Google's DeepMind research division claims its newest AI agent marks a significant step toward using the technology to tackle big problems in math and science. The system, known as AlphaEvolve, is based on the company's Gemini large language models (LLMs), with the addition of an "evolutionary" approach that evaluates and improves algorithms across a range of use cases.

AlphaEvolve is essentially an AI coding agent, but it goes deeper than a standard Gemini chatbot. When you talk to Gemini, there is always a risk of hallucination, where the AI makes up details due to the non-deterministic nature of the underlying technology. AlphaEvolve uses an interesting approach to increase its accuracy when handling complex algorithmic problems.

According to DeepMind , this AI uses an automatic evaluation system. When a researcher interacts with AlphaEvolve, they input a problem along with possible solutions and avenues to explore. The model generates multiple possible solutions, using the efficient Gemini Flash and the more detail-oriented Gemini Pro, and then each solution is analyzed by the evaluator. An evolutionary framework allows AlphaEvolve to focus on the best solution and improve upon it.

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Google's DeepMind research division claims its newest AI agent marks a significant step toward using the technology to tackle big problems in math and science. The system, known as AlphaEvolve, is based on the company's Gemini large language models (LLMs), with the addition of an "evolutionary" approach that evaluates and improves algorithms across a range of use cases.

AlphaEvolve is essentially an AI coding agent, but it goes deeper than a standard Gemini chatbot. When you talk to Gemini, there is always a risk of hallucination, where the AI makes up details due to the non-deterministic nature of the underlying technology. AlphaEvolve uses an interesting approach to increase its accuracy when handling complex algorithmic problems.

According to DeepMind , this AI uses an automatic evaluation system. When a researcher interacts with AlphaEvolve, they input a problem along with possible solutions and avenues to explore. The model generates multiple possible solutions, using the efficient Gemini Flash and the more detail-oriented Gemini Pro, and then each solution is analyzed by the evaluator. An evolutionary framework allows AlphaEvolve to focus on the best solution and improve upon it.

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Google's DeepMind research division claims its newest AI agent marks a significant step toward using the technology to tackle big problems in math and science. The system, known as AlphaEvolve, is based on the company's Gemini large language models (LLMs), with the addition of an "evolutionary" approach that evaluates and improves algorithms across a range of use cases.

AlphaEvolve is essentially an AI coding agent, but it goes deeper than a standard Gemini chatbot. When you talk to Gemini, there is always a risk of hallucination, where the AI makes up details due to the non-deterministic nature of the underlying technology. AlphaEvolve uses an interesting approach to increase its accuracy when handling complex algorithmic problems.

According to DeepMind , this AI uses an automatic evaluation system. When a researcher interacts with AlphaEvolve, they input a problem along with possible solutions and avenues to explore. The model generates multiple possible solutions, using the efficient Gemini Flash and the more detail-oriented Gemini Pro, and then each solution is analyzed by the evaluator. An evolutionary framework allows AlphaEvolve to focus on the best solution and improve upon it.

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Netflix is joining its streaming rivals in testing the amount and types of advertisements its subscribers are willing to endure for lower prices.

Today, at its second annual upfront to advertisers, the streaming leader announced that it has created interactive mid-roll ads and pause ads that incorporate generative AI. Subscribers can expect to start seeing the new types of ads in 2026, Media Play News reported.

“[Netflix] members pay as much attention to midroll ads as they do to the shows and movies themselves,” Amy Reinhard, president of advertising at Netflix, said, per the publication.

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Netflix is joining its streaming rivals in testing the amount and types of advertisements its subscribers are willing to endure for lower prices.

Today, at its second annual upfront to advertisers, the streaming leader announced that it has created interactive mid-roll ads and pause ads that incorporate generative AI. Subscribers can expect to start seeing the new types of ads in 2026, Media Play News reported.

“[Netflix] members pay as much attention to midroll ads as they do to the shows and movies themselves,” Amy Reinhard, president of advertising at Netflix, said, per the publication.

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Netflix is joining its streaming rivals in testing the amount and types of advertisements its subscribers are willing to endure for lower prices.

Today, at its second annual upfront to advertisers, the streaming leader announced that it has created interactive mid-roll ads and pause ads that incorporate generative AI. Subscribers can expect to start seeing the new types of ads in 2026, Media Play News reported.

“[Netflix] members pay as much attention to midroll ads as they do to the shows and movies themselves,” Amy Reinhard, president of advertising at Netflix, said, per the publication.

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The world of computers is dominated by binary. Silicon transistors are either conducting or they're not, and so we've developed a whole world of math and logical operations around those binary capabilities. And, for the most part, quantum computing has been developing along similar lines, using qubits that, when measured, will be found in one of two states.

In some cases, the use of binary values is a feature of the object being used to hold the qubit. For example, a technology called dual-rail qubits takes its value from which of two linked resonators holds a photon. But there are many other quantum objects that have access to far more than two states—think of something like all the possible energy states an electron could occupy when orbiting an atom. We can use things like this as qubits by only relying on the lowest two energy levels. But there's nothing stopping us from using more than two.

In Wednesday's issue of Nature, researchers describe creating qudits, the generic term for systems that hold quantum information—it's short for quantum digits. Using a system that can be in three or four possible states (qutrits and ququarts, respectively), they demonstrate the first error correction of higher-order quantum memory.

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The world of computers is dominated by binary. Silicon transistors are either conducting or they're not, and so we've developed a whole world of math and logical operations around those binary capabilities. And, for the most part, quantum computing has been developing along similar lines, using qubits that, when measured, will be found in one of two states.

In some cases, the use of binary values is a feature of the object being used to hold the qubit. For example, a technology called dual-rail qubits takes its value from which of two linked resonators holds a photon. But there are many other quantum objects that have access to far more than two states—think of something like all the possible energy states an electron could occupy when orbiting an atom. We can use things like this as qubits by only relying on the lowest two energy levels. But there's nothing stopping us from using more than two.

In Wednesday's issue of Nature, researchers describe creating qudits, the generic term for systems that hold quantum information—it's short for quantum digits. Using a system that can be in three or four possible states (qutrits and ququarts, respectively), they demonstrate the first error correction of higher-order quantum memory.

Read full article

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The world of computers is dominated by binary. Silicon transistors are either conducting or they're not, and so we've developed a whole world of math and logical operations around those binary capabilities. And, for the most part, quantum computing has been developing along similar lines, using qubits that, when measured, will be found in one of two states.

In some cases, the use of binary values is a feature of the object being used to hold the qubit. For example, a technology called dual-rail qubits takes its value from which of two linked resonators holds a photon. But there are many other quantum objects that have access to far more than two states—think of something like all the possible energy states an electron could occupy when orbiting an atom. We can use things like this as qubits by only relying on the lowest two energy levels. But there's nothing stopping us from using more than two.

In Wednesday's issue of Nature, researchers describe creating qudits, the generic term for systems that hold quantum information—it's short for quantum digits. Using a system that can be in three or four possible states (qutrits and ququarts, respectively), they demonstrate the first error correction of higher-order quantum memory.

Read full article

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