Genius Sports (GENI) Q2 2025 Earnings Call August 06, 2025 08:00 AM ET Company ParticipantsBrandon Bukstel - IR ManagerMark Locke - Co-Founder, CEO & DirectorNicholas Taylor - CFOBarry Jonas - Managing DirectorBenjamin Miller - VP - Global Investment ResearchConference Call ParticipantsJordan Bender - Senior Equity Research Analyst - Gaming & LeisureRyan Sigdahl - Senior Research AnalystJed Kelly - MD & Senior Analyst - Online Travel & InternetBernie Mcternan - Senior AnalystSteve Pizzella - Equity Research ...

Genius Sports (GENI) Q2 2025 Earnings Call August 06, 2025 08:00 AM ET Speaker0Thank you for standing by. My name is Van, and I will be your conference operator today. At this time, I would like to welcome everyone to the Genius Sports Second Quarter twenty twenty five Earnings Results Call. All lines have been placed on mute to prevent any background noise. After the speakers' remarks, there will be a question and answer session.Thank you. I would now like to turn the call over to Genius Sports. Please go ...

[Music] I'm going to be giving a quick presentation about the state of the union regarding AI vision. Um, so I'm Peter Robisho. I'm the ML lead at Rooflow, which is a platform for building and deploying vision models.Um, so a lot of people are really interested in LLMs these days. So I'm trying to pitch why computer vision matters. Uh so if you think about systems that interact with the real world, they have to use vision as one of their primary inputs because the the built world is sort of built around vis ...

Technology & Engineering - Google Photos' Magic Editor integrates complex CV and generative AI models into a seamless mobile experience [1] - The focus is on optimizing massive models for latency and size [1] - Crucial interplay exists with graphics rendering (OpenGL/Halide) [1] - The process involves turning research concepts into polished features for practical use [1] Product Development - The aim is to build tools that improve users' lives through greater expression, skill-building, and communication [1] Personnel - Kelvin Ma, a product engineer with 15 years of experience, is involved in developing innovative consumer applications used by millions [1]

[Music] This is the only picture we have of the world's first and last supersonic transport aircraft. It's called the Concord. And for this picture to be taken, the plane crew had to slow down from flying at twice the speed of sound to around 1.3% times the speed of sound.so that a Royal Air Force fighter jet could catch up and take it. This is the type of technology the aviation industry had around 25 or 30 years ago. Unfortunately, today we no longer have it.The reason behind this is what we call FOD or f ...

Core Viewpoint - The article discusses the significant increase in submissions to the ICCV 2025 conference, reflecting rapid growth in the computer vision field and the challenges faced in the peer review process due to the high volume of submissions [3][26][31]. Submission and Acceptance Data - ICCV 2025 received 11,239 valid submissions, with 2,699 papers accepted, resulting in an acceptance rate of 24% [3][4]. - In comparison, ICCV 2023 had 8,260 submissions and accepted 2,160 papers, yielding an acceptance rate of approximately 26.15% [6]. - Historical data shows ICCV 2021 had 6,152 submissions with a 26.20% acceptance rate, and ICCV 2019 had 4,323 submissions with a 25% acceptance rate [6]. Peer Review Challenges - Despite the increase in submissions, the acceptance rate has remained relatively stable, hovering around 25% to 26% [4]. - The ICCV 2025 conference implemented a new policy to enhance accountability and integrity, identifying 25 irresponsible reviewers and rejecting 29 associated papers [4][5]. - The article highlights the growing challenges in the peer review process as submission volumes exceed 10,000, with NIPS expected to surpass 30,000 submissions [31]. Recommendations for Peer Review System - The article advocates for a two-way feedback loop in the peer review process, allowing authors to evaluate review quality while reviewers receive formal recognition [34][38]. - It suggests a systematic reviewer reward mechanism to incentivize high-quality reviews [38]. - The need for reforms in the peer review system is emphasized to address issues of fairness and accountability [36][37].

Core Viewpoint - The article highlights the significant impact of Kaiming He joining Google DeepMind as a distinguished scientist, emphasizing his dual role in academia and industry, which is expected to accelerate the development of Artificial General Intelligence (AGI) at DeepMind [1][5][8]. Group 1: Kaiming He's Background and Achievements - Kaiming He is renowned for his contributions to computer vision and deep learning, particularly for introducing ResNet, which has fundamentally transformed deep learning [4][18]. - He has held prestigious positions, including being a research scientist at Microsoft Research Asia and Meta's FAIR, focusing on deep learning and computer vision [12][32]. - His academic credentials include a tenure as a lifelong associate professor at MIT, where he has published influential papers with over 713,370 citations [18][19]. Group 2: Impact on Google DeepMind - Kaiming He's expertise in computer vision and deep learning is expected to enhance DeepMind's capabilities, particularly in achieving AGI within the next 5-10 years, as stated by Demis Hassabis [7][8]. - His arrival is seen as a significant boost for DeepMind, potentially accelerating the development of advanced AI models [5][39]. Group 3: Research Contributions - Kaiming He has published several highly cited papers, including works on Faster R-CNN and Mask R-CNN, which are among the most referenced in their fields [21][24]. - His recent research includes innovative concepts such as fractal generative models and efficient one-step generative modeling frameworks, showcasing his continuous contribution to advancing AI technology [36][38].

