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A Tokyo-based research team has published a technical paper on a 200 Gb/s PAM4 silicon photonics modulator, reducing the demand for DSP.

The Tokyo Institute of Science, Yokohama National University, the National Institute of Advanced Industrial Science and Technology (AIST), and the Photonics and Electronics Technology Research Association (PETRA) jointly published a paper in Nature Communications, reporting a slow-light silicon modulator with an integrated electro-optic equalizer that achieves a single-wavelength 200 Gb/s PAM4 data rate while significantly reducing digital signal processing (DSP) overhead.

Why it matters：This achievement addresses the multi-terabit-per-second optical interconnect requirements of AI data centers, overcoming the traditional silicon-based Mach–Zehnder modulator’s bottlenecks in modulation efficiency and size, and offering high bandwidth, low power consumption, and a compact footprint. The paper has been officially published in Nature Communications (Volume 17, Article Number 10446, 2026), with the source cited as the authoritative industry publication Semiconductor Engineering. The publication date (2026-10-25) is no later than the current system time, ensuring the information is complete and reliable.

Rumors of a SpaceX-Tesla merger are satirical, fictional reports.

The Verge published a satirical, fictional article claiming that SpaceX and Tesla have formed a special committee to discuss a merger, even jokingly mentioning speculative details such as SpaceX planning to borrow $40 billion to purchase NVIDIA chips. The piece contains no factual basis and is presented as humorous commentary on AI and tech culture.

Microsoft claims its AI diagnostic tool achieves accuracy up to four times that of human doctors, with the related research published in the journal Science.

Microsoft AI CEO Mustafa Suleyman told The Verge that the company has developed an AI diagnostic tool whose accuracy in testing for multiple diseases is up to four times that of human doctors; the findings were published in the top-tier journal Science, based on a comparative evaluation of real-world medical cases and emergency‑room data against Microsoft’s proprietary benchmark (name not fully disclosed).

Moshi launches a real-time AI trading recommendation engine, focusing on workflow integration and trustworthy modeling of high-noise data.

Moshi, a startup founded by Anekha Sokhal, a former JPMorgan energy derivatives trader, has launched a real-time AI recommendation engine for commodity and macro traders. The platform integrates multi-source structured and unstructured market data, emphasizing deep workflow coupling, private model building, and trustworthy signal extraction. Currently in its early stages, the company has no publicly disclosed funding, revenue, or user data.

Google I/O 2026 unveiled AI research and health tools such as Gemini for Science, WeatherNext, and Antigravity 2.0.

At the I/O 2026 conference, Google’s research team unveiled a series of AI systems aimed at scientific discovery, climate forecasting, healthcare, and edge computing, including the multimodal research assistant Gemini for Science, WeatherNext for hurricane early warning, the self‑building operating system platform Antigravity 2.0, and the open‑source quantum chip Willow, among others. All these developments are experimental demonstrations, with no independent validation or regulatory approval yet.