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October 10, 2023
By
Blogpost
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
Blogpost
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
Get Started with Machine Learning Faster Using Amazon SageMaker JumpStart
Unlock Machine Learning with Amazon SageMaker JumpStart: A Comprehensive Guide
September 28, 2023
By
Blogpost
Creating a Chatbot with Amazon Bedrock | scraping/output/-6672370828350515768.txt | [
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Unlock Machine Learning with Amazon SageMaker JumpStart: A Comprehensive Guide
September 28, 2023
By
Blogpost
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
Blogpost
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
Blogpost | scraping/output/-6672370828350515768.txt | [
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September 6, 2023
By
Julie Plusquin
Blogpost
Amazon Bedrock: Building a solid foundation for Your AI Strategy
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got our hands on it as part of a limited preview. So, what’s in it for you?
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August 22, 2023
By
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Blogpost
From hype to action: charting your AI course | scraping/output/-6672370828350515768.txt | [
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] |
Understanding this behaviour and explaining why full control is unachievable
August 22, 2023
By
Michiel De Koninck
Blogpost
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.
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Addressing ethical considerations in LLM development.
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Visualising Dynamic Pricing modelling to build insights
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Deploy AI models in your OT environment and do it securely | scraping/output/-6672370828350515768.txt | [
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] |
July 24, 2023
By
Blogpost
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
Blogpost
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
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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
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Predicting enzyme heat stability during Christmas break | scraping/output/-6672370828350515768.txt | [
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] |
Harness large foundation models affordably with LoRA technique for business
applications.
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By
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Predicting enzyme heat stability during Christmas break
ML6 tackles enzyme stability prediction in Kaggle competition, pushing
boundaries in life sciences.
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AI image generation without copyright infringement using a Creative Commons
dataset.
June 29, 2023
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Comparing GCP and Azure from a developer's perspective.
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Different methods available for fine-tuning Large Language Models (LLMs) and
when to use them.
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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
Blogpost | scraping/output/-6672370828350515768.txt | [
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] |
June 2, 2023
By
Blogpost
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The Compliance-Friendly Guide to Using ChatGPT and other GPT models | scraping/output/-6672370828350515768.txt | [
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] |
Exploring Recent Developments in Open Source LLMs and Their Implications.
May 19, 2023
By
Michiel De Koninck
Blogpost
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
Blogpost
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
Blogpost
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. | scraping/output/-6672370828350515768.txt | [
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Blogpost
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
Blogpost
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
Opinion
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
By
Jens Bontinck
Generative AI
Insight | scraping/output/-6672370828350515768.txt | [
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April 18, 2023
By
Jens Bontinck
Generative AI
Insight
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
Opinion
Reflexion is all you need?
Jens Bontinck shares his view on a recent technique called Reflexion
April 13, 2023
By
Jens Bontinck
Blogpost
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
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
Blogpost
Improving Tennis Court Line Detection with Machine Learning
Improving tennis court line detection using machine learning for near real-
time predictions.
March 13, 2023
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Blogpost
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
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Blogpost
Who spoke when: Choosing the right speaker diarization tool
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case.
March 10, 2023
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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
Blogpost | scraping/output/-6672370828350515768.txt | [
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] |
January 3, 2023
By
Julien Schuermans
Blogpost
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.
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Key take-aways of Google Cloud’s new Visual Inspection AI tool.
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Explore the practical implementation of dynamic pricing, its effectiveness,
challenges, and the use of Thompson sampling to optimize prices.
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Examples of AI use cases for climate action and clean energy and sustainable
industry and agriculture.
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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. | scraping/output/-6672370828350515768.txt | [
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] |
December 1, 2022
By
Insight
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
Webinar
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
Blogpost
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
Blogpost
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
Insight
Semantic Search: Intro and business applications | scraping/output/-6672370828350515768.txt | [
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] |
November 7, 2022
By
Insight
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
Blogpost
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
Blogpost
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
Webinar
Webinar | Hybrid AI: successfully combining expert knowledge with ML models | scraping/output/-6672370828350515768.txt | [
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] |
October 26, 2022
By
Webinar
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
Blogpost
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
Blogpost
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
Blogpost
Triton Ensemble Model for deploying Transformers into production | scraping/output/-6672370828350515768.txt | [
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] |
September 19, 2022
By
Thomas Janssens
Blogpost
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
Blogpost
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.
September 6, 2022
By
Matthias Feys
Blogpost
Potential applications of discrete diffusion models | scraping/output/-6672370828350515768.txt | [
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September 6, 2022
By
Matthias Feys
Blogpost
Potential applications of discrete diffusion models
Applications of using VQGAN combined with Discrete Absorbing Diffusion models.
August 30, 2022
By
Bert Christiaens
Blogpost
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models explained
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to autoregressive models, and work our way to the discrete absorbing diffusion
model.
August 11, 2022
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Generative AI
Blogpost
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Developing a DALL-E-based logo generator.
August 2, 2022
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Hallucination Detection
Detecting errors in transformers generated abstractive summaries
July 13, 2022
By
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Insight
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
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Insight
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
Blogpost
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
Blogpost
Decoding Sentence Encoders
Learn more about the similarities and differences between transformers and
sentence-transformers.
June 13, 2022
By
Webinar
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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
Blogpost
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
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] |
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Semantic search: A practical Overview | scraping/output/-6672370828350515768.txt | [
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Blogpost | scraping/output/-6672370828350515768.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
Blogpost
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
Blogpost
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
Webinar
Webinar | Explainable AI explained by Google and ML6 | scraping/output/-6672370828350515768.txt | [
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] |
March 15, 2022
By
Webinar
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
Blogpost
Need for Speed: JAX
Train neural networks faster with JAX on TPUs
March 2, 2022
By
Jasper Van den Bossche
Blogpost
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
Computer vision | scraping/output/-6672370828350515768.txt | [
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Voice & Sound
Front-End Development
Data Protection & Security
Responsible/ Ethical AI
Infrastructure
Hardware & sensors
MLOps
Generative AI
Natural language processing
Computer vision
In-Line Quality Inspection & Process Steering
Transforming Cosmec’s In-Line Quality Inspection with AI
By accurately detecting broken bricks over 90% of the time, Cosmec
significantly reduced waste and boosted production efficiency.
