Latest news with #Gemini2.0
Tom's Guide
4 days ago
- Entertainment
- Tom's Guide
I asked AI to predict 2026 — here's the boldest forecasts from ChatGPT, Gemini, and Claude
We live in an era where AI models can generate art, code software and even predict protein structures. But can they predict cultural trends? As we hurtle toward the mid-2020s, predicting what comes next feels more challenging than ever. Technology evolves at breakneck speed; cultural shifts happen overnight on social media; and entire industries reinvent themselves annually. So I decided to turn to the experts — or at least the artificial ones. I posed the same question to ChatGPT-4o, Gemini 2.0 and Claude 3.7 Sonnet: Predict the biggest trends we'll see in 2026 across technology, culture, fashion, and entertainment. What's going to be in, what's going out, and why? Their responses were fascinating, surprisingly different, and revealed just how uniquely each AI approaches predictions. Here's what they told me. Technology was Gemini's strongest suit. It predicted that 2026 will be the year of "agentic AI" — AI systems that don't just respond to prompts but actually set goals and execute plans autonomously. Gemini also emphasized multimodal AI becoming mainstream, where your AI assistant can simultaneously analyze your screenshot, hear your voice command, and understand the context of your email. On culture, Gemini painted a fascinating picture of contradictions. It predicted a "Dark Mode" mindset taking hold, not just in UI design but in overall aesthetics. Think moodier fashion, darker music, and social media content that pushes back against toxic positivity. Simultaneously, it forecasted a "Cosy Comeback" with people craving comfort and slow living as an antidote to hustle culture. The AI also made a bold prediction about cultural preservation becoming trendy among young people, with brands needing to genuinely respect tradition rather than simply appropriating it for marketing. Fashion predictions were surprisingly specific. Gemini named exact colors for Spring/Summer 2026: Transformative Teal, Electric Fuchsia, Blue Aura, Amber Haze, and Jelly Mint. It predicted that plaid would become a neutral (wear it head-to-toe, apparently) and that brown teddy coats would be everywhere. In technology, ChatGPT made some counterintuitive predictions. While other AIs focused on AI advancement, ChatGPT predicted that "generic chatbots" would be out by 2026. The novelty of "just talking to a chatbot" will wear off unless the experience is highly personalized. It also boldly declared that "crypto-as-a-lifestyle" is over. It also predicted the rise of "AI-native apps", applications built entirely around AI interactions rather than having AI features bolted on. It also forecasted that local AI models would boom as people grow wary of cloud data collection. ChatGPT's cultural predictions felt the most human. It identified "digital decluttering" and "analog luxe" as major trends, predicting people will increasingly crave low-tech moments and artisanal experiences. This aligns with the growing backlash against screen time and digital overwhelm. It also predicted "AI-ethics as status" — where knowing how your AI works becomes the new social flex. Fashion-wise, ChatGPT predicted a "color comeback" after years of washed-out minimalism, calling it "dopamine dressing 2.0." It also forecasted the rise of "post-normcore utilitywear". Perhaps fittingly, ChatGPT was the only AI to coin terms that sounded like they'd already gone viral on TikTok. And its entertainment predictions were bold: it declared that "endless franchise reboots" would be out. Given superhero fatigue and the mixed reception of long-running franchises, this feels prescient. Claude took the most integrated approach, emphasizing "seamless integration" over isolated trends. It predicted AI-powered AR/VR experiences that adapt to individual users, emphasizing that by 2026, these technologies would feel natural rather than a novelty. Claude came with receipts: $200.87 billion AR/VR market by 2030, adding analytical heft to its predictions. In culture, Claude introduced the concept of "The Great Redirection", driven by elections in 64 countries with half the world's population voting in 2024-2025. This political angle was unique among the three AIs. Claude argued that all this voting would make people crave genuine, community-driven experiences over manufactured cultural trends. Claude also forecast "The Great Unretirement" with seniors returning to work, a trend that's already emerging but could accelerate by 2026. Fashion predictions centered on "Bio-Harmony". Claude went beyond typical trend forecasting to predict bio-engineered materials inspired by ecosystems, with garments designed as "second skins" that grow, evolve and biodegrade. By far, this was by far the most futuristic prediction across all three AIs. It's entertainment analysis was market-focused, predicting gaming would surpass $300 billion by 2028 and that advertising-supported streaming would become the primary growth model. It provided specific revenue projections, noting ad revenue would hit $1 trillion in 2026. This exercise revealed something fascinating about how different AI models approach uncertainty. Each seemed to default to its training strengths: Gemini acted like a data analyst, ChatGPT like a cultural critic, and Claude like a researcher trying to connect the dots None of the AIs claimed certainty — they all acknowledged that prediction is inherently speculative. But their different approaches suggests AI prediction works best as a group project, with each model bringing its own analytical superpowers to the table. As we head toward 2026, the truth will likely incorporate elements from all three perspectives. I thought it was really interesting that each AI's predictions revealed as much about its own "personality" as about the future itself.
