chunk
stringlengths 11
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| source
stringlengths 37
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| embeddings
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|---|---|---|
### Beyond general techniques
All techniques discussed in the previous sections are (at least in theory)
generally applicable to any neural network architecture, but additional
efficiency gains can be obtained by focusing on specific architectures.
One very relevant example is the transformer architecture which has dominated
the state of the art in many fields (especially NLP) for a few years now and
keeps on pushing the limits in terms of model sizes.
This is all good and well if you work for Google or OpenAI, but for many
people the hardware requirements are very cumbersome. It should therefore not
surprise you that a lot of research has been focused on trying to make the
transformer architecture (and more specifically the self-attention mechanism)
more efficient. An extensive overview of these techniques are out of scope for
this blogpost, but the talk about high performance natural language processing
at EMNLP 2020 is a great resource to dive into this topic.
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Large Language Models: to Fine-tune or not to Fine-tune? | scraping/output/-7655118339217718135.txt | [
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Elevating excellence : accelerating each others talent | scraping/output/-7655118339217718135.txt | [
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] |
Implementing ML models' code promotion across environments using CI/CD
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Placeholder tag
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January 11, 2024
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Unlocking the Secrets of Life: AI Protein Models Demystified
A brief overview of what proteins are, their characteristics and the
applications of protein engineering. | scraping/output/-7655118339217718135.txt | [
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Placeholder tag
Unlocking the Secrets of Life: AI Protein Models Demystified
A brief overview of what proteins are, their characteristics and the
applications of protein engineering.
November 15, 2023
By
Medha Hegde
Placeholder tag
Whisper Deployment Decisions: Part I — Evaluating Latency, Costs, and
Performance Metrics
Optimizing OpenAI Whisper deployment in production.
November 5, 2023
By
Placeholder tag
Assessing the impact of the EU AI Act proposal
How can model providers comply with regulation concerning training data &
copyright?
October 31, 2023
By
Michiel Van Lerbeirghe
Placeholder tag
LLAMA 2: So good they named it twice
Meta's LLAMA model takes center stage, but there's a catch: it's open-source,
but not entirely. Here's the story of LLAMA, its recent updates, and what it
means for you.
October 10, 2023
By
Placeholder tag
Webinar | Generative AI for corporate use - Fine tuning foundation models | scraping/output/-7655118339217718135.txt | [
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] |
October 10, 2023
By
Placeholder tag
Webinar | Generative AI for corporate use - Fine tuning foundation models
In this webinar, we explored the benefits and challenges of the fine-tuning
approach, discovering when to opt for fine-tuning, outlining the different
techniques and offering practical guidelines for getting started with fine
tuning.
October 5, 2023
By
Placeholder tag
The EU Data Governance Act (DGA): an engine for innovation
Discover the DGA's impact on data sharing, privacy, and innovation, and how
ML6 solutions align with its objectives.
October 2, 2023
By
Michiel Van Lerbeirghe
Placeholder tag
Get Started with Machine Learning Faster Using Amazon SageMaker JumpStart
Unlock Machine Learning with Amazon SageMaker JumpStart: A Comprehensive Guide
September 28, 2023
By
Placeholder tag
Creating a Chatbot with Amazon Bedrock | scraping/output/-7655118339217718135.txt | [
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Unlock Machine Learning with Amazon SageMaker JumpStart: A Comprehensive Guide
September 28, 2023
By
Placeholder tag
Creating a Chatbot with Amazon Bedrock
In an era where digital interactions dominate, chatbots play a crucial role in
enhancing user experience. Amazon Bedrock emerges as a beacon of simplicity,
allowing developers to harness the power of cutting-edge machine learning
models.
September 20, 2023
By
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AI internships at ML6: nine domains to steer your career towards
At the end of college years, it can be very confusing to start determining
your next steps. Are you 100% convinced about your choice of study? Do you
have doubts about how your education plays out in practice? These questions
certainly don't come as a surprise. There is a lot coming at you. For this
reason, it is very interesting to explore the field in practice. Luckily,
schools often offer that option through internships.
September 6, 2023
By
Julie Plusquin
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September 6, 2023
By
Julie Plusquin
Placeholder tag
Amazon Bedrock: Building a solid foundation for Your AI Strategy
Amazon recently announced their foundation model service: Amazon Bedrock. We
got our hands on it as part of a limited preview. So, what’s in it for you?
September 6, 2023
By
Placeholder tag
Fine-tuning Whisper for Dutch Language: The Crucial Role of Size
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Placeholder tag
From hype to action: charting your AI course | scraping/output/-7655118339217718135.txt | [
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] |
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] |
August 8, 2023
By
Placeholder tag
Dynamic Pricing from a visual perspective
Visualising Dynamic Pricing modelling to build insights
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Deploy AI models in your OT environment and do it securely
Securing AI deployment in OT environments.
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Unlocking the Power of Google's PaLM 2: Harnessing Generative Language Models
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models for achieving business success.
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Beating your competition in e-commerce with Dynamic Pricing
Learn how dynamic pricing has revolutionized e-commerce through probabilistic
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AI can play a crucial role in the field of HR, but should we blindly follow? | scraping/output/-7655118339217718135.txt | [
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] |
July 24, 2023
By
Placeholder tag
AI can play a crucial role in the field of HR, but should we blindly follow?
Is AI only applicable within factories and large corporate organizations?
Anything but. AI will (and already is) creating impact within the Human
Resources branch as well. We speak with Julie Plusquin, Talent Partner at ML6
about what the evolution within HR may look like.
July 20, 2023
By
Julie Plusquin
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Smart energy: how to make your home battery smart and save money?
Learn how to make your home battery smarter by connecting it to external
inputs such as energy prices and weather. This can save up to 35% on
electricity costs.
July 16, 2023
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Harnessing the Power of Foundation Models for your Business with LoRA
Harness large foundation models affordably with LoRA technique for business
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July 13, 2023
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Predicting enzyme heat stability during Christmas break | scraping/output/-7655118339217718135.txt | [
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] |
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June 19, 2023
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Placeholder tag
Developing AI Systems in the Foundation Model Age : An intro
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Developing AI Systems in the Foundation Model Age : From MLOps to FMOps
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Webinar | Generative AI for corporate use: How to get started with LLM
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started with Large Language Models
June 2, 2023
By
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Recording of the webinar on Generative AI for corporate use: How to get
started with Large Language Models
June 2, 2023
By
Placeholder tag
How specialized AI models can support designers in their daily work
Generative AI as your co-pilot: specialized AI models and their impact on
design innovation.
