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Nasher Miles awards digital mandate to itch – Brand Wagon News

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Nasher Miles, an Indian luggage and travel accessories brand, has handed over its digital mandate to itch, a creative agency, following a competitive multi-agency pitch. From what is understood, the partnership aims to strengthen Nasher Miles’ digital presence and engage modern travellers more effectively. itch will collaborate with the brand to design and implement a digital strategy that integrates creativity and authenticity, with a focus on building connections within the online travel community, the company stated. 

“In itch, we’ve found a partner who genuinely understands our aspirations for digital growth and innovation. Their fresh perspective and creative strengths align perfectly with our goals, and we’re excited to create memorable, impactful digital journeys that connect deeply with our customers,” Shruti Kedia, co-founder and marketing head, Nasher Miles, said. 

It is believed that the collaboration seeks to enhance Nasher Miles’ influence on digital platforms by fostering deeper engagement with audiences passionate about travel and adventure. By combining their expertise, the two organizations plan to create a seamless online experience. 

“We are thrilled to partner with Nasher Miles, a brand that shares our passion for bold, creative storytelling. Together, we’re excited to push boundaries and make travel feel even more personal, accessible, and engaging for the Indian audience.” Naman Agarwal, Co-Founder, itch, commented. 

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AI’s next leap demands a computing revolution

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July 20, 2025

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We stand at a technological crossroads remarkably similar to the early 2000s, when the internet’s explosive growth outpaced existing infrastructure capabilities. Just as dial-up connections couldn’t support the emerging digital economy, today’s classical computing systems are hitting fundamental limits that will constrain AI’s continued evolution. The solution lies in quantum computing – and the next five to six years will determine whether we successfully navigate this crucial transition.

The computational ceiling blocking AI advancement

Current AI systems face insurmountable mathematical barriers that mirror the bandwidth bottlenecks of early internet infrastructure. Training large language models like GPT-3 consumes 1,300 megawatt-hours of electricity, while classical optimization problems require exponentially increasing computational resources. Google’s recent demonstration starkly illustrates this divide: their Willow quantum processor completed calculations in five minutes that would take classical supercomputers 10 septillion years – while consuming 30,000 times less energy.

The parallels to early 2000s telecommunications are striking. Then, streaming video, cloud computing, and e-commerce demanded faster data speeds that existing infrastructure couldn’t provide. Today, AI applications like real-time molecular simulation, financial risk optimization, and large-scale pattern recognition are pushing against the physical limits of classical computing architectures. Just as the internet required fiber optic cables and broadband infrastructure, AI’s next phase demands quantum computational capabilities.

Breakthrough momentum accelerating toward mainstream adoption

The quantum computing landscape has undergone transformative changes in 2024-2025 that signal mainstream viability. Google’s Willow chip achieved below-threshold error correction – a critical milestone where quantum systems become more accurate as they scale up. IBM’s roadmap targets 200 logical qubits by 2029, while Microsoft’s topological qubit breakthrough promises inherent error resistance. These aren’t incremental improvements; they represent fundamental advances that make practical quantum-AI systems feasible.

Industry investments reflect this transition from research to commercial reality. Quantum startups raised $2 billion in 2024, representing a 138 per cent increase from the previous year. Major corporations are backing this confidence with substantial commitments: IBM’s $30 billion quantum R&D investment, Microsoft’s quantum-ready initiative for 2025, and Google’s $5 million quantum applications prize. The market consensus projects quantum computing revenue will exceed $1 billion in 2025 and reach $28-72 billion by 2035.

Expert consensus on the five-year transformation window

Leading quantum computing experts across multiple organizations align on a remarkably consistent timeline. IBM’s CEO predicts quantum advantage demonstrations by 2026, while Google targets useful quantum computers by 2029. Quantinuum’s roadmap promises universal fault-tolerant quantum computing by 2030. IonQ projects commercial quantum advantages in machine learning by 2027. This convergence suggests the 2025-2030 period will be as pivotal for quantum computing as 1995-2000 was for internet adoption.

The technical indicators support these projections. Current quantum systems achieve 99.9 per cent gate fidelity – crossing the threshold for practical applications. Multiple companies have demonstrated quantum advantages in specific domains: JPMorgan and Amazon reduced portfolio optimization problems by 80 per cent, while quantum-enhanced traffic optimization decreased Beijing congestion by 20 per cent. These proof-of-concept successes mirror the early internet’s transformative applications before widespread adoption.

Real-world quantum-AI applications emerging across industries

The most compelling evidence comes from actual deployments showing measurable improvements. Cleveland Clinic and IBM launched a dedicated healthcare quantum computer for protein interaction modeling in cancer research. Pfizer partnered with IBM for quantum molecular modeling in drug discovery. DHL optimized international shipping routes using quantum algorithms, reducing delivery times by 20 per cent.

These applications demonstrate quantum computing’s unique ability to solve problems that scale exponentially with classical approaches. Quantum systems process multiple possibilities simultaneously through superposition, enabling breakthrough capabilities in optimization, simulation, and machine learning that classical computers cannot replicate efficiently. The energy efficiency advantages are equally dramatic – quantum systems achieve 3-4 orders of magnitude better energy consumption for specific computational tasks.

