#multimodal AI integration

[in-sightjournal]

Ask A Genius 1353: GPT-5, AI Consciousness, and Crossover Country

Scott Douglas Jacobsen: You have been listening to a country song designed for people who do not usually enjoy country music—not the traditional kind aimed at long-time fans, but rather a version that tries to appeal to outsiders. Rick Rosner: There is crossover country, of course. However, in Albuquerque, I could only find stations playing formulaic country music on the radio. There is…

[oneaichat]

AI-Driven Content Creation Tools for Smarter Marketing | OneAIChat Boost your brand with AI-driven content creation—generate high-quality, engaging content faster and smarter with advanced artificial intelligence tools.

[ezzybrownmedia]

Why AI Agents Army is Better than ChatGPT

Amidst the dynamic realm of natural language processing and artificial intelligence, two prominent entities appear as business authorities: ChatGPT and AI Agents Army. Even though both are widely recognised for their capacity to produce text that resembles that of humans, AI Agents Army demonstrates superior performance and capabilities. The following is why: - Area of expertise AI Agents Army is designed and trained with the sole purpose of assisting businesses with customer service, whereas ChatGPT is a versatile language model capable of performing a vast array of applications. The AI Agents Army's exceptional performance in this specific field is a result of this area of expertise and the provision of customized solutions. REGISTER NOW!!! - Industry Focus In addition to e-commerce, healthcare, and finance, the AI Agents Army is highly established in numerous other sectors. It provides businesses and consumers with more accurate and relevant responses due to its industry-specific expertise, which empowers it to comprehend sector-specific context, terminology, and details. - Capacity for Multimodality  

  AI Agents Army surpasses the text-only functionalities of ChatGPT by integrating multimodal capabilities throughout the interaction process. Incorporating a wide range of communication channels (including text, audio, images, and videos), AI Agents Army offers users a heightened level of immersion and engagement.Read More… Read the full article

[jeffsperandeo]

ChatGPT’s First Year: The AI-mpressive Journey from Bytes to Insights

The Genesis of a Digital GiantChatGPT’s story is a testament to human ingenuity. Birthed by OpenAI, a company co-founded by the visionary Sam Altman, ChatGPT is the offspring of years of groundbreaking work in AI. OpenAI, once a non-profit, evolved into a capped-profit entity, striking a balance between ethical AI development and the need for sustainable growth. Altman, a figure both admired and…

[vlruso]

Unlocking Multimodal AI with Open AI: GPT-4Vs Vision Integration and Its Impact

📢 Introducing Open AI's GPT-4V: the next level of AI! 🌟 With vision integration, this language model analyzes images alongside text, unlocking new opportunities and challenges. 🌐 Find out how it works and its impact here: https://ift.tt/Wvotjzu. #AI #GPT4V #OpenAI List of Useful Links: AI Scrum Bot - ask about AI scrum and agile Our Telegram @itinai Twitter -  @itinaicom

[rideboomindia]

Based on the search results, here are some innovative technologies that RideBoom could implement to enhance the user experience and stay ahead of ONDC:

Enhanced Safety Measures: RideBoom has already implemented additional safety measures, including enhanced driver background checks, real-time trip monitoring, and improved emergency response protocols. [1] To stay ahead, they could further enhance safety by integrating advanced telematics and AI-powered driver monitoring systems to ensure safe driving behavior.

Personalized and Customizable Services: RideBoom could introduce a more personalized user experience by leveraging data analytics and machine learning to understand individual preferences and offer tailored services. This could include features like customizable ride preferences, personalized recommendations, and the ability to save preferred routes or driver profiles. [1]

Seamless Multimodal Integration: To provide a more comprehensive transportation solution, RideBoom could integrate with other modes of transportation, such as public transit, bike-sharing, or micro-mobility options. This would allow users to plan and book their entire journey seamlessly through the RideBoom app, enhancing the overall user experience. [1]

Sustainable and Eco-friendly Initiatives: RideBoom has already started introducing electric and hybrid vehicles to its fleet, but they could further expand their green initiatives. This could include offering incentives for eco-friendly ride choices, partnering with renewable energy providers, and implementing carbon offset programs to reduce the environmental impact of their operations. [1]

