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Electronic waste, conflict minerals, and poor labor conditions are just a few issues blighting the tech industry. Here’s how to shop more sustainably.

Our plan for this episode was to spend some time talking about antitrust regulation, because one of the biggest companies on the planet is currently in the midst of a trial that could fundamentally reshape the internet economy. And that was before Google lost its adtech trial! Two’s definitely a trend, in this case: Meta’s trial is just beginning, and we have a ruling in Google’s, but both companies are staring at a future that looks very different than the last 20 years. On this episode of The Vergecast, The Verge’s Alex Heath joins Nilay and David to talk through what the Google ruling means (with as little ad-tech talk as possible, we promise), as well as what it was like to be in the Meta courtroom all week. So far, the FTC’s case against Meta seems somewhat dubious, and might hinge a little too much on the power of MeWe. But that one is only just beginning, and there are many more questions about Instagram and WhatsApp still to come. After that, we talk about some big news in the AI world. OpenAI is working on a social network, with plans to rival X and become the place people do… something. Post their ripoff photos? Make funny jokes with the help of ChatGP … Read the full story at The Verge.

Google is the latest AI service provider to court users in higher education. Starting today, college students in the US can sign up for Google’s One AI Premium plan for free, shirking the usual $20 monthly subscription fee until June 30th, 2026. Applicants will need to sign up before the deal expires on June 30th, 2025 according to Google spokesperson Alex Joseph, and will need a valid .edu email address for verification. He told The Verge that students will be emailed toward the end of their plan, “so you will have plenty of time to cancel.” The plan includes 2TB of cloud storage and access to an assortment of Google’s AI offerings that aim to help students “study smarter.” Google One AI Premium includes Gemini 2.5 Pro-powered tools like Gemini Advanced, which is Google’s competitor to ChatGPT Plus, and Gemini Deep Research features that can be used to summarize complex topics and convert reports into a podcast-style audio format.  Users can also access NotebookLM Plus for more studying and audio summarization features, and integrate Google’s Gemini assistant directly into Docs, Sheets, and Slides. Some new tools are also included, such as Google’s new Veo 2 text-to-video AI model, and Whisk, which allows users to “mix text and image prompts to create something new.” OpenAI and Anthropic have announced their own education initiatives this month, similarly hoping to attract students with a free taste of their AI tools. Academia is an important market, so it’s hardly surprising that Google has thrown its own hat in the ring, especially with AI being the biggest threat to its web search empire.

Introduction AWS is a well-known cloud provider whose primary goal is to allocate server resources for software engineers to deploy their applications. AWS offers many services, one of which is EC2, providing virtual machines for running software applications in the cloud. However, for data-intensive applications, storing data inside EC2 instances is not always the optimal choice. While EC2 offers fast read and write speeds, it is not optimized for scalability. A better alternative is to use S3 storage instead. Storing data in EC2 vs S3 Amazon S3 was specifically designed for storing massive amounts of unstructured data: It has a highly reliable resilience system, thanks to which the durability rate exceeds 99.99%. S3 automatically replicates data across multiple servers to prevent potential data loss. It seamlessly integrates with other AWS services for data analytics and machine learning. Storing data in S3 is significantly more cost-effective compared to EC2. The main use case where EC2 might be preferred is when frequent data access is required. For example, during machine learning model training, where the dataset must be read repeatedly for each batch. In most other cases, S3 is the better choice. About this article The objective of this article is to demonstrate how to create a basic S3 Storage. By the end of the tutorial, we will have a functioning S3 storage that allows remote access to uploaded images. To keep the focus on key aspects, we will cover only the storage creation process and not dive into best security practices. Tutorial # 01. Create S3 storage To perform any operations related to S3 storage management, select the Storage option from the service menu. In the submenu that appears, choose S3. AWS organizes data into collections called buckets. To create a bucket, click Create bucket. Each bucket requires a unique global name. Most other settings can be left as default. Once all options are selected, click Create bucket. After a few seconds, AWS will redirect you to the bucket management panel. # 02. Create folder (optional step) Folders in S3 function similarly to standard computer folders, helping to organize hierarchical data. Additionally, any file stored in an S3 folder will have a URL prefix that includes the folder path. To create a folder, click the Create folder button. In the appearing window, choose a custom name for the folder. After clicking the Create folder button, the folder will be created! You can now navigate to it. Since no images have been uploaded yet, the folder is empty for now, but we will add images in step 4. # 03. Adjust data access As a reminder, our goal is to create a publicly visible image storage that allows remote access. To achieve this, we need to adjust data access policies. By clicking on the Permissions tab under the bucket name, you will see a list of options to modify access settings. We need to unblock public access, so click on the respective Edit button in the interface and uncheck all the checkboxes related to access blocking. After saving the changes, we should see an exclamation mark icon with the “Off” text. Then, navigate to the Bucket policy section and click Edit. To allow read access, insert the following policy text: # 04. Upload images Now it is time to upload images. To do that, navigate to the created “images” folder and click on the Upload button. Click on the Add files button, which will open a file explorer on your computer. Choose and import the images from there. Depending on the number and size of the imported images, AWS might take some time to process them. In this example, I have imported nine images. # 05. Access data After the images have been successfully imported, click on any of their filenames to get more information. In the opened panel, you will see metadata related to the chosen image. As we can see in the “Object URL” field, AWS created a unique URL for our image! Additionally, we can notice that the URL contains the images/ prefix, which corresponds exactly to the folder structure we defined above! Finally, since we have authorized read access, we can now publicly access this URL. If you click on the image URL and copy it into the browser’s address bar, the image will be displayed! The amazing part about this is that you can now create a URL template in the form https://<bucket_url>/<folder_path>/<filename>. By doing so, you can dynamically replace the <filename> field in a program to access images and perform data manipulation. Conclusion In this article, we have introduced the AWS S3 storage system, which is very useful for storing large amounts of unstructured data. With its advanced scalability and security mechanisms, S3 is perfect for organizing massive data volumes at a much lower cost compared to EC2 containers. All images are by the author unless noted otherwise. Connect with me Medium LinkedIn The post Beginner’s Guide to Creating a S3 Storage on AWS appeared first on Towards Data Science.

