Gemini app reaches 950 million monthly users, Google says

Google reports the Gemini app has reached 950 million monthly users amid a restructuring of its Google DeepMind teams. Sundar Pichai, CEO of Google and Alphabet, announced Demis Hassabis will transition to the newly created role of Chair of Google DeepMind and Chief Scientist of Alphabet, allowing him to focus on long-term artificial general intelligence research. “We have to accelerate all this work and stay focused on the AI frontier,” said Pichai, describing the company as “standing in the foothills of the singularity” as it prioritizes advancements in the field.

Gemini App Momentum: 950 Million Monthly Users Achieved

The Gemini application has rapidly become a significant player in the consumer AI space, reaching over 950 million monthly users according to recent figures released by Google. This adoption rate accompanies a broader restructuring within Google DeepMind, signaling a strategic shift in the company’s approach to artificial intelligence development and deployment. The surge in Gemini’s popularity demonstrates a significant appetite for accessible AI tools, exceeding initial expectations for user engagement.

Google reports the application is delivering “helpful experiences everywhere including AI Mode and AI Overviews,” indicating a successful integration into daily digital routines for a vast user base. This prioritization of long-term AGI research suggests a willingness to invest in fundamental science even amidst the demands of maintaining and expanding successful consumer products like the Gemini app.

The internal restructuring also sees Koray Kavukcuoglu stepping up as SVP of Google DeepMind, overseeing Gemini model development, frontier AI research, and the Gemini app and developer teams. Kavukcuoglu, a veteran of DeepMind with 13 years of service, previously led the team responsible for breakthroughs like WaveNet and DQN. This appointment underscores Google’s commitment to continued innovation in AI models and applications. Flash is in high demand, and Gemma models have surpassed 900 million downloads, further illustrating the company’s strong position in the AI ecosystem.

Pichai stated, emphasizing the current success and future potential of Google DeepMind’s portfolio. The company asserts it possesses expertise at every layer from infrastructure to cloud to frontier models to AI-first applications, positioning itself to lead the field in the years to come.

Demis Hassabis Transitions to Chair & Chief Scientist Roles

Google’s Gemini application has rapidly achieved a substantial user base, surpassing 950 million monthly users as the company undergoes internal restructuring at Google DeepMind. The move, detailed in communications from CEO Sundar Pichai, signals a heightened prioritization of long-term Artificial General Intelligence research alongside continued product development. In his new capacity, Hassabis will continue to advise Koray Kavukcuoglu, Josh, and the GDM leads, while also focusing on Isomorphic Labs and its mission to improve human health.

Koray Kavukcuoglu, currently Chief Technology Officer and Chief AI Architect, will step up as Senior Vice President of Google DeepMind, reporting directly to Pichai. This appointment underscores Google’s commitment to maintaining its position in AI model development and application.

Koray Kavukcuoglu to Lead Google DeepMind as SVP

Koray Kavukcuoglu will assume the role of Senior Vice President of Google DeepMind, a move signaling Google’s commitment to sustained advancement in artificial intelligence model development. Kavukcuoglu’s long tenure at DeepMind, spanning 13 years, has been instrumental in several key breakthroughs. He initially established the company’s deep learning team and subsequently spearheaded projects like WaveNet and DQN, demonstrating a consistent ability to translate research into tangible results.

The change comes as Google seeks to both accelerate AI work and prioritize long-term research into artificial general intelligence. The company reports Flash is in high demand, its Cyber model is live, and Gemma models have collectively surpassed 900 million downloads, demonstrating broad adoption of its AI tools beyond the flagship Gemini application.

Kavukcuoglu’s appointment underscores a strategic shift, allowing Hassabis to concentrate on the broader implications of AGI and its potential impact on society, while Kavukcuoglu focuses on the continued refinement and expansion of Google’s AI product line. Hassabis concluded, signaling optimism for the future of Google DeepMind under new leadership.

Jeff Dean & Sanjay Ghemawat Launch Independent ML Venture

The departure of Jeff Dean from Google after 27 years marks a significant shift in artificial intelligence research, as he and Sanjay Ghemawat are launching an independent public benefit corporation focused on accelerating discoveries in machine learning, science, and engineering. The new corporation will benefit from Google’s continued investment and utilize its cloud infrastructure, suggesting a collaborative, rather than competitive, relationship despite the founders’ departure.

Dean and Ghemawat’s combined expertise is considerable; the pair helped to drive some of the most significant technology transitions, from early search infrastructure to the neural networks that helped create the modern AI era. Google intends to collaborate with the new venture on a research framework for machine learning systems and related infrastructure advances, suggesting a continued reliance on their insights.

The timing of these changes suggests Google is attempting to balance the demands of rapid product deployment with the pursuit of more fundamental, long-term AI breakthroughs. The launch of Dean and Ghemawat’s independent venture, supported by Google’s investment, appears to be a strategic move to foster innovation outside a large corporate structure, while still maintaining a collaborative relationship and benefiting from their continued expertise.

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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