The capacity for artificial intelligence (AI) to formulate, evolve, and test altered thought patterns under dynamic conditions indicates advanced cognition that is crucial for scientific discovery. The existing AI development landscape falls into two categories: 1) frameworks over non-reasoning models that natively incorporate opinions on how humans think, and 2) reasoning models that abstract precise control of the reasoning intuition away from end users. While powerful, for scientists to maximize utility of AI in scientific discovery, they not only require accuracy and transparency in reasoning, but also steerability. Hence, we introduce an alternative approach that enables deep and precise control over the reasoning process called: a cognitive loop via in-situ optimization (CLIO). CLIO enables large language models (LLMs) to self-formulate ways of approaching a problem, adapt behavior when self-confidence is low, and ultimately provide scientists with a final belief or answer. Through CLIO's open design, scientists can observe uncertainty levels, understand how final belief states are formulated using graph structures, and interject corrections. Without any further post-training, OpenAI's GPT-4.1 with CLIO yields an accuracy of 22.37\% in text-based biology and medicine questions on Humanity's Last Exam (HLE). This yields a 13.82\% net or 161.64\% relative increase when compared to the base GPT-4.1 model and surpasses OpenAI's o3 performance in high and low reasoning effort modes. We further discovered that oscillations within internal uncertainty measures are key in determining the accuracy of CLIO's results, revealing how its open design and internal mechanisms can provide insight and control into scientific decision-making processes.

Despite widespread speculation about artificial intelligence's impact on the future of work, we lack systematic empirical evidence about how these systems are actually being used for different tasks. Here, we present a novel framework for measuring AI usage patterns across the economy. We leverage a recent privacy-preserving system to analyze over four million Claude.ai conversations through the lens of tasks and occupations in the U.S. Department of Labor's O*NET Database. Our analysis reveals that AI usage primarily concentrates in software development and writing tasks, which together account for nearly half of all total usage. However, usage of AI extends more broadly across the economy, with approximately 36% of occupations using AI for at least a quarter of their associated tasks. We also analyze how AI is being used for tasks, finding 57% of usage suggests augmentation of human capabilities (e.g., learning or iterating on an output) while 43% suggests automation (e.g., fulfilling a request with minimal human involvement). While our data and methods face important limitations and only paint a picture of AI usage on a single platform, they provide an automated, granular approach for tracking AI's evolving role in the economy and identifying leading indicators of future impact as these technologies continue to advance.

How are AI assistants being used in the real world? While model providers in theory have a window into this impact via their users' data, both privacy concerns and practical challenges have made analyzing this data difficult. To address these issues, we present Clio (Claude insights and observations), a privacy-preserving platform that uses AI assistants themselves to analyze and surface aggregated usage patterns across millions of conversations, without the need for human reviewers to read raw conversations. We validate this can be done with a high degree of accuracy and privacy by conducting extensive evaluations. We demonstrate Clio's usefulness in two broad ways. First, we share insights about how models are being used in the real world from one million Claude.ai Free and Pro conversations, ranging from providing advice on hairstyles to providing guidance on Git operations and concepts. We also identify the most common high-level use cases on Claude.ai (coding, writing, and research tasks) as well as patterns that differ across languages (e.g., conversations in Japanese discuss elder care and aging populations at higher-than-typical rates). Second, we use Clio to make our systems safer by identifying coordinated attempts to abuse our systems, monitoring for unknown unknowns during critical periods like launches of new capabilities or major world events, and improving our existing monitoring systems. We also discuss the limitations of our approach, as well as risks and ethical concerns. By enabling analysis of real-world AI usage, Clio provides a scalable platform for empirically grounded AI safety and governance.

