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Digital twins rely on combining data from different sources, including sensors, ERP systems, and other manufacturing software. The data must be precise, consistent, and complete. Model creation To create a digital twin, you need an accurate model of the physical system, which includes all relevant machines, materials, and processes. The model must predict performance and identify bottlenecks to be successful. Performance prediction The digital twin should predict the production system's performance based on various factors, including demand, raw materials, equipment availability, and maintenance schedules, using multiple performance metrics. Optimization The digital twin should be able to optimize the production process by identifying the most efficient use of resources, reducing waste, and improving the quality of output. Intuitive interface/dashboard Developing an intuitive dashboard for users is important since it's often their only way of interacting with the solution.
scraping/output/-1381642931706136732.txt
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Intuitive interface/dashboard Developing an intuitive dashboard for users is important since it's often their only way of interacting with the solution. ## Assets & Boilerplates View all No results found. There are no results with this criteria. Try changing your search. 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 contact # Get in touch We currently have four locations across Europe and are excited to make your business grow. Let us know how we can help. Name * Company * Email * How did you hear about us? How can we help you?Select one...I'm interested in getting strategic adviceI'm looking to build a solutionI want to build strategic AI assets I'm trying to understand how my company can get started with AI Message
scraping/output/-1381642931706136732.txt
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scraping/output/-1381642931706136732.txt
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scraping/output/-3687198579817695165.txt
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scraping/output/-3687198579817695165.txt
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scraping/output/-3687198579817695165.txt
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As a project manager you make sure that the goals and outcomes of your project are closely aligned with the needs and wishes of the client while monitoring resources, timelines and technological and other requirements. You will work with teams of highly-talented machine learning engineers delivering cutting- edge AI solutions. Some of your responsibilities:
scraping/output/-3687198579817695165.txt
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* Identify requirements with clients and stakeholders using ideation and prioritization techniques (e.g. interviews, workshops, refinements, …) * Translate client’s requirements and preferences into tangible goals for the technical team * Create task backlog and planning * Check deliverables and follow up resource planning, timelines and budgets * Set up and coordinate meetings with stakeholders and technical experts * Safeguard user, technical, legal, ethical, security and other requirements * Effectively pitch solutions, present results - adjust complex technical messaging to the audience at the customer * Probe clients for other opportunities * Flexibly interact with client’s ways of working, agile tooling * Create project documentation * Facilitate post project meetings to establish project takeaways * Coach squad members on projects and in their goals * Enable teamwork and group cohesion
scraping/output/-3687198579817695165.txt
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* Facilitate post project meetings to establish project takeaways * Coach squad members on projects and in their goals * Enable teamwork and group cohesion * Ensure clear responsibilities and quality of delivery on projects
scraping/output/-3687198579817695165.txt
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## Why you? * Excellent verbal and written communication in English and German (Berlin Office) or English and Dutch (Amsterdam Office) * Master’s degree or similar required through relevant experience * min. 5 years of experience as a project manager in IT, strong track record in managing data-projects * Experience with agile project management methodologies and tools * Ability to understand and refine client requirements * Ability to hold team members accountable for goals, and take responsibility for the work * Ability to plan, schedule, coordinate, and organize an interdisciplinary team * Familiarity with data processing, machine learning is a strong plus * Flexible, curious and willing to embrace change for the better ## Why ML6?
scraping/output/-3687198579817695165.txt
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## 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 Advisory & Delivery unit 💻 Locations Office Berlin 📍, Office Amsterdam 📍, Office Ghent 📍 ## Why others choose ML6 🤝 * ### Create intelligence with a lasting positive impact
scraping/output/-3687198579817695165.txt
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Advisory & Delivery unit 💻 Locations Office Berlin 📍, Office Amsterdam 📍, Office Ghent 📍 ## 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/-3687198579817695165.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. * ### 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. 📽️ Working as a Project Manager 00:00 % buffered ## 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?
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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/-3687198579817695165.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?
