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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 ‍ ## Related posts View all No results found. There are no results with this criteria. Try changing your search. Large Language Model Large Language Model Foundation Models Foundation Models Corporate Corporate People People Structured Data Structured Data Chat GPT Chat GPT Sustainability Sustainability Voice & Sound Voice & Sound Front-End Development
scraping/output/6475038328432950049.txt
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scraping/output/6475038328432950049.txt
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scraping/output/6475038328432950049.txt
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🚀 Unleash the Power of Large Language Models and Foundation Models - Read our insights and expertise on these trends in AI! Discover more Services Navigate intelligence Activate intelligence Build intelligence AI Solutions Custom solutions Solutions catalogue Domains of expertise References Client cases Customer testimonials Resources Resource library Blog Video & demo Press Events Open Source Our DNA How we work Mission & ambition Responsible AI Partners ML6 For Good Careers Jobs Team Working at ML6 Taal NLEN Contact us en defrNL Contact us PressRandstad verhoogt verkoopeffectiviteit met ML6 | Computable July 14, 2023 # Randstad verhoogt verkoopeffectiviteit met ML6 | Computable Subscribe to newsletter Sign up By clicking Sign Up you're confirming that you agree with our Terms and Conditions. Thank you! Your submission has been received! Oops! Something went wrong while submitting the form.
scraping/output/2401150086897253870.txt
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scraping/output/2401150086897253870.txt
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scraping/output/4712455591175985452.txt
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Thank you! Your submission has been received! Oops! Something went wrong while submitting the form. Share this post 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 ‍
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) 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. ‍ ‍ ‍ ‍ ‍
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 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) ‍ ## Related posts View all No results found. There are no results with this criteria. Try changing your search. Large Language Model Large Language Model Foundation Models Foundation Models Corporate Corporate People People Structured Data Structured Data Chat GPT Chat GPT Sustainability Sustainability Voice & Sound Voice & Sound Front-End Development
scraping/output/4712455591175985452.txt
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scraping/output/4712455591175985452.txt
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scraping/output/4712455591175985452.txt
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Structured Data 🚀 Unleash the Power of Large Language Models and Foundation Models - Read our insights and expertise on these trends in AI! Discover more Services Navigate intelligence Activate intelligence Build intelligence AI Solutions Custom solutions Solutions catalogue Domains of expertise References Client cases Customer testimonials Resources Resource library Blog Video & demo Press Events Open Source Our DNA How we work Mission & ambition Responsible AI Partners ML6 For Good Careers Jobs Team Working at ML6 Taal NLEN Contact us en defrNL Contact us Blogposts All Posts February 11, 2021 # Opinion — How to build a data architecture to drive innovation — today and tomorrow Contributors Robbe Sneyders Principal ML Engineer No items found. Subscribe to newsletter Sign up By clicking Sign Up you're confirming that you agree with our Terms and Conditions. Thank you! Your submission has been received!
scraping/output/478919339682848376.txt
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No items found. Subscribe to newsletter Sign up By clicking Sign Up you're confirming that you agree with our Terms and Conditions. Thank you! Your submission has been received! Oops! Something went wrong while submitting the form. Share this post 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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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. ‍ ## Related posts View all No results found. There are no results with this criteria. Try changing your search. Large Language Model Large Language Model Foundation Models 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 AI Infrastructure Infrastructure Hardware & sensors Hardware & sensors MLOps MLOps Generative AI Generative AI Natural language processing Natural language processing Computer vision Computer vision
scraping/output/478919339682848376.txt
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scraping/output/478919339682848376.txt
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scraping/output/-7543089521676933654.txt
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scraping/output/-7543089521676933654.txt
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Vendors Teamtailor Analytics These cookies collect information to help us understand how the site is being used. Vendors Teamtailor Marketing These cookies are used to make advertising messages more relevant to you. In some cases, they also deliver additional functions on the site. Vendors Youtube, Google Accept these cookies Decline non-necessary cookies * Join us 🕵️‍♀️ Employee Log in Candidate Log in to Connect Homepage ml6.eu Facebook X LinkedIn Mail Business Development unit 🤝 * Multiple locations # AI Client Partner ## Love taking ownership to further accelerate businesses with AI? 🚀 Join us ## Your challenge 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:
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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 ## Why others choose ML6 🤝
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 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
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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. * ### Byte size Benefits
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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.
