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We list the main reasons why fine-tuning Billy comes up short: * No source clarity. It’s difficult to prevent hallucination and your LLM has no clear distinction between “general” and “specific” knowledge. * No access restriction. Imagine a case where some users should be able to query the information of strategic documents while others shouldn’t. How would you tackle this? Your fine-tuned Billy just knows everything, he can’t choose to leave out knowledge at inference time. * Hosting an LLM is costly. Once you have a fine-tuned LLM, you have to keep it spinning. A large language model is well… large. Costs to keep it up and running will rack up. Do the benefits outweigh those costs? * Fine-tuning repetitions. Model retraining is required when you want the model to reflect changes to the knowledge base.
scraping/output/3628185678030734886.txt
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Luckily, all these problems are solvable. If answering questions in a verifiable way and preventing hallucination is what you are looking for: you may not need the hyper-modern bookworm, let’s just ask the good old librarian where to find the answers to your questions. ### With RAG to Riches The idea behind Retrieval-Augmented Generation (RAG) is quite straight- forward. Remember, the goal is to unlock the information in our knowledge base. Instead of unleashing (i.e. fine-tuning) our bookworm on it, we comprehensively index the information of our knowledge base. By indexing the embeddings of your internal knowledge base, you unlock smart search capabilities. In the schema above, we illustrate how the Smart Retriever functions like a librarian. Ideally, the librarian has perfect knowledge of what is in his library. For a visitor asking a certain question, he would know just which chapter of which book to recommend.
scraping/output/3628185678030734886.txt
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On a more technical level, this describes a semantic search engine. In this case, the embeddings are vectorial representations of document sections and they allow a mathematical description of the actual meaning stored in each section. By comparing embeddings, we can determine which text sections are similar in meaning to which other text sections. This is crucial for the retrieval process displayed below. Through leveraging our Smart Retriever, we can force our generator to stick to the content of our knowledge base that is most relevant for answering the question. Et voilà: Retrieval-Augmented Generation. In play are two crucial components: 1. The Smart Retriever (i.e. the librarian) 2. The Generator (i.e. the bookworm)
scraping/output/3628185678030734886.txt
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In play are two crucial components: 1. The Smart Retriever (i.e. the librarian) 2. The Generator (i.e. the bookworm) It should be clear by now why this approach is called Retrieval-Augmented Generation. Based on the question asked, you first retrieve the most relevant information from your internal knowledge base; you then augment the typical generation phase by passing that relevant information explicitly to the generator component. #### Key highlights of this RAG-based setup 1. Clear indication of the source upon which the answer was based. Allowing for validation of the answer returned by the generator. 2. Very unlikely to hallucinate, by restricting our generator component to the corpus of our knowledge base, it will admit it can’t formulate a response when no relevant sources were found by the retriever. 3. Maintainable Search Index. A knowledge base is a living thing, when it changes, we can adapt our Search Index to reflect those changes.
scraping/output/3628185678030734886.txt
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Aside from those highlights, the multi-lingual aspect of LLMs is a thing of beauty. You can have a knowledge base consisting of purely Italian recipes which your pasta-loving French friend can talk to in an all-French dialogue. #### Fine-tuning revisited Note that in the section above, we dismissed fine-tuning as a valuable option because we had little control on source clarity thus increasing the risk for hallucination. It must be noted that the RAG approach, powered by a general LLM, only works well as long as the specific knowledge base does not contain super specific jargon that the LLM can’t understand from its general training. Imagine you need the responses of your solution to follow ‘the tone and lingo’ that is present in your knowledge base. In this case the fine-tuning of your LLM seems less avoidable.
scraping/output/3628185678030734886.txt
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Imagine you need the responses of your solution to follow ‘the tone and lingo’ that is present in your knowledge base. In this case the fine-tuning of your LLM seems less avoidable. It could be a valid approach to be able to handle specific jargon and then incorporate your fine-tuned LLM in the RAG architecture to reap the combined benefits. Instead of working with a general bookworm, you would then use your specifically trained Billy to power the Generator and/or the Smart Retriever components. ### Why now? What’s new? Excellent question. Semantic Search (smart retrieval) has been around for quite some time and so has generative AI (some primitive forms have been around for decades). However, we have seen pivotal advancements over the last months. On a technological level, we’ve recently witnessed big leaps forward in LLM performance. These positively impact the RAG solution on two levels:
scraping/output/3628185678030734886.txt
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On a technological level, we’ve recently witnessed big leaps forward in LLM performance. These positively impact the RAG solution on two levels: * Embeddings (e.g. Embedding API by OpenAI or Google’s PaLM) * Generative capabilities (e.g. OpenAI’s ChatGPT solution) Accompanying that improved generative quality is the increase in traction. Previously, companies could not easily imagine the opportunities of a system relying on generative AI. Now however, thanks to the wide media coverage and adoption of tools like ChatGPT, overall interest has grown exponentially. So, though arguably mediocre versions of RAG might have been possible for quite some time, technological improvements and increased traction result in a fruitful market opportunity. ### Challenges on your way to success In this section, we aim to introduce you to some of the main challenges with setting up a successful RAG solution.