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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/-3687198579817695165.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 Jens Bontinck Head Advisory & Delivery ## Join us * Frontend Engineer
scraping/output/-3687198579817695165.txt
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Contact Julie Plusquin Talent Partner – Talent & Culture 🫶 ## Colleagues Jens Bontinck Head Advisory & Delivery ## Join us * Frontend Engineer Advisory & Delivery unit 💻 * Multiple locations * Data Engineer Advisory & Delivery unit 💻 * Office Berlin 📍 * Data Engineering Consultant Advisory & Delivery unit 💻 * Multiple locations More jobs Office Berlin 📍 Office Amsterdam 📍 Office Ghent 📍 ## About ML6
scraping/output/-3687198579817695165.txt
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* Data Engineering Consultant Advisory & Delivery unit 💻 * Multiple locations More jobs Office Berlin 📍 Office Amsterdam 📍 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/-3687198579817695165.txt
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scraping/output/-3687198579817695165.txt
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scraping/output/-315247701017859334.txt
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Responsible AI Partners ML6 For Good Careers Jobs Team Working at ML6 Taal NLEN Contact us en defrNL Contact us ML6 for good # Empowering nonprofits with AI Solutions At ML6, we believe in the power of AI to drive positive change and make a lasting impact on the world. Our ML6 for Good program is a testament to our commitment to using cutting-edge technology for the greater good. We partner with nonprofit organizations to accelerate their mission-critical initiatives by providing them with access to our AI expertise and solutions. Here's why "doing good" is at the heart of what we do. Why Do GoodOur impact so farContact our team ## Why Do Good matters to ML6 "Doing good" is one of our core values at ML6. As a leading company in the field of artificial intelligence, we recognize our responsibility to harness AI's potential for positive, long-term change. ‍
scraping/output/-315247701017859334.txt
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‍ With the ML6 for Good program, we extend our commitment to the nonprofit sector by offering our services "pro bono." This enables us to utilize our skills and tools to improve the world and the environment by granting nonprofits access to the same advanced AI solutions that businesses benefit from. ## Our impact so far Our dedication to "doing good" has led to meaningful collaborations with nonprofit organizations on several impactful projects. Here are a few examples of the work we've done in the past. ### IDA Foundation We partnered with IDA Foundation on three distinct use-cases, including "Forecasting of emergency health kits," "Pallet packaging optimization," and "Emergency event detection" based on social web data. These projects aimed to create data-driven approaches to anticipate disasters and optimize the transportation of critical supplies for countries in need. ‍ This is some text inside of a div block. This is some text inside of a div block. ### AROP VZW
scraping/output/-315247701017859334.txt
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This is some text inside of a div block. This is some text inside of a div block. ### AROP VZW We assisted Arop VZW by developing an initial version of an automatic visual quality inspection workflow for their sheltered workshops. This tool aids workers in their daily activities, enhancing the quality and efficiency of their work. ‍ This is some text inside of a div block. This is some text inside of a div block. ### Fruitpunch.ai and Reef.support In collaboration with Fruitpunch.ai and Reef.support, we've been working on a visual detection tool with image segmentation capabilities. This tool is designed to identify different types of corals and detect coral bleaching, contributing to raising awareness about the preservation of these vital ecosystems. ‍ This is some text inside of a div block. This is some text inside of a div block. ### Beeodiversity
scraping/output/-315247701017859334.txt
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This is some text inside of a div block. This is some text inside of a div block. ### Beeodiversity One ambitious project we're working on is the "Pesticide Predictor." We ask the question: Can we predict the presence of certain pesticides based on the kind of agricultural land surrounding a measuring point? By applying machine learning algorithms to the data collected from various agricultural regions, we aim to develop a tool that can alert farmers and environmental agencies to potential pesticide contamination risks. This innovative approach combines technology and nature to safeguard our environment, promoting sustainable agriculture and a healthier planet for all. This is some text inside of a div block. This is some text inside of a div block. ### Plan International
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This is some text inside of a div block. This is some text inside of a div block. ### Plan International By gaining insights into the donor journey, efficiency and effectiveness of fundraising could be enhanced. With insights gained from a data-driven approach, the ultimate goal is to enable fundraisers to take better and more personal care of existing donors based on their needs, while spending less resources and funds in the process. This is some text inside of a div block. This is some text inside of a div block. # Contact our team Our ML6 for Good team is led by Rosemarijn Dumont and Birgit Mullie. They're backed by a passionate group of ML6 employees eager to assist nonprofits. Together, this dedicated team collaborates with nonprofits, ensuring AI is a force for positive change. Name * Company * Email * Message We need your details to contact you. Your data will be processed according to the provisions of our privacy policy.
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scraping/output/-315247701017859334.txt
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scraping/output/-315247701017859334.txt
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scraping/output/9148560787307000418.txt
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scraping/output/9148560787307000418.txt
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scraping/output/-5229824330364300756.txt
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Thank you! Your submission has been received! Oops! Something went wrong while submitting the form. Share this post Another day, another change in the world of generative AI. At this point, even a blind person can see that the generative AI hype train has well and truly left the station and Google has officially jumped aboard. It is no secret that Microsoft has thrown the first punch in the fight for generative AI with its investment in OpenAI (and thus ChatGPT) but Google has decided to come out swinging. Will Google pull a Tyson Fury on us and rise from the canvas to win the fight, retaining its title as the go-to cloud for AI/ML? Let’s find out! 🔎 ### What does the Google generative AI landscape look like? At Google I/O 2023, generative AI was — to no one’s surprise — one of the main talking points. Big announcements were made and new services went live (in preview). Let’s take a closer look at what they came up with. * For users ‍
scraping/output/-5229824330364300756.txt
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* For users ‍ Google announced Duet AI which is an AI chatbot assistant (one might even call it a copilot) that is planned to be implemented in basically anything Google — from Workspace to GCP. Google search will get a similar feature but doesn’t fall under Duet. All in all nothing too world-shocking in terms of ideas — especially considering the moves Microsoft is making (e.g., Office 365 Copilot, Bing Chat, ...) — but they are practicing what they preach. * For builders The big one here is Generative AI App Builder which is Google’s fast and easy managed solution to enterprise knowledge discoverability. A LLM-powered chat interface connected to internal enterprise documents is such a valuable and general use case. Think about a chat interface where you can ask questions about information in technical manuals, legal documents, etc.
