Latest news with #Generation
Economic Times
11 hours ago
- Business
- Economic Times
LTIMindtree shares in focus after launch of BlueVerse AI ecosystem for enterprises
LTIMindtree shares will be in focus on Friday after the company announced the launch of BlueVerse, a new business unit offering a suite of artificial intelligence (AI) services and solutions for enterprises. ADVERTISEMENT The BlueVerse Marketplace currently features over 300 industry- and function-specific AI agents. It also includes productised services built on repeatable frameworks, accelerators, and sector-specific solution kits. At launch, the unit will offer pre-built solutions for marketing services and contact centre as a service (CCaaS), the latter leveraging context-aware AI agents to improve efficiency. The ecosystem also includes BlueVerse Foundry, which features a no-code designer and a flexible pro-code editor to help users build and deploy AI agents, tools, assistants, RAG (Retrieval-Augmented Generation) pipelines, and intelligent business processes. "BlueVerse is all about unlocking productivity for businesses at different levels by embedding AI across all functions of the enterprise. Backed by a strategic partnership ecosystem and deep AI expertise, it positions LTIMindtree as the partner of choice for future-ready organisations." said Venu Lambu, chief executive officer and managing director at LTIMindtree. Also Read: These 9 Nifty Microcap Index stocks trading below industry PE may rally up to 42% According to Trendlyne, the average target price for LTIMindtree is Rs 4,945, indicating a potential downside of around 8% from current levels. Of the 40 analysts tracking the stock, the consensus rating is 'Buy'. ADVERTISEMENT On the technical front, the stock's Relative Strength Index (RSI) stands at 65.3, indicating neutral momentum. The Moving Average Convergence Divergence (MACD) is at 155.2 — above both the centre and signal lines, which is seen as a bullish signal. Also Read: 8 debt-free penny stocks that surged 110-300% in the last 1 year. Do you own any? ADVERTISEMENT LTIMindtree shares closed 1.4% lower at Rs 5,371 on the BSE on Thursday. The stock has declined 5% year-to-date and is down 14% over the past six months. The company's current market capitalisation stands at Rs 1,59,170 crore. (Disclaimer: Recommendations, suggestions, views and opinions given by the experts are their own. These do not represent the views of the Economic Times) ADVERTISEMENT (You can now subscribe to our ETMarkets WhatsApp channel)
Time of India
11 hours ago
- Business
- Time of India
LTIMindtree shares in focus after launch of BlueVerse AI ecosystem for enterprises
Synopsis LTIMindtree shares: The ecosystem also encompasses BlueVerse Foundry, offering both a no-code designer and a versatile pro-code editor to enable users to create and launch AI agents, tools, assistants, RAG (Retrieval-Augmented Generation) pipelines, and smart business workflows.
Techday NZ
4 days ago
- Techday NZ
AI: The future belongs to those who put the humans in the machine first
In 1993, Ghost in the Machine imagined a future where consciousness could exist inside a computer. Three decades later, that vision has blurred into reality and machine intelligence is no longer a science fiction trope - it's a tool we use every day. But the real shift isn't just about building smarter systems; it's about building systems that support smarter humans. As generative AI spreads across legal practice, the advantage is no longer in what you know, but how well you reason because recall is easy - anyone can pull up case law. The real edge lies in interpretation, explanation and judgment. And while today's models don't always reason perfectly - neither do humans. The better question is: can AI help lawyers reason better? This is where things get interesting. More data ≠ better model Let's start with the false promise of infinite data. It's widely understood that throwing thousands of pages of legislation, regulation, case law and other legal documents at a model doesn't make it smarter. In fact, it often makes it worse because legal reasoning depends on, amongst other things, quality, relevance and clarity. A carefully curated dataset of law and precedent on an expertise domain in a particular jurisdiction (and potentially some related jurisdictions) can outperform a bloated corpus of global case law riddled with inconsistencies and irrelevance. Here, the model doesn't need to 'know the law' - it needs to retrieve it with precision and reason over the top with discipline. That's why in most practical applications in a specific domain of expertise, Retrieval-Augmented Generation (RAG) will probably beat full fine-tuning. RAG lets you plug into a general-purpose model that's already been trained on a vast body of knowledge, and then layer on your own curated legal content in real-time - without the need for full re-training. It's fast, flexible and keeps you close to the constantly evolving edge of legal precedent. If fine-tuning is like rewriting the engine, RAG is like swapping in smarter fuel - giving you a model that reasons over your trusted material instead of guessing based on a noisy global corpus. This is the difference between dumping legal textbooks on your desk and actually having a partner walk you through the implications. Reasoning over regurgitation Take a real-world query: "Can an employee working remotely in Melbourne still claim a