January 4, 2024
By
Jan Gerlo
In-Line Quality Inspection & Process Steering
Manufacturing
This is some text inside of a div block.
Case study
Transforming Bolt’s customer service with a Large Language Model
AI Revolutionizes Customer Support at Bolt Energie by using a Large Language
Model.
October 15, 2023
By
Birgit Mullie
Public & Professional Services
This is some text inside of a div block.
Case study
Understanding Stampix’ users through analysis of user-generated images | scraping/output/-6672370828350515768.txt | [
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October 15, 2023
By
Birgit Mullie
Public & Professional Services
This is some text inside of a div block.
Case study
Understanding Stampix’ users through analysis of user-generated images
To support Stampix’ journey towards a 1-click photobook experience Stampix and
ML6 leveraged machine learning using AWS SageMaker.
September 6, 2023
By
Sven Rymenans
CPG, Retail & Ecommerce
This is some text inside of a div block.
Case study
Strategic Advice
Accelerating Innovation in the Digital Insurance Sector for Policy Expert with
AWS
Policy Expert pushed innovation in digital insurance with AWS, leveraging AI &
ML for customer insights and real-time quotes.
July 25, 2023
By
Strategic Advice
Financial services
This is some text inside of a div block.
Case study
Data Driven Sales
Increasing sales effectiveness at Randstad by leveraging multiple data sources | scraping/output/-6672370828350515768.txt | [
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By
Strategic Advice
Financial services
This is some text inside of a div block.
Case study
Data Driven Sales
Increasing sales effectiveness at Randstad by leveraging multiple data sources
The sales effectiveness tool is currently actively used by around 10.000 sales
consultants spread over 6 countries. The hit rate has increased from the
historical 25% to 70%, meaning that today the sales consultants using the tool
are spending 70% of their time with the right clients - clients who have
actual and feasible potential for Randstad.
March 21, 2022
By
Matthias Feys
Data Driven Sales
Public & Professional Services
This is some text inside of a div block.
Case study
Generative AI
Tailor-made AI image generation services for crafters
ML6 has worked closely with Creative Fabrica to offer customers complete,
real-time content creation experiences that are tailored to their specific
needs.
April 16, 2023
By
Generative AI
Media & Entertainment | scraping/output/-6672370828350515768.txt | [
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April 16, 2023
By
Generative AI
Media & Entertainment
This is some text inside of a div block.
Case study
Content Management Platform
Building an AI-driven content platform for Funke Mediengruppe
Thanks to the AI solution, it is possible for the Funke group to process and
analyse content in near real time in a way that wasn't possible before. The
platform and its reusable components allow Funke to quickly test and implement
new content ideas or products in record time.
March 7, 2022
By
Content Management Platform
Media & Entertainment
This is some text inside of a div block.
Case study
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"Lights on highways are turned off when possible and the highways are
illuminated when necessary." Based on this lighting vision of the Flemish
government, the Agency for Roads & Traffic (AWV) is working with AI specialist
ML6 to automatically control motorway lighting based on artificial
intelligence. It will soon be possible to control highway lighting for
shorter periods and smaller regions. This will have beneficial economic,
environmental and social effects. The solution saves energy and light
pollution is countered when the lights do not have to be switched on (or can
be dimmed). Where it is required for safety, the lights are switched on. The
operator can make a data-driven decision, as a first step towards a full-
automatic control of lighting on the Flemish road network. This results in
energy savings and a reduction of light pollution, which are key objectives of
the project, are key objectives of the project, without compromising safety
for road users.
February 13, 2023 | scraping/output/-6672370828350515768.txt | [
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February 13, 2023
By
Jens Bontinck
Public & Professional Services
This is some text inside of a div block.
Case study
Strategic Advice
Ethical and Technical Assessment for an AI-based solution in the EU Council
Newsroom team
ML6 evaluated the technical feasibility and potential ethical risks involved
in using an AI-based solution for annotating media assets.
May 23, 2023
By
Strategic Advice
Public & Professional Services
This is some text inside of a div block.
Case study
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.
Case study
Fraud detection & prevention
Accelerating Keypoint's Digital Real Estate Management Platform with AI | scraping/output/-6672370828350515768.txt | [
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Public & Professional Services
This is some text inside of a div block.
Case study
Fraud detection & prevention
Accelerating Keypoint's Digital Real Estate Management Platform with AI
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.
December 30, 2021
By
Simon Logghe
Fraud detection & prevention
Public & Professional Services
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Case study
Strategic Advice
Enabling MLOps to shorten GfK’s development cycle
ML6 advised and supported GfK on setting up an MLOps way-of-working, enabling
GFK to bring insights to production in weeks instead of months, at much lower
costs.
September 7, 2022
By
Robbe Sneyders
Strategic Advice
Media & Entertainment
This is some text inside of a div block.
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September 7, 2022
By
Robbe Sneyders
Strategic Advice
Media & Entertainment
This is some text inside of a div block.
Case study
Intelligent/ Smart Machines
Optimizing the battery life of Saroléa motorcycles with AI
ML6 and Saroléa started to implement machine learning on the Saroléa
superbikes in 2018, with the initial focus on improving battery performance.
To date, The battery response models are being used to better estimate the
behaviour of the battery under certain conditions and, ultimately, to optimize
the control loops.
February 4, 2021
By
Nicolas Deruytter
Intelligent/ Smart Machines
This is some text inside of a div block.
