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Talent & Culture 🫢 * Multiple locations * Frontend Engineer Advisory & Delivery unit πŸ’» * Multiple locations * Data Engineer Advisory & Delivery unit πŸ’» * Office Berlin πŸ“ * Junior Talent Partner Talent & Culture 🫢 * Multiple locations * Alliance Manager - Azure Business Development unit 🀝 * Multiple locations * Innovation Lead Incubation Unit πŸ’‘ * Multiple locations * Data Engineering Consultant Advisory & Delivery unit πŸ’» * Multiple locations * Senior Software Engineer Advisory & Delivery unit πŸ’» * Multiple locations * AI Client Executive Business Development unit 🀝 * Multiple locations * AI Client Partner Business Development unit 🀝 * Multiple locations * AI Project Manager Advisory & Delivery unit πŸ’» * Multiple locations * Senior Data Engineer Advisory & Delivery unit πŸ’» * Multiple locations * Senior Machine Learning Engineer Advisory & Delivery unit πŸ’» * Multiple locations * Machine Learning Engineer
scraping/output/-1004585763649522392.txt
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* Senior Data Engineer Advisory & Delivery unit πŸ’» * Multiple locations * Senior Machine Learning Engineer Advisory & Delivery unit πŸ’» * Multiple locations * Machine Learning Engineer Advisory & Delivery unit πŸ’» * Multiple locations * Internship Multiple locations * < enter fancy job title here > Multiple locations ## Are you ready to apply? πŸš€ #### 1 ### Get to know each other Starting your ML6 journey means connecting with one of our recruiters who will guide you as they get to know you. #### 2 ### Technical matching We work with you to match your skills and interests with our projects, setting you up for success in your role at ML6. #### 3 ### Presentation time Time to show our team, as we involve different ML6 agents in the journey, how you can contribute to our innovative projects by demonstrating your skills & creativity. #### 4 ### Final meeting - wrapping up
scraping/output/-1004585763649522392.txt
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#### 4 ### Final meeting - wrapping up As you approach the finish line, we’ll have a final chat to reflect on your dream job and discuss final questions. * > Great, easy-going, fair recruitment - especially the feeling of being taken serious with short reply and follow-up cycles is awesome Candidate 00:00 % buffered ## About ML6
scraping/output/-1004585763649522392.txt
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* > Great, easy-going, fair recruitment - especially the feeling of being taken serious with short reply and follow-up cycles is awesome Candidate 00:00 % buffered ## 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.
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scraping/output/-1004585763649522392.txt
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scraping/output/-2864730860114532062.txt
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scraping/output/-2864730860114532062.txt
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πŸš€ 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 protecting personal data # Regulatory compliance through Pseudonymisation or Anonymisation Accelerate your path towards regulatory compliance, data sharing and privacy protection. Based on ML6’s proven approach, we guide you through your complex pseudonymisation or anonymisation use case while reducing cost and development time through reusable components.
scraping/output/-3291290598934202599.txt
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Business ValueOur solution in practiceResourcesGet in touch ## Business Value Pseudonymization and anonymisation are powerful measures to protect personal data, and often necessary to ensure compliance with data regulations, enable data sharing, mitigate risks and foster transparency and trust. Pseudonymisation and anonymisation are two distinct techniques used to protect personal data while still allowing its use for various purposes. Which technique to use depends on the specific use case and applicable regulation. Discover the difference between pseudonymisation & anonymisation here 1\. Pseudonymisation \- is a data processing technique where personally identifiable information (PII) is replaced with pseudonyms or artificial identifiers. The purpose is to prevent direct identification of individuals while retaining the possibility of re-identifying individuals with the help of additional data, typically held separately, and the process is reversible.
scraping/output/-3291290598934202599.txt
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2\. Anonymisation \- is an irreversible process that transforms personal data into a state where it can no longer be attributed to an identifiable individual, even through the use of additional information, eliminating any risk of re-identification. Anonymised data does not fall under the scope of data protection regulations such as GDPR, as it is no longer considered personal data. ### Transparency obligations in the public sector Efficiently pseudonomize documents that need to be openly published to comply with regulations such as CeReBro, Digital Transformation of Justice plan or Wet Open Overheid (WOO) and create transparency towards citizens. ‍ This is some text inside of a div block. This is some text inside of a div block. ### GDPR compliance of cloud usage
scraping/output/-3291290598934202599.txt
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This is some text inside of a div block. This is some text inside of a div block. ### GDPR compliance of cloud usage Comply with the General Data Protection Regulation (GDPR) by anonymizing your data when keeping it in the cloud. We guide you through the process so that you can focus on creating the most value from your data without legal risks. ‍ This is some text inside of a div block. This is some text inside of a div block. ### LLM training in regulated environments Ensure data privacy and mitigate risks of re-identification by anonymizing data used for training or fine-tuning Large Language Models (LLMs) on your specific enterprise data containing personal information. This is some text inside of a div block. This is some text inside of a div block. ## Tackling your complex use cases with a proven approach
scraping/output/-3291290598934202599.txt
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This is some text inside of a div block. This is some text inside of a div block. ## Tackling your complex use cases with a proven approach Our unique 5-step approach results in a solution tailored to your specific needs, while ensuring cost-efficiency and speed through the reuse of pre-built components. Our unique 5-step approach results in a solution tailored to your specific needs, while ensuring cost-efficiency and speed through the reuse of pre-built components. 1\. Define \- In tailored workshops, we together define the entities that need to be pseudonomized or anonymized 2\. Extract \- We extract the relevant entities (whether standard or custom entities) from your documents to detect the personal data of your needs. 3\. Randomize \- We pseudonomise or anonymise, depending on your specific needs, the detected personal data and replace each entity with statistically equivalent data to guarantee privacy and confidentiality
scraping/output/-3291290598934202599.txt
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4\. Visualize & Validate \- We offer a web application to visualize the outcomes to users and allow for user validation, integrated into your existing workflow 5\. Improve / Maintain - We maintain and support improvements of your pseudonymisation solution, enabling access to new features and capabilities and training of new models. Do you want to know more about our approach? Contact our experts ## Technical and functional specifications #### Use cases Standard supported On the roadmap or custom development Pseudonymisation Anonymisation N/A #### Languages Standard supported On the roadmap or custom development NL πŸ‡³πŸ‡± FR πŸ‡«πŸ‡· DE πŸ‡©πŸ‡ͺ ES πŸ‡ͺπŸ‡Έ EN πŸ‡ΊπŸ‡Έ #### Multi-cloud Standard supported On the roadmap or custom development Azure GCP AWS On Premise #### Entities Standard supported On the roadmap or custom development
scraping/output/-3291290598934202599.txt