travel allowance under their enterprise agreement?" An untrained model might respond with this: "There are hundreds of examples of travel allowances in Australian enterprise agreements…shall I find these for you and list them?" Helpful? Not really. A well-trained legal AI might say this instead: "It depends on the specific terms of the enterprise agreement that applies to the employee. Travel allowances are typically tied to physical attendance at a designated worksite and if an employee's role has been formally varied to remote or hybrid including under a flexible work arrangement, the allowance may no longer apply. You'd need to check whether the agreement defines a primary work location, whether remote work was agreed under (Section 65 of the Fair Work Act or otherwise) and whether there are any clauses preserving travel entitlements in such cases." Now we're not 'just' talking about answers; we're talking about prompts for strategic thinking. Scaling senior expertise, insight and judgment, not just recall The much deeper question is this: how do we train AI not just to answer; but to remind us to ask better questions? Clients don't pay us for information; they pay for interpretation and come to top-tier firms because they want the kind of insight only senior legal professionals can provide - the kind that draws on pattern recognition through lots of relevant experience, strategic insight and framing and an understanding of nuance built across decades of practice. The real opportunity lies in scaling what clients actually value most: the expertise of senior partners - including their insight, experience, judgment and contextual thinking. This means training AI to reason like a partner - to recognise what matters, frame choices, reason through trade-offs and flag what clients will care about We should be asking "How do we encode that?" How do we teach a model to say not just 'here's what the law says', but 'here's how you might think about this and here's what clients like yours have cared about in similar cases'. This represents an all important shift from knowledge to judgment and from retrieval to reasoning. Because the goal isn't to build a machine that knows everything but to build one that helps your lawyers engage with better questions, surface richer perspectives and unlock more strategic conversations that create value for clients. It's important to remember: AI hears what is said, but great lawyers listen for what isn't said. That's where real context lives - within tone, hesitation and the unspoken concerns that shape top-tier legal advice. To build AI that supports nuanced thinking, we need to train it on more than documents; we need to model real-world interactions and teach it to recognise the emotional cues that matter. This isn't about replacing human intelligence but about amplifying it, helping lawyers read between the lines and respond with sharper insight. This, in turn, might open up brand new use cases. Imagine if AI could listen in on client-lawyer conversations not just for note-taking but to proactively suggest risks, flag potential misunderstandings or surface relevant precedents in real time based on the emotional and contextual cues it detects. From knowledge to insight: What great training looks like If we want to AI to perform like a partner, we need the model not to give lawyers the answer but to do what a senior partner would do in conversation: "Here's what you need to think about... Here are two approaches clients tend to prefer... and here's a risk your peers might not spot." This kind of reasoning-first response can help younger lawyers engage with both the material and the client without needing to escalate every issue to their senior. Importantly, it's not about skipping the partner - it's about scaling their thinking. Scaling the apprenticeship model in ways not possible in the past. If you're not solving for: What the client really cares about, and why How to recognise the invisible threads between past matters, and current situations, options and decisions, How to ask the kinds of questions a senior prcatitioner would ask The kind of prompt to use to achieve this …then you're not training AI…you're just hoping like hell that it helps. This is also where RAG and training intersect. Rather than re-training the model from scratch, we can use RAG to ensure the model is drawing from the right content - legal guidance, judgment notes, contextual memos - while training it to reason the way our top partners do. Think of it less like coding a robot; and more like mentoring a junior lawyer with access to every precedent you've ever relied on. Some critics, including recent research, have questioned whether today's large language models can truly reason or reliably execute complex logical tasks. It's a fair challenge and one we acknowledge but it's also worth noting that ineffective reasoning isn't new. Inconsistency, bias and faulty heuristics have long been a part of human decision-making. The aim of legal AI isn't to introduce flawless reasoning, but to scale the kind of strategic thought partners already apply every day and to prompt richer thinking, not shortcut it. How to structure a real firm-level AI rollout As AI becomes embedded in professional services, casual experimentation is no longer enough. Legal firms need structured adoption strategies and one of the best frameworks could be what Wharton professor Ethan Mollick calls the 