chunk stringlengths 11 1k | source stringlengths 37 40 | embeddings list |
|---|---|---|
Talent & Culture 🫶 * Multiple locations
* Frontend Engineer
Advisory & Delivery unit 💻 * Multiple locations
* Data Engineer
Advisory & Delivery unit 💻 * Office Berlin 📍
* Junior Talent Partner
Talent & Culture 🫶 * Multiple locations
* Alliance Manager - Azure
Business Development unit 🤝 * Multiple... | scraping/output/-1004585763649522392.txt | [
0.018397454172372818,
0.004548073746263981,
-0.08646808564662933,
-0.03594604879617691,
0.08067051321268082,
-0.024021746590733528,
0.0040425327606499195,
0.03731200098991394,
0.048462655395269394,
-0.06323640048503876,
0.016213078051805496,
0.028195317834615707,
0.05323297530412674,
-0.03... |
* Senior Data Engineer
Advisory & Delivery unit 💻 * Multiple locations
* Senior Machine Learning Engineer
Advisory & Delivery unit 💻 * Multiple locations
* Machine Learning Engineer
Advisory & Delivery unit 💻 * Multiple locations
* Internship
Multiple locations
* < enter fancy job title here >
Multi... | scraping/output/-1004585763649522392.txt | [
-0.0007283671875484288,
-0.037412844598293304,
-0.06350451707839966,
0.005232657305896282,
0.06313145905733109,
-0.004192605149000883,
0.0017170272767543793,
0.065272256731987,
0.05213325843214989,
-0.01812204159796238,
0.010786747559905052,
0.04419934004545212,
0.04277288541197777,
-0.033... |
#### 4
### Final meeting - wrapping up
As you approach the finish line, we’ll have a final chat to reflect on your
dream job and discuss final questions.
* > Great, easy-going, fair recruitment - especially the feeling of being taken serious with short reply and follow-up cycles is awesome
Candidate
00:00
% buf... | scraping/output/-1004585763649522392.txt | [
0.01876642554998398,
-0.02130037173628807,
-0.06658577173948288,
-0.02547828108072281,
0.05678274855017662,
-0.021007027477025986,
0.04628348723053932,
0.04563310742378235,
0.07608724385499954,
0.004828146193176508,
0.018855217844247818,
0.0195747260004282,
0.04850985109806061,
-0.02232850... |
* > Great, easy-going, fair recruitment - especially the feeling of being taken serious with short reply and follow-up cycles is awesome
Candidate
00:00
% buffered
## About ML6
We guide the AI revolution towards positive impact. 🌐
Exciting developments are happening in the world of AI, offering unprecedented
op... | scraping/output/-1004585763649522392.txt | [
0.009200124070048332,
0.002780218841508031,
-0.09053421020507812,
-0.04132838547229767,
0.04207432270050049,
-0.03650028631091118,
0.04956664890050888,
0.04402409493923187,
0.05587296560406685,
-0.03945711627602577,
0.022861376404762268,
0.005411083810031414,
0.0716281458735466,
-0.0291174... |
Examples of serious misconduct (not limited to): safety of the individual
(aggression, discrimination, sexual harassment), data privacy issues, public
health, tax and fraud.
* Join us 🕵️♀️
* Data & privacy
* Manage cookies
ml6.eu/
Employee login
Candidate Connect login
Applicant tracking system by Teamta... | scraping/output/-1004585763649522392.txt | [
0.05424997955560684,
-0.010493537411093712,
-0.09148477762937546,
-0.08608821779489517,
0.03151591867208481,
-0.03368574008345604,
0.05130934715270996,
0.05987449735403061,
0.062128063291311264,
-0.03634663298726082,
-0.018089860677719116,
0.010559788905084133,
0.08272054046392441,
-0.0137... |
No items found.
🚀 Unleash the Power of Large Language Models and Foundation Models - Read our
insights and expertise on these trends in AI!
Discover more
Services
Navigate intelligence
Activate intelligence
Build intelligence
AI Solutions
Custom solutions
Solutions catalogue
Domains of expertise
References
... | scraping/output/-2864730860114532062.txt | [
0.05000491067767143,
-0.010341300629079342,
-0.042680297046899796,
-0.026224547997117043,
0.052057839930057526,
-0.05658881738781929,
0.07841837406158447,
0.08457168191671371,
0.03373835235834122,
-0.03769903630018234,
0.022519808262586594,
0.034452974796295166,
0.04128238558769226,
-0.055... |
Foundation Models
Corporate
People
Structured Data
Chat GPT
Sustainability
Voice & Sound
Front-End Development
Data Protection & Security
Responsible/ Ethical AI
Infrastructure
Hardware & sensors
MLOps
Generative AI
Natural language processing
Computer vision
Accelerating businesses with AI technology &... | scraping/output/-2864730860114532062.txt | [
0.014286217279732227,
-0.015707867220044136,
-0.039112672209739685,
-0.029195789247751236,
0.02637052722275257,
-0.053580962121486664,
0.08145482838153839,
0.08285489678382874,
0.056762415915727615,
-0.032076142728328705,
0.03387955203652382,
0.01853342354297638,
0.041100580245256424,
-0.0... |
🚀 Unleash the Power of Large Language Models and Foundation Models - Read our
insights and expertise on these trends in AI!
Discover more
Services
Navigate intelligence
Activate intelligence
Build intelligence
AI Solutions
Custom solutions
Solutions catalogue
Domains of expertise
References
Client cases
Cu... | scraping/output/-3291290598934202599.txt | [
0.02128366008400917,
-0.010606945492327213,
-0.06812969595193863,
-0.04510865360498428,
0.032983262091875076,
-0.07089028507471085,
0.0554133802652359,
0.08548329770565033,
0.03718092292547226,
-0.04561254382133484,
0.010294051840901375,
0.021384021267294884,
0.05317005142569542,
-0.047834... |
Business ValueOur solution in practiceResourcesGet in touch
## Business Value
Pseudonymization and anonymisation are powerful measures to protect personal
data, and often necessary to ensure compliance with data regulations, enable
data sharing, mitigate risks and foster transparency and trust.
Pseudonymisation and ... | scraping/output/-3291290598934202599.txt | [
0.03787929192185402,
-0.03351280838251114,
-0.034442245960235596,
-0.08823756128549576,
0.011390715837478638,
-0.05752900242805481,
0.043674200773239136,
0.06935654580593109,
0.032346032559871674,
-0.011846908368170261,
0.004955084063112736,
0.007823861204087734,
0.07861986011266708,
0.005... |
2\. Anonymisation \- is an irreversible process that transforms personal data
into a state where it can no longer be attributed to an identifiable
individual, even through the use of additional information, eliminating any
risk of re-identification. Anonymised data does not fall under the scope of
data protection regul... | scraping/output/-3291290598934202599.txt | [
0.046424500644207,
0.00956339668482542,
-0.036521535366773605,
-0.11643972247838974,
0.02868622913956642,
-0.008277412503957748,
0.05187803506851196,
0.05183986574411392,
0.04065045714378357,
-0.05251822993159294,
-0.01809554174542427,
0.0032779769971966743,
0.10121961683034897,
-0.0342688... |
This is some text inside of a div block.
This is some text inside of a div block.
### GDPR compliance of cloud usage
Comply with the General Data Protection Regulation (GDPR) by anonymizing your
data when keeping it in the cloud. We guide you through the process so that
you can focus on creating the most value from ... | scraping/output/-3291290598934202599.txt | [
0.026493674144148827,
0.009016918949782848,
-0.04903440549969673,
-0.07195837795734406,
0.043213170021772385,
-0.017106978222727776,
0.04045644402503967,
0.05794168636202812,
0.0358668677508831,
-0.016289271414279938,
-0.013560935854911804,
0.037476807832717896,
0.09338007122278214,
-0.048... |
This is some text inside of a div block.
This is some text inside of a div block.
## Tackling your complex use cases with a proven approach
Our unique 5-step approach results in a solution tailored to your specific
needs, while ensuring cost-efficiency and speed through the reuse of pre-built
components.