Case study
Strategic Advice
Building an AI-ready data architecture for SWISS KRONO | scraping/output/-6672370828350515768.txt | [
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February 4, 2021
By
Nicolas Deruytter
Intelligent/ Smart Machines
This is some text inside of a div block.
Case study
Strategic Advice
Building an AI-ready data architecture for SWISS KRONO
Together with SWISS KRONO, ML6 identified the different requirements for AI
use cases. Few key requirements are a solid data architecture and
infrastructure, multi-disciplinary teams and management/organisation support.
Having these different angles in balance will help the organisation further to
enable and embed use cases in its day-to-day operations. This enables every
organisation to benefit from the actual output on a larger scale.
May 16, 2022
By
Jens Bontinck
Strategic Advice
Manufacturing
This is some text inside of a div block.
Case study
Content Management Platform
Automating the creation of VRT news summaries by using Natural Language
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Strategic Advice
Manufacturing
This is some text inside of a div block.
Case study
Content Management Platform
Automating the creation of VRT news summaries by using Natural Language
Processing
The AI driven model is a powerful tool in assisting journalists in their
writing work by speeding up the summary creation as well as suggest
alternative wordings. In that way, human creativity and AI can enhance each
other’s complementary strengths and produce high quality results.
May 16, 2022
By
Jens Bontinck
Content Management Platform
Public & Professional Services
This is some text inside of a div block.
Case study
Route optimization
Shaping transport collaboration with Teleroute, part of Alpega Group
With the help of artificial intelligence and machine learning, the new premium
feauture TelerouteRecommends allows customers to get instant recommendations
of trade-lanes and solve problems carriers and freight forwarders might have
when looking for truck-capacity or loads. | scraping/output/-6672370828350515768.txt | [
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] |
May 13, 2022
By
Sven Rymenans
Route optimization
CPG, Retail & Ecommerce
This is some text inside of a div block.
Case study
Data Driven Sales
The AI-Driven NGO : A data-driven approach to creating a better future for
children
By gaining insights into the donor journey, efficiency and effectiveness of
fundraising could be enhanced.
November 19, 2021
By
Data Driven Sales
Public & Professional Services
This is some text inside of a div block.
Case study
Production Process Optimization
Accolade Wines is saving 1 million litres of wine per year with AI
ML6 helped Accolade Wines to implement a ML process to capture real time
insights during the manufacturing process and prevent wine loss from happening
with predictive analytics, enabling all 3 bottling lines to further reduce
losses and saving another 1 million litres of wine per year or the consumption
of 50 000 people per year. A 200% ROI for Accolade Wines.
September 8, 2021
By
Jeffrey Hagen | scraping/output/-6672370828350515768.txt | [
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September 8, 2021
By
Jeffrey Hagen
Production Process Optimization
Manufacturing
This is some text inside of a div block.
Case study
Recommendation & Matching engine
Building a recommendation engine for March Real Estate
Ml6 built a proactive recommendation tool resulting in a 7x boost in the
number of leads from the matching engine.
May 28, 2021
By
Recommendation & Matching engine
Public & Professional Services
This is some text inside of a div block.
Case study
Intelligent/ Smart Machines
Building autonomous combines with CNH Industrial
ML6 helped to move an algorithm to a multispectral camera. ML6 helped
downscale and optimize the algorithm and helped get it embedded by working
together with the camera provider. Through the collaboration, CNH was able to
go into production much faster. They now also have the necessary expertise to
deploy algorithms on an embedded system for future use cases.
March 5, 2021
By
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] |
March 5, 2021
By
Intelligent/ Smart Machines
This is some text inside of a div block.
Case study
Peter De Jaeger, Chief Innovation Officer, talking about their AI use case &
working with ML6
2:36
Shanna Cloet, IT Manager at Fost Plus, talking about their AI use case &
working with ML6.
2:53
Philippe Meire, Founder of March, talking about their AI use case & working
with ML6.
2:46
Heiko Weigelt, CIO at Funke Mediengruppe, talking about their AI use case &
working with ML6.
3:43
Otary x ML6 : Optimising the production of off-shore wind farms
3min25sec
What is MLOps and why should you care ?
1 min
Our CEO, Nicolas Deruytter, about the upcoming disruptions in technology.
22 min
AI is moving fast, and so is Julie, Talent Partner at ML6.
3min
Happy Holidays from ML6 | December '22
1min
Office Opening Amsterdam | February '23
4 min
Brand Video | This is ML6
6min
December 4, 2023
#### ML6 achieves Azure Solution Partner for Data & AI | scraping/output/-6672370828350515768.txt | [
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] |
1min
Office Opening Amsterdam | February '23
4 min
Brand Video | This is ML6
6min
December 4, 2023
#### ML6 achieves Azure Solution Partner for Data & AI
ML6 Attains Azure Solution Partner for Data & AI Status: Elevating Excellence
in AI Solutions and Data Management.
View more
October 31, 2023
#### Jaarlijks één miljoen liter wijn sparen dankzij AI
Accolade Wines schakelde de experts van ML6 in om hen te ondersteunen bij de
implementatie van een ML-proces waardoor ze real-time analyses uit de
productie konden halen.
View more
October 26, 2023
#### Gebruik AI-modellen in een OT-omgeving, maar wel op een veilige manier
Ontdek hoe AI-activiteiten kunnen worden beschermd tegen cyberaanvallen.
View more
October 12, 2023
#### Vier bedrijven strijden om onderscheiding Scale-up van het Jaar
Chili Publish, Loop Earplugs, ML6 en TechWolf zijn de vier kandidaten om Feops
op te volgen als Scale-up van het Jaar.