May 25, 2023
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Optical 3D Acquisition Methods: A Comprehensive Guide [Part 2]
Discover 3D optical acquisition methods: Stereo Vision, Structured Light, Time
of Flight, and LiDAR.
May 21, 2023
By
Francisco Pereira
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3D Computer Vision: Unlocking the Third Dimension [Part 1]
Discover the power of 3D computer vision and its impact on industries. Learn
about advantages, applications, and trends.
May 19, 2023
By
Francisco Pereira
In-Line Quality Inspection & Process Steering
Placeholder tag
Open source catching up to OpenAI
Exploring Recent Developments in Open Source LLMs and Their Implications.
May 19, 2023
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In-Line Quality Inspection & Process Steering
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Open source catching up to OpenAI
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May 19, 2023
By
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The Compliance-Friendly Guide to Using ChatGPT and other GPT models
In this blogpost we explore the different options out there around usage of
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May 17, 2023
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Generative AI
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Doubling down on learning and innovation
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May 17, 2023
By
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Placeholder tag
Low Rank Adaptation: A Technical Deep Dive | scraping/output/-7655118339217718135.txt | [
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] |
May 17, 2023
By
Kaat Van Doren
Placeholder tag
Low Rank Adaptation: A Technical Deep Dive
LoRA is revolutionizing fine-tuning for large AI models, enabling efficient
adaptation without straining resources. Explore its principles, effectiveness,
and impact on the open-source community.
May 17, 2023
By
Generative AI
Placeholder tag
AI Act Amendments (Joint committee vote 11/5/23)
Discover key amendments proposed in the EU AI Act, including banned and high-
risk applications, regulations for foundation models and generative AI, and
the impact on open-source components. Prepare for upcoming AI regulations and
their implications.
May 17, 2023
By
Placeholder tag
State of the LLM: Unlocking Business Potential with Large Language Models
Explore the potential of large language models (LLMs) for businesses: models,
tooling, challenges, and applications
May 16, 2023
By
Generative AI
Placeholder tag
How to label your way to accurate Automatic Speech Recognition (ASR) | scraping/output/-7655118339217718135.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/-7655118339217718135.txt | [
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] |
April 18, 2023
By
Jens Bontinck
Generative AI
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Improving Tennis Court Line Detection with Machine Learning | scraping/output/-7655118339217718135.txt | [
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0.03436810150742531,
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0.014714202843606472,
0.055542971938848495
] |
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/-7655118339217718135.txt | [
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] |
This blog post explores the current state of AutoML tools and their ability to
deliver on the promise of automating machine learning tasks without expert
knowledge.
January 3, 2023
By
Julien Schuermans
Placeholder tag
Welcome to the multi-language-pipeline universe
Apache Beam has lots of I/O connectors available, but not all connectors are
in your language of choice.
December 15, 2022
By
Andres Vervaecke
Placeholder tag
A first impression of Google Cloud’s new Visual Inspection AI tool
Key take-aways of Google Cloud’s new Visual Inspection AI tool.
December 14, 2022
By
Jérémy Keusters
Placeholder tag
Dynamic pricing in practice
Explore the practical implementation of dynamic pricing, its effectiveness,
challenges, and the use of Thompson sampling to optimize prices.
December 12, 2022
By
Placeholder tag
AI for the green transition - Applications and use cases [Part 1] | scraping/output/-7655118339217718135.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
Placeholder tag
Why every AI project needs an MLOps ecosystem in place
This blogpost covers the need for MLOps and how it helps to manage the
complexity of machine learning models and deployments. Secondly, it addresses
the business implications of implementing MLOps as a proper ecosystem. Lastly,
the challenges of building an MLOps capability are covered to ensure the
success of your machine learning initiatives at your organisation.
November 1, 2022
By
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Vertex AI is All You Need | scraping/output/-7655118339217718135.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/-7655118339217718135.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/-7655118339217718135.txt | [
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Placeholder tag
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NVIDIA Deepstream Quickstart | scraping/output/-7655118339217718135.txt | [
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Semantic search: A practical Overview | scraping/output/-7655118339217718135.txt | [
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A practical guide to image-based anomaly detection using Anomalib
A short introduction to unsupervised anomaly detection and how to apply it.
March 31, 2022
By
Sebastian Wehkamp
Placeholder tag
Building a coral segmentation model using sparse data
In light of a partnership with the organizations Reef Support and Fruitpunch
AI, we designed an image segmentation algorithm. The main challenge here was
the way the data is labelled, containing sparse pixel annotations created by
domain experts. The blogpost covers on a high level how we solved this
challenge, and how the challenge will be going forward.
March 28, 2022
By
Placeholder tag
3D object detection in the real world
In this blogpost, we look at object detection using 3D ‘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
Placeholder tag
Need for Speed: JAX
Train neural networks faster with JAX on TPUs
March 2, 2022
By
Jasper Van den Bossche
Placeholder tag
Training your neural network ten times faster using Jax on a TPU
Introduction blogpost to JAX : how it works, how it is different from
Tensorflow/Pytorch and why we think every machine learning engineer should add
it to its arsenal of tools.
February 3, 2022
By
Jasper Van den Bossche
Large Language Model
Foundation Models
Corporate
People
Structured Data
Chat GPT
Sustainability
Voice & Sound
Front-End Development
Data Protection & Security
Responsible/ Ethical AI
Infrastructure
Hardware & sensors
MLOps
Generative AI
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## Related posts
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How to detect small objects in (very) large images
We are often asked how to detect very small objects in high resolution, i.e.
very large images. A good example is finding objects in aerial images. The
goal of this blog post is to demonstrate a practical approach to this problem
using Slicing Aided Hyper Inference (SAHI).
September 11, 2021
By
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By subscribing you agree to with our Privacy Policy | scraping/output/1840059133410400175.txt | [
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### A quick overview of LLAMA 2 and a short recap of what led up to it
So Meta did a thing. LLAMA 2 is now out!
