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May 17, 2023
By
Kaat Van Doren
Placeholder tag
Low Rank Adaptation: A Technical Deep Dive
LoRA is revolutionizing fine-tuning for large AI models, enabling efficient
adaptation without straining resources. Explore its principles, effectiveness,
and impact on the open-source community.
May 17, 2023
By
Generative AI
Placeholder tag
AI Act Amendments (Joint committee vote 11/5/23)
Discover key amendments proposed in the EU AI Act, including banned and high-
risk applications, regulations for foundation models and generative AI, and
the impact on open-source components. Prepare for upcoming AI regulations and
their implications.
May 17, 2023
By
Placeholder tag
State of the LLM: Unlocking Business Potential with Large Language Models
Explore the potential of large language models (LLMs) for businesses: models,
tooling, challenges, and applications
May 16, 2023
By
Generative AI
Placeholder tag
How to label your way to accurate Automatic Speech Recognition (ASR) | scraping/output/-4850200093856018142.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/-4850200093856018142.txt | [
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] |
April 18, 2023
By
Jens Bontinck
Generative AI
Placeholder tag
ChatGPT & GPT-4: Gimmicks or game-changers?
Discover the truth behind the hype of ChatGPT & GPT-4. Read about the
potential game-changing capabilities of GPT-4 or if it's just another gimmick.
April 14, 2023
By
Generative AI
Placeholder tag
Reflexion is all you need?
Jens Bontinck shares his view on a recent technique called Reflexion
April 13, 2023
By
Jens Bontinck
Placeholder tag
Accelerating people - even if it goes beyond ML6
How we empower our agents in their own personal road to success.
March 31, 2023
By
Julie Plusquin
Placeholder tag
How to effectively label images for computer vision projects ?
In this blogpost we cover the most important aspects of tackling a computer
vision problem by labeling your own data.
March 22, 2023
By
Thomas Janssens
Placeholder tag
Improving Tennis Court Line Detection with Machine Learning | scraping/output/-4850200093856018142.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/-4850200093856018142.txt | [
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] |
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AI for the green transition - Applications and use cases [Part 1] | scraping/output/-4850200093856018142.txt | [
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December 12, 2022
By
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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
By
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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
your business processes.
November 7, 2022
By
Sven Rymenans
Content Management Platform
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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
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Vertex AI is All You Need | scraping/output/-4850200093856018142.txt | [
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How to approach uncertainty in machine learning : forecasting, regression and
classification models.
September 19, 2022
By
Thomas Janssens
Placeholder tag
Triton Ensemble Model for deploying Transformers into production
When deploying a Transformer model, the complexity increases since besides the
model one also needs to consider how to deploy the tokenizer. Is it best to
deploy the tokenizer on the server or to have the end users handle it and what
is the best way to deploy the tokenizer along with the model on the server so
that inference latency and costs are minimized?
September 6, 2022
By
Placeholder tag
Unblocking ML use cases that lack (usable) data
What do we do though if we don’t have any or enough usable data yet to get
started? One obvious option is to collect (more) data. However, today we want
to go beyond that. We will look at three other dimensions that can be relevant
to unblock ML use cases: data protection, external data, and synthetic data. | scraping/output/-4850200093856018142.txt | [
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] |
September 6, 2022
By
Matthias Feys
Placeholder tag
Potential applications of discrete diffusion models
Applications of using VQGAN combined with Discrete Absorbing Diffusion models.
August 30, 2022
By
Bert Christiaens
Placeholder tag
Using generative AI for image manipulation: discrete absorbing diffusion
models explained
We review and compare the previous SOTA generative models, from autoencoders
to autoregressive models, and work our way to the discrete absorbing diffusion
model.
August 11, 2022
By
Bert Christiaens
Generative AI
Placeholder tag
Can AI generate truly original logos?
Developing a DALL-E-based logo generator.
August 2, 2022
By
Placeholder tag
Hallucination Detection
Detecting errors in transformers generated abstractive summaries
July 13, 2022
By
Matthias Cami
Placeholder tag
Why a tech radar will not make you more innovative
How we tackle information management at ML6 and how our tech radar helps us do
this.
July 6, 2022
By | scraping/output/-4850200093856018142.txt | [
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Placeholder tag
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NVIDIA Deepstream Quickstart | scraping/output/-4850200093856018142.txt | [
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Semantic search: A practical Overview | scraping/output/-4850200093856018142.txt | [
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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
A practical overview on information retrieval, in particular Semantic Search.
May 9, 2022
By
Content Management Platform
Placeholder tag
From principles to practice : Why we created an Ethical AI unit at ML6
Why we created an Ethical AI unit at ML6 and what we learned so far.
May 3, 2022
By
Placeholder tag
How to discover good data augmentation policies for ML
In this blogpost we take a look at what data augmentation is and how you can
efficiently discover good data augmentation strategies for your model and your
use case.