scraping/output/3628185678030734886.txt
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### Challenges on your way to success In this section, we aim to introduce you to some of the main challenges with setting up a successful RAG solution. * Strong dependency on the performance of the Smart Retriever. The quality of the responses given by your Generative Component will depend directly on the relevancy of the information handed to it by the Smart Retriever. As mentioned above, we can thank LLM advancements for giving us rich and powerful text embeddings. But fetching these embeddings purely via API’s may not be your best option. You should be very conscious when designing your Semantic Search component, perhaps your knowledge base has specific jargon and you might need a custom fitted (i.e. fine-tuned) component to handle it. A more in-depth practical guide on Semantic Search can be found in this blogpost [1] . ‍
scraping/output/3628185678030734886.txt
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* Trade-off to be made in restriction to stick to info in knowledge base. As explained in the RAG architecture, we can force our LLM generative component to restrict itself to the information found in the relevant documents. While this ensures that hallucination (i.e. non-sensical answers) has little chance, it also means you are barely leveraging the information your LLM possesses. You might want your solution to use that knowledge as well but maybe only when requested by the user. ‍ * Conversational design to allow complex dialogue. While our depictions above have represented the user behaviour as asking merely a “one-shot question”, often your user might want to zoom in on the answer provided by your solution (in a ChatGPT-style conversation). Luckily, tools exist to aid you in this battle. The langchain framework offers a helping hand in getting this just right. ‍
scraping/output/3628185678030734886.txt
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* Prompt engineering as a way to steer generation toward succes. To get the answer of your generative component just right, you need to tell it exactly what kind of output you expect. Overall, this is far from rocket science. But getting your prompt setup just right for your use case takes time and deserves enough attention. It may be worthwhile looking at prompt management systems to make sure you can keep track of which prompting works best for which situations. ‍
scraping/output/3628185678030734886.txt
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* Choosing the right LLM: what does it cost and where does my data go? Throughout this text, we haven’t made any explicit choice regarding what LLM(s) to use in your solution. When choosing which LLM (API) to use, make sure to take privacy and cost restrictions into consideration. There are quite some decent options out there already. We have OpenAI’s GPT, Meta’s LLaMA, Google’s PaLM and with Elon Musk claiming to join the LLM scene, who knows where things will go. The exciting news is: more options will come and competition should drive LLM performance up and prices down. ‍
scraping/output/3628185678030734886.txt
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* Getting and keeping your LLM solution in production (LLMOps). As with all mature AI solutions: building them is one thing, getting/keeping them in production is another. The field of LLMOps focusses on the operationalisation of LLMs. Monitoring the performance of your LLM-based solution, keeping your knowledge base and search index up-to-date, processing conversational history… Before flinging your LLM solution into production, think wisely about how to maintain it and how to keep it fruitful in the long run. Charmed by the potential of RAG and intrigued by the related challenges, we now move on to looking at an actual RAG-based solution. ### Getting your hands dirty with a RAG If your interest is sparked by the concept of Retrieval-Augmented Generation, you may be asking yourself: Do I have what it takes to take a RAG-based solution for a spin? Well, if you have:
scraping/output/3628185678030734886.txt
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If your interest is sparked by the concept of Retrieval-Augmented Generation, you may be asking yourself: Do I have what it takes to take a RAG-based solution for a spin? Well, if you have: * specific knowledge: a moderate (preferably organised) database of “knowledge articles” that contain useful information that is not easily found on the world-wide web (e.g. technical documents, onboarding guidelines, handled support tickets…) * business value: a clear definition of business value if that information could be unlocked for the intended users Then yes, RAG might be the way to go for you. As an experiment, we recently built a small demo to showcase how this technology can be leveraged to support government staff in answering parliamentary questions more easily. In this case, the specific knowledge consists of: * a set of Flemish legislative documents * a set of parliamentary questions of the past The business value, meanwhile is in:
scraping/output/3628185678030734886.txt
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* a set of Flemish legislative documents * a set of parliamentary questions of the past The business value, meanwhile is in: * improving efficiency by automatically suggesting answers to parliamentary questions based on the Flemish knowledge base * improving transparency and user adoption through explicit citations Screenshot of demo solution built around the case of “application to help answer parliamentary questions” ‍ ‍ If you are looking for some guidelines on how to technically implement a similar solution, stay tuned for a follow-up blogpost where we aim to zoom in on the technical details of setting up RAG. ##### References * Mathias Leys (May 9, 2022) Semantic search: a practical overview https://blog.ml6.eu/semantic-search-a-practical-overview-bf2515e7be76 ‍ ## Related posts View all No results found. There are no results with this criteria. Try changing your search. Large Language Model Large Language Model Foundation Models
scraping/output/3628185678030734886.txt
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scraping/output/3628185678030734886.txt
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scraping/output/3628185678030734886.txt
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scraping/output/-2839507695242921612.txt
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scraping/output/-2839507695242921612.txt
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scraping/output/-2615541418613730807.txt
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“Hooray, the end of the year!” is the chant that echoes in many corporate hallways when Christmas is nearing. Lots of people take time off at the end of the year to find some quiet and “recharge their batteries” for the next one. Most people that do decide to keep working are — unknowingly — nearing the least productive week they’ve had in a long time. “Nothing new here” you think while you zoom out on our planet again. You notice the back of your head itching and realize that you have been wearing your VR glasses for way too long now. You sigh softly and start scratching your hair. Empty-headedly, you decide to give a last good spin to the sphere in front of you before taking the headset off. With a glazed look you see the world spinning in front of you until something odd catches your attention. “What in the hell was that?” you stutter while you start zooming in again. You take a closer look and count that there are four corporate hallways that are
scraping/output/-2615541418613730807.txt
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“What in the hell was that?” you stutter while you start zooming in again. You take a closer look and count that there are four corporate hallways that are kinda unusual, atypical.. weird even. In Ghent, Amsterdam, Berlin and Zurich, you find one corresponding hallway where people just don’t seem to be planning much holidays, nor are they preparing for a soothing week to conclude the year. Instead, you find that these hallways share an exciting feeling that is starting to build up in the days and weeks prior to Christmas. Soft whispers are turning into audible muffles, people start to move in quicker ways and the atmosphere is filling with longing. When the Christmas trees are being decorated the atmosphere spills over and the muffles turn into cries of exhilaration. “Finally” you hear. “Finally.. The ML6 Xmas Projects are back in town!”
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‍ ‍ ‍ ‍ ### What are Xmas Projects and are they essential to spawn superhuman kindergartners? Contrary to popular belief, our Xmas Projects are not related to the secret ingredient with which this monkey called Jojo created the Powerpuff Girls. Even though we haven’t — yet — been able to fully understand the chemistry of that particular event, we do have a good understanding of the chemistry our Xmas Projects bring about. And it’s worth writing a blogpost over. Xmas Projects are hackathon-like projects in which people lay down their day- to-day job for some time and get together out of common interest to explore new tools, learn tech and make an impact! The first edition of the Xmas Projects dates back to 2018 and have since been spanning the Christmas and New Year period. Over time, the concept has become an integral part of the way of working at ML6.
