AI Progress & AGI Definition - OpenAI is setting the research roadmap for the company, deciding on technical paths and long-term research directions [1] - The industry is progressing to a point where AI can converse naturally, solve math problems, and the focus is shifting towards its real-world impact [1] - The potential for automating the discovery and production of new technology is a key consideration for AI's impact [1][2] - OpenAI seeks to create general intelligence, prioritizing the automated researcher concept for significant technological advancements [2] - The industry is seeing incredible results in medicine, combining reasoning with domain knowledge and intuition [2] Benchmarks & Evaluation - Current benchmarks are facing saturation as models reach human-level performance on standardized intelligence measures [3] - The field has developed data-efficient ways to train for specific abilities, making benchmarks less representative of overall intelligence [3] - The industry needs to consider the reward utility of models and their ability to discover new insights, rather than just test-taking abilities [3] - Reasoning models and longer chain of thought are significant advancements, but continuous hard work is needed to make them work [4][5] Future Directions - Scaling remains important, and new directions include extending the horizon for models to plan and reason [5] - The industry should expect progress on interfaces, with AI becoming more persistent and capable of expressing itself in different forms [6] - Learning to code remains a valuable skill, fostering structured intellect and the ability to break down complicated problems [6]

Μεγάλη μου χαρά, μεγάλη μου τιμή να μαι εδώ πέρα σήμερα μαζί σας. Θέλω να ξεκινήσω με μια ερώτηση. Πόσοι γνωρίζετε αυτό το μηχάνημα που βλέπουμε τώρα στην οθόνη μας Ειλικρινά πείτε μου.Ευτυχώς δεν είμαι ο μεγαλύτερος στην στην αίθουσα αυτή. Το ξέρουνε κι άλλοι. Είναι ένας παλιός υπολογιστής λοιπόν.Home Micro, όπως λεγότανε τότε στην εποχή του εκεί περί το 80 και είχα τη χαρά να ταν το πρώτο τέλος πάντων έξυπτο μηχάνημα το οποίο διαχειρίστηκα. Believe it or not που λένε και στο χωριό μου στην Αρκαδία υπήρχε ...

Core Points - Gorilla Technology Group Inc. will hold a conference call on August 14, 2025, at 8:30 a.m. Eastern time to discuss its financial results for the six months ended June 30, 2025 [1] - The conference call will be available for live webcast and replay [2] Company Overview - Gorilla Technology Group Inc. is headquartered in London, U.K., and operates as a global solution provider in Security Intelligence, Network Intelligence, Business Intelligence, and IoT technology [3] - The company offers a variety of solutions across sectors such as Government & Public Services, Manufacturing, Telecom, Retail, Transportation & Logistics, Healthcare, and Education, utilizing AI and Deep Learning Technologies [3] Technology and Solutions - The company focuses on enhancing urban operations, security, and resilience through AI-driven technologies [4] - Key products include intelligent video surveillance, facial recognition, license plate recognition, edge computing, post-event analytics, and advanced cybersecurity technologies [4]

Core Insights - DEEPX has signed a partnership agreement with Baidu to enhance AI solutions in global industrial applications [1][12] - The collaboration will leverage Baidu's PaddlePaddle framework for various AI projects, including OCR, drones, and robotics [3][7] Company Overview - DEEPX specializes in low-power AI semiconductors and has a significant patent portfolio with over 350 patents pending [13][14] - The company is focused on developing high-performance AI chips that improve energy efficiency and enable advanced AI functionalities [14] Partnership Details - As an official ecosystem partner, DEEPX will co-develop products and participate in global customer promotion activities [3][12] - The partnership aims to enhance the practical applicability of PaddlePaddle-based AI models across various industries [11][12] Technology and Product Development - DEEPX's DX-M1 chip has demonstrated high performance in real-time applications, particularly in edge environments [6] - The company is also developing the V-NPU, a dedicated NPU card for vision AI, with mass production expected to begin in September [9] Future Initiatives - DEEPX and Baidu plan to showcase their collaboration at the 2025 Shenzhen Artificial General Intelligence Conference [10] - The partnership is expected to facilitate the adoption and scaling of AI products powered by DEEPX's NPUs among global partners [8]

ok now the most important part:"we are about understanding this miraculous technology called deep learning.""this is a work of passion.""i want to to recognize and deeply thank the team at openai""early glimpses of technology that will go much further.""we'll get back to scaling."- @merettmit is amazing to work with such a brilliant and driven group of people and discover the future together, and to provide such utility to people along the way. ...

1956, siamo in un piccolo college americano a Dartmh e un gruppo di scienziati si riunisce con una idea visionaria. Possiamo far pensare le macchine. Era la prima volta che qualcuno ad alta voce diceva che l'intelligenza, quella umana, potesse essere simulata.In quel gruppo c'erano persone come John McCarfy e Marvin Miskuzione, ma una convinzione. Se descriviamo abbastanza bene l'intelligenza, possiamo replicarla. Beh, per tanti era una follia, addirittura forse un salto nel buio.Anche noi, Pasquale abbiamo ...

