Taiwan's Yageo said it would keep Shibaura Electronics's most advanced technology in Japan if it successfully acquires the artificial intelligence sensor maker. The comments from Yageo founder and Chairman Pierre Chen come as Tokyo seeks to strike a balance between shareholder returns while ensuring cutting-edge AI technology stays at home. Shibaura's high-precision thermistors are key for monitoring the internal temperature of electronic devices to prevent overheating. That's especially important in AI, where data centers with large clusters of high-performance servers churn through troves of data. "It is not in Yageo's interest to see Shibaura's technology transfer to countries that Japan considers to be unfriendly," Chen told reporters in Taipei on Saturday.

In using "LLMs", we are referring to language models which evince in-context learning capabilities (also referred to as "foundation models" [13]). This behavior has traditionally been observed in models with at least a billion parameters.

The advent of large language models (LLMs) and their adoption by the legal community has given rise to the question: what types of legal reasoning can LLMs perform?

Fact-checking is extensively studied in the context of misinformation and disinformation, addressing objective inaccuracies. However, a softer form of misinformation involves responses that are factually correct but lack certain features such as clarity and relevance. This challenge is prevalent in formal Question-Answer (QA) settings such as press conferences in finance, politics, sports, and other domains, where subjective answers can obscure transparency. Despite this, there is a lack of manually annotated datasets for subjective features across multiple dimensions. To address this gap, we introduce SubjECTive-QA, a human annotated dataset on Earnings Call Transcripts' (ECTs) QA sessions as the answers given by company representatives are often open to subjective interpretations and scrutiny. The dataset includes 49, 446 annotations for long-form QA pairs across six features: Assertive, Cautious, Optimistic, Specific, Clear, and Relevant. These features are carefully selected to encompass the key attributes that reflect the tone of the answers provided during QA sessions across different domains. Our findings are that the best-performing Pre-trained Language Model (PLM), RoBERTa-base, has similar weighted F1 scores to Llama-3-70b-Chat on features with lower subjectivity, such as Relevant and Clear, with a mean difference of 2.17% in their weighted F1 scores. The models perform significantly better on features with higher subjectivity, such as Specific and Assertive, with a mean difference of 10.01% in their weighted F1 scores.

Nvidia beat Wall Street expectations in its quarterly earnings report on Wednesday, marking another in a string of financial wins for the computer hardware giant. It reported 44.1bn in revenue in the quarter ending in April, up 69% from the previous year. The company exceeded investors' predictions of 43.3bn in revenue. Adjusted earnings per share came in at 0.81, under investor expectations of an adjusted earnings per share of 88 cents. The company also reported 39.1bn in data center revenue, up 73% from the year prior.

SoftBank reported a 124% jump in quarterly profit on resilient AI demand that's supporting startup valuations and chip unit sales, a boost to its aggressive data center investment plans. The Tokyo-based company reported net income of 517.18 billion ( 3.5 billion) in its fiscal fourth quarter. It was helped by the Vision Fund, which swung to a profit of 26.1 billion mainly on a surge in the value of TikTok owner ByteDance and its strong international sales. The earnings come at a critical juncture for SoftBank as it plans to invest 30 billion in OpenAI while leading a 100 billion foray into building AI hardware in the United States. Maintaining a healthy cash flow and balance sheet is key to securing the billions of dollars needed at minimum cost.

SoftBank Group reported a 124% jump in quarterly profit on resilient AI demand that's supporting startup valuations and chip unit sales, a boost to its aggressive data center investment plans. The Tokyo-based company reported net income of 517.18 billion ( 3.5 billion) in its fiscal fourth quarter. It was helped by the Vision Fund, which swung to a profit of 26.1 billion. The earnings come at a critical juncture for SoftBank as it plans to invest 30 billion in OpenAI while leading a 100 billion foray into building AI hardware in the U.S. Maintaining a healthy cash flow and balance sheet is key to securing the billions of dollars needed at minimum cost.

Meta is set to report its first quarter earnings on Wednesday after the bell, and investors will be looking for news on whether the company met its quarterly revenue goals of somewhere between 39.5bn and 41.8bn. Wall Street is projecting the company will post 41.36bn in revenue on 5.21 in earnings per share. While Meta has repeatedly beaten Wall Street expectations in the past few quarters, analysts were disappointed by the first quarter revenue outlook Meta chief executive Mark Zuckerberg shared at the end of 2024. The company is also planning on spending up to 65bn on AI infrastructure by the end of 2025. Uncertainty over Donald Trump's sweeping tariffs may yet roil ad markets, clouding the company's financial outlook for near future quarters.