April 28, 2022
By
Arne Vandendorpe
Placeholder tag
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
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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
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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
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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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] |
Belgium
5
Mar
Event
VEK Career Day
Belgium
7
Mar
Event
UvA Skills Week
Netherlands
13
Mar
Event
Scale up fair Vlerick
Belgium
28
Mar
Event
Ghent Capital of Technology fair
Belgium
18
Apr
Event
Tech Meetup Jobfair Berlin
Germany
29
May
Event
TU Berlin Jobfair
Germany
## Past events
22
Feb
Event
[Webinar] Creating an AI-Ready Foundation for Labs
15
Feb
Event
Spaghetti Evening for a Good Cause with ML6 & Uilenspel! (Ghent edition)
Belgium
15
Feb
Event
Spaghetti Evening for a Good Cause! (Berlin edition)
Germany
15
Feb
Event
Spaghetti Evening for a Good Cause! (Amsterdam edition)
Netherlands
30
Jan
Event
Building AI Responsibly
Netherlands
24
Jan
Event
LLMs for LLMs: AI applications in the legal industry
11
Jan
Event
[Webinar] The AI Act in practice - preparing for the upcoming regulation
14
Nov
Event
Meetup | November's Talk Evening
Germany
7
Nov
Event
VTK Leuven Sector Night
Belgium
12
Oct
Event | scraping/output/2353435236380849981.txt | [
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] |
[Webinar] The AI Act in practice - preparing for the upcoming regulation
14
Nov
Event
Meetup | November's Talk Evening
Germany
7
Nov
Event
VTK Leuven Sector Night
Belgium
12
Oct
Event
ML6 at Leuven Slush'd
Belgium
5
Oct
Event
ABISS: the next intersection for innovative industrial interconnection
Belgium
5
Oct
Event
[Webinar] Generative AI for corporate use - Fine tuning foundation models
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December 7, 2023
# Unveiling Google's Gemini: a game-changer in the world of AI?
Contributors
Matthias Feys
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Thank you! Your submission has been received! | scraping/output/2864851427751051785.txt | [
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## What is Gemini, Google’s newest AI model?
Yesterday, Google launched the first version of their new family of models:
Gemini. The launch immediately sparked discussions - is Google, with these new
models, opening up the competition field with the OpenAI models? In this blog,
we’ll share our view on the Gemini launch.
### Text generation - patience, please!
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### Text generation - patience, please!
Until now, GPT4 has set the standard for text generation capabilities. With
Gemini, Google now wants to challenge this position. Gemini Ultra \- the
biggest, most capable version of the model - promises to compete with GPT4,
according to several benchmarks reported in the technical paper. Next to
Gemini Ultra, there is also Gemini Pro, with performance more comparable to
GPT3.5 and Gemini Nano, which would support on-device LLM support!
We should always be careful about jumping to conclusions based on these
benchmarks: specifically for LLMs, it’s difficult to abstract the impact of
the prompt used. Still, these results show the potential of Gemini in text
generation: for the first time, a model performs better than human experts on
the MMLU dataset, a benchmark generally used to evaluate LLMs against human
experts.
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As of December 13th, Gemini Pro will be available via Google AI Studio and
Vertex AI. In many places (but not yet in Europe), a fine-tuned version is
already available in Bard, allowing for easy experimentation with the new
model. The broad release of the Gemini Ultra is planned early next year as
mentioned in this release statement, after further refining and ensuring the
model is safe.
To experience the full text capabilities of Gemini Ultra you’ll need some
patience. Or, if you’re lucky, you can get early access to experiment as a
selected partner or customer. As a Google Cloud Premium Partner, we are in
close contact for early access and are keen to experiment to learn what this
model can mean for our customers.
### How Google's Gemini differentiates through multimodal capabilities
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### How Google's Gemini differentiates through multimodal capabilities
So, why are we so excited about Gemini today? While text generation has been a
major focus in the GenAI landscape, what really sets Gemini apart from other
generative models are its multimodal capabilities: Gemini has been trained
natively multimodal, it can take audio & visuals as input next to text and
return text and images as output. This enables:
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* Multimodal combinations: take a picture of your fridge, add a voice message to come up with a recipe, receive written instructions and a picture of how you can organise the plate? This type of interaction will become the new standard with this capability.
* Chart understanding: interpreting & reasoning on charts, enabling businesses to quickly extract insights from extensive reports, or make them more transparent.
* Video understanding: Gemini supports video as input natively, a capability not available in the GPT4 models.
* Transcription: Gemini Pro outperforms specialised audio models such as Whisper in transcription tasks.
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This more generalist, multimodal approach seems to us the most important
differentiator for Gemini in the current landscape. As we are learning from
building GenAI solutions with customers, businesses have information in very
different formats. These multimodal capabilities open up new possibilities for
businesses to interact with that information in a more intuitive way, for
example by using Multimodal RAG.