Arabian Post
23-06-2025
- Business
- Arabian Post
Hyperscalers Form ASIC Coalition to Challenge NVIDIA Dominance
Cloud computing giants AWS, Google, Microsoft, Meta and OpenAI are accelerating in-house development of custom application‑specific integrated circuits, aiming to erode NVIDIA's dominance in high‑performance AI datacentres. Industry reports highlight a projected annual growth rate of around 50% for ASIC purchases by hyperscalers, marking a strategic pivot in the AI hardware landscape. NVIDIA's premium-priced solutions—including Blackwell GPUs—have placed pressure on hyperscalers to secure more cost‑efficient, scalable systems. With single GPUs ranging from $70,000 to $80,000 and fully configured servers tallying up to $3 million, these companies are betting on internal design to manage costs and supply risks. Amazon Web Services has notably moved ahead with its in‑house chips—Trainium for training and Inferentia for inference—reporting 30 – 40% greater cost efficiency compared with NVIDIA hardware. AWS is also collaborating with Marvell and Taiwan's Alchip on next‑generation Trainium versions. Internal indications suggest AWS may deploy as many as half‑a‑million ASIC units in its data centres, an expansive scale‑up that could rival NVIDIA's installed base. ADVERTISEMENT Google, meanwhile, has scaled its TPU v6 Trillium chips, transitioning from single‑supplier to dual‑supplier design by partnering with MediaTek. With deployments reportedly hitting 100,000‑unit clusters to support Gemini 2.0 workloads, Google claims competitive cost-performance metrics relative to NVIDIA GPUs. Microsoft's forthcoming Maia 200 chip, co‑designed with GUC using TSMC's 3 nm process, is scheduled for commercial release in 2026. Meta's Meta Training and Inference Accelerator, developed alongside Broadcom, Socionext and GUC, is expected in early 2026 on TSMC's 3 nm node, featuring HBM3e memory—another step towards self‑sufficiency in AI compute. OpenAI has also announced a proprietary training processor, with mass production anticipated at TSMC by 2026. Market projections reflect this tectonic shift. ASICs are poised to claim between $100 billion and $130 billion of custom AI accelerator spend by 2030, with Broadcom estimating a market of $60 billion to $90 billion by 2027. Traditional ASIC powerhouses—Broadcom, Marvell, MediaTek, Alchip and GUC—are experiencing surging demand as they support hyperscaler transitions. Despite these developments, hyperscalers continue to reserve capacity for NVIDIA chips, recognising the GPU giant's entrenched ecosystem—especially its CUDA software stack—and the steep technical barriers to immediate elimination of GPU dependencies. The trend resembles historical transitions in specialised compute. Just as cryptocurrency mining moved from GPUs to ASICs for lower costs and greater efficiency, hyperscalers now aim to fragment the AI compute supply chain and diversify their hardware portfolios. ADVERTISEMENT TSMC stands to benefit significantly, serving as the foundry for both NVIDIA's mass‑market GPUs and hyperscaler ASICs. Its chairman emphasises that the competition between NVIDIA and cloud‑designed chips is ultimately beneficial to TSMC, ensuring a broad customer base. Broadcom has emerged as a frontrunner, with its ASIC and networking chipset revenues soaring 220% to $12.2 billion in 2024. Hyperscalers are investing in clusters featuring up to one million custom XPUs over open‑Ethernet networks—an architecture that places Broadcom and Marvell in strategic positions. Networking ASICs are expected to account for 15–20% of AI data‑centre silicon budgets, rising from the 5–10% range. Revenue trends reflect these structural shifts. Marvell has secured a multi‑year AI chip deal with AWS and anticipates its AI silicon revenue jumping from $550 million in 2024 to over $2.5 billion in 2026. Broadcom, similarly, is redirecting significant investment toward hyperscaler ASIC demand. Nevertheless, NVIDIA retains a commanding lead in AI training and general‑purpose GPU compute. Its end‑to‑end platform—from hardware to software—remains deeply embedded in the AI ecosystem. Custom ASICs, by contrast, offer task‑specific gains but lack the breadth of software compatibility that NVIDIA enables. Analysts caution that the AI compute landscape is evolving toward a more fragmented, mixed‑architecture model combining GPUs and ASICs. Hyperscalers' shift signals strategic recognition of rising costs, supply constraints, and performance demands. Yet, they also underscore persistent obstacles: software ecosystem maturity, long development cycles, and the complexity of large‑scale deployment. Questions remain regarding the timeframe in which hyperscalers can meaningfully shift workloads away from NVIDIA GPUs. Industry roadmaps project new ASIC deployments through 2026–27. Analysts expect GPU market share erosion may begin toward the end of the decade, provided in-house ASICs deliver consistent performance and efficiency. The stage is set for a multi‑year contest in datacentre compute. NVIDIA faces increasing pressure from hyperscalers building bespoke chips to optimise workloads and control supply. The next evolution of AI infrastructure may look less like a GPU‑centric world and more like a diverse ecosystem of specialised, interlocking processors.
