We promise this isn’t an April Fools’ Day post.

NVIDIA says their 3D scene rendering AI runs up to 1,000 times faster than older models without sacrificing image quality.

Training datasets are very important for experimenting with varied data to train new AI models. However, many commonly used public data sets contain labeling errors. This makes it challenging to train robust models, particularly for novel tasks. Many researchers use techniques such as employing a variety of data quality control procedures to overcome these shortcomings. However, there is no centralized repository consisting of examples of using these strategies.

Meta AI researchers have recently released Mephisto. It is a new platform to collect, share, and iterate on the most promising approaches to collecting training datasets for AI models. Researchers can exchange unique collecting strategies with Mephisto in a reusable and iterable format. It also allows them to change out components and quickly locate the exact annotations required, minimizing the barrier to custom task creation.

The team uncovers many common pathways for driving a complex annotation activity from concept to data collection in Mephisto. In addition to improving the quality of datasets, Mephisto also enhances the experience of the researchers and annotators who created the data set.

A new approach to in-memory computing proposes a new set up to create an artificial synapse that can both store and process data.

In this blossoming era of AI, efficient computational approaches to processing and storing large amounts of data are required. However, current computer designs have inherent performance limitations.

In recent years, research has been focused on the development of alternative computing architectures that mimic the brain. These devices, called neuromorphic computers, circumvent many of the issues associated with the traditional von Neumann architecture, which has been around since 1945 and is composed of processing and memory units.

Speech and language recognition technology is a rapidly developing field, which has led to the emergence of novel speech dialog systems, such as Amazon Alexa and Siri. A significant milestone in the development of dialog artificial intelligence (AI) systems is the addition of emotional intelligence. A system able to recognize the emotional states of the user, in addition to understanding language, would generate a more empathetic response, leading to a more immersive experience for the user.

“Multimodal sentiment analysis” is a group of methods that constitute the gold standard for an AI dialog system with sentiment detection. These methods can automatically analyze a person’s psychological state from their speech, voice color, facial expression, and posture and are crucial for human-centered AI systems. The technique could potentially realize an emotionally intelligent AI with beyond-human capabilities, which understands the user’s sentiment and generates a response accordingly.

However, current emotion estimation methods focus only on observable information and do not account for the information contained in unobservable signals, such as physiological signals. Such signals are a potential gold mine of emotions that could improve the sentiment estimation performance tremendously.