### How Gemini compares with PaLM 2
PaLM 2 deals with text, while Gemini is more versatile, handling various types
of data like text, images, and code. What makes Gemini different from PaLM 2
is its ability to learn from diverse sources. Since Bard (Google’s chat based
AI tool) is moving from PaLM 2 to Gemini models, we expect it to benefit from
Gemini's capabilities as time goes on.
## Our conclusion on Gemini
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] |
## Our conclusion on Gemini
Over the next weeks, we’ll start using these models at ML6 and find out what
value they can bring to our customers. Open questions remain (pricing,
regional availability, timing), but this much is clear: by launching Gemini,
Google has made a significant move in the GenAI race, and we can’t wait to see
what’s next.
If you’re interested to find out more about Gemini, or what GenAI can mean for
your business, don’t hesitate to reach out. We’d love to think along.
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Nicolas founded ML6 in December 2012. As CEO he leads the company and ensures
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] |
Nicolas founded ML6 in December 2012. As CEO he leads the company and ensures
sustainable growth. ML6 has been featured by Deloitte's fast 50 in '18, '19,
'20 and '21 following 10x growth.
Nicolas is energised by realising a meaningful impact at businesses, people
and society. Nicolas has been featured as a speaker in multiple events
covering topics from entrepreneurship, innovation to practical applications of
AI. Nicolas provides guest lectures at Vlerick business school in the AI & big
data programme on creating business value. He is a member of the AI4Belgium
thinkthank, advising the Government on the development and execution of the
Belgian AI Strategy. He was nominated as IT Person of the Year in 2021 and
2022. Nicolas is a member of the board of directors of the Flanders' Chambers
of Commerce and Industry (Voka).
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] |
Nicolas holds masters in engineering: operations research, industrial
engineering and mechatronics from Ghent University. He’s passionate about
empowering others, running, hiking, wintersport and enduro.
Visit my LinkedIn profile
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July 13, 2022
# Hallucination Detection
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Share this post | scraping/output/-7826392670882397316.txt | [
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This blogpost is derived from its interactive version on Hugging Face Spaces.
You can continue reading there if you want the benefits of playing around with
multiple examples or to provide your own input.
### Introduction
Recent work using transformers on large text corpora has shown great success
when fine-tuned on several different downstream NLP tasks. One such task is
that of text summarization. The goal of text summarization is to generate
concise and accurate summaries from input document(s). There are 2 types of
summarization: | scraping/output/-7826392670882397316.txt | [
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* Extractive summarization merely copies informative fragments from the input.
* Abstractive summarization may generate novel words. A good abstractive summary should cover principal information in the input and has to be linguistically fluent. This blogpost will focus on this more difficult task of abstractive summary generation. Furthermore we will focus mainly on hallucination errors, and less on sentence fluency.
Why is this important? Let’s say we want to summarize news articles for a
popular newspaper. If an article tells the story of Elon Musk buying Twitter,
we don’t want our summarization model to say that he bought Facebook instead.
Summarization could also be done for financial reports for example. In such
environments, these errors can be very critical, so we want to find a way to
detect them. | scraping/output/-7826392670882397316.txt | [
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0.06434155255556107
] |
To generate summaries we will use the PEGASUS model, producing abstractive
summaries from large articles. These summaries often contain sentences with
different kinds of errors. Rather than improving the core model, we will look
into possible post-processing steps to detect errors from the generated
summaries.
### Generating summaries
Below you can find the generated summary for an example article. For the full
article, different examples or to test things out yourself, we also made an
interactive Hugging Face space. We will discuss two approaches that we found
are able to detect some common errors. Based on these errors, one could then
score different summaries, indicating how factual a summary is for a given
article. The idea is that in production, you could generate a set of summaries
for the same article, with different parameters (or even different models). By
using post-processing error detection, we can then select the best possible
summary.
#### Example summary: | scraping/output/-7826392670882397316.txt | [
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] |
#### Example summary:
“The OnePlus 10 Pro is the company’s first flagship phone. It’s the result of
a merger between OnePlus and Oppo, which will be called “SuperVOOC” The phone
is launching in China first on January 11. There’s also no word on a US
release date yet. The 10 Pro will have a 6.7-inch display and three cameras on
the back. We don’t have a price yet, but OnePlus’ flagship prices have gone up
every year so far, and the 9 Pro was $969. The phone will go on sale January
11 in China and January 18 in the U.S.”