But what the hell is a LLAMA, how did we get here and what do you (yes, you)
need to know if you want to use it.
### What the hell is a LLAMA?
LLAMA is a domesticated South American camelid, widely used as a meat and pack
animal by Andean cultures since the Pre-Columbian era.
LLAMA is also Meta’s Large Language Model that got an update recently — which
is what we’re talking about here.
(llama is apparently also a bowling term for four strikes in a row — which may
win you a quiz one day)
### How did we get here?
How LLAMA 2 came to be is quite the story. So buckle up because we’re in for a
ride.
Let’s cast our minds back a few months. | scraping/output/5072385454336786681.txt | [
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### How did we get here?
How LLAMA 2 came to be is quite the story. So buckle up because we’re in for a
ride.
Let’s cast our minds back a few months.
\- Google taunted the demo gods and did a Tesla during Bard’s release (link).
\- Elon Musk pulled the old switcheroo — pushing for a halt on AI before
starting his own AI company. Classic Elon. Oldest trick in the book.
\- And Samsung decided to open-source their internal documents (link).
Good times.
BUT ALSO: Meta entered the chat.
Mark Zuckerberg laid out his carefully constructed vision that open-source
LLMs are the future and boldly released LLAMA as a completely open model — no
strings attached!
Which would have been a bold (or should I say re-Mark-able) move if not for
one major flaw: that is not at all what actually happened.
So let’s recap what did happen:
#### February: Meta releases LLAMA | scraping/output/5072385454336786681.txt | [
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] |
So let’s recap what did happen:
#### February: Meta releases LLAMA
Mark and his funky bunch announce that the LLM hype train will not leave the
station without them and release their very own model: LLAMA.
Or should I say LLAMA-RK. Sorry.
Unlike the usual suspects (aka Google, AWS, Microsoft and OpenAI), they decide
to not keep the model under lock and key 🔐.
Instead opting to share the model with a restricted group of researchers under
a non-commercial license.
In this way they want to “maintain integrity and prevent misuse”. Which they
will — unless someone leaks LLAMA but that would never happen.
#### March: Meta does an oopsie
Pretty much immediately, someone leaks LLAMA. As in: the entire thing. So now,
anyone can get their hands on the model and basically do whatever they want
with it — completely bypassing the restrictions. | scraping/output/5072385454336786681.txt | [
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] |
Meta underlines that — despite the leak — LLAMA’s license still applies.
You still can’t use it commercially and unauthorized people still can’t use it
at all.
#### March: Unauthorized people use LLAMA anyway
The AI community reacted to Meta’s warning with a collective “damn that’s
crazy” and proceeded to ignore it completely.
Live reaction from the AI community
At this point, the model is in the hands of the masses. And those hands are
notoriously sticky.
Meta realizes that they can’t sue literally everyone, basically accepts the L
and lets it slide.
Side note: they are much less lenient on their ban on commercial use and it’s
definitely feasible for them to sue businesses so don’t get any ideas.
#### March — July: People go ham on LLAMA
It didn’t take long for the AI community to do what it does best: optimize the
hell out of something.
And boy, did they optimize LLAMA. | scraping/output/5072385454336786681.txt | [
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#### March — July: People go ham on LLAMA
It didn’t take long for the AI community to do what it does best: optimize the
hell out of something.
And boy, did they optimize LLAMA.
In a matter of weeks, people managed to get LLAMA running on a phone. People
were training LLAMA variations such as Vicuna that rival Google’s Bard,
spending just a few hundred bucks.
source: https://lmsys.org/blog/2023-03-30-vicuna/
### July: Things have gone full-circle
The leak that originally bit Meta firmly in the derrière (pardon my French)
went full circle and ironically became their biggest advantage.
They had initially planned to give LLAMA to a select group of researchers who
could tweak & optimize it.
Instead, the model got leaked to the general public. Which is obviously a bad
look for a multi-billion dollar company but it also made it so that everyone
was optimizing LLAMA (not just a few researchers).
This accelerated progress beyond their most optimistic estimates. | scraping/output/5072385454336786681.txt | [
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Was this Meta’s master plan all along?
We’ll leave that thought to our colleagues in the conspiracy theory business.
#### July: Meta doubles down and releases LLAMA 2
Which brings us to today. Meta just released a big update to their LLAMA
model. And it is open-source. On purpose this time!
There are a few strings attached though which we’ll circle back to
(*cliffhanger*).
This more or less confirms that they are doubling down on the open-source
trend they inadvertently kickstarted.
Gotta love how things play out sometimes.
Let’s take a closer look at what exactly they released, what those infamous
few strings are and what this all means for you.
### What you need to know about LLAMA 2
Small disclaimer: all technical details are nicely & succinctly put in Meta’s
announcement so regurgitating the same information doesn’t add much.
Instead, we’ll skip over a lot of the fine print and focus solely on the
highlights that you (yes, you) should know. | scraping/output/5072385454336786681.txt | [
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Instead, we’ll skip over a lot of the fine print and focus solely on the
highlights that you (yes, you) should know.
#### It’s good but we’re just getting started
LLAMA 2 comes in four sizes (7B, 13B, 65B and 70B).
\- The smaller varieties achieve about the same performance as the current
open-source state-of-the-art (albeit with a much smaller model).
\- The bigger varieties are significantly better than the current open-source
state-of-the-art.
Obligatory side note: evaluating LLMs is notoriously difficult. Currently, the
evaluations are made based on GPT-4 evaluation, (subjective) human evaluation
and scores on standardized tests (i.e., think SATs and the likes).
So any statement about accuracies we make here are by no means very accurate
(ironically) but enough to get a general impression.
That’s all very nice but this is not the end of the road. Not even close. | scraping/output/5072385454336786681.txt | [
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That’s all very nice but this is not the end of the road. Not even close.
Just like when LLAMA got leaked, LLAMA 2 is now also in the hands of the many.
Especially because it’s not even a legal grey area now, those hands will be
firmly glued to their collective keyboards — working their magic and
optimizing the hell out of it.