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ML6 facilitates the necessary hardware/compute for the accepted Xmas Projects to realise their ideas. And indeed — instead of taking holidays, a lot of people actually prefer to work together with colleagues on these initiatives. Each year, an increasing amount of work is being put into all of our Xmas Projects, reaching another record of 230 manday equivalents in this last edition! 🔥 ‍ ### The process To maximize the impact and minimize overhead or restrictions, we have concretized all the necessary phases: ‍ * 💡 Ideation ‍ The ideas the Xmas Projects originate from are submitted by anyone during the complete year building up to the Christmas period. The final deadline for the ideation phase is by the end of November, typically. ‍
scraping/output/-2615541418613730807.txt
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‍ * 🗣️ Pitching ‍ At the start of December, The “Xmas Projects working group” gets together to apply a touch of curation by trying to find synergies to combine ideas, or tweak the scope of the proposed applications. The people who submitted these curated ideas are potential Xmas Project leads and need to pitch their idea to the whole organisation to get ‘funding’ and convince colleagues to work on their projects! ‍ * ✉️ Voting ‍ By the end of the second week of December, it’s up to everyone else to make a personal top 5 of the pitched projects. ‍ * 👪 Allocation ‍ Based on the responses, hardware availability and the needs of the Xmas Project, it’s up to the Xmas Projects working group to puzzle people and Xmas Projects accordingly! ‍
scraping/output/-2615541418613730807.txt
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* 👪 Allocation ‍ Based on the responses, hardware availability and the needs of the Xmas Project, it’s up to the Xmas Projects working group to puzzle people and Xmas Projects accordingly! ‍ * 🏁 Actual building ‍ In the most recent edition, we kicked off the Xmas Projects on the 24th of December. The Xmas Projects leads have made sure that the teams are informed and completed the necessary steps to hit the ground running. The goal is to come up with a practical outcome by the end of the Xmas Project period, which lasted until the 3rd of January this year! ‍ * 🧐 Demo ‍ During ML6' New Year’s Event, which is always hosted on the first Friday the year, every Xmas Project team presents the context, goals, way of working, learnings, — and of course — the demo! ‍
scraping/output/-2615541418613730807.txt
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‍ * 🚀 Integration/Followup ‍ The Xmas Projects working group and chapters (=research groups) come together after the demo’s to see if and how we can extend functionality and/or scale up solutions, be it for our own marketing campaigns, to continue helping the non- profit organisations we were partnering up with, or simply to continue innovating and learn stuff along the way! These Xmas Projects are in many cases the fertile soil our appealing internships are born from. Interested? Have a look at our internship/thesis landing page! ‍ ### Benefits ‍ As you might have guessed by now, Xmas Projects involve a substantial investment of people, time and resources. Of course, we never do something without a reason 😉. Over the years we’ve seen that there are quite a number of benefits linked to this initiative! ‍
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Of course, we never do something without a reason 😉. Over the years we’ve seen that there are quite a number of benefits linked to this initiative! ‍ * Tech learnings ‍ Many ideas are born from this crazy, out-of-the-box, “will this even work” mentality. By trying to reach new heights with AI, we challenge the status-quo and our technical knowledge. Our brains are figuratively molding. On the other hand, there are many people that deliberately choose for Xmas Projects involving topics they don’t have a lot of expertise in, simply because they want to get up to speed with new tools and libraries. ‍ * Test business cases ‍ Xmas Projects are not exclusively about development work. It is also the right time to assess the market, define a sales strategy and think big for existing ideas and chapter projects. ‍
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‍ * Ideas for customers ‍ The learnings we gather are directly applicable to many of the customer projects we are working on. It’s not uncommon to pitch the demo’s we came up with to our clients to help see if new technologies could advance their business cases. ‍ * Ownership ‍ During the Xmas Projects, stars are born! ⭐ Everyone has an equal chance to become a Xmas Project lead and thereby exercise their soft-and people skills! It’s a great way for juniors to put themselves on the map and get familiar with all aspects of end-to-end project ownership. ‍ * People cohesion ‍ Because the company is growing in vast numbers (33 new joiners in 2021) it’s hard to find the time to connect and get to know everyone. The Xmas Project teams consist of people with a common interest and mix up people from different countries, units, and seniorities. It’s a great way of getting to know others! ‍ ‍ ‍ ## List of Xmas Projects ‍
scraping/output/-2615541418613730807.txt
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‍ ‍ ‍ ## List of Xmas Projects ‍ Last edition, we decided to split the Xmas Projects into three logical clusters. 1. ML6 For Good 2. ML6 Goes Public 3. ML6 On Steroids ‍ ### ML6 For Good ‍ We’ve worked on a total of five projects for Non-Profit Organisations that would otherwise not have the budget or resources to tackle their problems with the help of AI. The work we’ve done in this pillar ties neatly with one our six core values, being “Do good”. ‍ * For IDA Foundation we worked on three different use-cases, including “Forecasting of emergency health kits”, “Pallet packaging optimization” and “Emergency event detection”, based on social web data. The goal of these three projects was to come up with a more data-driven way to anticipate disasters and optimize transport for countries in need. ‍
scraping/output/-2615541418613730807.txt
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‍ * We supported Arop VZW, by coming up with a first version of an automatic visual quality inspection workflow for their sheltered workshops, aiding the workers in their day-to-day activities! ‍ * Together with Fruitpunch.ai and Reef.support, we’ve been working on a visual detection tool (with image segmentation) to detect different types of corals and identify coral bleaching to raise awareness! You can read more in one of their LinkedIn posts. :) ‍ ‍ Example image showing the results of the advanced semi-automatic labeling procedure we set up. ‍ ‍ ### ML6 Goes Public ‍ Three out of eleven Xmas Projects of the last edition were geared towards pushing the newest techniques we’ve encountered in the last year and find out how they shine in a practical context. ‍ #### Souvenir photo booth
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‍ #### Souvenir photo booth ‍ Ever wanted someone to draw you like one of their French girls? Wait no more.. In this Xmas Project we created an interface for our smart mirror, replacing famous painted faces with yours! ‍ Scottish scientist, writer, and polymath Mary Somerville, painted by Thomas Phillips. Her face is merged with a colleague at ML6, who also happens to be a polymath. Can you guess who? A hint — He is known for cycling over 300 km in one sitting when ML6 organises charity sports events. People are literally afraid of betting on his sports performances. Charities on the other hand love him. ‍ #### Terms and Conditions Parser‍ I know you’re not perfect. I know you agreed to terms and conditions you never read or understood, just like the rest of us. Why are these documents so cumbersome and long anyways? Everyone hates them — and that’s why we decided to build a browser add-on to help you digest the most important stuff in it. ‍
scraping/output/-2615541418613730807.txt
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‍ Our browser add-on generates a general summary of the Terms and Conditions and also highlights the most important extracts of the original text ( Link to full video ) ‍ #### Creative inspiration platform ‍I can’t really say anything about this one. Can you keep a secret though? It’s about a new application to use and train GAN’s and the demo was really cool, believe me. It’s gonna be big, so stay tuned! ‍ ‍ ‍ ### ML6 On Steroids ‍ I know people think we at ML6 try to match our consumption of steroids to the amount of quick tips we externalize. But that’s a lie. We just happen to be very muscular, you know. 😎 The goal of this pillar is to test side ideas originated from the work we did on client related projects during the year. The findings from these Xmas Projects often give the client project an innovative boost, or bring interesting insights! ‍ ‍ #### ML6 Cyber Threat Map ‍
scraping/output/-2615541418613730807.txt
