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Dans son plan contre la fraude fiscale, Bercy cible les ultra riches

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Dans son plan contre la fraude fiscale, Bercy cible les ultra riches
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Le plan de lutte contre la fraude fiscale se précise. Le gouvernement a annoncé, le 8 mai, vouloir augmenter de 25% les contrôles fiscaux des “plus gros patrimoines” d’ici la fin du quinquennat et contrôler “tous les deux ans” les cent plus grandes capitalisations boursières. “Notre priorité: faire payer ce qu’ils doivent aux ultra-riches et aux multinationales qui fraudent”, a déclaré le ministre délégué chargé des Comptes publics Gabriel Attal dans un entretien au journal “Le Monde”, dévoilant des mesures du plan qui sera présenté officiellement mardi 9 mai. Annoncée de longue date et initialement attendue pour la fin du premier trimestre, la présentation de ce plan, présenté comme l’un des piliers de la stratégie gouvernementale pour rétablir les finances publiques, a été reportée à plusieurs reprises.

Création d’un service d’élite contre les fraudes internationales

Gabriel Attal annonce que les sanctions seront durcies à l’égard des fraudeurs, notamment “pour les fautes les plus graves” pour lesquelles une “sanction d’indignité fiscale et civique” pourra être prononcée, sous la forme d’une privation de réduction ou de crédit d’impôt ainsi que du droit de vote “pendant une certaine durée”, a dit le ministre. Il a également annoncé la création d’un service de renseignement fiscal à Bercy consacré à la lutte contre les grandes fraudes internationales, doté d’une centaine “d’agents d’élite” d’ici la fin du quinquennat, qui utiliseront les techniques de renseignement comme “les écoutes, la captation de données, la pose de balises”.

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La cible des plus fortunés et des multinationales est donc clairement affichée. Elle a aussi des objectifs politiques : l’exécutif prend les devants par rapport à la publication d’une étude explosive de l’Institut des Politiques publiques (Ecole d’Economie de Paris),en collaboration avec la Direction générale des Finances publiques (DGFIP). Attendue à la fin mai, elle  devrait démontrer que les ultra-riches arrivent à éviter massivement l’impôt. Un évitement lié à des montages d’optimisation fiscale, parfois à la limite de la légalité. En mettant l’accent sur les grandes fortunes, Bercy veut aussi faire taire les critiques sur les “cadeaux” aux plus aisés et aux grandes entreprises, au début du premier quinquennat Macron. 

 Des mesures en préparation sur la fraude sociale 

Tout en ciblant les plus aisés, le ministre insiste parallèlement sur l’idée d’”alléger la pression sur le petit contribuable, le petit patron, en massifiant ” les régularisations plutôt que d’avoir recours aux contrôles et en instaurant “une remise de pénalité automatique pour la première erreur”. Autre geste vers le commun des contribuables: la création d’une “pénalité inversée automatique en faveur du contribuable en cas d’erreur de l’administration”, a promis M. Attal.

Ce seront “1.500 effectifs supplémentaires” qui seront dédiés à la lutte contre la fraude fiscale d’ici 2027, a-t-il encore indiqué. Gabriel Attal avait rappelé la semaine dernière qu’en matière de fraude fiscale, “on a eu l’an dernier 14,6 milliards d’euros de mises en recouvrement notifiées par la DGFiP”. Concernant la fraude sociale, “on a recouvré 800 millions d’euros de cotisations sociales non versées du côté de l’Urssaf”. Il prépare aussi des mesures de lutte contre les fraudes sociales seront présentées “d’ici la fin du mois” de mai, avait-il précisé.

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La publication de ces chiffres suscite des critiques de la part de l’opposition : « Je crois vraiment qu’on n’en fait trop avec ces chiffres, réagit Éric Bocquet, sénateur communiste auteur de rapports parlementaires sur le sujet. On ne peut que se féliciter de ces milliards récupérés, évidemment. Mais tous les ministres se félicitent chaque année de résultats mirifiques, de recouvrements extraordinaires… Je tiens à rappeler qu’entre 2010 et 2018, les résultats oscillaient entre 16 et 21 milliards d’euros », a-t-il lancé sur Public Sénat. Cela placerait l’année 2022 en dessous des années 2010. « Il y a beaucoup de communication autour de ce sujet”. A voir si le plan détaillé, présenté ce mardi, pourra convaincre les sceptiques sur cette nouvelle initiative face à la fraude fiscale. 