Innovative Payment and Loyalty Solutions: To stay competitive with ONDC's zero-commission model, RideBoom could explore innovative payment options, such as integrated digital wallets, subscription-based services, or loyalty programs that offer rewards and discounts to frequent users. This could help attract and retain customers by providing more value-added services. [2]

Robust Data Analytics and Predictive Capabilities: RideBoom could leverage advanced data analytics and predictive modeling to optimize their operations, anticipate demand patterns, and proactively address user needs. This could include features like dynamic pricing, intelligent routing, and personalized recommendations to enhance the overall user experience. [1]

By implementing these innovative technologies, RideBoom can differentiate itself from ONDC, provide a more seamless and personalized user experience, and stay ahead of the competition in the on-demand transportation market.

[jcmarchi]

Moments Lab Secures $24 Million to Redefine Video Discovery With Agentic AI

New Post has been published on https://thedigitalinsider.com/moments-lab-secures-24-million-to-redefine-video-discovery-with-agentic-ai/

Moments Lab Secures $24 Million to Redefine Video Discovery With Agentic AI

Moments Lab, the AI company redefining how organizations work with video, has raised $24 million in new funding, led by Oxx with participation from Orange Ventures, Kadmos, Supernova Invest, and Elaia Partners. The investment will supercharge the company’s U.S. expansion and support continued development of its agentic AI platform — a system designed to turn massive video archives into instantly searchable and monetizable assets.

The heart of Moments Lab is MXT-2, a multimodal video-understanding AI that watches, hears, and interprets video with context-aware precision. It doesn’t just label content — it narrates it, identifying people, places, logos, and even cinematographic elements like shot types and pacing. This natural-language metadata turns hours of footage into structured, searchable intelligence, usable across creative, editorial, marketing, and monetization workflows.

But the true leap forward is the introduction of agentic AI — an autonomous system that can plan, reason, and adapt to a user’s intent. Instead of simply executing instructions, it understands prompts like “generate a highlight reel for social” and takes action: pulling scenes, suggesting titles, selecting formats, and aligning outputs with a brand’s voice or platform requirements.

“With MXT, we already index video faster than any human ever could,” said Philippe Petitpont, CEO and co-founder of Moments Lab. “But with agentic AI, we’re building the next layer — AI that acts as a teammate, doing everything from crafting rough cuts to uncovering storylines hidden deep in the archive.”

From Search to Storytelling: A Platform Built for Speed and Scale

Moments Lab is more than an indexing engine. It’s a full-stack platform that empowers media professionals to move at the speed of story. That starts with search — arguably the most painful part of working with video today.

Most production teams still rely on filenames, folders, and tribal knowledge to locate content. Moments Lab changes that with plain text search that behaves like Google for your video library. Users can simply type what they’re looking for — “CEO talking about sustainability” or “crowd cheering at sunset” — and retrieve exact clips within seconds.

Key features include:

AI video intelligence: MXT-2 doesn’t just tag content — it describes it using time-coded natural language, capturing what’s seen, heard, and implied.

Search anyone can use: Designed for accessibility, the platform allows non-technical users to search across thousands of hours of footage using everyday language.

Instant clipping and export: Once a moment is found, it can be clipped, trimmed, and exported or shared in seconds — no need for timecode handoffs or third-party tools.

Metadata-rich discovery: Filter by people, events, dates, locations, rights status, or any custom facet your workflow requires.

Quote and soundbite detection: Automatically transcribes audio and highlights the most impactful segments — perfect for interview footage and press conferences.

Content classification: Train the system to sort footage by theme, tone, or use case — from trailers to corporate reels to social clips.

Translation and multilingual support: Transcribes and translates speech, even in multilingual settings, making content globally usable.

This end-to-end functionality has made Moments Lab an indispensable partner for TV networks, sports rights holders, ad agencies, and global brands. Recent clients include Thomson Reuters, Amazon Ads, Sinclair, Hearst, and Banijay — all grappling with increasingly complex content libraries and growing demands for speed, personalization, and monetization.

