The big thing that we are targeting is producing an automated researcher. So automating the discovery of new ideas. The next set of evals and milestone that we're looking at will involve actual movement on things that are economically relevant. And I was talking to some some high schoolers and they're saying, "Oh, you know, actually the default way to code is vibe coding. I I do think you know the future hopefully will be vibe researching. " Thanks for coming Jacob and Mark. Jacob, you're the chief scientis ...

Links:- FreeCodeCamp: https://t.co/qS3FMKJ4t7- Deep Learning: https://t.co/2Pi0rUehre- Harvard: https://t.co/8bHwz8hW4l- Corey: https://t.co/HfiIUvo6fk- Project learning: https://t.co/mDIIQtJOJCLearn MCPs from scratch (with 11 projects): https://t.co/yzmieK4Z0c ...

Accessibility StatementSkip Navigation SYDNEY, Sept. 17, 2025 /PRNewswire/ -- Nuix ('the Company', ASX: NXL) today announces it has won a multiyear contract to supply forensic analysis software to the tax authority of German state Rhineland-Palatinate (Landesamt für Steuern Rheinland-Pfalz). Nuix Neo automates workflows, ingesting data from over 1,000 file types at scale. Using responsible AI, advanced automation, and deep link-analysis, the software enables investigators to uncover the truth in vast, compl ...

AI is transforming the way that we understand and treat diseases. And nowhere is this more evident than in how we study proteins. It used to take months of lab experiments to determine the structure of a protein.With the release of AlphaFold 2 in 2021. That process can be reduced to minutes with deep learning. This capability can now fit on your desk.The NVIDIA DGX Spark brings data center class performance to protein AI, powered by the Grace Blackwell architecture, with up to one Petaflop of compute and 12 ...

Summary of ZipRecruiter Conference Call Company Overview - **Company**: ZipRecruiter (NYSE: ZIP) - **Industry**: Online Recruiting - **Conference Date**: September 10, 2025 Key Points Company Journey and Strategy - ZipRecruiter was founded with the idea of creating a "magic button" to post jobs across various platforms, effectively turning the internet into a giant job board [4] - The company shifted focus from volume to quality, utilizing machine learning and deep learning to deliver high-quality candidates [5] - The current emphasis is on engagement, ensuring that employers and candidates can connect effectively [5] Competitive Landscape - The U.S. online recruiting market is valued at over $300 billion annually, with a significant portion still offline [6] - Key competitors include LinkedIn, Indeed, and ZipRecruiter, with the latter positioning itself as a matchmaker rather than just a job board [6][9] - ZipRecruiter aims to differentiate itself through technology that enables proactive engagement between employers and job seekers [9] Product Innovations - New tools include a resume database with messaging capabilities and a product called ZipIntro, which facilitates quick video interviews between employers and candidates [10][14] - The company has acquired BreakRoom, which provides structured information for job seekers, particularly in frontline roles [14][15] AI Integration - ZipRecruiter has been utilizing AI for nearly a decade, focusing on algorithmic matching to improve candidate-employer connections [17] - Future AI applications aim to enhance engagement speed between job seekers and employers [18] - AI is also being used internally to improve operational efficiency, particularly in coding and repetitive tasks [20][21] Market Dynamics - The labor market has experienced a significant downturn over the past 30 months, but recent data shows signs of stabilization and potential growth [31][32] - The company reported a 10% increase in unique employers in Q1 compared to the previous quarter, indicating a recovery trend [32][56] - The revenue mix is currently 80% from SMBs and 20% from enterprises, with a goal to shift to a 50/50 split over time [24][26] Financial Outlook - ZipRecruiter aims for a long-term adjusted EBITDA margin of 30%, currently operating at mid-single-digit margins due to ongoing investments [48][49] - The company maintains a strong capital position, prioritizing organic investments and potential M&A opportunities [51][52] Future Focus - Key areas of focus for the next year include enhancing product engagement metrics and expanding enterprise solutions [57] - The company is optimistic about achieving year-over-year growth in Q4 2025, driven by improved market conditions and product effectiveness [33][34] Additional Insights - The company recognizes the importance of brand recognition, with over 80% awareness among both employers and job seekers [13] - The integration with third-party applicant tracking systems poses challenges for enterprise sales, but significant progress has been made [28] This summary encapsulates the essential insights from the ZipRecruiter conference call, highlighting the company's strategic direction, competitive positioning, and market outlook.

[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 ...