Our unique... | scraping/output/-3291290598934202599.txt | [
0.019389990717172623,
-0.02396550588309765,
-0.045234907418489456,
-0.09234379976987839,
0.04012955725193024,
-0.0255605336278677,
0.011302691884338856,
0.047278959304094315,
0.0720621719956398,
-0.03347501531243324,
-0.0016834648558869958,
0.040099695324897766,
0.0865788385272026,
-0.0291... |
4\. Visualize & Validate \- We offer a web application to visualize the
outcomes to users and allow for user validation, integrated into your existing
workflow
5\. Improve / Maintain - We maintain and support improvements of your
pseudonymisation solution, enabling access to new features and capabilities
and training ... | scraping/output/-3291290598934202599.txt | [
0.028698522597551346,
-0.04067642614245415,
-0.03450025990605354,
-0.06538759171962738,
0.04501752927899361,
-0.05935923755168915,
0.043256212025880814,
0.07325216382741928,
0.03769892081618309,
-0.03939054161310196,
0.0279471967369318,
0.040158238261938095,
0.03896960988640785,
-0.0393629... |
ES 🇪🇸
EN 🇺🇸
#### Multi-cloud
Standard supported
On the roadmap or custom development
Azure
GCP
AWS
On Premise
#### Entities
Standard supported
On the roadmap or custom development
Company ID
Bank account number
ID number
Company name
Country
Date
City
Street
Name
Price
IP address ... | scraping/output/-3291290598934202599.txt | [
0.004631957970559597,
-0.01522041205316782,
-0.051885347813367844,
-0.04275621846318245,
0.04281219094991684,
-0.03413823992013931,
0.05544658750295639,
0.06634855270385742,
0.051888369023799896,
-0.030973060056567192,
0.01935267075896263,
0.015452542342245579,
-0.003885351587086916,
-0.05... |
Corporate
People
Structured Data
Chat GPT
Sustainability
Voice & Sound
Front-End Development
Data Protection & Security
Responsible/ Ethical AI
Infrastructure
Hardware & sensors
MLOps
Generative AI
Natural language processing
Computer vision
Questions?
# Contact our experts
Discover how our solution ca... | scraping/output/-3291290598934202599.txt | [
0.007779045030474663,
-0.016735194250941277,
-0.03107251413166523,
-0.061029497534036636,
0.045789994299411774,
-0.04030410945415497,
0.0663737878203392,
0.0834689512848854,
0.06314288824796677,
-0.03580530732870102,
0.036216214299201965,
0.021789031103253365,
0.03346693888306618,
-0.03276... |
Oops! Something went wrong while submitting the form.
Talk to
Bruno Borghmans
Sales Consultant
Contact Bruno
Accelerating businesses with AI technology & experts
Contact:
info@ml6.eu
+32 9 265 95 50
Contact us
Join our newsletter
By subscribing you agree to with our Privacy Policy
Thank you! Your submissi... | scraping/output/-3291290598934202599.txt | [
0.016138318926095963,
-0.010210227221250534,
-0.05704629793763161,
-0.05732695013284683,
0.035020507872104645,
-0.05847790464758873,
0.05741460993885994,
0.08696532994508743,
0.048742108047008514,
-0.02362157218158245,
0.03573183715343475,
0.028802698478102684,
0.04014031961560249,
-0.0401... |
MLOps
🚀 Unleash the Power of Large Language Models and Foundation Models - Read our
insights and expertise on these trends in AI!
Discover more
Services
Navigate intelligence
Activate intelligence
Build intelligence
AI Solutions
Custom solutions
Solutions catalogue
Domains of expertise
References
Client ca... | scraping/output/8745283474802914343.txt | [
0.015386035665869713,
-0.019497424364089966,
-0.042927637696266174,
-0.02217172645032406,
0.06474093347787857,
-0.0678514614701271,
0.07436618953943253,
0.06450121104717255,
0.040061287581920624,
-0.020934175699949265,
0.022014182060956955,
0.036888327449560165,
0.034031398594379425,
-0.04... |
Corporate
People
Structured Data
Chat GPT
Sustainability
Voice & Sound
Front-End Development
Data Protection & Security
Responsible/ Ethical AI
Infrastructure
Hardware & sensors
MLOps
Generative AI
Natural language processing
Computer vision
Accelerating businesses with AI technology & experts
Contact:
... | scraping/output/8745283474802914343.txt | [
0.01861809752881527,
-0.007257629185914993,
-0.04283001273870468,
-0.027256501838564873,
0.033577196300029755,
-0.05109598487615585,
0.07161897420883179,
0.08220640569925308,
0.05122101679444313,
-0.035757746547460556,
0.03975615277886391,
0.0325358584523201,
0.03885256499052048,
-0.031850... |
Computer vision
Hardware & sensors
🚀 Unleash the Power of Large Language Models and Foundation Models - Read our
insights and expertise on these trends in AI!
Discover more
Services
Navigate intelligence
Activate intelligence
Build intelligence
AI Solutions
Custom solutions
Solutions catalogue
Domains of ex... | scraping/output/2712597742853724339.txt | [
0.0347863994538784,
-0.03711559250950813,
-0.0757649689912796,
-0.04083468019962311,
0.04909005016088486,
-0.05562927573919296,
0.05441842973232269,
0.054466184228658676,
0.03966910392045975,
-0.042570993304252625,
0.033937323838472366,
0.016211409121751785,
0.036665525287389755,
-0.024926... |
Placeholder tag
Developing an AI solution for product photography: what we learned
Reshaping Product Photography with generative AI
August 23, 2023
By
Bert Christiaens
Placeholder tag
Using generative AI for image manipulation: discrete absorbing diffusion
models explained
We review and compare the previous SOT... | scraping/output/2712597742853724339.txt | [
0.06853377819061279,
-0.020099535584449768,
-0.0469486229121685,
-0.050170302391052246,
0.06124073639512062,
-0.0546211376786232,
0.07301034033298492,
0.05191010609269142,
0.02593172900378704,
-0.007735419552773237,
0.047295648604631424,
0.007617522031068802,
0.024991704151034355,
-0.03318... |
Voice & Sound
Front-End Development
Data Protection & Security
Responsible/ Ethical AI
Infrastructure
Hardware & sensors
MLOps
Generative AI
Natural language processing
Computer vision
Accelerating businesses with AI technology & experts
Contact:
info@ml6.eu
+32 9 265 95 50
Contact us
Join our newslett... | scraping/output/2712597742853724339.txt | [
0.014850117266178131,
-0.0024797169025987387,
-0.04479318857192993,
-0.04882791265845299,
0.03959949314594269,
-0.050171706825494766,
0.06961895525455475,
0.08040126413106918,
0.04982632026076317,
-0.04775577783584595,
0.024838251993060112,
0.0380239300429821,
0.023058420047163963,
-0.0259... |
🚀 Unleash the Power of Large Language Models and Foundation Models - Read our
insights and expertise on these trends in AI!
Discover more
Services
Navigate intelligence
Activate intelligence
Build intelligence
AI Solutions
Custom solutions
Solutions catalogue
Domains of expertise
References
Client cases
Cu... | scraping/output/-8837970835713973964.txt | [
0.05216103792190552,
-0.017672691494226456,
-0.057224009186029434,
-0.028398269787430763,
0.06267928332090378,
-0.027450988069176674,
0.051360782235860825,
0.07483086735010147,
0.03853161260485649,
-0.026476819068193436,
0.048589691519737244,
0.020846715196967125,
0.04051675274968147,
-0.0... |
View more
MLSox, our April Fool's sock-matching app
tag
View more
Notebook on TensorFlow Data Validation
tag
View more
Jeroom dancing through pose transfer (with VT4/GoPlay)
tag
View more
How to spot a deepfake?
tag
View more
HuggingFace Knowledge Graphs
tag
Knowledge graphs in particular are a great too... | scraping/output/-8837970835713973964.txt | [
0.021795839071273804,
-0.0297381654381752,
-0.0529448427259922,
-0.008868638426065445,
0.05614910647273064,
-0.04223088175058365,
0.05441813915967941,
0.06017610803246498,
0.05051372945308685,
-0.029755987226963043,
0.0317496620118618,
0.06956850737333298,
0.02871619164943695,
-0.036502361... |
dOCRtor
tag
Simulate common OCR errors for Dutch texts and use the finetune the character-
based ByT5 model to correct them.