In fact, we are already seeing the first signs of the open-source magic, e.g.:
\- Baby Llama: LLAMA 2 implementation fully in C
\- Stable Beluga 2: Instruction fine-tuned LLAMA 2 by stability.ai
\- …
Expect lots more open-source progress based on LLAMA 2.
#### It’s open-source but also not really?
This is where the infamous few strings attached come in.
Here’s the deal:
Is it open-source? Yes.
Can you use it commercially? Yes. | scraping/output/5072385454336786681.txt | [
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#### It’s open-source but also not really?
This is where the infamous few strings attached come in.
Here’s the deal:
Is it open-source? Yes.
Can you use it commercially? Yes.
BUT there are some caveats to be aware of:
\- If you have more than 700mln monthly active users, you need a license from
Meta. Which probably isn’t an issue for you but it is for the cloud
providers.- You can’t use LLAMA 2 (or its output) to improve other LLMs.
Again, this shouldn’t be much of an issue for you but it is for Meta’s
competitors.
\- You can’t use LLAMA 2 in an illegal, unethical or irresponsible way. Meta’s
acceptable use definition of unethical / irresponsible that is. This is very
reminiscent of the OpenRAIL initiative that e.g., StableDiffusion adopted in
their license.
We highly recommend taking a look at Meta’s acceptable use policy. Although
overall, it is largely what you would expect.
If you want to read the full LLAMA 2 license, by all means. | scraping/output/5072385454336786681.txt | [
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We highly recommend taking a look at Meta’s acceptable use policy. Although
overall, it is largely what you would expect.
If you want to read the full LLAMA 2 license, by all means.
Side note: the license refers to their acceptable use policy via URL.
So if they made another oopsie and forgot something important, they can still
go back and add it without needing to touch the actual license itself.
If you want to use the model and play it extra safe, we’d suggest making a
dated PDF copy of the policy.
#### Solid business by Meta
All in all, it’s pretty smart business from Meta.
With their LLAMA 2 license, they basically combine the advantages of open-
source with the advantages of closed-source: | scraping/output/5072385454336786681.txt | [
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] |
All in all, it’s pretty smart business from Meta.
With their LLAMA 2 license, they basically combine the advantages of open-
source with the advantages of closed-source:
* On one side, the general public can ethically use LLAMA 2 however they please. So Meta will benefit from all the open-source progress that will be made.
* On the flip side, other model providers essentially can’t use LLAMA 2 at all which protects Meta’s competitive edge.
And the major cloud providers can’t resell LLAMA 2 without a commercial
agreement with Meta which is exactly how they want to monetize it.
Don’t believe me? Mark Zuckerberg said this during Meta’s quarterly earnings
call:
“If you’re someone like Microsoft, Amazon or Google, and you’re going to
basically be reselling the services, that’s something that we think we should
get some portion of the revenue for”
~ Mark Zuckerberg, July 27 2023 | scraping/output/5072385454336786681.txt | [
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So basically everyone can ethically use it except for their competitors and
the parties they can make the big bucks from 💸. Pretty clever.
So clever in fact that we could see these halfopen-source/pseudopen-source
licenses becoming a wider industry standard (*foreshadowing*).
If this turns out to be spot on, you heard it here first.
If his MMA career doesn’t work out, this Mark Zuckerberg guy should look into
starting a company.
#### Meta Microsoft
Microsoft is officially Meta’s preferred partner for LLAMA 2.
However, the exact scope of being a LLAMA 2 preferred partner is not crystal
clear. As, for example, the model is also available on AWS and Google Cloud so
we know that it’s not an exclusivity agreement.
Either way, it is interesting to see Microsoft doubling down on their
partnership with Meta after their partnership with OpenAI. | scraping/output/5072385454336786681.txt | [
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Either way, it is interesting to see Microsoft doubling down on their
partnership with Meta after their partnership with OpenAI.
\- Does Microsoft see Meta’s open-source LLM strategy as the long-term play
and OpenAI’s closed-source GPTs as more of a short-term bet to gain a first-
mover advantage in the LLM space?
\- Or are they simply opportunistically betting on all the most promising
horses in the race to generative AI leadership? And one of these horses just
happens to be a llama.
\- Maybe they want to position OpenAI’s and Meta’s offerings differently and
believe that there are synergies to banking on both.
Shameless speculation aside, the only real answer is that we don’t know and
neither do you. Time will tell though.
### Conclusion
Meta stumbled its way onto the open-source LLM podium and decided to stick
around.
They released LLAMA 2 which is looking very decent but the real value will
come from the general public tinkering around with it. | scraping/output/5072385454336786681.txt | [
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] |
They released LLAMA 2 which is looking very decent but the real value will
come from the general public tinkering around with it.
It’s pretty much open-source. As in: you can do whatever you want with it
(both commercially and non-commercially)— except:
\- if your name is Google, Amazon or Microsoft
\- if you’re planning to do something illegal, unethical or irresponsible.
Meta’s definition of unethical and irresponsible that is.
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Voice & Sound
Front-End Development
Front-End Development
Data Protection & Security
Data Protection & Security
Responsible/ Ethical AI
Responsible/ Ethical AI
Infrastructure
Infrastructure
Hardware & sensors
Hardware & sensors
MLOps
MLOps
Generative AI
Generative AI
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# Hardware & Sensors
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# Hardware & Sensors
Extra computing power for when you need it the most
Learn moreContact us
Compute, i.e. digital brainpower, and data are the main resources for the
development and use of AI solutions and Machine learning models. In most
cases, cloud providers supply these resources.
However, many complex business situations require additional compute resources
to be installed and managed on-premise. Examples are:
1. Machine learning for automatic quality assurance in manufacturing. Sorting products on a high-speed conveyor belt requires fast and accurate decision-making. Any latency in data processing would result in wasted materials and high costs.
2. Processing highly sensitive data that requires private, on-premises servers to meet GDPR requirements. | scraping/output/105758856737077937.txt | [
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] |
Our hardware solutions help you overcome these challenges, but we take it a
step further with our sensor expertise. This unlocks a whole new set of AI
business opportunities. Our use of depth cameras, temperature and pressure
sensors adds - literally - a new dimension to your data.