The model hallucinated! It was giving me OK answers and then it just started hallucinating. We’ve all heard or experienced it. Natural Language Generation models can sometimes hallucinate, i.e., they start generating text that is not quite accurate for the prompt provided. In layman’s terms, they start making stuff up that’s not strictly related to the context given or plainly inaccurate. Some hallucinations can be understandable, for example, mentioning something related but not exactly the topic in question, other times it may look like legitimate information but it’s simply not correct, it’s made up. This is clearly a problem when we start using generative models to complete tasks and we intend to consume the information they generated to make decisions. The problem is not necessarily tied to how the model is generating the text, but in the information it’s using to generate a response. Once you train an LLM, the information encoded in the training data is crystalized, it becomes a static representation of everything the model knows up until that point in time. In order to make the model update its world view or its knowledge base, it needs to be retrained. However, training Large Language Models requires time and money. One of the main motivations for developing RAG s the increasing demand for factually accurate, contextually relevant, and up-to-date generated content.[1] When thinking about a way to make generative models aware of the wealth of new information that is created everyday, researchers started exploring efficient ways to keep these models-up-to-date that didn’t require continuously re-training models. They came up with the idea for Hybrid Models, meaning, generative models that have a way of fetching external information that can complement the data the LLM already knows and was trained on. These modela have a information retrieval component that allows the model to access up-to-date data, and the generative capabilities they are already well known for. The goal being to ensure both fluency and factual correctness when producing text. This hybrid model architecture is called Retrieval Augmented Generation, or RAG for short. The RAG era Given the critical need to keep models updated in a time and cost effective way, RAG has become an increasingly popular architecture. Its retrieval mechanism pulls information from external sources that are not encoded in the LLM. For example, you can see RAG in action, in the real world, when you ask Gemini something about the Brooklyn Bridge. At the bottom you’ll see the external sources where it pulled information from. Example of external sources being shown as part of the output of the RAG model. (Image by author) By grounding the final output on information obtained from the retrieval module, the outcome of these Generative AI applications, is less likely to propagate any biases originating from the outdated, point-in-time view of the training data they used. The second piece of the Rag Architecture is what is the most visible to us, consumers, the generation model. This is typically an LLM that processes the information retrieved and generates human-like text. RAG combines retrieval mechanisms with generative language models to enhance the accuracy of outputs[1] As for its internal architecture, the retrieval module, relies on dense vectors to identify the relevant documents to use, while the generative model, utilizes the typical LLM architecture based on transformers. A basic flow of the RAG system along with its component. Image and caption taken from paper referenced in [1] (Image by Author) This architecture addresses very important pain-points of generative models, but it’s not a silver bullet. It also comes with some challenges and limitations. The Retrieval module may struggle in getting the most up-to-date documents. This part of the architecture relies heavily on Dense Passage Retrieval (DPR)[2, 3]. Compared to other techniques such as BM25, which is based on TF-IDF, DPR does a much better job at finding the semantic similarity between query and documents. It leverages semantic meaning, instead of simple keyword matching is especially useful in open-domain applications, i.e., think about tools like Gemini or ChatGPT, which are not necessarily experts in a