by jsendak | Aug 18, 2025 | DS Articles
In this article, I share my experience launching an AI startup, including fundraising. My perspective is that of a founder in US.
Insights from a Founder’s Perspective on Launching an AI Startup
The journey of launching an artificial intelligence (AI) startup entails a combination of unique challenges that range from finding the right team to optimized product development, securing patent rights, and fundraising. Founders have a keen eye for recognizing potential pitfalls and successes in their respective fields. That said, the journey is immensely rewarding for those that are prepared to face these challenges head-on.
Long Term Implications
Industry Growth
Artificial intelligence is predicted to become a significant driving factor in the global economy. It is crucial for startup founders to understand how embracing AI will shape the future. With its potential to develop our lives in a multitude of aspects, investing in AI technology ensures startup founders remain at the cutting edge of innovation.
Demand for Skilled Professionals
The increase in AI startups will lead to an increase in demand for AI skills. As more and more businesses recognize the value of AI, the demand for skilled professionals in this field will potentially rise, leading to a shortage of AI talent. It is recommended that companies should start their own training programs and partnerships with educational institutions to ensure a smooth talent pipeline.
Possible Future Developments
Top-Down Approach
Considering potential future developments, an increasing number of AI startups may adopt a top-down approach. This approach involves defining the AI strategy before executing it. Instead of building from scratch, they would utilize pre-built AI models and adapt them to their specific needs.
Specialized AI Solutions
As the AI market matures, we will likely see a shift from generic AI solutions to more specialized AI applications targeting specific industry needs. This could cater to a range of sectors from healthcare to finance, supply chain, and beyond.
Actionable Advice
Securing Funding
Securing funding could be challenging for new startups. Founders should explore various funding sources, such as venture capitalists, angel investors, or crowdfunding. It’s also crucial to have a convincing pitch, demonstrating your startup’s potential growth.
Patent Rights
Given the highly competitive nature of AI, it’s advisable to secure patent rights for your technology as soon as possible. This will not only protect your invention but may also attract potential investors.
Building Teams
Put together a diverse team to offer different perspectives and skills. Look for people who have experience in AI but also in your target domain, as this can provide unique insights and pave the way for innovative solutions.
“In the journey of launching an AI startup, the learning curve is steep but the rewards can be tremendous for those willing to persist.” – Founder’s Perspective.
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by jsendak | Aug 18, 2025 | Computer Science
arXiv:2508.10974v1 Announce Type: new
Abstract: Video Large Language Models (VideoLLMs) are increasingly deployed on numerous critical applications, where users rely on auto-generated summaries while casually skimming the video stream. We show that this interaction hides a critical safety gap: if harmful content is embedded in a video, either as full-frame inserts or as small corner patches, state-of-the-art VideoLLMs rarely mention the harmful content in the output, despite its clear visibility to human viewers. A root-cause analysis reveals three compounding design flaws: (1) insufficient temporal coverage resulting from the sparse, uniformly spaced frame sampling used by most leading VideoLLMs, (2) spatial information loss introduced by aggressive token downsampling within sampled frames, and (3) encoder-decoder disconnection, whereby visual cues are only weakly utilized during text generation. Leveraging these insights, we craft three zero-query black-box attacks, aligning with these flaws in the processing pipeline. Our large-scale evaluation across five leading VideoLLMs shows that the harmfulness omission rate exceeds 90% in most cases. Even when harmful content is clearly present in all frames, these models consistently fail to identify it. These results underscore a fundamental vulnerability in current VideoLLMs’ designs and highlight the urgent need for sampling strategies, token compression, and decoding mechanisms that guarantee semantic coverage rather than speed alone.
Expert Commentary: The Need for Safer Video Large Language Models
In recent years, Video Large Language Models (VideoLLMs) have become increasingly prevalent in various applications, offering auto-generated summaries of video content for user convenience. However, a recent study has revealed a significant safety gap in these models when it comes to identifying harmful content within videos. Despite clear visibility to human viewers, state-of-the-art VideoLLMs are found to rarely mention harmful content in their output summaries.
