In Figure 2, we examine the probability of acquiring a '7' as a function of the number of acquired We see that XWED initially focuses on 7s but then diversifies. The XWED behavior is preferable: we are initially unsure about the loss of these points, but once the loss is well characterized for the 7s we should explore other areas as well. B.2 Constant π Fails for Distribution Shift. Figure B.1 (a) shows that, for LURE suffered high variance in Figure 3. In Figure B.1 (b), we observe that ASE continues to Figure B.2 demonstrates that ASEs continue to outperform all other baselines for the task of This result highlights the importance of the adaptive nature of both ASE-and LUREbased active testing. Figure B.2: V ariant of the experiments of 7.3 where we estimate the accuracy of the main model. We here investigate a variation of the experiments in 7.3: reducing the size of the training set to Despite this, Figure B.3 demonstrates that ASEs continue to outperform all baselines.

As Artificial Intelligence (AI) integrates into diverse areas, particularly in content generation, ensuring rightful ownership and ethical use becomes paramount. AI service providers are expected to prioritize responsibly sourcing training data and obtaining licenses from data owners. However, existing studies primarily center on safeguarding static copyrights, which simply treats metadata/datasets as non-fungible items with transferable/trading capabilities, neglecting the dynamic nature of training procedures that can shape an ongoing trajectory. In this paper, we present \textsc{IBis}, a blockchain-based framework tailored for AI model training workflows. \textsc{IBis} integrates on-chain registries for datasets, licenses and models, alongside off-chain signing services to facilitate collaboration among multiple participants. Our framework addresses concerns regarding data and model provenance and copyright compliance. \textsc{IBis} enables iterative model retraining and fine-tuning, and offers flexible license checks and renewals. Further, \textsc{IBis} provides APIs designed for seamless integration with existing contract management software, minimizing disruptions to established model training processes. We implement \textsc{IBis} using Daml on the Canton blockchain. Evaluation results showcase the feasibility and scalability of \textsc{IBis} across varying numbers of users, datasets, models, and licenses.

Description:   *** Cannot provide sponsorship upon conversion. What is the specific title of the position? Lead Data and AI Engineer Work location? Preferred Locations - MA or MN (Client facilities). 100% telecommute is also considered. Work hours (ex. 9am-5pm day/night shifts rotating shifts etc)? 9-5 Please provide a summary of the project/initiative that this candidate will be working on? We are establishing Agile Data Warehouse in cloud and many new AI practices to enable personalization in various capabilities to improve employee experience. Please describe the team the candidate will be working with - how many members? 10 – 12 team members What is the break-down of the teams skill sets (ex: 1 PM 4 Developers etc.)? 1 PM, 3 Product owners 2 Sprint teams consisting - 1 Scrum Masters and 12 developers What are the top 5-10 responsibilities for this position (please be detailed as to what the candidate is expected to do or complete on a daily basis)? • Identify opportunities for Data Engineering and AI to enhance the core product platform, select the best machine learning techniques to the specific business problem and then build the models that solve the problem. • Architect and design AI/ML and Analytics solutions and cloud services • Own the end-end process, from recognizing the problem to implementing the solution. • Establish DataOps and MLOps principles and best practices What does the ideal candidate background look like (ex: healthcare specific background specific industry experience etc.)? a. Hands on experience with modern application – microservices, Cloud and CI/CD b. 5-7 years of hands on Data and AI engineering work c. Good communication with developing architecture and design documentation What skills/attributes are required (please be detailed as to number of years of experience for each skill)? • Bachelor's Degree or master's degree in Computer Science. • 5+ years of hands-on software engineering experience. • Demonstrated AI/ML solution design experience • Proven work experience in Spark, Python, SQL, Any RDBMS. • Familiarity with Azure Data Lake, Synapse, ADF, Power BI. • Experience building, deploying and maintaining ML models in production • Experience with MLOps tools such as ModelDB, MLFlow and Kubeflow. • Familiar with best practices in the data engineering and MLOps community. • Ability to convey complex concepts and ideas in a clear and concise manner to a wide range of audience internal business stakeholders, outside partners and technology teams. • To be able to work in a fast-paced agile development environment. • Proven track record in working with diverse teams to achieve goals • Strong problem solving and troubleshooting skills with the ability to exercise mature judgment. What skills/attributes are preferred (what will set a candidate apart)? • Experience with AzureML • Expert in Azure Synapse, Azure Container Registry, Azure App services Of the required skills listed, which would you consider the top 3? Please list your expectations regarding years of experience for each requirement. a. AI/ ML Solution design b. Strong problem solving and troubleshooting skills with the ability to exercise mature judgment. c. MLOps What will the interview process look like? (Video phone or in person? How many rounds? How technical will the interviews be?) a. How many rounds? 2-3 b. Video vs. phone? Video c. How technical will the interviews be? Mostly technical

"Many a true word hath been spoken in jest." "O, beware, my lord, of jealousy; It is the green-ey'd monster, which doth mock The meat it feeds on." "There was a star danced, and under that was I born." Who can write like Shakespeare? Or even spell like Shakespeare?

DoNotPay, the "robot lawyer" service that helps you contest parking tickets and even sue people, is launching a new tool to help customers understand license agreements. Called "Do Not Sign," the service is included with DoNotPay's monthly $3 subscription fee, and it lets users upload, scan, or copy and paste the URLs of any license agreements they'd like to check. The service uses machine learning to highlight clauses it thinks users need to know about, including options to opt out from data collection. Agreeing to lengthy license agreements is an almost weekly occurrence for many people, with modern smart devices forcing you to hit "agree" on every new contract. Do Not Sign isn't a replacement for a real lawyer, but it's better than accepting a license agreement sight unseen so you can start using a shiny new gadget, service, or app without delay.

Digital transformation initiatives are driving government agencies to develop integrated IT asset management strategies that can meet resource demands and reduce analyst workloads. To meet growing service and asset requests, agencies are looking to IT asset management solutions that incorporate robotic process automation and artificial intelligence. IT asset management has typically been viewed as an operational solution that enables an agency to properly document IT assets along with associated contracts, license agreements and disposal information. However, in recent years, IT asset management has become an important part of an overall security strategy for many agencies after several highly publicized security breaches. Incorporating RPA with AI into next-generation IT asset management solutions will also help federal agencies that are struggling to meet IT asset management objectives due to limited resources.

"Many a true word hath been spoken in jest." "O, beware, my lord, of jealousy; It is the green-ey'd monster, which doth mock The meat it feeds on." "There was a star danced, and under that was I born." Who can write like Shakespeare? Or even just spell like Shakespeare?

As artificial intelligence (AI) is becoming mature, every industry wants to adopt it. Enterprises want to use it to unlock the hidden insight from data and use that to make strategic choices for companies. Many enterprises are continuously evaluating different use cases and experimenting with data using different AI frameworks. Having an infrastructure that can support different machine learning and deep learning (MLDL) frameworks is one of the challenges for enterprises in experimenting with AI. In many cases, it is helpful to be closer to data where you want to perform AI.