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ES πŸ‡ͺπŸ‡Έ EN πŸ‡ΊπŸ‡Έ #### Multi-cloud Standard supported On the roadmap or custom development Azure GCP AWS On Premise #### Entities Standard supported On the roadmap or custom development Company ID Bank account number ID number Company name Country Date City Street Name Price IP address Phone number License plate To be announced ## Resources Content Management Platform Increasing efficiency and inspiration at FedNot by using advanced NLP techniques ML6 built an AI solution that makes these documents searchable in an intelligent way using advanced NLP techniques and pseudonymization. July 14, 2021 By Content Management Platform Public & Professional Services This is some text inside of a div block. 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
scraping/output/-3291290598934202599.txt
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scraping/output/-3291290598934202599.txt
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scraping/output/-3291290598934202599.txt
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scraping/output/8745283474802914343.txt
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scraping/output/8745283474802914343.txt
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Computer vision Hardware & sensors πŸš€ 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 # Bert Christiaens Computer vision Hardware & sensors Visit my LinkedIn profile Our know-how ## Related posts Placeholder tag Developing an AI solution for product photography: what we learned Reshaping Product Photography with generative AI August 23, 2023 By Bert Christiaens Placeholder tag
scraping/output/2712597742853724339.txt
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Placeholder tag Developing an AI solution for product photography: what we learned Reshaping Product Photography with generative AI August 23, 2023 By Bert Christiaens Placeholder tag Using generative AI for image manipulation: discrete absorbing diffusion models explained We review and compare the previous SOTA generative models, from autoencoders to autoregressive models, and work our way to the discrete absorbing diffusion model. August 11, 2022 By Bert Christiaens Generative AI Placeholder tag Potential applications of discrete diffusion models Applications of using VQGAN combined with Discrete Absorbing Diffusion models. August 30, 2022 By Bert Christiaens 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
scraping/output/2712597742853724339.txt
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scraping/output/2712597742853724339.txt
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πŸš€ 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 Video & demo ## Demo Wim De Vilder speaking Moroccan through faceswap (with VRT) tag View more Multivariate Time Series Similarity tag This demo is a result of research conducted in the field of multivariate time series similarities. The goal was to develop a fast and scalable system, able to unsupervisedly find the 5 most similar datapoints for a given reference. View more
scraping/output/-8837970835713973964.txt
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View more MLSox, our April Fool's sock-matching app tag View more Notebook on TensorFlow Data Validation tag View more Jeroom dancing through pose transfer (with VT4/GoPlay) tag View more How to spot a deepfake? tag View more HuggingFace Knowledge Graphs tag Knowledge graphs in particular are a great tool to flexibly represent complex information, but building one can be a time-consuming process, or can require lots of labelled examples. Enter REBEL designed to tackle these problems, and generate knowledge graphs directly from text. View more Infrastructure tag Our preferred infrastructure is GCP but we also embrace multi cloud and edge setups. View more Generating a face based on a text description tag View more dOCRtor tag Simulate common OCR errors for Dutch texts and use the finetune the character- based ByT5 model to correct them. View more 5 tips on how to work with imbalanced datasets tag
scraping/output/-8837970835713973964.txt
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dOCRtor tag Simulate common OCR errors for Dutch texts and use the finetune the character- based ByT5 model to correct them. View more 5 tips on how to work with imbalanced datasets tag Short tutorial (5 tips in a single document) on how to work with imbalanced datasets. View more Connexion tag Have you heard about Connexion? We are convinced of the API-first approach for microservices. It allows us to clearly separate definition and implementation, efficiently collaborate with the stakeholders and reduce applications developing costs. Connexion is a great Python framework and the best choice for our needs. We are very proud to be part of the community and to help move Connexion forward! View more CICD and automation tag
scraping/output/-8837970835713973964.txt
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scraping/output/-8837970835713973964.txt
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scraping/output/-8837970835713973964.txt
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πŸš€ 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 # Natural Language Processing Let your computers speak your language Learn moreContact us ## Make machines talk
scraping/output/-4043632892115440660.txt
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Jobs Team Working at ML6 Taal NLEN Contact us en defrNL Contact us # Natural Language Processing Let your computers speak your language Learn moreContact us ## Make machines talk Natural Language Processing (NLP) is a field of artificial intelligence that teaches computers to understand and generate human language. Powerful applications range from understanding and automatically answering customer queries to automated content moderation tools that can identify and remove harmful or inappropriate language. As we continue to explore the unlimited potential of NLP, we already have some innovative solutions in store: ## Semantic search Semantic search improves search accuracy by analyzing the relationships between words and concepts to deliver search results that better match the user's intent, rather than just matching keywords. ## Text classification and clustering
scraping/output/-4043632892115440660.txt
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## Text classification and clustering Text classification and clustering automatically categorize text based on its content, such as news articles or customer feedback, into predefined categories like topics or sentiment for better analysis and organization. ## Pseudonymisation Pseudonymisation replaces personal data with pseudonyms to protect privacy and comply with data protection regulations like GDPR. This allows the use of data for NLP tasks while reducing the risk of re-identification. ## Sentiment analysis Sentiment analysis involves automatically determining the sentiment of a piece of text, such as whether it is positive, negative, or neutral. This can be used to analyze customer feedback, social media posts, or product reviews. ## Document processing Document processing is the conversion of analog documents into digital format, which involves analyzing the layout, extracting information, and creating digital images for archiving or further use. ## Text generation
scraping/output/-4043632892115440660.txt
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## Text generation Text generation uses large language models to create new and understandable natural language output, such as weather reports, patient reports, image captions, or chatbots. ## Client cases Discover how our expertise in Hardware & Sensors leads businesses to success 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 ## Typical challenges Thanks to our linguistics and computer science expertise, we’re able to overcome functional and technical challenges NLP challenges such as: ## Ambiguity
scraping/output/-4043632892115440660.txt
