Articles tagged with "AI-research"
Tesla's First Ever Annual Revenue Drop Is Not The Concerning Part - CleanTechnica
Tesla reported its first-ever annual revenue decline, with total revenue dropping 3% from $97.7 billion in 2024 to $94.8 billion in 2025. Vehicle revenue fell more sharply by 11% in Q4 2025 compared to the previous year, alongside a 16% drop in vehicle deliveries in the same quarter and an 8.6% decline for the full year. Despite these sales decreases, operating expenses surged 39% in Q4 2025, leading to an 11% drop in operating income and a dramatic 61% plunge in net income year over year—from $2.13 billion to $840 million. Other financial metrics also showed deterioration, including a 60% drop in Q4 earnings per share, a 21% decline in net cash from operations, and a 30% fall in free cash flow. Tesla attributed rising operating costs primarily to increased spending on AI, R&D projects, and sales, general, and
Teslaelectric-vehiclesenergy-storageAI-researchautonomous-drivingfinancial-performancerenewable-energyTaking humanoid soccer to the next level: An interview with RoboCup trustee Alessandra Rossi - Robohub
The article features an interview with Alessandra Rossi, a trustee of RoboCup and Assistant Professor of Computer Science at the University of Naples “Federico II,” who has been deeply involved in the RoboCup humanoid soccer league since 2016. Rossi’s engagement has grown from participating as a team member and leader of the UK’s Bold Hearts humanoid KidSize team to serving on the Technical and Organizing Committees, the Executive Committee of the Humanoid League, and most recently, the RoboCup Board of Trustees. She has also contributed to educational initiatives, such as an online robotics module using RoboCup as a teaching benchmark, and co-authored a significant paper on current and future challenges in humanoid robotics, highlighting collaboration across RoboCup leagues. Looking ahead, RoboCup aims to realize its ambitious 2050 goal: a fully autonomous humanoid robot team defeating the reigning FIFA World Cup champions. To accelerate progress, the Federation plans key changes, including a stronger emphasis on humanoid robots and the
roboticshumanoid-robotsRoboCupAI-researchautonomous-robotsrobotics-competitionhumanoid-soccerOpenAI Pushing Propaganda Over Research, Researchers Who Quit Argue - CleanTechnica
The article from CleanTechnica highlights growing concerns from former OpenAI researchers who allege that the company is prioritizing propaganda and advocacy over transparent, critical economic research on AI’s impacts. A key criticism is that OpenAI is increasingly guarded about publishing findings that suggest AI could harm the economy, particularly by exacerbating job losses and economic inequality. This shift is seen as driven by OpenAI’s transition from an open-source nonprofit to a for-profit entity aiming for a $1 trillion valuation, which may incentivize downplaying negative consequences to protect its market position and investor interests. Beyond economic risks, former OpenAI staff have also raised alarms about the company’s risky approach to AI development and its reluctance to openly discuss important safety and policy issues. The article further contextualizes these concerns within broader political and social dynamics, noting close ties between OpenAI leadership and wealthy political figures, which may reduce regulatory oversight and prioritize billionaire interests over those of ordinary people. While acknowledging potential benefits of AI, the piece underscores the
energyartificial-intelligenceelectricity-demandpollutioneconomic-impactAI-researchOpenAIWhy Cohere’s ex-AI research lead is betting against the scaling race
The article discusses a growing skepticism within the AI research community about the prevailing strategy of scaling large language models (LLMs) by increasing computational power and data center size. Sara Hooker, former VP of AI Research at Cohere and a Google Brain alumna, exemplifies this shift with her new startup, Adaption Labs. Hooker argues that merely scaling LLMs has become inefficient and unlikely to produce truly intelligent systems capable of adapting and learning continuously from real-world experiences. Instead, her company focuses on building AI that can adapt in real time, a capability current reinforcement learning (RL) methods fail to deliver effectively in production environments. Hooker highlights that existing AI models, despite their size and complexity, do not learn from mistakes once deployed, limiting their practical intelligence. She envisions AI systems that can efficiently learn from their environment, which would democratize AI control and customization beyond a few dominant labs. This perspective aligns with recent academic findings and shifts in the AI community, including skepticism from prominent researchers
energyartificial-intelligenceAI-researchdata-centersmachine-learninglarge-language-modelsAI-scalabilityTesla’s record sales quarter barely boosted profit
In the third quarter of 2025, Tesla achieved a record vehicle delivery of 497,099 units, generating $21.2 billion in revenue—its highest in over a year—largely driven by U.S. customers rushing to benefit from an expiring federal EV tax credit. Despite this strong sales performance, Tesla's profit was only $1.4 billion, a modest increase of $200 million from the previous quarter but still 37% lower than the same quarter in 2024. The company attributed the subdued profit growth to a 50% rise in operating expenses compared to the prior year, fueled by increased spending on AI and R&D projects, as well as nearly $240 million in restructuring charges, possibly linked to the recent shutdown of its Dojo supercomputer initiative. Looking ahead, Tesla faces pressure to deliver another record-breaking quarter to match or exceed prior years' shipment volumes, with some potential support from new, more affordable versions of the Model 3 and Model Y.