Geeky Gadgets
29-05-2025
- Business
- Geeky Gadgets
Gemini TTS Native Audio Out : The Future of Human-Like Audio Content
What if your audiobook could whisper secrets, your podcast could laugh with its audience, or your virtual assistant could interrupt with perfect timing—just like a real conversation? With the advent of Gemini 2.5 Text-to-Speech (TTS), these possibilities are no longer confined to imagination. This new model by Google introduces native audio output that doesn't just replicate speech but redefines it, offering a level of expressiveness and realism that feels almost human. Whether you're a creator seeking to immerse your audience or a developer building lifelike interactions, Gemini 2.5 promises to transform how we think about audio content. Sam Witteveen explore the features that set Gemini 2.5 apart, from its customizable speech styles to its ability to simulate natural, multi-speaker conversations. You'll discover how this technology is reshaping industries like audiobook narration, AI-driven podcasts, and interactive dialogues, offering unprecedented levels of personalization and creative freedom. But it's not all smooth sailing—challenges like balancing expressiveness with naturalness and navigating multi-speaker setups remain. As we unpack its potential and limitations, consider how this innovation might inspire new ways to connect, create, and communicate through sound. Gemini 2.5 TTS Overview Key Features That Differentiate Gemini 2.5 Building on the foundation of its predecessor, Gemini 2.0, the 2.5 model incorporates several advanced features that elevate its speech generation capabilities. These features include: Customizable Speech Styles: Users can adjust tone, emotion, and delivery to suit specific contexts, such as whispering, laughter, or a more formal tone. Users can adjust tone, emotion, and delivery to suit specific contexts, such as whispering, laughter, or a more formal tone. Natural Interaction Simulation: The model supports realistic conversational elements, including interruptions and overlapping dialogue, making it ideal for storytelling or AI-driven podcasts. The model supports realistic conversational elements, including interruptions and overlapping dialogue, making it ideal for storytelling or AI-driven podcasts. Multi-Speaker Audio Generation: It enables the creation of dynamic, multi-voice content, with distinct personalities assigned to each speaker. These enhancements make Gemini 2.5 a powerful tool for applications that demand nuanced and expressive audio delivery. Its ability to simulate natural interactions and provide customizable speech styles sets it apart from other TTS models. Applications Across Industries Gemini 2.5 TTS is designed to cater to a broad spectrum of industries and use cases, offering practical solutions for creating high-quality audio content. Some of its most impactful applications include: Audiobook Narration: The model's expressive tones and emotional depth bring stories to life, enhancing listener engagement and immersion. The model's expressive tones and emotional depth bring stories to life, enhancing listener engagement and immersion. AI-Generated Podcasts: With its ability to produce multi-speaker content featuring natural conversational flow, Gemini 2.5 is well-suited for creating engaging podcasts. With its ability to produce multi-speaker content featuring natural conversational flow, Gemini 2.5 is well-suited for creating engaging podcasts. Interactive Dialogues: It supports the development of realistic dialogues for virtual assistants, training simulations, and creative projects. These use cases demonstrate the model's versatility and its potential to transform how audio content is produced, offering new levels of personalization and realism. Gemini TTS Advanced Text-to-Speech Model Watch this video on YouTube. Take a look at other insightful guides from our broad collection that might capture your interest in AI voice. Technical Capabilities and Accessibility Gemini 2.5 TTS is accessible through Google AI Studio, providing an intuitive platform for users to explore its features. Developers can also use the Gemini API for seamless integration, allowing programmatic customization of prompts, speech styles, and voice configurations. Key technical highlights include: Multi-Language Support: The model can generate speech in multiple languages, making it suitable for global applications and diverse audiences. The model can generate speech in multiple languages, making it suitable for global applications and diverse audiences. Voice Customization: Users can select from a variety of voice options to align with specific project requirements. Users can select from a variety of voice options to align with specific project requirements. Cloud-Based Infrastructure: Advanced processing capabilities are available through the cloud, making sure dynamic and efficient speech synthesis. While the model excels in expressiveness and versatility, some users may find multi-speaker setups challenging to configure effectively. Additionally, the expressive nature of the output may occasionally feel exaggerated, depending on the context. Comparison with Open source Alternatives Gemini 2.5 TTS competes with open source models like Kakoro, which offer advantages such as real-time processing and greater control over data through local deployment. These features make open source models appealing for privacy-conscious users or latency-sensitive applications. However, Gemini 2.5's cloud-based infrastructure enables more sophisticated features, such as dynamic speech synthesis and natural interaction simulation. The