scraping/output/-5229824330364300756.txt
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Turning this into a standardized offering is the textbook definition of a no- brainer. The ease of development looks very impressive. Turns out these Google folks know what they’re doing when it comes to looking for information. Wonder if they have done this before? For more on the underlying Retrieval-Augmented Generation (RAG) technology behind Generative AI App Builder, click here. * For tuners There is also a solution for people who want to dig a level deeper and interact and tune the underlying ML models directly. Enter Generative AI Studio and Model Garden which are both in Vertex AI. Model Garden is the place where you can browse through the available models to find the one that fits your use case. Overview of model cards available on Vertex Model Garden Generative AI Studio is the place where you can interact with the models and tune them to meet your use case and/or data.
scraping/output/-5229824330364300756.txt
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Overview of model cards available on Vertex Model Garden Generative AI Studio is the place where you can interact with the models and tune them to meet your use case and/or data. Security and efficiency stand out: when you tune a model, you only train a small adapter model whereas the actual foundation model stays frozen. Both data and adapter model never leave your cloud tenant! Note: at the time of writing none of the above has public access except for Generative AI Studio and Model Garden which is in public preview. ### The stand-outs In general, we notice a clear convergence in the cloud generative AI offerings towards the same set of ideas. Great minds think alike but clearly cloud providers do too. That said, Google’s genAI stack is by no means a copy and there are some definite differences. Let’s take a look at our first impressions: * One size does not fit all
scraping/output/-5229824330364300756.txt
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That said, Google’s genAI stack is by no means a copy and there are some definite differences. Let’s take a look at our first impressions: * One size does not fit all With PaLM 2, Google accentuated the wide range of sizes it comes in: Gecko, Otter, Bison and Unicorn. Since only Bison is available in preview (at time of writing), it is too early to compare but this versatility is definitely an interesting avenue with Gecko even poised to be able to run on a mobile device. * Promise of fine-tuning The few-shot and fine-tuning behavior is looking very promising — even with as little as 5–10 labeled examples. We notice significant improvements, especially for niche situations such as the legal or medical domains. * Safety first
scraping/output/-5229824330364300756.txt
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* Safety first It is no secret that generative models are prone to the odd ethical hiccup if let completely loose. Therefore Google is focussing heavily on putting in place guardrails that should inhibit any unethical input from coming in and any unethical output from coming out. To this end, Google uses so-called safetyAttributes that quantify how toxic, hateful, insulting, etc. it predicts a certain in-/output to be. This is a big step towards peace-of-mind that a company who uses generative AI won’t end up making headlines like Microsoft Tay. ### What can we expect? Of course it is still early days. What can we expect going forward? * Europe compliance This is a big one: at the time of writing there is no Google generative AI offering where all data stays within Europe! For sensitive use cases, this is of course a BIG no-no in the eyes of GDPR. Obviously Google is more than aware of this and will be working overtime to make this possible in the (near) future.
scraping/output/-5229824330364300756.txt
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Obviously Google is more than aware of this and will be working overtime to make this possible in the (near) future. * To churn or not to churn We already covered the convergence in offerings by the major players. Whether people will return from e.g., Bing Chat back to Google search after Google catches up with its generative AI efforts will be the burning question on any AI enthusiast’s mind. * Better language coverage As always, advances in NLP are always English-centered at first. Generative AI Studio is no different as it currently only supports English. However, PaLM 2 is highly multilingual so we assume this limited support is a more a due to the bias/toxicity/… guardrails Google has put in place rather than the capabilities of the underlying model itself. * Multimodality Although Google has image, text and speech generative AI offerings, there is no multimodal generative AI yet.
scraping/output/-5229824330364300756.txt
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* Multimodality Although Google has image, text and speech generative AI offerings, there is no multimodal generative AI yet. Considering multimodality is a booming trend, it is not difficult to envision a world where Google releases multimodal offerings in the near future. * GenAI-backed ML APIs Google is practicing what it preaches and we fully expect this to extend to its suite of ML APIs. Offerings such as Google Contact Center AI and Dialogflow seem like obvious candidates to be backed by LLMs. Given the impressive fine-tuning performance, we are especially excited by domain-specific models such as MED-PaLM2 and if/how these will be commoditized. ### Conclusion Things are heating up in the generative AI landscape. OpenAI has hit Google where it hurts (using their own technology no less) but they have come out swinging.
