As AI rapidly transitions from experimentation to infrastructure, its implications are no longer confined to labs or startups. In 2025, organizations must confront AI not just as a productivity lever, but as a strategic and often existential risk domain. Three AI-centered priorities now dominate enterprise and government agendas: Agentic AI, AI Governance Platforms, and Disinformation Security. This article explores what these imperatives mean, what’s driving their urgency, and how leaders can respond. 1. Agentic AI: From Assistants to Autonomous Actors What it is: Agentic AI refers to systems that can plan, decide, and act independently within defined boundaries. Unlike traditional passive AI models that respond to explicit prompts, agentic systems proactively pursue goals, whether automating workflows, managing inventory, or coordinating software development. Why it matters in 2025: Open-source frameworks like AutoGPT and BabyAGI have demonstrated early capabilities and are rapidly evolving. Enterprises are deploying domain-specific agents to reduce human-in-the-loop dependencies in areas like IT ops, marketing, and customer support. Regulatory and ethical frameworks have yet to catch up, leaving critical questions around accountability and control unanswered. Key challenge: Balancing control with autonomy. How can organizations ensure agentic AI aligns with human intent, without micromanaging every decision it makes? 2. AI...

Why Chat Bots on WhatsApp Matter in 2025 In today’s fast-moving digital world, instant messaging isn’t just something nice to have – it’s part of how we live. Whether we’re chatting with friends, checking in with family or getting updates from favorite brands, we expect quick, smooth replies right when we need them. That’s why chat bot for WhatsApp are becoming such a big deal in 2025. So many people use WhatsApp, a figure that amounts to 2 billion and includes virtually every country in the world and specifically, every village and town as well. Therefore, it is logical to think that the use of these applications will continue to increase in the future, with the majority of our everyday activities being carried out there. Now people enjoy consuming, getting information or using services by conversing and messaging with their chat partners. From quick searches and information to valuable topics, to quick talks, they are the new assistants who have moved to this level. This level of intelligence has taken root in our day-to-day lives and we owe it all to AI. Talking to apps is becoming super common and chatbots are showing up everywhere to make life easier. Whether...

PLAID is a multimodal generative model that simultaneously generates protein 1D sequence and 3D structure, by learning the latent space of protein folding models. The awarding of the 2024 Nobel Prize to AlphaFold2 marks an important moment of recognition for the of AI role in biology. What comes next after protein folding? In PLAID, we develop a method that learns to sample from the latent space of protein folding models to generate new proteins. It can accept compositional function and organism prompts, and can be trained on sequence databases, which are 2-4 orders of magnitude larger than structure databases. Unlike many previous protein structure generative models, PLAID addresses the multimodal co-generation problem setting: simultaneously generating both discrete sequence and continuous all-atom structural coordinates.

Introduction to Ads Audience Targeting Assistant GPT The primary role of the Ads Audience Targeting Assistant GPT is to analyze campaign materials, such as landing pages, ad copy, or ad banners, and provide data-driven recommendations for audience targeting based on the provided input. Understanding and reaching the correct audience is essential. Traditional methods often involve guesswork and broad targeting strategies. However, the Ads Audience Targeting Assistant GPT reduces the need for intuition and generalization by utilizing sophisticated content analysis. It uses AI and ChatGPT knowledge to provide audience-targeting suggestions. By adopting this strategy, the outcome is a more tailored and efficient advertisement targeting method, ensuring that the ads are directed toward the specific audience segments with the highest probability of being interested in the product or service. The Audience Targeting Assistant GPT can suggest target audiences on multiple digital advertising platforms, including  Google Ads, Facebook Ads, Twitter Ads, Tiktok Ads, and LinkedIn. This allows business owners or marketers to find ideal customers across marketing channels. Generic ChatGPT vs. Ads Audience Targeting Assistant GPT While the generic Chat GPT is a powerful tool in its own right, offering a wide range of functionalities from answering questions to generating text-based content, the...

Today’s businesses constantly seek ways to accelerate innovation processes, reduce costs, and minimize risks. However, the traditional waterfall model is often time-consuming and expensive. So, how can companies innovate quickly and efficiently without breaking the bank? Enter design sprints, a game-changing approach revolutionizing AI product development. They provide a flexible, five-day framework for rapid prototyping and user testing. This approach, pioneered by Google Ventures, has been adopted by leading companies like Slack, Spotify, and Google to enhance their innovation capabilities. But what makes design sprints so effective? In this article, we’ll explore the key features and real-world applications of design sprints, demonstrating how they can supercharge your innovation process, reduce costs, and minimize risks—all while creating solutions your users will love. Finally, we will also present the success story of our client, who, thanks to our AI Design Sprint, validated the idea of a Home Care Matching Application.  Let’s dive in! Why the Waterfall Method Is Ineffective for Rapid Innovation The waterfall method, characterized by its sequential stages, requires each phase to be completed before moving on to the next. This extensive upfront planning is not only time-consuming but also costly. Additionally, changing the design stage later on is challenging,...

Today, I’ll tell you about our ETL migration from AWS Glue to the Snowflake and dbt modern data stack. My team’s mission is to centralize and ensure data reliability from multiple business domains. We build an analytical foundation to support product and strategic teams’ decision-making. As part of this effort, we migrated 28 gold models that power 13 data products, including dashboards, profiling tools, and analytical studies. These models serve as a cornerstone for enabling data-driven insights.

MIT today launched its Initiative for New Manufacturing (INM), an Institute-wide effort to reinfuse U.S. industrial production with leading-edge technologies, bolster crucial U.S. economic sectors, and ignite job creation. The initiative will encompass advanced research, innovative education programs, and partnership with companies across many sectors, in a bid to help transform manufacturing and elevate its impact. “We want to work with firms big and small, in cities, small towns and everywhere in between, to help them adopt new approaches for increased productivity,” MIT President Sally A. Kornbluth wrote in a letter to the Institute community this morning. “We want to deliberately design high-quality, human-centered manufacturing jobs that bring new life to communities across the country.” Kornbluth added: “Helping America build a future of new manufacturing is a perfect job for MIT — and I’m convinced that there is no more important work we can do to meet the moment and serve the nation now.” The Initiative for New Manufacturing also announced its first six founding industry consortium members: Amgen, Flextronics International USA, GE Vernova, PTC, Sanofi, and Siemens. Participants in the INM Industry Consortium will support seed projects proposed by MIT researchers, initially in the area of artificial intelligence for manufacturing....