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Computer vision ## Typical challenges Thanks to our linguistics and computer science expertise, we’re able to overcome functional and technical challenges NLP challenges such as: ## Ambiguity Natural language is subjective and ambiguous, which makes it difficult for machines to accurately process language due to multiple meanings that can depend on the context and speaker. ## Data quality and quantity Machine learning models, including NLP, need large amounts of high-quality data, such as diverse, well-labeled, and clean data, to reduce bias and increase accuracy and reliability. ## Model complexity NLP models are complex and require technical expertise and computational resources to tune many layers and parameters for optimal performance. ## Performance trade-offs NLP systems balance accuracy and speed. This means that more complex models are usually more accurate but slower, while simpler models are faster but less accurate. ## High level outline of the solution
scraping/output/-4043632892115440660.txt
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## High level outline of the solution Defining the problem The first step in using NLP is to define the problem that needs to be solved. This involves identifying the data to be analyzed, the questions to be answered, or the business objectives to be met. Data preparation After defining the problem, data needs to be collected and prepared for analysis or model training. This involves tasks such as tokenization, stemming, and removing stopwords. Choosing the NLP technique(s) Choose the appropriate NLP technique for the specific problem, such as sentiment analysis, topic modelling, or entity recognition. Train Model(s) Train model(s) on data using machine learning algorithms like Naive Bayes, Support Vector Machines, or Transformers depending on the problem. Not all solutions require a custom trained model. Testing and evealuation
scraping/output/-4043632892115440660.txt
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scraping/output/-4043632892115440660.txt
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scraping/output/-4043632892115440660.txt
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scraping/output/-4043632892115440660.txt
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scraping/output/6352475188966086889.txt
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scraping/output/6352475188966086889.txt
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Thank you! Your submission has been received! Oops! Something went wrong while submitting the form. Tag Showing 0 of 100 Placeholder 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 Placeholder tag 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 Placeholder tag 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 Placeholder tag Elevating excellence : accelerating each others talent
scraping/output/6352475188966086889.txt
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Implementing ML models' code promotion across environments using CI/CD pipelines. February 8, 2024 By Miro Goettler 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 β€˜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 Placeholder tag 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 Placeholder tag
scraping/output/6352475188966086889.txt
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January 11, 2024 By Michiel Van Lerbeirghe Placeholder tag 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 Placeholder tag 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 Placeholder tag 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 Placeholder tag Unlocking the Secrets of Life: AI Protein Models Demystified A brief overview of what proteins are, their characteristics and the applications of protein engineering.
scraping/output/6352475188966086889.txt
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Placeholder tag 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 Placeholder tag Whisper Deployment Decisions: Part I β€” Evaluating Latency, Costs, and Performance Metrics Optimizing OpenAI Whisper deployment in production. November 5, 2023 By Placeholder tag 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 Placeholder tag 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 Placeholder tag Webinar | Generative AI for corporate use - Fine tuning foundation models
scraping/output/6352475188966086889.txt
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October 10, 2023 By Placeholder tag Webinar | Generative AI for corporate use - Fine tuning foundation models In this webinar, we explored the benefits and challenges of the fine-tuning approach, discovering when to opt for fine-tuning, outlining the different techniques and offering practical guidelines for getting started with fine tuning. October 5, 2023 By Placeholder tag The EU Data Governance Act (DGA): an engine for innovation Discover the DGA's impact on data sharing, privacy, and innovation, and how ML6 solutions align with its objectives. October 2, 2023 By Michiel Van Lerbeirghe Placeholder tag Get Started with Machine Learning Faster Using Amazon SageMaker JumpStart Unlock Machine Learning with Amazon SageMaker JumpStart: A Comprehensive Guide September 28, 2023 By Placeholder tag Creating a Chatbot with Amazon Bedrock
scraping/output/6352475188966086889.txt
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Unlock Machine Learning with Amazon SageMaker JumpStart: A Comprehensive Guide September 28, 2023 By Placeholder tag Creating a Chatbot with Amazon Bedrock In an era where digital interactions dominate, chatbots play a crucial role in enhancing user experience. Amazon Bedrock emerges as a beacon of simplicity, allowing developers to harness the power of cutting-edge machine learning models. September 20, 2023 By 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
scraping/output/6352475188966086889.txt
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September 6, 2023 By Julie Plusquin Placeholder tag Amazon Bedrock: Building a solid foundation for Your AI Strategy Amazon recently announced their foundation model service: Amazon Bedrock. We got our hands on it as part of a limited preview. So, what’s in it for you? September 6, 2023 By 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 Placeholder tag Developing an AI solution for product photography: what we learned Reshaping Product Photography with generative AI August 23, 2023 By Bert Christiaens Placeholder tag Why Open AI's API models can't be forced to behave deterministically Understanding this behaviour and explaining why full control is unachievable August 22, 2023 By Michiel De Koninck Placeholder tag From hype to action: charting your AI course
scraping/output/6352475188966086889.txt
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Understanding this behaviour and explaining why full control is unachievable August 22, 2023 By Michiel De Koninck Placeholder tag From hype to action: charting your AI course With the growing maturity of AI models and techniques, the technical art of the possible is no longer the most important ceiling.Company specific boundaries will come from the strategy of a company and the values it adheres to. August 22, 2023 By Strategic Advice Placeholder tag How you can think about the future of LLMs The magical world of ChatGPT, troves of information, and levels of automation. August 10, 2023 By Jens Bontinck Placeholder tag Navigating Ethical Considerations: Developing and Deploying Large Language Models (LLMs) Responsibly Addressing ethical considerations in LLM development. August 8, 2023 By Placeholder tag Dynamic Pricing from a visual perspective Visualising Dynamic Pricing modelling to build insights August 3, 2023 By Placeholder tag
scraping/output/6352475188966086889.txt
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August 8, 2023 By Placeholder tag Dynamic Pricing from a visual perspective Visualising Dynamic Pricing modelling to build insights August 3, 2023 By Placeholder tag Deploy AI models in your OT environment and do it securely Securing AI deployment in OT environments. August 3, 2023 By Rob Vandenberghe Placeholder tag Unlocking the Power of Google's PaLM 2: Harnessing Generative Language Models for Business Success Discover the potential of Google's PaLM 2 as we harness generative language models for achieving business success. July 26, 2023 By Brent De Hauwere Placeholder tag Beating your competition in e-commerce with Dynamic Pricing Learn how dynamic pricing has revolutionized e-commerce through probabilistic statistics, iterative modeling, and customer feedback. July 24, 2023 By Placeholder tag AI can play a crucial role in the field of HR, but should we blindly follow?
