Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Gift Ideas For Everyone On Your List Mashable @Home In My Bag Safety Net Look Up Versus Trending Now Say More Creator Playbook Mashable Selects Getting Technical with Mashable All Series Apple's new AI assistant can help you produce songs and edit your pitch decks. Chase joined Mashable's Social Good team in 2020, covering online stories about digital activism, climate justice, accessibility, and media representation. Apple brings more creator tools to Final Cut Pro, including an AI assistant. Apple is giving the public a taste of its new AI features and powerful devices ahead of tomorrow's Pro And Tell event. The tech giant already announced a new M6-powered, 14-inch MacBook Pro and a new iPad Mini 8, complete with an OLED screen and an internal A20 Pro chip.

The chip that debuted back in August has made its way to two more of Apple's computers. Plus, the company's subscription-based suite of creative software got an upgrade. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. We may earn revenue from the products available on this page and participate in affiliate programs. The $899 Mac mini was the first machine to get Apple's M6 chip back in August, and now it moves into two Macs with built-in displays: the 14-inch MacBook Pro at $1,999 and the iMac at $1,499.

Apple has updated its Adobe-rivaling Apple Creator Studio software suite just in time for the arrival of the new M6 iMac and 14-inch MacBook Pro. One of the biggest additions is built-in MotionVFX tools for the Final Cut Pro app, following Apple's acquisition of the former earlier this year. In a press release, Apple said the integration of MotionVFX provides editors with "rapid access to extensive professional video effects and content." These include graphics, themes, titles and transitions, as well as specialized rotoscoping and color grading tools. The Content Hub that lives within Final Cut Pro also contains curated stock videos, photos and backgrounds that can be used in projects.

Mathematical verification must be a national mission in the AI era, write Patrick Shafto, Ken Ono, and Scott Duke Kominers.

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Control algorithms in production environments typically require domain experts to tune their parameters and logic for specific scenarios. However, existing research predominantly focuses on algorithmic performance under ideal or default configurations, overlooking the critical aspect of Tuning Potential. To bridge this gap, we introduce Crucible, an agent that employs an LLM-driven, multi-level expert simulation to turn algorithms and defines a formalized metric to quantitatively evaluate their Tuning Potential. We demonstrate Crucible's effectiveness across a wide spectrum of case studies, from classic control tasks to complex computer systems, and validate its findings in a real-world deployment. Our experimental results reveal that Crucible systematically quantifies the tunable space across different algorithms. Furthermore, Crucibleprovides a new dimension for algorithm analysis and design, which ultimately leads to performance improvements.

We present a novel approach to formalise and solve search-based problems using large language models, which significantly improves upon previous state-of-theart results. We demonstrate the efficacy of this approach on benchmarks like the logic puzzles tasks in ZebraLogicBench. Instead of letting the LLM attempt to directly solve the puzzles, our method prompts the model to formalise the problem in a logic-focused, human-readable, domain-specific language (DSL) called Logic.py. This formalised representation is then solved using a constraint solver, leveraging the strengths of both the language model and the solver. Our approach achieves a remarkable 65% absolute improvement over the baseline performance of Llama 3.1 70B on ZebraLogicBench, increasing its accuracy to over 90%. This significant advancement demonstrates the potential of combining language models with domain-specific languages and auxiliary tools on traditionally challenging tasks for LLMs.

Multimodal symbolic logical reasoning, which aims to deduce new facts from multimodal input via formal logic, is critical in high-stakes applications such as autonomous driving and medical diagnosis, as its rigorous, deterministic reasoning helps prevent serious consequences. To evaluate such capabilities of current state-of-the-art vision language models (VLMs), we introduce MuSLR, the first multimodal symbolic logical reasoning grounded in formal logical rules. We curate a benchmark dataset for MuSLR comprising 1,093 instances across 7 domains, including 35 atomic symbolic logic and 976 logical combinations, with reasoning depths ranging from 2 to 9. We evaluate 7 state-of-the-art VLMs on our benchmark and find that they all struggle with multimodal symbolic reasoning, with the best model, GPT-4.1, achieving only 46.8%. Thus, we propose LogiCAM, a modular framework that applies formal logical rules to multimodal inputs, boosting GPT-4.1's

It has been observed that transformers with greater depth (that is, more layers) have more capabilities, but can we establish formally which capabilities are gained? We answer this question with a theoretical proof followed by an empirical study. First, we consider transformers that round to fixed precision except inside attention. We show that this subclass of transformers is expressively equivalent to the programming language C-RASP and this equivalence preserves depth. Second, we prove that deeper C-RASP programs are more expressive than shallower C-RASP programs, implying that deeper transformers are more expressive than shallower transformers (within the subclass mentioned above). The same is also proven for transformers with positional encodings (like RoPE and ALiBi). These results are established by studying a temporal logic with counting operators equivalent to C-RASP. Finally, we provide empirical evidence that our theory predicts the depth required for transformers without positional encodings to length-generalize on a family of sequential dependency tasks.

Texico's analog brain games use playing cards, toy train tracks, and scrap paper. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The Japanese company's games can help users learn the principles of coding with less screentime. Breakthroughs, discoveries, and DIY tips sent six days a week. Parents around the world are responding to growing research showing that excessive screen time, especially for young children, may have negative cognitive effects .