(Avec AFP)

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Microsoft previews AI ‘building blocks’ for .NET

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Core benefits of the Microsoft.Extensions.AI libraries include:

  • Providing a consistent set of APIs and conventions for integrating AI services into .NET applications.
  • Allowing .NET library authors to use AI services without being tied to a specific provider.
  • Enabling .NET developers to experiment with different packages using the same underlying abstractions, maintaining a single API throughout an application.
  • Simplifying the addition of new capabilities and facilitating the componentization and testing of applications.

Instructions on getting started with the Microsoft.Extensions.AI packages can be found in the October 8 blog post. Microsoft’s current focus is on creating abstractions that can be implemented across various services, the company said. There is no plan to release APIs tailored to any specific provider’s services. Microsoft’s goal is to act as a unifying layer within the .NET ecosystem, enabling developers to choose preferred frameworks and libraries while ensuring integration and collaboration across the ecosystem.

In explaining the libraries, Microsoft’s Luis Quintanilla, program manager for the developer division, said AI capabilities are rapidly evolving, with common patterns emerging for functionality such as chat, embeddings, and tool calling. Unified abstractions are crucial for developers to work across different sources, he said.

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Deno 2.0 arrives, ready to battle Node.js

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Deno 2.0, a major update to the open source Deno runtime for JavaScript, TypeScript, and WebAssembly, is now available as a production release. It emphasizes backward compatibility with the rival Node.js runtime and NPM and a stabilized standard library.

Proponents say the Deno 2.0 update was designed to make JavaScript development simpler and more scalable. The production release, from Deno Land, was announced October 9 and can be installed from dotcom–2.deno.

“Deno 2.0 is fully compatible with existing Node.js projects, allowing developers to run their existing applications seamlessly while also taking advantage of Deno’s modern, all-in-one toolchain,” Ryan Dahl, creator of both Deno and Node.js, said. “It’s designed to help teams cut through the complexity of today’s JavaScript landscape with zero-config tooling, native TypeScript, and robust support for frameworks like Next.js, Astro, and more.” Dahl said full compatibility with Node.js and NPM makes it easy to adopt Deno incrementally. Long-term support (LTS) releases, meanwhile, provide stability in production environments.

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Gemini Code Assist Enterprise woos enterprise developers

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Gemini Code Assist Enterprise woos enterprise developers
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Google Cloud has announced Gemini Code Assist Enterprise, billed as an enterprise-grade tool that lets developers generate or transform code that is more accurate and relevant to their applications.

Generally available on October 9, Gemini Code Assist Enterprise is a cloud-based, AI-powered application development solution that works across the technology stack to provide better contextual suggestions, enterprise-grade security commitments, and integrations across Google Cloud. The tool supports developers in being more versatile and working with a wider set of services faster, Google Cloud said

Supported by Gemini’s large token context window, Gemini Code Assist Enterprise moves beyond AI-powered coding assistance in the IDE, Google Cloud said. The company emphasized the following features of Gemini Code Assist Enterprise:

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Electron vs. Tauri: Which cross-platform framework is for you?

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Electron vs. Tauri: Which cross-platform framework is for you?
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Unlike Electron, not very many mainstream desktop applications are built with Tauri, at least, not yet. Some of that may be due to Electron’s legacy presence, or the relative complexity of Rust versus JavaScript. But a fair number of apps, both commercial and open source, have been written with Tauri. The pgMagic GUI client for PostgreSQL, the Payload file transfer tool, and the Noor team chat app are examples.

Payload, a file transfer app written in Tauri

Payload, a file transfer app written in Tauri. The runtime memory footprint is a mere 4MB on Microsoft Windows, but the app has access to just about all the same system-level functionality as an Electron app.

IDG

Which is better: Tauri or Electron?

Right now, Electron remains the most prominent and well-understood of the cross-platform UI frameworks. For all the criticism levied against it, it’s still a popular default choice for delivering cross-platform applications with good system integration and a rich GUI. But complaints about Electron’s memory consumption and the size of its binaries are valid, and they aren’t going away soon. They’re intimately tied to the design of Electron apps, and only a redesign of either Electron or the underlying browser components will fix that issue.