## Client cases
Discover how our expertise in Computer vision leads businesses to success
Placeholder tag
Automating the creation of VRT news summaries by using Natural Language
Processing
The AI driven model is a powerful tool in assisting journalists in their
writing work by speeding up the summary creation as well as suggest
alternative wordings. In that way, human creativity and AI can enhance each
other’s complementary strengths and produce high quality results.
May 16, 2022
By
Jens Bontinck
This is some text inside of a div block.
Public & Professional Services
This is some text inside of a div block.
Large Language Model
Foundation Models
Corporate
People
Structured Data
Chat GPT | scraping/output/105758856737077937.txt | [
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Public & Professional Services
This is some text inside of a div block.
Large Language Model
Foundation Models
Corporate
People
Structured Data
Chat GPT
Sustainability
Voice & Sound
Front-End Development
Data Protection & Security
Responsible/ Ethical AI
Infrastructure
Hardware & sensors
MLOps
Generative AI
Natural language processing
Computer vision
## Typical challenges
Our hardware and sensor solutions overcome even the most complex functional
and technical obstacles, making sure you achieve all your business goals.
### Limited compute power and memory
When the unlimited scaling potential of the cloud isn’t accessible, we
carefully select and tune our AI models to stay within our own hardware's
capabilities so we don’t compromise on accuracy or performance.
### Diagnosability and transparency
Built to be an indispensable part of your installation, we always make sure
our solutions are transparent so you can diagnose and troubleshoot problems
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### Diagnosability and transparency
Built to be an indispensable part of your installation, we always make sure
our solutions are transparent so you can diagnose and troubleshoot problems
yourself.
### Noisy sensor data and sensor drift
Effective integration with physical sensors requires checking the sensory
input for any noise, drift or outliers. We make this possible through alerting
mechanisms or by using an AI solution that is robust enough to deal with
these.
### Security
Using on-premises hardware poses additional security risks because you cannot
rely on a cloud provider's managed security services. However, we always take
this into account when solving your business problem. In addition, we advise
you to maintain a separation between IT and OT in manufacturing environments.
## High level outline of the solution | scraping/output/105758856737077937.txt | [
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## High level outline of the solution
We typically recommend a hybrid architecture in which we only deploy what is
absolutely necessary on-premise. All other solution components that can be
moved to the cloud will be moved.
Using the conveyor belt example from earlier, we would keep our trained AI
model and data collection on-premises, but the collected data would be
uploaded to the cloud for storage, human annotation, and retraining of a
superior AI model. The superior model would then be used on-premises. Since
the uploaded data is in the cloud, it allows us to test a wide range of
solutions without being limited by hardware.
## Resources
View all
Placeholder tag
Google’s EdgeTPU benchmarked : >10x speedup vs Intel’s Movidius
Google's EdgeTPU Dev Board surpasses Intel's Movidius Stick with a >10x
speedup in inference time.
March 13, 2019
By
Arne Vandendorpe
Large Language Model
Foundation Models
Corporate
People
Structured Data
Chat GPT
Sustainability | scraping/output/105758856737077937.txt | [
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March 13, 2019
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December 12, 2022
# Dynamic pricing in practice
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### Why care about dynamic pricing? | scraping/output/5683155270269197020.txt | [
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### Why care about dynamic pricing?
Dynamic pricing aims to actively adapt product prices based on insights about
customer behavior.
This pricing strategy has proven exceptionally effective in a wide range of
industries: from e-commerce (e.g., TB International) to sports (e.g.,
Ticketmaster) and from food delivery (e.g., GO-JEK) to automotive (e.g.,
Carro) as it allows to adjust prices to changes in demand patterns.
It is hardly surprising that recent times of extraordinary uncertainty and
volatility caused a surge in the adoption of dynamic pricing strategies with
an estimated 21% of e-commerce businesses reportedly already using dynamic
pricing and an additional 15% planning to adopt the strategy in the upcoming
year. | scraping/output/5683155270269197020.txt | [
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To find a big success story, we should look no further than Amazon who (on
average) change their products’ prices once every 10 minutes. They attribute
roughly 25% of their e-commerce profits to their pricing strategy.
In what follows we’ll take a practical look at the working principles &
challenges in the field of dynamic pricing, followed by some final
considerations.
### Back to basics
The beginning is usually a good place to start so we’ll kick things off there.
The one crucial piece of information we need in order to find the optimal
price is how demand behaves over different price points.
If we can make a decent guess of what we can expect demand to be for a wide
range of prices, we can figure out which price optimizes our target (i.e.,
revenue, profit, …).
For the keen economists amongst you, this is beginning to sound a lot like a
demand curve. | scraping/output/5683155270269197020.txt | [
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For the keen economists amongst you, this is beginning to sound a lot like a
demand curve.
Estimating a demand curve, sounds easy enough right?
Let’s assume we have demand with constant price elasticity; so a certain
percent change in price will cause a constant percent change in demand,
independent of the price level. In economics, this is often used as a proxy
for demand curves in the wild.
So our demand data looks something like this:
Alright now we can get out our trusted regression toolbox and fit a nice curve
through the data because we know that our constant-elasticity demand function
has this form:
with shape parameter a and price elasticity η
Now that we have a reasonable estimate of our demand function, we can derive
our expected profit at different price points because we know the following
holds: | scraping/output/5683155270269197020.txt | [
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] |
Now that we have a reasonable estimate of our demand function, we can derive
our expected profit at different price points because we know the following
holds:
Note that fixed costs (e.g., rent, insurance, etc.), per definition, don’t
vary when demand or price changes. Therefore, fixed costs have no influence on
the behavior of dynamic pricing algorithms.
Finally, we can dust off our good old high-school math book and find the price
which we expect will optimize profit which was ultimately the goal of all
this.
Voilà there you have it: we should price this product at 4.24 and we can
expect a bottom-line profit of 7.34
So can we kick back & relax now?
Well, there are a few issues with what we just did.
### The demands they are a-changin’
We arrive at our first bit of bad news: unfortunately, you can’t just estimate
a demand curve once and be done with it. | scraping/output/5683155270269197020.txt | [
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### The demands they are a-changin’
We arrive at our first bit of bad news: unfortunately, you can’t just estimate
a demand curve once and be done with it.