scraping/output/-5229824330364300756.txt
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### Conclusion Things are heating up in the generative AI landscape. OpenAI has hit Google where it hurts (using their own technology no less) but they have come out swinging. Google released services that are largely in line with its competitors and it is practicing what it preaches in its own products (e.g., Google search, Workspace, etc.). As far as first impressions go, it looks like generative AI on Google will become competitive with Azure and AWS. The big question on everyone’s mind is whether Microsoft can convert its shock upset into generative AI dominance or whether Google will retain its title as the AI champion. ‍ The jury is still out on that one. ‍ ## 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
scraping/output/-5229824330364300756.txt
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scraping/output/-5229824330364300756.txt
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scraping/output/-5229824330364300756.txt
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Structured Data Front-End Development 🚀 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 March Real Estate # Building a recommendation engine for March Real Estate ## Impact Since integrating the proactive recommendation tool, the number of leads from the matching engine skyrocketed to almost 7x the number of new contacts per day. 45% of March leads are now realized using the March Matching Engine.
scraping/output/-4652357889146377325.txt
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By using a data-driven approach, March also gets a high response rate from companies. Four weeks after the initial contact 70 percent responded, and 10 percent of the companies contacted indicated that they are actually looking for a new location at that moment. ‍ ## Intro to the customer The Belgian startup March is a fast-growing commercial property company specialised in matching companies looking to rent or buy commercial real estate with developers and owners. In the generally conservative real estate industry, March differentiates itself as an innovative proptech player which has the human element at heart. At its core, the company uses data intelligence to generate high-impact leads, thereby freeing the time of its real estate agents to focus on the customer. ## Challenge
scraping/output/-4652357889146377325.txt
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## Challenge A lot of factors influence the location decision of business space for different companies in various industries. March’s vision was to use all these variables in a fast and efficient way for targeted prospecting in order to find the optimal match between companies and business space owners. That is why they got in touch with ML6. “45% of our leads are now coming from the March Matching Engine. That is very much in line with our ambition to be twice as efficient in our industry than our competitors. We are on the right track and can continue to work on making our algorithm even more accurate by adding more features and further fine- tuning the model.” Co-founder & CEO March By March - Philippe Meire - Case Quote ## Solution
scraping/output/-4652357889146377325.txt
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Co-founder & CEO March By March - Philippe Meire - Case Quote ## Solution March, together with ML6 and experience design studio Bothrs, developed an internal online tool that makes it easy to determine high-potential prospects for a particular property, based on their suitability for that location as determined by machine learning algorithms. The March Companions can leverage personalised information to approach these leads, allowing March to pivot from a reactive to a proactive approach. The March Matching Engine starts from a large collection of private and public data. More than 50 features analyse the location, the building and the parcel of each property in Belgium. This allows employees to generate reports for potential clients, show them related property scores and bring all valuable factors together in a single, convenient overview. In addition, the tool has a few other features, such as personal and auto-generated offers. By ## Results
scraping/output/-4652357889146377325.txt
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By ## Results Since integrating the proactive recommendation tool, the number of leads from the matching engine skyrocketed to almost 7x the number of new contacts per day. 45% of March leads are now realized using the March Matching Engine. By using a data-driven approach, March also gets a high response rate from companies. Four weeks after the initial contact 70 percent responded, and 10 percent of the companies contacted indicated that they are actually looking for a new location at that moment. ‍ Accelerating businesses with AI technology & experts Contact: info@ml6.eu +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
scraping/output/-4652357889146377325.txt
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scraping/output/-4652357889146377325.txt
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scraping/output/7322456078592268314.txt
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scraping/output/7322456078592268314.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 # Julie Plusquin No items found. As a Talent Partner, Julie focuses on attracting, recruiting and retaining talent within our organisation. She loves analysing human behaviour and investing in personal growth. She is convinced that the right place can be found for every talent.
scraping/output/4068675949401372701.txt
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Julie likes to step away from traditional recruitment methods and loves listening to the person & their story behind the 'traditional CV'. Every story is different and everyone gets energised by something different. She also likes to do research about evidence-based management theories in a business psychology context. Are you in for a talk with her to explore the possibilities? Feel free to reach out via Linkedin ! ‍ Visit my LinkedIn profile Our know-how ## Related posts Placeholder tag 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?
