Global AI Machine Learning market, a new research report that examines the current and futuristic growth of this market.

We developed four variants of the model, ranging in size from small (124 million parameters) to large ( 1.5 billion parameters). We chose a staged release process, releasing the smallest model in February, but withholding larger models due to concerns about the potential for misuse, such as generating fake news content, impersonating others in email, or automating abusive social media content production [ 46 ]. We released the next model size in May as part of a staged release process. We are now releasing our 774 million parameter model. While large language models' flexibility and generative capabilities raise misuse concerns, they also have a range of beneficial uses - they can assist in prose, poetry, and programming; analyze dataset biases; and more.

The invention of an artificial super-intelligence has been a central theme in science fiction since at least the 19th century. From E.M. Forster's short story The Machine Stops (1909) to the recent HBO television series Westworld, writers have tended to portray this possibility as an unmitigated disaster. But this issue is no longer one of fiction. Prominent contemporary scientists and engineers are now also worried that super-AI could one day surpass human intelligence (an event known as the "singularity") and become humanity's "worst mistake". Current trends suggest we are set to enter an international arms race for such a technology.

OpenAI, the AI company that Elon Musk founded and then quit has just released a more powerful version of its AI text-writing software. The company still won't release their full software - that can be used to write fake news and messages en masse - due to fears it might be misused. OpenAI says its text-writing system is so advanced it can write news stories and even fiction that passes as human. A user can feed the system text - anything from a few sentences to pages of it - and the system will then continue that same text in an uncannily well-written, contextually relevant, human style. However, after releasing its original system, GPT-2, in February, the company said the full software was too dangerous to release to the public - a weaker version was made available. Now, the company has announced it has released a version of GPT-2 that is six times more powerful.

Music genre classification is a widely researched topic in music information retrieval (MIR). Being able to automatically tag genres will benefit music streaming service providers such as JOOX, Apple Music, and Spotify for their content-based recommendation. However, most studies on music classification have been done on western songs which differ from Thai songs. Lukthung, a distinctive and long-established type of Thai music, is one of the most popular music genres in Thailand and has a specific group of listeners. In this paper, we develop neural networks to classify such Lukthung genre from others using both lyrics and audios. Words used in Lukthung songs are particularly poetical, and their musical styles are uniquely composed of traditional Thai instruments. We leverage these two main characteristics by building a lyrics model based on bag-of-words (BoW), and an audio model using a convolutional neural network (CNN) architecture. We then aggregate the intermediate features learned from both models to build a final classifier. Our results show that the proposed three models outperform all of the standard classifiers where the combined model yields the best $F_1$ score of 0.86, allowing Lukthung classification to be applicable to personalized recommendation for Thai audience.

The concept of randomness is easy to grasp on an intuitive level but challenging to characterize in rigorous mathematical terms. In "Algorithmic Randomness" (May 2019), Rod Downey and Denis R. Hirschfeldt present a comprehensive discussion of this issue, incorporating the distinct perspectives of "statisticians, coders, and gamblers." Randomness is also a concern to "modelers" who depend on simulation models driven by random number generators or analytic models built using probabilistic assumptions. In such cases, the underlying mathematical model is often an ergodic stochastic process, and the issue is whether the output of the simulator's random number generator or the observed behavior of the real-world system being modeled is "random enough" to establish confidence in the model's predictions. In a sense, this highly pragmatic perspective represents a less restrictive approach to the issue of randomness: if any of the strong criteria described by the authors are satisfied, the output of the simulator's random number generator or the observed behavior of the system being modeled should be sufficiently random to establish confidence in a model's predictions.

The consequences of fabricated news stories may have lingering effects on your perception. According to a new study, voters may develop false memories after reading a fake news report. And, they're more likely to do so if the narrative lines up with their own beliefs. Researchers presented over 3,000 eligible voters in Ireland with legitimate and made-up stories ahead of the 2018 referendum on legalizing abortion. In subsequent questioning – and after being told that some of the reports were fake – nearly half of participants reported a memory for at least one of the fabricated events, and many tended to be steadfast in these beliefs.

What's the status quo of Federated Machine Learning and how to establish an … "FML enables machine learning engineers and data scientists to work …

A family on the brink of splitting up become the owners of a cutting-edge robot being sought by a corporation, homicide investigators and terrorists.

In this work, we attempt to solve the Hit Song Science problem, which aims to predict which songs will become chart-topping hits. We constructed a dataset with approximately 1.8 million hit and non-hit songs and extracted their audio features using the Spotify Web API. We test four models on our dataset. Our best model was random forest, which was able to predict Billboard song success with 88% accuracy.