scraping/output/6352475188966086889.txt
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July 24, 2023 By 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 Placeholder tag Smart energy: how to make your home battery smart and save money? Learn how to make your home battery smarter by connecting it to external inputs such as energy prices and weather. This can save up to 35% on electricity costs. July 16, 2023 By Juta Staes Placeholder tag Harnessing the Power of Foundation Models for your Business with LoRA Harness large foundation models affordably with LoRA technique for business applications. July 13, 2023 By Placeholder tag Predicting enzyme heat stability during Christmas break
scraping/output/6352475188966086889.txt
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Harness large foundation models affordably with LoRA technique for business applications. July 13, 2023 By Placeholder tag Predicting enzyme heat stability during Christmas break ML6 tackles enzyme stability prediction in Kaggle competition, pushing boundaries in life sciences. July 10, 2023 By Pieter Coussement Placeholder tag AI image generation without copyright infringement AI image generation without copyright infringement using a Creative Commons dataset. June 29, 2023 By Philippe Moussali Placeholder tag Battle of the Clouds: GCP vs Azure Comparing GCP and Azure from a developer's perspective. June 26, 2023 By Michiel De Koninck Placeholder tag PEFT (Parameter-Efficient Fine-Tuning) Different methods available for fine-tuning Large Language Models (LLMs) and when to use them. June 19, 2023 By Placeholder tag Developing AI Systems in the Foundation Model Age : An intro Introduction to Foundation Models and choosing the right one. June 7, 2023 By
scraping/output/6352475188966086889.txt
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June 19, 2023 By Placeholder tag Developing AI Systems in the Foundation Model Age : An intro Introduction to Foundation Models and choosing the right one. June 7, 2023 By Generative AI Placeholder tag Developing AI Systems in the Foundation Model Age : From MLOps to FMOps The structure of FMOps to optimize your foundation model workflows. June 7, 2023 By Generative AI Placeholder tag Stability AI Unveils Stable Animation SDK Unleash Astonishing Animations with Stability AI's Stable Animation SDK June 6, 2023 By Brent De Hauwere Generative AI Placeholder tag Google’s new Generative AI offering Google just released its Generative AI offering. What is it and what does it mean for you? June 5, 2023 By Placeholder tag Webinar | Generative AI for corporate use: How to get started with LLM Recording of the webinar on Generative AI for corporate use: How to get started with Large Language Models June 2, 2023 By Placeholder tag
scraping/output/6352475188966086889.txt
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Recording of the webinar on Generative AI for corporate use: How to get started with Large Language Models June 2, 2023 By Placeholder tag How specialized AI models can support designers in their daily work Generative AI as your co-pilot: specialized AI models and their impact on design innovation. May 25, 2023 By Generative AI Placeholder tag Optical 3D Acquisition Methods: A Comprehensive Guide [Part 2] Discover 3D optical acquisition methods: Stereo Vision, Structured Light, Time of Flight, and LiDAR. May 21, 2023 By Francisco Pereira Placeholder tag 3D Computer Vision: Unlocking the Third Dimension [Part 1] Discover the power of 3D computer vision and its impact on industries. Learn about advantages, applications, and trends. May 19, 2023 By Francisco Pereira In-Line Quality Inspection & Process Steering Placeholder tag Open source catching up to OpenAI Exploring Recent Developments in Open Source LLMs and Their Implications. May 19, 2023 By
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In-Line Quality Inspection & Process Steering Placeholder tag Open source catching up to OpenAI Exploring Recent Developments in Open Source LLMs and Their Implications. May 19, 2023 By Michiel De Koninck Placeholder tag The Compliance-Friendly Guide to Using ChatGPT and other GPT models In this blogpost we explore the different options out there around usage of ChatGPT and other GPT models. For every option we we determine the risks around compliance, security and privacy. May 17, 2023 By Rob Vandenberghe Generative AI Placeholder tag Doubling down on learning and innovation In this article, we delve into the mind of Kaat Van Doren, Head of Talent & Culture at ML6, as she explores the role of curiosity within the organization and her own professional life at a podcast sit down with Cevora. May 17, 2023 By Kaat Van Doren Placeholder tag Low Rank Adaptation: A Technical Deep Dive
scraping/output/6352475188966086889.txt
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May 17, 2023 By Kaat Van Doren Placeholder tag Low Rank Adaptation: A Technical Deep Dive LoRA is revolutionizing fine-tuning for large AI models, enabling efficient adaptation without straining resources. Explore its principles, effectiveness, and impact on the open-source community. May 17, 2023 By Generative AI Placeholder tag AI Act Amendments (Joint committee vote 11/5/23) Discover key amendments proposed in the EU AI Act, including banned and high- risk applications, regulations for foundation models and generative AI, and the impact on open-source components. Prepare for upcoming AI regulations and their implications. May 17, 2023 By Placeholder tag State of the LLM: Unlocking Business Potential with Large Language Models Explore the potential of large language models (LLMs) for businesses: models, tooling, challenges, and applications May 16, 2023 By Generative AI Placeholder tag How to label your way to accurate Automatic Speech Recognition (ASR)
scraping/output/6352475188966086889.txt
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May 16, 2023 By Generative AI Placeholder tag How to label your way to accurate Automatic Speech Recognition (ASR) This blog post explores the process of labelling speech data for Automatic Speech Recognition (ASR). May 10, 2023 By Placeholder tag Leveraging LLMs on your domain-specific knowledge base We'll cover the basics of language models and how they can be trained on your organization's data to improve search accuracy, automate tagging, and even generate new content May 9, 2023 By Michiel De Koninck Generative AI Placeholder tag Unpacking AutoGPT: A Critical Analysis of the Autonomous Anatomy by Jens Jens Bontinck, Head of Delivery & Advice, shares his view on the status quo of AutoGPT. In this blogpost, you will not learn how to run it, nor will I go into details of the technical solution. Therefore, if you are a product owner, a functional analist, or a commercial enthousiast, this should be right up your alley. Happy to read your feedback! April 18, 2023
scraping/output/6352475188966086889.txt
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April 18, 2023 By Jens Bontinck Generative AI Placeholder tag ChatGPT & GPT-4: Gimmicks or game-changers? Discover the truth behind the hype of ChatGPT & GPT-4. Read about the potential game-changing capabilities of GPT-4 or if it's just another gimmick. April 14, 2023 By Generative AI Placeholder tag Reflexion is all you need? Jens Bontinck shares his view on a recent technique called Reflexion April 13, 2023 By Jens Bontinck 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 How to effectively label images for computer vision projects ? In this blogpost we cover the most important aspects of tackling a computer vision problem by labeling your own data. March 22, 2023 By Thomas Janssens Placeholder tag Improving Tennis Court Line Detection with Machine Learning
scraping/output/6352475188966086889.txt
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March 22, 2023 By Thomas Janssens Placeholder tag Improving Tennis Court Line Detection with Machine Learning Improving tennis court line detection using machine learning for near real- time predictions. March 13, 2023 By Jules Talloen Placeholder tag Who spoke when: Choosing the right speaker diarization tool A guide on how to choose the right speaker diarization framework for your use case. March 10, 2023 By Philippe Moussali 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 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.
