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Talent & Culture 🫶 * Multiple locations
* Frontend Engineer
Advisory & Delivery unit 💻 * Multiple locations
* Data Engineer
Advisory & Delivery unit 💻 * Office Berlin 📍
* Junior Talent Partner
Talent & Culture 🫶 * Multiple locations
* Alliance Manager - Azure
Business Development unit 🤝 * Multiple locations
* Innovation Lead
Incubation Unit 💡 * Multiple locations
* Data Engineering Consultant
Advisory & Delivery unit 💻 * Multiple locations
* Senior Software Engineer
Advisory & Delivery unit 💻 * Multiple locations
* AI Client Executive
Business Development unit 🤝 * Multiple locations
* AI Client Partner
Business Development unit 🤝 * Multiple locations
* AI Project Manager
Advisory & Delivery unit 💻 * Multiple locations
* Senior Data Engineer
Advisory & Delivery unit 💻 * Multiple locations
* Senior Machine Learning Engineer
Advisory & Delivery unit 💻 * Multiple locations
* Machine Learning Engineer | scraping/output/-1004585763649522392.txt | [
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* Senior Data Engineer
Advisory & Delivery unit 💻 * Multiple locations
* Senior Machine Learning Engineer
Advisory & Delivery unit 💻 * Multiple locations
* Machine Learning Engineer
Advisory & Delivery unit 💻 * Multiple locations
* Internship
Multiple locations
* < enter fancy job title here >
Multiple locations
## Are you ready to apply? 🚀
#### 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. | scraping/output/-1004585763649522392.txt | [
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Applicant tracking system by Teamtailor | scraping/output/-1004585763649522392.txt | [
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Infrastructure | scraping/output/-2864730860114532062.txt | [
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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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### 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.
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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.
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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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## 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 🇳🇱
FR 🇫🇷
DE 🇩🇪
ES 🇪🇸
EN 🇺🇸
#### 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 🇪🇸
EN 🇺🇸
#### 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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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
Questions?
# Contact our experts
Discover how our solution can support you in your complex anonymisation or
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Accelerating businesses with AI technology & experts | scraping/output/-3291290598934202599.txt | [
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## 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
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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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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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Sign up | scraping/output/-8837970835713973964.txt | [
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# Natural Language Processing
Let your computers speak your language
Learn moreContact us
## Make machines talk | scraping/output/-4043632892115440660.txt | [
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] |
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Working at ML6
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# 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.
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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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] |
Testing and evealuation
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approach is working.
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optimized for cost and inference time. Continuous monitoring is needed to
detect performance changes that call for action.
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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 ‘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
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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 — 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
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Placeholder tag
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In an era where digital interactions dominate, chatbots play a crucial role in
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AI internships at ML6: nine domains to steer your career towards
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certainly don't come as a surprise. There is a lot coming at you. For this
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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
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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
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Placeholder tag
Navigating Ethical Considerations: Developing and Deploying Large Language
Models (LLMs) Responsibly
Addressing ethical considerations in LLM development.
August 8, 2023
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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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] |
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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
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] |
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Placeholder tag
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By | scraping/output/6352475188966086889.txt | [
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In-Line Quality Inspection & Process Steering
Placeholder tag
Open source catching up to OpenAI
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Placeholder tag
The Compliance-Friendly Guide to Using ChatGPT and other GPT models
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Generative AI
Placeholder tag
Doubling down on learning and innovation
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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.
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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
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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
Jérémy 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
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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
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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
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November 15, 2022
By
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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
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Semantic Search: Intro and business applications
Semantic search from a business point of view and how it can bring value to
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Content Management Platform
Placeholder tag
Why every AI project needs an MLOps ecosystem in place
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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
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Vertex AI is All You Need
Deploying a Scikit-learn Pipeline + LightGBM Classifier Model on Vertex AI for
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Webinar | Hybrid AI: successfully combining expert knowledge with ML models
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for optimal outcomes. Learn its relevance, integration methods, and real-world
examples.
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Hybrid Machine Learning: Marrying NLP and RegEx
Common hybrid NLP design patterns and look at example situations of when you
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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
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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
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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 Lorré
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
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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
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NVIDIA Deepstream Quickstart
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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
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EU AI Act : what you need to know
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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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] |
Why we open sourced two Dutch summarization datasets
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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 ‘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
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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
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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?
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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?
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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 “sounding board” to discuss ethically sensitive
projects that emerge during the sales process. Given that there is often no
“one 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 —
including people from all different units at ML6 — 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?
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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 — 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.
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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 — 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 — modifying existing processes is often easier than inventing new ones.
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] |
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PressML6 moet Justitie transparanter en efficiënter maken
September 20, 2023
# ML6 moet Justitie transparanter en efficiënter maken
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Keypoint
# Accelerating Keypoint's Digital Real Estate Management Platform with AI
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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 ‘water’. By applying anomaly detection on water sensor data Keypoint aims to detect leaks and receive notifications through its platform.
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“ML6 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
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Privacy Notice | scraping/output/9065848743917767112.txt | [
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May 17, 2022
# More things in heaven and earth
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Matthias Cami
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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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