View more
September 27, 2023 | scraping/output/-6672370828350515768.txt | [
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] |
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#### Justitie België zet AI in om rechterlijke uitspraken te digitaliseren | scraping/output/-6672370828350515768.txt | [
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View more
September 15, 2023
#### Justitie België zet AI in om rechterlijke uitspraken te digitaliseren
De federale overheidsdienst Justitie van België heeft ML6 gevraagd om te
ondersteunen met het anonimiseren, automatisch categoriseren en samenvatten
van rechterlijke beslissingen.
View more
September 15, 2023
#### ML6 en de Belgische justitie slaan handen ineen voor digitalisatie van
rechterlijke uitspraken
De federale overheidsdienst Justitie van België heeft ML6 gekozen om hen te
ondersteunen met het anonimiseren, automatisch categoriseren en samenvatten
van rechterlijke beslissingen.
View more
September 15, 2023
#### ML6 digitaliseert uitspraken Belgische justitie
De federale overheidsdienst (FOD) Justitie van België heeft ML6 gekozen voor
het ondersteunen met het anonimiseren, automatisch categoriseren en
samenvatten van rechterlijke beslissingen.
View more
September 20, 2023
#### ML6 moet Justitie transparanter en efficiënter maken | scraping/output/-6672370828350515768.txt | [
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] |
View more
September 20, 2023
#### ML6 moet Justitie transparanter en efficiënter maken
Het Gentse ML6 moet de Belgische FOD Justitie transparanter en efficiënter
maken. Het gaat onder meer AI inzetten bij het verwerken van vonnissen.
View more
September 13, 2023
#### ML6 achieves AWS Advanced Tier Services Partner
ML6 is now Amazon Web Services (AWS) Advanced Tier Services Partner
View more
August 17, 2023
#### Nieuwe Machine Learning-oplossing optimaliseert kwaliteitscontrole
productiebedrijven
Snel en betaalbaar procesoptimalisatie doorvoeren en Overall Equipment
Effectiveness (OEE) verhogen.
View more
August 30, 2023
#### Nieuwe machine learning-oplossing voor geoptimaliseerde
kwaliteitscontrole
ML6 heeft onlangs een baanbrekende machine learning-oplossing onthuld die zich
richt op de kwaliteitscontrole binnen deze processen.
View more
August 23, 2023
#### Machine learning ook toegepast voor kwaliteitscontrole in productie | scraping/output/-6672370828350515768.txt | [
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July 14, 2023 | scraping/output/-6672370828350515768.txt | [
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] |
Het aanpakken van klimaatverandering is een belangrijk maatschappelijk doel
waar ML6 met de inzet van ai zijn steentje aan wil bijdragen.
View more
July 14, 2023
#### Randstad verhoogt verkoopeffectiviteit met ML6 | Computable
Randstad en ML6 hebben gezamenlijk een ai-project opgezet om de
verkoopeffectiviteit te verbeteren door gebruik te maken van een datagedreven
aanpak.
View more
May 4, 2023
#### Naar een groenere wereld met ai | Channelweb
Jeffrey praatte met Computable over de positieve impact van AI op het klimaat
View more
May 17, 2023
#### Brengt AutoGPT ons stap dichter bij autonome ai? | Channelweb
Jens Bontinck legt uit wat AutoGPT is en waarom het zo populair is.
View more
May 26, 2023
#### Gentse ML6 is ‘Tech scale-up van het jaar’
ML6 krijgt opnieuw een erkenning voor zijn snelle groei.
View more
May 26, 2023
#### ML6 is Tech Scale-up of the Year 2023
Gentse specialist in AI & machine learning ML6 is Tech Scale-up of the Year
2023
View more | scraping/output/-6672370828350515768.txt | [
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] |
View more
May 26, 2023
#### ML6 is Tech Scale-up of the Year 2023
Gentse specialist in AI & machine learning ML6 is Tech Scale-up of the Year
2023
View more
May 10, 2023
#### Roy Meijer (NAJK) over de toekomst van jonge boeren - interview met Jens
Bontinck
Radio-interview met Jens Bontinck over AutoGPT (vanaf minuut: 1:18:10)
View more
May 17, 2023
#### Brengt AutoGPT ons stap dichter bij autonome ai? | Computable.nl
Jens praatte met Computable over de introductie van AutoGPT.
View more
May 10, 2023
#### AI heeft ruimte nodig en geen restricties of pauze
Opiniestuk door Edle Everaert over AI en de laatste ontwikkelingen
View more
May 4, 2023
#### Naar een groenere wereld met ai | Computable.nl
Jeffrey praatte met Computable over de positieve impact van AI op het klimaat
View more
#### Jeffrey (33) maakt zelflerende algoritmes: ‘Systeem kan zien of cellen
kwaadaardig zijn of niet’| AD | scraping/output/-6672370828350515768.txt | [
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] |
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] |
Simon praat met Business AM radio over onze ranking bij de FT1000, onze
groeiambities en het belang van Artificiële Intelligentie.
View more
March 8, 2023
#### ML6 haalt Financial Times top-1.000 van snelste groeiers | Kanaal Z
De Gentse Gentse scale-up in artificiële intelligentie ML6 is de Financial
Times top duizend van snelst groeiende bedrijven in Europa binnengetuimeld, op
plaats 386.
View more
March 28, 2023
#### Kunstmatige intelligentie is een spiegel van de maatschappij. ‘Je krijgt
terug wat je erin stopt’ | Trouw.nl
The Dutch newspaper Trouw interviewed our CEO Nicolas Deruytter to talk about
the relation between AI, cultural values, ethics, and our demographic
position.
View more
March 29, 2023
#### ML6 haalt Financial Times 1000-lijst van snelst groeiende Europese
bedrijven
View more
January 13, 2023
#### AI in stroomversnelling: Dit zijn de trends voor 2023 | Het
ondernemersbelang | scraping/output/-6672370828350515768.txt | [
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] |
#### ML6 haalt Financial Times 1000-lijst van snelst groeiende Europese
bedrijven
View more
January 13, 2023
#### AI in stroomversnelling: Dit zijn de trends voor 2023 | Het
ondernemersbelang
Nu artificiële intelligentie in veel sectoren wordt gebruikt, volgen de AI-
ontwikkelingen elkaar razendsnel op. Ook dit jaar kunnen we weer interessante
ontwikkelingen verwachten. Nicolas Deruytter, oprichter en CEO van ML6 deelt
vier trends voor 2023.
