FBI under pressure over open airport five miles from Charlie Kirk assassination hit as private jet'vanishes' after shooting MSNBC analyst Matthew Dowd fired over'disgusting' on-air comments about Charlie Kirk shortly after conservative star was assassinated Elite sniper breaks down Charlie Kirk assassin's sick plot... and reveals tiny detail everyone's missed: The gun. MAUREEN CALLAHAN: Charlie Kirk's body wasn't even cold... before the fighting started again. Do these ghouls not see where this is headed? Charlie Kirk's powerful tribute to murdered Ukrainian refugee hours before his own assassination: 'America will never be the same' Musk dethroned as richest person by forgotten Wall Street darling's founder as stock soars 42% Charlie Kirk dead at 31: What we know so far about MAGA star's death at Utah campus that sent shockwaves around the world as FBI botches arrest and Trump promises ultimate punishment TMZ forced to apologize after staff heard erupting in laughter as Charlie Kirk's death was announced Sweater weather starts here - the cozy, chic pieces from Soft Surroundings you'll actually wear all season Trump issues Oval Office address over Charlie Kirk's assassination: 'This is a dark moment for America' Fierce debate erupts over'non-human' technology in space after video captures UFO surviving Hellfire strike Is this Charlie Kirk's killer? This Oscar-nominated actress, 68, will soon reunite with her ex in Spain for their daughter's wedding, can you guess who?

Large language models (LLMs) gained immense popularity due to their impressive capabilities in unstructured conversations. However, they underperform compared to previous approaches in task-oriented dialogue (TOD), wherein reasoning and accessing external information are crucial. Empowering LLMs with advanced prompting strategies such as reasoning and acting (ReAct) has shown promise in solving complex tasks traditionally requiring reinforcement learning. In this work, we apply the ReAct strategy to guide LLMs performing TOD. We evaluate ReAct-based LLMs (ReAct-LLMs) both in simulation and with real users. While ReAct-LLMs seem to underperform state-of-the-art approaches in simulation, human evaluation indicates higher user satisfaction rate compared to handcrafted systems despite having a lower success rate.

It started with a single tweet in November 2019. David Heinemeier Hansson, a high-profile tech entrepreneur, lashed out at Apple's newly launched credit card, calling it "sexist" for offering his wife a credit limit 20 times lower than his own. The allegations spread like wildfire, with Hansson stressing that artificial intelligence – now widely used to make lending decisions – was to blame. "It does not matter what the intent of individual Apple reps are, it matters what THE ALGORITHM they've placed their complete faith in does. And what it does is discriminate. While Apple and its underwriters Goldman Sachs were ultimately cleared by US regulators of violating fair lending rules last year, it rekindled a wider debate around AI use across public and private industries. Politicians in the European Union are now planning to introduce the first comprehensive global template for regulating AI, as institutions increasingly automate routine tasks in an attempt to boost efficiency and ...

Swiss Re, the world's leading reinsurance organisation, in collaboration with MachineHack, is set to launch a Machine Learning Hackathon from March 11th to 28th to predict accident risk scores for unique postcodes. The top three winners stand a chance to win prizes worth INR 1.5 lakh. Swiss Re applies fresh perspectives, knowledge and capital to anticipate and manage risk to create smarter solutions. Swiss Re's Global Business Solutions Center (BSC) in Bangalore has more than 1,300 professionals leveraging experience, expertise and out-of-the-box thinking to create new business opportunities. Click here to participate in the hackathon.

We study the problem of estimating potential revenue or demand at business facilities and understanding its generating mechanism. This problem arises in different fields such as operation research or urban science, and more generally, it is crucial for businesses' planning and decision making. We develop a Bayesian spatial interaction model, henceforth BSIM, which provides probabilistic predictions about revenues generated by a particular business location provided their features and the potential customers' characteristics in a given region. BSIM explicitly accounts for the competition among the competitive facilities through a probability value determined by evaluating a store-specific Gaussian distribution at a given customer location. We propose a scalable variational inference framework that, while being significantly faster than competing Markov Chain Monte Carlo inference schemes, exhibits comparable performances in terms of parameters identification and uncertainty quantification. We demonstrate the benefits of BSIM in various synthetic settings characterised by an increasing number of stores and customers. Finally, we construct a real-world, large spatial dataset for pub activities in London, UK, which includes over 1,500 pubs and 150,000 customer regions. We demonstrate how BSIM outperforms competing approaches on this large dataset in terms of prediction performances while providing results that are both interpretable and consistent with related indicators observed for the London region.