scraping/output/6352475188966086889.txt
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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 Placeholder tag Welcome to the multi-language-pipeline universe Apache Beam has lots of I/O connectors available, but not all connectors are in your language of choice. December 15, 2022 By Andres Vervaecke Placeholder tag A first impression of Google Cloud’s new Visual Inspection AI tool Key take-aways of Google Cloud’s new Visual Inspection AI tool. December 14, 2022 By JΓ©rΓ©my Keusters Placeholder tag Dynamic pricing in practice Explore the practical implementation of dynamic pricing, its effectiveness, challenges, and the use of Thompson sampling to optimize prices. December 12, 2022 By Placeholder tag AI for the green transition - Applications and use cases [Part 1]
scraping/output/6352475188966086889.txt
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December 12, 2022 By Placeholder tag AI for the green transition - Applications and use cases [Part 1] Examples of AI use cases for climate action and clean energy and sustainable industry and agriculture. December 1, 2022 By Placeholder tag AI for the green transition - Applications and use cases [Part 2] Examples of AI use cases for transport & mobility and environment & oceans. December 1, 2022 By Placeholder tag Webinar | Sustainable AI: What can be done today Discover how AI is driving sustainability in energy systems and manufacturing, with pragmatic use cases for decarbonization and waste reduction. November 24, 2022 By Jeffrey Hagen Placeholder tag A Threatmap for Log4Shell attacks on Google Cloud Log4Shell got security teams around the world on their toes In this blog post, we take a look at how and why we built a live threatmap on top of Google Cloud to detect and visualize cyber attacks and log4j exploits. November 15, 2022 By Placeholder tag
scraping/output/6352475188966086889.txt
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November 15, 2022 By Placeholder tag Challenges for Sign Language Translation Identified gaps in the architectures for SLR/SLT by considering and exploiting existing state-of-the-art architectures. November 7, 2022 By Placeholder tag Semantic Search: Intro and business applications Semantic search from a business point of view and how it can bring value to your business processes. November 7, 2022 By Sven Rymenans Content Management Platform Placeholder tag Why every AI project needs an MLOps ecosystem in place This blogpost covers the need for MLOps and how it helps to manage the complexity of machine learning models and deployments. Secondly, it addresses the business implications of implementing MLOps as a proper ecosystem. Lastly, the challenges of building an MLOps capability are covered to ensure the success of your machine learning initiatives at your organisation. November 1, 2022 By Placeholder tag Vertex AI is All You Need
scraping/output/6352475188966086889.txt
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November 1, 2022 By Placeholder tag Vertex AI is All You Need Deploying a Scikit-learn Pipeline + LightGBM Classifier Model on Vertex AI for Explainable Online Predictions October 26, 2022 By Placeholder tag Webinar | Hybrid AI: successfully combining expert knowledge with ML models Explore the field of Hybrid AI, combining machine learning and expert rules for optimal outcomes. Learn its relevance, integration methods, and real-world examples. October 17, 2022 By Matthias Feys Placeholder tag Hybrid Machine Learning: Marrying NLP and RegEx Common hybrid NLP design patterns and look at example situations of when you should opt for which pattern. October 5, 2022 By Placeholder tag Know your unknowns: a short primer on uncertainty in machine learning How to approach uncertainty in machine learning : forecasting, regression and classification models. September 19, 2022 By Thomas Janssens Placeholder tag
scraping/output/6352475188966086889.txt
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How to approach uncertainty in machine learning : forecasting, regression and classification models. September 19, 2022 By Thomas Janssens Placeholder tag Triton Ensemble Model for deploying Transformers into production When deploying a Transformer model, the complexity increases since besides the model one also needs to consider how to deploy the tokenizer. Is it best to deploy the tokenizer on the server or to have the end users handle it and what is the best way to deploy the tokenizer along with the model on the server so that inference latency and costs are minimized? September 6, 2022 By Placeholder tag Unblocking ML use cases that lack (usable) data What do we do though if we don’t have any or enough usable data yet to get started? One obvious option is to collect (more) data. However, today we want to go beyond that. We will look at three other dimensions that can be relevant to unblock ML use cases: data protection, external data, and synthetic data.