The security imperative driving quantum adoption

Beyond performance advantages, quantum computing addresses critical security challenges that will force rapid adoption. Current encryption methods protecting AI systems will become vulnerable to quantum attacks within this decade. The US government has mandated federal agencies transition to quantum-safe cryptography, while NIST released new post-quantum encryption standards in 2024. Organizations face a “harvest now, decrypt later” threat where adversaries collect encrypted data today for future quantum decryption.

This security imperative creates unavoidable pressure for quantum adoption. Satellite-based quantum communication networks are already operational, with China’s quantum network spanning 12,000 kilometers and similar projects launching globally. The intersection of quantum security and AI protection will drive widespread infrastructure upgrades in the coming years.

Preparing for the quantum era transformation

The evidence overwhelmingly suggests we’re approaching a technological inflection point where quantum computing transitions from experimental curiosity to essential infrastructure. Just as businesses that failed to adapt to internet connectivity fell behind in the early 2000s, organizations that ignore quantum computing risk losing competitive advantage in the AI-driven economy.

The quantum revolution isn’t coming- it’s here. The next five to six years will determine which organizations successfully navigate this transition and which turn into casualties of technological change. AI systems must re-engineer themselves to leverage quantum capabilities, requiring new algorithms, architectures, and approaches that blend quantum and classical computing.

This represents more than incremental improvement; it’s a fundamental paradigm shift that will reshape how we approach computation, security, and artificial intelligence. The question isn’t whether quantum computing will transform AI – it’s whether we’ll be ready for the transformation.

(Krishna Kumar is a technology explorer & strategist based in Austin, Texas in the US. Rakshitha Reddy is AI developer based in Atlanta, US)



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OpenAI’s artificial intelligence (AI) model has achieved a monumental achievement in catching up wit..

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July 20, 2025

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Using a closed inference model under experimentation in my department, I scored 35 out of 42 points in IMO 2025 and have struggled with AI for a long time given the same time as humans…10 to 30% accuracy for other models

The closed inference giant language model (LLM), which OpenAI is experimenting internally, has achieved the equivalent of a gold medal at the International Mathematical Olympiad (IMO). [Source = Alexander OpenAI Research Scientist X]

OpenAI’s artificial intelligence (AI) model has achieved a monumental achievement in catching up with human intelligence at the International Mathematical Olympiad.

In other words, OpenAI has shown its robustness with overwhelming AI performance at a time when rumors of a crisis are emerging due to the failure to acquire start-up Windsurf and the subsequent outflow of talent.

“We achieved gold medal-level performance at the International Mathematical Olympiad (IMO 2025) this year with a universal inference model,” OpenAI CEO Sam Altman said on his X(X) on the 19th (local time).

“When I first started OpenAI, it was a dream story. This is an important indicator of how advanced AI has developed over the past decade, he said, explaining the significance of this achievement.

The results are based on the Large Language Model for Inference (LLM), which is being experimented internally by a small team led by Alexander Way, a research scientist at OpenAI.

IMO is a prestigious Olympiad that has been underway since 1959, and students under the age of 20 representing each country participate. It is characterized by requiring mathematical thinking and creative ideas, not just problems that can be solved by memorizing formulas.

According to OpenAI, the test was conducted under the same conditions as human candidates. IMO, which consists of a total of 6 questions, is a method of solving 3 questions for 4 hours and 30 minutes a day over 2 days.

OpenAI’s model scored 35 points out of 42 while solving 5 out of 6 questions, setting a record equivalent to the gold medal spot.

In this year’s IMO, humans still performed better than AI with six perfect scores, but it is evaluated as a symbolic event that shows how close LLM’s rapidly developing performance is to human intelligence.

LLMs so far have hardly reached the silver and bronze medals in IMO, let alone the gold medal.

Google DeepMind’s “AlphaProof” and “AlphaGeometry 2” scored silver medals last year, but these models were specialized only in the math field.

Noam Brown, an open AI research scientist, said, “The achievements that AI has previously shown in Go and poker are the result of researchers training AI to master only that specific area for years. However, this model is not an IMO-specific model, but an inference LLM that combines a new method of experimental general-purpose technology.”

According to MathArena of the Federal Institute of Technology (ETH Zurich), Switzerland, which tracks the mathematical performance of major models, all of the world’s leading models such as Google’s Gemini 2.5 Pro, xAI’s Grok 4 and DeepSeek’s R1 did not even make it to the bronze medal list at this year’s IMO 2025.

The specific secret of OpenAI’s breakthrough achievement has not been disclosed.

“We have developed a new technology that enables LLM to perform much better tasks that are difficult to verify,” Brown said. “O1 (existing inference model) thinks for seconds, and ‘deep research’ functions take minutes, but this model thinks for hours.”

Meanwhile, this result is not a third-party verification state as it is conducted as a closed-door experimental model that has not been officially released by OpenAI. Mass Arena said in response, “We are excited to see steep progress in this area and look forward to the launch of the model, enabling independent evaluation through public benchmarks.”



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Application of machine learning algorithms and SHAP explanations to predict fertility preference among reproductive women in Somalia

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