At the annual Hot Chips conference, IBM (NYSE: IBM) today unveiled details of the upcoming new IBM Telum Processor, designed to bring deep learning inference to enterprise workloads to help address fraud in real-time. Telum is IBM's first processor that contains on-chip acceleration for AI inferencing while a transaction is taking place. Three years in development, the breakthrough of this new on-chip hardware acceleration is designed to help customers achieve business insights at scale across banking, finance, trading, insurance applications and customer interactions. A Telum-based system is planned for the first half of 2022. According to recent Morning Consult research commissioned by IBM, 90% of respondents said that being able to build and run AI projects wherever their data resides is important1.

At the annual Hot Chips conference, IBM (NYSE: IBM) today unveiled details of the upcoming new IBM Telum Processor, designed to bring deep learning inference to enterprise workloads to help address fraud in real-time. Telum is IBM's first processor that contains on-chip acceleration for AI inferencing while a transaction is taking place. Three years in development, the breakthrough of this new on-chip hardware acceleration is designed to help customers achieve business insights at scale across banking, finance, trading, insurance applications and customer interactions. A Telum-based system is planned for the first half of 2022. According to recent Morning Consult research commissioned by IBM, 90% of respondents said that being able to build and run AI projects wherever their data resides is important1.

This paper presents a generative AI model for automated music composition with LSTM networks that takes a novel approach at encoding musical information which is based on movement in music rather than absolute pitch. Melodies are encoded as a series of intervals rather than a series of pitches, and chords are encoded as the set of intervals that each chord note makes with the melody at each timestep. Experimental results show promise as they sound musical and tonal. There are also weaknesses to this method, mainly excessive modulations in the compositions, but that is expected from the nature of the encoding. This issue is discussed later in the paper and is a potential topic for future work.

The past decade has seen a substantial rise in the amount of mis- and disinformation online, from targeted disinformation campaigns to influence politics, to the unintentional spreading of misinformation about public health. This development has spurred research in the area of automatic fact checking, from approaches to detect check-worthy claims and determining the stance of tweets towards claims, to methods to determine the veracity of claims given evidence documents. These automatic methods are often content-based, using natural language processing methods, which in turn utilise deep neural networks to learn higher-order features from text in order to make predictions. As deep neural networks are black-box models, their inner workings cannot be easily explained. At the same time, it is desirable to explain how they arrive at certain decisions, especially if they are to be used for decision making. While this has been known for some time, the issues this raises have been exacerbated by models increasing in size, and by EU legislation requiring models to be used for decision making to provide explanations, and, very recently, by legislation requiring online platforms operating in the EU to provide transparent reporting on their services. Despite this, current solutions for explainability are still lacking in the area of fact checking. This thesis presents my research on automatic fact checking, including claim check-worthiness detection, stance detection and veracity prediction. Its contributions go beyond fact checking, with the thesis proposing more general machine learning solutions for natural language processing in the area of learning with limited labelled data. Finally, the thesis presents some first solutions for explainable fact checking.

This paper describes a novel dataset consisting of sentences with semantic similarity annotations. The data originate from the journalistic domain in the Czech language. We describe the process of collecting and annotating the data in detail. The dataset contains 138,556 human annotations divided into train and test sets. In total, 485 journalism students participated in the creation process. To increase the reliability of the test set, we compute the annotation as an average of 9 individual annotations. We evaluate the quality of the dataset by measuring inter and intra annotation annotators' agreements. Beside agreement numbers, we provide detailed statistics of the collected dataset. We conclude our paper with a baseline experiment of building a system for predicting the semantic similarity of sentences. Due to the massive number of training annotations (116 956), the model can perform significantly better than an average annotator (0,92 versus 0,86 of Person's correlation coefficients).

As I scan my Netflix page, which rectangular tiles leap to the forefront of my attention? There's Sexify, with its promotional photo of a woman's face seemingly captured at the peak of passion. Or maybe I should watch Lucifer, its star staring shirtlessly into my soul, his chest so uncannily hairless it looks like a video game character's. The answer to What Lies Below might be a jacked aquatic geneticist with a Superman jaw line, but other mysteries are left teasingly unsolved by Netflix's most popular titles. For a while this spring, Why Did You Kill Me? jostled for space on the Top 10 list with Who Killed Sara?

Last December, Universal Music Publishing Group bought up Bob Dylan's entire discography in a deal estimated at more than $300 million. Similarly, Stevie Nicks sold an 80 percent share of her works to Primary Wave Music for an estimated $100 million that same month. But as all this money changes hands for the industry's biggest stars, one songwriting startup has plans to open the firehose of music royalties to the everyman. "You see these huge deals, like the Bob Dylan deal with the publishing rights and all this money," Alex Mitchell, co-founder and CEO of Boomy told Engadget. "It started with a recognition that most people are going to be left out of that and it caused us to have a conversation about equity in the music industry, 'how do we fairly remunerate artists, what's the role of labels,' there's just chaos happening in the music industry right now." Mitchell realized that one major obstacle keeping amateur musicians from becoming published musicians was a technological one.

After a recent $200 million funding round from top Chinese venture capital firms, Shenzhen-based AI startup SmartMore has big plans to go public and expand across Southeast Asia, starting with Singapore. The funding round, closed in June, included existing investors Sequoia Capital China, Lenovo Capital and Forbes Midas listee Anna Fang's ZhenFund. IDG Capital, CoStone Capital and Green Pine Capital Partners also participated in the round. The two-year-old startup is now valued at more than $1 billion, says Li Ruiyu, cofounder and head of product, in a video interview. "This round of funding will be used to penetrate deeper into our market, increase investment in R&D, and drive large-scale implementation of our intelligent manufacturing solutions," says Li, 28, who made the 30 Under 30 Asia list this year.

… with the aid of machine-learning algorithms, detect and identify gunshot … art with artificial intelligence, barely anyone used it to make actual art.

This game-changing course introduces you to new-age technologies in Artificial Intelligence music creation to help you become a music star in no time. Why learn this music course and how is this a differentiator for music creators? This course can change your life if you are a music composer. Because, we will tell you the most popular Artificial Intelligence Music Creation tools that can help you compose music tracks without you – having any music knowledge whatsoever. We will also detail about the latest discovery tools in Music Mashups and also we will go through the complete tutorial of Adobe Amper and Jukedeck – great AI music assistants.