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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/8080948775203877080.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/8080948775203877080.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
Placeholder tag
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
Placeholder tag | scraping/output/8080948775203877080.txt | [
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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?
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Fine-tuning Whisper for Dutch Language: The Crucial Role of Size
Unlocking ASR Excellence: Fine-Tuning Whisper for Dutch Language
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Sharon Grundmann
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Developing an AI solution for product photography: what we learned
Reshaping Product Photography with generative AI
August 23, 2023
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Why Open AI's API models can't be forced to behave deterministically
Understanding this behaviour and explaining why full control is unachievable
August 22, 2023
By
Michiel De Koninck
Placeholder tag
From hype to action: charting your AI course | scraping/output/8080948775203877080.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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Discover the potential of Google's PaLM 2 as we harness generative language
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/8080948775203877080.txt | [
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] |
July 24, 2023
By
Placeholder tag
AI can play a crucial role in the field of HR, but should we blindly follow?
Is AI only applicable within factories and large corporate organizations?
Anything but. AI will (and already is) creating impact within the Human
Resources branch as well. We speak with Julie Plusquin, Talent Partner at ML6
about what the evolution within HR may look like.
July 20, 2023
By
Julie Plusquin
Placeholder tag
Smart energy: how to make your home battery smart and save money?
Learn how to make your home battery smarter by connecting it to external
inputs such as energy prices and weather. This can save up to 35% on
electricity costs.
July 16, 2023
By
Juta Staes
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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
By
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Predicting enzyme heat stability during Christmas break | scraping/output/8080948775203877080.txt | [
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] |
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June 19, 2023
By
Placeholder tag
Developing AI Systems in the Foundation Model Age : An intro
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June 7, 2023
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Developing AI Systems in the Foundation Model Age : From MLOps to FMOps
The structure of FMOps to optimize your foundation model workflows.
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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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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/8080948775203877080.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/8080948775203877080.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/8080948775203877080.txt | [
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] |
April 18, 2023
By
Jens Bontinck
Generative AI
Placeholder tag
ChatGPT & GPT-4: Gimmicks or game-changers?
Discover the truth behind the hype of ChatGPT & GPT-4. Read about the
potential game-changing capabilities of GPT-4 or if it's just another gimmick.
April 14, 2023
By
Generative AI
Placeholder tag
Reflexion is all you need?
Jens Bontinck shares his view on a recent technique called Reflexion
April 13, 2023
By
Jens Bontinck
Placeholder tag
Accelerating people - even if it goes beyond ML6
How we empower our agents in their own personal road to success.
March 31, 2023
By
Julie Plusquin
Placeholder tag
How to effectively label images for computer vision projects ?
In this blogpost we cover the most important aspects of tackling a computer
vision problem by labeling your own data.
March 22, 2023
By
Thomas Janssens
Placeholder tag
Improving Tennis Court Line Detection with Machine Learning | scraping/output/8080948775203877080.txt | [
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] |
March 22, 2023
By
Thomas Janssens
Placeholder tag
Improving Tennis Court Line Detection with Machine Learning
Improving tennis court line detection using machine learning for near real-
time predictions.
March 13, 2023
By
Jules Talloen
Placeholder tag
Who spoke when: Choosing the right speaker diarization tool
A guide on how to choose the right speaker diarization framework for your use
case.
March 10, 2023
By
Philippe Moussali
Placeholder tag
A sea of job opportunities, but where to start? Perhaps in learning to swim?
Tips on figuring out how to navigate the recruitment process and determine
which companies are a good match for you.
March 9, 2023
By
Julie Plusquin
Placeholder tag
What to Expect from Automated Machine Learning: Zooming in on Technical
Aspects and Zooming Out to the Bigger Picture
This blog post explores the current state of AutoML tools and their ability to
deliver on the promise of automating machine learning tasks without expert
knowledge. | scraping/output/8080948775203877080.txt | [
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] |
This blog post explores the current state of AutoML tools and their ability to
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AI for the green transition - Applications and use cases [Part 1] | scraping/output/8080948775203877080.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
Placeholder tag
Vertex AI is All You Need | scraping/output/8080948775203877080.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/8080948775203877080.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/8080948775203877080.txt | [
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Placeholder tag
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Placeholder tag
NVIDIA Deepstream Quickstart | scraping/output/8080948775203877080.txt | [
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Semantic search: A practical Overview | scraping/output/8080948775203877080.txt | [
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Why we open sourced two Dutch summarization datasets
Open Sourcing Dutch Summarization Datasets for Transfer Learning
May 12, 2022
By
Placeholder tag
Semantic search: A practical Overview
A practical overview on information retrieval, in particular Semantic Search.
May 9, 2022
By
Content Management Platform
Placeholder tag
From principles to practice : Why we created an Ethical AI unit at ML6
Why we created an Ethical AI unit at ML6 and what we learned so far.
May 3, 2022
By
Placeholder tag
How to discover good data augmentation policies for ML
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efficiently discover good data augmentation strategies for your model and your
use case.
April 28, 2022
By
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Placeholder tag
A practical guide to image-based anomaly detection using Anomalib
A short introduction to unsupervised anomaly detection and how to apply it.