Built for Integration, Trained for Precision

MXT-2 is trained on 1.5 billion+ data points, reducing hallucinations and delivering high confidence outputs that teams can rely on. Unlike proprietary AI stacks that lock metadata in unreadable formats, Moments Lab keeps everything in open text, ensuring full compatibility with downstream tools like Adobe Premiere, Final Cut Pro, Brightcove, YouTube, and enterprise MAM/CMS platforms via API or no-code integrations.

“The real power of our system is not just speed, but adaptability,” said Fred Petitpont, co-founder and CTO. “Whether you’re a broadcaster clipping sports highlights or a brand licensing footage to partners, our AI works the way your team already does — just 100x faster.”

The platform is already being used to power everything from archive migration to live event clipping, editorial research, and content licensing. Users can share secure links with collaborators, sell footage to external buyers, and even train the system to align with niche editorial styles or compliance guidelines.

From Startup to Standard-Setter

Founded in 2016 by twin brothers Frederic Petitpont and Phil Petitpont, Moments Lab began with a simple question: What if you could Google your video library? Today, it’s answering that — and more — with a platform that redefines how creative and editorial teams work with media. It has become the most awarded indexing AI in the video industry since 2023 and shows no signs of slowing down.

“When we first saw MXT in action, it felt like magic,” said Gökçe Ceylan, Principal at Oxx. “This is exactly the kind of product and team we look for — technically brilliant, customer-obsessed, and solving a real, growing need.”

With this new round of funding, Moments Lab is poised to lead a category that didn’t exist five years ago — agentic AI for video — and define the future of content discovery.

[in-sightjournal]

Ask A Genius 1332: ChatGPT, AGI, and the Future of Multimodal AI

Rick Rosner: You had ChatGPT summarize my life using publicly available sources, which it processed into a coherent narrative. There were several minor errors—for example, it claimed I spent ten years in high school. That is inaccurate. I returned to high school a few times over the course of a decade, but I did not attend for ten continuous years. Despite these inaccuracies, the summary…

[chattingwithmodels]

Google Gen AI SDK, Gemini Developer API, and Python 3.13

A Technical Overview and Compatibility Analysis 🧠 TL;DR – Google Gen AI SDK + Gemini API + Python 3.13 Integration 🚀 🔍 Overview Google’s Gen AI SDK and Gemini Developer API provide cutting-edge tools for working with generative AI across text, images, code, audio, and video. The SDK offers a unified interface to interact with Gemini models via both Developer API and Vertex AI 🌐. 🧰 SDK…

[govindhtech]

Pegasus 1.2: High-Performance Video Language Model

Pegasus 1.2 revolutionises long-form video AI with high accuracy and low latency. Scalable video querying is supported by this commercial tool.

TwelveLabs and Amazon Web Services (AWS) announced that Amazon Bedrock will soon provide Marengo and Pegasus, TwelveLabs' cutting-edge multimodal foundation models. Amazon Bedrock, a managed service, lets developers access top AI models from leading organisations via a single API. With seamless access to TwelveLabs' comprehensive video comprehension capabilities, developers and companies can revolutionise how they search for, assess, and derive insights from video content using AWS's security, privacy, and performance. TwelveLabs models were initially offered by AWS.

Introducing Pegasus 1.2

Unlike many academic contexts, real-world video applications face two challenges:

Real-world videos might be seconds or hours lengthy.

Proper temporal understanding is needed.

TwelveLabs is announcing Pegasus 1.2, a substantial industry-grade video language model upgrade, to meet commercial demands. Pegasus 1.2 interprets long films at cutting-edge levels. With low latency, low cost, and best-in-class accuracy, model can handle hour-long videos. Their embedded storage ingeniously caches movies, making it faster and cheaper to query the same film repeatedly.

Pegasus 1.2 is a cutting-edge technology that delivers corporate value through its intelligent, focused system architecture and excels in production-grade video processing pipelines.

Superior video language model for extended videos

Business requires handling long films, yet processing time and time-to-value are important concerns. As input films increase longer, a standard video processing/inference system cannot handle orders of magnitude more frames, making it unsuitable for general adoption and commercial use. A commercial system must also answer input prompts and enquiries accurately across larger time periods.

