If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Artificial intelligence is turning old pictures of people into short, animated clips that show them moving and blinking. The feature, called Deep Nostalgia, comes from genealogy company MyHeritage. It uses machine learning to create facial expressions and movements that look super realistic, Tom's Guide reported Tuesday. In a blog post, MyHeritage shared social media posts from users who were thrilled to see their loved ones who'd passed come to life, if only for a few moments. The clips show the people in black-and-white or faded photos tilting their heads and looking around.
Open Knowledge Graphs (such as DBpedia, Wikidata, YAGO) have been recognized as the backbone of diverse applications in the field of data mining and information retrieval. Hence, the completeness and correctness of the Knowledge Graphs (KGs) are vital. Most of these KGs are mostly created either via an automated information extraction from Wikipedia snapshots or information accumulation provided by the users or using heuristics. However, it has been observed that the type information of these KGs is often noisy, incomplete, and incorrect. To deal with this problem a multi-label classification approach is proposed in this work for entity typing using KG embeddings. We compare our approach with the current state-of-the-art type prediction method and report on experiments with the KGs.
Customised video conferencing backgrounds have gotten an artificially intelligent upgrade: real-time animated deepfakes that transform your face into that of a celebrity. Karim Iskakov at the Skolkovo Institute of Science and Technology in Moscow, and Ali Aliev, a software developer in Moscow, have developed a program that lets you create deepfakes in real time during video calls. The program, called Avatarify, works with video conferencing applications such as Zoom or Skype. All it requires is a headshot of the person you want to appear to be. Demonstrating to New Scientist via a Zoom call, Aliev used the program to appear to be speaking as several figures including Boris Johnson, Donald Trump, Albert Einstein and the Mona Lisa.
Albert Einstein's favourite childhood toy that he cherished all his life will go up for sale next month for £46,000 ($60,000). The German game, titled'Perlen Mosaik Spiel' (Mosaic Pearl Game), consists of 520 coloured beads that can be inserted into a punch-hole frame to create patterns. The game shows some general signs of wear and tear, including minor chipping to the title label and'intriguing' pencil markings inside the box, which may represent his first autograph. The board game would have been cherished by the young and gifted physicist, who went on to win the 1921 Nobel Prize in Physics. Seller Bonhams New York describes the set as'a rare and joyous Einstein artefact of museum quality' and'an essential learning tool for the young genius'.
In this paper we propose and study the novel problem of explaining node embeddings by finding embedded human interpretable subspaces in already trained unsupervised node representation embeddings. We use an external knowledge base that is organized as a taxonomy of human-understandable concepts over entities as a guide to identify subspaces in node embeddings learned from an entity graph derived from Wikipedia. We propose a method that given a concept finds a linear transformation to a subspace where the structure of the concept is retained. Our initial experiments show that we obtain low error in finding fine-grained concepts.
Great spirits have always faced violent opposition from mediocre minds. Fuzzy or Techie?! Why #AI needs more interdisciplinary thinkers @IBMthinkLeaders https://t.co/ZggRLF45zv Becuase who (trolls) has time to read and fact check and their only job is to attack and harass . Unknown faces hounded me and made personal attacks, labelled me as Left wing marxist while everyone knows I am a staunch BJP supporter and believe in Modiji's vision for India. Going to IIT next month and have only one advice for Techies- Read more of Philosophy #books:-) Netizsche, Arthur Schopenhauer, Kant, Plato, Hegel -- Ruchi (@rucsb) April 30, 2019 Please add Adhi Shankara, Vivekananda, Kautilya, Panini to that list?
Trying to comprehend the nature of an AI superintelligence is akin to trying to comprehend the mind of God. There's an element of the mystical in attempting to define the intentions of a being infinitely more intelligent and therefore infinitely more powerful than you--a being that may or may not even exist. There's even an actual religion, founded by a former Uber and Waymo self-driving engineer, dedicated to the worship of God based on artificial intelligence. But the existential fear of superintelligence isn't just spiritual vaporware, and we have a sense, at least, of how it might come to be. Researchers like Eliezer Yudkowsky suggest that the development of superintelligence could start with the creation of a "seed AI"--an artificial intelligence program that is able to achieve recursive self-improvement. We've already done that with narrow AI-- AlphaGo Zero started out knowing nothing about the game Go, but quickly managed to improve to a human level and then far beyond.
The work presented in this master thesis consists of extracting a set of events from texts written in natural language. For this purpose, we have based ourselves on the basic notions of the information extraction as well as the open information extraction. First, we applied an open information extraction(OIE) system for the relationship extraction, to highlight the importance of OIEs in event extraction, and we used the ontology to the event modeling. We tested the results of our approach with test metrics. As a result, the two-level event extraction approach has shown good performance results but requires a lot of expert intervention in the construction of classifiers and this will take time. In this context we have proposed an approach that reduces the expert intervention in the relation extraction, the recognition of entities and the reasoning which are automatic and based on techniques of adaptation and correspondence. Finally, to prove the relevance of the extracted results, we conducted a set of experiments using different test metrics as well as a comparative study.
A drone flies outside the Massachusetts Institute of Technology's Kresge Auditorium during the 2018 Solve conference. The project connects tech entrepreneurs with leaders in government, business and academia to tackle world problems. MIT's recent billion-dollar commitment to its new AI-focused school, the Stephen A. Schwarzman College of Computing, represents an essential advance, not for its magnitude but for its plans to infect the rest of the university with AI. Announced earlier this month, MIT's new school's mission includes engaging across MIT to explore how AI might impact research across fields from engineering and social sciences to the humanities. MIT's president, Rafael Reif, explained the purpose of the school is to "educate bilinguals of the future."
This paper presents a framework for generating adventure games from open data. Focusing on the murder mystery type of adventure games, the generator is able to transform open data from Wikipedia articles, OpenStreetMap and images from Wikimedia Commons into WikiMysteries. Every WikiMystery game revolves around the murder of a person with a Wikipedia article and populates the game with suspects who must be arrested by the player if guilty of the murder or absolved if innocent. Starting from only one person as the victim, an extensive generative pipeline finds suspects, their alibis, and paths connecting them from open data, transforms open data into cities, buildings, non-player characters, locks and keys and dialog options. The paper describes in detail each generative step, provides a specific playthrough of one WikiMystery where Albert Einstein is murdered, and evaluates the outcomes of games generated for the 100 most influential people of the 20th century.