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We list the main reasons why fine-tuning Billy comes up short:
* No source clarity. It’s difficult to prevent hallucination and your LLM has no clear distinction between “general” and “specific” knowledge.
* No access restriction. Imagine a case where some users should be able to query the information of strategic documents while others shouldn’t. How would you tackle this? Your fine-tuned Billy just knows everything, he can’t choose to leave out knowledge at inference time.
* Hosting an LLM is costly. Once you have a fine-tuned LLM, you have to keep it spinning. A large language model is well… large. Costs to keep it up and running will rack up. Do the benefits outweigh those costs?
* Fine-tuning repetitions. Model retraining is required when you want the model to reflect changes to the knowledge base. | scraping/output/3628185678030734886.txt | [
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Luckily, all these problems are solvable. If answering questions in a
verifiable way and preventing hallucination is what you are looking for: you
may not need the hyper-modern bookworm, let’s just ask the good old librarian
where to find the answers to your questions.
### With RAG to Riches
The idea behind Retrieval-Augmented Generation (RAG) is quite straight-
forward. Remember, the goal is to unlock the information in our knowledge
base. Instead of unleashing (i.e. fine-tuning) our bookworm on it, we
comprehensively index the information of our knowledge base.
By indexing the embeddings of your internal knowledge base, you unlock smart
search capabilities.
In the schema above, we illustrate how the Smart Retriever functions like a
librarian. Ideally, the librarian has perfect knowledge of what is in his
library. For a visitor asking a certain question, he would know just which
chapter of which book to recommend. | scraping/output/3628185678030734886.txt | [
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On a more technical level, this describes a semantic search engine. In this
case, the embeddings are vectorial representations of document sections and
they allow a mathematical description of the actual meaning stored in each
section. By comparing embeddings, we can determine which text sections are
similar in meaning to which other text sections. This is crucial for the
retrieval process displayed below.
Through leveraging our Smart Retriever, we can force our generator to stick to
the content of our knowledge base that is most relevant for answering the
question. Et voilà: Retrieval-Augmented Generation.
In play are two crucial components:
1. The Smart Retriever (i.e. the librarian)
2. The Generator (i.e. the bookworm) | scraping/output/3628185678030734886.txt | [
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In play are two crucial components:
1. The Smart Retriever (i.e. the librarian)
2. The Generator (i.e. the bookworm)
It should be clear by now why this approach is called Retrieval-Augmented
Generation. Based on the question asked, you first retrieve the most relevant
information from your internal knowledge base; you then augment the typical
generation phase by passing that relevant information explicitly to the
generator component.
#### Key highlights of this RAG-based setup
1. Clear indication of the source upon which the answer was based. Allowing for validation of the answer returned by the generator.
2. Very unlikely to hallucinate, by restricting our generator component to the corpus of our knowledge base, it will admit it can’t formulate a response when no relevant sources were found by the retriever.
3. Maintainable Search Index. A knowledge base is a living thing, when it changes, we can adapt our Search Index to reflect those changes. | scraping/output/3628185678030734886.txt | [
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Aside from those highlights, the multi-lingual aspect of LLMs is a thing of
beauty. You can have a knowledge base consisting of purely Italian recipes
which your pasta-loving French friend can talk to in an all-French dialogue.
#### Fine-tuning revisited
Note that in the section above, we dismissed fine-tuning as a valuable option
because we had little control on source clarity thus increasing the risk for
hallucination.
It must be noted that the RAG approach, powered by a general LLM, only works
well as long as the specific knowledge base does not contain super specific
jargon that the LLM can’t understand from its general training.
Imagine you need the responses of your solution to follow ‘the tone and lingo’
that is present in your knowledge base. In this case the fine-tuning of your
LLM seems less avoidable. | scraping/output/3628185678030734886.txt | [
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Imagine you need the responses of your solution to follow ‘the tone and lingo’
that is present in your knowledge base. In this case the fine-tuning of your
LLM seems less avoidable.
It could be a valid approach to be able to handle specific jargon and then
incorporate your fine-tuned LLM in the RAG architecture to reap the combined
benefits. Instead of working with a general bookworm, you would then use your
specifically trained Billy to power the Generator and/or the Smart Retriever
components.
### Why now? What’s new?
Excellent question.
Semantic Search (smart retrieval) has been around for quite some time and so
has generative AI (some primitive forms have been around for decades).
However, we have seen pivotal advancements over the last months.
On a technological level, we’ve recently witnessed big leaps forward in LLM
performance. These positively impact the RAG solution on two levels: | scraping/output/3628185678030734886.txt | [
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On a technological level, we’ve recently witnessed big leaps forward in LLM
performance. These positively impact the RAG solution on two levels:
* Embeddings (e.g. Embedding API by OpenAI or Google’s PaLM)
* Generative capabilities (e.g. OpenAI’s ChatGPT solution)
Accompanying that improved generative quality is the increase in traction.
Previously, companies could not easily imagine the opportunities of a system
relying on generative AI. Now however, thanks to the wide media coverage and
adoption of tools like ChatGPT, overall interest has grown exponentially.
So, though arguably mediocre versions of RAG might have been possible for
quite some time, technological improvements and increased traction result in a
fruitful market opportunity.
### Challenges on your way to success
In this section, we aim to introduce you to some of the main challenges with
setting up a successful RAG solution. | scraping/output/3628185678030734886.txt | [
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### Challenges on your way to success
In this section, we aim to introduce you to some of the main challenges with
setting up a successful RAG solution.
* Strong dependency on the performance of the Smart Retriever.
The quality of the responses given by your Generative Component will depend
directly on the relevancy of the information handed to it by the Smart
Retriever. As mentioned above, we can thank LLM advancements for giving us
rich and powerful text embeddings. But fetching these embeddings purely via
API’s may not be your best option. You should be very conscious when designing
your Semantic Search component, perhaps your knowledge base has specific
jargon and you might need a custom fitted (i.e. fine-tuned) component to
handle it. A more in-depth practical guide on Semantic Search can be found in
this blogpost [1] .
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] |
* Trade-off to be made in restriction to stick to info in knowledge base.
As explained in the RAG architecture, we can force our LLM generative
component to restrict itself to the information found in the relevant
documents. While this ensures that hallucination (i.e. non-sensical answers)
has little chance, it also means you are barely leveraging the information
your LLM possesses. You might want your solution to use that knowledge as well
but maybe only when requested by the user.