Tauri apps are designed differently from the ground up to use less disk space and less memory. But that comes at the cost of it being a newer technology that relies heavily on Rust—a relatively new language, also with a relatively new development ecosystem. A commitment to Tauri requires a commitment to both Rust and JavaScript, for the back end and front end, respectively.

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WebSockets under the hood with Node.js

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WebSockets under the hood with Node.js
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// script.js
const wsUri = "ws://localhost:3000";
const outputDiv = document.getElementById("output");
const messageInput = document.getElementById("message");
const sendButton = document.getElementById("send-btn");

let websocket;

function connect() {
    websocket = new WebSocket(wsUri);

    websocket.onopen = function (event) {
        outputDiv.innerHTML += "

Connected to server!

"; }; websocket.onmessage = function (event) { const receivedMessage = event.data; outputDiv.innerHTML += "

Received: " + receivedMessage + "

"; }; websocket.onerror = function (event) { outputDiv.innerHTML += "

Error: " + event.error + "

"; }; websocket.onclose = function (event) { outputDiv.innerHTML += "

Connection closed.

"; }; } sendButton.addEventListener("click", function () { const message = messageInput.value; if (websocket && websocket.readyState === WebSocket.OPEN) { websocket.send(message); messageInput.value = ""; } else { outputDiv.innerHTML += "

Error: Connection not open.

"; } }); connect(); // Connect immediately

This script sets up several event handlers using the browser-native API. We start up the WebSocket as soon as the script is loaded and watch for open, onclose, onmessage, and onerror events. Each one appends its updates to the DOM. The most important one is onmessage, where we accept the message from the server and display it.

The Click handler on the button itself takes the input typed in by the user (messageInput.value) and uses the WebSocket object to send it to the server with the send() function. Then we reset the value of the input to a blank string.

Assuming the back end is still running and available at ws://localhost:3000, we can now run the front end. We can use http-server as a simple way to run the front end. It’s a simple way to host static files in a web server, akin to Python’s http module or Java’s Simple Web Server, but for Node. It can be installed as a global NPM package or simply run with npx, from the client directory:

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Julia language adds lower-overhead Memory type

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Julia language adds lower-overhead Memory type
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A new version of the dynamically typed, high-performance Julia language for numerical computing has been released. Julia 1.11 features a new Memory type, a lower-level container that provides an alternative to Array.

Downloadable from julialang.org, Julia 1.11 was released October 7 following two alphas, two betas, and four release candidates. Introduced with Julia 1.11, the Memory type has less overhead and a faster constructor than Array, making it a good choice for situations that do not need all the features of Array, according to release notes. Most of the Array type now is implemented in Julia on top of Memory as well, thus leading to significant speedups for functions such as push!, along with more maintainable code.

Also in Julia 1.11, public is a new keyword. Symbols marked with public are considered public API, while symbols marked with export also are now treated as public API. The difference between export and public is that public names do not become available when using a package module. Additionally, tab completion has become more powerful and gains inline hinting when there is a singular completion available that can be completed with tab.

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Embracing your inner on-premises self

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Embracing your inner on-premises self
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Vendor dissatisfaction. Enterprises are not happy with major cloud providers due to service outages, egress fees, or lack of transparency around pricing and service-level commitments. Businesses are rethinking their cloud reliance and exploring on-premises alternatives.

A new way of thinking

I’ve often pointed out that the IT world is moving to heterogeneity and ubiquity, meaning that no approach, cloud or on-premises, will rise to the top and become “the standard.” I view this as a good thing, as long as we’re prepared to deal with the complexity it will bring. Thus far, many enterprises have stubbed their toe on that issue.

I’m now having these conversations often, whereas in the past, it was not spoken about in polite company–and certainly not at cloud conferences. Indeed, the concepts of multicloud and optimization are seeping back into the talks at significant events, when just a few years ago, presenters were pulling those slides out of their decks.