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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 🫶 ## 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 📍 Office Berlin 📍 Office Ghent 📍 ## About ML6
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 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. Join us Business Development unit 🤝 * Multiple locations # AI Client Partner ## Love taking ownership to further accelerate businesses with AI? 🚀 Loading application form * Join us 🕵️‍♀️ * Data & privacy * Manage cookies ml6.eu/ Employee login Candidate Connect login Applicant tracking system by Teamtailor
scraping/output/-7543089521676933654.txt
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scraping/output/300095212007731430.txt
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Data Engineer | Squad Lead Subscribe to newsletter Sign up By clicking Sign Up you're confirming that you agree with our Terms and Conditions. Thank you! Your submission has been received! Oops! Something went wrong while submitting the form. Share this post 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
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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
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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
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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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##### 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
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#### 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
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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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scraping/output/300095212007731430.txt
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scraping/output/300095212007731430.txt
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No items found. 🚀 Unleash the Power of Large Language Models and Foundation Models - Read our insights and expertise on these trends in AI! Discover more Services Navigate intelligence Activate intelligence Build intelligence AI Solutions Custom solutions Solutions catalogue Domains of expertise References Client cases Customer testimonials Resources Resource library Blog Video & demo Press Events Open Source Our DNA How we work Mission & ambition Responsible AI Partners ML6 For Good Careers Jobs Team Working at ML6 Taal NLEN Contact us en defrNL Contact us # Sharon Grundmann No items found. Visit my LinkedIn profile Our know-how ## Related posts Placeholder tag Fine-tuning Whisper for Dutch Language: The Crucial Role of Size Unlocking ASR Excellence: Fine-Tuning Whisper for Dutch Language September 5, 2023 By Sharon Grundmann Large Language Model Foundation Models Corporate People Structured Data Chat GPT Sustainability
scraping/output/-6223937804146990080.txt
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scraping/output/-6223937804146990080.txt
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Computer vision Structured Data 🚀 Unleash the Power of Large Language Models and Foundation Models - Read our insights and expertise on these trends in AI! Discover more Services Navigate intelligence Activate intelligence Build intelligence AI Solutions Custom solutions Solutions catalogue Domains of expertise References Client cases Customer testimonials Resources Resource library Blog Video & demo Press Events Open Source Our DNA How we work Mission & ambition Responsible AI Partners ML6 For Good Careers Jobs Team Working at ML6 Taal NLEN Contact us en defrNL Contact us All client cases Accolade Wines # Accolade Wines is saving 1 million litres of wine per year with AI ## Impact
scraping/output/-6457410234367962254.txt
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Careers Jobs Team Working at ML6 Taal NLEN Contact us en defrNL Contact us 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 solutions to multiple lines and/or countries.
scraping/output/-6457410234367962254.txt
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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 Contact us Join our newsletter By subscribing you agree to with our Privacy Policy Thank you! Your submission has been received! Oops! Something went wrong while submitting the form. Services Navigate Intelligence Activate IntelligenceBuild Intelligence Solutions Custom AI solutionsSolutions catalogueDomains of expertise References Client casesCustomer testimonials Resources Resource libraryBlogpostsVideo & DemoEventsOpen Source About
scraping/output/-6457410234367962254.txt
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scraping/output/-6457410234367962254.txt
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scraping/output/-6672370828350515768.txt
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Intelligent/ Smart Machines Generative AI Data Driven Sales Recommendation & Matching engine Computer Vision Diagnostics In-Line Quality Inspection & Process Steering Digital Twin Production Planning Content Management Platform Client & Prospect Profiling Industry Minerals Chemicals Food & Beverages Machinery & Equipment Recycling Manufacturing Media & Entertainment Public & Professional Services CPG, Retail & Ecommerce Life Sciences & Healthcare Financial services Expertise Domain Large Language Model Foundation Models Corporate People Structured Data Chat GPT Sustainability Voice & Sound Front-End Development Data Protection & Security Responsible/ Ethical AI Infrastructure Hardware & sensors MLOps Generative AI Natural language processing Computer vision Clear all Thank you! Your submission has been received! Oops! Something went wrong while submitting the form. Filters Tag Large Language Models: to Fine-tune or not to Fine-tune?
scraping/output/-6672370828350515768.txt
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Clear all Thank you! Your submission has been received! Oops! Something went wrong while submitting the form. Filters Tag Large Language Models: to Fine-tune or not to Fine-tune? Fine-tune large language models for custom behavior and knowledge, weighing open source vs. closed source options. February 20, 2024 By Michiel De Koninck Benefit your AI investments with this paradigm shift In this article we explain why the time for AI investments is now. We share our strategic framework to use AI in business transformation, aiming to maximise your AI investments. February 13, 2024 By Jens Bontinck Blogpost Promoting Code Across Environments for Reliable and Efficient Model Deployment Implementing ML models' code promotion across environments using CI/CD pipelines. February 8, 2024 By Miro Goettler Blogpost 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 Insight Webinar | The AI Act in practice - preparing for the upcoming regulation 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. January 8, 2024 By Sebastian Wehkamp Blogpost Unveiling Google's Gemini: a game-changer in the world of AI? Explore Google's latest AI model, Gemini, challenging GPT4. December 7, 2023 By Matthias Feys Blogpost 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 Pauline Nissen Blogpost Unlocking the Secrets of Life: AI Protein Models Demystified A brief overview of what proteins are, their characteristics and the applications of protein engineering. November 15, 2023 By Medha Hegde Blogpost
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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 Blogpost Whisper Deployment Decisions: Part I — Evaluating Latency, Costs, and Performance Metrics Optimizing OpenAI Whisper deployment in production. November 5, 2023 By Blogpost Assessing the impact of the EU AI Act proposal How can model providers comply with regulation concerning training data & copyright? October 31, 2023 By Michiel Van Lerbeirghe Blogpost LLAMA 2: So good they named it twice Meta's LLAMA model takes center stage, but there's a catch: it's open-source, but not entirely. Here's the story of LLAMA, its recent updates, and what it means for you. October 10, 2023 By Blogpost Webinar | Generative AI for corporate use - Fine tuning foundation models
scraping/output/-6672370828350515768.txt
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