March 31, 2022
By
Sebastian Wehkamp
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A practical guide to image-based anomaly detection using Anomalib
A short introduction to unsupervised anomaly detection and how to apply it.
March 31, 2022
By
Sebastian Wehkamp
Placeholder tag
Building a coral segmentation model using sparse data
In light of a partnership with the organizations Reef Support and Fruitpunch
AI, we designed an image segmentation algorithm. The main challenge here was
the way the data is labelled, containing sparse pixel annotations created by
domain experts. The blogpost covers on a high level how we solved this
challenge, and how the challenge will be going forward.
March 28, 2022
By
Placeholder tag
3D object detection in the real world
In this blogpost, we look at object detection using 3D ‘depth’ sensor data. We
explain how two recent approaches, VoteNet and 3DETR, tackle the problem and
benchmark their performance out of the box and after fine-tuning.
March 15, 2022
By
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] |
March 15, 2022
By
Placeholder tag
Webinar | Explainable AI explained by Google and ML6
Discover the importance of Explainable AI (XAI) in understanding machine
learning models and explore practical case studies, including Google's Vertex
Explainable AI tool.
March 9, 2022
By
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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November 7, 2022
# Challenges for Sign Language Translation
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> Wouldn’t it be cool if people without knowledge of Sign Language can
> understand Sign Language? Then what’s stopping us (researchers & developers)
> from achieving this goal?
The work in this blog was done as part of my internship at ML6 and MSc Thesis
at TU Delft.
### Introduction to Sign Language
| scraping/output/3909810078930943999.txt | [
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The work in this blog was done as part of my internship at ML6 and MSc Thesis
at TU Delft.
### Introduction to Sign Language
Sign Language (SL) is the primary language for the deaf and mute community.
According to the World Federation of the Deaf , there are more than 70 million
deaf people around the world that use sign language. It is a natural and
complete language that has its own linguistic intricacies. Every spoken
language has its own sign language, like American Sign Language (ASL), Chinese
Sign Language (CSL), German Sign Language (DGS), and so on. In total, there
are around 300 different sign languages. Sign languages are not a one-to-one
mapping of spoken languages, they have their own definite grammar. | scraping/output/3909810078930943999.txt | [
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For instance, a well-constructed question must be accompanied by the correct
eyebrow position. When a person is asking questions related to who, where,
what, why, and when, the eyebrows are expected in a certain position. If the
question is regarding a yes/no situation, the eyebrows are expected in some
particular way. SL does not only use hand gestures to communicate but also
includes facial expressions, hand movements and positions, and body posture.
Any change in them can change the entire meaning of the sign. That is why it
is generally hard for someone with no knowledge of sign languages to
understand them. | scraping/output/3909810078930943999.txt | [
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] |
All of these factors make translation into spoken language difficult. There
are mainly two research areas going on in Sign Language interpretation, i.e.
Sign Language Recognition (SLR) and Sign Language Translation (SLT), which we
touch on later in the blog and utilize a state-of-the-art architecture for
translation. We will also be discussing and listing down some of the crucial
gaps in the architecture and current research setting for SLT in a real-time
setting.
Some facts about Sign Language (Image Source)
### Sign Language Recognition and Translation
SLR is about recognizing actions from sign language. It is considered to be
the naïve gesture recognition problem but not just limited to alphabets and
numbers. It focuses on recognizing a sequence of continuous signs but
disregards the underlying rich grammatical and linguistic structures of sign
language that differ from spoken language. The main goal is to interpret the
signs, isolated or in a continuous sequence. | scraping/output/3909810078930943999.txt | [
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On the other hand, SLT is about interpreting sign language in terms of natural
language with its grammar, keeping in mind the language. The primary objective
of SLT is to translate sign language videos into spoken language forms, taking
into account the different grammatical aspects of the language. It is a
relatively new problem and is complex as it involves considering facial
features and body postures as well along with hand movements and positions.
The image below clearly presents the difference between Continuous SLR and
SLT.
Difference between SLR and SLT (image taken from [1])
### SLT with VAC_CSLR + Transformer | scraping/output/3909810078930943999.txt | [
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Difference between SLR and SLT (image taken from [1])
### SLT with VAC_CSLR + Transformer
There are several gaps and challenges in the current research landscape for
SLT in real-time human interaction. To get a better idea of these gaps, we
utilized a state-of-the-art architecture for Continuous SLR proposed in
“Visual alignment constraint (VAC) for continuous sign language recognition
(CSLR).” research paper by Min, Yuecong, et al [2]. To use of this
architecture for the SLT problem, we added a two-layer transformer for
translation over the VAC_CSLR architecture, as shown in the image below.