View more
January 16, 2023
#### AI in stroomversnelling: dit zijn de trends voor 2023 | Managersonline.nl
AI in stroomversnelling: dit zijn de trends voor 2023
View more
January 11, 2023
#### AI in stroomversnelling: dit zijn de trends voor 2023
View more
April 19, 2023
#### 'België dreigt nieuwe industriële revolutie mis te lopen'
View more
March 17, 2023
#### ML6 haalt Financial Times 1000-lijst van snelst groeiende Europese
bedrijven | scraping/output/-6672370828350515768.txt | [
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] |
April 19, 2023
#### 'België dreigt nieuwe industriële revolutie mis te lopen'
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March 17, 2023
#### ML6 haalt Financial Times 1000-lijst van snelst groeiende Europese
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Specialist in AI-oplossingen krijgt met FT1000-verkiezing erkenning voor
snelle, organische en duurzame groei
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GitHub | scraping/output/-6672370828350515768.txt | [
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] |
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GitHub
GitHub
#### Text Augmentation using large-scale LMs and prompt engineering
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GitHub
GitHub
#### Gender debaising of datasets using CDA
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GitHub
GitHub
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Join our newsletter | scraping/output/-6672370828350515768.txt | [
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March 14, 2019
# Google’s Edge TPU. What? How? Why?
Contributors
Stan Callewaert
Manufacturing Business Consultant
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Share this post | scraping/output/157057350857647893.txt | [
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As a premium partner of Google, we at ML6 were able to get early access to the
newest machine learning toy: the Edge TPU! The Edge TPU is basically the
Raspberry Pi of machine learning. It’s a device that performs inference at the
Edge with its TPU.
The Edge TPU (mounted on the Coral dev board). Credits for this image go to
Google.
Wait what?! That last sentence contained a lot of vague and very technical
words. Don’t worry, all of these words will become clear throughout this
blogpost.
### Cloud vs Edge
The Edge TPU obviously runs on the edge, but what is the edge and why wouldn’t
we want to run everything on the cloud?
Cloud vs Edge | scraping/output/157057350857647893.txt | [
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] |
### Cloud vs Edge
The Edge TPU obviously runs on the edge, but what is the edge and why wouldn’t
we want to run everything on the cloud?
Cloud vs Edge
Running code in the cloud means that you use CPUs, GPUs and TPUs of a company
that makes those available to you via your browser. The main advantage of
running code in the cloud is that you can assign the necessary amount of
computing power for that specific code (training large models can take a lot
of computation).
The edge is the opposite of the cloud. It means that you are running your code
on premise (which basically means that you are able to physically touch the
device the code is running on). The main advantage of running code on the edge
is that there is no network latency. As IoT devices usually generate frequent
data, running code on the edge is perfect for IoT based solutions.
### CPU vs GPU vs TPU | scraping/output/157057350857647893.txt | [
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### CPU vs GPU vs TPU
A TPU (Tensor Processing Unit) is another kind of processing unit like a CPU
or a GPU. There are, however, some big differences between those. The biggest
difference is that a TPU is an ASIC, an Application-Specific Integrated
Circuit). An ASIC is optimized to perform a specific kind of application. For
a TPU this specific task is performing multiply-add operations which are
typically used in neural networks. As you probably know, CPUs and GPUs are not
optimized to do one specific kind of application so these are not ASICs.
Since we are comparing CPUs, GPUs and TPUs, let’s quickly look at how they
respectively perform multiply-add operations with their architecture:
Multiply-add operation on CPU. Credits for this image go to this Google Cloud
article. | scraping/output/157057350857647893.txt | [
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Multiply-add operation on CPU. Credits for this image go to this Google Cloud
article.
A CPU performs the multiply-add operation by reading each input and weight
from memory, multiplying them with its ALU (the calculator in the figure
above), writing them back to memory and finally adding up all the multiplied
values.
Modern CPUs are strengthened by a massive cache, branch prediction and high
clock rate on each of its cores. Which all contribute to a lower latency of
the CPU
A GPU does the same thing but has thousands of ALU’s to perform its
calculations. A calculation can be parallelised over all ALU’s. This is called
a SIMD and a perfect example of this is the muliply-add operation in neural
networks.
A GPU does however not use the fancy features which lower the latency
(mentioned above). It also needs to orchestrate its thousands of ALU’s which
further decreases the latency. | scraping/output/157057350857647893.txt | [
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A GPU does however not use the fancy features which lower the latency
(mentioned above). It also needs to orchestrate its thousands of ALU’s which
further decreases the latency.
In short, a GPU drastically increases its throughput by parallelising its
computation in exchange for an increase in its latency. Or in other words:
A CPU is a Spartan warrior which is strong and well-trained while the GPU is
like a giant army of peasants which can defeat the Spartan because they are
with that ma
A TPU, on the other hand, operates very differently. Its ALU’s are directly
connected to each other without using the memory. They can directly give pass
information which will drastically decrease latency.
In the figure above you can see that all weights of the neural network are
loaded into the ALU’s. Once this is done, the input of the neural network will
be loaded into these ALU’s to perform the multiply-add operations. This
process can be observed in the figure below. | scraping/output/157057350857647893.txt | [
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] |
Multiply-add operation on TPU. Credits for this image go to this Google Cloud
article.
As you can see in the figure above, all the input for the neural network does
not get inserted into the ALU’s at the same time but rather in a step-by-step
basis from left to right. This is done to prevent memory access as the output
of ALU’s will propagate to the next ALU’s. This is all done with the
methodology of a systolic array which is graphically shown in the figure
below.