View more
5 tips on how to work with imbalanced datasets
tag
Short tutorial (5 tips in a single document) on how to work with imbalanced
datasets.
View more
Connexion
tag
Have you heard... | scraping/output/-8837970835713973964.txt | [
0.047460831701755524,
-0.05814126506447792,
-0.06339646875858307,
-0.032224006950855255,
0.034354355186223984,
-0.020178088918328285,
0.05228409916162491,
0.08166603744029999,
0.05738301947712898,
-0.03279963508248329,
0.026691090315580368,
0.03870274871587753,
0.023903142660856247,
-0.051... |
View more
CICD and automation
tag
CICD and automation in general is part of our development culture. We aim to
avoid any manual action when possible which enables delivery of high quality
software faster and allows us to focus on solving business problems. We are
fans of a code review process. Code review leads to b... | scraping/output/-8837970835713973964.txt | [
-0.00008466351573588327,
-0.04932272806763649,
-0.08158993721008301,
-0.037682484835386276,
0.008885044604539871,
-0.0602848045527935,
0.06361288577318192,
0.06788258254528046,
0.058819182217121124,
-0.028827132657170296,
0.019391288980841637,
0.037954945117235184,
0.04342081770300865,
-0.... |
View more
Sign up for our newsletter
Stay up to date
Sign up
By clicking Sign Up you're confirming that you agree with our Terms and
Conditions.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Accelerating businesses with AI technology & experts
Contact:
info... | scraping/output/-8837970835713973964.txt | [
0.01278764009475708,
-0.022701455280184746,
-0.03913550078868866,
-0.03562725707888603,
0.02521420642733574,
-0.055632513016462326,
0.06911419332027435,
0.08768397569656372,
0.06214305758476257,
-0.0264141783118248,
0.049501996487379074,
0.04418303817510605,
0.03791223093867302,
-0.0347098... |
🚀 Unleash the Power of Large Language Models and Foundation Models - Read our
insights and expertise on these trends in AI!
Discover more
Services
Navigate intelligence
Activate intelligence
Build intelligence
AI Solutions
Custom solutions
Solutions catalogue
Domains of expertise
References
Client cases
Cu... | scraping/output/-4043632892115440660.txt | [
0.015936443582177162,
-0.009072811342775822,
-0.05842701345682144,
-0.038373325020074844,
0.06799544394016266,
-0.06532663106918335,
0.07173769921064377,
0.08436999469995499,
0.038425274193286896,
-0.04954468831419945,
0.017360512167215347,
0.02102534845471382,
0.04002130776643753,
-0.0410... |
Jobs
Team
Working at ML6
Taal
NLEN
Contact us
en
defrNL
Contact us
# Natural Language Processing
Let your computers speak your language
Learn moreContact us
## Make machines talk
Natural Language Processing (NLP) is a field of artificial intelligence that
teaches computers to understand and generate human ... | scraping/output/-4043632892115440660.txt | [
0.045654296875,
-0.0014807877596467733,
-0.09810465574264526,
-0.04093119129538536,
0.08413282036781311,
-0.03015523962676525,
0.020117880776524544,
0.054143909364938736,
0.060527291148900986,
-0.04718426987528801,
0.03172321617603302,
0.010147239081561565,
0.04321546107530594,
-0.05851158... |
## Text classification and clustering
Text classification and clustering automatically categorize text based on its
content, such as news articles or customer feedback, into predefined
categories like topics or sentiment for better analysis and organization.
## Pseudonymisation
Pseudonymisation replaces personal dat... | scraping/output/-4043632892115440660.txt | [
0.038705211132764816,
-0.019618848338723183,
-0.05873486399650574,
-0.05522051826119423,
0.05460717901587486,
-0.049951884895563126,
0.05054517835378647,
0.07056199014186859,
0.056496139615774155,
-0.021085353568196297,
0.024745555594563484,
0.04872060567140579,
0.06960246711969376,
-0.038... |
## Text generation
Text generation uses large language models to create new and understandable
natural language output, such as weather reports, patient reports, image
captions, or chatbots.
## Client cases
Discover how our expertise in Hardware & Sensors leads businesses to success
No results found.
There are no ... | scraping/output/-4043632892115440660.txt | [
0.005945390090346336,
-0.02335571125149727,
-0.04087519645690918,
-0.08610015362501144,
0.08186003565788269,
-0.053160108625888824,
0.06786405295133591,
0.048684217035770416,
0.040708027780056,
-0.040568768978118896,
0.031733181327581406,
0.02660372294485569,
0.034584399312734604,
-0.04444... |
Computer vision
## Typical challenges
Thanks to our linguistics and computer science expertise, we’re able to
overcome functional and technical challenges NLP challenges such as:
## Ambiguity
Natural language is subjective and ambiguous, which makes it difficult for
machines to accurately process language due to mu... | scraping/output/-4043632892115440660.txt | [
0.03882850706577301,
-0.04518800228834152,
-0.06575498729944229,
-0.058740027248859406,
0.08004678785800934,
-0.0633181557059288,
0.0592152364552021,
0.049415964633226395,
0.0814267247915268,
-0.037694256752729416,
0.044710274785757065,
0.002861193148419261,
0.06727267056703568,
-0.0744375... |
## High level outline of the solution
Defining the problem
The first step in using NLP is to define the problem that needs to be solved.
This involves identifying the data to be analyzed, the questions to be
answered, or the business objectives to be met.
Data preparation
After defining the problem, data needs to b... | scraping/output/-4043632892115440660.txt | [
0.04773981496691704,
-0.011372184380888939,
-0.06026708707213402,
-0.05503351241350174,
0.05835617706179619,
-0.06778206676244736,
0.037120454013347626,
0.06054374948143959,
0.06610443443059921,
0.005463605280965567,
0.03884345293045044,
0.04152526333928108,
0.051843252032995224,
-0.056586... |
Testing and evealuation
Test and evaluate the performance of the chosen NLP technique(s) by measuring
metrics such as accuracy, precision, and recall to determine how well the
approach is working.
Deploy and monitor
Based on the client's requirements, the deployment of the NLP solution must be
optimized for cost and... | scraping/output/-4043632892115440660.txt | [
0.014326750300824642,
-0.02952791564166546,
-0.02636384405195713,
-0.04712340980768204,
0.0429864302277565,
-0.04301225766539574,
0.08199550956487656,
0.08603407442569733,
0.08707019686698914,
-0.043176162987947464,
0.016758853569626808,
0.044051866978406906,
0.0325048454105854,
-0.0236770... |
Computer vision
contact US
## Contact Our NLP experts for AI-powered language solutions
Discover how Our NLP solutions can revolutionize your business communications
Name *
Company *
Email *
How did you hear about us?
How can we help you?Select one...I'm interested in getting strategic adviceI'm
looking to buil... | scraping/output/-4043632892115440660.txt | [
0.011602425016462803,
-0.024388913065195084,
-0.07292918860912323,
-0.03631608933210373,
0.04557332396507263,
-0.04213781654834747,
0.06392655521631241,
0.08023449033498764,
0.07701943069696426,
-0.048073042184114456,
0.025168394669890404,
0.022888386622071266,
0.03164200857281685,
-0.0354... |
Join our newsletter
By subscribing you agree to with our Privacy Policy
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Services
Navigate Intelligence Activate IntelligenceBuild Intelligence
Solutions
Custom AI solutionsSolutions catalogueDomains of expertise
... | scraping/output/-4043632892115440660.txt | [
0.011886990629136562,
-0.019043080508708954,
-0.027456941083073616,
-0.04586682841181755,
0.03762790933251381,
-0.05721542239189148,
0.0641404315829277,
0.10131411254405975,
0.05475594475865364,
-0.025892814621329308,
0.0642801895737648,
0.05204171687364578,
0.048060305416584015,
-0.023868... |
🚀 Unleash the Power of Large Language Models and Foundation Models - Read our
insights and expertise on these trends in AI!