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‍ ‍ #### ML6 Cyber Threat Map ‍ Hackers are boring (except the white hat hackers, these peeps are chill). Because hackers end up trying the same hacks all over, Google Cloud has come up with a tool called Cloud Armor that is able to protect your applications from general hacking attacks. Pretty sweet right! We thought it’d be cool to also visualize where and which attacks we are protected from. Entering the ML6 Cyber Threat Map! If you’re interested, my colleague Rob Vandenberghe already wrote a blogpost about the topic. ‍ ‍ The Threat Map we built on top of Cloud Armor. ‍ ‍ #### Multimodal House Price Prediction ‍ We have been building up lots of expertise in the real estate sector, spanning different clients (including FedNot). When it comes to detecting house prices, ML practitioners mostly work with classic columnar data. In this Xmas Project we wanted to find out whether adding visual data or textual data improves prediction performance.‍ ‍ #### Ping Pong AI
scraping/output/-2615541418613730807.txt
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‍ #### Ping Pong AI ‍ At ML6 we not only like to do sports together, but also work on sports data together! We’ve done multiple projects in the sports sector, including a really interesting football match analytics use-case. Over the years we found out that sports including small objects (particular ball sports mostly) involve extra complexities. No better sport to do analysis for than ping-pong in that regard! During this Xmas Project we have been looking into both the audio and visual aspects for registration and event detection. ‍ A game of table tennis ( full video ) analyzed by the tools we’ve set up! Video source: OpenTTGames dataset. ‍ ### Conclusion
scraping/output/-2615541418613730807.txt
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‍ A game of table tennis ( full video ) analyzed by the tools we’ve set up! Video source: OpenTTGames dataset. ‍ ### Conclusion For four years in a row, ML6 halts their typical business and undergoes a transformation from a specialized AI consultancy firm to a multi-week hackathon facilitator in which ML6 agents get to spend time on projects they feel most attracted to. Both parts of this duality contribute to what we do best: accelerate intelligence. Xmas Projects are great. They are innovative by nature and enable people to think atypical and big. The way we designed Xmas Projects ensures that we are challenged in aspects we simply don’t see popping up in our daily jobs, are able to make a social impact and have a lot of fun at the same time. Personally speaking, I think it’s one of the most enjoyable and rewarding parts of working at ML6. What’s keeping you from organising a similar concept in your organisation, or joining us? ‍ ## Related posts View all
scraping/output/-2615541418613730807.txt
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scraping/output/-2615541418613730807.txt
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scraping/output/-2615541418613730807.txt
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scraping/output/-3048720869722842573.txt
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No items found. Subscribe to newsletter 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. Share this post In this blogpost, Jens Bontinck, Head of Delivery & Advice, shares his view on the status quo of AutoGPT. In this blogpost, you will not learn how to run it, nor will I go into details of the technical solution. Therefore, if you are a product owner, a functional analist, or a commercial enthousiast, this should be right up your alley. Happy to read your feedback! ‍
scraping/output/-3048720869722842573.txt
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‍ I have been closely following the development of AutoGPT, created by Significant Gravitas. AutoGPT stands for Autonomous Generative Pre-training Transformer and is an open-source experiment that is drawing a lot of attention at the moment of writing(GitHub link). AutoGPT goes beyond an ordinary chatbot, and its decision-making power reduces the need for human intervention and thus allows for future automation and value creation. Did you skipped a heartbeat when seeing the first possibilities, or did the current missing features made you rather nervous? Auto-GPT is drawing a lot of attention these days on GitHub ### The anatomy of how AutoGPT works?
scraping/output/-3048720869722842573.txt
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Auto-GPT is drawing a lot of attention these days on GitHub ### The anatomy of how AutoGPT works? AutoGPT is provided with an identity/role and a task, providing details about what it is supposed to do. This is fairly easy, and the perceived power of AutoGPT lies in what it does with that starting context. AutoGPT can be seen as an agent that seeks to complete the task autonomously by using a framework that allows it to reason and act. Each task is handled by an ‘Execution Agent’ (GPT-4) and provides input to one or more other GPT-4 agents, which is adding new (sub-)tasks to be completed by the agent. In short, AutoGPT is able to unravel a bigger objective in smaller tasks and mainly act as an orchestrator to achieve the initial objective. AutoGPT is able to unravel a bigger objective in smaller tasks and I see it currently as a ‘ task orchestrator’ to achieve the initial objective. ‍ source: Lesswrong — https://www.lesswrong.com/posts/566kBoPi76t8KAkoD/on- autogpt
scraping/output/-3048720869722842573.txt
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‍ source: Lesswrong — https://www.lesswrong.com/posts/566kBoPi76t8KAkoD/on- autogpt ### Why is it perceived better than ChatGPT? Some enthousiasts degraded ChatGPT directly when wrapping their head around the first results of AutoGPT. In essence, I believe the difference boils down to following features, allowing AutoGPT to differentiate from ChatGPT: * Smart(er) (and faster) prompting helps LLMs overcome their inherent limitations in areas such as the content window and math problem-solving. * Internet access allows AutoGPT to access more and up-to-date information. * (long term) Memory supports keeping a context across the executed tasks.
scraping/output/-3048720869722842573.txt
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Another facet that is contributing to this overall perception is that AutoGPT doesn’t require us to trigger the next steps (besides the approval to execute commands, for cost purposes I believe), while ChatGPT required a human (you!) to swiftly come with a well-crafted prompt to assure the quality and effectiveness of the subsequent outputs. This ‘compounding effect’ of an agent handling a series of tasks by itself is new to us, and is leaving a big impact to the community today. ### Impact on Business Applications The current possibilities of AutoGPT seem endless, but we are in the early days of its development (yes, it’s hyping today). Today, AutoGPT is still limited by a set of pre-defined commands (of which more will come every day) and its true scale will lie in the number of atomic tasks it supports. Also, these days you quickly experience that AutoGPT can get stuck in a loop, is not able to handle the tasks and thus not able to provide you the expected output.
scraping/output/-3048720869722842573.txt
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However, the genie is out of the bottle, and more and more people will test and improve AutoGPT. Everyday AutoGPT will be able to handle more and more atomic tasks and the perception of it’s power will further increase. ‍ An overview of additional atomic tasks (e.g. Bing Search, email, Twilio, Telegram, …) In the coming months, I anticipate the business impact of AutoGPT will still be limited to the fairly easy processes, of which the subtasks are known and ‘supported’ . Over time, we will have a clearer distinction between use cases where AutoGPT is useful (e.g. initial research, composing an itinerary for your next travel destination, …) and where it is not doing a great job at all. For these successful use cases, it will further revolutionise the way we work and interact with machines and the role of humans overall.
scraping/output/-3048720869722842573.txt
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One key element to keep in mind: as long as third parties do not allow access to perform operations via an API, AutoGPT will not suddenly (and magically) be able to execute the tasks successfully. People will need to tell to AutoGPT how to handle atomic tasks (e.g. how to send an email, how to browse, how to query the latest flights to Malta, … how to launch a rocket…) in order for AutoGPT to benefit from it. ‍ ### Is This a Next Step towards AGI? AutoGPT is not the only system that performs similar functions. Other systems include Microsoft Jarvis and BabyAGI. All three initiatives are a promising a first step towards AGI, Artificial General Intelligence, and I believe the evolution will go faster and faster. From that perspective, I believe it is good that there is much time spent on investigating the possibilities of such systems today already.
