[Applause] Let's start with a small exercise. So to warm you up, I will have three questions to you. First of all, how many of you have used recently artificial intelligence.Okay. How many of you have used some kind of medicines le recently. Some of you.And how many of you know that AI can be used to find new drugs. Okay, great. Fantastic.So my name is Kamas Kazabel and today I will uncover like a hidden layers for you a world of medicines and how artificial intelligence can be used to find new drugs. But l ...

Core Viewpoint - Google DeepMind has introduced the Gemini 2.5 Flash Image model, which features native image generation and editing capabilities, enhancing user interaction through multi-turn dialogue and maintaining scene consistency, marking a significant advancement in state-of-the-art (SOTA) image generation technology [2][30]. Team Behind the Development - Logan Kilpatrick, a senior product manager at Google DeepMind, leads the development of Google AI Studio and Gemini API, previously known for his role at OpenAI and experience at Apple and NASA [6][9]. - Kaushik Shivakumar, a research engineer at Google DeepMind, focuses on robotics and multi-modal learning, contributing to the development of Gemini 2.5 [12][14]. - Robert Riachi, another research engineer, specializes in multi-modal AI models, particularly in image generation and editing, and has worked on the Gemini series [17][20]. - Nicole Brichtova, the visual generation product lead, emphasizes the integration of generative models in various Google products and their potential in creative applications [24][26]. - Mostafa Dehghani, a research scientist, works on machine learning and deep learning, contributing to significant projects like the development of multi-modal models [29]. Technical Highlights of Gemini 2.5 - The model showcases advanced image editing capabilities while maintaining scene consistency, allowing for quick generation of high-quality images [32][34]. - It can creatively interpret vague instructions, enabling users to engage in multi-turn interactions without lengthy prompts [38][46]. - Gemini 2.5 has improved text rendering capabilities, addressing previous shortcomings in generating readable text within images [39][41]. - The model integrates image understanding with generation, enhancing its ability to learn from various modalities, including images, videos, and audio [43][45]. - The introduction of an "interleaved generation mechanism" allows for pixel-level editing through iterative instructions, improving user experience [46][49]. Comparison with Other Models - Gemini aims to integrate all modalities towards achieving artificial general intelligence (AGI), distinguishing itself from Imagen, which focuses on text-to-image tasks [50][51]. - For tasks requiring speed and cost-effectiveness, Imagen remains a suitable choice, while Gemini excels in complex multi-modal workflows and creative scenarios [52]. Future Outlook - The team envisions future models exhibiting higher intelligence, generating results that exceed user expectations even when instructions are not strictly followed [53]. - There is excitement around the potential for future models to produce aesthetically pleasing and functional visual content, such as accurate charts and infographics [53].

Project Overview - The project focuses on using deep learning and open AI to aid in archaeological site discovery, specifically in the Amazon rainforest [2][4][10] - The team developed a scalable system (AKOS) integrating open AI to enhance the system's intelligence [1] - The approach involves training classifiers on satellite images and lighter data to classify segments of the Amazon forest, dividing the region into 3x3 km tiles [2] - The team created an interactive website where users can explore potential sites in detail [3] Technical Approach - Deep learning is considered a valuable tool for discovering archaeological sites, especially in the Amazon [10] - GPT models are used as collaborators, assisting in decision-making by providing multiple solutions and discussing their strengths and weaknesses [12] - The team used GPT for summarization, providing detailed descriptions of potential spots to help archaeologists understand why the model chose them [16] Results and Findings - The model successfully identified potential discovery sites, which were confirmed through manual analysis based on archaeological knowledge [3][14] - The deep learning approach is scalable enough to scan the entire Amazon rainforest in a reasonable time [4] - Configuration changes improved the visibility of features, leading to the identification of over 100 potential sites [3] Future Plans - The team intends to make their work public to gather feedback and inspire further research [18] - They plan to improve their approach and share it with a broader community, potentially inspiring applications in other fields [18]

AI Model Vulnerability - AI 深度学习模型，应用于自动驾驶汽车、金融和医疗保健等领域，可能遭到破坏 [1]

[Music] Hello everyone. Um, today I going to talk about machine learning for everyone. My name is Glenn Glen Queen and the president at CSTU California Science and the Technology University.My talk is about machine learning core. Okay. Um that's why my own experience know from my teaching or from my talk with people you know even though everyone's talking about AI so you ask this very basic questions what is AI then how the machine learns so that's the two basic questions and I found most of time people may ...

Market Overview - The Video Analytics Market is projected to grow from USD 8.3 billion in 2023 to USD 22.6 billion by 2028, reflecting a Compound Annual Growth Rate (CAGR) of 22.3% during the forecast period [1]. Market Dynamics Drivers - Applications such as perimeter intrusion and boundary control are significant drivers for the video analytics market, particularly in the critical infrastructure sector, where continuous security is paramount [3]. - Increasing investments and focus from governing institutions on public safety, along with the need to analyze unstructured video surveillance data in real time, are also contributing to market growth [5]. Restraints - The performance limitations of edge-based video analytics systems, despite advancements in chipsets, may pose challenges to widespread adoption [4]. Opportunities - The emergence of edge technologies and devices, along with the integration of deep learning, is expected to enhance the capabilities of video analytics and drive further adoption [5][6]. Deployment Models - The cloud segment is anticipated to grow at a higher CAGR during the forecast period, driven by the benefits of lower costs, reduced operational expenditure, and enhanced flexibility and scalability [7]. Market Segmentation By Vertical - The government and defense sector is expected to hold the largest market share in 2023, focusing on city surveillance and border security initiatives [8]. Recent Trends - Recent terror attacks in Europe and the US have underscored the necessity for effective video analytics solutions in city surveillance, which are crucial for ensuring operational efficiencies and public safety [9].

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]

AI Technology Evolution - AI algorithms are advancing rapidly, doubling in capability in under 6 months, significantly faster than the 18 months for computer transistors based on Moore's Law [6][7] - The breakthrough of large language models, stemming from research papers in 2017-2018, has led to AI becoming a general-purpose technology impacting various aspects of life [4][11][12] - The field is evolving towards AI producing new knowledge, requiring algorithms to advance further to generate insights from patterns across different scientific domains [18][19] AI Applications and Impact - AI is impacting education through personalized teaching, with over 70% of educators viewing it positively [13] - AI is transforming various sectors, including healthcare, with AI-developed drugs entering clinical trials as early as 2020 and breakthroughs in protein structure prediction in 2023 [14][15] - AI enables creative content generation, including presentations, music, and art, exemplified by AI tools like Ghost Writer [16][17] Challenges and Considerations - AI systems are data-dependent and can perpetuate biases present in the training data, highlighting the need for awareness regarding uploaded content [23][24][25] - AI training and inference are energy-intensive, requiring GPUs that consume approximately 10 times more power than CPUs, leading to significant energy demands [26][27] - The industry needs to establish guard rails and ethical frameworks for AI development, as reflected in the EU AI Act, to ensure human oversight and address potential risks [35][36] - Deepfakes pose a significant threat due to their realistic nature, requiring heightened awareness and caution when consuming online content [32][33][34]

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]