* Conversational design to allow complex dialogue.
While our depictions above have represented the user behaviour as asking
merely a “one-shot question”, often your user might want to zoom in on the
answer provided by your solution (in a ChatGPT-style conversation). Luckily,
tools exist to aid you in this battle. The langchain framework offers a
helping hand in getting this just right.
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* Prompt engineering as a way to steer generation toward succes.
To get the answer of your generative component just right, you need to tell it
exactly what kind of output you expect. Overall, this is far from rocket
science. But getting your prompt setup just right for your use case takes time
and deserves enough attention. It may be worthwhile looking at prompt
management systems to make sure you can keep track of which prompting works
best for which situations.
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* Choosing the right LLM: what does it cost and where does my data go?
Throughout this text, we haven’t made any explicit choice regarding what
LLM(s) to use in your solution. When choosing which LLM (API) to use, make
sure to take privacy and cost restrictions into consideration. There are quite
some decent options out there already. We have OpenAI’s GPT, Meta’s LLaMA,
Google’s PaLM and with Elon Musk claiming to join the LLM scene, who knows
where things will go. The exciting news is: more options will come and
competition should drive LLM performance up and prices down.
| scraping/output/3628185678030734886.txt | [
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* Getting and keeping your LLM solution in production (LLMOps).
As with all mature AI solutions: building them is one thing, getting/keeping
them in production is another. The field of LLMOps focusses on the
operationalisation of LLMs. Monitoring the performance of your LLM-based
solution, keeping your knowledge base and search index up-to-date, processing
conversational history…
Before flinging your LLM solution into production, think wisely about how to
maintain it and how to keep it fruitful in the long run.
Charmed by the potential of RAG and intrigued by the related challenges, we
now move on to looking at an actual RAG-based solution.
### Getting your hands dirty with a RAG
If your interest is sparked by the concept of Retrieval-Augmented Generation,
you may be asking yourself:
Do I have what it takes to take a RAG-based solution for a spin?
Well, if you have: | scraping/output/3628185678030734886.txt | [
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If your interest is sparked by the concept of Retrieval-Augmented Generation,
you may be asking yourself:
Do I have what it takes to take a RAG-based solution for a spin?
Well, if you have:
* specific knowledge: a moderate (preferably organised) database of “knowledge articles” that contain useful information that is not easily found on the world-wide web (e.g. technical documents, onboarding guidelines, handled support tickets…)
* business value: a clear definition of business value if that information could be unlocked for the intended users
Then yes, RAG might be the way to go for you.
As an experiment, we recently built a small demo to showcase how this
technology can be leveraged to support government staff in answering
parliamentary questions more easily.
In this case, the specific knowledge consists of:
* a set of Flemish legislative documents
* a set of parliamentary questions of the past
The business value, meanwhile is in: | scraping/output/3628185678030734886.txt | [
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* a set of Flemish legislative documents
* a set of parliamentary questions of the past
The business value, meanwhile is in:
* improving efficiency by automatically suggesting answers to parliamentary questions based on the Flemish knowledge base
* improving transparency and user adoption through explicit citations
Screenshot of demo solution built around the case of “application to help
answer parliamentary questions”
If you are looking for some guidelines on how to technically implement a
similar solution, stay tuned for a follow-up blogpost where we aim to zoom in
on the technical details of setting up RAG.
##### References
* Mathias Leys (May 9, 2022) Semantic search: a practical overview https://blog.ml6.eu/semantic-search-a-practical-overview-bf2515e7be76
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] |
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Oops! Something went wrong while submitting the form. | scraping/output/3628185678030734886.txt | [
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PressOost-Vlaamse Ambassadeur ‘middelgrote’ bedrijven Skyhaus: technologie op
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March 31, 2021
# Oost-Vlaamse Ambassadeur ‘middelgrote’ bedrijven Skyhaus: technologie op
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Thank you! Your submission has been received! | scraping/output/-2839507695242921612.txt | [
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January 19, 2022
# What better time than Christmas to foster innovation?
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“Hooray, the end of the year!” is the chant that echoes in many corporate
hallways when Christmas is nearing. Lots of people take time off at the end of
the year to find some quiet and “recharge their batteries” for the next one.
Most people that do decide to keep working are — unknowingly — nearing the
least productive week they’ve had in a long time.
“Nothing new here” you think while you zoom out on our planet again. You
notice the back of your head itching and realize that you have been wearing
your VR glasses for way too long now. You sigh softly and start scratching
your hair. Empty-headedly, you decide to give a last good spin to the sphere
in front of you before taking the headset off. With a glazed look you see the
world spinning in front of you until something odd catches your attention.
“What in the hell was that?” you stutter while you start zooming in again. You
take a closer look and count that there are four corporate hallways that are | scraping/output/-2615541418613730807.txt | [
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“What in the hell was that?” you stutter while you start zooming in again. You
take a closer look and count that there are four corporate hallways that are
kinda unusual, atypical.. weird even.
In Ghent, Amsterdam, Berlin and Zurich, you find one corresponding hallway
where people just don’t seem to be planning much holidays, nor are they
preparing for a soothing week to conclude the year. Instead, you find that
these hallways share an exciting feeling that is starting to build up in the
days and weeks prior to Christmas. Soft whispers are turning into audible
muffles, people start to move in quicker ways and the atmosphere is filling
with longing. When the Christmas trees are being decorated the atmosphere
spills over and the muffles turn into cries of exhilaration.
“Finally” you hear.
“Finally.. The ML6 Xmas Projects are back in town!” | scraping/output/-2615541418613730807.txt | [
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] |
### What are Xmas Projects and are they essential to spawn superhuman
kindergartners?
Contrary to popular belief, our Xmas Projects are not related to the secret
ingredient with which this monkey called Jojo created the Powerpuff Girls.
Even though we haven’t — yet — been able to fully understand the chemistry of
that particular event, we do have a good understanding of the chemistry our
Xmas Projects bring about. And it’s worth writing a blogpost over.