scraping/output/-3048720869722842573.txt
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Today we know already that if AutoGPT knows how to perform an atomic task, it is able to generate results in a much higher rate than our brain. However it is still in our hands to provide the possibility to interact with e.g. a rocket via an API. In other words, if we create a way that allows everyone to interact with a rocket launcher, than AutoGPT could learn how to interact with it. Another reasonable threat we should anticipate on, is the ability of AutoGPT, and thus the LLMs behind it, to sound like human and to reason like human. This could encourage the instructor to take action outside of the possibility of AutoGPT. A drastic example to make my point: if you ask AutoGPT to solve a dispute, it could identify that sending a rocket is the best thing to do and that you should do it tomorrow. (Don’t do it!) ‍ ### Conclusion
scraping/output/-3048720869722842573.txt
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‍ ### Conclusion In conclusion, AutoGPT is an exciting development in the field of AI, and we should closely follow its progress. Future versions will have improvements on functionality, memory, UI, … which all will contribute to the overall perception of AutoGPT as a first step to AGI. These rapid evolutions will provide further insights in the significant benefits to businesses it will bring. I believe we must also be aware of the fact that AutoGPT is still an experiment and therefore I tend to be more conservative than ‘the internet’ today. AutoGPT is still quite expensive to run, ends up in infinite loops quite often and is not capable of ‘everything’ (as stated by some online). The genie is out of the bottle but it will take some additional human intelligence to maximise the value of AutoGPT. ‍ ## Related posts View all No results found. There are no results with this criteria. Try changing your search. Large Language Model Large Language Model Foundation Models
scraping/output/-3048720869722842573.txt
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‍ ## Related posts View all No results found. There are no results with this criteria. Try changing your search. Large Language Model Large Language Model Foundation Models Foundation Models Corporate Corporate People People Structured Data Structured Data Chat GPT Chat GPT Sustainability Sustainability Voice & Sound Voice & Sound Front-End Development Front-End Development Data Protection & Security Data Protection & Security Responsible/ Ethical AI Responsible/ Ethical AI Infrastructure Infrastructure Hardware & sensors Hardware & sensors MLOps MLOps Generative AI Generative AI 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 Thank you! Your submission has been received!
scraping/output/-3048720869722842573.txt
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scraping/output/-3048720869722842573.txt
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scraping/output/-2267828673903295176.txt
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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. ML6, a leading AI & Machine Learning company in Europe with offices in Ghent, Amsterdam and Berlin, announced today that it has been selected as Google Cloud Services Partner of the Year for Benelux 2023. The company is being recognized for its ability to deliver impactful business value to their clients by solving complex challenges, building and running custom AI solutions on Google Cloud - and helping their clients expand their own AI capabilities. Simon Logghe, Managing Director at ML6, expressed his gratitude for the received recognition:
scraping/output/-2267828673903295176.txt
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“We really value this sign of trust in our long standing and fruitful collaboration. We are looking forward to accelerating our Go-To-Market with the huge GenAI opportunity. We would like to extend a special thank you to the BeNeLux partner team. They go above and beyond to help us grow together with Google Cloud and are, based on our experience, a leading partner organisation in Europe.” ‍ “Google Cloud’s partner awards recognize the significant impact and customer success that our partners have driven over the past year," said Kevin Ichhpurani, Corporate Vice President, Global Ecosystem and Channels at Google Cloud. "We're delighted to recognize ML6 as a 2023 Google Cloud Partner Award winner, and look forward to a continued strong partnership in support of our mutual customers." ‍
scraping/output/-2267828673903295176.txt
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‍ Last year, ML6 successfully worked on a broad range of impactful Google Cloud projects, enabling its clients to be a frontrunner in their industries by innovating through AI. As many AI innovations take place in a business' core, projects are often confidential and cannot be shared publicly. To name a few of our publicly available, exciting projects: ‍ 1\. Helping Randstad to increase sales effectiveness by leveraging multiple data sources on Google Cloud. Together with Randstad, ML6 has built a sales effectiveness tool, which is actively used by around 10.000 sales consultants spread over 6 countries. The hit rate has increased from the historical 25% to 70%, meaning that today the sales consultants using the tool are spending 70% of their time with the right clients. Based on this demonstrated value the tool will be further deployed to all Randstad locations. ‍
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‍ 2\. Supporting the Belgian Federation of Notaries (FedNot) with knowledge and skill management through various applications including advanced anonymization and pseudonymization techniques for protection of personal data, a custom semantic search engine covering offering secure access to tens of millions of notary deed clauses, a real estate price estimator and multiple automatic information extraction tools for generating structured (meta)data from contracts. ‍ 3\. Building a data lake in Google Cloud for the Flemish Agency for Road and Traffic (AWV), allowing the exploitation of data for AI use cases such as the automatic switching of road lighting along the highways or to make smart decisions for sprinkling salt on roads during winter. ‍
scraping/output/-2267828673903295176.txt
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‍ The benefits include substantial energy savings, reduced light pollution and a minimised environmental impact achieved through a more targeted use of salt. The success of this project has been recognised by the nomination in the category ‘IT Project of the Year in Public Market’ by Computable Belgium. ‍ About ML6 ‍ ML6 is a leading tech scale-up that helps their clients to navigate, activate and build trustworthy AI with the aim to innovate and gain a competitive edge. Through its services, ML6 helps clients realise business impact by building and deploying custom end-to-end AI solutions and by bringing client's AI capabilities to the next level. ML6 has built a team of over 100 senior AI experts, based in Ghent, Amsterdam and Berlin. ML6 has received Google Cloud sustainability award for Benelux in 2022 and Google Cloud Partner award in 2015. ‍ Accelerating businesses with AI technology & experts Contact: info@ml6.eu +32 9 265 95 50 Contact us Join our newsletter
scraping/output/-2267828673903295176.txt
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‍ 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 received! Oops! Something went wrong while submitting the form. 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 2024 Privacy Notice
scraping/output/-2267828673903295176.txt
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Structured Data 🚀 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 Customer testimonials Resources Resource library Blog Video & demo Press Events Open Source Our DNA How we work Mission & ambition Responsible AI Partners ML6 For Good Careers Jobs Team Working at ML6 Taal NLEN Contact us en defrNL Contact us All client cases Randstad # Increasing sales effectiveness at Randstad by leveraging multiple data sources ## Impact
scraping/output/-854568260067003203.txt
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Jobs Team Working at ML6 Taal NLEN Contact us en defrNL Contact us All client cases Randstad # Increasing sales effectiveness at Randstad by leveraging multiple data sources ## Impact The sales effectiveness tool is currently actively used by around 10.000 sales consultants spread over 6 countries. The hit rate has increased from the historical 25% to 70%, meaning that today the sales consultants using the tool are spending 70% of their time with the right clients - clients who have actual and feasible potential for Randstad. ## Intro to the customer As one of the leading multinational human resource consulting firms, Randstad has 39,530 employees spread over 38 countries.
