO
and ProtNLM models.
Source: Structure, Function
##### DeepGO | scraping/output/300095212007731430.txt | [
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] |
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 hierarchically
according to GO classes. A CNN is used to obtain embedding representations of
protein sequences, after which a classification model is used to refine
features for each class and lastly, uses a model to allow for multi-model data
integration. This model was state-of-the-art at the time of its publication as
a sequence-based protein function prediction tool.
##### DeepFRI | scraping/output/300095212007731430.txt | [
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] |
##### 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) is then
used on these features to construct protein-level features and predict
probabilities for each function. Performance is enhanced by including
predicted structure during the training process.
##### GAT-GO | scraping/output/300095212007731430.txt | [
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] |
##### 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 to
outperform the existing function predictors by making use of high-capacity
pretrained protein embeddings, predicted protein structure and sequential
features.
##### SPROF-GO | scraping/output/300095212007731430.txt | [
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] |
##### 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 (MLPs) to
produce an attention vector and a hidden embedding matrix. Since the GO
classification system is structured in a class-subclass manner, the function
prediction problem is modelled as a hierarchical multi-label classification
task, with the classes arranged as a Directed Acyclic Graph (DAG). This model
beats all other state of the art models and can also generalise to non-
homologous and unseen proteins.
##### ProtNLM | scraping/output/300095212007731430.txt | [
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] |
##### 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
description of the function of a protein from its primary sequence. The
functioning of this model is analogous to that of an image-captioning model,
where instead of an image we use a protein sequence. This model is now used by
UniProt in their automatic annotation pipeline to add descriptions and names
for ~49 million uncharacterised protein sequences.
#### Protein Design
Protein Design Interactions
Here, we describe two models: RFDiffision and ProT-VAE, both involved in
protein design, which is the design of proteins with the purpose of it
performing a specific function. Some of the models described above are used to
facilitate this process.
##### RFDiffusion | scraping/output/300095212007731430.txt | [
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] |
##### 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
process involves first generating a random protein backbone using RFDiffusion,
then using ProteinMPNN to design a sequence that folds to this backbone
structure, and finally evaluating the generated structure using AlphaFold. It
also allows for conditioning to be able to generate, for e.g., a protein with
high affinity binding to a target protein or a diverse protein assembly with a
desired symmetry. RFDiffusion is able to design proteins previously unobserved
in nature.
##### ProT-VAE | scraping/output/300095212007731430.txt | [
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##### 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 protein sequences. The VAE is trained on specific families of
proteins while the ProtT5 model was trained on millions of protein sequences,
after being initialised using T5 NLP weights. The ProT-VAE model is able to
furnish “data-drive protein engineering”, and is available on NVIDIA’s BioNeMo
framework (claimed to be soon open-sourced).
#### Conclusion | scraping/output/300095212007731430.txt | [
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] |
#### 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) are
unlimited. Understanding proteins and their functioning with the combined use
of Protein Language Models, Protein Structure Prediction models, Inverse
Folding models and Function Prediction models can facilitate protein design
with transformative effects. We will continue to monitor advancements while
converting the most recent research into valuable applications within the
field. If you are interested in our relevant current work, check out this
press release!
For more information, you can contact me here: [email protected]
##### References
* Protein Folding Problem Explanation
* Overview of Protein Models
* Podcast with David Baker about Protein Design | scraping/output/300095212007731430.txt | [
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] |
##### References
* Protein Folding Problem Explanation
* Overview of Protein Models
* Podcast with David Baker about Protein Design
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Fine-tuning Whisper for Dutch Language: The Crucial Role of Size
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September 5, 2023
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Sustainability | scraping/output/-6223937804146990080.txt | [
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September 5, 2023
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Accolade Wines
# Accolade Wines is saving 1 million litres of wine per year with AI
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] |
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All client cases
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 prevent wine loss from happening
with predictive analytics, enabling all 3 bottling lines to further reduce
losses and saving another 1 million litres of wine per year or the consumption
of 50 000 people per year. A 200% ROI for Accolade Wines.
## Intro to the customer
Park, the largest wine bottling facility in GreatBritain. Bottling over 250
million bottles of wine eachyear, The Park is a Carbon Neutral facility, and 4
timeswinner of the UK’s Sustainable Manufacturer of theYear.
## Challenge | 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
wine management system could be used to monitor and improve wine yield to
unprecedented levels
By
## Solution
Together with Accolade Wines, ML6 built a solution for wine loss detection by
implementing sensors in place for the process & having a realtime dashboard
for individual components. The idea was to prevent wine loss from happening on
time using rule based and ML alerts. We created a real-time Grafana dashboard
that visualizes the end-to-end process and helps identify when the wine loss
occurs so operators can directly act on it.
The solution was built on Google Cloud to iterate quickly and scale up the
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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.
The new rule-based alert system shown in the main dashboard (above) monitors
for big discrepancies in volume during the bottling process. So when there is
a sizable difference in the amount of wine going in and coming out of any part
of the manufacturing line, an alert is fired so that operators can investigate
what’s causing the loss and make changes instantly.
We then mapped out information collected on a macro and a micro level.
Operators can see the total wine volume in the system over time (top left) and
the difference in the amount of wine that comes in and out of the facility
(top right). For digging into the details, there are also graphs for flow
rates (bottom) that measure the amount of liters per second that flow at
certain times during the bottling process.
By
## Results | scraping/output/-6457410234367962254.txt | [
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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
insights provided by ML6, enabled all 3 bottling lines to further reduce
losses by over half of that. Resulting in saving another 1 million litres of
wine per year.
Accelerating businesses with AI technology & experts
Contact:
[email protected]
+32 9 265 95 50
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Elevating excellence : accelerating each others talent | scraping/output/-6672370828350515768.txt | [
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Implementing ML models' code promotion across environments using CI/CD
pipelines.
February 8, 2024
By
Miro Goettler
Blogpost
Elevating excellence : accelerating each others talent
A company can only be as successful as the success of its people. Everything
depends on a strong ‘people and talent approach’ that is committed to
individual development and growth. At ML6, we consciously attract people who
can bring new insights and skills. People who dare to think about innovation
disruptively, but who always fit within our cultural values. So what does that
look like more concretely you might wonder?
February 5, 2024
By
Julie Plusquin
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Explore the impact of the EU AI Act on businesses, focusing on the interplay
with existing regulations, cybersecurity considerations, and practical
approaches to implement Responsible AI.
January 11, 2024
By
Michiel Van Lerbeirghe | scraping/output/-6672370828350515768.txt | [
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January 11, 2024
By
Michiel Van Lerbeirghe
Elevating Your Retrieval Game: Insights from Real-world Deployments
Discover how to optimize Retrieval Augmented Generation (RAG) by improving
retriever performance, utilizing metrics for evaluation, and enabling follow-
up questions for enhanced user experience.
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By
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Blogpost
Unveiling Google's Gemini: a game-changer in the world of AI?
Explore Google's latest AI model, Gemini, challenging GPT4.
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The impact of multimodality in the landscape of Large Language Models
Since the recent introduction of GPT-4 Vision, there has been a growing
interest in multimodality. Read what does it mean for you.
November 22, 2023
By
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A brief overview of what proteins are, their characteristics and the
applications of protein engineering.
November 15, 2023
By
Medha Hegde
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Webinar | Generative AI for corporate use - Fine tuning foundation models | scraping/output/-6672370828350515768.txt | [
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