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February 5, 2024 By Julie Plusquin Placeholder tag AI internships at ML6: nine domains to steer your career towards At the end of college years, it can be very confusing to start determining your next steps. Are you 100% convinced about your choice of study? Do you have doubts about how your education plays out in practice? These questions certainly don't come as a surprise. There is a lot coming at you. For this reason, it is very interesting to explore the field in practice. Luckily, schools often offer that option through internships. September 6, 2023 By Julie Plusquin Placeholder tag AI can play a crucial role in the field of HR, but should we blindly follow? Is AI only applicable within factories and large corporate organizations? Anything but. AI will (and already is) creating impact within the Human Resources branch as well. We speak with Julie Plusquin, Talent Partner at ML6 about what the evolution within HR may look like. July 20, 2023 By Julie Plusquin
scraping/output/4068675949401372701.txt
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July 20, 2023 By Julie Plusquin Placeholder tag Accelerating people - even if it goes beyond ML6 How we empower our agents in their own personal road to success. March 31, 2023 By Julie Plusquin Placeholder tag A sea of job opportunities, but where to start? Perhaps in learning to swim? Tips on figuring out how to navigate the recruitment process and determine which companies are a good match for you. March 9, 2023 By Julie Plusquin 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 Accelerating businesses with AI technology & experts Contact: info@ml6.eu +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!
scraping/output/4068675949401372701.txt
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scraping/output/4068675949401372701.txt
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Natural language processing 🚀 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 September 6, 2022 # Triton Ensemble Model for deploying Transformers into production Contributors 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
scraping/output/1562919865548539780.txt
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Thank you! Your submission has been received! Oops! Something went wrong while submitting the form. Share this post The blog post explains how to deploy large-scale transformer models efficiently in production using the Triton inference server. The post discusses the challenges associated with deploying transformer models and the benefits of using Triton for deployment. It also describes the ensemble modeling technique and how it can be used to improve the performance of transformer models in production.
scraping/output/1562919865548539780.txt
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You will learn about the Triton inference server, its benefits and how it can be used for deploying large-scale transformer models. You will also learn about ensemble modeling and how it can help improve the performance of transformer models. The post includes code examples and step-by-step instructions for deploying transformer models using Triton and ensemble modeling. By the end of the post, you will have a good understanding of how to deploy large-scale transformer models in production using Triton and ensemble modeling. The blogpost can be found on our Medium channel by clicking this link. ## 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/1562919865548539780.txt
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scraping/output/1562919865548539780.txt
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scraping/output/1562919865548539780.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 Open source # A(G)I for everyone FondantConnexionApache BeamHugging FaceGithub ## Fondant Production-ready data processing made easy and shareable
scraping/output/2241256086645873529.txt
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NLEN Contact us en defrNL Contact us Open source # A(G)I for everyone FondantConnexionApache BeamHugging FaceGithub ## Fondant Production-ready data processing made easy and shareable Fondant aims to become the open-source hub for people to share and reuse data processing components and data pipelines. Today, Fondant is being used for a multitude of use cases, such as multimodal RAG and multi-agent workflows. In the future, Fondant will empower users to prepare data for AGI solutions. Visit our website page and start testing and contributing to Fondant! Discover Fondant ## Connexion Learn more Connexion is the Python web framework that enables you to build API-first. We have been maintaining it since 2021, working towards a major 3.0 release. With around 5 million downloads per month, it is a staple of the Python API ecosystem. ### Connexion Docs View more Infrastructure ### Connexion Repository View more ### Why we decided to help maintain connexion
scraping/output/2241256086645873529.txt
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### Connexion Docs View more Infrastructure ### Connexion Repository View more ### Why we decided to help maintain connexion View more ## Apache Beam Learn more Apache Beam is a framework for unified batch and streaming pipelines, which is mostly known as the framework underlying Google Cloud Dataflow, but can be run on almost any distributed processing backend such as Apache Flink or Spark. We have ported Apache Beam to Python 3, added ML inference functionality, and created a lot of content for the community. #### Apache Beam Blogpost View more #### Apache Beam AI/ML Docs View more #### Apache Beam Repo View more ## Hugging face View all We leverage Hugging Face a lot in our solutions, but we also contribute back. We have published models, datasets, and demos for the community. Check our space for more. Natural language processing ### German Toxic Comment Detection View more Structured Data ### Dynamic Pricing View more Natural language processing
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scraping/output/2241256086645873529.txt
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scraping/output/2241256086645873529.txt
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scraping/output/-8846698550655468162.txt