'Lab, Library, and Leadership' model for making AI work in complex organisations. In his breakdown: Lab = the experimental sandbox where teams pilot real-world use cases with feedback loops and measurable impact. Library = the curated knowledge base of prompts, best practices, guardrails and insights (not just raw documents, but how to use these well). Leadership = the top-down cultural shift that's needed to legitimise, resource and scale these efforts. For law firms, this maps elegantly to our current pressing challenges: the Lab is where legal teams experiment with tools like RAG based models on live matters. The Library is the evolving playbook of prompt templates, safe document sources and past legal reasoning. And Leadership (arguably the most vital) is what determines whether those ideas ever leave the lab and reach real matters and clients. As Mollick puts it, "AI does not currently replace people, but it does change what people with AI are capable of." The firms that win in this next chapter won't just use AI - they'll teach their people how to build with it. And critically, they'll keep teaching it. Most models, including GPT-4, are built on datasets with a cut-off and as a consequence they are often months or even years out of date. If you're not feeding the machine fresh experiences and insights, you're working with a version of reality that's already stale. This isn't a 'one and done' deployment - it's an ongoing dialogue and by structuring feedback loops from live matters, debriefs and partner insights, firms can ensure the model evolves alongside the business, not behind it. Putting humans in the machine Ultimately, legal AI isn't about machine innovation; it's about human innovation and the real challenge is how to capture and scale the experience, insight, judgment and strategic thinking of senior lawyers. That requires sitting down with partners to map how they approach a question, what trade-offs they consider and how they advise clients through complexity. That's the real creativity and that's what we need to encode into the machine. Lawyer 2.0 isn't just AI-assisted - it's trained by the best, for the benefit of the many. The future of legal work will belong to those who put humans in the machine first.
Yahoo
13-06-2025
- Health
- Yahoo
New Nimbus COVID-19 variant — What you need to know
SALT LAKE CITY () — With reports of a new variant of COVID-19 out there, it's important to remain informed and prepare. Public health experts are saying that the new Nimbus variant is more contagious, and they're recommending COVID boosters. The official name of the new variant is NB.1.8.1, but it has been nicknamed Nimbus. spoke with Dr. Kelly Oakeson, Chief Scientist for Next Generation Sequencing and Bioinformatics at the Utah Public Health Lab with the Utah Department of Health about what you need to know about this new COVID-19 variant. According to Dr. Oakeson, the CDC is predicting that the Nimbus variant makes up anywhere from 38-50% of the current COVID cases in the United States. He also said that we've seen it in Utah, through a handful of clinical cases, and it's been detected in wastewater. As we have seen with previous variants, new mutations have made the virus more transmissible. 'It's better at attaching to our cells and infecting our cells and making us sick, but it also has mutations as well that help us avoid our immune response, right?' Dr. Oakeson explained. 'It has ways of avoiding our antibodies that we have built up against COVID, either from vaccination or from infection.' Symptoms are similar to current COVID symptoms: cough, fatigue, fever, loss of taste and smell, etc., he said. Four measles cases now reported in Arizona, first of this year What can you do to protect yourself? Dr. Oakeson recommended the same measures people have been taking all along to protect themselves against COVID. 'If you're not feeling well, stay in bed, rest up. If you have to go out and you're going to be in large places, put a mask on,' Dr Oakeson said. 'We know these N95, these surgical masks do a good job at helping prevent spread of respiratory viruses.' She also recommended getting a COVID booster if you haven't yet. 'If you got one last fall, and depending on your health status, you're probably okay. There are recommendations for people that are immunosuppressed or immunocompromised to get boosters more often,' he explained. If you haven't gotten a booster in the past year, Dr. Oakeson said that it's probably time to go out and get one. The formulation of the newest booster will provide some protection against the new variant, he said. From a public health perspective, Dr. Oakeson said that the biggest concern is a summer surge, where large groups of people would be infected, putting pressure on hospital systems. 'We tend to see COVID come in waves, you know, in the summer and then again in the winter, so we're keeping an eye out to see how that wave crests here as cases start increasing,' he said. New Nimbus COVID-19 variant — What you need to know Wildfire burns 1500 acres in France Canyon in Garfield County, not contained Judge blocks Trump's National Guard deployment in Los Angeles Rubio: US 'not involved' in Israel's strike inside Iran Highland man threatened to kill his wife and himself with rifle over financial dispute, charges say Copyright 2025 Nexstar Media, Inc. All rights reserved. This material may not be published, broadcast, rewritten, or redistributed.
