Meta has trained an AI agent to play a boardgame that involves chatting with other players to persuade them to support its strategies -- and then betraying them. The company, which owns Facebook, Instagram and WhatsApp, says that its Cicero AI may have widespread applications in the near future including developing smarter virtual assistants with the combined use of technologies such as natural language processing (NLP) and strategic reasoning, according to a blog post released by the company. In a research article in the academic journal Science, Meta said its Cicero AI achieved human-level performance at the strategy boardgame Diplomacy in an online league where it played 40 games against 82 humans, ranking in the top 10% of participants who played more than one game. Diplomacy pits seven players against one another for control of a map of Europe. Each turn begins with players negotiating with one another for support for their plans and concludes with them simultaneously trying to execute their moves.

Have you ever wanted to build a customized recommender system for yourself? If Yes! Then this is the course you are looking for. Have you ever thought how YouTube adjust your feed as per your favorite content? Why is your Netflix recommending you your favorite TV shows? Have you ever wanted to build a customized recommender system for yourself?

Artificial intelligence (AI) has changed how modern applications interact with us, and machine learning has driven the modernization of various processes and systems. We all know that artificial intelligence has strengthened its hold on how we interact with computers. Siri, Alexa, Cortana and Hey Google are now natural parts of our life, and references to them regularly appear in mainstream entertainment, such as TV shows and advertisements. Artificial intelligence and machine learning are also a natural part of our evolving ecosystem. People understand, for example, that the "social media algorithm"--the infamous algorithm that decides who sees what social media content--is driven by artificial intelligence.

Black Friday may still be a few hours away, but we're already seeing a bunch of great deals on our favorite tech. This comes after a slow trickle of deals popping up across the web ever since the start of November. While we don't have the supply chain issues we did last year, it's still a good idea to start your holiday shopping as early as possible -- even if it's just a few hours before the biggest sale day of the year. The sooner you check off items from your list, the sooner they'll arrive and you'll be ready to go for the holidays. To make things easier for you, we've collected the best early Black Friday tech deals here so you don't have to go searching for them.

Task-oriented dialogue (TOD) systems have been widely used by mobile phone intelligent assistants to accomplish tasks such as calendar scheduling or hotel reservation. Current TOD systems usually focus on multi-turn text/speech interaction, then they would call back-end APIs designed for TODs to perform the task. However, this API-based architecture greatly limits the information-searching capability of intelligent assistants and may even lead to task failure if TOD-specific APIs are not available or the task is too complicated to be executed by the provided APIs. In this paper, we propose a new TOD architecture: GUI-based task-oriented dialogue system (GUI-TOD). A GUI-TOD system can directly perform GUI operations on real APPs and execute tasks without invoking TOD-specific backend APIs. Furthermore, we release META-GUI, a dataset for training a Multi-modal convErsaTional Agent on mobile GUI. We also propose a multi-model action prediction and response model, which show promising results on META-GUI. The dataset, codes and leaderboard are publicly available.

Predicting conversion rate (e.g., the probability that a user will purchase an item) is a fundamental problem in machine learning based recommender systems. However, accurate conversion labels are revealed after a long delay, which harms the timeliness of recommender systems. Previous literature concentrates on utilizing early conversions to mitigate such a delayed feedback problem. In this paper, we show that post-click user behaviors are also informative to conversion rate prediction and can be used to improve timeliness. We propose a generalized delayed feedback model (GDFM) that unifies both post-click behaviors and early conversions as stochastic post-click information, which could be utilized to train GDFM in a streaming manner efficiently. Based on GDFM, we further establish a novel perspective that the performance gap introduced by delayed feedback can be attributed to a temporal gap and a sampling gap. Inspired by our analysis, we propose to measure the quality of post-click information with a combination of temporal distance and sample complexity. The training objective is re-weighted accordingly to highlight informative and timely signals. We validate our analysis on public datasets, and experimental performance confirms the effectiveness of our method.

Today, we're announcing CICERO, the first artificial intelligence (AI) agent to achieve human-level performance in the popular strategy game Diplomacy. Diplomacy has been viewed as a nearly impossible challenge in AI because it requires players to understand people's motivations and perspectives, make complex plans and adjust strategies, and use language to convince people to form alliances. CICERO marks the beginning of a new era for AI that can collaborate with people in gameplay using strategic reasoning and natural language processing, and the learnings from technology like this could one day lead to intelligent assistants that can collaborate with people. While CICERO is only capable of playing Diplomacy, the technology behind it is relevant to many other applications. For example, current AI assistants can complete simple question-answer tasks, like telling you the weather -- but what if they could hold a long-term conversation with the goal of teaching you a new skill?

Ever wanted to learn to make a recommendation engine in Python? I mean, recommendation engines are a seriously valuable data science product. Just ask NetFlix and Amazon! They've made cargo ships of cash off these quirky little contraptions. They're actually not all that complicated to make either.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Phone banking is starting to get a dramatic personality shift, thanks in no small part to artificial intelligence (AI) and conversational AI. The first generation of phone banking was largely driven by interactive voice response (IVR) technology. That's the touch tone-driven technology that provides the monotonous voice tone telling you to "push 3 for your bank balance." IVR is a technology that was never particularly loved by anyone but it has done the job for many banks around the world for decades, albeit in a suboptimal approach.

Online platforms in the Internet Economy commonly incorporate recommender systems that recommend arms (e.g., products) to agents (e.g., users). In such platforms, a myopic agent has a natural incentive to exploit, by choosing the best product given the current information rather than to explore various alternatives to collect information that will be used for other agents. We propose a novel recommender system that respects agents' incentives and enjoys asymptotically optimal performances expressed by the regret in repeated games. We model such an incentive-aware recommender system as a multi-agent bandit problem in a two-sided market which is equipped with an incentive constraint induced by agents' opportunity costs. If the opportunity costs are known to the principal, we show that there exists an incentive-compatible recommendation policy, which pools recommendations across a genuinely good arm and an unknown arm via a randomized and adaptive approach. On the other hand, if the opportunity costs are unknown to the principal, we propose a policy that randomly pools recommendations across all arms and uses each arm's cumulative loss as feedback for exploration. We show that both policies also satisfy an ex-post fairness criterion, which protects agents from over-exploitation.