Discover more
Services
Navigate intelligence
Activate intelligence
Build intelligence
AI Solutions
Custom solutions
Solutions catalogue
Domains of expertise
References
Client cases
Cu... | scraping/output/6352475188966086889.txt | [
0.01587342657148838,
-0.0031606052070856094,
-0.05754377320408821,
-0.016799764707684517,
0.048250362277030945,
-0.061100687831640244,
0.08332850784063339,
0.07063151895999908,
0.04029364883899689,
-0.042753610759973526,
0.01875586248934269,
0.029912639409303665,
0.04375998303294182,
-0.04... |
Data Protection & Security
Responsible/ Ethical AI
Infrastructure
Hardware & sensors
MLOps
Generative AI
Natural language processing
Computer vision
Solutions
Strategic Advice
Production Process Optimization
Route optimization
Fraud detection & prevention
Intelligent/ Smart Machines
Generative AI
Data Dr... | scraping/output/6352475188966086889.txt | [
0.008445857092738152,
-0.01347228791564703,
-0.03100721724331379,
-0.04567379876971245,
0.04699769243597984,
-0.044226694852113724,
0.07279657572507858,
0.06349313259124756,
0.048480428755283356,
-0.021170003339648247,
0.03822014108300209,
0.02805163338780403,
0.04054704308509827,
-0.03291... |
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Tag
Showing 0 of 100
Placeholder tag
Large Language Models: to Fine-tune or not to Fine-tune?
Fine-tune large language models for custom behavior and knowledge, weighing
open source vs. closed source options.
Febr... | scraping/output/6352475188966086889.txt | [
0.004163207020610571,
-0.044608280062675476,
-0.05111328139901161,
-0.06510157883167267,
0.03134666010737419,
-0.05198116600513458,
0.046269491314888,
0.06383343040943146,
0.033898260444402695,
-0.02149992436170578,
0.0268230140209198,
0.038049496710300446,
0.029521046206355095,
-0.0644786... |
Implementing ML models' code promotion across environments using CI/CD
pipelines.
February 8, 2024
By
Miro Goettler
Placeholder tag
Elevating excellence : accelerating each others talent
A company can only be as successful as the success of its people. Everything
depends on a strong ‘people and talent approach’ t... | scraping/output/6352475188966086889.txt | [
0.0442940890789032,
-0.02348184958100319,
-0.09607820212841034,
-0.045445945113897324,
0.0347851924598217,
-0.03471764922142029,
0.014747277833521366,
0.07282928377389908,
0.026639025658369064,
0.007556920871138573,
0.006779070943593979,
0.02544998936355114,
0.04800880327820778,
-0.0552708... |
January 11, 2024
By
Michiel Van Lerbeirghe
Placeholder tag
Elevating Your Retrieval Game: Insights from Real-world Deployments
Discover how to optimize Retrieval Augmented Generation (RAG) by improving
retriever performance, utilizing metrics for evaluation, and enabling follow-
up questions for enhanced user expe... | scraping/output/6352475188966086889.txt | [
0.028962677344679832,
-0.035702090710401535,
-0.04794851318001747,
-0.022064536809921265,
0.05888858810067177,
-0.06468803435564041,
0.04433409124612808,
0.0955069288611412,
0.048457350581884384,
-0.0698467567563057,
0.021419396623969078,
0.04700399935245514,
0.0005189074436202645,
-0.0560... |
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... | scraping/output/6352475188966086889.txt | [
0.04043424502015114,
-0.04295074939727783,
-0.06669630855321884,
-0.04178757593035698,
0.06725434213876724,
-0.04870228096842766,
0.07106278091669083,
0.10195865482091904,
0.03922097384929657,
-0.029565416276454926,
0.016765352338552475,
0.02743653394281864,
0.018873946741223335,
-0.037191... |
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 gettin... | scraping/output/6352475188966086889.txt | [
0.019806694239377975,
-0.026134194806218147,
-0.06332681328058243,
-0.010044511407613754,
0.02059423178434372,
-0.05311508849263191,
0.06312695890665054,
0.058561358600854874,
0.03189791738986969,
-0.018130503594875336,
0.01571059226989746,
0.057258352637290955,
0.01320625375956297,
-0.040... |
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,
all... | scraping/output/6352475188966086889.txt | [
0.03464360535144806,
-0.011445813812315464,
-0.06976886838674545,
0.0068448069505393505,
0.02371973916888237,
-0.041822608560323715,
0.028352893888950348,
0.06855709850788116,
0.05154673010110855,
-0.00921644363552332,
0.0158857274800539,
0.05916321277618408,
0.02865178883075714,
-0.048800... |
September 6, 2023
By
Julie Plusquin
Placeholder tag
Amazon Bedrock: Building a solid foundation for Your AI Strategy
Amazon recently announced their foundation model service: Amazon Bedrock. We
got our hands on it as part of a limited preview. So, what’s in it for you?
September 6, 2023
By
Placeholder tag
Fine... | scraping/output/6352475188966086889.txt | [
0.0420503169298172,
-0.03349326178431511,
-0.06281305849552155,
-0.009273305535316467,
0.03733206167817116,
-0.056187890470027924,
0.04743679240345955,
0.05778787285089493,
0.024305356666445732,
-0.016559716314077377,
0.0395098477602005,
0.03228244557976723,
0.0366651676595211,
-0.03140704... |
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
With the growing maturity of AI models and techniques, the technical art of
the possible is no longer the most important ceiling.Company s... | scraping/output/6352475188966086889.txt | [
0.02164091169834137,
-0.03057204745709896,
-0.07230804860591888,
-0.026395687833428383,
0.027224034070968628,
-0.04658052697777748,
0.02633841522037983,
0.07706650346517563,
0.06600280851125717,
-0.032116618007421494,
0.044382285326719284,
0.05556553229689598,
0.0389348529279232,
-0.066321... |
August 8, 2023
By
Placeholder tag
Dynamic Pricing from a visual perspective
Visualising Dynamic Pricing modelling to build insights
August 3, 2023
By
Placeholder tag
Deploy AI models in your OT environment and do it securely
Securing AI deployment in OT environments.
August 3, 2023
By
Rob Vandenberghe
Plac... | scraping/output/6352475188966086889.txt | [
0.022832389920949936,
-0.0174116138368845,
-0.06568112969398499,
-0.018896978348493576,
0.051142625510692596,
-0.056160952895879745,
0.045489855110645294,
0.07548698037862778,
0.047266438603401184,
-0.022106988355517387,
0.04946640878915787,
0.05983053520321846,
0.027855293825268745,
-0.02... |
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, Tale... | scraping/output/6352475188966086889.txt | [
0.021619459614157677,
-0.03235149756073952,
-0.07707365602254868,
-0.030799230560660362,
0.028978293761610985,
-0.042651426047086716,
0.011177802458405495,
0.09004959464073181,
0.051078036427497864,
-0.021603185683488846,
0.001456088968552649,
0.03845975175499916,
0.015982450917363167,
-0.... |
Harness large foundation models affordably with LoRA technique for business
applications.
July 13, 2023
By
Placeholder tag
Predicting enzyme heat stability during Christmas break
ML6 tackles enzyme stability prediction in Kaggle competition, pushing
boundaries in life sciences.
July 10, 2023
By
Pieter Coussemen... | scraping/output/6352475188966086889.txt | [
0.020302385091781616,
-0.030309146270155907,
-0.056907255202531815,
-0.040918998420238495,
0.0381096750497818,
-0.04939401522278786,
0.06417149305343628,
0.06290686875581741,
0.027900870889425278,
-0.018747441470623016,
0.03204742819070816,
0.015584397129714489,
0.0022707246243953705,
-0.1... |
June 19, 2023
By
Placeholder tag
Developing AI Systems in the Foundation Model Age : An intro
Introduction to Foundation Models and choosing the right one.