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Responsible AI Partners ML6 For Good Careers Jobs Team Working at ML6 Taal NLEN Contact us en defrNL Contact us # Security Keep your knowledge in safe hands Learn moreContact us When it comes to building robust ML solutions, we always play it safe. Cybercrime is on the rise and has become a major global problem. If cybercrime were a country, it would have the third largest economy in the world. All the more reason why we prioritize security, especially since we work with valuable data sets. In other words, protecting your data and revenue streams from theft and loss is key to the success of your business. We take care of that by creating secure solutions, protecting ransomware and ensuring you are compliant with regulations. ### Build secure solutions
scraping/output/-8846698550655468162.txt
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### Build secure solutions We help clients manage cyber threats, data breaches, and privacy concerns while innovating with new tech. We offer cybersecurity consulting, risk assessments, security control implementation, and incident response planning. ### Mitigate against ransomware attacks We help clients avoid or reduce damage from ransomware attacks, keeping their critical business processes running and revenue flowing. ### GDPR Compliant We keep your valuable or personal data safe from hackers and make sure you comply with regulations such as GDPR. This protects you from financial penalties and reputational damage. ### Typical challenges
scraping/output/-8846698550655468162.txt
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### Typical challenges We build our solutions on AWS, Azure, or GCP, because they provide security features such as physical security and network protection. The level of responsibility of the cloud provider depends on the type of service (IaaS, PaaS, SaaS). In the case of edge deployments, we take care of all security aspects. We believe that communication is key when it comes to security. This is also reflected in our security program, which makes our engineers aware of their responsibilities through clear communication and training. ### High level outline of the solution
scraping/output/-8846698550655468162.txt
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### High level outline of the solution We prioritize security in all our work by following best practices and using secure tools and templates. Our building blocks for ML challenges consider security responsibilities and industry standards, making our codebase the safest and easiest way to start new projects. Need further proof of our commitment to information security? We’re happy to share that we’re SO/IEC 27001:2017 certified, the internationally acclaimed standard for information security management. ### Partnerships vs. doing things ourselves We can make our solutions even more secure by collaborating with the best in the business. Working closely with AWS, Azure, and GCP, we take advantage of their PaaS and SaaS solutions to minimize risks and maximize protection. Plus, we're always in touch with their security experts to stay up-to-date on the latest tech to keep your data safe from harm. ## Related posts View all Placeholder tag
scraping/output/-8846698550655468162.txt
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scraping/output/-8846698550655468162.txt
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scraping/output/-8846698550655468162.txt
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scraping/output/-8846698550655468162.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 Blogposts All Posts August 3, 2023 # Dynamic Pricing from a visual perspective Contributors 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
scraping/output/-6435728918303101593.txt
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Thank you! Your submission has been received! Oops! Something went wrong while submitting the form. Share this post ### Visualising Dynamic Pricing modelling to build insights The nice thing about Dynamic Pricing is that it offers a level of explainability. The demo below shows an experiment lasting two weeks in which we are training a dynamic pricing model that is interacting with a fictitious market. For this experiment, 100 data points (=potential clients being shown a price) were generated daily, based on the prices that were chosen by the model. The model is then retrained overnight on the daily that came in the day prior. In a matter of a few days, the suggested prices start revolving around the optimal price which is indicated by the vertical dashed line - while at the same time exploring nearby prices still. Video 1. A demo indicating the visual and explainable nature of Dynamic Pricing models.
scraping/output/-6435728918303101593.txt
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Video 1. A demo indicating the visual and explainable nature of Dynamic Pricing models. Want to learn more? Please have a look at the blogpost which explains the characteristics of Dynamic Pricing modelling from a business perspective. ## 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/-6435728918303101593.txt
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scraping/output/-6435728918303101593.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 # Julien Schuermans No items found. Julien has a background in Electrical Engineering and Computer Science and obtained an advanced Masters’ degree in Artificial Intelligence. At ML6 he has worked for clients in a wide variety of industries: manufacturing, real estate, network security, and law enforcement.
scraping/output/-627206788474025535.txt
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During these projects, Julien has been responsible for the design and implementation of end-to-end ML pipelines and powerful, scalable APIs running 24/7 in the cloud or on-premise. One of his microservice-based solutions is handling up to 600k messages per minute! These projects really sparked his interest in the opportunities & challenges of structured data. As the Chapter Lead of the Structured Data research division, Julien guides a team of experts in the domains of tabular data and time series. His mission is twofold: First of all, he keeps ML6 engineers up to date with the latest research and best practices. The chapter generates, collects and distributes valuable insights for projects dealing with topics such as anomaly detection, forecasting or clustering.