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SAP Build gains AI capabilities to help build autonomous agents

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SAP Build gains AI capabilities to help build autonomous agents
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Joule will also be integrated into SAP Build Work Zone, a low-code tool for creating web sites. Joule’s generative AI capabilities will provide support while navigating data from connected business systems. All this will be available in SAP Build Work Zone standard edition, the SAP Start site, and the SAP Mobile Start app.

New capabilities such as code explanation and documentation search in SAP Build Code will assist Java and JavaScript developers, who will also be able to automate workflows in SAP Build Process Automation, with assistance from generative AI.

Early next year, SAP plans to extend Joule to help developers using ABAP (Advanced Business Application Programming), SAP’s high-level programming language, to generate high-quality code and unit tests that comply with SAP’s ABAP Cloud development model. Joule will also be able to generate explanations for legacy code, to ease the path to modernizing legacy codebases and the migration to a “clean core”, a modern ERP system without hard-coded customizations.

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Databricks says with its new Databricks Apps platform, you can build tailored enterprise apps in 5 minutes

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“This experience is not unique, but provides parity with other development environments,” said Park.

He pointed out that Databricks Apps has a lot of competition from business intelligence vendors supporting data apps such as Tableau, Qlik, Sisense and Qrvey. It also vies for market share with “mega vendors” including Microsoft, Oracle, SAP, Salesforce, ServiceNow, and Zoho. Then there are low-code and no-code apps such as Mendix, Appian, and Quickbase at the fringes of the market.

The most important “tactical capabilities” Databricks brings to the table with the new platform, Park noted, is the ability to reuse existing governance, launch from an open-ended serverless environment, and provide a single tool to manage data, infrastructure, and code applications all at once.

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California’s vetoed AI bill: Bullet dodged, but not for long

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California’s vetoed AI bill: Bullet dodged, but not for long
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Artificial intelligence has the power to revolutionize industries, drive economic growth, and improve our quality of life. But like any powerful, widely available technology, AI also poses significant risks.

California’s now vetoed legislation, SB 1047 — the Safe and Secure Innovation for Frontier Artificial Intelligence Models Act — sought to combat “catastrophic” risks from AI by regulating developers of AI models. While lawmakers should be commended for trying to get ahead of the potential dangers posed by AI, SB 1047 fundamentally missed the mark. It tackled hypothetical AI risks of the distant future instead of the actual AI risk of today, and focused on organizations that are easy to regulate instead of the malicious actors that actually inflict harm.

The result was a law that did little to improve actual safety and risks stifling AI innovation, investment, and diminishing the United States’ leadership in AI. However, there can be no doubt that AI regulation is coming. Beyond the EU AI Act and Chinese laws on AI, 45 US states introduced AI bills in 2024. All enterprises looking to leverage AI and machine learning must prepare for additional regulation by boosting their AI governance capabilities as soon as possible. 

Addressing unlikely risks at the cost of ignoring present dangers

There are many real ways in which AI can be used to inflict harm today. Examples of deepfakes for fraud, misinformation, and non-consensual pornography are already becoming common. However, SB 1047 seemed more concerned with hypothetical catastrophic risks from AI than with the very real and present threats that AI poses today. Most of the catastrophic risks envisioned by the law are science fiction, such as the ability of AI models to develop new nuclear or biological weapons. It is unclear how today’s AI models would cause these catastrophic events, and it is unlikely that these models will have any such capabilities for the foreseeable future, if ever. 

SB 1047 was also focused on commercial developers of AI models rather than those who actively cause harm using AI. While there are basic ways in which AI developers can ensure that their models are safe — e.g. guardrails on generating harmful speech or images or divulging sensitive data — they have little control over how downstream users apply their AI models. Developers of the giant, generic AI models targeted by the law will always be limited in the steps they can take to de-risk their models for the potentially infinite number of use cases to which their models can be applied. Making AI developers responsible for downstream risks is akin to making steel manufacturers responsible for the safety of the guns or cars that are manufactured with it. In both cases you can only effectively ensure safety and mitigate risk by regulating the downstream use cases, which this law did not do.    

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Further, the reality is that today’s AI risks, and those of the foreseeable future, stem from those who intentionally exploit AI for illegal activities. These actors operate outside the law and are unlikely to comply with any regulatory framework, but they are also unlikely to use the commercial AI models created by the developers that SB 1047 intended to regulate. Why use a commercial AI model — where you and your activities are tracked — when you can use widely available open source AI models instead?  