Xmas Projects are hackathon-like projects in which people lay down their day-
to-day job for some time and get together out of common interest to explore
new tools, learn tech and make an impact! The first edition of the Xmas
Projects dates back to 2018 and have since been spanning the Christmas and New
Year period. Over time, the concept has become an integral part of the way of
working at ML6. | scraping/output/-2615541418613730807.txt | [
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ML6 facilitates the necessary hardware/compute for the accepted Xmas Projects
to realise their ideas. And indeed — instead of taking holidays, a lot of
people actually prefer to work together with colleagues on these initiatives.
Each year, an increasing amount of work is being put into all of our Xmas
Projects, reaching another record of 230 manday equivalents in this last
edition! 🔥
### The process
To maximize the impact and minimize overhead or restrictions, we have
concretized all the necessary phases:
* 💡 Ideation
The ideas the Xmas Projects originate from are submitted by anyone during the
complete year building up to the Christmas period. The final deadline for the
ideation phase is by the end of November, typically.
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* 🗣️ Pitching
At the start of December, The “Xmas Projects working group” gets together to
apply a touch of curation by trying to find synergies to combine ideas, or
tweak the scope of the proposed applications. The people who submitted these
curated ideas are potential Xmas Project leads and need to pitch their idea to
the whole organisation to get ‘funding’ and convince colleagues to work on
their projects!
* ✉️ Voting
By the end of the second week of December, it’s up to everyone else to make a
personal top 5 of the pitched projects.
* 👪 Allocation
Based on the responses, hardware availability and the needs of the Xmas
Project, it’s up to the Xmas Projects working group to puzzle people and Xmas
Projects accordingly!
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* 👪 Allocation
Based on the responses, hardware availability and the needs of the Xmas
Project, it’s up to the Xmas Projects working group to puzzle people and Xmas
Projects accordingly!
* 🏁 Actual building
In the most recent edition, we kicked off the Xmas Projects on the 24th of
December. The Xmas Projects leads have made sure that the teams are informed
and completed the necessary steps to hit the ground running. The goal is to
come up with a practical outcome by the end of the Xmas Project period, which
lasted until the 3rd of January this year!
* 🧐 Demo
During ML6' New Year’s Event, which is always hosted on the first Friday the
year, every Xmas Project team presents the context, goals, way of working,
learnings, — and of course — the demo!
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] |
* 🚀 Integration/Followup
The Xmas Projects working group and chapters (=research groups) come together
after the demo’s to see if and how we can extend functionality and/or scale up
solutions, be it for our own marketing campaigns, to continue helping the non-
profit organisations we were partnering up with, or simply to continue
innovating and learn stuff along the way!
These Xmas Projects are in many cases the fertile soil our appealing
internships are born from. Interested? Have a look at our internship/thesis
landing page!
### Benefits
As you might have guessed by now, Xmas Projects involve a substantial
investment of people, time and resources.
Of course, we never do something without a reason 😉. Over the years we’ve seen
that there are quite a number of benefits linked to this initiative!
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Of course, we never do something without a reason 😉. Over the years we’ve seen
that there are quite a number of benefits linked to this initiative!
* Tech learnings
Many ideas are born from this crazy, out-of-the-box, “will this even work”
mentality. By trying to reach new heights with AI, we challenge the status-quo
and our technical knowledge. Our brains are figuratively molding.
On the other hand, there are many people that deliberately choose for Xmas
Projects involving topics they don’t have a lot of expertise in, simply
because they want to get up to speed with new tools and libraries.
* Test business cases
Xmas Projects are not exclusively about development work. It is also the right
time to assess the market, define a sales strategy and think big for existing
ideas and chapter projects.
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] |
* Ideas for customers
The learnings we gather are directly applicable to many of the customer
projects we are working on. It’s not uncommon to pitch the demo’s we came up
with to our clients to help see if new technologies could advance their
business cases.
* Ownership
During the Xmas Projects, stars are born! ⭐
Everyone has an equal chance to become a Xmas Project lead and thereby
exercise their soft-and people skills! It’s a great way for juniors to put
themselves on the map and get familiar with all aspects of end-to-end project
ownership.
* People cohesion
Because the company is growing in vast numbers (33 new joiners in 2021) it’s
hard to find the time to connect and get to know everyone. The Xmas Project
teams consist of people with a common interest and mix up people from
different countries, units, and seniorities. It’s a great way of getting to
know others!
## List of Xmas Projects
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] |
## List of Xmas Projects
Last edition, we decided to split the Xmas Projects into three logical
clusters.
1. ML6 For Good
2. ML6 Goes Public
3. ML6 On Steroids
### ML6 For Good
We’ve worked on a total of five projects for Non-Profit Organisations that
would otherwise not have the budget or resources to tackle their problems with
the help of AI. The work we’ve done in this pillar ties neatly with one our
six core values, being “Do good”.
* For IDA Foundation we worked on three different use-cases, including “Forecasting of emergency health kits”, “Pallet packaging optimization” and “Emergency event detection”, based on social web data. The goal of these three projects was to come up with a more data-driven way to anticipate disasters and optimize transport for countries in need.
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* We supported Arop VZW, by coming up with a first version of an automatic visual quality inspection workflow for their sheltered workshops, aiding the workers in their day-to-day activities!
* Together with Fruitpunch.ai and Reef.support, we’ve been working on a visual detection tool (with image segmentation) to detect different types of corals and identify coral bleaching to raise awareness! You can read more in one of their LinkedIn posts. :)
Example image showing the results of the advanced semi-automatic labeling
procedure we set up.
### ML6 Goes Public
Three out of eleven Xmas Projects of the last edition were geared towards
pushing the newest techniques we’ve encountered in the last year and find out
how they shine in a practical context.
#### Souvenir photo booth | scraping/output/-2615541418613730807.txt | [
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#### Souvenir photo booth
Ever wanted someone to draw you like one of their French girls? Wait no more..
In this Xmas Project we created an interface for our smart mirror, replacing
famous painted faces with yours!
Scottish scientist, writer, and polymath Mary Somerville, painted by Thomas
Phillips. Her face is merged with a colleague at ML6, who also happens to be a
polymath. Can you guess who? A hint — He is known for cycling over 300 km in
one sitting when ML6 organises charity sports events. People are literally
afraid of betting on his sports performances. Charities on the other hand love
him.