Group 1: Core Insights - The article emphasizes the importance of foundational skills in programming and software engineering for entering the AI field, with Python being the preferred language due to its ease of use and comprehensive ecosystem [1][2][4] - It highlights that while many AI roles stem from machine learning, the most sought-after positions are closer to software engineering, necessitating knowledge of languages like Java, GO, or Rust [1][2] - Continuous practice and real-world application are deemed essential for mastering programming languages, rather than solely relying on courses or books [2] Group 2: Recommended Resources - A variety of resources are suggested for learning Python, including a beginner's course that can be completed in four hours and a highly regarded specialization course [5] - For mathematics and statistics, specific books and courses are recommended to understand the underlying principles of machine learning and AI [9][10] - The article lists essential resources for deep learning and large language models, emphasizing the significance of frameworks like PyTorch and TensorFlow in the industry [13][14] Group 3: AI Engineering and Productization - The article stresses the need for skills in productizing AI models, indicating that most AI roles resemble traditional software engineering rather than pure machine learning engineering [11] - It mentions the importance of learning MLOps for model deployment, covering aspects like containerization and cloud systems [11] - The article concludes with advice on becoming an expert in the field through project-based learning and self-reflection [14]

Core Insights - Taboola.com Inc.'s Realize platform significantly enhances the efficiency and performance of the company's advertising operations, targeting a $55 billion market through its AI-driven engine [1][4] - The platform utilizes deep-learning algorithms for real-time user signal analysis and historical behavior, leading to improved user engagement and campaign results, which encourages advertisers to increase their investments [2] - Realize empowers mid-sized and smaller advertisers with simplified, self-serve campaign tools, allowing for growth in the advertiser base without increasing operational costs [3] Competitive Landscape - Competitors like The Trade Desk and Magnite do not possess a proprietary platform like Realize but are establishing their niches in the digital advertising space [5][6] - The Trade Desk focuses on transparent, data-driven programmatic solutions, enhancing its leadership in digital advertising through AI innovation and partnerships [5] - Magnite aims to maximize publisher revenues through transparent monetization strategies, solidifying its position as the largest independent sell-side platform [6] Financial Performance - Taboola's shares have declined by 12% year to date, underperforming the industry [7] - The company is currently trading at a price-to-earnings multiple of 18.1, which is lower than the industry average of 28.4, indicating an affordable valuation [10] - Consensus estimates for Taboola's EPS for 2025 and 2026 show no movement over the past 60 days, with projections indicating year-over-year increases [11][12]

Computing Power Evolution - The industry has witnessed a dramatic growth in computing power over the past 5 decades, transitioning from early CPUs to GPUs and now specialized AI processors [4] - GPUs and accelerators have rapidly outpaced traditional CPUs in compute performance, initially driven by gaming [4] - Apple's M4 chip features a neural engine delivering 38 trillion operations per second, establishing it as the most efficient desktop SOC on the market [3] - NVIDIA's B200 delivers 20 quadrillion operations per second at low precision in AI data centers [3] Hardware and AI Development - The development of CUDA by Nvidia in 2006 enabled GPUs to handle more than just graphics, paving the way for deep learning breakthroughs [6] - The "hardware lottery" highlights that progress stems from available technology, not necessarily perfect solutions, as GPUs were adapted for neural networks [7] - As AI scales, general-purpose chips are becoming insufficient, necessitating a rethinking of the entire system [7] Efficiency and Optimization - Quantization is used to reduce the size of numbers in AI, enabling smaller, more power-efficient, and compact AI models [8][10] - Reducing the size of parameters allows for more data movement across the system per second, decreasing bottlenecks in memory and network interconnects [10][11] - Wafer Scale Engine 2 achieves similar compute performance to 200 A100 GPUs while using significantly less power (25kW vs 160kW) [12] Future Trends - Photonic computing, using light instead of electrons, promises faster data transfer, higher bandwidth, and lower energy use, which is key for AI [15] - Thermodynamic computing harnesses physical randomness for generative models, offering efficiency in creating images, audio, and molecules [16] - AI supercomputers, composed of thousands or millions of chips, are essential for breakthroughs, requiring fault tolerance and dynamic rerouting capabilities [17][20] Global Collaboration - Over a third of all US AI research involves international collaborators, highlighting the importance of global connectedness for progress [22] - The AI supply chain is complex, spanning multiple continents and involving intricate manufacturing processes [22]

AI发展与应用 - DeepMind 的使命是负责任地构建 AI，以造福人类，深度学习已成为解决图像分类、语音识别和机器翻译等问题的最佳方法 [5][6] - Transformer 架构促成了大型语言模型的构建，这些模型在大量公开数据上进行训练，能够解决广泛的问题 [8] - 现代基础模型（LLM）已超越文本，成为多模态模型，能够处理文本、手写文本和图像，为个性化辅导等学习方式带来可能性 [11][12] - Gemini 1.5 Pro 能够处理高达 1 million 多模态 tokens 的上下文窗口，可以处理大量信息作为输入 [15] - AI Agents 不仅限于简单的聊天机器人，还可以进行语音交互，甚至在 3D 世界中进行实时交互 [16] AI的包容性与可及性 - 行业致力于弥合英语和其他语言（特别是印度语言）之间 AI 能力的差距，目标是开发能够理解 125 种以上印度语言的模型 [19][20][21][22] - Vani 项目与印度科学研究所合作，旨在收集印度各个角落的语音数据，目标是从印度每个地区收集数据，以覆盖更多零语料库语言 [24][25] AI在特定领域的应用 - 行业正在构建数字农业堆栈的基础层，利用卫星图像识别农田边界、作物类型和水源，为农民提供个性化服务，如作物保险 [26][27][28] - AlphaFold 通过预测蛋白质结构，将原本需要 5 年的研究缩短到几秒钟，并在不到一年的时间内完成了 200 million 个蛋白质结构的预测，并免费提供数据，极大地加速了科学发现 [29][30][31][32] 未来展望 - 行业期望 AI 能够帮助更多人，使他们能够做出诺贝尔奖级别的贡献 [35]