Dn) 1 as n, which means the most posterior mass falls in the neighbourhood of true parameter. Remarkonthenotation: ν() is similar toν() defined in Section 2.1 of the main text. Thenotationsweusedinthis proof are the same as in the proof of Theorem 2.1. Theorem 2.2 implies that a faithful prediction interval can be constructed for the sparse neural network learned by the proposed algorithms. In practice, for a normal regression problem with noise N(0,σ2), to construct the prediction interval for a test pointx0, the terms σ2 and Σ = γ µ(β,x0)TH 1 γ µ(β,x0) in Theorem 2.2 need to be estimated from data.

The widespread adoption of outsourced neural network inference presents significant privacy challenges, as sensitive user data is processed on untrusted remote servers. Secure inference offers a privacy-preserving solution, but existing frameworks suffer from high computational overhead and communication costs, rendering them impractical for real-world deployment. We introduce SecONNds, a non-intrusive secure inference framework optimized for large ImageNet-scale Convolutional Neural Networks. SecONNds integrates a novel fully Boolean Goldreich-Micali-Wigderson (GMW) protocol for secure comparison -- addressing Yao's millionaires' problem -- using preprocessed Beaver's bit triples generated from Silent Random Oblivious Transfer. Our novel protocol achieves an online speedup of 17$\times$ in nonlinear operations compared to state-of-the-art solutions while reducing communication overhead. To further enhance performance, SecONNds employs Number Theoretic Transform (NTT) preprocessing and leverages GPU acceleration for homomorphic encryption operations, resulting in speedups of 1.6$\times$ on CPU and 2.2$\times$ on GPU for linear operations. We also present SecONNds-P, a bit-exact variant that ensures verifiable full-precision results in secure computation, matching the results of plaintext computations. Evaluated on a 37-bit quantized SqueezeNet model, SecONNds achieves an end-to-end inference time of 2.8 s on GPU and 3.6 s on CPU, with a total communication of just 420 MiB. SecONNds' efficiency and reduced computational load make it well-suited for deploying privacy-sensitive applications in resource-constrained environments. SecONNds is open source and can be accessed from: https://github.com/shashankballa/SecONNds.

The government said Tuesday it will provide around 45 billion ( 305 million) for a cutting-edge semiconductor project promoted by Japanese telecom giant NTT, major U.S. chipmaker Intel and its South Korean counterpart SK Hynix. The funding for the project to develop optical semiconductors that allow high-speed data processing with lower power consumption comes as Japan, in cooperation with the United States and South Korea, aims to gain a stronger footing in the semiconductor industry, amid China's growing influence in the sector. Semiconductor research and development has been boosted by the expansion of artificial intelligence and other digital technologies that require the processing of huge amounts of data. "We hope the (project), by enabling faster communications and realizing reduced power consumption, will be a game changer in the future," Ken Saito, minister of economy, trade and industry, told a news conference. NTT is pushing the development of optical semiconductors as a key technology for its Innovative Optical and Wireless Network (IOWN), an advanced platform featuring high-capacity communication with reduced time lag.

Telecom giant Nippon Telegraph and Telephone will launch a business-use generative artificial intelligence platform in March, in an effort to catch up with foreign rivals in the fast-expanding market. The AI platform has higher Japanese language processing capabilities than ChatGPT, a widely used AI chatbot developed by U.S.-based OpenAI, NTT said earlier in the month. The new AI model, called tsuzumi, named after a Japanese hand drum used in traditional events, can read documents containing charts and diagrams. NTT said it aims for annual sales of over ¥100 billion ($670 million) in this AI platform business by 2027. "The market size will grow bigger and bigger as many companies compete with each other," NTT President Akira Shimada said during a news conference in early November.

Nippon Telegraph and Telephone (NTT) plans to test driverless vehicle technology with Toyota and invest in a U.S. startup developing self-driving systems, a spokesperson for the telecommunications firm said on Monday. NTT aims to start tests with autonomous buses and taxis as early as 2025 and invest about ¥10 billion ($66.91 million) in U.S. startup May Mobility, the spokesperson said, highlighting growing momentum behind self-driving technology in Japan. The Nikkei newspaper first reported on Monday that NTT will invest in May Mobility, adding that both NTT and Toyota would jointly develop vehicles. Both the NTT spokesperson and a Toyota spokesperson said they had no plans for joint development. Toyota did not comment further.

Generalizing machine learning (ML) models for network traffic dynamics tends to be considered a lost cause. Hence for every new task, we design new models and train them on model-specific datasets closely mimicking the deployment environments. Yet, an ML architecture called_Transformer_ has enabled previously unimaginable generalization in other domains. Nowadays, one can download a model pre-trained on massive datasets and only fine-tune it for a specific task and context with comparatively little time and data. These fine-tuned models are now state-of-the-art for many benchmarks. We believe this progress could translate to networking and propose a Network Traffic Transformer (NTT), a transformer adapted to learn network dynamics from packet traces. Our initial results are promising: NTT seems able to generalize to new prediction tasks and environments. This study suggests there is still hope for generalization, though it calls for a lot of future research.

Amazon enhanced Polly - the cloud-based text-to-speech service - to deliver natural and realistic speech synthesis. The service can now be leveraged to present domain-specific style such as newscast and sportscast. Though text-to-speech existed for more than two decades, it is never used in mainstream media due to the lack of natural and realistic modulation. Except for automated announcements that read out from existing datastores, the technology never replaced human voice and speech. Thanks to the advancements in AI, text-to-speech has evolved to become more natural and realistic to an extent that it may be hard to distinguish it from a human voice.

Nippon Telegraph and Telephone Corp. (NTT) said Thursday it will launch a trial for a farming support service using drones and artificial intelligence technology, with a goal of commercializing the service in Japan and other Asian countries. The new system, which connects drones with GPS satellites, is anticipated to help the farm industry in the nation amid a serious labor shortage. NTT aims to raise crop output by up to 30 percent through the new service. The telecommunications giant will conduct the trial service on 8 hectares of a rice field in Fukushima Prefecture from later this month to March 2021. It aims to launch the service on a commercial basis in Japan in two years.

Amazon's Alexa continues to learn new party tricks, with the latest being a "newscaster style" speaking voice that will be launching on enabled devices in a few weeks' time. You can listen to samples of the speaking style below, and the results, well, they speak for themselves. The voice can't be mistaken for a human, but it does incorporate stresses into sentences in the same way you'd expect from a TV or radio newscaster. According to Amazon's own surveys, users prefer it to Alexa's regular speaking style when listening to articles (though getting news from smart speakers still has lots of other problems). Amazon says the new speaking style is enabled by the company's development of "neural text-to-speech" technology or NTTS.

NTT Corp. and Toyota Motor Corp. announced Monday the launch of a joint research project to promote the development and use of "partner robots" tasked with helping people in their everyday lives. NTT will provide its Corevo artificial intelligence technology that enhances nonverbal interactions between humans and robots as well as improving voice recognition and dialogue control techniques. They will be incorporated in an existing Human Support Robot developed by Toyota. That robot is capable of basic functions crucial to daily life, including picking up objects from the floor and communicating with families and caregivers. Partner robots that operate in the home helping with tasks such as housework and nursing have been garnering attention amid the increasing labor shortage in quickly graying Japan, Toyota said.