A fragmented patchwork of ineffective AI regulation

Proposed laws such as SB 1047 also contribute to a growing problem: the patchwork of inconsistent AI regulations across states and municipalities. Forty-five states introduced, and 31 enacted, some form of AI regulation in 2024 (source). This fractured regulatory landscape creates an environment where navigating compliance becomes a costly challenge, particularly for AI startups who lack the resources to meet a myriad of conflicting state requirements. 

More dangerous still, the evolving patchwork of regulations threatens to undermine the safety it seeks to promote. Malicious actors will exploit the uncertainty and differences in regulations across states, and will evade the jurisdiction of state and municipal regulators.

Generally, the fragmented regulatory environment will make companies more hesitant to deploy AI technologies as they worry about the uncertainty of compliance with a widening array of regulations. It delays the adoption of AI by organizations leading to a spiral of lower impact, and less innovation, and potentially driving AI development and investment elsewhere. Poorly crafted AI regulation can squander the US leadership in AI and curtail a technology that is currently our best shot at improving growth and our quality of life.

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A better approach: Unified, adaptive federal regulation

A far better solution to managing AI risks would be a unified federal regulatory approach that is adaptable, practical, and focused on real-world threats. Such a framework would provide consistency, reduce compliance costs, and establish safeguards that evolve alongside AI technologies. The federal government is uniquely positioned to create a comprehensive regulatory environment that supports innovation while protecting society from the genuine risks posed by AI.

A federal approach would ensure consistent standards across the country, reducing compliance burdens and allowing AI developers to focus on real safety measures rather than navigating a patchwork of conflicting state regulations. Crucially, this approach must be dynamic, evolving alongside AI technologies and informed by the real-world risks that emerge. Federal agencies are the best mechanism available today to ensure that regulation adapts as the technology, and its risks, evolve.

Building resilience: What organizations can do now

Regardless of how AI regulation evolves, there is much that organizations can do now to reduce the risk of misuse and prepare for future compliance. Advanced data science teams in heavily regulated industries — such as finance, insurance, and healthcare — offer a template for how to govern AI effectively. These teams have developed robust processes for managing risk, ensuring compliance, and maximizing the impact of AI technologies.

Key practices include controlling access to data, infrastructure, code, and models, testing and validating AI models throughout their life cycle, and ensuring auditability and reproducibility of AI outcomes. These measures provide transparency and accountability, making it easier for companies to demonstrate compliance with any future regulations. Moreover, organizations that invest in these capabilities are not just protecting themselves from regulatory risk; they are positioning themselves as leaders in AI adoption and impact.

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The danger of good intentions

While the intention behind SB 1047 was laudable, its approach was flawed. It focused on organizations that are easy to regulate versus where the actual risk lies. By focusing on unlikely future threats rather than today’s real risks, placing undue burdens on developers, and contributing to a fragmented regulatory landscape, SB 1047 threatened to undermine the very goals it sought to achieve. Effective AI regulation must be targeted, adaptable, and consistent, addressing actual risks without stifling innovation.

There is a lot that organizations can do to reduce their risks and comply with future regulation, but inconsistent, poorly crafted regulation will hinder innovation and will even increase risk. The EU AI Act serves as a stark cautionary tale. Its sweeping scope, astronomical fines, and vague definitions create far more risks to the future prosperity of EU citizens than it realistically limits actors intent on causing harm with AI. The scariest thing in AI is, increasingly, AI regulation itself.

Kjell Carlsson is the head of AI strategy at Domino Data Lab, where he advises organizations on scaling impact with AI. Previously, he covered AI as a principal analyst at Forrester Research, where he advised leaders on topics ranging from computer vision, MLOps, AutoML, and conversation intelligence to next-generation AI technologies. Carlsson is also the host of the Data Science Leaders podcast. He received his Ph.D. from Harvard University.

Generative AI Insights provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss the challenges and opportunities of generative artificial intelligence. The selection is wide-ranging, from technology deep dives to case studies to expert opinion, but also subjective, based on our judgment of which topics and treatments will best serve InfoWorld’s technically sophisticated audience. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Contact doug_dineley@foundryco.com.

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