scraping/output/-627206788474025535.txt
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Secondly he supports the Labs and Business Development units with technical expertise during the pre contractual phase: Julien has been involved with spotting opportunities and drawing out the best possible solutions for technical problems. Visit my LinkedIn profile Our know-how ## Related posts Placeholder tag Homomorphic Encryption for Deep Learning with Intel’s HE-Transformer Homomorphic Encryption for Deep Learning with Intel’s HE-Transformer - Exploring the possibilities of hosting an ML model to perform inference on encrypted data using homomorphic encryption. July 16, 2019 By Julien Schuermans Placeholder tag What to Expect from Automated Machine Learning: Zooming in on Technical Aspects and Zooming Out to the Bigger Picture This blog post explores the current state of AutoML tools and their ability to deliver on the promise of automating machine learning tasks without expert knowledge. January 3, 2023 By Julien Schuermans Large Language Model
scraping/output/-627206788474025535.txt
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January 3, 2023 By Julien Schuermans 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 Accelerating businesses with AI technology & experts Contact: info@ml6.eu +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 Mission How we workResponsible AICareers Copyright 2024 Privacy Notice
scraping/output/-627206788474025535.txt
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scraping/output/-1858539412038625659.txt
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scraping/output/-1858539412038625659.txt
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scraping/output/-1575485975548286455.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 ### Introduction In this blogpost, we’ll examine the applications of using VQGAN combined with Discrete Absorbing Diffusion models from the amazing paper: Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes.
scraping/output/-1575485975548286455.txt
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Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes. If you are curious about the clever techniques that made this possible, the paper itself and our technical blogpost will definitely be something for you! In the technical blogpost we gave an overview of the previous SOTA generative models, to then arrive at a new class of models, the diffusion models. We went in depth on the architecture of the Discrete Absorbing Diffusion models. In this blogpost, we will take a step back and look how we can leverage the characteristics of this model for creative purposes. ‍ ### Recap technical blogpost In the technical blogpostI we discussed two models:
scraping/output/-1575485975548286455.txt
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‍ ### Recap technical blogpost In the technical blogpostI we discussed two models: * VQGAN, a model that represents data in a 16x16 grid of discrete latent codes and is able to decode them back to image space * the Discrete Absorbing Diffusion model, a transformer model that learns which combinations of discrete latent codes result in realistic and consistent images In the technical blogpost we saw that a discrete diffusion model doesn’t generate these latent codes from left-to-right, such as an autoregressive model, but can generate them in a random order. Out-of-order generation of latent codes ‍ This out-of-order and bidirectional sampling allows us to use novel techniques to edit and generate images: * Image generation from scratch * Conditional image generation * Image inpainting * Stitching images * Generating larger images ‍ ### Image generation from scratch
scraping/output/-1575485975548286455.txt
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* Image generation from scratch * Conditional image generation * Image inpainting * Stitching images * Generating larger images ‍ ### Image generation from scratch As in most generative models, we can generate images from scratch. The diffusion model starts with an empty 1I6x16 grid, denoted by MASK tokens, and iteratively generates tokens to fill up the grid. Once every code is generated, the VQGAN decoder uses these generated codes to create globally coherent images competitive with other generative models. ‍ Faces generated from scratch by sampling from a discrete diffusion model trained on the FFHQ dataset Churches generated from scratch by sampling from a discrete diffusion model trained on the LSUN churches dataset ‍ ### Conditional image generation ‍ Now let’s talk about conditional image generation. This is a fancy way to say that we choose a part of the image, to steer the generation of the rest of the image.
scraping/output/-1575485975548286455.txt
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‍ Now let’s talk about conditional image generation. This is a fancy way to say that we choose a part of the image, to steer the generation of the rest of the image. With the existing autoregressive models, it is possible to use conditional image generation based on a partial image. The idea here is to give the model the top part of an image as context and then further complete the content by predicting the next pixels one-by-one in a top-to-bottom, left-to-right fashion. Instead of predicting pixels, we could also predict discrete codes in a unidirectional manner, which is an idea that was explored in the paper: Taming transformers for high-resolution image synthesis. Impressive, right? However, it is only possible to condition on the top part of an image, due to the unidirectional nature of autoregressive models. ‍ Unidirectional prediction in autoregressive models ‍
scraping/output/-1575485975548286455.txt
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‍ Unidirectional prediction in autoregressive models ‍ That’s where our diffusion model can do better! We can encode an image with the VQGAN encoder, giving us a grid of latent variables. Then we choose which parts of the image we want to keep and let the model generate everything around it. This is a big improvement in flexibility compared to autoregressive models since we are no longer restricted to specific parts of an image. As a demonstration, in the following figure we want to keep the tower in the middle with different generated backgrounds. We keep the codes in the middle region 🏯, replace the other codes with the MASK value and ask the model to generate multiple new images. ‍ Left: generated image with fixed region in the middle. Right: full generated image ‍ As you can see, the content and structure of the middle region stays fixed for the most part, only adapting slightly to better fit the surrounding pixels. Feels great to tell the model what to do 😎! ‍
scraping/output/-1575485975548286455.txt