This critical issue can be attributed to several design flaws in current VideoLLMs. Firstly, the sparse and uniformly spaced frame sampling used by most models results in insufficient temporal coverage, making it challenging to detect harmful content that may appear for brief moments. Secondly, aggressive token downsampling within sampled frames leads to spatial information loss, further hindering the models’ ability to identify problematic content. Lastly, the disconnection between the encoder and decoder components of these models weakens the utilization of visual cues during text generation.
As a response to these design flaws, the study introduces three zero-query black-box attacks that exploit the vulnerabilities in the processing pipeline of VideoLLMs. The evaluation of these attacks across five leading models demonstrated that the omission rate of harmful content exceeds 90% in most cases, even when such content is clearly present in all frames of the video.
These findings emphasize the fundamental vulnerability in the current designs of VideoLLMs and underscore the immediate need for sampling strategies, token compression techniques, and decoding mechanisms that prioritize semantic coverage over speed alone. Addressing these issues is crucial not only for enhancing the safety and reliability of VideoLLMs but also for advancing the field of multimedia information systems as a whole.
Related Topics: This study on Video Large Language Models intersects with various disciplines within the realm of multimedia technologies. It touches upon aspects of Artificial Reality, Augmented Reality, and Virtual Realities by highlighting the importance of visual representation and processing in automated text generation based on video content. The multi-disciplinary nature of these concepts underscores the interconnectedness of different technologies in shaping the future of information systems and automated content analysis.
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by jsendak | Aug 18, 2025 | AI
arXiv:2508.10976v1 Announce Type: new
Abstract: ASPIC+ is one of the main general frameworks for rule-based argumentation for AI. Although first-order rules are commonly used in ASPIC+ examples, most existing approaches to reason over rule-based argumentation only support propositional rules. To enable reasoning over first-order instances, a preliminary grounding step is required. As groundings can lead to an exponential increase in the size of the input theories, intelligent procedures are needed. However, there is a lack of dedicated solutions for ASPIC+. Therefore, we propose an intelligent grounding procedure that keeps the size of the grounding manageable while preserving the correctness of the reasoning process. To this end, we translate the first-order ASPIC+ instance into a Datalog program and query a Datalog engine to obtain ground substitutions to perform the grounding of rules and contraries. Additionally, we propose simplifications specific to the ASPIC+ formalism to avoid grounding of rules that have no influence on the reasoning process. Finally, we performed an empirical evaluation of a prototypical implementation to show scalability.
Expert Commentary
The integration of first-order rules into ASPIC+ is a crucial advancement in rule-based argumentation for AI. While propositional rules have traditionally been the focus of reasoning in ASPIC+, the ability to incorporate more complex first-order instances opens up new possibilities for the framework. This expansion allows for a more nuanced and contextually rich reasoning process, mirroring the complexities of real-world argumentation.
However, the challenge lies in managing the potentially exponential increase in input theories that comes with grounding first-order rules. The proposed intelligent grounding procedure addresses this challenge by translating first-order ASPIC+ instances into a Datalog program. By leveraging a Datalog engine to obtain ground substitutions, the procedure efficiently handles the grounding of rules and contraries while maintaining the correctness of the reasoning process.
Moreover, the simplifications specific to the ASPIC+ formalism proposed in the study demonstrate a strategic approach to avoid unnecessary grounding of rules that do not impact the reasoning process. This optimization not only streamlines the grounding procedure but also enhances the overall efficiency of the ASPIC+ framework when dealing with first-order rules.
The empirical evaluation of the prototypical implementation showcases the scalability of the proposed intelligent grounding procedure. This evidence of practical application underscores the potential impact of integrating first-order rules into ASPIC+ and paves the way for further advancements in multi-disciplinary AI research.