Moreover, RWTH Phoenix Weather 14T dataset [3] was used to train both networks
separately. This dataset is extracted from weather forecast airings of the
German tv station PHOENIX. It has 9 different signers, gloss-level annotations
with a vocabulary of 1,066 different signs and translations into German spoken
language with a vocabulary of 2,887 different words. | scraping/output/3909810078930943999.txt | [
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The architecture is based on a two-step, Sign-to-Gloss Gloss-to-Text,
translation where the first step is to obtain glosses from the video sequence,
and in the next step, the glosses are converted into spoken language
sentences. After the training and testing phase, the model was utilized in a
real-time setting. It was tested on the different videos with translation
happening on the go, in sets of frames using OpenCV. MediaPipe was used to
identify when to start and end a sign sequence. | scraping/output/3909810078930943999.txt | [
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The first stage required utilizing the VAC_CSLR network to obtain glosses from
the video sequences. The Visual Alignment Constraint network focuses on
enhancing the feature extractor with alignment supervision by proposing two
auxiliary losses: the Visual Enhancement (VE) loss and the Visual Alignment
(VA) loss. The VE loss provides direct supervision for the feature extractor,
which itself is enhanced with the addition of an auxiliary classifier on
visual features to get the auxiliary logits. This auxiliary loss makes the
feature extractor make predictions based on local visual information only. | scraping/output/3909810078930943999.txt | [
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] |
Then, to compensate for the contextual information that VE loss lacks, the VA
loss is proposed. The VA loss is implemented as a knowledge distillation loss
which regards the entire network and the visual feature extractor as the
teacher and student models, respectively. The final objective function is
composed of the primary Connectionist Temporal Classification (CTC) loss, the
visual enhancement loss, and the visual alignment loss. In the second stage to
obtain translation from glosses, a two-layered Transformer was used to
maximize the log-likelihood over all gloss-text pairs.
We referred to the original Transformer [4] implementation for more details.
VAC_CSLR + Transformer Architecture
VE loss, VA loss, and Total Loss (respectively, clockwise from top left)
### Experiment Setup on the VAC_CLSR + Transformer Network
| scraping/output/3909810078930943999.txt | [
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] |
VAC_CSLR + Transformer Architecture
VE loss, VA loss, and Total Loss (respectively, clockwise from top left)
### Experiment Setup on the VAC_CLSR + Transformer Network
After hyper-parameter tuning and model validation, the model was applied to
different videos from the published datasets and clips from various SL-
friendly news channels. The videos were mainly selected from German SL sources
as the models were trained on a German SL dataset. We utilized random videos
from RWTH-Phoenix-Weather 2014, RWTH-Phoenix-Weather 2014-T dataset, and took
SL snippets from Tagesschau, a news show in Germany, for evaluation. These
videos were not very long, just a sentence long (so, up to 8–10 seconds). | scraping/output/3909810078930943999.txt | [
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] |
In the translation pipeline, a video is broken down into frames of images and
on every image a MediaPipe holistic model is run, which identifies key points
from the image. If the identified key points contain left or right-hand key
points then the SLR model starts taking frames for prediction. The set of
frames is decided on the basis of key-point detection of the left or right
hand from the MediaPipe holistic model i.e. till one of the hands is in the
frame. After we get the glosses from the VAC model, these glosses are passed
to the Transformer model which provides the spoken translations. The final
translations were compared to the actual text for the SL video sequence.
In addition to this, we also applied different transformations to the frames
captured from videos. Here are the transformations that were applied: | scraping/output/3909810078930943999.txt | [
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0.014376451261341572,
0.01798834837973118,
0.02119084633886814,
0.06257633119821548
] |
In addition to this, we also applied different transformations to the frames
captured from videos. Here are the transformations that were applied:
★ Segmentation masks: A mask is used to segment an image. It is used to
identify the parts of an image containing a particular object, in this case, a
human. It was mainly used to avoid noise in the images, with the background
being insignificant for prediction.
★ Image rotation: It is a common image augmentation operation. The image is
rotated at various angles to capture the different aspects of the image
features in different orientations.
★ Image resizing: In this, the size of the image was changed by the central
cropping method at different dimensions.
★ Image scaling: This is different from image resizing as it happens on the
entire image by resampling. The images were scaled randomly between 0.5 to 1.5
interval.
### Short Demo
### Gaps observed for real-time SLT | scraping/output/3909810078930943999.txt | [
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] |
### Short Demo
### Gaps observed for real-time SLT
After several experiments on the architecture with different videos, we listed
down the gaps we observed and which are important to improve SLT for real-
world application. Here are the observed gaps: | scraping/output/3909810078930943999.txt | [
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1. Limited number of datasets available:
In current research for SL, almost all research papers mention the need for
more data to progress the research quality. The datasets available are mostly
of alphabets, numbers, and individual words. There are also datasets for
Continuous SLR that contain gloss representations for the SL sequences, but,
for SLT, spoken translations are also required. There are very few datasets
that contain spoken translations as well in the dataset.
The main reason is that the SLT problem is comparatively new and also for
spoken translation annotations, human SL interpreters are required to
translate the entire video dataset. It is important because the problem of SLT
is crucial for real-world applications which connect people with SL knowledge
to the ones that do not have this knowledge.
Another aspect of the limited datasets available is that most of the SL
corpora are either unavailable for use due to the presence of corrupted or | scraping/output/3909810078930943999.txt | [
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to the ones that do not have this knowledge.
Another aspect of the limited datasets available is that most of the SL
corpora are either unavailable for use due to the presence of corrupted or
unreachable data, or available under heavy restrictions and licensing terms.