June 7, 2023
By
Generative AI
Placeholder tag
Developing AI Systems in the Foundation Model Age : From MLOps to FMOps
The structure of FMOps to optimize y... | scraping/output/6352475188966086889.txt | [
0.01359410211443901,
-0.030456475913524628,
-0.05138414353132248,
-0.03782283514738083,
0.05366911739110947,
-0.0425565131008625,
0.054072797298431396,
0.06529361754655838,
0.04186248406767845,
-0.014400986954569817,
0.03336877003312111,
0.04139094054698944,
0.008310463279485703,
-0.058050... |
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
... | scraping/output/6352475188966086889.txt | [
0.03962283954024315,
-0.03054170496761799,
-0.045086994767189026,
-0.017145950347185135,
0.0411205030977726,
-0.043158676475286484,
0.044477034360170364,
0.0793333500623703,
0.028250275179743767,
-0.03503820672631264,
0.010212482884526253,
0.03538784012198448,
-0.005566130857914686,
-0.061... |
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
By
Michiel De Koninck
Placeholder tag
The Compliance-Friendly Guide to Using ChatGPT and other GPT models
In this blogpost we ex... | scraping/output/6352475188966086889.txt | [
0.058086808770895004,
-0.020278513431549072,
-0.05578317865729332,
-0.015855904668569565,
0.02413903921842575,
-0.041789811104536057,
0.062142420560121536,
0.10532284528017044,
0.030692555010318756,
-0.014584400691092014,
0.0014973870711401105,
0.04466751590371132,
0.0024527208879590034,
-... |
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... | scraping/output/6352475188966086889.txt | [
0.04038924723863602,
-0.0457107312977314,
-0.05422743782401085,
-0.043830662965774536,
0.045476898550987244,
-0.049809981137514114,
0.05631855130195618,
0.07568877935409546,
0.02286272682249546,
-0.026762960478663445,
0.028997935354709625,
0.0322859100997448,
-0.0074376692064106464,
-0.064... |
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 c... | scraping/output/6352475188966086889.txt | [
0.04185687378048897,
-0.026310741901397705,
-0.0550723671913147,
-0.03584709018468857,
0.04546832665801048,
-0.057748276740312576,
0.041084855794906616,
0.08459562063217163,
0.032472413033246994,
-0.03562064468860626,
0.01939774490892887,
0.06299106031656265,
0.029789404943585396,
-0.06316... |
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
Reflex... | scraping/output/6352475188966086889.txt | [
0.053545936942100525,
-0.03782419487833977,
-0.05212913453578949,
-0.024356180801987648,
0.02574796974658966,
-0.03426764905452728,
0.043678827583789825,
0.07863734662532806,
0.038655009120702744,
-0.04503074660897255,
0.0419938750565052,
0.06966918706893921,
0.0262687299400568,
-0.0500350... |
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 too... | scraping/output/6352475188966086889.txt | [
0.03264211490750313,
-0.019151214510202408,
-0.06149417161941528,
-0.031442102044820786,
0.0462736040353775,
-0.04977171868085861,
0.02182946540415287,
0.06698412448167801,
0.027336595579981804,
-0.0164143368601799,
0.03836158290505409,
0.04959762096405029,
0.02006833627820015,
-0.04443031... |
This blog post explores the current state of AutoML tools and their ability to
deliver on the promise of automating machine learning tasks without expert
knowledge.
January 3, 2023
By
Julien Schuermans
Placeholder tag
Welcome to the multi-language-pipeline universe
Apache Beam has lots of I/O connectors available... | scraping/output/6352475188966086889.txt | [
0.00401599146425724,
-0.042945608496665955,
-0.07535773515701294,
-0.013837834820151329,
0.033784281462430954,
-0.03916577249765396,
0.051870182156562805,
0.04653466120362282,
0.03468075022101402,
-0.029437987133860588,
0.026835961267352104,
0.05629102885723114,
0.010426958091557026,
-0.04... |
December 12, 2022
By
Placeholder tag
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
Placeholder tag
AI for the green transition - Applications and use cases [Part 2]
Exa... | scraping/output/6352475188966086889.txt | [
0.021521704271435738,
-0.034179363399744034,
-0.031203515827655792,
-0.031448278576135635,
0.0403982512652874,
-0.032685235142707825,
0.032360903918743134,
0.07010974735021591,
0.013656171970069408,
-0.047137562185525894,
0.004086367320269346,
0.030551590025424957,
-0.024563202634453773,
-... |
November 15, 2022
By
Placeholder tag
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
Placeholder tag
Semantic Search: Intro and business applications
Semantic search from a busin... | scraping/output/6352475188966086889.txt | [
0.028238167986273766,
-0.0453951396048069,
-0.06736123561859131,
-0.02573644183576107,
0.053515274077653885,
-0.05396334454417229,
0.017324496060609818,
0.05850750207901001,
0.027920005843043327,
-0.01288428995758295,
0.012334201484918594,
0.042110055685043335,
0.007298301439732313,
-0.058... |
November 1, 2022
By
Placeholder tag
Vertex AI is All You Need
Deploying a Scikit-learn Pipeline + LightGBM Classifier Model on Vertex AI for
Explainable Online Predictions
October 26, 2022
By
Placeholder tag
Webinar | Hybrid AI: successfully combining expert knowledge with ML models
Explore the field of Hybrid... | scraping/output/6352475188966086889.txt | [
0.04089369252324104,
-0.00015911136870272458,
-0.08520948886871338,
-0.03410385176539421,
0.04816675931215286,
-0.04461756721138954,
0.03783285990357399,
0.07857326418161392,
0.024936683475971222,
-0.02584979496896267,
0.027239970862865448,
0.03995930776000023,
0.01815500669181347,
-0.0810... |
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 ... | scraping/output/6352475188966086889.txt | [
0.04186270758509636,
-0.03241873160004616,
-0.03740321099758148,
-0.03468739986419678,
0.020468641072511673,
-0.03905176743865013,
0.050384677946567535,
0.06293356418609619,
0.030985238030552864,
0.017730526626110077,
-0.000468855956569314,
0.0679856687784195,
0.03186805918812752,
-0.05438... |
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
mod... | scraping/output/6352475188966086889.txt | [
0.06520471721887589,
-0.013573273085057735,
-0.06866700947284698,
-0.025342490524053574,
0.038030240684747696,
-0.065396748483181,
0.06393010169267654,
0.06846677511930466,
0.02090686932206154,
-0.02727007493376732,
0.038238588720560074,
0.03577288240194321,
0.016887342557311058,
-0.053601... |
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
Georges Lorré
Placeholder tag
The art of pooling embeddings
In this blogpost we will discover the complexity of pooling that ... | scraping/output/6352475188966086889.txt | [
0.03322914242744446,
-0.03312409669160843,
-0.0715578943490982,
-0.012029634788632393,
0.02608497068285942,
-0.03471825644373894,
0.02034207619726658,
0.07646007835865021,
0.04500653222203255,
-0.018467307090759277,
0.019636735320091248,
0.062235355377197266,
0.015769800171256065,
-0.05264... |
Placeholder tag
Using Synthetic Data to boost the Performance of your Object Detection Model
A practical guide to generating artificial data using Unity Perception.
May 22, 2022
By
Jules Talloen
Placeholder tag
BERT is eating your cash: quantization and ONNXRuntime to save money
Insights in how to run your Tran... | scraping/output/6352475188966086889.txt | [
0.058168675750494,
-0.03038027510046959,
-0.0779946967959404,
-0.010555526241660118,
0.03968038037419319,
-0.00331749115139246,
0.04099757969379425,
0.031400538980960846,
0.026379600167274475,
-0.01828361302614212,
0.041145555675029755,
0.051160894334316254,
0.030227843672037125,
-0.058416... |
By
Placeholder tag
More things in heaven and earth
Assessing Detic for object detection of thousands of classes.