particular domain, but know a little bit about everything. However, DPR has its shortcomings too. The dense vector representation can lead to irrelevant or off-topic documents being retrieved. DPR models seem to retrieve information based on knowledge that already exists within their parameters, i.e, facts must be already encoded in order to be accessible by retrieval[2]. […] if we extend our definition of retrieval to also encompass the ability to navigate and elucidate concepts previously unknown or unencountered by the model—a capacity akin to how humans research and retrieve information—our findings imply that DPR models fall short of this mark.[2] To mitigate these challenges, researchers thought about adding more sophisticated query expansion and contextual disambiguation.  Query expansion is a set of techniques that modify the original user query by adding relevant terms, with the goal of establishing a connection between the intent of the user’s query with relevant documents[4]. There are also cases when the generative module fails to fully take into account, into its responses, the information gathered in the retrieval phase. To address this, there have been new improvements on attention and hierarchical fusion techniques [5]. Model performance is an important metric, especially when the goal of these applications is to seamlessly be part of our day-to-day lives, and make the most mundane tasks almost effortless. However, running RAG end-to-end can be computationally expensive. For every query the user makes, there needs to be one step for information retrieval, and another for text generation. This is where new techniques, such as Model Pruning [6] and Knowledge Distillation [7] come into play, to ensure that even with the additional step of searching for up-to-date information outside of the trained model data, the overall system is still performant. Lastly, while the information retrieval module in the RAG architecture is intended to mitigate bias by accessing external sources that are more up-to-date than the data the model was trained on, it may actually not fully eliminate bias. If the external sources are not meticulously chosen, they can continue to add bias or even amplify existing biases from the training data. Conclusion Utilizing RAG in generative applications provides a significant improvement on the model’s capacity to stay up-to-date, and gives its users more accurate results. When used in domain-specific applications, its potential is even clearer. With a narrower scope and an external library of documents pertaining only to a particular domain, these models have the ability to do a more effective retrieval of new information. However, ensuring generative models are constantly up-to-date is far from a solved problem. Technical challenges, such as, handling unstructured data or ensuring model performance, continue to be active research topics. Hope you enjoyed learning a bit more about RAG, and the role this type of architecture plays in making generative applications stay up-to-date without requiring to retrain the model. Thanks for reading! References A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions. (2024). Shailja Gupta and Rajesh Ranjan and Surya Narayan Singh. (ArXiv) Retrieval-Augmented Generation: Is Dense Passage Retrieval Retrieving. (2024). Benjamin Reichman and Larry Heck— (link) Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D. & Yih, W. T. (2020). Dense passage retrieval for open-domain question answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 6769-6781).(Arxiv) Hamin Koo and Minseon Kim and Sung Ju Hwang. (2024).Optimizing Query Generation for Enhanced Document Retrieval in RAG. (Arxiv) Izacard, G., & Grave, E. (2021). Leveraging passage retrieval with generative models for open domain question answering. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (pp. 874-880). (Arxiv) Han, S., Pool, J., Tran, J., & Dally, W. J. (2015). Learning both weights and connections for efficient neural network. In Advances in Neural Information Processing Systems (pp. 1135-1143). (Arxiv) Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter. ArXiv. /abs/1910.01108 (Arxiv) The post Retrieval Augmented Generation (RAG) — An Introduction appeared first on Towards Data Science.