Why? Because demand is influenced by many factors (e.g., market trends,
competitor actions, human behavior, etc.) that tend to change a lot over time.
Below you can see an (exaggerated) example of what we’re talking about:
Now, you may think we can solve this issue by periodically re-estimating the
demand curve.
And you would be very right! But also very wrong as this leads us nicely to
the next issue.
### Where are we getting this data anyways?
So far, we have assumed that we get (and keep getting) data on demand levels
at different price points.
Not only is this assumption unrealistic, but it is also very undesirable
Why? Because getting demand data on a wide spectrum of price points implies
that we are spending a significant amount of time setting prices that are
either too high or too low! | scraping/output/5683155270269197020.txt | [
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Why? Because getting demand data on a wide spectrum of price points implies
that we are spending a significant amount of time setting prices that are
either too high or too low!
Which is ironically exactly the opposite of what we set out to achieve.
In practice, our demand observations will rather look something like this:
As we can see, we have tried three price points in the past (€7.5, €10 and
€11) and collected demand data.
On a side note: keep in mind that we still assume the same latent demand curve
and optimal price point of €4.24
So (for the sake of the example) we have been massively overpricing our
product in the past.
This limited data brings along a major challenge in estimating the demand
curve though.
Intuitively, it makes sense that we can make a reasonable estimate of expected
demand at €8 or €9, given the observed demand at €7.5 and €10.
But can we extrapolate further to €2 or €20 with the same reasonable
confidence? Probably not. | scraping/output/5683155270269197020.txt | [
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] |
This is a nice example of a very well-known problem in statistics called the
“exploration-exploitation trade-off”
Exploration: We want to explore the demand for a diverse enough range of price
points so that we can accurately estimate our demand curve.
Exploitation: We want to exploit all the knowledge we have gained through
exploring and actually do what we set out to do: set our price at an optimal
level.
### Enter: Thompson Sampling
As we mentioned, this is a well-known problem in statistics. So luckily for
us, there is a pretty neat solution in the form of Thompson sampling!
Basically, instead of estimating one demand function based on the data
available to us, we will estimate a probability distribution of demand
functions or simply put, for every possible demand function that fits our
functional form (i.e. constant elasticity) we will estimate the probability
that it is the correct one, given our data. | scraping/output/5683155270269197020.txt | [
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] |
Or mathematically speaking, we will place a prior distribution on the
parameters that define our demand function and update these priors to
posterior distributions via Bayes’ rule, thus obtaining a posterior
distribution for our demand function
Thompson sampling then entails just sampling a demand function out of this
distribution, calculating the optimal price given this demand function,
observing demand for this new price point and using this information to refine
our demand function estimates.
So:
\- When we are less certain of our estimates, we will sample more diverse
demand functions, which means that we will also explore more diverse price
points. Thus, we will explore.
\- When we are more certain of our estimates, we will sample a demand function
close to the real one & set a price close to the optimal price more often.
Thus, we will exploit. | scraping/output/5683155270269197020.txt | [
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\- When we are more certain of our estimates, we will sample a demand function
close to the real one & set a price close to the optimal price more often.
Thus, we will exploit.
With that said, we’ll take another look at our constrained data and see
whether Thompson sampling gets us any closer to the optimal price of €4.24
Let’s start working our mathemagic:
We’ll start off by placing semi-informed priors on the parameters that make up
our demand function.
These priors are semi-informed because we have the prior knowledge that price
elasticity is most likely between 0 and 1. As for the other parameters, we
have little knowledge about them so we can place a pretty uninformative prior.
If that made sense to you, great. If it didn’t, don’t worry about it
Now that our priors are taken care of, we can update these beliefs by
incorporating the data at the €7.5, €10 and €11 price levels we have available
to us. | scraping/output/5683155270269197020.txt | [
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] |
Now that our priors are taken care of, we can update these beliefs by
incorporating the data at the €7.5, €10 and €11 price levels we have available
to us.
The resulting demand & profit curve distributions look a little something like
this:
It’s time to sample one demand curve out of this posterior distribution.
The lucky curve is:
This results in the following expected profit curve
And eventually, we arrive at a new price: €5.25! Which is indeed considerably
closer to the actual optimal price of €4.24
Now that we have our first updated price point, why stop there?
With “pure” Thompson sampling, we would sample a new demand curve (and thus
price point) out of the posterior distribution every time. But since we are
mainly interested in seeing the convergence behavior of Thompson sampling,
let’s simulate 10 demand points at this fixed €5.25 price point.
We know the drill by now.
Let’s recalculate our posteriors with this extra information. | scraping/output/5683155270269197020.txt | [
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] |
We know the drill by now.
Let’s recalculate our posteriors with this extra information.
We immediately notice that the demand (and profit) posteriors are much less
spread apart this time around which implies that we are more confident in our
predictions.
Now, we can sample just one curve from the distribution.
And finally, we arrive at a price point of €4.04 which is eerily close to the
actual optimum of €4.24
### Some things to think about
Because we have purposefully kept the example above quite simple, you may
still be wondering what happens when added complexities show up.
Let’s discuss some of those concerns FAQ-style:
### Isn’t this constant-elasticity model a bit too simple to work in practice?
Brief answer: usually yes it is.
Luckily, more flexible methods exist. | scraping/output/5683155270269197020.txt | [
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### Isn’t this constant-elasticity model a bit too simple to work in practice?
Brief answer: usually yes it is.
Luckily, more flexible methods exist.
We would recommend using Gaussian Processes. We won’t go into how these work
here but the main idea is that it doesn’t impose a restrictive functional form
onto the demand function but rather lets the data speak for itself.
If you do want to learn more, we recommend these links: 1, 2, 3
### Price optimization is much more complex than just optimizing a simple
profit function?
It sure is. In reality, there are many added complexities that come into play,
such as inventory/capacity constraints, complex cost structures, …
The nice thing about our setup is that it consists of three components that
you can change pretty much independently from each other.
This means that you can make the price optimization pillar arbitrarily
custom/complex. As long as it takes in a demand function and spits out a
price. | scraping/output/5683155270269197020.txt | [
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] |
This means that you can make the price optimization pillar arbitrarily
custom/complex. As long as it takes in a demand function and spits out a
price.