scraping/output/6352475188966086889.txt
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September 6, 2022 By Matthias Feys Placeholder tag Potential applications of discrete diffusion models Applications of using VQGAN combined with Discrete Absorbing Diffusion models. August 30, 2022 By Bert Christiaens Placeholder tag Using generative AI for image manipulation: discrete absorbing diffusion models explained We review and compare the previous SOTA generative models, from autoencoders to autoregressive models, and work our way to the discrete absorbing diffusion model. August 11, 2022 By Bert Christiaens Generative AI Placeholder tag Can AI generate truly original logos? Developing a DALL-E-based logo generator. August 2, 2022 By Placeholder tag Hallucination Detection Detecting errors in transformers generated abstractive summaries July 13, 2022 By Matthias Cami Placeholder tag Why a tech radar will not make you more innovative How we tackle information management at ML6 and how our tech radar helps us do this. July 6, 2022 By
scraping/output/6352475188966086889.txt
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By Matthias Cami Placeholder tag Why a tech radar will not make you more innovative How we tackle information management at ML6 and how our tech radar helps us do this. July 6, 2022 By Georges LorrΓ© Placeholder tag The art of pooling embeddings In this blogpost we will discover the complexity of pooling that hides behind its apparent simplicity. June 22, 2022 By Placeholder tag Decoding Sentence Encoders Learn more about the similarities and differences between transformers and sentence-transformers. June 13, 2022 By Placeholder tag Webinar | Data centric AI: improving data to get to better results Learn how to improve training data quality and maximize AI model accuracy in our webinar on data-centric AI, with practical tips and advanced approaches. June 9, 2022 By Matthias Feys Placeholder tag Using Synthetic Data to boost the Performance of your Object Detection Model A practical guide to generating artificial data using Unity Perception. May 22, 2022 By
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Placeholder tag Using Synthetic Data to boost the Performance of your Object Detection Model A practical guide to generating artificial data using Unity Perception. May 22, 2022 By Jules Talloen Placeholder tag BERT is eating your cash: quantization and ONNXRuntime to save money Insights in how to run your Transformer in the cloud in a cost-effective way. May 18, 2022 By Placeholder tag Unlocking the full potential of data How to maximise the value of data to drive better performance of ML models while minimising the cost of doing so. May 17, 2022 By Placeholder tag More things in heaven and earth Assessing Detic for object detection of thousands of classes. May 17, 2022 By Matthias Cami Placeholder tag NVIDIA Deepstream Quickstart
scraping/output/6352475188966086889.txt
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By Placeholder tag More things in heaven and earth Assessing Detic for object detection of thousands of classes. May 17, 2022 By Matthias Cami Placeholder tag NVIDIA Deepstream Quickstart In this blogpost we’ll set up a docker container to run an NVIDIA deepstream pipeline on a GPU or a Jetson and show you some tricks to get the most out of it! Deepstream’s documentation, guides and sample projects are few and far between, so this article aims to be a reference to get you from Deepstream zero to Deepstream-hey-I-can-make -a-pipeline-now and beyond! May 14, 2022 By Jules Talloen Placeholder tag EU AI Act : what you need to know Abbreviated version of the AI act proposal : what you need to know. May 12, 2022 By Placeholder tag Why we open sourced two Dutch summarization datasets Open Sourcing Dutch Summarization Datasets for Transfer Learning May 12, 2022 By Placeholder tag Semantic search: A practical Overview
scraping/output/6352475188966086889.txt
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scraping/output/6352475188966086889.txt
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A practical guide to image-based anomaly detection using Anomalib A short introduction to unsupervised anomaly detection and how to apply it. March 31, 2022 By Sebastian Wehkamp Placeholder tag Building a coral segmentation model using sparse data In light of a partnership with the organizations Reef Support and Fruitpunch AI, we designed an image segmentation algorithm. The main challenge here was the way the data is labelled, containing sparse pixel annotations created by domain experts. The blogpost covers on a high level how we solved this challenge, and how the challenge will be going forward. March 28, 2022 By Placeholder tag 3D object detection in the real world In this blogpost, we look at object detection using 3D β€˜depth’ sensor data. We explain how two recent approaches, VoteNet and 3DETR, tackle the problem and benchmark their performance out of the box and after fine-tuning. March 15, 2022 By Placeholder tag
scraping/output/6352475188966086889.txt
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March 15, 2022 By Placeholder tag Webinar | Explainable AI explained by Google and ML6 Discover the importance of Explainable AI (XAI) in understanding machine learning models and explore practical case studies, including Google's Vertex Explainable AI tool. March 9, 2022 By Placeholder tag Need for Speed: JAX Train neural networks faster with JAX on TPUs March 2, 2022 By Jasper Van den Bossche Placeholder tag Training your neural network ten times faster using Jax on a TPU Introduction blogpost to JAX : how it works, how it is different from Tensorflow/Pytorch and why we think every machine learning engineer should add it to its arsenal of tools. February 3, 2022 By Jasper Van den Bossche 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
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scraping/output/6352475188966086889.txt
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scraping/output/6352475188966086889.txt
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scraping/output/-2077808234674879873.txt
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Yannik is responsible for ML6’ sales activities in the German Market. He identifies business opportunities, drives ML-projects and maintains relationships with key accounts across all industries. ‍ Having graduated in Business & Economics at the WU Vienna, Yannik thinks business. He aims to fully understand the client’s situation in order to provide the optimal help. His professional experience in different industries gives him a clear advantage. ‍ In his free-time he listens to music and likes reading a nice book. On the weekends you will probably find him dancing and on the tennis court. Visit my LinkedIn profile Our know-how ## Related posts 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
scraping/output/-2077808234674879873.txt
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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: [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 Mission How we workResponsible AICareers Copyright 2024 Privacy Notice
scraping/output/-2077808234674879873.txt
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scraping/output/-7496122496336218647.txt
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Thank you! Your submission has been received! Oops! Something went wrong while submitting the form. Share this post At ML6, we are all passionate about technology and motivated by the potential benefits AI can bring to our clients and to society. We are however also aware that AI can carry certain ethical concerns and risks, which need to be addressed. Responsibility & Ethical AI has always been an important topic for ML6. In the past, we have taken many steps already to define what trustworthy AI means for us, what our principles are and which applications we pledge to not develop. With this framework in mind, we took a big step to structurally move our principles into practice. The result was the creation of our Ethical AI Unit at ML6, a group of ML6 agents dedicated to the topic of Ethical AI. ‍ ### Why did we create the Ethical AI unit? ‍
scraping/output/-7496122496336218647.txt
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‍ ### Why did we create the Ethical AI unit? ‍ As mentioned above, considering Ethics within all that we do is important to us. It fosters trust in our solutions and in our mission both with clients as well as with our employees. The Ethical AI unit is our way to stay at the forefront of the rapidly evolving field, and to institutionalise our ethical principles. It enables us to advise our clients on ethical risks and upcoming regulatory requirements, and to integrate best practices in our processes and in the design and development of all our AI solutions. Our goal is to maximise the value of AI while identifying and mitigating potential risks. ‍ ### What does the unit do and who is part of it? ‍
scraping/output/-7496122496336218647.txt
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‍ ### What does the unit do and who is part of it? ‍ The Ethical AI unit at ML6 has two main purposes. The first purpose of the Ethics unit is to serve as a β€œsounding board” to discuss ethically sensitive projects that emerge during the sales process. Given that there is often no β€œone right answer” to ethical questions, it is important to discuss these cases in a diverse group, taking different perspectives into consideration. This is how the Ethics unit enables ML6 to identify potential ethical risks early on, and define risk mitigation measures to take during design and development of the AI solution.