Latency

To evaluate Pegasus 1.2's speed, it compares time-to-first-token (TTFT) for 3–60-minute videos utilising frontier model APIs GPT-4o and Gemini 1.5 Pro. Pegasus 1.2 consistently displays time-to-first-token latency for films up to 15 minutes and responds faster to lengthier material because to its video-focused model design and optimised inference engine.

Performance

Pegasus 1.2 is compared to frontier model APIs using VideoMME-Long, a subset of Video-MME that contains films longer than 30 minutes. Pegasus 1.2 excels above all flagship APIs, displaying cutting-edge performance.

Pricing

Cost Pegasus 1.2 provides best-in-class commercial video processing at low cost. TwelveLabs focusses on long videos and accurate temporal information rather than everything. Its highly optimised system performs well at a competitive price with a focused approach.

Better still, system can generate many video-to-text without costing much. Pegasus 1.2 produces rich video embeddings from indexed movies and saves them in the database for future API queries, allowing clients to build continually at little cost. Google Gemini 1.5 Pro's cache cost is $4.5 per hour of storage, or 1 million tokens, which is around the token count for an hour of video. However, integrated storage costs $0.09 per video hour per month, x36,000 less. Concept benefits customers with large video archives that need to understand everything cheaply.

Model Overview & Limitations

Architecture

Pegasus 1.2's encoder-decoder architecture for video understanding includes a video encoder, tokeniser, and big language model. Though efficient, its design allows for full textual and visual data analysis.

These pieces provide a cohesive system that can understand long-term contextual information and fine-grained specifics. It architecture illustrates that tiny models may interpret video by making careful design decisions and solving fundamental multimodal processing difficulties creatively.

Restrictions

Safety and bias

Pegasus 1.2 contains safety protections, but like any AI model, it might produce objectionable or hazardous material without enough oversight and control. Video foundation model safety and ethics are being studied. It will provide a complete assessment and ethics report after more testing and input.

Hallucinations

Occasionally, Pegasus 1.2 may produce incorrect findings. Despite advances since Pegasus 1.1 to reduce hallucinations, users should be aware of this constraint, especially for precise and factual tasks.

[newspatron]

Google Gemini: The Ultimate Guide to the Most Advanced AI Model Ever

We hope you enjoyed this article and found it informative and insightful. We would love to hear your feedback and suggestions, so please feel free to leave a comment below or contact us through our website. Thank you for reading and stay tuned for more

Google Gemini: A Revolutionary AI Model that Can Shape the Future of Technology and Society. Artificial intelligence (AI) is one of the most exciting and rapidly evolving fields of technology today. From personal assistants to self-driving cars, AI is transforming various aspects of our lives and society. However, the current state of AI is still far from achieving human-like intelligence and…

[cookiesnerd]

ChatGPT and Google Gemini are both advanced AI language models designed for different types of conversational tasks, each with unique strengths. ChatGPT, developed by OpenAI, is primarily focused on text-based interactions. It excels in generating structured responses for writing, coding support, and research assistance. ChatGPT’s paid versions unlock additional features like image generation with DALL-E and web browsing for more current information, which makes it ideal for in-depth text-focused tasks​.

In contrast, Google Gemini is a multimodal AI, meaning it handles both text and images and can retrieve real-time information from the web. This gives Gemini a distinct advantage for tasks requiring up-to-date data or visual content, like image-based queries or projects involving creative visuals. It integrates well with Google's ecosystem, making it highly versatile for users who need both text and visual support in their interactions. While ChatGPT is preferred for text depth and clarity, Gemini’s multimodal and real-time capabilities make it a more flexible choice for creative and data-current tasks​

[jeffsperandeo]

Neurons and NPCs: Wiring the Future of Gaming AI

The Dawn of Dynamic Realms | AI-Driven NPCs Reshaping Video Gaming Introduction:A revolution is unfolding in the world of video gaming. Artificial Intelligence (AI) is transforming Non-Player Characters (NPCs) from scripted entities into dynamic, lifelike figures. This evolution is not just technological; it’s a redefinition of interaction and narrative immersion, offering new dimensions to…

[dpfocu]

OpenAI’s 12 Days of “Shipmas”: Summary and Reflections

Over 12 days, from December 5 to December 16, OpenAI hosted its “12 Days of Shipmas” event, revealing a series of innovations and updates across its AI ecosystem. Here’s a summary of the key announcements and their implications:

Day 1: Full Launch of o1 Model and ChatGPT Pro

OpenAI officially launched the o1 model in its full version, offering significant improvements in accuracy (34% fewer errors) and performance. The introduction of ChatGPT Pro, priced at $200/month, gives users access to these advanced features without usage caps.