#### Terms and Conditions Parser
I know you’re not perfect. I know you agreed to terms and conditions you never
read or understood, just like the rest of us. Why are these documents so
cumbersome and long anyways? Everyone hates them — and that’s why we decided
to build a browser add-on to help you digest the most important stuff in it.
| scraping/output/-2615541418613730807.txt | [
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Our browser add-on generates a general summary of the Terms and Conditions and
also highlights the most important extracts of the original text ( Link to
full video )
#### Creative inspiration platform
I can’t really say anything about this one. Can you keep a secret though?
It’s about a new application to use and train GAN’s and the demo was really
cool, believe me. It’s gonna be big, so stay tuned!
### ML6 On Steroids
I know people think we at ML6 try to match our consumption of steroids to the
amount of quick tips we externalize. But that’s a lie. We just happen to be
very muscular, you know. 😎
The goal of this pillar is to test side ideas originated from the work we did
on client related projects during the year. The findings from these Xmas
Projects often give the client project an innovative boost, or bring
interesting insights!
#### ML6 Cyber Threat Map
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] |
#### ML6 Cyber Threat Map
Hackers are boring (except the white hat hackers, these peeps are chill).
Because hackers end up trying the same hacks all over, Google Cloud has come
up with a tool called Cloud Armor that is able to protect your applications
from general hacking attacks. Pretty sweet right! We thought it’d be cool to
also visualize where and which attacks we are protected from. Entering the ML6
Cyber Threat Map! If you’re interested, my colleague Rob Vandenberghe already
wrote a blogpost about the topic.
The Threat Map we built on top of Cloud Armor.
#### Multimodal House Price Prediction
We have been building up lots of expertise in the real estate sector, spanning
different clients (including FedNot). When it comes to detecting house prices,
ML practitioners mostly work with classic columnar data. In this Xmas Project
we wanted to find out whether adding visual data or textual data improves
prediction performance.
#### Ping Pong AI | scraping/output/-2615541418613730807.txt | [
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] |
#### Ping Pong AI
At ML6 we not only like to do sports together, but also work on sports data
together! We’ve done multiple projects in the sports sector, including a
really interesting football match analytics use-case. Over the years we found
out that sports including small objects (particular ball sports mostly)
involve extra complexities. No better sport to do analysis for than ping-pong
in that regard! During this Xmas Project we have been looking into both the
audio and visual aspects for registration and event detection.
A game of table tennis ( full video ) analyzed by the tools we’ve set up!
Video source: OpenTTGames dataset.
### Conclusion | scraping/output/-2615541418613730807.txt | [
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A game of table tennis ( full video ) analyzed by the tools we’ve set up!
Video source: OpenTTGames dataset.
### Conclusion
For four years in a row, ML6 halts their typical business and undergoes a
transformation from a specialized AI consultancy firm to a multi-week
hackathon facilitator in which ML6 agents get to spend time on projects they
feel most attracted to. Both parts of this duality contribute to what we do
best: accelerate intelligence.
Xmas Projects are great. They are innovative by nature and enable people to
think atypical and big. The way we designed Xmas Projects ensures that we are
challenged in aspects we simply don’t see popping up in our daily jobs, are
able to make a social impact and have a lot of fun at the same time.
Personally speaking, I think it’s one of the most enjoyable and rewarding
parts of working at ML6.
What’s keeping you from organising a similar concept in your organisation, or
joining us?
## Related posts
View all | scraping/output/-2615541418613730807.txt | [
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April 18, 2023
# Unpacking AutoGPT: A Critical Analysis of the Autonomous Anatomy by Jens
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Jens Bontinck
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In this blogpost, 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!
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I have been closely following the development of AutoGPT, created by
Significant Gravitas. AutoGPT stands for Autonomous Generative Pre-training
Transformer and is an open-source experiment that is drawing a lot of
attention at the moment of writing(GitHub link). AutoGPT goes beyond an
ordinary chatbot, and its decision-making power reduces the need for human
intervention and thus allows for future automation and value creation. Did you
skipped a heartbeat when seeing the first possibilities, or did the current
missing features made you rather nervous?
Auto-GPT is drawing a lot of attention these days on GitHub
### The anatomy of how AutoGPT works? | scraping/output/-3048720869722842573.txt | [
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Auto-GPT is drawing a lot of attention these days on GitHub
### The anatomy of how AutoGPT works?
AutoGPT is provided with an identity/role and a task, providing details about
what it is supposed to do. This is fairly easy, and the perceived power of
AutoGPT lies in what it does with that starting context. AutoGPT can be seen
as an agent that seeks to complete the task autonomously by using a framework
that allows it to reason and act. Each task is handled by an ‘Execution Agent’
(GPT-4) and provides input to one or more other GPT-4 agents, which is adding
new (sub-)tasks to be completed by the agent. In short, AutoGPT is able to
unravel a bigger objective in smaller tasks and mainly act as an orchestrator
to achieve the initial objective.
AutoGPT is able to unravel a bigger objective in smaller tasks and I see it
currently as a ‘ task orchestrator’ to achieve the initial objective.
source: Lesswrong — https://www.lesswrong.com/posts/566kBoPi76t8KAkoD/on-
autogpt | scraping/output/-3048720869722842573.txt | [
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] |
source: Lesswrong — https://www.lesswrong.com/posts/566kBoPi76t8KAkoD/on-
autogpt
### Why is it perceived better than ChatGPT?
Some enthousiasts degraded ChatGPT directly when wrapping their head around
the first results of AutoGPT. In essence, I believe the difference boils down
to following features, allowing AutoGPT to differentiate from ChatGPT:
* Smart(er) (and faster) prompting helps LLMs overcome their inherent limitations in areas such as the content window and math problem-solving.
* Internet access allows AutoGPT to access more and up-to-date information.
* (long term) Memory supports keeping a context across the executed tasks. | scraping/output/-3048720869722842573.txt | [
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Another facet that is contributing to this overall perception is that AutoGPT
doesn’t require us to trigger the next steps (besides the approval to execute
commands, for cost purposes I believe), while ChatGPT required a human (you!)
to swiftly come with a well-crafted prompt to assure the quality and
effectiveness of the subsequent outputs. This ‘compounding effect’ of an agent
handling a series of tasks by itself is new to us, and is leaving a big impact
to the community today.
### Impact on Business Applications
The current possibilities of AutoGPT seem endless, but we are in the early
days of its development (yes, it’s hyping today). Today, AutoGPT is still
limited by a set of pre-defined commands (of which more will come every day)
and its true scale will lie in the number of atomic tasks it supports.