SL data is particularly challenging to anonymize due to the need for valuable
facial and other physical features in signing videos, therefore restricting
its open distribution.
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2. Domain restricted data:
Most of the benchmark datasets currently present are collected from a certain
SL media source which is domain-specific. Like the current benchmark dataset
for SLT, the RWTH-Phoenix-Weather 2014T dataset of German Sign Language,
contains videos from the daily weather forecast airings of the German public
TV station PHOENIX featuring sign language interpretation.
If a model is trained on a domain-specific dataset, then it is possible that
it has not generalized well and has a limited vocabulary i.e. vocabulary
specific to the domain. Most of the open-source SL sources, like news
channels, are domain-specific so, it becomes challenging to develop a dataset
that is open-domain.
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3. Lack of variety in datasets:
In the available datasets, there has been a lack of variety in terms of the
number of signers, physical orientation of signers, and camera viewpoints of
signers. There has been an average of 10–20 signers across various datasets,
with the RWTH-Phoenix-Weather 2014T Dataset having just 9 signers. An
increased number of native signers gives a better understanding of sign
representation. In SL there are different dialects, this makes variations in
signs for the same word. So, it is possible that the same word or phrase can
be signed in different ways by different people, or the sign sequence of the
same word may differ from one region to another. Therefore, it is better to
capture this variation as much as possible by selecting a variety of signers.
Another aspect related to variety is the camera viewpoint from which the
signer is captured for the dataset collection. Generally, for a real-time | scraping/output/3909810078930943999.txt | [
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Another aspect related to variety is the camera viewpoint from which the
signer is captured for the dataset collection. Generally, for a real-time
application, it is not necessary that the signer will always be captured from
the front by the camera. Currently, more than 85% of the datasets do not have
multiple views.
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4. Architecture transferability across different SL:
Recently, the amount of research related to SLR/SLT has been increasing. The
architectures are capturing various aspects of an SL video sequence. However,
after scrutinizing different results from these types of research it is quite
apparent that the accuracy results (WER score and BLEU score[5]) are not
similar when the same architecture is tried on a different language dataset.
For example, for an SLT architecture proposed in one of the research papers,
on RWTH-Phoenix-Weather 2014T dataset it got 22.17 BLEU; on Public DGS Corpus
just 3.2 BLEU (the higher the better). Therefore, these results indicate that
current architectures are not appropriate for real-world applications, either
more data is needed for these models or approaches that are more
linguistically sophisticated are required.
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5. Hardware restrictions for deep architectures:
Another technical gap that is worth mentioning is the limits of hardware for
conventional deep learning architectures. The model architecture that has
multiple layers with millions of parameters is expected to be heavy in size
and might require high resources and computational power. Often the target
devices have limited resources and it would be heavy to compute especially for
a real-time application.
It is an important aspect in the light of real-world applications as real-
world applications are expected to be robust and swift in delivering outputs.
### Conclusion | scraping/output/3909810078930943999.txt | [
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### Conclusion
In this blog, we showed the identified gaps in the architectures for SLR/SLT
by considering and exploiting existing state-of-the-art architectures. Our
mentioned gaps suggest that there needs to be more advancement in
architectures and datasets to achieve high-level real-world applications. We
conclude that although the current architectures for SLR/SLT might not be
fully equipped for a real-world application for SL interpretation, the
progress in terms of datasets and architectures looks promising. As the
problem of SLT at hand is difficult, various aspects of SL must be considered
to solve this challenging problem.
References | scraping/output/3909810078930943999.txt | [
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information processing systems 30 (2017).
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https://aclanthology.org/P02-1040.pdf | scraping/output/3909810078930943999.txt | [
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### Introduction
At ML6, security is in our blood. Our security program is embedded in all the
business processes. Starting from the first sales contact, delivering a proof
of concept, up until the delivery of production applications.
To prove our dedication towards information security, our security program is
certified against the ISO/IEC 27001:2017 standard. This internationally
accepted standard provides requirements for establishing, implementing,
maintaining and continually improving information security within an
organization.
In this blogpost, we explain the why, what and how behind our ISO 27001
certification.
#### Why? | scraping/output/700730908545582736.txt | [
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In this blogpost, we explain the why, what and how behind our ISO 27001
certification.
#### Why?
Building robust ML solutions means that we put a big emphasis on security.
Furthermore a lot of our solutions rely on very valuable datasets, requiring
even a bigger commitment to security, especially with the ever increasing
amount of cyber attacks.
To clearly showcase our focus on security and our commitment to build not only
functional but also secure ML solutions, we started the process to become ISO
27001 certified.
#### What?
ISO 27001 is a standard for Information Security Management Systems (ISMS).
This standard is internationally agreed by experts in the information security
field as the best way of managing information security. | scraping/output/700730908545582736.txt | [
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However, being certified is not an end in itself. The ultimate goal of our
ISMS is to have a structured and reliable approach to protect valuable
information from all sorts of risks. The certification is just a manner to
showcase to our customers, employees, partners etc. that they can trust ML6 to
handle their data in a responsible and secure manner.
#### How?
Implementation of an ISMS can be mapped on the PDCA circle of Edwards Deming.