scraping/output/-854568260067003203.txt
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## Intro to the customer As one of the leading multinational human resource consulting firms, Randstad has 39,530 employees spread over 38 countries. Making sure that these people focus their time on contacting companies who have real potential for Randstad, and enabling them to build conversations based on accurate and relevant information has a direct impact on the success of Randstad in serving its clients and hence realizing its own core ambition. ## Challenge While Randstad already had the data and insights in the ‘right companies’, with attractive commercial potential, this knowledge was not yet used optimally to steer the activities of the sales organisation on a day-to-day basis. ‍ It therefore embarked on a mission to create an AI enabled solution that allows a better and continuous use of data by proposing concrete, actionable tasks to sales consultants through an intuitive end-user application. ‍
scraping/output/-854568260067003203.txt
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‍ "Partnering with ML6 has allowed Randstad to embark on this AI journey in a collaborative and co-creation way-of-working; ML6 facilitated the creation and upskilling of the Randstad technical team and hence setting them up for the future." Global VP, Digital Strategy & Innovation By Randstad ## Solution ML6, as AI specialist partner, is driving the setup and training of the data intelligence components of the solution, as well as the implementation of the AI ML Ops architecture to ensure that the solution can easily be scaled across teams and countries, and retraining in a continuous, automated way.
scraping/output/-854568260067003203.txt
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This 'sales effectiveness tool’ consists of four different solution components. Core is the scoring system that assesses the potential of an individual company for Randstad, as well as how easy or difficult it will be to get to that potential. This company segmentation in an attractiveness quadrant is done based on company profile data, but also leveraging market data to predict job needs for the next 12 weeks and to spot company buying signals for Randstad services. ‍
scraping/output/-854568260067003203.txt
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The data-driven territory design aims to make the scoring specific by geographic area, increasing the relevance of the generated insights for local sales teams. The resulting kanban script - an automated and actionable to-do list - provides individual sales consultants on a weekly basis with an overview of companies to focus on; and insights about that company/industry as a basis to structure the customer discussion.Finally, the market insights solution component uses additional external data to provide the sales consultants with further context, for example a valid business reason to call the company, instant customer industry information, supporting data for expectations management/ fillability and pricing negotiations.The 'sales effectiveness tool’ is conceived to formally and continuously leverage data in an outside-in approach, acquiring up to date, factual data from selected, structured external data sources and applying time series based forecasting to
scraping/output/-854568260067003203.txt
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an outside-in approach, acquiring up to date, factual data from selected, structured external data sources and applying time series based forecasting to generate insights and triggers.
scraping/output/-854568260067003203.txt
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‍ The Dun & Bradstreet company repository serves as the source for a worldview on all companies, existing Randstad clients as well as prospects, including a comprehensive view on their profile features. Job demand data is gathered from multiple sources like textkernel, careerjet, burning glass and jobdigger. Finally, economic data per geography (eg gdp, employment) are acquired from Oxford Economics.Defining and imposing data governance principles like global definitions and data protection (e.g. no manual override) ensure data quality and hence the relevancy of the weekly recommendations for sales consultants. ‍ The onset of the Covid pandemic, as a textbook example of a high-impact, low- frequency (HILF) event, has triggered the addition of Natural Language Processing (NLP) techniques to spot extra data on irregular, unpredictable events. ‍
scraping/output/-854568260067003203.txt
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‍ "ML6 helped us to build and deploy an intelligent matching engine that has the potential to have a big impact on our candidate and customer journey's. Hence also influencing our top and bottom line. " CIO (Randstad Belgium) By Randstad ## Results The hit rate has increased from the historical 25% to 70%, meaning that today the sales consultants using the tool are spending 70% of their time with the right clients - clients who have actual and feasible potential for Randstad. And that significant and demonstrated commercial business value is only the start. The sales effectiveness tool has been built in line with the global digital factory strategy of Randstad; readying it for deployment to a growing number of countries, while retraining and refining the AI models on a monthly basis. ‍
scraping/output/-854568260067003203.txt
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‍ The rollout across all 38 Randstad countries is planned to be done with explicit attention to tailoring of recommendations to varying market positions and business lines or concepts. The setup of automated data ingestion and processing pipelines, and retraining pipelines drives the scalability of the solution and its ability to keep learning from new data. It will also speed up the integration of new strategic insights and new data sources which are discovered by using the tool, hence further pushing the relevance of recommended actions. ‍ 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 received! Oops! Something went wrong while submitting the form. Services Navigate Intelligence Activate IntelligenceBuild Intelligence Solutions Custom AI solutionsSolutions catalogueDomains of expertise References
scraping/output/-854568260067003203.txt
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scraping/output/-854568260067003203.txt
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Computer vision MLOps People 🚀 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 Customer testimonials Resources Resource library Blog Video & demo Press Events Open Source Our DNA How we work Mission & ambition Responsible AI Partners ML6 For Good Careers Jobs Team Working at ML6 Taal NLEN Contact us en defrNL Contact us # Thomas Janssens Computer vision MLOps People Visit my LinkedIn profile Our know-how ## Related posts Placeholder tag Know your unknowns: a short primer on uncertainty in machine learning How to approach uncertainty in machine learning : forecasting, regression and classification models. September 19, 2022 By Thomas Janssens Placeholder tag
scraping/output/-731701728420759485.txt
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How to approach uncertainty in machine learning : forecasting, regression and classification models. September 19, 2022 By Thomas Janssens Placeholder tag How to effectively label images for computer vision projects ? In this blogpost we cover the most important aspects of tackling a computer vision problem by labeling your own data. March 22, 2023 By Thomas Janssens Large Language Model Foundation Models Corporate People Structured Data Chat GPT Sustainability Voice & Sound Front-End Development Data Protection & Security Responsible/ Ethical AI Infrastructure Hardware & sensors MLOps Generative AI Natural language processing Computer vision 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 received! Oops! Something went wrong while submitting the form. Services
scraping/output/-731701728420759485.txt
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scraping/output/-731701728420759485.txt
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scraping/output/-2743735034926628820.txt
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scraping/output/-2743735034926628820.txt
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Share this post ML6 presents a recommended CI/CD pattern for promoting ML model code across environments using Amazon SageMaker. This blogpost deep-dives into MLOps practices, highlighting the need for reliable and efficient model deployment. By focusing on code promotion rather than the model itself, ML6 streamlines ML workflows, accelerates time-to- market, and enhances overall MLOps capabilities. The post includes insights, architectural details, tech stack, CI/CD workflow, and a practical example of fine-tuning a pre-trained model for medical transcription classification. Through collaboration with AWS, ML6 offers an innovative solution, leveraging Terraform and SageMaker Pipelines, to enable users to adopt this deployment pattern effectively. By implementing these practices, organizations can achieve reproducibility, reliability, and efficiency in ML model deployment. Read the full blogpost on our Medium channel. ## Related posts View all No results found.