scraping/output/-7496122496336218647.txt
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The second purpose of the unit is in keeping up with the evolving field of Ethical AI and implementing ethical and legal considerations and best practices into our processes at ML6. This includes preparing for and giving feedback on the upcoming EU AI Act and other relevant regulation, researching topics such as bias & fairness, implementing best practices, and creating awareness internally and externally on the topic. The Ethical AI unit has specifically been created with diversity in mind β€” including people from all different units at ML6 β€” from Engineers to Sales to Management -, different geographical locations, backgrounds and genders. This is strongly aligned with the two purposes of the unit and enables us to take different perspectives and knowledge into account. ‍ ### What did we learn so far & what tips would we give to other organisations? ‍
scraping/output/-7496122496336218647.txt
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‍ ### What did we learn so far & what tips would we give to other organisations? ‍ Since starting the Ethical AI unit, we have learned a lot. Let us share a few practical tips on what to watch out for if you want to start structurally integrating Ethical considerations into your organisation and processes: ‍ * Get leadership buy-in. Whether it’s creating a unit, or putting a stronger focus on ethics and responsibility β€” it all starts with buy-in from leadership. Ethical AI needs coordination across different units or departments, with sometimes conflicting incentives. Making sure that your leadership is on board can help overcome potential hurdles. ‍
scraping/output/-7496122496336218647.txt
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* Focus on the value Ethical AI can bring to your organisation. Trust in and adoption of AI, higher motivation for employees, mitigation of ethical and legal risks β€” these are just some examples of positive effects implementing ethics structurally in your company can have. Articulate clearly what value Ethical AI can bring to your organisation. ‍ * Communicate transparently about ethics. There is often no right answer when it comes to ethics. Having clear processes in place, discussing the topic, and raising awareness might sometimes be even more important than a concrete decision taken. ‍ * Define processes together with the people from concerned departments. They know their processes best and need to be committed to the new ways of working. Ask for feedback and actively involve a diverse group of people. Also leverage existing structures as much as possible β€” modifying existing processes is often easier than inventing new ones. ‍
scraping/output/-7496122496336218647.txt
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‍ This is in a nutshell what our new Ethical AI unit is and what we learned in the process of building it up. Reach out if you have questions or want to talk about Ethical AI! ‍ ## 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 Accelerating businesses with AI technology & experts Contact:
scraping/output/-7496122496336218647.txt
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scraping/output/-7496122496336218647.txt
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scraping/output/5757173050822662678.txt
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scraping/output/5757173050822662678.txt
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scraping/output/6950471906497477906.txt
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scraping/output/6950471906497477906.txt
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Natural language processing Structured Data Computer vision πŸš€ 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 Keypoint # Accelerating Keypoint's Digital Real Estate Management Platform with AI ## Impact
scraping/output/9065848743917767112.txt
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Careers Jobs Team Working at ML6 Taal NLEN Contact us en defrNL Contact us All client cases Keypoint # Accelerating Keypoint's Digital Real Estate Management Platform with AI ## Impact By embedding artificial intelligence in its platforms, Keypoint is able to reduce fraud cases and will guarantee all home repairs are completed properly and at a fair price. Next to that, water damage claims will be prevented through the implementation of smart sensors and AI. ## Intro to the customer Keypoint is the digital assistant for all parties with decision-making power on repairs, maintenance and building management.
scraping/output/9065848743917767112.txt
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## Intro to the customer Keypoint is the digital assistant for all parties with decision-making power on repairs, maintenance and building management. Its digital collaboration platform links all the parties involved and makes claim management highly automated and efficient. A claim becomes digital, transparent and relevant at the right time for everyone. To this end, Keypoint also builds numerous links with property management software for its respective target markets. In addition, Keypoint manages its own repair network across Belgium that can repair all types of assignments, both within and outside the insurance sector. ‍ ## Challenge
scraping/output/9065848743917767112.txt
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‍ ## Challenge In a rapidly growing property market, both insurers and residents require professional, 24/7 ad-hoc insurance services that provide reliable and fast solutions. With Connect, Keypoint provides a unique platform that brings all involved parties together. Through Repair, Keypoint specialises in the organisation of repairs and maintenance of buildings across Belgium. However, Keypoint sees an increase in the need for faster responses (e.g. claim prevention) and more optimisation (e.g. estimate checks through picture recognition and pricing databases). Therefore, Keypoint considered the next step for the evolution of the Keypoint’s Connect platform to be the integration of Artificial Intelligence and linking it with smart sensors (IoT). ‍ Through the implementation of AI, Keypoint aims to make its platform more effective for:
scraping/output/9065848743917767112.txt
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‍ Through the implementation of AI, Keypoint aims to make its platform more effective for: * Repairs pricing: With the implementation of AI, Keypoint can check estimates based on historical data (e.g. similar claims in the past) and via the creation of a pricing database (automatic database creation via approved estimates) compared with the claim pictures and received estimate. * Fraud detection: In Flanders about four in 1,000 cases in 2017 turned out to be fraudulent. More than one in three fraud cases involved exaggerated or invented claims of the extent of damage or loss. * Claim prevention: Preventing claims before they occur: based on Assuralia’s statistics, around 40%+ of property claims are related to β€˜water’. By applying anomaly detection on water sensor data Keypoint aims to detect leaks and receive notifications through its platform. ‍
scraping/output/9065848743917767112.txt
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‍ β€œML6 really supported us to accelerate the adoption of artificial intelligence in our platform. From helping us securing the necessary innovation funds, to exploring different use cases (based on various data sources, eg. images, text, tabular data) and setting up the necessary pipelines and APIs.” Managing Director at Keypoint By Keypoint Testimonial - Jonathan Goderis ## Solution ML6 accelerated the adoption of AI in Keypoint's platforms by focussing on two technical challenges: 1. Automatic processing of repair offers (NLP) 2. Fraud detection on claims images and weather data (Computer vision and Structured Data) ‍ #### Automatic processing of repair offers