Using a systolic array for the multiply-add operation. Credits for this image
go to this Google Cloud article. | scraping/output/157057350857647893.txt | [
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] |
Using a systolic array for the multiply-add operation. Credits for this image
go to this Google Cloud article.
Each grey cell in the figure above represents an ALU in the TPU (which
contains a weight). In ALU’s a multiplication-add operation is performed by
taking the input an ALU got from the top, multiplying it by its weight and
then summing it up with the value it got from the left. The result of this is
propagated to the right to further complete the multiply-add operation. The
input the ALU got from the top gets propagated to the bottom to perform the
multiply-add operation for the next neuron in the neural network layer.
At the end of each row, the result of the multiply-add operation for each
neuron in the layer can be found without using memory in between operations.
Using this systolic array significantly increases the performance of the Edge
TPU. If you want to know how much exactly, you can check out our benchmark
blogpost.
### Quantization | scraping/output/157057350857647893.txt | [
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Using this systolic array significantly increases the performance of the Edge
TPU. If you want to know how much exactly, you can check out our benchmark
blogpost.
### Quantization
A last important note on TPUs is quantization. Since Google’s Edge TPU uses
8-bit weights to do its calculations while typically 32-bit weights are uses
we should be able to convert weights from 32 bits to 8 bits. This process is
called quantization.
Quantization basically rounds the more accurate 32-bit number to the nearest
8-bit number. This process is visually shown in the figure below.
Quantization
By rounding numbers, accuracy decreases. However, neural networks are very
good in generalization (e.g. dropout) and therefore do not take a big hit when
quantization is applied as shown in the figure below.
Accuracy of non-quantized models vs quantized models.
The advantages of quantization are more significant. It reduces computation
and memory needs which leads to more energy efficient computation. | scraping/output/157057350857647893.txt | [
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Accuracy of non-quantized models vs quantized models.
The advantages of quantization are more significant. It reduces computation
and memory needs which leads to more energy efficient computation.
### In conclusion
The Edge TPU performs inference faster than any other processing unit
architecture. It is not only faster, its also more eco-friendly by using
quantization and using less memory operations.
We, at ML6, are fans!
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Hardware & sensors | scraping/output/157057350857647893.txt | [
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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 | scraping/output/-3779760048647440922.txt | [
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] |
Discover the potential of Google's PaLM 2 as we harness generative language
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July 26, 2023
By
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Navigate Intelligence Activate IntelligenceBuild Intelligence | scraping/output/-3779760048647440922.txt | [
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No items found.
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# Fé Zenner
No items found.
As Finance Director, Fé and the finance team make sure that finance operations
run smoothly and that the right financial insights serve the right purposes. | scraping/output/6512065842551337239.txt | [
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# Fé Zenner
No items found.
As Finance Director, Fé and the finance team make sure that finance operations
run smoothly and that the right financial insights serve the right purposes.
Fé studied Commercial Sciences at KU leuven with a masters in International
Relations. She started her career by working 3,5 years as a Corporate Credit
Analyst in the oldest bank of the world ´Banca Monte dei Paschi di Siena’.
After that, she joined intuo, a SaaS company, during the start-up phase, where
she combined Finance and HR. She set up the finance department and optimized
and executed finance operations and shaped HR both on a national and
international level to be prepared for the fast and international growth and
finally the acquisition by Unit4 that followed. | scraping/output/6512065842551337239.txt | [
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In October 2020, Fé started working at ML6 as HR Business Partner with a
strong focus on Comp & Ben & Soft HR related matters. She made the internal
transition from HR Business Partner to Finance Director in november 2021. Fé
is more of a generalist than a specialist and when it comes to finance, she is
particularly interested in the story behind the numbers, finance analytics,
data based decision making and optimizations in operations.
Visit my LinkedIn profile
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### Introduction
A short while ago, we were happy to receive the news that ML6 won the 2020 ‘AI
Innovator of the Year’ award in the prestigious Data News Awards for
Excellence. This award recognizes IT companies that set the trend in creating
innovation and adopting artificial intelligence technologies. The jury
specifically recognized our ability to innovate together with our clients,
which is something we are very proud of.
To celebrate our award, we would like to share some insights on how we foster
innovation at ML6. Everyone at ML6 works hard every day to make sure that we
stay at the forefront of innovation and create value with our clients, and
while it’s hard to capture this spirit into words, we’ll try our best. To
limit the scope of this post, we’ll mostly focus on our delivery team, but
we’re happy to talk to anyone who would like to know more! | scraping/output/8531092381639395649.txt | [
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### Accelerate Intelligence
Before diving into our organizational structure, we want to give a clear
overview of the mindset and key principles at its base. In the end, we believe
that these are the most important to get right. There is no silver bullet when
it comes to organizational structures. It comes down to translating your
values into something that works for your specific company and is supported by
the people in your organization.