Commentary: The Pro tier targets professionals who rely heavily on AI for business-critical tasks, though the price point might limit access for smaller enterprises.

Day 2: Reinforced Fine-Tuning

OpenAI showcased its reinforced fine-tuning technique, leveraging user feedback to improve model precision. This approach promises enhanced adaptability to specific user needs.

Day 3: Sora - Text-to-Video

Sora, OpenAI’s text-to-video generator, debuted as a tool for creators. Users can input textual descriptions to generate videos, opening new doors in multimedia content production.

Commentary: While innovative, Sora’s real-world application hinges on its ability to handle complex scenes effectively.

Day 4: Canvas - Enhanced Writing and Coding Tool

Canvas emerged as an all-in-one environment for coding and content creation, offering superior editing and code-generation capabilities.

Day 5: Deep Integration with Apple Ecosystem

OpenAI announced seamless integration with Apple’s ecosystem, enhancing accessibility and user experience for iOS/macOS users.

Day 6: Improved Voice and Vision Features

Enhanced voice recognition and visual processing capabilities were unveiled, making AI interactions more intuitive and efficient.

Day 7: Projects Feature

The new “Projects” feature allows users to manage AI-powered initiatives collaboratively, streamlining workflows.

Day 8: ChatGPT with Built-in Search

Search functionality within ChatGPT enables real-time access to the latest web information, enriching its knowledge base.

Day 9: Voice Calling with ChatGPT

Voice capabilities now allow users to interact with ChatGPT via phone, providing a conversational edge to AI usage.

Day 10: WhatsApp Integration

ChatGPT’s integration with WhatsApp broadens its accessibility, making AI assistance readily available on one of the most popular messaging platforms.

Day 11: Release of o3 Model

OpenAI launched the o3 model, featuring groundbreaking reasoning capabilities. It excels in areas such as mathematics, coding, and physics, sometimes outperforming human experts.

Commentary: This leap in reasoning could redefine problem-solving across industries, though ethical and operational concerns about dependency on AI remain.

Day 12: Wrap-Up and Future Vision

The final day summarized achievements and hinted at OpenAI’s roadmap, emphasizing the dual goals of refining user experience and expanding market reach.

Reflections

OpenAI’s 12-day spree showcased impressive advancements, from multimodal AI capabilities to practical integrations. However, challenges remain. High subscription costs and potential data privacy concerns could limit adoption, especially among individual users and smaller businesses.

Additionally, as the competition in AI shifts from technical superiority to holistic user experience and ecosystem integration, OpenAI must navigate a crowded field where user satisfaction and practical usability are critical for sustained growth.

Final Thoughts: OpenAI has demonstrated its commitment to innovation, but the journey ahead will require balancing cutting-edge technology with user-centric strategies. The next phase will likely focus on scalability, affordability, and real-world problem-solving to maintain its leadership in AI.

What are your thoughts on OpenAI’s recent developments? Share in the comments!

[skyjohn009]

Distributed Acoustic Sensing Market to Experience Significant Growth

Distributed Acoustic Sensing Market to Experience Significant Growth

Straits Research has published a comprehensive report on the global Distributed Acoustic Sensing Market, projecting a significant growth rate of 11.58% from 2024 to 2032. The market size is expected to reach USD 1,617.72 million by 2032, up from USD 673.32 million in 2024.

Market Definition

Distributed Acoustic Sensing (DAS) is a cutting-edge technology that enables real-time monitoring of acoustic signals along the entire length of a fiber optic cable. This innovative solution has far-reaching applications across various industries, including oil and gas, power and utility, transportation, security and surveillance, and environmental and infrastructure monitoring.

