Engineering trust, accelerating growth and sculpting change are the three predominant trends Gartner has selected for its Hype Cycle for Emerging Technologies, 2021. They will drive organizations to explore emerging technologies such as nonfungible tokens (NFT), sovereign cloud, data fabric, generative AI and composable networks to help secure competitive advantage, the research firm said. Gartner's hype cycle provides a high-level view of important emerging trends that organizations must track, along with the specific technologies that must be monitored through the themes of trust, growth and change, said Philip Dawson, research vice president at Gartner. Engineering Trust: Trust demands security and reliability, Gartner said. However, it can also extend to building innovations as a resilient core and foundation for IT to deliver business value.

Nonfungible tokens (NFT) have 2 to 5 years before plateauing after hitting a "trough of disillusionment" and active metadata management, composable applications and generative AI are in the same camp, according to Gartner's 2021 Hype Cycle for Emerging Technologies. Now the hype cycle report from Gartner is always good for debate. But the hype cycle is even better for tech buyers who need to know what buzzword bingo they're about to receive from vendors. Gartner's emerging technology hype cycle distills more than 1,500 technologies into a list of must know tools. Also: What is quantum computing?

Generative Pre-trained Transformer 3 is an autoregressive language model that uses deep learning to produce human-like text created by OpenAI. Get down the article below to learn more. Artificial intelligence has reached new heights, and it doesn't show signs of stopping anytime soon. It has penetrated every walk of life, and it influences us in ways we're often not aware of. The latest breakthrough in AI is GPT-3, which can produce near-perfect text.

In it's next act, AI will transform from an augmentative technology to a more direct generator of products and data, according to Gartner's 2021 Hype Cycle. Enterprises have long sought AI for its ability to supercharge a workforce, picking up slack through automated tasks and a cost-effective option for repetitive labor, compared to humans. The next act in enterprise AI sees the technology becoming a standalone maker. The technology generates synthetic data to train its own models or identify groundbreaking products as solutions mature and adoption widens, as showcased in Gartner's Hype Cycle for Emerging Technologies 2021 report, published Monday. Called "Generative AI,", the technology is set to reach the plateau of productivity in the next two to five years.

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.

In early August 2021, OpenAI (which is partly controlled by Elon Musk) launched Codex, a revolutionary new AI system which can automatically write code in a variety of languages, using only plaintext prompts as input.

The ability to jointly profile the transcriptional and chromatin landscape of single-cells has emerged as a powerful technique to identify cellular populations and shed light on their regulation of gene expression. Current computational methods analyze jointly profiled (paired) or individual data modalities (unpaired), but do not offer a principled method to analyze both paired and unpaired samples jointly. Here we present MultiVI, a probabilistic framework that leverages deep neural networks to jointly analyze scRNA, scATAC and multiomic (scRNA scATAC) data. MultiVI creates an informative low-dimensional latent space that accurately reflects both chromatin and transcriptional properties of the cells even when one of the modalities is missing. We use public datasets to demonstrate that MultiVI is stable, easy to use, and outperforms current approaches for the joint analysis of paired and unpaired data.

Last week, artificial intelligence company OpenAI launched Codex, a new deep-learning-driven platform which writes fully functioning software code automatically. The system -- which was trained on a vast corpus of publicly available code-- originally debuted as part of Github's Copilot, a feature which helps programmers improve or update their software automatically. Codex is based on OpenAI's wildly successful GPT-3. When I tested GPT-3 last year, I felt like I was witnessing a technological revolution. The system can generate everything from fully-formed blog posts to songs, recipes -- even sea shanties.

Last year, OpenAI released the third version of its Generative Pretrained Transformer model (GPT-3), to much excitement amongst the tech and business communities -- so much, in fact, that OpenAI's CEO tweeted "the hype is way too much." GPT-3 has astonished observers with groundbreaking examples of code, news articles, translations and even poetry which evaluators have difficulty distinguishing from human-written output. Fundamentally, it simply autocompletes: give it a prompt, and it'll predict what comes next. But the enormous dataset it was trained on, along with the sheer complexity of its architecture, has enabled it to achieve the best results yet. So, how exactly does this technology work, and where could it take us?

We've created an improved version of OpenAI Codex, our AI system that translates natural language to code, and we are releasing it through our API in private beta starting today. Codex is the model that powers GitHub Copilot, which we built and launched in partnership with GitHub a month ago. Proficient in more than a dozen programming languages, Codex can now interpret simple commands in natural language and execute them on the user's behalf--making it possible to build a natural language interface to existing applications. We are now inviting businesses and developers to build on top of OpenAI Codex through our API. OpenAI Codex is a descendant of GPT-3; its training data contains both natural language and billions of lines of source code from publicly available sources, including code in public GitHub repositories.