May 17, 2022
By
Matthias Cami
Placeholder tag
NVIDIA Deepstream Quickstart
In this blogpost we’ll set up a docker container to run an NVIDIA deepstream
pipeline on a GPU or a Jetson and show you som... | scraping/output/6352475188966086889.txt | [
0.048023637384176254,
-0.011458941735327244,
-0.07568702101707458,
-0.021418500691652298,
0.04407224431633949,
-0.03572029620409012,
0.024285176768898964,
0.061117589473724365,
0.0330624133348465,
-0.031638018786907196,
0.041742708534002304,
0.05149964243173599,
0.009842926636338234,
-0.03... |
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
P... | scraping/output/6352475188966086889.txt | [
0.07551345974206924,
-0.028657300397753716,
-0.09707032144069672,
-0.009028096683323383,
0.037340033799409866,
-0.03707217797636986,
0.02722894586622715,
0.07318122684955597,
0.017360715195536613,
-0.015843670815229416,
0.030318666249513626,
0.06448545306921005,
0.01947162114083767,
-0.048... |
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... | scraping/output/6352475188966086889.txt | [
0.09078167378902435,
-0.035743482410907745,
-0.07276047766208649,
-0.038680221885442734,
0.012837103568017483,
-0.04107063636183739,
0.02327171340584755,
0.07215270400047302,
0.026379186660051346,
-0.03826403617858887,
0.03950275108218193,
0.04321404919028282,
0.0048958612605929375,
-0.065... |
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
... | scraping/output/6352475188966086889.txt | [
0.03770681098103523,
-0.0239914171397686,
-0.058258719742298126,
-0.026974866166710854,
0.05942097678780556,
-0.03164629638195038,
0.05153887718915939,
0.05373571813106537,
0.04997430369257927,
-0.008989468216896057,
0.030334986746311188,
0.04882609099149704,
0.020438306033611298,
-0.03831... |
Sustainability
Voice & Sound
Front-End Development
Data Protection & Security
Responsible/ Ethical AI
Infrastructure
Hardware & sensors
MLOps
Generative AI
Natural language processing
Computer vision
No results found.
There are no results with this criteria. Try changing your search.
Sign up for our news... | scraping/output/6352475188966086889.txt | [
0.012950560078024864,
-0.01402312982827425,
-0.03652187064290047,
-0.03795423358678818,
0.04713821783661842,
-0.054993804544210434,
0.07277526706457138,
0.078623928129673,
0.06155436113476753,
-0.03137453645467758,
0.04157847538590431,
0.0419505313038826,
0.015789056196808815,
-0.036431752... |
Services
Navigate Intelligence Activate IntelligenceBuild Intelligence
Solutions
Custom AI solutionsSolutions catalogueDomains of expertise
References
Client casesCustomer testimonials
Resources
Resource libraryBlogpostsVideo & DemoEventsOpen Source
About
Mission How we workResponsible AICareers
Copyright 202... | scraping/output/6352475188966086889.txt | [
0.013621602207422256,
-0.021486999467015266,
-0.03108375519514084,
-0.04486671835184097,
0.04013408347964287,
-0.062043726444244385,
0.07488805055618286,
0.10037354379892349,
0.04806864261627197,
-0.030610058456659317,
0.05472322180867195,
0.05536283552646637,
0.04954789951443672,
-0.02173... |
No items found.
🚀 Unleash the Power of Large Language Models and Foundation Models - Read our
insights and expertise on these trends in AI!
Discover more
Services
Navigate intelligence
Activate intelligence
Build intelligence
AI Solutions
Custom solutions
Solutions catalogue
Domains of expertise
References
... | scraping/output/-2077808234674879873.txt | [
0.032358743250370026,
-0.001775876386091113,
-0.05521208420395851,
-0.027075817808508873,
0.028992170467972755,
-0.05605560913681984,
0.04636340215802193,
0.048450130969285965,
-0.0015737125650048256,
-0.010660163126885891,
0.03185894340276718,
0.017271870747208595,
0.003928042948246002,
-... |
Yannik is responsible for ML6’ sales activities in the German Market. He
identifies business opportunities, drives ML-projects and maintains
relationships with key accounts across all industries.
Having graduated in Business & Economics at the WU Vienna, Yannik thinks
business. He aims to fully understand the clien... | scraping/output/-2077808234674879873.txt | [
0.017298348248004913,
-0.002006043214350939,
-0.07112782448530197,
-0.028042597696185112,
0.0440962053835392,
-0.05493626371026039,
0.040881551802158356,
0.027232211083173752,
0.016337363049387932,
0.013980643823742867,
0.03405708074569702,
0.012354932725429535,
0.02498890645802021,
-0.045... |
Corporate
People
Structured Data
Chat GPT
Sustainability
Voice & Sound
Front-End Development
Data Protection & Security
Responsible/ Ethical AI
Infrastructure
Hardware & sensors
MLOps
Generative AI
Natural language processing
Computer vision
Accelerating businesses with AI technology & experts
Contact:
... | scraping/output/-2077808234674879873.txt | [
0.018618082627654076,
-0.007257630582898855,
-0.04283001646399498,
-0.027256516739726067,
0.033577192574739456,
-0.05109598487615585,
0.0716189593076706,
0.08220640569925308,
0.05122102424502373,
-0.03575774282217026,
0.03975618630647659,
0.03253583982586861,
0.03885255381464958,
-0.031850... |
Responsible/ Ethical AI
🚀 Unleash the Power of Large Language Models and Foundation Models - Read our
insights and expertise on these trends in AI!
Discover more
Services
Navigate intelligence
Activate intelligence
Build intelligence
AI Solutions
Custom solutions
Solutions catalogue
Domains of expertise
Ref... | scraping/output/-7496122496336218647.txt | [
0.024999452754855156,
-0.011026467196643353,
-0.06503705680370331,
-0.025334974750876427,
0.0467790924012661,
-0.04219423234462738,
0.06815133988857269,
0.09216724336147308,
0.05609096959233284,
-0.06047169119119644,
0.029811955988407135,
0.033992014825344086,
0.051398564130067825,
-0.0500... |
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Share this post
At ML6, we are all passionate about technology and motivated by the potential
benefits AI can bring to our clients and to society. We are however also aware
that AI can carry certain ethical concerns a... | scraping/output/-7496122496336218647.txt | [
0.019663993269205093,
0.004698819015175104,
-0.06330393999814987,
-0.02238466590642929,
0.04469984024763107,
-0.03865501284599304,
0.05729690566658974,
0.058432240039110184,
0.07998664677143097,
-0.042911987751722336,
0.04463912174105644,
0.005771049298346043,
0.06837523728609085,
-0.04400... |
### Why did we create the Ethical AI unit?
As mentioned above, considering Ethics within all that we do is important to
us. It fosters trust in our solutions and in our mission both with clients as
well as with our employees.