trade-offs include potential latency and reliance on cloud services, which may not suit all use cases. Nevertheless, for applications that prioritize advanced expressiveness and realism, Gemini 2.5 stands out as a compelling option. Opportunities and Challenges The preview of Gemini 2.5 TTS highlights its potential to redefine audio content creation. Its ability to generate expressive, multi-speaker audio opens up opportunities for innovative applications, including immersive storytelling, professional training tools, and AI-driven media production. However, certain challenges remain: Balancing Naturalness and Expressiveness: Some speech outputs may feel overly dramatic, requiring further refinement to achieve a more natural tone. Some speech outputs may feel overly dramatic, requiring further refinement to achieve a more natural tone. Complexity in Multi-Speaker Configurations: Setting up distinct voices for multi-speaker scenarios can be intricate and time-consuming. Setting up distinct voices for multi-speaker scenarios can be intricate and time-consuming. Unclear Pricing Structure: Limited information on costs and token usage may deter potential users from fully adopting the model. Despite these challenges, Gemini 2.5's innovative capabilities position it as a fantastic tool in the text-to-speech landscape. As the technology evolves, it promises to unlock new possibilities for creating engaging, personalized audio content. Media Credit: Sam Witteveen Filed Under: AI, Top News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
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Business Standard
21-05-2025
- Business
- Business Standard
Google introduces 'AI Mode' for agentic web search experience: How it works
At the keynote session of Google I/O 2025, Google shared details on the evolution of its AI Mode, designed to deliver an agentic web search experience. Initially introduced in March, AI Mode has since been enhanced to offer users faster, more intuitive, and more comprehensive search results. Here are the new updates coming to the AI Mode: AI Mode: Automating complex search tasks Google said that the AI Mode represents a significant shift in how web searches are conducted. Instead of requiring users to sift through multiple pages of results, the tool autonomously handles the more tedious aspects of online research. For complex topics – where standard search methods may prove insufficient – AI Mode acts as a digital agent. It uses a technique called query fan-out, which dissects the user's primary query into smaller, more specific sub-queries. It then conducts multiple searches in parallel, collecting information from a wide range of sources. The system compiles this data into a single, in-depth response, complete with source links for verification. According to Google, this entire process is completed within seconds, significantly reducing the time and effort typically required to gather relevant and trustworthy information. AI Mode: Agentic web search rollout The rollout will initially kick off in the US and support tasks such as buying event tickets, making restaurant reservations, and booking local appointments. Google is partnering with platforms including Ticketmaster, StubHub, Resy, and Vagaro to deliver a seamless, integrated user experience. What Is Project Mariner Project Mariner is an experimental initiative built on Google's Gemini 2.0 AI model, aiming to redefine human-computer interaction within web browsers. According to Google, Project Mariner is designed to understand and interpret on-screen content, including text, images, code, and form elements. It enables the AI to interact meaningfully with websites by processing both visual and structural components, allowing it to complete complex tasks such as filling out forms, navigating UI elements, and compiling personalised search results.
Engadget
20-05-2025
- Business
- Engadget
AI Mode is now rolling out to everyone in the US
Google has begun rolling out AI Mode to every Search user in the US. The company announced the expansion during its I/O 2025 conference. Google first began previewing AI Mode with testers in its Labs program at the start of March. Since then, it has been gradually rolling out the feature to more people, including in recent weeks regular Search users. For the uninitiated, AI Mode is a chatbot built directly into Google Search. It lives in a separate tab, and was designed by the company to tackle more complicated queries than people have historically used its search engine to answer. For instance, you can use AI Mode to generate a comparison between different fitness trackers. Before today, the chatbot was powered by Gemini 2.0. Now it's running a custom version of Gemini 2.5. What's more, Google plans to bring many of AI Mode's capabilities to other parts of the Search experience. "AI Mode is where we'll first bring Gemini's frontier capabilities, and it's also a glimpse of what's to come," the company wrote in a blog post published during the event. "As we get feedback, we'll graduate many features and capabilities from AI Mode right into the core search experience in AI Overviews." Looking to the future, Google plans to bring Deep Research to AI Mode. Google was among the first companies to debut the tool back in December . Since then, most AI companies, including OpenAI , have gone on to offer their take on Deep Research, which you can use to prompt Gemini and other chatbots to take extra time to create a comprehensive report on a subject. With today's announcement, Google is making the tool available in a place where more of its users are likely to encounter it. Developing...