Task oriented dialogue (TOD) requires the complex interleaving of a number of individually controllable components with strong guarantees for explainability and verifiability. This has made it difficult to adopt the multi-turn multi-domain dialogue generation capabilities of streamlined end-to-end open-domain dialogue systems. In this paper, we present a new framework, DLGNet-Task, a unified task-oriented dialogue system which employs autoregressive transformer networks such as DLGNet and GPT-2/3 to complete user tasks in multi-turn multi-domain conversations. Our framework enjoys the controllable, verifiable, and explainable outputs of modular approaches, and the low development, deployment and maintenance cost of end-to-end systems. Treating open-domain system components as additional TOD system modules allows DLGNet-Task to learn the joint distribution of the inputs and outputs of all the functional blocks of existing modular approaches such as, natural language understanding (NLU), state tracking, action policy, as well as natural language generation (NLG). Rather than training the modules individually, as is common in real-world systems, we trained them jointly with appropriate module separations. When evaluated on the MultiWOZ2.1 dataset, DLGNet-Task shows comparable performance to the existing state-of-the-art approaches. Furthermore, using DLGNet-Task in conversational AI systems reduces the level of effort required for developing, deploying, and maintaining intelligent assistants at scale.

Hierarchical neural networks are often used to model inherent structures within dialogues. For goal-oriented dialogues, these models miss a mechanism adhering to the goals and neglect the distinct conversational patterns between two interlocutors. In this work, we propose Goal-Embedded Dual Hierarchical Attentional Encoder-Decoder (G-DuHA) able to center around goals and capture interlocutor-level disparity while modeling goal-oriented dialogues. Experiments on dialogue generation, response generation, and human evaluations demonstrate that the proposed model successfully generates higher-quality, more diverse and goal-centric dialogues. Moreover, we apply data augmentation via goal-oriented dialogue generation for task-oriented dialog systems with better performance achieved.

I previously worked for a year and a half at an Airbnb property management company, as head of the team responsible for pricing, revenue and analysis. One thing I find particularly interesting is how to figure out what price to charge for a listing on the site. Although'it's a two bedroom in Manchester' will get you reasonably far, there are actually a huge number of factors that can influence a listing's price. As part of a bigger project on using deep learning to predict Airbnb prices, I found myself thrown back into the murky world of property data. Geospatial data can be very complex and messy -- and user-entered geospatial data doubly so.

Consulting firm Accenture has a new tool to help businesses detect and eliminate gender, racial and ethnic bias in artificial intelligence software. Companies and governments are increasingly turning to machine-learning algorithms to help make critical decisions, including who to hire, who gets insurance or a mortgage, who receives government benefits and even whether to grant a prisoner parole. One of the arguments for using such software is that, if correctly designed and trained, it can potentially make decisions free from the prejudices that often impact human choices. But, in a number of well-publicized examples, algorithms have been found to discriminate against minorities and women. For instance, an algorithm many U.S. cities and states used to help make bail decisions was twice as likely to falsely label black prisoners as being at high-risk for re-offending as white prisoners, according to a 2016 investigation by ProPublica.

Over the past few months my blogs have attempted to demystify some of the techniques used by Data Scientists to build models or process large amounts of data. For all the flashy techniques and algorithms this is not where Data Scientists spend 90% of their time. The hard yards of any analysis lies in the data munging. By Data Munging we mean the process of taking raw data, understanding it, cleaning it and preparing it for analysis or modelling. It is by no means the glamorous part of data science however if done well it plays a more important role in getting to powerful models and insights than what algorithm you use.