### Entity matching
The first method we will discuss is called Named Entity Recognition (NER). NER
is the task of identifying and categorising key information (entities) in
text. An entity can be a singular word or a series of words that consistently
refers to the same thing. Common entity classes are person names,
organisations, locations and so on. By applying NER to both the article and
its summary, we can spot possible hallucinations. | scraping/output/-7826392670882397316.txt | [
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] |
Hallucinations are words generated by the model that are not supported by the
source input. Deep learning based generation is prone to hallucinate
unintended text. These hallucinations degrade system performance and fail to
meet user expectations in many real-world scenarios. By applying entity
matching, we can improve this problem for the downstream task of summary
generation.
In theory all entities in the summary (such as dates, locations and so on),
should also be present in the article. Thus we can extract all entities from
the summary and compare them to the entities of the original article, spotting
potential hallucinations. The more unmatched entities we find, the lower the
factualness score of the summary.
Entity matching applied to the example summary | scraping/output/-7826392670882397316.txt | [
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] |
Entity matching applied to the example summary
We call this technique entity matching and here you can see what this looks
like when we apply this method on the summary. Entities in the summary are
marked green when the entity also exists in the article, while unmatched
entities are marked red .
As you can see we have 2 unmatched entities: “January 18” and “U.S”. The first
one is a hallucinated entity in the summary, that does not exist in the
article. U.S. does occur in the article, but as “US” instead of “U.S.”. This
could be solved by comparing to a list of abbreviations or with a specific
embedder for abbreviations but is currently not implemented.
### Dependency comparison | scraping/output/-7826392670882397316.txt | [
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### Dependency comparison
The second method we use for post-processing is called Dependency Parsing: the
process in which the grammatical structure in a sentence is analysed, to find
out related words as well as the type of the relationship between them. For
the sentence “Jan’s wife is called Sarah” you would get the following
dependency graph:
Here, “Jan” is the “poss” (possession modifier) of “wife”. If suddenly the
summary would read “Jan’s husband…”, there would be a dependency in the
summary that is non-existent in the article itself (namely “Jan” is the “poss”
of “husband”).However, often new dependencies are introduced in the summary
that are still correct, as can be seen in the example below. | scraping/output/-7826392670882397316.txt | [
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] |
“The borders of Ukraine” have a different dependency between “borders” and
“Ukraine” than “Ukraine’s borders”, while both descriptions have the same
meaning. So just matching all dependencies between article and summary (as we
did with entity matching) would not be a robust method. More on the different
sorts of dependencies and their description can be found here.
However, we have found that there are specific dependencies that are often an
indication of a wrongly constructed sentence when there is no article match.
We (currently) use 2 common dependencies which — when present in the summary
but not in the article — are highly indicative of factualness errors.
Furthermore, we only check dependencies between an existing entity and its
direct connections. Below we highlight all unmatched dependencies that satisfy
the discussed constraints for the current example. For more interactive
examples, we again refer to the interactive space.
First unmatched dependency from the summary | scraping/output/-7826392670882397316.txt | [
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0.043405476957559586
] |
First unmatched dependency from the summary
One of the dependencies that, when found in the summary but not in the
article, indicates a possible error is the “amod” (adjectival modifier)
dependency. Applied to this summary, we have “First” as the entity, and it is
the adjectival modifier of the word “phone”. And indeed, this unmatched
dependency indicates an actual error here. The sentence is not factual, since
the article talks about a new type of flagship phone, and not the first
flagship phone. This error was found by filtering on this specific kind of
dependency. Empirical results showed that unmatched amod dependencies often
suggest that the summary sentence contains an error.
Second unmatched dependency from the summary | scraping/output/-7826392670882397316.txt | [
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] |
Second unmatched dependency from the summary
Another dependency that we use is the “pobj” (object of preposition)
dependency. Furthermore, we only match pobj dependencies when the target word
is “in”, as in this example. In this case the sentence itself contains a
factual error (because the article states “there’s no word on a US release
date yet”). However, this could have already been found with entity matching
(as January 18 is unmatched), and the unmatched dependency can not be
completely blamed for this error here.
### Bringing it together | scraping/output/-7826392670882397316.txt | [
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] |
### Bringing it together
We have presented 2 methods that try to detect errors in summaries via post-
processing steps. Entity matching can be used to solve hallucinations, while
dependency comparison can be used to filter out some bad sentences (and thus
worse summaries). These methods highlight the possibilities of post-processing
AI-made summaries, but are only a first introduction. As the methods were
empirically tested they are definitely not sufficiently robust for general
use-cases. But for some different examples where you can play around with the
presented methods, we refer to the interactive Hugging Face space. | scraping/output/-7826392670882397316.txt | [
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] |
Below we generate 3 different kind of summaries (for the example article), and
based on the two discussed methods, their errors are detected to estimate a
summary score. Based on this basic approach, the best summary (read: the one
that a human would prefer or indicate as the best one) will hopefully be at
the top. We also highlight the entities as done before, but note that the
rankings are done on a combination of unmatched entities and dependencies
(with the latter not shown here).
