Online community platforms require dynamic personalized retrieval and recommendation that can continuously adapt to evolving user interests and new documents. However, optimizing models to handle such changes in real-time remains a major challenge in large-scale industrial settings. To address this, we propose the Interest-aware Representation and Alignment (IRA) framework, an efficient and scalable approach that dynamically adapts to new interactions through a cumulative structure. IRA leverages two key mechanisms: (1) Interest Units that capture diverse user interests as contextual texts, while reinforcing or fading over time through cumulative updates, and (2) a retrieval process that measures the relevance between Interest Units and documents based solely on semantic relationships, eliminating dependence on click signals to mitigate temporal biases. By integrating cumulative Interest Unit updates with the retrieval process, IRA continuously adapts to evolving user preferences, ensuring robust and fine-grained personalization without being constrained by past training distributions. We validate the effectiveness of IRA through extensive experiments on real-world datasets, including its deployment in the Home Section of NAVER's CAFE, South Korea's leading community platform.

In this position paper, we argue that human evaluation of generative large language models (LLMs) should be a multidisciplinary undertaking that draws upon insights from disciplines such as user experience research and human behavioral psychology to ensure that the experimental design and results are reliable. The conclusions from these evaluations, thus, must consider factors such as usability, aesthetics, and cognitive biases. We highlight how cognitive biases can conflate fluent information and truthfulness, and how cognitive uncertainty affects the reliability of rating scores such as Likert. Furthermore, the evaluation should differentiate the capabilities and weaknesses of increasingly powerful large language models -- which requires effective test sets. The scalability of human evaluation is also crucial to wider adoption. Hence, to design an effective human evaluation system in the age of generative NLP, we propose the ConSiDERS-The-Human evaluation framework consisting of 6 pillars --Consistency, Scoring Critera, Differentiating, User Experience, Responsible, and Scalability.

Given a time-series of noisy measured outputs of a dynamical system z[k], k=1...N, the Identifying Regulation with Adversarial Surrogates (IRAS) algorithm aims to find a non-trivial first integral of the system, namely, a scalar function g() such that g(z[i]) = g(z[j]), for all i,j. IRAS has been suggested recently and was used successfully in several learning tasks in models from biology and physics. Here, we give the first rigorous analysis of this algorithm in a specific setting. We assume that the observations admit a linear first integral and that they are contaminated by Gaussian noise. We show that in this case the IRAS iterations are closely related to the self-consistent-field (SCF) iterations for solving a generalized Rayleigh quotient minimization problem. Using this approach, we derive several sufficient conditions guaranteeing local convergence of IRAS to the correct first integral.

Bayes' Theorem is something that confuses and frustrates many, but is not as awful as many make it out to be. While the formula for "Bae's Theorem" given in the graphic above is silly, doesn't make mathematical sense, and borders on being NSFW, it does help illustrate what the problem statement is (something that throws many, as intuitively it seems kind of backwards). Given that Netflix is occurring, one would want to know the probability of'chill', NOT the other way around. Granted, the right side of the equation is complete nonsense, but the left-side is actually a good mnemonic device, especially given that part of the reason so many students tune-out while learning mathematics is due to the dry sterility of the presentation. The theorem essentially states that: the probability of event A given event B is equal to the probability of B given event A times the probability of event A divided by the probability of B. Which seems very complex without breaking it down bit by bit.