Also, these days you quickly experience that AutoGPT can get stuck in a loop,
is not able to handle the tasks and thus not able to provide you the expected
output. | scraping/output/-3048720869722842573.txt | [
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However, the genie is out of the bottle, and more and more people will test
and improve AutoGPT. Everyday AutoGPT will be able to handle more and more
atomic tasks and the perception of it’s power will further increase.
An overview of additional atomic tasks (e.g. Bing Search, email, Twilio,
Telegram, …)
In the coming months, I anticipate the business impact of AutoGPT will still
be limited to the fairly easy processes, of which the subtasks are known and
‘supported’ . Over time, we will have a clearer distinction between use cases
where AutoGPT is useful (e.g. initial research, composing an itinerary for
your next travel destination, …) and where it is not doing a great job at all.
For these successful use cases, it will further revolutionise the way we work
and interact with machines and the role of humans overall. | scraping/output/-3048720869722842573.txt | [
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One key element to keep in mind: as long as third parties do not allow access
to perform operations via an API, AutoGPT will not suddenly (and magically) be
able to execute the tasks successfully. People will need to tell to AutoGPT
how to handle atomic tasks (e.g. how to send an email, how to browse, how to
query the latest flights to Malta, … how to launch a rocket…) in order for
AutoGPT to benefit from it.
### Is This a Next Step towards AGI?
AutoGPT is not the only system that performs similar functions. Other systems
include Microsoft Jarvis and BabyAGI. All three initiatives are a promising a
first step towards AGI, Artificial General Intelligence, and I believe the
evolution will go faster and faster. From that perspective, I believe it is
good that there is much time spent on investigating the possibilities of such
systems today already. | scraping/output/-3048720869722842573.txt | [
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Today we know already that if AutoGPT knows how to perform an atomic task, it
is able to generate results in a much higher rate than our brain. However it
is still in our hands to provide the possibility to interact with e.g. a
rocket via an API. In other words, if we create a way that allows everyone to
interact with a rocket launcher, than AutoGPT could learn how to interact with
it.
Another reasonable threat we should anticipate on, is the ability of AutoGPT,
and thus the LLMs behind it, to sound like human and to reason like human.
This could encourage the instructor to take action outside of the possibility
of AutoGPT. A drastic example to make my point: if you ask AutoGPT to solve a
dispute, it could identify that sending a rocket is the best thing to do and
that you should do it tomorrow. (Don’t do it!)
### Conclusion | scraping/output/-3048720869722842573.txt | [
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] |
### Conclusion
In conclusion, AutoGPT is an exciting development in the field of AI, and we
should closely follow its progress. Future versions will have improvements on
functionality, memory, UI, … which all will contribute to the overall
perception of AutoGPT as a first step to AGI. These rapid evolutions will
provide further insights in the significant benefits to businesses it will
bring.
I believe we must also be aware of the fact that AutoGPT is still an
experiment and therefore I tend to be more conservative than ‘the internet’
today. AutoGPT is still quite expensive to run, ends up in infinite loops
quite often and is not capable of ‘everything’ (as stated by some online). The
genie is out of the bottle but it will take some additional human intelligence
to maximise the value of AutoGPT.
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Foundation Models | scraping/output/-3048720869722842573.txt | [
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] |
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ML6, a leading AI & Machine Learning company in Europe with offices in Ghent,
Amsterdam and Berlin, announced today that it has been selected as Google
Cloud Services Partner of the Year for Benelux 2023. The company is being
recognized for its ability to deliver impactful business value to their
clients by solving complex challenges, building and running custom AI
solutions on Google Cloud - and helping their clients expand their own AI
capabilities.
Simon Logghe, Managing Director at ML6, expressed his gratitude for the
received recognition: | scraping/output/-2267828673903295176.txt | [
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“We really value this sign of trust in our long standing and fruitful
collaboration. We are looking forward to accelerating our Go-To-Market with
the huge GenAI opportunity. We would like to extend a special thank you to the
BeNeLux partner team. They go above and beyond to help us grow together with
Google Cloud and are, based on our experience, a leading partner organisation
in Europe.”
“Google Cloud’s partner awards recognize the significant impact and customer
success that our partners have driven over the past year," said Kevin
Ichhpurani, Corporate Vice President, Global Ecosystem and Channels at Google
Cloud. "We're delighted to recognize ML6 as a 2023 Google Cloud Partner Award
winner, and look forward to a continued strong partnership in support of our
mutual customers."
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Last year, ML6 successfully worked on a broad range of impactful Google Cloud
projects, enabling its clients to be a frontrunner in their industries by
innovating through AI. As many AI innovations take place in a business' core,
projects are often confidential and cannot be shared publicly. To name a few
of our publicly available, exciting projects:
1\. Helping Randstad to increase sales effectiveness by leveraging multiple
data sources on Google Cloud. Together with Randstad, ML6 has built a sales
effectiveness tool, which is 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. Based on this demonstrated value the
tool will be further deployed to all Randstad locations.
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] |
2\. Supporting the Belgian Federation of Notaries (FedNot) with knowledge and
skill management through various applications including advanced anonymization
and pseudonymization techniques for protection of personal data, a custom
semantic search engine covering offering secure access to tens of millions of
notary deed clauses, a real estate price estimator and multiple automatic
information extraction tools for generating structured (meta)data from
contracts.
3\. Building a data lake in Google Cloud for the Flemish Agency for Road and
Traffic (AWV), allowing the exploitation of data for AI use cases such as the
automatic switching of road lighting along the highways or to make smart
decisions for sprinkling salt on roads during winter.
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The benefits include substantial energy savings, reduced light pollution and a
minimised environmental impact achieved through a more targeted use of salt.
The success of this project has been recognised by the nomination in the
category ‘IT Project of the Year in Public Market’ by Computable Belgium.
About ML6
ML6 is a leading tech scale-up that helps their clients to navigate, activate
and build
trustworthy AI with the aim to innovate and gain a competitive edge. Through
its services, ML6 helps clients realise business impact by building and
deploying custom end-to-end AI solutions and by bringing client's AI
capabilities to the next level. ML6 has built a team of over 100 senior AI
experts, based in Ghent, Amsterdam and Berlin. ML6 has received Google Cloud
sustainability award for Benelux in 2022 and Google Cloud Partner award in
2015.