The ML6 vision | scraping/output/8531092381639395649.txt | [
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The ML6 vision
Let’s start with our company vision: accelerate intelligence. Working in the
artificial intelligence sector, people understandably assume that this is the
intelligence our vision references, but in reality it goes far beyond. On the
one hand, we want to accelerate intelligence and create value at our clients
by strategically deploying innovative technology. On the other hand, we want
to accelerate intelligence internally by providing opportunities for our
people to grow. Together, this creates an accelerating flywheel, which we aim
to get spinning as fast as possible. | scraping/output/8531092381639395649.txt | [
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We believe that innovation cannot be pushed, but needs to be nurtured. People
need an environment where they can experiment, fail, and grow. An environment
that provides support for their needs, from exciting and challenging projects
with great impact, to a team of experts they can spar with and learn from, to
coaching with a strong focus on people. An environment with minimal regulation
to allow freedom, but maximal clarity to enable efficient collaboration. To
borrow the definition from Netflix, we aim to provide context, not control. | scraping/output/8531092381639395649.txt | [
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Next to this, we believe that innovation should not be reserved for the lucky
few, but should be part of everyone’s DNA in our organization. That’s why we
don’t have a separate innovation team, but integrate innovation across our
whole organization. This starts with recruitment, where our main focus is not
on proven experience but on potential to grow, and continues from there. We
organize a lot of company-wide events and initiatives, in which everyone can
participate. From brainstorms on specific use cases or business opportunities
to our yearly Xmas projects, where we close down our business for two weeks
during the holidays so people can collaborate on innovative projects of their
choosing. If you’ve met us at events or have been to one of our offices, you
might have seen some demos of these projects, such as our smart mirror,
reinforcement learning arcade games, or self-flying drone. | scraping/output/8531092381639395649.txt | [
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But of course providing two weeks of play time before returning to business as
usual will not magically create innovation. It’s necessary to stimulate these
values year around, which is why we baked them into our organizational
structure.
### The Delivery Unit
ML6 is organized into self-steering units, each with its own unique purpose
shared by all its members. We will not go into detail about the different
units and how they collaborate, but to give you an idea, there are among
others a business development unit, focused on finding new clients and
projects, an HR unit, focused on everything relating to people, and a delivery
unit, on which we will be focusing here. The purpose of the delivery unit is
to create value at our clients by delivering our projects with the highest
quality and to stay up to date with the most recent developments in the field. | scraping/output/8531092381639395649.txt | [
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When ML6 was smaller, it was easy to keep an overview of all the different
ongoing projects and everyone’s expertise. Planning of the projects could
happen with near perfect information and knowledge sharing was as easy as
asking the right person for help. But as ML6 kept growing, it soon became
clear that this was not a scalable approach. To address these growing pains,
we took some inspiration from the Spotify model, and organized the delivery
unit into squads and chapters. | scraping/output/8531092381639395649.txt | [
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While the Spotify model was a great inspiration, it’s important to note that
we could not simply copy paste it, but used it as input to create our own
organizational model fit for our unique context and culture. One major
difference with Spotify is that we are a service instead of a product company,
which creates very different dynamics, difficulties and opportunities. There
is no central product gluing different teams together and each project has
direct external stakeholders, but on the other hand there are a lot of
opportunities for people to take ownership and work on a wide array of
interesting use cases and technologies.
Product company (left) vs ML6 (right)
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Product company (left) vs ML6 (right)
Over the past years, we went through multiple iterations of this model to find
something that works for us. Something that turns these difficulties into
advantages and allows us to exploit our opportunities. We will keep iterating
to further improve our organization while we grow and adapt to continuously
changing circumstances. But for now, we’ll discuss a snapshot of the current
state.
### Squads | scraping/output/8531092381639395649.txt | [
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] |
### Squads
The first part of our model are the squads. Squads are self-steering multi-
disciplinary teams of around 10 people, focused on delivering their projects
with the highest quality. They’re a combination of project managers and
machine learning, software, and data engineers, which allows them to deliver
projects end-to-end. Every project within ML6 is owned by exactly one squad
and each squad has a portfolio of projects that they own and deliver as a
team. People will often work on multiple projects in their squad at once,
allowing them to use their skills across projects.
To enable the squads to perform optimally, they have decision power on their
own projects, planning & recruitment. This allows for a distributed approach
to planning and project delivery, which enables us to scale horizontally
instead of vertically, and provides everyone at ML6 with ownership of the
projects they work on. | scraping/output/8531092381639395649.txt | [
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Working in squads allows us to deliver our projects in an agile way. Small
teams prevent bureaucracy and enable quick decision making, which allows us to
move fast and innovate with our clients. At the same time, our squads can rely
on the support of the whole ML6 organization, providing benefits of scale. One
of the most important ways our squads are supported is via the chapters.
### Chapters
Chapters are the second part of our model. They are cross-squad expert teams
with a focus on a specific technical domain. We have chapters focusing on
‘vertical’ machine learning domains, such as our NLP chapter (check out their
latest tool: a GPT2 Dutch text generator), and chapters focusing on
‘horizontal’ domains, such as our ‘machine learning in production’ chapter.
While we grow, we continue to expand the coverage of our chapters into new
domains and technologies.
Squads & chapters
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Squads & chapters
One of the main goals of the chapters is to enable cross-squad knowledge
sharing. Knowledge we obtain on projects in one squad is funneled to the
chapter, which combines all knowledge linked to their domain, and makes it
available to everyone.
Of course our projects are not the only source of knowledge for our chapters.
They keep track of the state of the art in their domain by exploring new tools
and papers, and perform their own applied research on dedicated research
projects, often in collaboration with other companies and universities. They
also guide internships and theses, providing a playground for students and
receiving knowledge in return. | scraping/output/8531092381639395649.txt | [
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This knowledge is then shared with everyone via internal tooling, examples,
best practices and talks. The chapters maintain a wide array of internal
repositories providing boilerplate code, examples and documentation, which
gives us a head start on every new project. Each quarter, these materials and
the chapter research are highlighted in a series of talks and workshops on our
chapter conference. While it’s impossible to stay up to date about the
advances in the different machine learning domains individually, these
conferences help us stay up to date together. | scraping/output/8531092381639395649.txt | [
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Not all knowledge can be captured in materials though, or it would at least be
extremely difficult and inefficient. Additionally, knowing everyone’s
expertise and tapping shoulders to get their input becomes a lot harder now
that ML6 has grown a lot. Our chapters therefore also provide a clear contact
point for any domain related questions, making it easier to connect to the
right people. This way, the chapter experts can support both our projects and
sales efforts in their domain. Since everyone in the delivery unit is part of
both a squad and a chapter, there’s a distributed network of knowledge and the
distance that information needs to travel is small, enabling a quick flow of
chapter innovation to squads, projects and clients.