This circle is typically used in quality management to control and optimize
processes and products. Mapping the ISO 27001 standard to the 4 steps of the
PDCA circle gives a clear overview of how the ISMS can control and
continuously improve security within ML6.
##### Plan | scraping/output/700730908545582736.txt | [
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##### Plan
The planning phase requires you to first establish the context of the
organization. This context includes external and internal factors that are
relevant to the ISMS. Being aware of the context helps to have a clear
understanding of what the requirements and objectives for the ISMS will be,
who needs to be involved, which resources will be needed etc.
After identifying the context, the standard requires to identify and assess
information security risks that are relevant to the organisation. The internal
and external factors defined in the context of the organization serve as input
for the risk assessment.
Once the risk assessment is completed, the organization defines a risk
treatment plan to reduce the risks to acceptable levels.
##### Do | scraping/output/700730908545582736.txt | [
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Once the risk assessment is completed, the organization defines a risk
treatment plan to reduce the risks to acceptable levels.
##### Do
The Do-step exists out of executing the risk treatment plan to reduce security
risks in various areas of the organization like human resources, asset
management, supplier management, software development, compliance, access
control etc. The ISO standard puts forward 114 controls that can be
implemented to reduce risks.
This is where it was important for ML6 to avoid bureaucratic processes that
limit the flexibility and agility of a scale up.
Especially for our development teams this was a major point of attention.
Customers want to work with ML6 because we can deliver projects in a fast,
effective and efficient way, not because we need 10 days and 30 mails to set
up basic infrastructure before the project can actually start. | scraping/output/700730908545582736.txt | [
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] |
Our approach is to make sure that the secure way is the easiest way to work by
sticking to security-first best practices for all our internal tooling &
boilerplates. All our chapters are consistently striving to make our agents
as effective as possible by providing building blocks or boilerplates for
different types of ML challenges. Having these leverage infrastructure as code
best practices & making them well adhered to strict Google Cloud
Organizational Policies, has as a consequence that our joint codebase is not
only the best way to kickstart new projects, but also the most secure and
easiest way given that the security best practices are already included.
##### Check
After implementing the risk reducing measures it is important to track if the
measures that were taken are actually effective in reducing the security risks
and if the entire ISMS runs as expected. | scraping/output/700730908545582736.txt | [
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After implementing the risk reducing measures it is important to track if the
measures that were taken are actually effective in reducing the security risks
and if the entire ISMS runs as expected.
Monitoring of information security KPIs can help measure the performance of
ISMS and if security objectives are achieved.
An Internal Audit is a great way to verify that the ISMS is following the ISO
27001 standard and if internal policies and procedures are being followed.
The standard also requires the management involved in the check phase.
Management should review the organization’s ISMS to ensure its continuing
suitability, adequacy and effectiveness.
##### Act
The goal of the Check phase is to bring up points where the ISMS has failed to
meet the ISO standards or to reduce the information security risks. | scraping/output/700730908545582736.txt | [
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] |
##### Act
The goal of the Check phase is to bring up points where the ISMS has failed to
meet the ISO standards or to reduce the information security risks.
In the Act phase, corrective actions can be defined to further improve the
ISMS. These actions will translate into updating the context of the
organization and the risk assessment.
This brings us back to the Plan phase and therefore completing the PDCA
circle. In this manner ML6 has set up a continuous improvement cycle to manage
information security.
Do you have questions or would you like to get more information about our
certification? Our Head of Security, Rob Vandenberghe, is happy to help.
[email protected]
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Chat GPT | scraping/output/700730908545582736.txt | [
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# Jean Frerot
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# Jean Frerot
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As a customer engineer at ML6, Jean aims to inspire customers and peers to
solve difficult challenges with the proper technical tools. Working hands on
with the customer to identify the most valuable solutions is the driving
factor for Jean. As a member of the Labs unit, Jean is positioned in between
the Sales Department and the ML Engineers. This Unique position allows Jean to
work both on the Why and the How of a solution.
Jean is passionate about presenting complex technical solutions in
understandable and actionable ways, allowing even the most non-technical
audience to see the value and potential of AI. It is this strength that has
allowed Jean to feature as a public speaker in multiple events including his
keynote presentation at the World Summit AI. | scraping/output/1279492535031311424.txt | [
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] |
Jean comes from a strong technical background in machine learning. Like many
of his peers at ML6, he is a Google Cloud certified Data Engineer. A top
priority for Jean is keeping up to date with the latest and greatest the AI
landscape has to offer.
Visit my LinkedIn profile
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Services | scraping/output/1279492535031311424.txt | [
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Infrastructure | scraping/output/1192487889532925635.txt | [
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# NLP Trend: A few examples on how to process long documents
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Thank you! Your submission has been received! | scraping/output/3151389603688665733.txt | [
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### Introduction
Machine Learning is at its most disruptive time, being more and more impactful
in real world applications. This is especially the case in one domain: Long
document processing.