Accelerating businesses with AI technology & experts
Contact:
info@ml6.eu
+32 9 265 95 50
Contact us
Join our newsletter | scraping/output/-2267828673903295176.txt | [
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Randstad
# Increasing sales effectiveness at Randstad by leveraging multiple data
sources
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Randstad
# Increasing sales effectiveness at Randstad by leveraging multiple data
sources
## Impact
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.
## Intro to the customer
As one of the leading multinational human resource consulting firms, Randstad
has 39,530 employees spread over 38 countries. | scraping/output/-854568260067003203.txt | [
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## Intro to the customer
As one of the leading multinational human resource consulting firms, Randstad
has 39,530 employees spread over 38 countries.
Making sure that these people focus their time on contacting companies who
have real potential for Randstad, and enabling them to build conversations
based on accurate and relevant information has a direct impact on the success
of Randstad in serving its clients and hence realizing its own core ambition.
## Challenge
While Randstad already had the data and insights in the ‘right companies’,
with attractive commercial potential, this knowledge was not yet used
optimally to steer the activities of the sales organisation on a day-to-day
basis.
It therefore embarked on a mission to create an AI enabled solution that
allows a better and continuous use of data by proposing concrete, actionable
tasks to sales consultants through an intuitive end-user application.
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"Partnering with ML6 has allowed Randstad to embark on this AI journey in a
collaborative and co-creation way-of-working; ML6 facilitated the creation and
upskilling of the Randstad technical team and hence setting them up for the
future."
Global VP, Digital Strategy & Innovation
By
Randstad
## Solution
ML6, as AI specialist partner, is driving the setup and training of the data
intelligence components of the solution, as well as the implementation of the
AI ML Ops architecture to ensure that the solution can easily be scaled across
teams and countries, and retraining in a continuous, automated way. | scraping/output/-854568260067003203.txt | [
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This 'sales effectiveness tool’ consists of four different solution
components. Core is the scoring system that assesses the potential of an
individual company for Randstad, as well as how easy or difficult it will be
to get to that potential. This company segmentation in an attractiveness
quadrant is done based on company profile data, but also leveraging market
data to predict job needs for the next 12 weeks and to spot company buying
signals for Randstad services.
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The data-driven territory design aims to make the scoring specific by
geographic area, increasing the relevance of the generated insights for local
sales teams. The resulting kanban script - an automated and actionable to-do
list - provides individual sales consultants on a weekly basis with an
overview of companies to focus on; and insights about that company/industry as
a basis to structure the customer discussion.Finally, the market insights
solution component uses additional external data to provide the sales
consultants with further context, for example a valid business reason to call
the company, instant customer industry information, supporting data for
expectations management/ fillability and pricing negotiations.The 'sales
effectiveness tool’ is conceived to formally and continuously leverage data in
an outside-in approach, acquiring up to date, factual data from selected,
structured external data sources and applying time series based forecasting to | scraping/output/-854568260067003203.txt | [
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an outside-in approach, acquiring up to date, factual data from selected,
structured external data sources and applying time series based forecasting to
generate insights and triggers. | scraping/output/-854568260067003203.txt | [
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] |
The Dun & Bradstreet company repository serves as the source for a worldview
on all companies, existing Randstad clients as well as prospects, including a
comprehensive view on their profile features. Job demand data is gathered from
multiple sources like textkernel, careerjet, burning glass and jobdigger.
Finally, economic data per geography (eg gdp, employment) are acquired from
Oxford Economics.Defining and imposing data governance principles like global
definitions and data protection (e.g. no manual override) ensure data quality
and hence the relevancy of the weekly recommendations for sales consultants.
The onset of the Covid pandemic, as a textbook example of a high-impact, low-
frequency (HILF) event, has triggered the addition of Natural Language
Processing (NLP) techniques to spot extra data on irregular, unpredictable
events.
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"ML6 helped us to build and deploy an intelligent matching engine that has the
potential to have a big impact on our candidate and customer journey's. Hence
also influencing our top and bottom line. "
CIO (Randstad Belgium)
By
Randstad
## Results
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.
And that significant and demonstrated commercial business value is only the
start. The sales effectiveness tool has been built in line with the global
digital factory strategy of Randstad; readying it for deployment to a growing
number of countries, while retraining and refining the AI models on a monthly
basis.
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The rollout across all 38 Randstad countries is planned to be done with
explicit attention to tailoring of recommendations to varying market positions
and business lines or concepts. The setup of automated data ingestion and
processing pipelines, and retraining pipelines drives the scalability of the
solution and its ability to keep learning from new data. It will also speed up
the integration of new strategic insights and new data sources which are
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## Related posts
Placeholder tag
Know your unknowns: a short primer on uncertainty in machine learning
How to approach uncertainty in machine learning : forecasting, regression and
classification models.
September 19, 2022
By
Thomas Janssens
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] |
How to approach uncertainty in machine learning : forecasting, regression and
classification models.
September 19, 2022
By
Thomas Janssens
Placeholder tag
How to effectively label images for computer vision projects ?
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February 8, 2024
# Promoting Code Across Environments for Reliable and Efficient Model
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Share this post
ML6 presents a recommended CI/CD pattern for promoting ML model code across
environments using Amazon SageMaker.
This blogpost deep-dives into MLOps practices, highlighting the need for
reliable and efficient model deployment. By focusing on code promotion rather
than the model itself, ML6 streamlines ML workflows, accelerates time-to-
market, and enhances overall MLOps capabilities. The post includes insights,
architectural details, tech stack, CI/CD workflow, and a practical example of
fine-tuning a pre-trained model for medical transcription classification.
Through collaboration with AWS, ML6 offers an innovative solution, leveraging
Terraform and SageMaker Pipelines, to enable users to adopt this deployment
pattern effectively. By implementing these practices, organizations can
achieve reproducibility, reliability, and efficiency in ML model deployment.
Read the full blogpost on our Medium channel.