The Ethical AI unit is our way to stay at the forefront of the rapidly
evolving field... | scraping/output/-7496122496336218647.txt | [
-0.012660003267228603,
-0.02284008264541626,
-0.06657925993204117,
-0.032717373222112656,
0.05962666869163513,
-0.015797102823853493,
0.0431813970208168,
0.059669021517038345,
0.0757354348897934,
-0.0734119713306427,
0.025842126458883286,
0.01942863129079342,
0.11649105697870255,
-0.043675... |
### What does the unit do and who is part of it?
The Ethical AI unit at ML6 has two main purposes. The first purpose of the
Ethics unit is to serve as a “sounding board” to discuss ethically sensitive
projects that emerge during the sales process. Given that there is often no
“one right answer” to ethical quest... | scraping/output/-7496122496336218647.txt | [
0.05345497280359268,
0.014883924275636673,
-0.07697984576225281,
-0.020711582154035568,
0.08541901409626007,
-0.013100523501634598,
0.03380562365055084,
0.03310379385948181,
0.06721506267786026,
-0.047127608209848404,
0.035658784210681915,
-0.005225909873843193,
0.04987724497914314,
-0.024... |
The second purpose of the unit is in keeping up with the evolving field of
Ethical AI and implementing ethical and legal considerations and best
practices into our processes at ML6. This includes preparing for and giving
feedback on the upcoming EU AI Act and other relevant regulation, researching
topics such as bias &... | scraping/output/-7496122496336218647.txt | [
0.048997800797224045,
-0.0026783470530062914,
-0.07496080547571182,
-0.019240116700530052,
0.09119726717472076,
-0.02779543213546276,
0.03637860715389252,
0.03468446433544159,
0.0642457902431488,
-0.023836633190512657,
0.03168083727359772,
-0.007964183576405048,
0.07088853418827057,
-0.014... |
### What did we learn so far & what tips would we give to other organisations?
Since starting the Ethical AI unit, we have learned a lot. Let us share a few
practical tips on what to watch out for if you want to start structurally
integrating Ethical considerations into your organisation and processes:
* ... | scraping/output/-7496122496336218647.txt | [
0.049812041223049164,
-0.017330056056380272,
-0.09338448196649551,
-0.04395290091633797,
0.05870845913887024,
-0.043945636600255966,
0.018191423267126083,
0.05097715184092522,
0.054820217192173004,
-0.0373758040368557,
0.03967929631471634,
0.02933025173842907,
0.0607396624982357,
-0.012586... |
* Focus on the value Ethical AI can bring to your organisation. Trust in and adoption of AI, higher motivation for employees, mitigation of ethical and legal risks — these are just some examples of positive effects implementing ethics structurally in your company can have. Articulate clearly what value Ethical AI can b... | scraping/output/-7496122496336218647.txt | [
0.04733330011367798,
-0.02496710792183876,
-0.07691169530153275,
-0.031541742384433746,
0.0498523935675621,
-0.03683985024690628,
0.036336950957775116,
0.06617255508899689,
0.050491590052843094,
-0.023945050314068794,
0.011821086518466473,
-0.0013207216979935765,
0.09960737079381943,
-0.00... |
This is in a nutshell what our new Ethical AI unit is and what we learned in
the process of building it up. Reach out if you have questions or want to talk
about Ethical AI!
## Related posts
View all
No results found.
There are no results with this criteria. Try changing your search.
Large Language Mod... | scraping/output/-7496122496336218647.txt | [
0.02312280982732773,
-0.016249971464276314,
-0.03463764116168022,
-0.031224148347973824,
0.0813644602894783,
-0.036963481456041336,
0.05900583043694496,
0.0669250562787056,
0.06237674877047539,
-0.05485028773546219,
0.03498466685414314,
0.03874141350388527,
0.05770129710435867,
-0.05083628... |
MLOps
MLOps
Generative AI
Generative AI
Natural language processing
Natural language processing
Computer vision
Computer vision
Accelerating businesses with AI technology & experts
Contact:
info@ml6.eu
+32 9 265 95 50
Contact us
Join our newsletter
By subscribing you agree to with our Privacy Policy
Th... | scraping/output/-7496122496336218647.txt | [
0.015403167344629765,
-0.02116716466844082,
-0.03985954076051712,
-0.055103495717048645,
0.04636254534125328,
-0.05647806078195572,
0.07218355685472488,
0.08009413629770279,
0.049801986664533615,
-0.03156987577676773,
0.03237299248576164,
0.03448078781366348,
0.030711987987160683,
-0.03649... |
🚀 Unleash the Power of Large Language Models and Foundation Models - Read our
insights and expertise on these trends in AI!
Discover more
Services
Navigate intelligence
Activate intelligence
Build intelligence
AI Solutions
Custom solutions
Solutions catalogue
Domains of expertise
References
Client cases
Cu... | scraping/output/5757173050822662678.txt | [
0.0281209796667099,
-0.016542723402380943,
-0.06821992248296738,
-0.03242390230298042,
0.046335723251104355,
-0.04930826276540756,
0.07001185417175293,
0.09629367291927338,
0.03980190306901932,
-0.01901610754430294,
0.01816706359386444,
0.0404641255736351,
0.06068415194749832,
-0.041456278... |
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Accelerating businesses with AI technology & experts
Contact:
info@ml6.eu
+32 9 265 95 50
Contact us
Join our newsletter
By subscribing you agree to with our Privacy Policy
Thank you! Your submission has been ... | scraping/output/5757173050822662678.txt | [
0.008686822839081287,
-0.01621544361114502,
-0.046348486095666885,
-0.05193077400326729,
0.028213592246174812,
-0.05448892340064049,
0.0634782537817955,
0.0962090939283371,
0.06179625913500786,
-0.024815665557980537,
0.03879239410161972,
0.03456404060125351,
0.03972557559609413,
-0.0274430... |
No items found.
🚀 Unleash the Power of Large Language Models and Foundation Models - Read our
insights and expertise on these trends in AI!
Discover more
Services
Navigate intelligence
Activate intelligence
Build intelligence
AI Solutions
Custom solutions
Solutions catalogue
Domains of expertise
References
... | scraping/output/6950471906497477906.txt | [
0.01626879721879959,
-0.014245322905480862,
-0.04603898525238037,
-0.012760600075125694,
0.05198344960808754,
-0.048142969608306885,
0.07734235376119614,
0.08800538629293442,
0.03173315152525902,
-0.03504042327404022,
0.0274207703769207,
0.03345886245369911,
0.03628656268119812,
-0.0511344... |
Foundation Models
Corporate
People
Structured Data
Chat GPT
Sustainability
Voice & Sound
Front-End Development
Data Protection & Security
Responsible/ Ethical AI
Infrastructure
Hardware & sensors
MLOps
Generative AI
Natural language processing
Computer vision
Accelerating businesses with AI technology &... | scraping/output/6950471906497477906.txt | [
0.014286249876022339,
-0.01570786163210869,
-0.03911266103386879,
-0.029195789247751236,
0.026370521634817123,
-0.053580962121486664,
0.0814548209309578,
0.08285495638847351,
0.05676240846514702,
-0.032076139003038406,
0.03387953341007233,
0.018533427268266678,
0.04110054299235344,
-0.0283... |
Natural language processing
Structured Data
Computer vision
🚀 Unleash the Power of Large Language Models and Foundation Models - Read our
insights and expertise on these trends in AI!
Discover more
Services
Navigate intelligence
Activate intelligence
Build intelligence
AI Solutions
Custom solutions
Solution... | scraping/output/9065848743917767112.txt | [
0.029775455594062805,
-0.011824777349829674,
-0.057559579610824585,
-0.04855014383792877,
0.052898745983839035,
-0.06229134276509285,
0.0797378420829773,
0.08133220672607422,
0.05357678607106209,
-0.04578495770692825,
0.012764178216457367,
0.028292076662182808,
0.019205519929528236,
-0.028... |
Careers
Jobs
Team
Working at ML6
Taal
NLEN
Contact us
en
defrNL
Contact us
All client cases
Keypoint
# Accelerating Keypoint's Digital Real Estate Management Platform with AI
## Impact
By embedding artificial intelligence in its platforms, Keypoint is able to
reduce fraud cases and will guarantee all home... | scraping/output/9065848743917767112.txt | [
0.031538739800453186,
0.0006826111348345876,
-0.08496825397014618,
-0.04112878441810608,
0.044017452746629715,
-0.023203646764159203,
0.042215973138809204,
0.02150735631585121,
0.07417995482683182,
-0.022838672623038292,
0.022844675928354263,
0.022368844598531723,
0.008981309831142426,
-0.... |
## Intro to the customer
Keypoint is the digital assistant for all parties with decision-making power
on repairs, maintenance and building management.