Businesses in industries such as media, finance, jurisdiction, education, etc.
tend to accumulate a large quantity of medium to long documents. More often
than not, the documents and the database containing them are not so well
structured. It’s a big opportunity cost not to be able to organize and extract
any valuable information from them. | scraping/output/3151389603688665733.txt | [
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And yet, from a technical stand point it’s very challenging to process such
long documents. Indeed, the NLP research world is all about benchmarking
models on small text tasks. The state of the art models coming out of that
usually have a maximum number of input token allowed - i.e. roughly a maximum
number of words allowed in your input text.
But our team of experts at ML6 needs to find creative solutions on how to
tackle that problem. Here are a few examples on how they do just that:
1. Models adapted to long documents
2. Splitting up long documents
3. Summarizing long documents
### Models adapted to long documents
In a nutshell: some models can handle long sequences better, though they’re
still limited on input size and aren’t necessarily adapted to every task. We
benchmarked some of those models, to see which would be the most performant.
You can find our results here.
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More technically speaking: as said before, typical NLP model are quite limited
on their input size. Traditional RNN/LSTM and now Transformers model are
trained with a fixed one. To make it a tad more specific, models like BERT
with a full attention mechanism become quadratic dependent on it (training one
with long maximum input size makes your memory explode). Typically, a
transformer model will have a maximum input size of 512 tokens. So for a
traditional model this blogpost could already be too long to process (about
~800 words). | scraping/output/3151389603688665733.txt | [
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Now the smart people of the Allen Institute for AI and of Google Research saw
that problem and implemented respectively the Longformer model (see paper) and
the Big Bird model (see paper). By combining random, window and global
attention mechanisms (so essentially sparse attention layers), these models
would only get linearly impacted by input length. They could outperform models
like RoBERTa on longer input tasks and provide input length of up to 4096
tokens (BigBird).
If you want some more info about it, head to our quick-tip (codes included)
about it.
### Splitting up long documents
Note that the problem here is to tackle long documents and not just text. One
approach we took for a large scale project we had is break down documents in
clauses, which can then be tackled by a language model individually. | scraping/output/3151389603688665733.txt | [
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] |
In this case, we relied on an object detection model. Essentially, we worked
on scans of documents and the model could visualize where different clauses
were. Typically this kind of method works very well to extract structured
information, like tables or IDs. You can learn more about our solution in this
video (also includes some more info about the context and what is done on the
text of each clause).
Extract from: https://www.youtube.com/watch?v=0Q8zUEV2JeI&ab_channel=ML6
For each problem its solution: This type of project usually require a pretty
custom approach. It can for instance use manual rules to extract specific
section of the text (e.g. find contact information in a large database of job
offers).
### Summarizing long documents
A less conventional approach is to use extractive text summarization. So you
use an extra model to extract the most relevant piece of your text. | scraping/output/3151389603688665733.txt | [
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] |
### Summarizing long documents
A less conventional approach is to use extractive text summarization. So you
use an extra model to extract the most relevant piece of your text.
That means combining a summarization model on top of the other modeling
techniques you’ll use on its output → more chance of issues. Not necessarily
recommended.
But yet again, in specific use cases it can be a convenient tool (e.g.
summarizing long meetings to then extract relevant piece of information). You
can have a glimpse of what is possible with summarization models in this
blogpost and even try it yourself with this demo.
Using the summarization service — Check out newssummarizer.ml6.eu!
### Conclusion
It is clear that being able to process and extract key information from a
database of large documents is extremely valuable for a lot of institutions.
Data is the new oil, we now need ways of drilling it. | scraping/output/3151389603688665733.txt | [
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] |
On the other hand, it is very challenging from a technical stand point to
process those long documents. We did face that challenge at ML6 and tried to
layout few ways of doing so.
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Contact: | scraping/output/3151389603688665733.txt | [
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] |
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August 22, 2023
# Why Open AI's API models can't be forced to behave deterministically
Contributors
Michiel De Koninck
Machine Learning Engineer and LLM specialist
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Machine Learning Engineer and LLM specialist
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## ChatGPT, Dall-E, ... can't be forced to behave deterministically
A donkey never hits its head on the same stone twice. And it seems this is a
thing that ChatGPT and donkeys have in common: ChatGPT is no fool, it doesn’t
always make the same mistake. Instead it makes a different mistake each time.
Nice? Try forcing it to always make the same mistake. Bummer: that’s just not
possible. | scraping/output/-6332124900812207379.txt | [
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We observe that OpenAI’s Generative API-callable models (DALL-E, ChatGPT, …)
cannot be controlled to act deterministically. In other words: they produce
inconsistent results even when their temperature, the parameter that controls
their “creativity”, is dialed to zero. More information on temperature can be
found in the OpenAI API documentation. Instead of offering solutions, this
blogpost aims to explain this behaviour. Because knowledge is power, right?
But how relevant is this really? Does this question pop up regularly?
Apparently; yes. It currently tops the list of the OpenAI GPT list of FAQ.
Screenshot taken from https://platform.openai.com/docs/guides/gpt/faq
It’s likely that, if you are building an application that relies on the output
of a GPT-model, you (at least in a testing phase) want to be able to have the
model behave deterministically so that you can rely on reproducible behaviour
to some extent. For example: | scraping/output/-6332124900812207379.txt | [
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* (Generative Language) GPT context:
you want to show your boss that your “guided Angular code generator” delivers
the right blob of code for a certain request 100% of the time.