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May 22, 2022
# Using Synthetic Data to boost the Performance of your Object Detection Model
Contributors
Jules Talloen
Machine Learning Engineer
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Thank you! Your submission has been received! | scraping/output/2304708636802131724.txt | [
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A practical guide to generating artificial data using Unity Perception
Anyone who has ever trained a machine learning model will tell you that it is
the data that makes the model and that, in general, more data and labels will
lead to better performance. Collecting data and especially labeling them is
time-consuming and therefore expensive. Hence, machine learning professionals
are increasingly looking at more efficient ways of ‘augmenting’ their dataset
by using artificially generated variations of data samples, but also
increasingly at the use of hybrid or fully synthetic data. | scraping/output/2304708636802131724.txt | [
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Game engine company Unity offers a tool called Unity Perception which allows
you to simulate objects in different ways and take virtual pictures of your
simulations. In this way, it becomes possible to generate a high number of
labeled images from your object scans which can be used as training data.
In this blogpost, I present the results of my internship at ML6, in the course
of which I investigated the use of Unity Perception for generating training
data for an object detection model. More particularly, I will describe how I:
1. obtained object scans using a Lidar camera
2. generated a synthetic dataset for these objects using Unity
3. trained object detection models using both synthetic and real-world images
4. assessed the potential benefits of adding synthetic images to the training set
### Scanning the Objects | scraping/output/2304708636802131724.txt | [
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### Scanning the Objects
To create object meshes I used an app called Scaniverse on a phone with a
lidar camera. To do this well there are some requirements. You must be able to
capture the object from all sides and you require good and equal lighting on
all angles of the object
So I put the objects on a pedestal with good lighting, scanned the objects
using the app and afterwards took a separate picture of the bottom surface to
add it to the object mesh in Blender. With Scaniverse you can export your
objects as fbx or obj files, both work with Blender and Unity.
Example object scan in Blender
Not every object was easy to scan however. Especially objects with reflective
surfaces, thin or see-through segments were problematic to scan using this
method. If the scans are not great, there is still the possibility to edit
them afterwards using Blender.
Mostly for cropping and recentering them; or combining them with a bottom side
to get a complete object. | scraping/output/2304708636802131724.txt | [
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Mostly for cropping and recentering them; or combining them with a bottom side
to get a complete object.
I also made multiple scans for each object to increase the variation of the
objects in the synthetic images, and took around 200 pictures of each object I
scanned in various rooms and positions. I tried to have the objects in diverse
locations and took pictures from different angles. I made sure the pictures
were sometimes more blurry or only partially showing the object to further
increase variation.
Below are scans of the 3 objects I worked with: a shoe, a Coca Cola can and a
Kellogg’s cereal box.
I chose these objects because:
1. They were easy to move and manually take pictures of
2. They’ll fit with the indoor scene backgrounds
3. They are very different in terms of color, structure and reflective properties
Example object scans in Unity perception
### Generating Synthetic Data in Unity Perception | scraping/output/2304708636802131724.txt | [
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Example object scans in Unity perception
### Generating Synthetic Data in Unity Perception
Unity Perception is a toolkit for generating datasets. It is relatively new
and for now only works on camera-based use cases. It is a plugin for Unity, a
game engine and cross-platform IDE.
To generate training images I used a simulation that positions your objects
with random rotations, scales and positions in front of a background and
before a light source and a camera. The objects were drawn with equal
probability; every class was equally represented.
Unity perception UI | scraping/output/2304708636802131724.txt | [
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] |
Unity perception UI
For more variation I randomized the lighting intensity and used different
smoothness properties for the objects and different types of background
generation. I used the same background generation scheme as used by unity in
their tutorial for Unity Perception consisting of random objects with random
textures as their background. This ensures a very different background for
each simulation with no other recognisable objects aside from the object
classes. For general purposes this is a good baseline to begin with (this
situation is referenced as Simulation 1 in the graph below).
Tutorial background simulation | scraping/output/2304708636802131724.txt | [
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] |
Tutorial background simulation
However, training on only these images did not yield very good results so I
tried switching to a random background image instead. These background images
were sampled from an online dataset of indoor scenes. This drastically
improved results, as I will show in the graph below. The dataset these
background images are sampled from should be chosen depending on the natural
context of your object class.
Simulation with background image
To further improve the scene I added random objects but now in fewer numbers
and in the foreground. These objects sometimes partly obscure classes and also
make for a more diverse scene.
Simulation with background image and random objects
Another improvement I made is using multiple object meshes for the same object
class. This improves the results for more complex objects.
### Training Object Detection Models | scraping/output/2304708636802131724.txt | [
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Another improvement I made is using multiple object meshes for the same object
class. This improves the results for more complex objects.
### Training Object Detection Models
I used the Tensorflow Object Detection API to train the models. I imported
pre-trained models from the Tensorflow model zoo and then fine-tuned them on
my data. I chose to work with a mobilenet, since it is a relatively fast model
that allows me to obtain decent results in a short time.
When taking pictures, I moved the objects to a different location for every
picture and took pictures from many angles. I also took pictures in which
objects were either partially obscured or not entirely in frame; all the
pictures were taken with the same phone in the same quality. | scraping/output/2304708636802131724.txt | [
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I used LabelImg to manually label the pictures before partitioning them into a
training (80%), validation (10%) and test (10%) set. The validation set was
used to choose model hyperparameters and the test set for final evaluation.
The pictures for the test set were taken in different rooms than those for the
training set; same for the validation set.
I made sure every class was equally represented in all the partitions. I added
the synthetic images to the training set; the validation and test sets
consisted only of real-world pictures.
To measure the performance of the models I used the mean average precision
(mAP) at 0.5 Intersection over Union score and the average recall (AR) at 10
predictions (True Positive when object detected in top 10 predictions).
The mAP is good at measuring how accurate the detections are. While The AR
measures how well the model finds all the positives (see also here).
### Experiments | scraping/output/2304708636802131724.txt | [
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] |
The mAP is good at measuring how accurate the detections are. While The AR
measures how well the model finds all the positives (see also here).
### Experiments
I trained models using only real-world pictures, only synthetic images, or a
mixture of both. By comparing results, I show the benefit of adding synthetic
images and the best real-world/synthetic split in training images to work
with. The mixed models were trained using the same sampling scheme for both
real-world and synthetic images. With different sampling schemes, you can
likely make even better models. However I used the same sampling for the sake
of simplicity.