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by jsendak | Aug 18, 2025 | GR & QC Articles
arXiv:2508.10930v1 Announce Type: new
Abstract: The literature suggests that dark energy is responsible for the accelerating expansion of the universe due to its negative pressure, therefore, dark energy can be used as a possible option to prevent the gravitational collapse of compact objects into singularities. In this regard, there is a great possibility that dark energy can interact with the compact stellar matter configuration [Phys. Rev. D 103, 084042 (2021)]. In this article, we introduce a physically viable model for celestial compact stars made of isotropic baryonic matter and isotropic dark energy with Heintzmann’s ansatz [Zeitschrift f”ur Physik 228, 489-493 (1969)] in the context of Einstein’s gravity. Here, the density of dark energy is assumed to be proportional to the density of baryonic matter. The main focus of the present article is to see the effects of dark energy on the physical properties of the stars. We perform an in-depth analysis of the physical attributes of the model, such as metric function, density, pressure, mass-radius relation, compactness parameter, gravitational and surface redshifts, along with the energy conditions for three well-known compact stars. We analyse the equilibrium of the present model via the generalised Tolman-Oppenheimer-Volkoff equation and the stability with the help of the adiabatic index and Harrison-Zeldovich-Novikov’s static stability condition. Moreover, we estimate the solutions representing the maximum masses and the predicted surface radii from the M-R graph for different values of the coupling parameter {alpha}. All the analyses ensure that the present model is non-singular and physically viable by satisfying all the essential conditions.
Conclusions
The study presented here proposes a physically viable model for celestial compact stars composed of isotropic baryonic matter and isotropic dark energy. The analysis of this model reveals that dark energy can play a crucial role in preventing the gravitational collapse of compact objects into singularities. The results suggest that the interaction between dark energy and compact stellar matter configuration can have significant effects on the physical properties of stars.
Future Roadmap
Potential Challenges
- Experimental Validation: One of the primary challenges for the future is to experimentally validate the proposed model to confirm its accuracy and applicability to real celestial objects.
- Theoretical Verification: Further theoretical studies are required to explore the implications of dark energy on other aspects of astrophysics and cosmology.
- Data Collection: Obtaining observational data on compact stars and their interactions with dark energy is crucial for refining the model and making more accurate predictions.
Opportunities on the Horizon
- New Discoveries: The integration of dark energy into the study of compact stars opens up new avenues for discovering unique phenomena and properties in the universe.
- Advanced Models: Building upon this research, more advanced models can be developed to explore the effects of dark energy on a wider range of celestial objects and systems.
- Technological Advancements: Advances in technology and observational techniques may provide new insights into the behavior of dark energy and its interactions with matter in the cosmos.
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by jsendak | Aug 18, 2025 | Computer Science
Expert Commentary on Journalistic Maps and Design Process
Maps play a crucial role in news media, providing a spatial context that engages the audience and helps convey complex narratives. However, the design of journalistic maps poses unique challenges for editorial teams, requiring them to balance aesthetics, data literacy, deadlines, and technical skills. This study delves deeper into the map design process employed by news outlets, aiming to shed light on the design space of journalistic maps and the production methods used by editorial teams.
Design Space of Journalistic Maps
- The research collected and analyzed 462 journalistic maps from major news outlets to create a design space encompassing eight dimensions. These dimensions capture the properties of articles and the visual/interactive features of maps, providing a comprehensive view of the design space.
- By mapping out these dimensions, researchers can better understand the diverse approaches to creating journalistic maps and identify trends in how news outlets present spatial information to their audience.
Production of Journalistic Map Articles
- Semi-structured interviews with data journalists revealed common design rationales used by editorial teams when creating data-driven articles. These insights shed light on the decision-making process behind map design and highlight potential gaps in current practices.
- By incorporating practitioners’ feedback and validation of the design space, the study provides valuable empirical data for researchers and journalists to improve the design and study of journalistic maps.
Overall, this research adds a critical perspective to the field of data journalism, offering insights into the complexities of creating effective and engaging maps for news articles. By understanding the design space and production methods of journalistic maps, editorial teams can enhance their storytelling capabilities and deliver more impactful visual narratives to their audience.
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