You can tune the other two steps as much as you like too.
### Changing prices has a huge impact. How can I mitigate this during
experimentation?
There are a few things we can do to minimize risk:
A/B testing: You can do a gradual roll-out of the new pricing system where a
small (but increasing) percentage of your transactions are based on this new
system. This allows you to start small & track/grow the impact over time.
Limit products: Similarly to A/B testing, you can also segment on the product-
level. For instance, you can start gradually rolling out dynamic pricing for
one product type and extend this over time. | scraping/output/5683155270269197020.txt | [
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] |
Bound price range: Theoretically, Thompson sampling in its purest form can
lead to any arbitrary price point (albeit with an increasingly low
probability). In order to limit the risk here, you can simply place an
upper/lower bound on the price range you are comfortable experimenting in.
On top of all this, Bayesian methods (by design) explicitly quantify
uncertainty. This allows you to have a very concrete view of the demand
estimates’ variance
### What if I have multiple products that can cannibalize each other?
Here it really depends
1\. If you have a handful of products, we can simply reformulate our objective
while keeping our methods analogous.
Instead of tuning one price to optimize profit for the demand function of one
product, we tune N prices to optimize profit for the joint demand function of
N products. This joint demand function can then account for correlations in
demand within products.
| scraping/output/5683155270269197020.txt | [
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2.If you have hundreds, thousands or more products, we’re sure you can imagine
that the procedure described above becomes increasingly infeasible.
A practical alternative is to group substitutable products into “baskets” and
define the “price of the basket” as the average price of all products in the
basket.
If we assume that the products in baskets are substitutable but the products
in different baskets are not, we can optimize basket prices independently from
one another.
Finally, if we also assume that cannibalization remains constant if the ratio
of prices remains constant, we can calculate individual product prices as a
fixed ratio of its basket price.
For example, if a “burger basket” consists of a hamburger (€1) and a
cheeseburger (€3), then the “burger price” is ((€1 + €3) / 2 =) €2. So a
hamburger costs 50% of the burger price and a cheeseburger costs 150% of the
burger price. | scraping/output/5683155270269197020.txt | [
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If we change the burger’s price to €3, a hamburger will cost (50% * €3 =) €1.5
and a cheeseburger will cost (150% * €3 =) €4.5 because we assume that the
cannibalization effect between hamburgers & cheeseburgers is the same when
hamburgers cost €1 & cheeseburgers cost €3 and when hamburgers cost €1.5 &
cheeseburgers cost €4.5
### Is dynamic pricing even relevant for slow-selling products?
The boring answer is that it depends. It depends on how dynamic the market is,
the quality of the prior information, …
But obviously, this isn’t very helpful.
In general, we notice that you can already get quite far with limited data,
especially if you have an accurate prior belief on how the demand likely
behaves.
For reference, in our simple example where we showed a Thompson sampling
update, we were already able to gain a lot of confidence in our estimates with
just 10 extra demand observations.
### Demo time | scraping/output/5683155270269197020.txt | [
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] |
### Demo time
Now that we have covered the theory, you can go ahead and try it out for
yourself!
You can access our interactive demo here:
https://huggingface.co/spaces/ml6team/dynamic-pricing
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## Does the combination of people & technology sound like music to your ears?
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There is a reason why our team is not called the "HR" team, but rather "Talent
& Culture”. We see, recognize and invest in talent. | scraping/output/6539848297541217069.txt | [
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Join us
There is a reason why our team is not called the "HR" team, but rather "Talent
& Culture”. We see, recognize and invest in talent.
Being a Talent Partner at ML6 means connecting with the exceptional talent we
have whilst accelerating the expansion of our team. We highly value our people
and therefore want to keep attracting talent with you in the driving seat of
recruitment with a focus on our technical profiles!
You will be the first touchpoint that potential new colleagues meet and will
play a crucial role in their candidate journey. Next to this you will closely
collaborate with colleagues to make sure we hire the perfect matches with our
ML6 DNA.
Does the combination of people & technology sound like music to your ears?
Join us and accelerate your career!
## Your challenge
Recruitment strategy | scraping/output/6539848297541217069.txt | [
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## Your challenge
Recruitment strategy
* Collaborate with our Head of Talent & Culture & Talent Partner to keep our hiring plans aligned with the business needs
* Collaborate with our hiring managers to set out the right requirements to match our current team continuously
* Full ownership of the recruitment cycle for our technical profiles: work out and design the hiring process that fits the role & person we are looking for
Sourcing
* Know where to find the exceptional tech talent that will accelerate ML6
* Proactively reach out & source good potential candidates
* Host & attend events to keep us connected to the right candidates
* Build out a talent community
Candidate experience | scraping/output/6539848297541217069.txt | [
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Candidate experience
* Own the recruitment cycle of our different technical positions across the different countries (BE/NL/DE)
* Drive the selection process from resume screening to interviewing and offering
* Master our ATS system and try out new features
* Showcase our ML6 DNA throughout the process to attract the right candidates
Young graduates
* Be involved in the hiring of technical interns & young graduates
* Collaborate with universities & associations that fit our talent pools
* Follow-up directly with the participants after the events you attend
Employer branding
* Suggest and create an employer brand focused on techies that matches our organisation
Multiple HR Projects
* Work on different HR Projects (data driven recruit, culture, employer branding...)
## Why you? | scraping/output/6539848297541217069.txt | [
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* Work on different HR Projects (data driven recruit, culture, employer branding...)
## Why you?
* You have a master’s degree preferable in an HR related field (or communication, business management, economics...)
* You have experience in recruiting technical profiles (or other relevant experience within tech) or are ambitious to set your first steps into tech recruitment
* You view recruitment from the bigger picture, and know what different elements are needed to reach and attract the right technical audience
* You love to talk ‘tech’ and initiate different events to inspire the right audience
* You have a bigger interest in HR in general
* You get what drives and motivates people
* You are communicative in English (German or Dutch are a plus)
* You are proactive and hands on
* You are flexible, curious and willing to embrace change for the better
* You are ready to accelerate your career and join this dynamic team where no day is the same!
##
Why ML6? | scraping/output/6539848297541217069.txt | [
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##
Why ML6?