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‍ ### Regenerating image regions (inpainting) ‍ Similar to the conditional image generation, we can also perform image inpainting 🎨🖌. Let’s suppose we generate an image and we like most of it, except for one region that feels just not quite right. No problem, we can simply mask out the latent codes from the grid that correspond to this unwanted region and let the diffusion model regenerate them. To show how this works, let’s generate some new mouths for! By regenerating these codes multiple times, we can get many variations. ‍ Regeneration of a region of the image while keeping the other content fixed ‍ Very cool! We get 9 new images with exactly the same hair, eyes and background but with completely different mouths 👄 . ‍ ### Stitching images together
scraping/output/-1575485975548286455.txt
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‍ Very cool! We get 9 new images with exactly the same hair, eyes and background but with completely different mouths 👄 . ‍ ### Stitching images together Now the real fun can start. Due to the grid structure of the VQGANs latent space, the codes learned by the VQGAN are highly spatially correlated to the content of the generated images. This means that the latent codes that correspond to the region of the eyes of a generated face will contain information about the eyes. Okay, but now what? Let’s take the latent codes of the 👀 of image A and codes of the 👄 of image B. We now paste them in a grid and mask out all the other tokens and have our diffusion model do what it’s best at. ‍ The latent variables corresponding to the mouth and eyes are taken from the left faces and put into an empty grid with masked tokens. The masked tokens are predicted by the diffusion model and decoded into the face on the right. ‍
scraping/output/-1575485975548286455.txt
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‍ We see that the diffusion model has nicely filled in the masked regions to create a coherent face while staying true to the original look of the mouth and eyes. Comparison of the eye and mouth regions from the source (left) and generated (right) images. ‍ Let’s get creative and apply this idea to a model trained on churches. The pope wants you to build a new church and particularly loves the base of the famous Notre Dame in Paris and the tower from the magnificent Sint-Baafs cathedral in Ghent. No problem, you can just take out the codes corresponding to wanted regions and paste them onto an empty latent space. All that is left is to ask the diffusion model to fill in the empty regions and decode them with VQ-VAE and you can easily generate an endless amount of new churches that comply with the constraints. Left: generated image with highlighted masked region. Right: full generated image ‍ ‍
scraping/output/-1575485975548286455.txt
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Left: generated image with highlighted masked region. Right: full generated image ‍ ‍ As you can see, the content of the towers and the base stays the same and the model realistically fills in the rest. The pope is very happy with the results and gives you a VIP ticket to skip the line at Saint Peter’s gates. Job well done! If you want some crazier results, you can adjust the sampling temperature of the diffusion model to get some more variation (while trading off some global consistency). ‍ ### Generating larger images ‍ The last cool application of this model is that it allows us to generate images that are larger than the images the model was trained on. This is accomplished by dividing the latent space of the larger image into multiple overlapping grids that match the original 16x16 shape. At each prediction step we compute the probabilities of new tokens and aggregate them across the different grids. ‍
scraping/output/-1575485975548286455.txt
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‍ The process of producing larger images than trained on. The latent grid is divided into smaller grids that each calculate the probabilities of their masked tokens. These probabilities are aggregated across the grids to get a probability map of the original size, from which a new code can be sampled. Afterwards, the full grid of codes is decoded by the VQGAN decoder. ‍ This trick allows us to generate globally consistent images, even though the model was never trained for it. ‍ Generated images of higher resolution ‍ ### Conclusion In this blogpost we have discussed how the bidirectional and iterative nature of the recently emerging diffusion models, combined with the discrete representations of VQGANs and the long-range modelling capabilities of transformers allows us to have more control over the latent space. This architecture produces high-quality and consistent images while adding the ability to edit images in a conceptual discrete space.
scraping/output/-1575485975548286455.txt
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Keep an eye out, because it’s not the last you’ll see of these diffusion models (in fact, various amazing new papers have been released while writing this post, such as DALL-E 2, ImageGen, Stable Diffusion…). And don’t forget to check out the technical blogpost to learn what happens behind the scenes!🤓 ‍ ## 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
scraping/output/-1575485975548286455.txt
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scraping/output/-1575485975548286455.txt
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Large Language Model 🚀 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 June 19, 2023 # PEFT (Parameter-Efficient Fine-Tuning) Contributors 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
scraping/output/6096204450617945317.txt
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Thank you! Your submission has been received! Oops! Something went wrong while submitting the form. Share this post Navigating the Parameter-Efficient Fine-tuning (PEFT) landscape can seem like a daunting task. As AI and machine learning continue to evolve, it becomes increasingly important to understand the different methods available for fine- tuning Large Language Models (LLMs) and when to use them.In this overview, we dive deep into four prominent PEFT methods: Prompt Tuning, LoRA (Low-Rank Adaptation), Adapters, and Prefix Tuning. Each slide explains what the method is and when it's best to use it.Remember, choosing a PEFT method is all about aligning with your objectives. Whether you're aiming for diverse output, task- specific attention patterns, multiple tasks on the same model, or modifying learned representations, there's a PEFT method for you. Scroll down to expand your knowledge on PEFT methods and make an informed decision next time you need to fine-tune an LLM!
scraping/output/6096204450617945317.txt
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