Sign up to receive the Future Tense newsletter every other Saturday. On Aug. 30, my heart broke a tiny bit. That day, the Guardian published a remarkable interview with Frank Oz, Jim Henson's longtime collaborator and the puppeteer behind Fozzie Bear, Miss Piggy, and other classic Muppets. Oz hasn't been involved with the Muppets since 2007, three years after Disney purchased the franchise. He tells the Guardian: "I'd love to do the Muppets again but Disney doesn't want me, and Sesame Street hasn't asked me for 10 years. They don't want me because I won't follow orders and I won't do the kind of Muppets they believe in. He added of the post-Disney Muppet movies and TV shows: "The soul's not there.

Machine learning is one of the technology that has become more and more popular with time and machine learning is the subset of the Artificial Intelligence which comes to your knowledge when you are connected to IT industry. Most of the companies like Netflix, Google and smaller companies uses Machine learning algoithms to predict the insights from the data. Although terms like artificial intelligence, machine learning and deep learning are used interchangeably but, they are not the same thing. Machine learning is the subset of artificial intelligence and deep learning is a subset of machine learning. Alan Turing's vision towards machine learning is being explained in one of his seminal paper such as " Machine learning is an application of artificial intelligence where a computer/machine learns from the past experiences (input data) and make future predictions. The performance of such a system should be at least human level."

A.I. is this year's buzzword of choice across the Tech industry, and speculation about what this field can achieve is already running rife. Let's separate fact from fiction and make some sense of all the hype. As we start the new year, the Tech propaganda machine is already ramping up its next generation of buzzwords, promising paradigm shifts and silver bullets that will make whole industries obsolete, enable huge efficiency gains, and make the world a better place. Blockchain, which used to top keyword search trends and social media posts, suffered a significant decline in interest, partly due to the fact that its initial hype was residual from the Bitcoin bubble. It seems that this year's buzzword of choice is going to be Artificial Intelligence.

The automatic identification of complex features in images has already become a reality thanks to artificial neural networks. Some examples of software exploiting this technique are Facebook's automatic tagging system, Google's image search engine and the animal and plant recognition system used by iNaturalist. We know that these networks are inspired by the human brain, but their working mechanism is still mysterious. New research, conducted by SISSA in association with the Technical University of Munich and published for the 33rd Annual NeurIPS Conference, proposes a new approach for studying deep neural networks and sheds new light on the image elaboration processes that these networks are able to carry out. Similar to what happens in the visual system, neural networks used for automatic image recognition analyse the content progressively, through a chain of processing stages.

Sequential modelling entails making sense of sequential data, which naturally occurs in a wide array of domains. One example is systems that interact with users, log user actions and behaviour, and make recommendations of items of potential interest to users on the basis of their previous interactions. In such cases, the sequential order of user interactions is often indicative of what the user is interested in next. Similarly, for systems that automatically infer the semantics of text, capturing the sequential order of words in a sentence is essential, as even a slight re-ordering could significantly alter its original meaning. This thesis makes methodological contributions and new investigations of sequential modelling for the specific application areas of systems that recommend music tracks to listeners and systems that process text semantics in order to automatically fact-check claims, or "speed read" text for efficient further classification.

A multi-turn dialogue always follows a specific topic thread, and topic shift at the discourse level occurs naturally as the conversation progresses, necessitating the model's ability to capture different topics and generate topic-aware responses. Previous research has either predicted the topic first and then generated the relevant response, or simply applied the attention mechanism to all topics, ignoring the joint distribution of the topic prediction and response generation models and resulting in uncontrollable and unrelated responses. In this paper, we propose a joint framework with a topic refinement mechanism to learn these two tasks simultaneously. Specifically, we design a three-pass iteration mechanism to generate coarse response first, then predict corresponding topics, and finally generate refined response conditioned on predicted topics. Moreover, we utilize GPT2DoubleHeads and BERT for the topic prediction task respectively, aiming to investigate the effects of joint learning and the understanding ability of GPT model. Experimental results demonstrate that our proposed framework achieves new state-of-the-art performance at response generation task and the great potential understanding capability of GPT model.

We study the matrix completion problem that leverages hierarchical similarity graphs as side information in the context of recommender systems. Under a hierarchical stochastic block model that well respects practically-relevant social graphs and a low-rank rating matrix model, we characterize the exact information-theoretic limit on the number of observed matrix entries (i.e., optimal sample complexity) by proving sharp upper and lower bounds on the sample complexity. In the achievability proof, we demonstrate that probability of error of the maximum likelihood estimator vanishes for sufficiently large number of users and items, if all sufficient conditions are satisfied. On the other hand, the converse (impossibility) proof is based on the genie-aided maximum likelihood estimator. Under each necessary condition, we present examples of a genie-aided estimator to prove that the probability of error does not vanish for sufficiently large number of users and items. One important consequence of this result is that exploiting the hierarchical structure of social graphs yields a substantial gain in sample complexity relative to the one that simply identifies different groups without resorting to the relational structure across them. More specifically, we analyze the optimal sample complexity and identify different regimes whose characteristics rely on quality metrics of side information of the hierarchical similarity graph. Finally, we present simulation results to corroborate our theoretical findings and show that the characterized information-theoretic limit can be asymptotically achieved. N recent years, personalized recommender systems have emerged in an extensive range of Web applications to predict the preferences of its users and provide them with new and relevant items based on the scarce data about the users and/or items [2]. There are two major paradigms of recommender systems: (i) content-based filtering systems; (ii) collaborative filtering systems. Content-based filtering approach exploits a profile of users' preferences and/or properties of the items to carry out the recommendation task.

Kind of a weird question, but this arose from my curiosity regarding the use of machine learning to answer "purely qualitative" questions. From my limited prior knowledge of machine learning, most every algorithm/strategy that I have read uses some sort combination of numerical parameters or categorical parameters to break a problem down into inputs/outputs. Solutions are also evaluated in terms of these parameters or with a right/wrong(thinking picture identification). For instance, how could inputs be selected when trying to decide on what meal to eat given a choice between 3? Or where to go on vacation next? How would solutions be evaluated and compared?

The Tech5 talent search is back again. We scoured, measured, and assessed scaleups from all corners of the continent to bring you the top 100 for 2021. Based on performance, growth, and potential, these companies have proven they have what it takes to join the exclusive Tech5 community. And just what does that mean? The Tech5 community is a network of top European founders designed to help them connect, get access to bespoke events, and gain media exposure.

While TV shows and movies adapted from games remain, generally, rubbish, there is no such curse the other way round. This seminal James Bond tie-in is the best example, showing that first-person shooters – previously the esoteric concern of hefty PCs – could excel on consoles. Its four-player split screen also taught an entire generation how to swear wholeheartedly at their peers. And to settle it once and for all: Oddjob is too small. Therefore playing as him is definitely – definitely – cheating.