Another way of training these models is by first training them only on
synthetic data and then fine-tuning them on real-world data. For me this gave
similar but slightly worse results. However, the models were rather simple and
I am sure this can be improved upon. | scraping/output/2304708636802131724.txt | [
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I trained mixed models on the 4 different types of background simulations
shown in the previous section. The table below summarizes the mAP/AR results
for these simulations:
Simulation 1: Tutorial simulation; simulation 2: Using background images;
simulation 3: Using background images with random objects; simulation 4: Using
multiple meshes for each object. models trained with 1000 synthetic and 500
real-world images
### Performance Comparison between Different Synthetic/Real-world Splits
First, I wanted to see how good the synthetic-only models were and what is the
least amount of real-world pictures needed to obtain decent results. I trained
the synthetic-only models with 1000/2000/5000/100000 synthetic images; all had
around the same scores around 0.3 mAP@0.5IoU, which is not great. Some
examples results for the different models can be found in the pictures at the
end of this blogpost. | scraping/output/2304708636802131724.txt | [
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Then I trained models with 0, 30, 100, 300 and 530 real-world pictures in
addition to the 1000 synthetic images and compared results.
Comparison between models trained on different amounts of real-world images
I noticed that the mAP and AR scores increased as I added more real-world
images to the training set until I had added 300 real-world images; the extra
230 images in the last model of 530 real-world training images did not seem to
help much. The 1000 synthetic/300 real-world image model could detect most
objects in the images, missing mainly some of the shoe detections. | scraping/output/2304708636802131724.txt | [
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] |
So far I only showed that a mix of both is better than synthetic-only, but not
how much the synthetic data improves our models. Thus I trained a model only
on the 300 real-world images. This performed very poorly, even worse than the
synthetic-only model. When evaluating this model, I noticed the model was
almost exclusively detecting the Coca Cola cans; showing me that while 100
pictures is enough to train an object detection model for the Coca Cola cans,
it was not sufficient for the other 2 classes. However for testing purposes I
kept using the same training set of approximately 100 pictures for each class.
I then added just 300 synthetic images to the training set and already noticed
that the scores more than doubled. When I kept adding more synthetic data
however, the models started to perform more poorly. Perhaps using a different
sampling scheme for the real-world data could allow me to add more synthetic
images to the training set. | scraping/output/2304708636802131724.txt | [
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Comparison between models trained on different amounts of synthetic images
### Performance Comparison between Different Classes
While manually evaluating the models I noticed major differences between how
well some of the models were detecting the classes. Thus I evaluated them for
each class separately and looked at the results.
models trained on 5000 synthetic images, 500 real-world images and a mixture
of 2000 synthetic and 300 real-world images; evaluated on each class
separately
The shoe class scores badly in most of the models; this is the most complex
object I used and also the least colorful, which may contribute to its low
scores. By complex I mean that it looks different from every angle, and has
some thin segments that are moveable in the shoelaces. | scraping/output/2304708636802131724.txt | [
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The real-world models are very good at recognising the Coca Cola cans, however
for the other classes they are failing. Using the synthetic models however,
the cola cans are not so easily detected. This may be due to their reflective
properties, which are not simulated well in Unity Perception. However, there
are options in Unity to better simulate reflections, which I did not fully
make use of but certainly could help here.
The Kellogg’s cereal box is a “simpler” object and scores very well in
synthetic-only data; this shows that for these kinds of objects a synthetic-
only dataset can already provide great results on its own.
Example detection of model trained on 300 real-world images
Example detection of model trained on 1000 synthetic images
Example detection of model trained on 300 real-world images and 1000 synthetic
images
### Conclusion | scraping/output/2304708636802131724.txt | [
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Example detection of model trained on 1000 synthetic images
Example detection of model trained on 300 real-world images and 1000 synthetic
images
### Conclusion
In this post I showed that adding synthetic images to object detection models
can greatly improve your models. I explained step by step how to do this from
scanning the objects to training the object detection models. I showed that
best results are obtained with a model trained on a mix of both real-world and
synthetic images and that you can already achieve decent results with few
real-world images if you add synthetic data. I also concluded that simple
objects, like the Kellogg’s box, benefit greatly from the addition of
synthetic images. It is harder to obtain representative meshes from objects
with thin, see-through or reflective segments; however even models trained on
those objects get better results with the addition of synthetic data. | scraping/output/2304708636802131724.txt | [
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Perhaps for models with a very large set of qualitative real-world images, the
addition of synthetic data will not result in improvements. However, for
models with few qualitative real-world images such as my example, the addition
of synthetic data is a major boost to results. To conclude I am satisfied with
the results as the improvements of adding synthetic data were even greater
than expected. I hope you find this post useful and it inspires you to try
this and further improve the simulations to achieve even better results!
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Data Protection & Security | scraping/output/2304708636802131724.txt | [
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] |
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Employee Log in Candidate Log in to Connect Homepage ml6.eu
# Privacy Policy
### Privacy policy for recruitment using Teamtailor | scraping/output/-5576522641546504031.txt | [
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* Join us 🕵️♀️
Employee Log in Candidate Log in to Connect Homepage ml6.eu
# Privacy Policy
### Privacy policy for recruitment using Teamtailor
The service for handling recruitments and simplifying the hiring process (the
"Service") is powered by Teamtailor on behalf of ML6 ("Controller" “we” “us”
etc.). It is important that the persons using the Service ("Users”) feel safe
with, and are informed about, how we handle User's personal data in the
recruitment process. We strive to maintain the highest possible standard
regarding the protection of personal data. We process, manage, use, and
protect User's Personal Data in accordance with this Privacy Policy ("Privacy
Policy").
### 1\. General
We are the controller in accordance with current privacy legislations. The
Users’ personal data is processed with the purpose of managing and
facilitating recruitment of employees to our business.
### 2\. Collection of personal data | scraping/output/-5576522641546504031.txt | [
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### 2\. Collection of personal data
We are responsible for the processing of the personal data that the Users
contributes to the Service, or for the personal data that we in other ways
collects with regards to the Service.
When and how we collect personal data
We collect personal data about Users from Users when Users;
* make an application through the Service or otherwise, adding personal data about themselves either personally or by using a third-party source such as Facebook or LinkedIn; and
* use the Service to connect with our staff, adding personal data about themselves either personally or by using a third-party source such as Facebook or LinkedIn.
* provides identifiable data in the chat (provided through the website that uses the Service) and such data is of relevance to the application procedure; | scraping/output/-5576522641546504031.txt | [
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