## Related posts
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Responsible/ Ethical AI
Responsible/ Ethical AI | scraping/output/-7826392670882397316.txt | [
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Copyright 2024
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Watch our on-demand webinar: 'AI in manufacturing: how to overcome common
challenges and what's next', as we’ve seen a catalyzation in the number of
artificial intelligence (AI) projects in the manufacturing industry.
Due to the COVID 19 pandemic, manufacturers are forced to rethink operations
like product production & assembly.
In this webinar, ML6 and Google Cloud dive into the typical challenges
manufacturers face when wanting to use AI in their production lines. Based on
our extensive experience in the industry, we’ll share learnings on how to
efficiently and effectively tackle these challenges in order to move projects
forward quickly.
What you’ll learn during this webinar : | scraping/output/-6682690983500893324.txt | [
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What you’ll learn during this webinar :
* How to break down data silos and derive useful and intelligent insights with Google Cloud and AI;
* How you can overcome common challenges manufacturers face based on practical ML6 use cases with proven ROI, common pitfalls, realistic timelines and budget in process and semi-process industry.
* How ML6 can help you scale internal knowledge using machine learning retraining programmes on VertexAi.
#### Get access to the webinar by filling in the form below.
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Responsible/ Ethical AI | scraping/output/-6682690983500893324.txt | [
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COSMEC
# Transforming Cosmec’s In-Line Quality Inspection with AI
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COSMEC
# Transforming Cosmec’s In-Line Quality Inspection with AI
## Impact
In the competitive world of heavy clay processing and brick production,
ensuring product quality and consistency is critical for success. Cosmec, an
Italian industrial automation specialist, faced the challenge of achieving
this goal for their customers by partnering with ML6. By integrating ML6’s in-
line quality solution, they achieved automated identification of broken bricks
with over 90% accuracy. This breakthrough has led to substantial reductions in
downstream waste and an increased customer satisfaction for the brick
producers that are Cosmec’s customers. The machine vision solution, seamlessly
integrated with Cosmec's brick handling equipment, exemplifies ML6's
commitment to delivering solutions that yield tangible positive impacts.
## Intro to the customer | scraping/output/6247993532751003894.txt | [
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## Intro to the customer
Cosmec is known for providing excellent industrial automation solutions,
marked by innovation and a strong commitment to top-notch quality The
company's expertise spans a diverse range of industries, including heavy clay
processing, where it has established a reputation for excellence. Cosmec's
drive for continuous improvement and its embrace of advanced technologies led
it to seek a partnership with ML6, recognizing the transformative power of AI
in addressing the challenges of in-line quality inspection.
## Challenge | scraping/output/6247993532751003894.txt | [
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## Challenge
Using natural raw materials in heavy clay processing comes with variations
that make it tough to ensure consistent quality for Cosmec's customers. These
material fluctuations lead to inconsistencies during production, affecting
both product quality and process yield. Traditional quality inspection
methods, relying on manual inspection and subjective assessments, were proving
inadequate in addressing these challenges. Cosmec needed a solution that could
automate the inspection process, providing real-time insights and enabling
proactive interventions to ensure consistent product quality.
ML6 is a game changer for companies seeking to demystify and adopt AI. They
have made the collaboration effortless and allowed us to rapidly embed AI into
our product suite, giving us a competitive edge in the marketplace.
Sales manager
By
Mirko Rorer
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] |
Sales manager
By
Mirko Rorer
## Solution
ML6's expertise in AI and its proven track record in developing cutting-edge
solutions made it an ideal partner for Cosmec. ML6's team collaborated closely
with Cosmec to understand the specific requirements of the heavy clay
processing industry and to develop a tailored in-line quality solution.
Leveraging its advanced machine vision and machine learning capabilities, ML6
designed a system that could accurately identify and classify defects in real-
time, enabling Cosmec to take immediate corrective actions and maintain
consistent product quality.
By
## Results | scraping/output/6247993532751003894.txt | [
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] |
By
## Results
The implementation of ML6's in-line quality solution has had a transformative
impact on the operations of Cosmec's customers. By accurately detecting broken
bricks over 90% of the time, it significantly reduced waste and boosted
production efficiency. Moreover, the real-time insights provided by the system
have empowered their customers to proactively identify and address potential
quality issues, further enhancing the consistency and overall quality of its
products. As a result, Cosmec has experienced a marked increase in customer
satisfaction, further solidifying its position as a leader in the heavy clay
processing industry.
Accelerating businesses with AI technology & experts
Contact:
info@ml6.eu
+32 9 265 95 50
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February 5, 2024
# Elevating excellence : accelerating each others talent
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Julie Plusquin
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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?