Robust multi-vehicle path-planning is important for ensuring the safety of multi-vehicle systems in applications like transportation, search and rescue, and robotic exploration. Chance-constrained methods like Iterative Risk Allocation (IRA)(Ono and Williams 2008) have been developed for situations where environmental disturbances are unbounded. However, chance-constrained methods for the multi-vehicle case generally use centralized strategies where the vehicle set is planned with couplings between all vehicle pairs. This approach is intractable as fleet size increases because computation time is exponential with respect to the number of vehicles being planned over due to a polynomial increase in coupling constraints between vehicle pairs. We present a faster approach for chance-constrained multi-vehicle path-planning that relies upon a decentralized path-planning method called Risk-A ware Decentralized Model Predictive Control (RADMPC) to rapidly approximate a centralized IRA approach. The RADMPC approximation is evaluated for vehicle interactions to determine the vehicle sets that should be planned in a coupled manner. Applying IRA to the smaller vehicle sets determined from the RADMPC approximation rapidly plans safe paths for the entire fleet. A Monte Carlo simulation analysis demonstrates the correctness of our approach and a significant improvement in computation time compared to a centralized IRA approach.

A panel of industry experts gathered at RSA 2018 in San Francisco to explore the role that machine learning and artificial intelligence is playing in the current cyber landscape. After opening the discussion by asking the panel to each give their own definition of what machine learning is, Ira asked the speakers to define what types of applications are most appropriate for the use of machine learning and AI. Hillard: The places where it is most mature is around speech and image processing, and also around fraud detection. "The technology should be an enabler to solving a problem but sometimes it gets lost in what's being accomplished." Friedrichs: Most people have woken up to the fact that machine learning and AI are not the panacea that marketing tells us they are, but they can add to the feature set of a product.

Mumbai, January 27, 2017: HDFC Bank Ltd., today announced the launch of IRA, its interactive humanoid, at the Kamala Mills branch in Mumbai. IRA, which stands for Intelligent Robotic Assistant, will help branch staff in servicing customers. With this launch, HDFC Bank becomes the first bank in the country to introduce a humanoid for customer service. Developed using Robotics and Artificial Intelligence technologies, IRA will be positioned near the Welcome Desk, where it will greet customers and guide them to the relevant counter in the branch such as Cash Deposit, Foreign Exchange, Loans, among others in the first phase. After the customer selects an option, IRA will offer to guide the customer to the respective counter, by displaying'Take Me There' on the screen.

A fortnight ago the bank had announced that it will soon introduce a humanoid IRA - intelligent robotic assistant to help its branch staff in servicing customers. "In the next 18-24 months we are looking at atleast 15-20 humanoids in our branches with different manifestations and versions, it may be different looking with a different name," said Nitin Chugh, Country Head, Digital Banking, HDFC Bank. "Specific humanoids will be built to cater to needs of customers in those branches." Developed using Robotics and Artificial Intelligence technologies, IRA will be positioned near the Welcome Desk, where it will greet customers and guide them to the relevant counter in the branch such as Cash Deposit, Foreign Exchange, Loans, among others in the first phase. In the next phase, IRA's capabilities will be enhanced further by introducing features such as Voice and Face recognition for customer identification, Voice-guided navigation, Balance enquiry, and Cheque deposit among others.

HDFC Bank Ltd., today announced the launch of IRA, its interactive humanoid, at the Kamala Mills branch in Mumbai. IRA, which stands for Intelligent Robotic Assistant, will help branch staff in servicing customers. With this launch, HDFC Bank becomes the first bank in the country to introduce a humanoid for customer service. Developed using Robotics and Artificial Intelligence technologies, IRA will be positioned near the Welcome Desk, where it will greet customers and guide them to the relevant counter in the branch such as Cash Deposit, Foreign Exchange, Loans, among others in the first phase. Upon entering the Kamala Mills branch, IRA will greet the customer, before displaying a list of banking services he can avail at the branch.

Moneycontrol Bureau HDFC Bank's interactive humanoid IRA (Intelligent Robotic Assistant) which has been deployed at their Kamla Mills branch in Mumbai will guide customers to the various counters. This includes cash deposit, foreign exchange, loans among others in the first phase. IRA's language of communication will be English in the first phase. Nitin Chugh, country head, digital banking at HDFC Bank said that based on the response of the customers, they will take a decision on IRA's deployment in other branches across the country. HDFC Bank has developed IRA in partnership with Asimov Robotics, a start-up based in Kochi.