* (Generative Vision) DALL-E context:
you want to show your colleagues that your prompt delivers exactly the same
wonderful image of “fruit juice in a soup bowl” as it did yesterday when you
were working on the marketing for your son’s lemonade stand.
But enough introduction, because we value both structure and blowing things
out of proportion, we will now analyse the behaviour of GPT models
specifically by taking you from observation through understanding towards
explanation. We have to go into quite the level of detail here but through
simplified visualisation, we hope to alleviate the need for any knowledge of
complicated mathematical concepts. Hooray for simplifications!
## The Observation
To summarise again, the question that we will answer here boils down to: | scraping/output/-6332124900812207379.txt | [
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] |
## The Observation
To summarise again, the question that we will answer here boils down to:
”why don’t I consistently get the same answers from a call to any OpenAI
Generative API when the temperature is 0?"
Note that, while relevant discussions and resources on this topic can be
found;
* OpenAI Forum: “a question on Determinism”
* Twitter Post with reply from OpenAI engineer
* Nvidia on Determinism in Deep Learning (slide 51)
We deemed none of these explanations to be satisfactory in delivering a
complete and comprehensive answer. Great news because this allows us to fill
the void and offer you that sweet satisfaction.
## The Understanding
“it is hard to understand exactly how a black box system works. But if the
black box evolved from boxes that were, you know, less black and more
transparent, then some quality assumptions can still be made.”
\- someone, at some point in time, possibly | scraping/output/-6332124900812207379.txt | [
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As clear from the fake quote above: we can’t know exactly how the closed
source LLMs (Large Language Models) ChatGPT or GPT-4 work under the hood (e.g.
paper GPT-4). But from their more transparent predecessors (e.g. GPT-2 paper
[2] and open source code) and open source competitors (e.g. LLaMA paper), we
know the gist of the relevant transformer-based architecture.
Below, we zoom-in on the parts of the architecture that we estimate to be most
relevant for the observation of non-deterministic behaviour. Feel free to
quickly skim through this and skip to the “Explanation” section, if you are
familiar with the basics of textual generative models’ architecture.
### How does the model deliver output text?
| scraping/output/-6332124900812207379.txt | [
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] |
### How does the model deliver output text?
First; one inference forward pass through the LLM network delivers a single
“token” which represents the “most granular unit of text the model
understands” (can be thought of as a syllable but can just as well represent a
word as a whole). For the sake of interpretability, we consider each “token”
in our story to represent an entire word. This simplification has no impact on
further conclusions.
Simplified representation of one forward pass through an LLM network | scraping/output/-6332124900812207379.txt | [
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] |
Simplified representation of one forward pass through an LLM network
That being said, an LLM network has a vocabulary of tokens and through its
seemingly magical understanding of input text, it is very good at indicating
which tokens from its vocabulary are most likely to follow the given input.
This indication happens through (see drawing above) assigning probabilities.
For example P(token_0) represents the estimated probability that the word
chewing (represented by token_0) follows the given sequence of input tokens (
the cow is …). The probability of the word bowling (represented by
token_50.256) is hopefully lower than that of the word chewing or grazing in
this context.
We emphasise that, for an LLM to generate a complete sequence of text, it has
to iteratively pass through the network multiple times: each forward pass
serves to select exactly one token which in turn contributes to determining
the next token. | scraping/output/-6332124900812207379.txt | [
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] |
The crucial piece of the puzzle now comes from how just one output token is
chosen using this list of token probabilities. Below, we briefly dive into the
world of sampling. For a more extended understanding of sampling methods, we
recommend reading through this Hugging Face blogpost.
### Top notch sampling: Top-p sampling
Modern LLMs use (a variant of) top-p sampling (i.e. nucleus sampling
introduced in this 2018 paper) for sampling the response. This method only
considers the tokens whose cumulative probability exceed the probability p and
then redistributes the probability mass across the remaining tokens so that
the sum of probabilities is 1. If you’re now thinking “wait what”,
congratulations, you’re not a statistician! Feel free to re-read that sentence
and then jump into the more understandable visual explanation below:
Example of top-p sampling (i.e. nucleus sampling) based on the Hugging Face
blogpost. | scraping/output/-6332124900812207379.txt | [
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Example of top-p sampling (i.e. nucleus sampling) based on the Hugging Face
blogpost.
Say that we set p=0.92. On the first pass starting from only the word "the”,
we need 6 tokens to exceed that probability of 92% (they sum to 94%). We can
sample a token from these six words by considering their redistributed
probabilities (where the word nice will have the highest chance of being
picked). For the next pass, we find that the 3 most likely tokens together
already easily exceed the threshold of 92% and thus the eventual token is
sampled from only those three.
The nice thing is, the amount of tokens to sample from dynamically depends on
the level of “uncertainty” of the model. If the model deems a small subset of
tokens to be most relevant (for example because the input tokens consist of
"the", "car" thus restricting the context) it will only sample from those. | scraping/output/-6332124900812207379.txt | [
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Relevant anecdote: a couple of years ago at ML6 we created a basic ‘terms &
conditions summariser’ that by chance generated the word “milk” in a summary
and completely shifted towards talking about food just because it didn’t use
nucleus sampling.