Looking for a dynamic environment where you can create real business impact
with AI? Look no further. Don’t expect a big corporate organisation with pre-
defined jobroles and plans for the coming years. Expect the unexpected.
Growth, change and impact. Expect to work for the most innovative and biggest
companies across countries. Expect exponential personal growth in your career.
We provide an attractive salary package for your expertise and an optimised
benefits program where we stay true to our caring and learning goals. In a
nutshell, we've built a place where we truly love working, we think you will
too.
Scroll down to find out more about why others choose ML6! 👇
Department
Talent & Culture 🫶
Locations
Office Ghent 📍, Office Berlin 📍, Office Amsterdam 📍
## Why others choose ML6 🤝
* ### Create intelligence with a lasting positive impact | scraping/output/6539848297541217069.txt | [
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Department
Talent & Culture 🫶
Locations
Office Ghent 📍, Office Berlin 📍, Office Amsterdam 📍
## Why others choose ML6 🤝
* ### Create intelligence with a lasting positive impact
As a leader in AI, we work hard to ensure that AI is designed, developed and
deployed to create positive impact. We take our responsibility to build secure
and sustainable technology seriously and have integrated ethics within our way
of working. This shows in every client case and do good projects we do.
* ### Innovate and share knowledge
Learning is part of our DNA. Whether it's through our team's expertise,
creating spin-offs or the latest tech breakthrough, our culture will empower
you to deliver top-notch AI solutions to our customers. We organise chapter
conferences, guest lectures, meet ups, dedicate time to research, blogposts, a
personal training catalog and so much more.
* ### We achieve more together | scraping/output/6539848297541217069.txt | [
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* ### We achieve more together
Expect to build lasting relationships that extend beyond the office walls.
Thanks to our collaborative and diverse culture, we achieve the extraordinary.
From our ML6 agents to our ML6 Maffia, we’ve created a safe environment where
mutual trust and open feedback is cherished. To foster our interpersonal
connections, we love to see each other's pretty faces at the office.
* ### Fast growing and international
We're gearing up for our next growth milestone. Don’t expect a big corporate
organisation with pre-defined jobroles and plans for the coming years. Expect
the unexpected. Growth, change and impact. Expect to work for the most
innovative and biggest companies across countries. Expect exponential personal
growth in your career.
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* ### Byte size Benefits
Last but not least, we provide an attractive salary package for your expertise
and an optimised benefits program where we stay true to our caring and
learning goals. In a nutshell, we've built a place where we truly love
working, we think you will too.
Contact Julie Plusquin Talent Partner – Talent & Culture 🫶
## Colleagues
Kaat Van Doren
Head Talent & Culture
## Join us
* Talent Reward Officer
Talent & Culture 🫶 * Multiple locations
More jobs
## FAQ
* ### Can I work remotely at ML6?
There is a reason why we don’t work (fully) remotely at ML6 and why we love
being present at our offices so often. At ML6, our culture is based on
interpersonal connections and our strength lies in our personal collaboration.
With our clients, and with each other. We work from home about 2 days a week.
Our offices are situated in Berlin, Amsterdam and Ghent.
* ### What to expect when joining ML6? | scraping/output/6539848297541217069.txt | [
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* ### What to expect when joining ML6?
Don’t expect a big corporate organisation with pre-defined jobroles and a
defined plan for the coming years. Expect the unexpected. Growth, change and
impact. Learn from engaged experts with a like minded passion for technology.
Expect to work for the most innovative and biggest companies across countries.
We are all T-shaped consultants working on a shared goal.
* ### Am I a fit for the company?
We are an AI Consulting company on a mission. Guiding the AI revolution
towards positive impact. You can become part of this mission. We are always on
the look for business minded people with a consultative approach. We dedicate
great effort in building a caring culture full of learning opportunities that
accelerates us to accomplish our main purpose. You can be in the driver’s seat
of your career. You can be the next ML6 agent to create value for our
customers and company.
Are you up for the challenge? | scraping/output/6539848297541217069.txt | [
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Are you up for the challenge?
* ### What does the application process look like?
It depends on the role that you're applying for. For our technical roles, we
always start with a technical matching (ex. coding challenge) and get to know
interview. Every role at ML6 receives a presentation challenge where we will
evaluate how you can contribute to our innovative projects by demonstrating
your skills & creativity. As you approach the finish line, we schedule a final
interview (physically) to reflect on your dream job at ML6.
* ### How is ML6 structured?
We work in Units and with Unit Coaches. For more information about our
structure, we recommend you to watch this video.
* ### What are examples of projects that ML6 worked on? | scraping/output/6539848297541217069.txt | [
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We work in Units and with Unit Coaches. For more information about our
structure, we recommend you to watch this video.
* ### What are examples of projects that ML6 worked on?
At ML6, we boost and increase revenue growth for other companies, by
implementing AI solutions. We do this for a broad range of industries. From
manufacturing, to healthcare or media and entertainment industries (and lots
more!).
You can find some of our client cases and testimonials here.
* ### I would love to meet up with someone from ML6
You can find all of our upcoming meet ups and events here. If you would like
to schedule a first get-to-know, we recommend you to apply officially via our
jobsite where we will follow up with you soon.
* ### What about the ethical aspect of AI?
At ML6 we take our responsibility to build secure, reliable and sustainable
technology seriously and integrate ethics within our way of working. We have
our own Ethical Advisory board to discuss sensitive projects. | scraping/output/6539848297541217069.txt | [
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] |
* ### Why is ML6 called ML6?
ML stands for our main expertise, Machine Learning. Next to that, we don't
take ourselves always very seriously. Our people are 'ML6 agents', wink to
MI6. Seen the James Bond movies? Our meeting rooms for example are named after
them. After all, we can be a bit geeky at times ;-)
* ### What does a typical AI project look like?
This really depends on the scope of the project. A project can run for a
couple of months, to even years. Our ML6ies work on projects together with a
team of colleagues. This team can have different skills like data engineering,
software, machine learning and project management to deliver top notch service
to our clients. They sometimes work on different projects simultaneously or
combine project work with an internal project or research topic.
Office Ghent 📍 Office Berlin 📍 Office Amsterdam 📍
## About ML6 | scraping/output/6539848297541217069.txt | [
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