energyelectric-vehiclesTeslaautomotive-industryAI-researchR&DEV-tax-creditCortical Labs' CL1 turns living neurons into programmable processors
Cortical Labs, led by neuroscientist Brett Kagan, has developed the CL1, the world’s first commercial biological computer that uses 800,000 lab-grown human neurons reprogrammed from skin or blood samples to process information. Unlike traditional silicon-based processors, these living neurons can learn, adapt, and in some cases outperform machine learning systems. The CL1, priced at $35,000 and shipping since summer 2025, includes a custom life-support system for the neurons and operates with significantly lower energy consumption compared to conventional data centers. Its early adopters span diverse fields such as pharmaceuticals, finance, gaming, and AI research. The journey from the initial scientific proof of concept, DishBrain, to the commercial CL1 product took about two and a half to three years and involved extensive engineering challenges. Moving beyond lab-scale experiments required building a scalable, reproducible system, which meant developing everything from low-level code and kernel-level software to custom hardware including FPGAs and printed
biological-computingsynthetic-intelligenceneural-networksbrain-computer-interfaceenergy-efficient-computingregenerative-medicineAI-researchKaren Hao on the Empire of AI, AGI evangelists, and the cost of belief
Karen Hao’s analysis, as presented in her book and discussed in a TechCrunch event, frames the AI industry—particularly OpenAI—as an emerging empire driven by the ideology of artificial general intelligence (AGI) that promises to “benefit all humanity.” Hao argues that OpenAI wields unprecedented economic and political power, reshaping geopolitics and daily life much like a colonial empire. This AGI-driven mission has justified rapid, large-scale expansion of AI development, often at the expense of safety, efficiency, and ethical considerations. The industry’s focus on speed and scale—primarily by leveraging vast data and supercomputing resources—has sidelined alternative approaches that might prioritize algorithmic innovation and sustainability but progress more slowly. Hao highlights that this relentless pursuit of AGI has led to enormous financial expenditures by major tech companies, with OpenAI alone projecting massive spending through 2029, and others like Meta and Google investing heavily in AI infrastructure. Despite these investments, the promised broad societal benefits
energyartificial-intelligenceAGIdata-centerscomputational-resourcestechnology-industryAI-researchFundamental Research Labs nabs $30M to build AI agents across verticals
Fundamental Research Labs, an applied AI research company formerly known as Altera, has secured $30 million in Series A funding led by Prosus, with participation from Stripe CEO Patrick Collison. The company operates with an unconventional structure, maintaining multiple teams focused on diverse AI applications across verticals, including gaming, prosumer apps, core research, and platform development. Founded by Dr. Robert Yang, a former MIT faculty member, the startup aims to be a “historical” company rather than follow a typical startup model. It is already generating revenue by charging users for its AI agents after a seven-day trial period. Among its products is Shortcut, a spreadsheet-based AI agent described as a “superhuman excel agent” that outperforms first-year analysts from top firms like McKinsey and Goldman Sachs in accuracy and speed. The company’s offerings also include a general-purpose consumer assistant and other AI tools like Fairies. Prosus investment partner Sandeep Bakshi highlighted the team’s mission-driven
robotartificial-intelligenceAI-agentsautomationproductivity-appsdigital-humansAI-researchInterview with Amar Halilovic: Explainable AI for robotics - Robohub
Amar Halilovic, a PhD student at Ulm University in Germany, is conducting research on explainable AI (XAI) for robotics, focusing on how robots can generate explanations of their actions—particularly in navigation—that align with human preferences and expectations. His work involves developing frameworks for environmental explanations, especially in failure scenarios, using black-box and generative methods to produce textual and visual explanations. He also studies how to plan explanation attributes such as timing, representation, and duration, and is currently exploring dynamic selection of explanation strategies based on context and user preferences. Halilovic finds it particularly interesting how people interpret robot behavior differently depending on urgency or failure context, and how explanation expectations shift accordingly. Moving forward, he plans to extend his framework to enable real-time adaptation, allowing robots to learn from user feedback and adjust explanations on the fly. He also aims to conduct more user studies to validate the effectiveness of these explanations in real-world human-robot interaction settings. His motivation for studying explainable robot navigation stems from a broader interest in human-machine interaction and the importance of understandable AI for trust and usability. Before his PhD, Amar studied Electrical Engineering and Computer Science in Bosnia and Herzegovina and Sweden. Outside of research, he enjoys traveling and photography and values building a supportive network of mentors and peers for success in doctoral studies. His interdisciplinary approach combines symbolic planning and machine learning to create context-sensitive, explainable robot systems that adapt to diverse human needs.
roboticsexplainable-AIhuman-robot-interactionrobot-navigationAI-researchPhD-researchautonomous-robots