##### A strong brand | scraping/output/-7866457806601395761.txt | [
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##### A strong brand
It starts with bringing out a strong brand. We are known as one of the largest
and fastest growing AI consulting companies in Europe. We won several awards
in 2023 that attest to this. Clients consciously choose to engage with ML6
because of our end-end solutions and customized guidance. We dare to challenge
our clients and ourselves and are always on top of the latest innovations
which we also do enough research on. Next to our strong client work, people
choose consciously to apply at ML6 for our strong company culture and values
that we all carry with us and expose to the outside world.
##### Changing market, changing skills | scraping/output/-7866457806601395761.txt | [
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] |
##### Changing market, changing skills
We notice that the demands in the market are evolving. Organizations are
becoming more mature and ready for technological innovation. Because
technology is also evolving so rapidly, it also means that the skills of our
people are evolving with it. Whereas a few years ago a lot of attention was
paid to pure machine learning skills, today it is more important than ever to
be able to use them multi-functionally. Our people are getting exposed to
software engineering, data engineering, pre-sales and much more.
##### Our A-team, building the A-team | scraping/output/-7866457806601395761.txt | [
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##### Our A-team, building the A-team
Each one of us is a consultant. All the work we do is for and with customers
and other stakeholders. This means that our engineers must be capable of
translating technical solutions to specific use cases/industries and
communicating their results clearly to non technical stakeholders as well. Our
application process challenges various aspects that are crucial to get started
at our company. From consulting skills, to pure coding skills and teamwork.
And yes, we won't beat around the bush, our process is quite challenging. We
deliberately involve different stakeholders in our process because we believe
in the fact that our A-team of today can help build out the A-team of
tomorrow. Next to that, we strongly believe in the fact that recruitment is
not just an HR responsibility.
##### Culture | scraping/output/-7866457806601395761.txt | [
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##### Culture
Today, we hire about 1% of all candidates who apply at ML6. That's why
creating the right expectations with your audience is crucial early in the
process. It starts with your brand and communication. We strongly believe that
our company culture contributes to this. For example, people looking for a
fully remote job won’t be happy at ML6. Neither will people who like a lot of
rules and policies. Committing to the right "attraction strategy" is therefore
gaining importance. As we have a strong collaborative culture where knowledge
sharing is key, talented people love to be surrounded by talented people to
accelerate together.
##### Growth | scraping/output/-7866457806601395761.txt | [
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##### Growth
2024 will be a very exciting year for ML6. In terms of people growth, we want
to grow more than 50% internationally. Our focus lies on Belgium, the
Netherlands and Germany. Given our further internationalization and the
projects we already have underway in other countries (e.g. UK, Nordics, US),
we certainly do not rule out expanding our 3 offices in the future. With that
in mind, it will become even more important to closely monitor our talent
density and preserve our company culture. Tomorrow's ML6 agents will
contribute to and shape our culture further.
##### Opportunities | scraping/output/-7866457806601395761.txt | [
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] |
##### Opportunities
What exactly does that mean in terms of opportunities? As mentioned earlier,
we are looking for engineer- consultants, multi-functional people. We don't
want to create predefined boxes for people into which they feel stifled, given
interests and skills are very individual (throwback to when we deleted our
jobtitles). We currently have several technical roles open where we are
looking for talented people with knowledge and skills that will help us
accelerate our AI projects. For example, people who are eager to share
knowledge and (co-) drive projects. This can include experience with different
clouds (AWS, Azure ...) or experience with specific AI domains (biology,
energy ...). Simply put, people who are eager to create impact with AI for
businesses. In addition to technical roles, our company is also growing in the
functional area, HR area, Business Development area, and so much more.
#####
Explore all opportunities here
####
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# Jens Bontinck
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Employee Log in Candidate Log in to Connect Homepage ml6.eu
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# Jens Bontinck
Head Advisory & Delivery – Advisory & Delivery unit 💻
jens.bontinck@ml6.eu
0472454130
Jens Bontinck is the Head of Delivery & Advice at ML6, leading a team of 70 AI
and Data experts in creating top-quality AI solutions and providing guidance.
With a strong background in technology and AI strategy, he is a highly
knowledgeable and passionate tech leader. Jens regularly shares his insights
as a guest lecturer at esteemed institutions like Vlerick Business School and
UGent, and through his blog. He challenges the status quo and shares his
honest thoughts on the current and future evolutions of AI. | scraping/output/8429349735837569769.txt | [
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Jens kickstarted his career as a developer at RealDolmen, after he graduated
from Ghent University as Master of Science in Information Engineering
Technology. Over time he built experience on a functional and strategic level
and focused on Digital Strategy at LoQutus. Having a clear understanding of
both the technical and strategic domain, Jens is able to deliver innovative
and competitive insights on a day-to-day basis.