Core Viewpoint - Kaiming He, a prominent figure in AI and computer vision, has officially joined Google DeepMind as a distinguished scientist while retaining his position as a tenured associate professor at MIT, marking a significant boost for DeepMind's ambitions in artificial general intelligence (AGI) [2][5][6]. Group 1: Kaiming He's Background and Achievements - Kaiming He is renowned for his contributions to deep learning, particularly for developing ResNet, which has fundamentally transformed the trajectory of deep learning and serves as a cornerstone for modern AI models [5][17]. - His academic influence is substantial, with over 713,370 citations for his papers, showcasing his impact in the fields of computer vision and deep learning [17][18]. - He has received numerous prestigious awards, including the best paper awards at major conferences such as CVPR and ICCV, highlighting his significant contributions to the field [23][26]. Group 2: Implications of His Joining DeepMind - Kaiming He's expertise in computer vision and deep learning is expected to accelerate DeepMind's efforts towards achieving AGI, a goal that Demis Hassabis has indicated could be realized within the next 5-10 years [8][9]. - His recent research focuses on developing models that learn representations from complex environments, aiming to enhance human intelligence through more capable AI systems [16][17]. - The addition of Kaiming He to DeepMind is seen as a strategic advantage, potentially leading to innovative breakthroughs in AI model development [6][37].

Investment Rating - The industry view is rated as Attractive [5] Core Insights - The report highlights advancements in computer vision technologies from Waymo, GOOGL, and META, emphasizing the increasing value of data and the potential long-term advantages for GOOGL and META [3][4] - Waymo's improvements in simulation for autonomous driving are noted as a significant development, enhancing the scalability and efficiency of validation processes [4][7] - GOOGL's robotics research shows promise in generalization and cross-embodiment capabilities, indicating potential for deployment across various robotic applications [10][11] - META outlines a three-stage approach to model development for agent interactions, focusing on efficiency and productivity improvements [12][40] Summary by Sections Autonomous Driving - Waymo has made notable advancements in simulation, allowing for high-fidelity simulations that improve validation processes [4] - The importance of generalization in building scalable systems is emphasized, with evidence that scaling compute, data, and parameters leads to better model performance [8] - Challenges remain in extreme weather conditions, particularly snow and flooding, which require further improvements [9] GOOGL Robotics - GOOGL's early research indicates a strong potential for its robotics models to generalize across different types of robots, enhancing adaptability [10][11] META's Agentic Technologies - META's three-stage model development approach aims to enhance human-agent interactions, focusing on instinctive, deliberate, and collaborative systems [12] - The company is positioned to leverage AI investments for improved engagement and monetization across its platforms [40] Price Targets - GOOGL's price target is set at $185.00, implying a ~12X 2025e adjusted EBITDA [15] - META's price target is set at $650.00, implying a ~12.1X 2026e adjusted EBITDA [35]

Core Viewpoint - The CVPR 2025 awards have been announced, recognizing outstanding contributions in the field of computer vision, particularly highlighting young scholars and innovative research papers [1][2]. Group 1: Young Scholar Awards - The awards are aimed at early-career researchers who have obtained their PhD within the last seven years, acknowledging their significant contributions to computer vision [2]. - Notable recipients include Su Hao, a PhD student of Fei-Fei Li, who contributed to the renowned ImageNet project [3]. - Xie Saining, recognized for his work on ResNeXt and MAE, has also made impactful contributions to the field [4]. Group 2: Best Paper Award - The Best Paper award was given to "VGGT: Visual Geometry Grounded Transformer," co-authored by researchers from Meta and Oxford University, led by Wang Jianyuan [5]. - VGGT is the first large Transformer model capable of end-to-end predicting complete 3D scene information in a single feedforward pass, outperforming existing geometric and deep learning methods [5]. Group 3: Best Student Paper - The Best Student Paper award went to "Neural Inverse Rendering from Propagating Light," developed by a collaboration between the University of Toronto and Carnegie Mellon University [7]. - This paper introduces a physics-based neural inverse rendering method that reconstructs scene geometry and materials from multi-view, time-resolved light propagation data [9][25]. Group 4: Honorable Mentions - Four papers received Honorable Mentions, including: - "MegaSaM: Accurate, Fast, and Robust Structure and Motion from Casual Dynamic Videos," which presents a system for estimating camera parameters and depth maps from dynamic scenes [10][32]. - "Navigation World Models," which proposes a controllable video generation model for predicting future visual observations based on past actions [10][38]. - "Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models," which introduces a new family of open-source vision-language models [10][45]. - "3D Student Splatting and Scooping," which presents a new 3D model that improves upon existing Gaussian splatting techniques [10][52]. Group 5: Technical Innovations - VGGT employs an alternating attention mechanism to process both frame-wise and global self-attention, allowing for efficient memory usage while integrating multi-frame scene information [13][18]. - The "Neural Inverse Rendering" method utilizes a time-resolved radiance cache to understand light propagation, enhancing scene reconstruction capabilities [25][27]. - The "MegaSaM" system optimizes depth estimation and camera parameter accuracy in dynamic environments, outperforming traditional methods [32][35]. - The "Navigation World Model" adapts to new constraints in navigation tasks, demonstrating flexibility in unfamiliar environments [38][42]. - The "Molmo" model family is built from scratch without relying on closed-source data, enhancing the understanding of high-performance vision-language models [45][46].