Associated Press
12-06-2025
- Business
- Associated Press
Understand Tech Unveils Major Platform Update to Empower Secure, Scalable AI Deployment for Enterprises
PARIS, FRANCE / ACCESS Newswire / June 12, 2025 / Understand Tech, a leading enterprise AI platform, today announced a significant product update designed to meet the evolving demands of security-conscious organizations. The latest enhancements focus on on-premise deployment, intelligent document generation, advanced AI capabilities, and expanded integration with enterprise tools-solidifying Understand Tech's position as a trusted solution for regulated and large-scale enterprise environments. On-Premise AI: Security at the Core With heightened concerns around data privacy and compliance, Understand Tech now supports full on-premise deployment of its AI platform. This includes Understand AI, a powerful local Large Language Model (LLM) that runs entirely offline-eliminating the need for external API calls and offering complete control over data processing. In addition, organizations leveraging AWS can now deploy the platform effortlessly using Infrastructure as Code, enabled through public Terraform templates. The platform also now supports Single Sign-On (SSO) via OpenID Connect, ensuring streamlined and secure access management. Chained Prompts: Solving Long-Form AI Output The update introduces Chained Prompts, a groundbreaking feature that overcomes the context and length limitations of traditional LLMs. Enterprises can now upload a structured sequence of prompts, which are processed step-by-step and merged into a single cohesive output. This capability is ideal for complex document generation such as contracts, technical specifications, test suites, and compliance reports. Check out the Video: Enhanced LLM Capabilities and RAG Optimization Understanding AI has been upgraded to deliver improved multi-step reasoning, structural coherence, and factual accuracy. Optimized for Retrieval-Augmented Generation (RAG), the model can now deliver more relevant and accurate results, while remaining fully offline or hosted in private cloud environments. Refined User Experience A redesigned user interface brings a host of usability upgrades including streaming replies for real-time responsiveness and a vertical model selector for simplified navigation between AI models. The chat interface also now includes a larger interactive area and streamlined navigation to enhance productivity. Deep Integration and Customization With the launch of a Custom Chat Widget, enterprises can now embed Understand Tech's assistant within their own platforms, fully customizing visual elements including iconography, color schemes, branding, and welcome messages creating a seamless user experience. Understand Tech has also integrated directly with n8n, a popular open-source workflow automation tool. This native integration enables businesses to trigger backend logic from within the chat interface, allowing actions such as sending alerts, updating databases, or calling internal APIs-paving the way for intelligent automation. In addition, CRM integration has been expanded to include Zoho CRM, alongside existing support for HubSpot, allowing businesses to directly capture and sync user information and chat transcripts. Looking Ahead: Agentic AI on the Horizon Building on this foundation, Understand Tech is actively developing Agentic AI capabilities. Set to roll out in Q3 2025, users will soon be able to configure assistants capable of scheduling meetings, triggering workflows, and executing API calls autonomously-all driven by natural conversation logic. This development is a direct response to enterprise feedback, reflecting Understand Tech's ongoing commitment to innovation and customer-centric design. Availability All features are now live and available for both cloud-based and on-premise deployments. About Understand Tech Understand Tech is an enterprise-focused AI platform dedicated to building secure, scalable, and action-oriented AI systems. With a mission to empower enterprises through flexible deployment, robust compliance, and seamless integration, Understand Tech is redefining what's possible with AI in regulated and complex environments. For more information, demos, or technical support: [email protected] SOURCE: Understand Tech press release