C'est une nouvelle qui en fera sourire certains. Huawei, qui a débuté la production de sa puce IA la plus avancée, l'Ascend 910C, prévoit de...

Stimuler l'innovation tout en protégeant les données personnelles. C'est l'objectif du nouveau "bac à sable" dédié à la silver économie,...

Muriel Robin est furibarde. La comédienne et humoriste nous apprend par le biais de son compte Instagram, le 15 avril, que son image et sa voix, générées par l’intelligence artificielle (IA), la font apparaître dans une vidéo vantant un produit minceur ; elle va engager des poursuites judiciaires. On a eu droit récemment à la chanteuse Taylor Swift en actrice porno, l’acteur Tom Hanks faisant la promotion d’une mutuelle dentaire ou Scarlett Johansson (ainsi que de faux Steven Spielberg, Jack Black ou Adam Sandler) dénonçant, sans qu’on lui ait demandé son avis, les dérapages antisémites du rappeur Kanye West. Ces exemples parmi d’autres, auxquels on peut ajouter les millions de gens anonymes piégés par des vidéos nommées deepfakes, constituent le symptôme d’une IA incontrôlable. Les violations du droit d’auteur se font à la pelle. Le cas du cinéaste d’animation japonais Hayao Miyazaki, auteur de Princesse Mononoké (1997) ou du Voyage de Chihiro (2001), est exemplaire. Voulant afficher sur la Toile un autoportrait plus « sympa » qu’une photo, des internautes ont plagié le style du Japonais au moyen d’OpenAI. Emmanuel Macron, Gabriel Attal ou l’eurodéputée LFI Manon Aubry l’ont fait aussi. La Maison Blanche également. Lire aussi | Article réservé à nos abonnés Pourquoi les images de ChatGPT imitant le Studio Ghibli font polémique Le problème n’est pas que Myazaki, 84 ans, ait qualifié par le passé l’IA d’« insulte à la vie même ». Ni que son fils ait confié le 2 avril à l’Agence France-Presse que « rien ne pourra remplacer le talent » de son père. Pas plus que le débat juridique soulevé – l’appropriation d’une esthétique est-elle possible ? Non, ce qui est exemplaire, c’est que, d’un chef d’Etat à un anonyme, tout le monde croit avec sincérité que l’IA est une sorte de filtre qui efface l’auteur. Inconsciemment, on se dit que ce n’est pas si grave, voire que cela contribue à la démocratisation de l’art. Déséquilibre des forces vertigineux Cette approche fait penser à l’époque où l’Internet a fait exploser le piratage des musiques et des films et où certains y voyaient un espace de liberté pour les consommateurs face à des géants s’en mettant plein les poches. On disait que le piratage, c’était fun, c’était libre, tendance, presque révolutionnaire. Il vous reste 63.79% de cet article à lire. La suite est réservée aux abonnés.

Quand la revue « Esprit » se préoccupe d’IA Dans son numéro d’avril, la publication analyse les conséquences de l’intelligence artificielle contre ce qui fait notre humanité, à savoir l’esprit. Article réservé aux abonnés La revue des revues. C’est à une révolution technologique en cours, celle de l’intelligence artificielle (IA), qu’est consacré le numéro d’avril de la revue Esprit. Comme d’autres ruptures technologiques avant elle, elle nous somme « de réinterroger notre devenir à l’aune des possibles qu’elle ouvre, autant que notre présent, qu’elle transforme déjà », écrivent, dans leur introduction, le professeur de lettres et de philosophie Nicolas Léger et le doctorant en philosophie Adrien Tallent. Pour ces deux chercheurs se joue, avec l’IA, « une mise en concurrence de la technologie avec ce que nous avions jusqu’ici coutume de définir comme propre à l’humain : son esprit ». Cette mise en concurrence prend les traits d’une véritable guerre tant les fronts sont nombreux : ces machines intelligentes se sont invitées dans toutes les dimensions de la vie humaine : sociale, économique, politique, technique. La rupture anthropologique est profonde. Pour le philosophe suisse Mark Hunyadi, les « tech titans » (géants de la tech) transforment progressivement notre rapport au monde. En empêchant les hommes de penser et de juger, l’IA érode les facultés humaines jusqu’à faire de notre esprit un « organe atrophié ». L’individu est poussé à se conformer en permanence à l’ordre établi : « la part restante de la raison, sa part non alignée, se réduit comme peau de chagrin ». « Le “techno-cocon” se referme » L’IA risque de changer notre rapport aux autres, ajoute l’écrivain Alain Damasio dans un entretien publié dans la revue. Il assure que les intelligences artificielles personnalisées – qu’il nomme « MyIA » – vont bientôt envahir nos vies : elles épouseront nos goûts et feront tout pour nous plaire en puisant dans nos données pour s’adapter à nous. « Le “techno-cocon” se referme. Il me paraît évident que si notre rapport à l’altérité a déjà été affaibli par les réseaux, il le sera encore plus par MyIA », regrette-t-il. Lire aussi | Article réservé à nos abonnés Doter l’IA d’une personnalité n’est pas sans risque La revue Esprit ne se contente pas d’alerter sur les risques que fait peser l’IA : elle invite aussi les humains à lui résister et à lui opposer des représentations du monde alternatives. En la matière, l’art et la fiction sont une ressource essentielle, estime le critique littéraire Alexandre Gefen. « Pour un artiste, utiliser une IA, c’est souvent la “hacker”, l’emmener dans des chemins de traverse, la forcer à une créativité moins normée. » « Les artistes utilisant l’IA, ajoute-t-il, sont particulièrement bien placés pour penser l’IA, en analyser les biais, en remettre en question l’éthique : la réflexivité de l’art s’exerce à plein. » Il vous reste 17.36% de cet article à lire. La suite est réservée aux abonnés.