Deep learning inference on embedded devices is a burgeoning field with myriad applications because tiny embedded devices are omnipresent. But we must overcome major challenges before we can benefit from this opportunity. Embedded processors are severely resource constrained. Their nearest mobile counterparts exhibit at least a 100---1,000x difference in compute capability, memory availability, and power consumption. As a result, the machine-learning (ML) models and associated ML inference framework must not only execute efficiently but also operate in a few kilobytes of memory. Also, the embedded devices' ecosystem is heavily fragmented. To maximize efficiency, system vendors often omit many features that commonly appear in mainstream systems, including dynamic memory allocation and virtual memory, that allow for cross-platform interoperability. The hardware comes in many flavors (e.g., instruction-set architecture and FPU support, or lack thereof). We introduce TensorFlow Lite Micro (TF Micro), an open-source ML inference framework for running deep-learning models on embedded systems. TF Micro tackles the efficiency requirements imposed by embedded-system resource constraints and the fragmentation challenges that make cross-platform interoperability nearly impossible. The framework adopts a unique interpreter-based approach that provides flexibility while overcoming these challenges. This paper explains the design decisions behind TF Micro and describes its implementation details. Also, we present an evaluation to demonstrate its low resource requirement and minimal run-time performance overhead.

Emotions and their evolution play a central role in creating a captivating story. In this paper, we present the first study on modeling the emotional trajectory of the protagonist in neural storytelling. We design methods that generate stories that adhere to given story titles and desired emotion arcs for the protagonist. Our models include Emotion Supervision (EmoSup) and two Emotion-Reinforced (EmoRL) models. The EmoRL models use special rewards designed to regularize the story generation process through reinforcement learning. Our automatic and manual evaluations demonstrate that these models are significantly better at generating stories that follow the desired emotion arcs compared to baseline methods, without sacrificing story quality.

We as human may have often wondered if the intelligence of human can be copied and machines can work the same way as us. While it is still a distant dream but we are not very far away. In the path to artificial intelligence lets have an overview of what it really means and how data science is helping us achieve it. A) Artificial Intelligence: It is an important science that actually helps in daily activities nowadays. The end goal of any machine learning or deep learning algorithm is achieving artificial intelligence.

OpenAI recently launched Jukebox, a model that generates music with singing in the raw audio domain. As a generative model for music, Jukebox can handle the long context of raw audio using an autoencoder. Jukebox's autoencoder processes the audio files using a multiscale VQ-VAE to compress it to discrete codes and modeling those using autoregressive Transformers. Provided with a genre, artist, and lyrics as input, Jukebox can output a new music sample produced from scratch. This is a type of innovation that expands the boundaries of generative models to a new level.

This is a big question, and I'm not a particularly big person. As such, these are all likely to be obvious observations to someone deep in the literature and theory. What I find however is that there are a base of unspoken intuitions that underlie expert understanding of a field, that are never directly stated in the literature, because they can't be easily proved with the rigor that the literature demands. And as a result, the insights exist only in conversation and subtext, which make them inaccessible to the casual reader. Because I have no need of rigor to post on the internet, (or even a need to be correct) I'm going to post some of those intuitions here as I understand them.

Artificial intelligence researchers have not been successful in giving intelligent agents the common-sense knowledge they need to reason about the world. Without this knowledge, it is impossible for intelligent agents to truly interact with the world. Traditionally, there have been two unsuccessful approaches to getting computers to reason about the world--symbolic logic and deep learning. A new project, called COMET, tries to bring these two approaches together. Although it has not yet succeeded, it offers the possibility of progress.

In research published today in Patterns, a team of engineers led by Wang demonstrated how a deep learning algorithm can be applied to a conventional computerized tomography (CT) scan in order to produce images that would typically require a higher level of imaging technology known as dual-energy CT. Wenxiang Cong, a research scientist at Rensselaer, is first author on this paper. Wang and Cong were also joined by coauthors from Shanghai First-Imaging Tech, and researchers from GE Research. "We hope that this technique will help extract more information from a regular single-spectrum X-ray CT scan, make it more quantitative, and improve diagnosis," said Wang, who is also the director of the Biomedical Imaging Center within the Center for Biotechnology and Interdisciplinary Studies (CBIS) at Rensselaer. Conventional CT scans produce images that show the shape of tissues within the body, but they don't give doctors sufficient information about the composition of those tissues.

Imagine that before you could make dinner, you first had to rebuild the kitchen, specifically designed for each recipe. You'd spend way more time on preparation, than actually cooking. For computational biologists, it's been a similar time-consuming process for analyzing genomics data. Before they can even begin their analysis, they spend a lot of valuable time formatting and preparing huge data sets to feed into deep learning models. To streamline this process, researchers from the Max Delbrueck Center for Molecular Medicine in the Helmholtz Association (MDC) developed a universal programming tool that converts a wide variety of genomics data into the required format for analysis by deep learning models.

GPT-3 has taken the world by storm. There are thousands of tweets about it with numerous mind-blowing use cases which you can see for yourself if you search Twitter for #gpt3 hashtag. In this text, I have taken a bunch of them to show general trends. In brief, GPT-3 allows humans to communicate with machines in Simple English. GPT-3 definitely will influence how we communicate with our devices and lower the level of technical sophistication one needs to build new applications.

Artificial intelligence (AI) is a difficult term to define because experts continue to argue about its definition. We'll get into those arguments later, but for now, think of AI as the technology through which computers execute tasks that would normally require human intellect. Humans and animals have a natural intellect, but computers and other intelligent agents have artificial intelligence that engineers and scientists design.AI differs from machine learning and deep learning, though the topics are related. Machine learning is a subcategory within AI in which a machine learns and performs functions it wasn't specifically programmed to do (using what some argue to be logic). Deep learning is a subcategory of machine learning that allows machines to analyze multi-layer algorithms or neural networks.