Its digital collaboration platform links all the parties involved and makes
claim management highly automated and efficient. A claim becomes digital,
transparent and r... | scraping/output/9065848743917767112.txt | [
0.024975422769784927,
-0.00939352996647358,
-0.07645903527736664,
-0.035446614027023315,
0.04594064876437187,
-0.04539890214800835,
0.08359160274267197,
0.012471213936805725,
0.05675220116972923,
-0.01138534676283598,
0.038258012384176254,
0.030207904055714607,
0.013829871080815792,
-0.043... |
## Challenge
In a rapidly growing property market, both insurers and residents require
professional, 24/7 ad-hoc insurance services that provide reliable and fast
solutions. With Connect, Keypoint provides a unique platform that brings all
involved parties together. Through Repair, Keypoint specialises in the
organ... | scraping/output/9065848743917767112.txt | [
0.023425985127687454,
-0.027515515685081482,
-0.05249642953276634,
-0.028732577338814735,
0.0878632515668869,
-0.05431151017546654,
0.05718972533941269,
0.0013856814475730062,
0.050992630422115326,
-0.041753482073545456,
0.05016939342021942,
0.04578277841210365,
0.03300175443291664,
-0.045... |
Through the implementation of AI, Keypoint aims to make its platform more
effective for:
* Repairs pricing: With the implementation of AI, Keypoint can check estimates based on historical data (e.g. similar claims in the past) and via the creation of a pricing database (automatic database creation via approved ... | scraping/output/9065848743917767112.txt | [
0.06188594177365303,
-0.040664054453372955,
-0.05606038123369217,
-0.0516948439180851,
0.06344123184680939,
-0.003950598184019327,
0.053527556359767914,
0.023826347663998604,
0.05132010206580162,
-0.02895735763013363,
0.0620027557015419,
0.0446166954934597,
0.0471130907535553,
-0.019281921... |
“ML6 really supported us to accelerate the adoption of artificial intelligence
in our platform. From helping us securing the necessary innovation funds, to
exploring different use cases (based on various data sources, eg. images,
text, tabular data) and setting up the necessary pipelines and APIs.”
Managing Directo... | scraping/output/9065848743917767112.txt | [
0.03228895738720894,
-0.019812682643532753,
-0.06951866298913956,
-0.026264900341629982,
0.057017069309949875,
-0.036348991096019745,
0.0325220488011837,
0.0008153235539793968,
0.059797197580337524,
0.007760915905237198,
0.030815785750746727,
0.007118741981685162,
0.027698040008544922,
-0.... |
1. Automatic processing of repair offers (NLP)
2. Fraud detection on claims images and weather data (Computer vision and Structured Data)
#### Automatic processing of repair offers
Based on object detection, OCR techniques, text classification and named
entity recognition, we were able to build a pipeline that c... | scraping/output/9065848743917767112.txt | [
0.0315973274409771,
-0.07945234328508377,
-0.07168467342853546,
-0.0378195196390152,
0.07641280442476273,
-0.03230436518788338,
0.04456499218940735,
0.010405432432889938,
0.034758180379867554,
0.003905118443071842,
0.04980691894888878,
0.03304464742541313,
0.0753079429268837,
-0.0220609474... |
#### Fraud detection on claims images and weather data
Most claims contain one or more pictures of the damage. These are mostly taken
by the customer and provide evidence for the damage claim. Based on the type
of damage (known in the claim), we could verify through computer vision
techniques whether or not the con... | scraping/output/9065848743917767112.txt | [
0.021309886127710342,
-0.04903227090835571,
-0.04555581510066986,
-0.06856399029493332,
0.02734825201332569,
-0.014298874884843826,
0.04584430158138275,
0.01201009750366211,
0.054959241300821304,
-0.027515169233083725,
0.03940560668706894,
0.05881794914603233,
0.06182332709431648,
-0.00213... |
By
## Results
By embedding artificial intelligence in its platforms, Keypoint is able to
reduce fraud cases and will guarantee all home repairs are completed properly
and at a fair price. Next to that, water damage claims will be prevented
through the implementation of smart sensors and AI.
Accelerating busine... | scraping/output/9065848743917767112.txt | [
0.03410330414772034,
-0.028714489191770554,
-0.046056799590587616,
-0.0455663688480854,
0.036986399441957474,
-0.029643399640917778,
0.06538475304841995,
0.05245053768157959,
0.06540189683437347,
-0.029836643487215042,
0.04521255940198898,
0.040109891444444656,
0.01732374168932438,
-0.0274... |
Computer vision
🚀 Unleash the Power of Large Language Models and Foundation Models - Read our
insights and expertise on these trends in AI!
Discover more
Services
Navigate intelligence
Activate intelligence
Build intelligence
AI Solutions
Custom solutions
Solutions catalogue
Domains of expertise
References
... | scraping/output/6475038328432950049.txt | [
0.026726864278316498,
-0.012788578867912292,
-0.05691727250814438,
-0.01316539291292429,
0.04435348883271217,
-0.06715724617242813,
0.08410976082086563,
0.08273458480834961,
0.048635333776474,
-0.03411495313048363,
0.024080365896224976,
0.03633711859583855,
0.04412808641791344,
-0.05052587... |
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Share this post
Object detection models are typically trained to detect only a limited number
of object classes. The widely used MS COCO (Microsoft Common Objects in
Context) dataset for example only contains eighty c... | scraping/output/6475038328432950049.txt | [
0.043783560395240784,
-0.002696750219911337,
-0.04582533240318298,
-0.04221218079328537,
0.06033513695001602,
-0.06122220307588577,
0.02181367389857769,
0.01824880950152874,
0.07545233517885208,
-0.02450922131538391,
0.016581740230321884,
0.0842970460653305,
0.06954833120107651,
-0.0530972... |
### Detic: detecting twenty-thousand classes
Object detection is composed of two sub-problems: finding the object
(localization) and identifying it (classification). Conventional methods
couple these sub-problems and consequently rely on box labels for all classes.
However, detection datasets remain much smaller in si... | scraping/output/6475038328432950049.txt | [
0.05020389333367348,
-0.02453736402094364,
-0.05503842607140541,
-0.054284363985061646,
0.06069043651223183,
-0.05003787577152252,
0.030431313440203667,
0.033957161009311676,
0.04710543528199196,
-0.003958758898079395,
0.050400201231241226,
0.06147562339901924,
0.05880625173449516,
-0.0198... |
Detic is the first known model that trains a detector on all twenty-one-
thousand classes of the ImageNet dataset. Consequently, it has the capability
of detecting a large number of objects, making it a very suitable baseline
model for a wide variety of tasks. Furthermore, to generalise to larger
vocabularies, Detic le... | scraping/output/6475038328432950049.txt | [
0.06010567396879196,
0.015928098931908607,
-0.05603446066379547,
-0.04325077682733536,
0.059400103986263275,
-0.03988282009959221,
0.0444779098033905,
0.011069259606301785,
0.03182889148592949,
-0.0194108709692955,
0.036640994250774384,
0.02058715559542179,
0.0774381011724472,
-0.051570180... |
Approach overview, image taken from the original paper [1]
Combining aforementioned methods, training of the model is done on a mix of
detection data and image-labeled data. When using detection data, Detic uses
standard detection losses to train the classifier (W) and the box prediction
branch (B) of a detector. When... | scraping/output/6475038328432950049.txt | [
0.045917022973299026,
-0.05773289129137993,
-0.03412683308124542,
-0.058954548090696335,
0.08859177678823471,
-0.013410699553787708,
0.0603652223944664,
0.04007292911410332,
0.006798672489821911,
0.010031183250248432,
0.04808267578482628,
0.01267942227423191,
0.05295475944876671,
-0.059580... |
### Examples
#### (a) analyzing a social media feed
Imagine you would like to automatically analyze a social media feed for
occurrences of certain objects. Object detectors trained for specific use
cases in sports, animal detection, healthcare etc. would not get desired
results in this context. This is because social... | scraping/output/6475038328432950049.txt | [
0.01802331954240799,
-0.02681988663971424,
-0.022190062329173088,
-0.06775118410587311,
0.07643252611160278,
-0.011854746378958225,
0.046319302171468735,
0.013038228265941143,
0.03604070469737053,
0.0011215056292712688,
-0.0040136901661753654,
0.040198300033807755,
0.06420048326253891,
-0.... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.