Request Sapmle Link:https://straitsresearch.com/report/distributed-acoustic-sensing-market/request-sample

Latest Trends

The Distributed Acoustic Sensing Market is driven by several key trends, including:

Increasing demand for real-time monitoring: The need for real-time monitoring and data analysis is on the rise, driven by the growing importance of predictive maintenance, asset optimization, and operational efficiency.

Advancements in fiber optic technology: Advances in fiber optic technology have enabled the development of more sensitive and accurate DAS systems, expanding their range of applications.

Growing adoption in the oil and gas industry: The oil and gas industry is increasingly adopting DAS technology for monitoring and optimizing well operations, reducing costs, and improving safety.

Emerging applications in smart cities and infrastructure monitoring: DAS technology is being explored for various smart city applications, including traffic management, public safety, and infrastructure monitoring.

Key Opportunities

The Distributed Acoustic Sensing Market presents several key opportunities for growth and innovation, including:

Integration with other sensing technologies: The integration of DAS with other sensing technologies, such as seismic and electromagnetic sensing, can enhance its capabilities and expand its range of applications.

Development of advanced data analytics and AI algorithms: The development of advanced data analytics and AI algorithms can help unlock the full potential of DAS technology, enabling more accurate and actionable insights.

Expansion into new markets and industries: The Distributed Acoustic Sensing Market has significant potential for growth in new markets and industries, including renewable energy, transportation, and smart cities.

Key Players

The Distributed Acoustic Sensing Market is characterized by the presence of several key players, including:

Halliburton Co.

Hifi Engineering Inc.

Silixa Ltd.

Schlumberger Limited

Banweaver

Omnisens SA

Future Fibre Technologies Ltd.

Baker Hughes Inc.

Qintiq Group PLC

Fotech Solutions Ltd.

Buy Now:https://straitsresearch.com/buy-now/distributed-acoustic-sensing-market

Market Segmentation

The Distributed Acoustic Sensing Market can be segmented into two main categories:

By Fiber Type: The market can be segmented into single-mode fiber and multimode fiber.

By Vertical: The market can be segmented into oil and gas, power and utility, transportation, security and surveillance, and environmental and infrastructure monitoring.

About Straits Research

Straits Research is a leading provider of business intelligence, specializing in research, analytics, and advisory services. Our team of experts provides in-depth insights and comprehensive reports to help businesses make informed decisions.

[techblogmeta]

Why Gemini is Better than ChatGpt?

Gemini's Advantages Over ChatGPT

Both Gemini and ChatGPT are sophisticated AI models made to communicate with people like a human and help with a variety of tasks. But in some situations, Gemini stands out as a more sophisticated and adaptable option because to a number of characteristics it offers:

1. Multimodal Proficiency Gemini provides smooth multimodal interaction, enabling users to communicate with speech, text, and image inputs. Gemini is therefore well-suited for visually complex queries or situations where integrating media enhances comprehension since it can comprehend and produce answers that incorporate many forms of content.

2. Improved comprehension of context Geminis are better at comprehending and remembering context in lengthier interactions. It can manage intricate conversations, providing more precise and tailored answers without losing sight of previous debate points.

3. Original Work From excellent writing to eye-catching graphics and artistic representations, Gemini is a master at producing unique content. It is a favored option for projects demanding innovation due to its exceptional capacity to produce distinctive products.

4. Knowledge and Updates in Real Time In contrast to ChatGPT, which uses a static knowledge base that is updated on a regular basis, Gemini uses more dynamic learning techniques to make sure it stays current with data trends and recent events.

5. Customization and User-Friendly Interface With Gemini's improved customization options and more user-friendly interface, users can adjust replies, tone, and style to suit their own requirements. This flexibility is especially helpful for professionals and companies trying to keep their branding consistent.

6. More Comprehensive Integration Gemini is very flexible for both personal and commercial use because it integrates more easily into third-party tools, workflows, and apps because to its native support for a variety of platforms and APIs.

7. Improved Security and Privacy Users can feel secure knowing that their data is protected during interactions thanks to Gemini's emphasis on user data privacy, which includes greater encryption and adherence to international standards.