scraping/output/-2743735034926628820.txt
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scraping/output/-2743735034926628820.txt
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scraping/output/-2743735034926628820.txt
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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 Client cases Customer testimonials Resources Resource library Blog Video & demo Press Events Open Source Our DNA How we work Mission & ambition Responsible AI Partners ML6 For Good Careers Jobs Team Working at ML6 Taal NLEN Contact us en defrNL Contact us Blogposts All Posts May 22, 2022 # Using Synthetic Data to boost the Performance of your Object Detection Model Contributors Jules Talloen Machine Learning Engineer No items found. Subscribe to newsletter Sign up By clicking Sign Up you're confirming that you agree with our Terms and Conditions. Thank you! Your submission has been received!
scraping/output/2304708636802131724.txt
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No items found. Subscribe to newsletter 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. Share this post A practical guide to generating artificial data using Unity Perception Anyone who has ever trained a machine learning model will tell you that it is the data that makes the model and that, in general, more data and labels will lead to better performance. Collecting data and especially labeling them is time-consuming and therefore expensive. Hence, machine learning professionals are increasingly looking at more efficient ways of ‘augmenting’ their dataset by using artificially generated variations of data samples, but also increasingly at the use of hybrid or fully synthetic data.
scraping/output/2304708636802131724.txt
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Game engine company Unity offers a tool called Unity Perception which allows you to simulate objects in different ways and take virtual pictures of your simulations. In this way, it becomes possible to generate a high number of labeled images from your object scans which can be used as training data. In this blogpost, I present the results of my internship at ML6, in the course of which I investigated the use of Unity Perception for generating training data for an object detection model. More particularly, I will describe how I: 1. obtained object scans using a Lidar camera 2. generated a synthetic dataset for these objects using Unity 3. trained object detection models using both synthetic and real-world images 4. assessed the potential benefits of adding synthetic images to the training set ### Scanning the Objects
scraping/output/2304708636802131724.txt
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### Scanning the Objects To create object meshes I used an app called Scaniverse on a phone with a lidar camera. To do this well there are some requirements. You must be able to capture the object from all sides and you require good and equal lighting on all angles of the object So I put the objects on a pedestal with good lighting, scanned the objects using the app and afterwards took a separate picture of the bottom surface to add it to the object mesh in Blender. With Scaniverse you can export your objects as fbx or obj files, both work with Blender and Unity. Example object scan in Blender Not every object was easy to scan however. Especially objects with reflective surfaces, thin or see-through segments were problematic to scan using this method. If the scans are not great, there is still the possibility to edit them afterwards using Blender. Mostly for cropping and recentering them; or combining them with a bottom side to get a complete object.
scraping/output/2304708636802131724.txt
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Mostly for cropping and recentering them; or combining them with a bottom side to get a complete object. I also made multiple scans for each object to increase the variation of the objects in the synthetic images, and took around 200 pictures of each object I scanned in various rooms and positions. I tried to have the objects in diverse locations and took pictures from different angles. I made sure the pictures were sometimes more blurry or only partially showing the object to further increase variation. Below are scans of the 3 objects I worked with: a shoe, a Coca Cola can and a Kellogg’s cereal box. I chose these objects because: 1. They were easy to move and manually take pictures of 2. They’ll fit with the indoor scene backgrounds 3. They are very different in terms of color, structure and reflective properties Example object scans in Unity perception ### Generating Synthetic Data in Unity Perception
scraping/output/2304708636802131724.txt
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Example object scans in Unity perception ### Generating Synthetic Data in Unity Perception Unity Perception is a toolkit for generating datasets. It is relatively new and for now only works on camera-based use cases. It is a plugin for Unity, a game engine and cross-platform IDE. To generate training images I used a simulation that positions your objects with random rotations, scales and positions in front of a background and before a light source and a camera. The objects were drawn with equal probability; every class was equally represented. Unity perception UI
scraping/output/2304708636802131724.txt
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Unity perception UI For more variation I randomized the lighting intensity and used different smoothness properties for the objects and different types of background generation. I used the same background generation scheme as used by unity in their tutorial for Unity Perception consisting of random objects with random textures as their background. This ensures a very different background for each simulation with no other recognisable objects aside from the object classes. For general purposes this is a good baseline to begin with (this situation is referenced as Simulation 1 in the graph below). ‍ Tutorial background simulation
scraping/output/2304708636802131724.txt
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‍ Tutorial background simulation However, training on only these images did not yield very good results so I tried switching to a random background image instead. These background images were sampled from an online dataset of indoor scenes. This drastically improved results, as I will show in the graph below. The dataset these background images are sampled from should be chosen depending on the natural context of your object class. ‍ Simulation with background image To further improve the scene I added random objects but now in fewer numbers and in the foreground. These objects sometimes partly obscure classes and also make for a more diverse scene. ‍ Simulation with background image and random objects ‍ Another improvement I made is using multiple object meshes for the same object class. This improves the results for more complex objects. ### Training Object Detection Models
scraping/output/2304708636802131724.txt
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‍ Another improvement I made is using multiple object meshes for the same object class. This improves the results for more complex objects. ### Training Object Detection Models I used the Tensorflow Object Detection API to train the models. I imported pre-trained models from the Tensorflow model zoo and then fine-tuned them on my data. I chose to work with a mobilenet, since it is a relatively fast model that allows me to obtain decent results in a short time. When taking pictures, I moved the objects to a different location for every picture and took pictures from many angles. I also took pictures in which objects were either partially obscured or not entirely in frame; all the pictures were taken with the same phone in the same quality.
scraping/output/2304708636802131724.txt
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I used LabelImg to manually label the pictures before partitioning them into a training (80%), validation (10%) and test (10%) set. The validation set was used to choose model hyperparameters and the test set for final evaluation. The pictures for the test set were taken in different rooms than those for the training set; same for the validation set. I made sure every class was equally represented in all the partitions. I added the synthetic images to the training set; the validation and test sets consisted only of real-world pictures. To measure the performance of the models I used the mean average precision (mAP) at 0.5 Intersection over Union score and the average recall (AR) at 10 predictions (True Positive when object detected in top 10 predictions). The mAP is good at measuring how accurate the detections are. While The AR measures how well the model finds all the positives (see also here). ### Experiments
scraping/output/2304708636802131724.txt
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The mAP is good at measuring how accurate the detections are. While The AR measures how well the model finds all the positives (see also here). ### Experiments I trained models using only real-world pictures, only synthetic images, or a mixture of both. By comparing results, I show the benefit of adding synthetic images and the best real-world/synthetic split in training images to work with. The mixed models were trained using the same sampling scheme for both real-world and synthetic images. With different sampling schemes, you can likely make even better models. However I used the same sampling for the sake of simplicity. Another way of training these models is by first training them only on synthetic data and then fine-tuning them on real-world data. For me this gave similar but slightly worse results. However, the models were rather simple and I am sure this can be improved upon.