Modern tools for class-agnostic image segmentation (e.g., SegmentAnything) and open-set semantic understanding (e.g., CLIP) provide unprecedented opportunities for robot perception and mapping. While traditional closed-set metric-semantic maps were restricted to tens or hundreds of semantic classes, we can now build maps with a plethora of objects and countless semantic variations. This leaves us with a fundamental question: what is the right granularity for the objects (and, more generally, for the semantic concepts) the robot has to include in its map representation? While related work implicitly chooses a level of granularity by tuning thresholds for object detection, we argue that such a choice is intrinsically task-dependent. The first contribution of this paper is to propose a task-driven 3D scene understanding problem, where the robot is given a list of tasks in natural language and has to select the granularity and the subset of objects and scene structure to retain in its map that is sufficient to complete the tasks. We show that this problem can be naturally formulated using the Information Bottleneck (IB), an established information-theoretic framework. The second contribution is an algorithm for task-driven 3D scene understanding based on an Agglomerative IB approach, that is able to cluster 3D primitives in the environment into task-relevant objects and regions and executes incrementally. The third contribution is to integrate our task-driven clustering algorithm into a real-time pipeline, named Clio, that constructs a hierarchical 3D scene graph of the environment online using only onboard compute, as the robot explores it. Our final contribution is an extensive experimental campaign showing that Clio not only allows real-time construction of compact open-set 3D scene graphs, but also improves the accuracy of task execution by limiting the map to relevant semantic concepts.

In this work, we introduce a novel workflow that couples robotics to machine-learning for efficient optimization of a non-aqueous battery electrolyte. A custom-built automated experiment named "Clio" is coupled to Dragonfly - a Bayesian optimization-based experiment planner. Clio autonomously optimizes electrolyte conductivity over a single-salt, ternary solvent design space. Using this workflow, we identify 6 fast-charging electrolytes in 2 work-days and 42 experiments (compared with 60 days using exhaustive search of the 1000 possible candidates, or 6 days assuming only 10% of candidates are evaluated). Our method finds the highest reported conductivity electrolyte in a design space heavily explored by previous literature, converging on a high-conductivity mixture that demonstrates subtle electrolyte chemical physics.

In-house legal teams including Vodafone, Featurespace and Ferrero are using London-based legaltech firm Luminance's new artificial intelligence platform to help their departments get a better grip on their contracting issues. The platform – Luminance Corporate – streamlines the contract lifecycle process by automating contract drafting, version control and renewal, enabling in-house lawyers to better understand, manage and negotiate their contracts. The platform's AI capabilities also provide insights on those contracts, meaning lawyers don't have to manually search for key information. Rosemary Martin, group general counsel and company secretary at Vodafone, said: "Good technology can make a really positive difference in corporate legal departments. Being able to rapidly analyse contracts and display critical information means lawyers no longer have to waste time trawling through their contracts."

For the second year in a row, we surveyed a number of great minds in the legal community for their opinions on legal tech. From bitcoin and blockchain to A.I. and chatbots, there's plenty to get excited about. Respondents to this year's survey included: Here's what they had to say: There were several contenders for the biggest legal tech news story of 2017, with A.I. taking a top spot yet again. "The barista at my local Starbucks who thought about going to law school one time was getting ready to launch a legal tech company that focused on A.I. indigent defense via crowdsourcing along the blockchain," said Keith Lee. "That's how prevalent it's been."

Last week, hosts Jim Calloway and Sharon Nelson talked to Andrew Arruda, the CEO of ROSS Intelligence, about how artificial intelligence can assist lawyers, not replace them. They also discussed the biggest misconceptions about AI. Recorded at ABA TECHSHOW 2017, this episode of The Un-Billable Hour focuses on litigation finance underwriting for commercial cases. Joshua Lenon, lawyer-in-residence for Clio, and Eva Shang, founder of Legalist, discuss how Legalist, a service dedicated to financing litigation underwriting, works and how it integrates with Clio. They discuss the different e-discovery products available, sanctions, and discovery interactions with social media.

The people who are selling these machines want them to augment human intelligence, not replace it #ROSS #LegalTech https://t.co/TfcQ4Fm9mf With news that ROSS, the world's first AI lawyer, built upon IBM's Watson was'hired' by law firm Baker & Hostetler, there was much weeping and gnashing of teeth by organic, flesh-and-bone lawyers. They were then summarily rounded up by their new machine overlords and sealed in pods where their bioelectric energy was harnessed and used to power the very computers that now subjugated them. But for all the alarm-raising over the rise of AI and what it means for tomorrow's legal professionals, does machine intelligence pose a legitimate threat to the practice of law by human beings? Will clients in the near future be represented in court by Lawbot 3000?