scraping/output/9065848743917767112.txt
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1. Automatic processing of repair offers (NLP) 2. Fraud detection on claims images and weather data (Computer vision and Structured Data) ‍ #### Automatic processing of repair offers Based on object detection, OCR techniques, text classification and named entity recognition, we were able to build a pipeline that could parse a multitude of offers from different repair vendors in a structured manner. This way the prices (and other information) from offers can be automatically extracted and compared, line item by line item, with market prices for that line item. This way a price estimator was built to indicate whether an offer is overpriced or not. ‍ #### Fraud detection on claims images and weather data
scraping/output/9065848743917767112.txt
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‍ #### Fraud detection on claims images and weather data Most claims contain one or more pictures of the damage. These are mostly taken by the customer and provide evidence for the damage claim. Based on the type of damage (known in the claim), we could verify through computer vision techniques whether or not the content of the picture was in line with it. Next to that, we provided Google Streetview Images for the address of the claim. This helped the human in the loop to verify whether the requested claim was in line with the sort of house, roof, windows, etc. and whether any damage already existed before the date in the claim. For storm damage, we can detect fraudulent claims based on weather information and the time and location where the damage occurred. Also, we look at the number of similar claims in the same time period, to verify whether the claim is justified. Outliers are evaluated case by case. ‍ ‍ By ## Results
scraping/output/9065848743917767112.txt
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‍ ‍ By ## Results By embedding artificial intelligence in its platforms, Keypoint is able to reduce fraud cases and will guarantee all home repairs are completed properly and at a fair price. Next to that, water damage claims will be prevented through the implementation of smart sensors and AI. 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 Mission How we workResponsible AICareers Copyright 2024 Privacy Notice
scraping/output/9065848743917767112.txt
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Computer vision πŸš€ 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 May 17, 2022 # More things in heaven and earth Contributors Matthias Cami Machine Learning 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! Oops! Something went wrong while submitting the form. Share this post
scraping/output/6475038328432950049.txt
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Thank you! Your submission has been received! Oops! Something went wrong while submitting the form. Share this post Object detection models are typically trained to detect only a limited number of object classes. The widely used MS COCO (Microsoft Common Objects in Context) dataset for example only contains eighty classes, ranging from person to toothbrush. Extending this set is cumbersome and typically involves collecting a range of images for each object class to be detected, labeling them, and fine-tuning an existing model. But what if there were an easy way to teach a model new categories out of the box? Even extend it with thousands of categories? That is exactly what the recently published Detic model promises. In this blogpost we discuss how this new model works and assess its strengths and weaknesses by testing it against a number of potential use cases. ### Detic: detecting twenty-thousand classes
scraping/output/6475038328432950049.txt
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### Detic: detecting twenty-thousand classes Object detection is composed of two sub-problems: finding the object (localization) and identifying it (classification). Conventional methods couple these sub-problems and consequently rely on box labels for all classes. However, detection datasets remain much smaller in size and amount of object classes (vocabulary), compared to image classification datasets. The latter have richer vocabularies because datasets are larger and easier to collect. Incorporating the image classification data in the training of the classifiers of a detector allows expanding the vocabulary of detectors: from hundreds to tens of thousands of concepts! Detic [1] (Detector with image classes) does exactly this, by using image-level supervision in addition to detection supervision. The model also decouples the localization and classification sub- problems. Illustration of how a CLIP model works, original image from the CLIP paper [2]
scraping/output/6475038328432950049.txt
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Detic is the first known model that trains a detector on all twenty-one- thousand classes of the ImageNet dataset. Consequently, it has the capability of detecting a large number of objects, making it a very suitable baseline model for a wide variety of tasks. Furthermore, to generalise to larger vocabularies, Detic leverages open-ended CLIP [2] (Contrastive Language-Image Pre-training) embeddings. CLIP jointly trains an image encoder and a text encoder to predict the correct pairings of a batch of (image, text) training examples. At test time, the learned text encoder embeds the names or descriptions of the target dataset’s classes. By using embeddings of words instead of a fixed set of object categories, a classifier is created that can recognize concepts without having explicitly seen any examples of them. This is also called zero-shot learning. My colleague Juta has a really neat write- up on the subject of CLIP and multimodal AI in general, go check it out!
scraping/output/6475038328432950049.txt
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Approach overview, image taken from the original paper [1] Combining aforementioned methods, training of the model is done on a mix of detection data and image-labeled data. When using detection data, Detic uses standard detection losses to train the classifier (W) and the box prediction branch (B) of a detector. When using image-labeled data, only the classifier is trained by using a modified classification loss. The classification loss trains the features extracted from the largest-sized proposal predicted by the network. Similar to CLIP, the model can use a custom vocabulary of class embeddings at prediction time to generalise to different classes, without retraining. ### Examples #### (a) analyzing a social media feed
scraping/output/6475038328432950049.txt
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### Examples #### (a) analyzing a social media feed Imagine you would like to automatically analyze a social media feed for occurrences of certain objects. Object detectors trained for specific use cases in sports, animal detection, healthcare etc. would not get desired results in this context. This is because social media posts are not limited to certain topics and these images contain a wide variety of possible objects. However, since traditional detectors have a rather small vocabulary, you are constrained to how many categories you can look for. This finite set of classes also limits the analysis you can accomplish on these types of images. Since Detic is able to significantly expand the vocabulary, it presents itself as a good solution to this problem.
scraping/output/6475038328432950049.txt
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