You can follow Jens’ latests thoughts via jensbontinck.be
## Work with me
* Senior Software Engineer Advisory & Delivery unit 💻 * Multiple locations
* AI Project Manager Advisory & Delivery unit 💻 * Multiple locations
* Senior Data Engineer Advisory & Delivery unit 💻 * Multiple locations
## Some of my colleagues
Robbe Sneyders
Principal Engineer
## About ML6 | scraping/output/8429349735837569769.txt | [
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## Some of my colleagues
Robbe Sneyders
Principal Engineer
## About ML6
We guide the AI revolution towards positive impact. 🌐
Exciting developments are happening in the world of AI, offering unprecedented
opportunities for businesses to innovate and gain a competitive edge. Now more
than ever is the time to create intelligence with lasting positive impact. And
you can be a part of this!
1. We deliver end-to-end projects where we accompany organisations through the entire AI journey
2. We build bespoke hybrid ML solutions
3. We navigate and set up advisory projects
As ML6 highly values diversity, inclusion and transparent communication, we've
implemented a formal channel to report serious misconduct here.
Examples of serious misconduct (not limited to): safety of the individual
(aggression, discrimination, sexual harassment), data privacy issues, public
health, tax and fraud.
* Join us 🕵️♀️
* Data & privacy
* Manage cookies
ml6.eu/
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# Jules Talloen
Computer vision
Natural language processing | scraping/output/7646080843164400831.txt | [
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Responsible AI
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# Jules Talloen
Computer vision
Natural language processing
As a full stack ML engineer, Jules has an interest in the entire development
process, from state-of-the-art research to data pipelines and web development.
To stay at the forefront of innovation, he is continuously monitoring the
latest research and enables applications built on top of the latest
advancements. Using his technical expertise and drive for perfectionism, Jules
translates business needs into high-quality, scalable and performant data
processing solutions. | scraping/output/7646080843164400831.txt | [
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] |
Jules has experience both in the field of natural language processing and
computer vision and has delivered projects ranging from text based semantic
filtering to smart CCTV surveillance systems. As a member of the Computer
Vision chapter, he mainly focuses on video analytics, e.g. tracking and action
recognition. He leverages his communication skills to share his technical
expertise, both internally and externally.
Jules graduated summa cum laude as a Computer Science Engineer with a major in
Artificial Intelligence from the University of Ghent. During his thesis, he
combined the field of computational neuroscience with deep learning and
published a framework for a more biological plausible type of learning.
Additionally, he was involved in multiple research projects where he further
spiked his research interest.
Visit my LinkedIn profile
Our know-how
## Related posts
Placeholder tag
Getting started with custom NVIDIA Deepstream 6.0 pipelines in Python | scraping/output/7646080843164400831.txt | [
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] |
Visit my LinkedIn profile
Our know-how
## Related posts
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Getting started with custom NVIDIA Deepstream 6.0 pipelines in Python
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NVIDIA Deepstream Quickstart | scraping/output/7646080843164400831.txt | [
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] |
A practical guide to generating artificial data using Unity Perception.
May 22, 2022
By
Jules Talloen
Placeholder tag
NVIDIA Deepstream Quickstart
In this blogpost we’ll set up a docker container to run an NVIDIA deepstream
pipeline on a GPU or a Jetson and show you some tricks to get the most out of
it! Deepstream’s documentation, guides and sample projects are few and far
between, so this article aims to be a reference to get you from Deepstream
zero to Deepstream-hey-I-can-make -a-pipeline-now and beyond!
May 14, 2022
By
Jules Talloen
Placeholder tag
Beyond frames: The new frontier in Video Analysis
Beyond Frames: Unlocking the Power of Video Analysis
July 13, 2021
By
Jules Talloen
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 | scraping/output/7646080843164400831.txt | [
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SOlution
# Digital twin production planning
Make informed decisions by running different scenarios through a virtual model
of your production system.
Business ValueClient casesChallengesThe solutionAssets & BoilerplatesGet in
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Make informed decisions by running different scenarios through a virtual model
of your production system.
Business ValueClient casesChallengesThe solutionAssets & BoilerplatesGet in
touch
Imagine knowing exactly what to produce, when to produce and in what batch
sizes. That is now possible thanks to our digital twin production planning
solutions.
Digital Twin Production Planning is when your business uses a virtual model to
simulate its physical production system. This helps you make informed
decisions on your entire manufacturing process. By predicting potential
problems and testing different scenarios, you can optimize production, reduce
costs, and improve efficiency.
Dynamic scenario analysis | scraping/output/-1381642931706136732.txt | [
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Dynamic scenario analysis
Digital twin production planning uses dynamic scenario analysis to assess
production capacity and bottlenecks in real-time. It considers various factors
that can impact production, such as different temperatures, offering a
complete view of the production process and its environment. The system is
designed to be easy to use, with visualization tools that help users quickly
understand the impact of different production schemes.
