A job application form signed by Apple co-founder Steve Jobs as a teenager back in 1973 -- one that hints at his computer skills -- is being sold at auction in London. The paperwork for the unspecified position dates from a year before Mr Jobs joined then video game start-up Atari as a technician and worked alongside Steve Wozniak. The duo would go on to found the Apple Computer Company, releasing their first machine, the Apple-1, just two years later. Had teenaged Jobs' application been successful, he may not have met Mr Wozniak and the landscape of modern computing would likely have ended up very different. London auctioneer Charterfields is auctioning the item online on February 24, 2021 -- with bids set to open at £15,000 (which is around $20,950).

Dating back to the Industrial Revolution, people have speculated that machines would render human ... [ ] work obsolete. Unlike in earlier eras, artificial intelligence will prove this prophecy true. "When looms weave by themselves, man's slavery will end." Stanford is hosting an event next month named "Intelligence Augmentation: AI Empowering People to Solve Global Challenges." This title is telling and typical.

Most existing personalization systems promote items that match a user's previous choices or those that are popular among similar users. This results in recommendations that are highly similar to the ones users are already exposed to, resulting in their isolation inside familiar but insulated information silos. In this context, we develop a novel recommendation framework with a goal of improving information diversity using a modified random walk exploration of the user-item graph. We focus on the problem of political content recommendation, while addressing a general problem applicable to personalization tasks in other social and information networks. For recommending political content on social networks, we first propose a new model to estimate the ideological positions for both users and the content they share, which is able to recover ideological positions with high accuracy. Based on these estimated positions, we generate diversified personalized recommendations using our new random-walk based recommendation algorithm. With experimental evaluations on large datasets of Twitter discussions, we show that our method based on \emph{random walks with erasure} is able to generate more ideologically diverse recommendations. Our approach does not depend on the availability of labels regarding the bias of users or content producers. With experiments on open benchmark datasets from other social and information networks, we also demonstrate the effectiveness of our method in recommending diverse long-tail items.

Fake News Detection is an essential problem in the field of Natural Language Processing. The benefits of an effective solution in this area are manifold for the goodwill of society. On a surface level, it broadly matches with the general problem of text classification. Researchers have proposed various approaches to tackle fake news using simple as well as some complex techniques. In this paper, we try to make a comparison between the present Deep Learning techniques by representing the news instances in some vector space using a combination of common mathematical operations with available vector space representations. We do a number of experiments using various combinations and permutations. Finally, we conclude with a sound analysis of the results and evaluate the reasons for such results.

News recommendation calls for deep insights of news articles' underlying semantics. Therefore, pretrained language models (PLMs), like BERT and RoBERTa, may substantially contribute to the recommendation quality. However, it's extremely challenging to have news recommenders trained together with such big models: the learning of news recommenders requires intensive news encoding operations, whose cost is prohibitive if PLMs are used as the news encoder. In this paper, we propose a novel framework, SpeedyFeed, which efficiently trains PLMs-based news recommenders of superior quality. SpeedyFeed is highlighted for its light-weighted encoding pipeline, which gives rise to three major advantages. Firstly, it makes the intermedia results fully reusable for the training workflow, which removes most of the repetitive but redundant encoding operations. Secondly, it improves the data efficiency of the training workflow, where non-informative data can be eliminated from encoding. Thirdly, it further saves the cost by leveraging simplified news encoding and compact news representation. SpeedyFeed leads to more than 100$\times$ acceleration of the training process, which enables big models to be trained efficiently and effectively over massive user data. The well-trained PLMs-based model significantly outperforms the state-of-the-art news recommenders in comprehensive offline experiments. It is applied to Microsoft News to empower the training of large-scale production models, which demonstrate highly competitive online performances. SpeedyFeed is also a model-agnostic framework, thus being potentially applicable to a wide spectrum of content-based recommender systems. We've made the source code open to the public so as to facilitate research and applications in related areas.

Automatic music generation has become an epicenter research topic for many scientists in artificial intelligence, who are also interested in the music industry. Being a balanced combination of math and art, music in collaboration with A.I. can simplify the generation process for new musical pieces, and ease the interpretation of it to a tangible level. On the other hand, the artistic nature of music and its mingling with the senses and feelings of the composer makes the artificial generation and mathematical modeling of it infeasible. In fact, there are no clear evaluation measures that can combine the objective music grammar and structure with the subjective audience satisfaction goal. Also, original music contains different elements that it is inevitable to put together. Therefore, in this paper, a method based on a genetic multi-objective evolutionary optimization algorithm for the generation of polyphonic music (melody with rhythm and harmony or appropriate chords) is introduced in which three specific goals determine the qualifications of the music generated. One of the goals is the rules and regulations of music, which, along with the other two goals, including the scores of music experts and ordinary listeners, fits the cycle of evolution to get the most optimal response. The scoring of experts and listeners separately is modeled using a Bi-LSTM neural network and has been incorporated in the fitness function of the algorithm. The results show that the proposed method is able to generate difficult and pleasant pieces with desired styles and lengths, along with harmonic sounds that follow the grammar while attracting the listener, at the same time.

In computer science, the main outlets for peer-reviewed research are not journals but conferences, where accepted papers are presented in the form of talks or posters. In June, 2019, at a large artificial-intelligence conference in Long Beach, California, called Computer Vision and Pattern Recognition, I stopped to look at a poster for a project called Speech2Face. Using machine learning, researchers had developed an algorithm that generated images of faces from recordings of speech. A neat idea, I thought, but one with unimpressive results: at best, the faces matched the speakers' sex, age, and ethnicity--attributes that a casual listener might guess. That December, I saw a similar poster at another large A.I. conference, Neural Information Processing Systems (NeurIPS), in Vancouver, Canada.

In the retail sector, Amazon increasingly uses AI systems to direct and monitor staff in its warehouses. This has led to several reports of employees being overworked, accusations that Amazon has repeatedly denied. Amazon says that if the AI notices a worker underperforming, he or she gets additional support and training, which comes from a human.

System-provided explanations for recommendations are an important component towards transparent and trustworthy AI. In state-of-the-art research, this is a one-way signal, though, to improve user acceptance. In this paper, we turn the role of explanations around and investigate how they can contribute to enhancing the quality of generated recommendations themselves. We devise a human-in-the-loop framework, called ELIXIR, where user feedback on explanations is leveraged for pairwise learning of user preferences. ELIXIR leverages feedback on pairs of recommendations and explanations to learn user-specific latent preference vectors, overcoming sparseness by label propagation with item-similarity-based neighborhoods. Our framework is instantiated using generalized graph recommendation via Random Walk with Restart. Insightful experiments with a real user study show significant improvements in movie and book recommendations over item-level feedback.

Graphs are one of the most efficacious structures for representing datapoints and their relations, and they have been largely exploited for different applications. Previously, the higher-order relations between the nodes have been modeled by a generalization of graphs known as hypergraphs. In hypergraphs, the edges are defined by a set of nodes i.e., hyperedges to demonstrate the higher order relationships between the data. However, there is no explicit higher-order generalization for nodes themselves. In this work, we introduce a novel generalization of graphs i.e., K-Nearest Hyperplanes graph (KNH) where the nodes are defined by higher order Euclidean subspaces for multi-view modeling of the nodes. In fact, in KNH, nodes are hyperplanes or more precisely m-flats instead of datapoints. We experimentally evaluate the KNH graph on two multi-aspect datasets for misinformation detection. The experimental results suggest that multi-view modeling of articles using KNH graph outperforms the classic KNN graph in terms of classification performance.