scraping/output/2304708636802131724.txt
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I trained mixed models on the 4 different types of background simulations shown in the previous section. The table below summarizes the mAP/AR results for these simulations: ‍ Simulation 1: Tutorial simulation; simulation 2: Using background images; simulation 3: Using background images with random objects; simulation 4: Using multiple meshes for each object. models trained with 1000 synthetic and 500 real-world images ### Performance Comparison between Different Synthetic/Real-world Splits First, I wanted to see how good the synthetic-only models were and what is the least amount of real-world pictures needed to obtain decent results. I trained the synthetic-only models with 1000/2000/5000/100000 synthetic images; all had around the same scores around 0.3 mAP@0.5IoU, which is not great. Some examples results for the different models can be found in the pictures at the end of this blogpost.
scraping/output/2304708636802131724.txt
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Then I trained models with 0, 30, 100, 300 and 530 real-world pictures in addition to the 1000 synthetic images and compared results. Comparison between models trained on different amounts of real-world images I noticed that the mAP and AR scores increased as I added more real-world images to the training set until I had added 300 real-world images; the extra 230 images in the last model of 530 real-world training images did not seem to help much. The 1000 synthetic/300 real-world image model could detect most objects in the images, missing mainly some of the shoe detections.
scraping/output/2304708636802131724.txt
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So far I only showed that a mix of both is better than synthetic-only, but not how much the synthetic data improves our models. Thus I trained a model only on the 300 real-world images. This performed very poorly, even worse than the synthetic-only model. When evaluating this model, I noticed the model was almost exclusively detecting the Coca Cola cans; showing me that while 100 pictures is enough to train an object detection model for the Coca Cola cans, it was not sufficient for the other 2 classes. However for testing purposes I kept using the same training set of approximately 100 pictures for each class. I then added just 300 synthetic images to the training set and already noticed that the scores more than doubled. When I kept adding more synthetic data however, the models started to perform more poorly. Perhaps using a different sampling scheme for the real-world data could allow me to add more synthetic images to the training set.
scraping/output/2304708636802131724.txt
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Comparison between models trained on different amounts of synthetic images ### Performance Comparison between Different Classes While manually evaluating the models I noticed major differences between how well some of the models were detecting the classes. Thus I evaluated them for each class separately and looked at the results. models trained on 5000 synthetic images, 500 real-world images and a mixture of 2000 synthetic and 300 real-world images; evaluated on each class separately The shoe class scores badly in most of the models; this is the most complex object I used and also the least colorful, which may contribute to its low scores. By complex I mean that it looks different from every angle, and has some thin segments that are moveable in the shoelaces.
scraping/output/2304708636802131724.txt
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The real-world models are very good at recognising the Coca Cola cans, however for the other classes they are failing. Using the synthetic models however, the cola cans are not so easily detected. This may be due to their reflective properties, which are not simulated well in Unity Perception. However, there are options in Unity to better simulate reflections, which I did not fully make use of but certainly could help here. The Kellogg’s cereal box is a “simpler” object and scores very well in synthetic-only data; this shows that for these kinds of objects a synthetic- only dataset can already provide great results on its own. Example detection of model trained on 300 real-world images Example detection of model trained on 1000 synthetic images Example detection of model trained on 300 real-world images and 1000 synthetic images ### Conclusion
scraping/output/2304708636802131724.txt
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Example detection of model trained on 1000 synthetic images Example detection of model trained on 300 real-world images and 1000 synthetic images ### Conclusion In this post I showed that adding synthetic images to object detection models can greatly improve your models. I explained step by step how to do this from scanning the objects to training the object detection models. I showed that best results are obtained with a model trained on a mix of both real-world and synthetic images and that you can already achieve decent results with few real-world images if you add synthetic data. I also concluded that simple objects, like the Kellogg’s box, benefit greatly from the addition of synthetic images. It is harder to obtain representative meshes from objects with thin, see-through or reflective segments; however even models trained on those objects get better results with the addition of synthetic data.
scraping/output/2304708636802131724.txt
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Perhaps for models with a very large set of qualitative real-world images, the addition of synthetic data will not result in improvements. However, for models with few qualitative real-world images such as my example, the addition of synthetic data is a major boost to results. To conclude I am satisfied with the results as the improvements of adding synthetic data were even greater than expected. I hope you find this post useful and it inspires you to try this and further improve the simulations to achieve even better results! ‍ ## Related posts View all No results found. There are no results with this criteria. Try changing your search. Large Language Model Large Language Model Foundation Models Foundation Models Corporate Corporate People People Structured Data Structured Data Chat GPT Chat GPT Sustainability Sustainability Voice & Sound Voice & Sound Front-End Development Front-End Development Data Protection & Security Data Protection & Security
scraping/output/2304708636802131724.txt
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scraping/output/2304708636802131724.txt
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scraping/output/2304708636802131724.txt
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scraping/output/-5576522641546504031.txt
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scraping/output/-5576522641546504031.txt
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Vendors Teamtailor Analytics These cookies collect information to help us understand how the site is being used. Vendors Teamtailor Marketing These cookies are used to make advertising messages more relevant to you. In some cases, they also deliver additional functions on the site. Vendors Youtube, Google Accept these cookies Decline non-necessary cookies * Join us 🕵️‍♀️ Employee Log in Candidate Log in to Connect Homepage ml6.eu # Privacy Policy ### Privacy policy for recruitment using Teamtailor
scraping/output/-5576522641546504031.txt
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* Join us 🕵️‍♀️ Employee Log in Candidate Log in to Connect Homepage ml6.eu # Privacy Policy ### Privacy policy for recruitment using Teamtailor The service for handling recruitments and simplifying the hiring process (the "Service") is powered by Teamtailor on behalf of ML6 ("Controller" “we” “us” etc.). It is important that the persons using the Service ("Users”) feel safe with, and are informed about, how we handle User's personal data in the recruitment process. We strive to maintain the highest possible standard regarding the protection of personal data. We process, manage, use, and protect User's Personal Data in accordance with this Privacy Policy ("Privacy Policy"). ### 1\. General We are the controller in accordance with current privacy legislations. The Users’ personal data is processed with the purpose of managing and facilitating recruitment of employees to our business. ### 2\. Collection of personal data
scraping/output/-5576522641546504031.txt
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### 2\. Collection of personal data We are responsible for the processing of the personal data that the Users contributes to the Service, or for the personal data that we in other ways collects with regards to the Service. When and how we collect personal data We collect personal data about Users from Users when Users; * make an application through the Service or otherwise, adding personal data about themselves either personally or by using a third-party source such as Facebook or LinkedIn; and * use the Service to connect with our staff, adding personal data about themselves either personally or by using a third-party source such as Facebook or LinkedIn. * provides identifiable data in the chat (provided through the website that uses the Service) and such data is of relevance to the application procedure;
scraping/output/-5576522641546504031.txt
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