### Then what is Temperature controlling?
Okay so nice, we now understand what the top_p parameter from the OpenAI API
documentation refers to. Note that by default, this parameter sets p=100%
meaning that all output tokens are taken into account. If on the other hand
p=0%, the first token to be checked which algorithmically is the one with the
highest probability will always be chosen as it immediately exceeds the super
low threshold by itself.
Okay but what role does the temperature parameter play?
Imagine you have a set of output tokens (possibly with re-distributed
probability if you play with the top_p parameter) to sample from:
Sampling actual output tokens based on the remaining tokens in play | scraping/output/-6332124900812207379.txt | [
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Sampling actual output tokens based on the remaining tokens in play
What the temperature does is: it controls the relative weights in the
probability distribution. It controls the extent to which differences in
probability play a role in the sampling. Take the example above: for the token
input sequence "The” we would (by default) expect the word nice to have a 75%
chance of being chosen P(“nice”)=75%. This is what happens at temperature t=1.
This parameter can be chosen between 0 and 2.
At temperature t=0 this sampling technique turns into what we call greedy
search/argmax sampling where the token with the highest probability is always
selected (here: P(“nice”)=100% ).
At temperature t=2 the difference between the more probable and less probable
tokens is reduced at sample time. For the example on the left above, this
would result in: P("nice")=58% , P("dog")=32% , P("car")=10%. | scraping/output/-6332124900812207379.txt | [
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0.02310582436621189,
0.04517427459359169,
0.0599394291639328,
0.04094085097312927
] |
For those interested, the formula to calculate the sampling probabilities
impacted by temperature t is added below (where K represents the total amount
of tokens considered in the sampling):
### Conclusion: secrets begone
You can now consider yourself a true warrior of the OpenAI’s Generative APIs
because the extract below has no secrets for you any longer.
Note the statement “we generally recommend altering this or top_p/temperature
but not both”. And that is just a suggestion to keep your changes more or less
interpretable as you play with the values. Setting either of these values to
their deterministic limits (i.e. p=0 or top_p=0) has the same effect.
Extract from the OpenAI completion API documentation
We remember that: by default, sampling happens across the entirety of the
token vocabulary (top_p=1) and the probability distribution is left unaffected
(temperature=1).
## The Explanation
| scraping/output/-6332124900812207379.txt | [
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] |
We remember that: by default, sampling happens across the entirety of the
token vocabulary (top_p=1) and the probability distribution is left unaffected
(temperature=1).
## The Explanation
Wielding the knowledge above, we know exactly what should happen if we fix
temperature=0. Namely: the one token with the most likely probability will be
chosen.
But what if at some point during generation, lightning strikes and two tokens
get assigned exactly the same probability?
Lightning strikes ⚡: we encounter two tokens that have exactly the same
probability
### Lightning may strike: a non-zero chance
That case of at least two tokens having the same probability may seem
unlikely, but there are some factors contributing to its not so small
likelihood: | scraping/output/-6332124900812207379.txt | [
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* Model Uncertainty: in cases where the model is far from certain that one token is the ideal choice for the next token, the odds that tokens that are among the favourites have similar probabilities is higher.
* Limited Precision: the odds for two probabilities to be exactly the same decreases with the amount of bits that are used to represent them. If you have: 1 bit = 2 possible numbers, 2 bits= 4 possible numbers, 8 bits= 256 possible numbers. If you have only 8 bits to represent a number in a network (which is a result of a sum of multiplications from other 8-bit numbers) the odds of outcomes to be exactly the same is larger than when you have 32 bits at every step along the way. We note that if quantisation choices are made to optimise the speed and cost of inference, the precision of the represented numbers is limited further. | scraping/output/-6332124900812207379.txt | [
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* Forward Pass Bonanza: with the amount of tokens typically necessary to construct a relevant reply, the chance of lightning striking steadily rises. If Pᵢ is the probability of this happening for one forward passi then the total chance of this happening is the sum of that probability for all forward passes needed to generate the total reply. As a simplified example, imagine fixed odds of lightning striking P_i=0.0001%, then for a reply that needs 200 passes through the network, the odds of lightning striking for the entire reply would be P=0.02% which isn’t all too small. | scraping/output/-6332124900812207379.txt | [
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And of course we note that lightning has to strike only once to change the
entire generative behaviour; if another token is selected once, then the
probability for following forward passes will be directly impacted, resulting
in a different “answer path”. You can imagine that getting the word “milk”
once, results in a completely different terms & conditions summary down the
line.
### Okay, so lightning can strike. But what if it does?
The obvious next question is then: if two tokens indeed have exactly the same
probability, what happens next?
Well, when computers need to make a pick between a set of equally
valid/probable options, the decisive “coin toss” power is handed to a seed.
The seed controls the value of a pseudorandom number generator. A more
intuitive simplification is shown below.
Illustration on how a seed can be imagined to affect seemingly random
decisions | scraping/output/-6332124900812207379.txt | [
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