Sensitivity and bottleneck analysis
Digital twin production planning supports sensitivity and bottleneck analysis,
allowing your business to test and evaluate different production strategies.
It helps you make informed decisions by answering questions like, "Should we
prioritize producing a high-value product, even if it slows down other
production lines?" or "Should we produce a lower-value product to keep the
factory running at full speed?"
Optimizing investments | scraping/output/-1381642931706136732.txt | [
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Optimizing investments
Digital twin production planning s a powerful tool that helps your business
make better investment decisions. How? By accurately estimating ROI. This way,
you can make informed decisions and optimize your production processes, which
helps you succeed in a fast-paced production world.
## Business Value
Ready to boost your manufacturing process? Thanks to digital twin production
planning, you can make your business more data-driven and efficient. It’s the
best way to gain insight into new ways to improve your production processes
and to think beyond day-to-day operational decisions.
Gradually introducing digital twins will keep your business prepared for any
upcoming situation in the future and allow the system to make its own
adjustments, without needing human intervention.
### Support data-driven decision-making | scraping/output/-1381642931706136732.txt | [
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] |
### Support data-driven decision-making
Make quick informed decisions using simulations to test multiple strategies
and ideas. Digital twin production planning also suggests the best parameters
for achieving a target. It can handle more parameters and values than a human
can, making it useful for product and service design, resource management,
portfolio decisions and pre-sales analysis. For example, it can help you
determine whether meeting a client’s needs will be sustainable in the
production process.
### Proactive risk mitigation
Predictive risk management strategies can help you prevent problems in
manufacturing. Real-time reporting and predictive maintenance through the use
of digital twin technology can alert manufacturers to potential problems
before they occur. This proactive approach helps you manage your manufacturing
operations more effectively.
### Process optimization and operational excellence | scraping/output/-1381642931706136732.txt | [
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### Process optimization and operational excellence
Improve processes by using a digital twin to get a complete view of the
production chain and its environment. The digital twin can also help with
production planning and make sure that plants are running efficiently. By
digitizing processes and testing them under different conditions, you can
identify areas where innovation can improve the performance of your business.
### Cost reduction
Beyond predictive maintenance, digital twins can reduce costs in unexpected
ways. They can, for example, optimize planning capacity and resource
management, such as using trucks efficiently for production or increasing
output while taking the entire fleet into account.
## Client cases
No results found.
There are no results with this criteria. Try changing your search.
Large Language Model
Foundation Models
Corporate
People
Structured Data
Chat GPT
Sustainability
Voice & Sound
Front-End Development
Data Protection & Security | scraping/output/-1381642931706136732.txt | [
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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
## Challenges
When creating a content management platform, there are some functional and
technical challenges to consider, such as:
## Making implicit domain knowledge explicit
Digital twins generate models of business rules that are normally only
available to experts and decision makers. This implicit domain knowledge can
be extracted and used to manage complex product specifications by creating a
plan of how things currently work.
## Early capture and alignment on domain experts' expectations | scraping/output/-1381642931706136732.txt | [
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## Early capture and alignment on domain experts' expectations
To avoid losing stakeholders’ trust when outcomes don't meet expectations,
clear and transparent communication is important. We use an iterative
development approach, delivering small simulations or functionalities one at a
time. We start simple and add complexity gradually.
## Dependency on domain expert vision for prioritization
To make sure we're working on the most important simulation scenarios, we
listen to what the stakeholders want. We focus on those scenarios in each
development cycle and get feedback from them. Together, we set goals and work
with ML6 coaches to help the organization be more agile.
## A digital twin often implies a new way of working | scraping/output/-1381642931706136732.txt | [
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## A digital twin often implies a new way of working
A new way of working can be intimidating. However, we help your business in
making this transition by aligning it with your organization's strategy and
processes. That’s why we always analyze the current and desired states of the
business before we begin the transition.
## Value through user adoption
To make the tool easy to use, we need to know who will be using it and what
they need. We work with key users to co-create the interface and identify
their needs. We help guide users through this process.
## Data is spread and data owners need to be involved
To implement a digital twin smoothly, data owners should understand how their
data will be used and contribute to its development. The quality of the
simulation depends on the quality of the data.
## Required amount of business rules | scraping/output/-1381642931706136732.txt | [
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## Required amount of business rules
Building a digital twin can be made easier by reducing the number of business
rules that must be taken into account. Typically, the best solution is a
digital twin that employs both machine learning and business rules.
## Data quality and completeness
The digital twin often requires data from various sources, each with its own
level of quality. Since the digital twin only works with high-quality data,
extra technical work is necessary to check and maintain the data quality.
## The solution
A content management platform is made up of several essential building blocks
that work together to create a powerful and efficient system:
Data integration
Digital twins rely on combining data from different sources, including
sensors, ERP systems, and other manufacturing software. The data must be
precise, consistent, and complete.
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