### Conclusion | scraping/output/8531092381639395649.txt | [
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] |
### Conclusion
We like to believe that with this setup, we’ve created an environment where we
can innovate together with our clients. An environment which provides both
freedom and guidance for our people to make the best decisions in delivering
their projects. The squads and chapters both have their own goals and
objectives, but could not function without each other. Together, they make up
a knowledge network which allows us to accelerate intelligence.
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Infrastructure | scraping/output/8531092381639395649.txt | [
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Computer vision
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# Stan Callewaert
Computer vision | scraping/output/2284813685548846639.txt | [
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] |
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
# Stan Callewaert
Computer vision
Stan is both involved in business and technical topics in ML6. He started as a
Machine Learning Engineer that wrote code, gave technical presentations and
wrote blogposts. He slowly grew into an advisory role where he identified,
explored and exploited AI topics together with his clients. From this advisory
role, he started to get more and more involved in business topics, leading up
to the role he currently has as business consultant.
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] |
Stan his journey has one common theme: the manufacturing industry. He wrote
code, gave advice and does business with all kinds of manufacturing companies
and has developed in-depth knowledge by implementing, advising and seeing lots
of things at lots of manufacturing companies. That's why you will see that the
official title of Stan is: AI for Manufacturing Business Consultant.
Some clients that Stan worked together with over the past few years are:
Procter & Gamble, ASML, Jacob Douwe Egberts, Bekaert, Clarebout, Mettler
Toledo and many more. As one can see, these companies are from a wide range of
subindustries: chemical, food, machinery, metal and many more. This didn't
only give Stan general knowledge on how to help manufacturers with innovative
solutions but also gave him in-depth subindustry knowledge.
Some examples of blogposts Stan wrote and talks he gave at can be found here: | scraping/output/2284813685548846639.txt | [
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] |
Some examples of blogposts Stan wrote and talks he gave at can be found here:
* Business talk about AI in Manufacturing
* Technical talk about reinforcement learning
* Technical blogpost about TPU's
Stan is open to having a chat with people that have something to do with the
manufacturing industry so don't hesitate to contact him at this email address:
stan.callewaert@ml6.eu.
Visit my LinkedIn profile
Our know-how
## Related posts
Placeholder tag
5 AI Applications to Enhance your Retail Strategy Today
5 AI Applications to Enhance Retail Strategy Today
July 10, 2019
By
Stan Callewaert
Placeholder tag
Google’s Edge TPU. What? How? Why?
Exploring Google's Edge TPU: What it is, how it works, and why it matters
March 14, 2019
By
Stan Callewaert
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Infrastructure | scraping/output/2284813685548846639.txt | [
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] |
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In today's fast-paced world, staying up-to-date with the latest advancements
and trends in your field is crucial. As a result, knowledge management systems
have become increasingly popular, providing organizations with a centralized
location to store and access their knowledge. However, not all knowledge
management systems are created equal. In this blog post, we'll explore how
leveraging language models, such as the recently released LLM, can enhance the
effectiveness of 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.
Let's dive in!
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### LLMs, meet your limits … and exceed them
An LLM is a Large Language Model. OpenAI’s GPT-4 is one example, Meta’s LLamA
is another. We make the conscious choice here to stick to the general LLM term
to refer to these models. Bear in mind: each of these models were trained on a
gigantic set of (publicly available) data.
It has been clearly demonstrated by now that these LLMs have a meaningful
understanding of general language and that they are able to (re)produce
information relevant to the information that was present in their training
data. This is why generative tools like ChatGPT perform astonishingly well at
answering questions about topics that the LLM encountered during its training.
But what remains out of the direct grasp of those massive LLMs is the data
that is so valuable within each organisation: the internal knowledge base. The
question that thus massively pops up is: | scraping/output/3628185678030734886.txt | [
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] |
But what remains out of the direct grasp of those massive LLMs is the data
that is so valuable within each organisation: the internal knowledge base. The
question that thus massively pops up is:
How can we leverage the power of these LLMs in unlocking information stored in
a specific knowledge base upon which it wasn’t originally trained?
Oh okay, so to do this, can’t we just introduce our internal knowledge base as
extra data upon which the LLM should be trained? Or, if you will, can we fine-
tune the LLM on our specific knowledge base.
Yes, you most likely can. But for reliable question answering, it might not be
the way to go.
### Why fine-tuning won’t always cut it
Meet Billy the Bookworm. Billy is a large language model and he has devoured a
gigantic amount of online information, empowering with enormous knowledge.
Bily however, smart as he is, has not read through the books in your very
specific home library. | scraping/output/3628185678030734886.txt | [
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] |
Fine-tuning is this: presenting Billy the Bookworm with all the books in your
very specific knowledge base and letting him gobble up all that tasty extra
information. This way, the LLM bookworm Billy doesn’t just know all of that
general information, he also “knows” a lot about the contents of your specific
knowledge base.
Classical approach of fine-tuning on domain specific data (all icons from
flaticon)
Congratulations, trough this fine-tuning proces you’ve turned Billy into a
very specific Billy that knows so much about your specific domain! Below we
show how you could start putting Billy to work. By posing questions to your
improved bookworm, you can expect answers that use both the information from
its gigantic general training set and information stored in your specific
knowledge base.
Leveraging the fine-tuned LLM to ask questions about your internal knowledge
base.
| scraping/output/3628185678030734886.txt | [
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] |
Leveraging the fine-tuned LLM to ask questions about your internal knowledge
base.
While certainly powerful, the crucial problem with this solution approach, is
that you still have little insights into how your bookworm came up with its
answers. Moreover, fine-tuning an LLM has its (costly) consequences.
We list the main reasons why fine-tuning Billy comes up short: | scraping/output/3628185678030734886.txt | [
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