The U.S. Patent and Trademark Office (USPTO) recently added a new artificial intelligence (AI)-based "Similarity Search" feature to the prior art search tools available to examiners. This Similarity Search feature is designed to be an enhanced replacement of the Patent Linguistic Utility Service (PLUS) search tool and provides examiners with optional new search capabilities to access prior art alongside traditional document retrieval approaches. The PLUS search tool received as input a keyword list generated from scanned portions of the Specification being searched and produced a list of only U.S. patents and published U.S. applications that most closely match the Specification. The new AI-based Similarity Search tool receives the full text of the Specification as input and outputs a list of both domestic and foreign patent documents that are similar to the Specification being searched. Further, an examiner can refine the AI-based Similarity Search queries by emphasizing certain Cooperative Patent Classification (CPC) symbols and certain paragraphs, sentences, or words in a Specification to focus on specific concepts in the Specification being searched.

In many evolutionary computation systems, parent selection methods can affect, among other things, convergence to a solution. In this paper, we present a study comparing the role of two commonly used parent selection methods in evolving machine learning pipelines in an automated machine learning system called Tree-based Pipeline Optimization Tool (TPOT). Specifically, we demonstrate, using experiments on multiple datasets, that lexicase selection leads to significantly faster convergence as compared to NSGA-II in TPOT. We also compare the exploration of parts of the search space by these selection methods using a trie data structure that contains information about the pipelines explored in a particular run.

Unlike satellite images, which are typically acquired and processed in near-real-time, global land cover products have historically been produced on an annual basis, often with substantial lag times between image processing and dataset release. We developed a new automated approach for globally consistent, high resolution, near real-time (NRT) land use land cover (LULC) classification leveraging deep learning on 10 m Sentinel-2 imagery. We utilize a highly scalable cloud-based system to apply this approach and provide an open, continuous feed of LULC predictions in parallel with Sentinel-2 acquisitions. This first-of-its-kind NRT product, which we collectively refer to as Dynamic World, accommodates a variety of user needs ranging from extremely up-to-date LULC data to custom global composites representing user-specified date ranges. Furthermore, the continuous nature of the product’s outputs enables refinement, extension, and even redefinition of the LULC classification. In combination, these unique attributes enable unprecedented flexibility for a diverse community of users across a variety of disciplines.

The original Chapter 10 lab made use of keras, an R package for deep learning that relies on Python. Getting keras to work on your computer can be a bit of a challenge. Installation instructions are available here. RStudio has recently released a new R package for deep learning, called torch, that does not require a Python installation. Daniel Falbel and Sigrid Keydana, two of the torch developers, translated our keras version of the Chapter 10 lab to torch.

Digital human avatar technology provider Didimo Inc. is bringing generative artificial intelligence to the world of video game character creation. With today's launch of Popul8, Didimo says, game developers now have a simple way to create hundreds of unique game characters in a fraction of the usual time and cost. Popul8 is a generative AI tool that builds on Didimo's existing avatar creation toolset, accelerating the time it takes for developers to create extremely lifelike, 3D game and metaverse characters with full control over their appearance. Traditionally, it has taken developers countless hours to create realistic-looking characters for their computer games, with painstaking attention to detail a very necessary part of the design process. With Popul8, it becomes possible to design animated and diverse avatars in a matter of minutes. The tool allows for consistent designs, with users able to upload template characters from any game to ensure their new avatars match its aesthetic style.

A balanced scale to represent a balanced and fair system, the infinity symbol for the infinite potential of such a program, on a shield that stands for honor and protection. While TruthGPT is a pipe dream, the idea of a chatbot or artificial intelligence t

Is it possible that some of the greatest AI inventions are overlooking something major? In this episode, I dive into what seems to be the missing piece in artificial intelligence and AI research. Listen in to learn how a different approach could bridge the gap between the world of AI and everyday function and usability. "Steve Jobs showed that with Apple. That they were not just science and engineering creations they were theater, they were art.

Portals.co is a free-to-use AI-powered customer feedback platform that allows you to extract deeper levels of customer feedback information, by using AI-generated follow-up questions. Our widget can be integrated with your website via a single line of code. Enhance your customer feedback pathways for free, with Portals.co.

Adobe takes home the award thanks to its new, exciting update to Premiere Pro: text-based editing. At NAB, Adobe showed us why Premiere Pro is the go-to editing software for so many editors. While text-based editing was the highlight for us, Adobe also unveiled an impressive range of new features across its Creative Cloud video programs. Adobe showcased new features in Premiere Pro that will be shipping in May. These included text-based editing along with an AI-based workflow powered by Adobe Sensei.

An article titled "On misbehaviour and fault tolerance in machine learning systems," by doctoral researcher Lalli Myllyaho was named one of the best papers in 2022 by the Journal of Systems and Software. "The fundamental idea of the study is that if you put critical systems in the hands of artificial intelligence and algorithms, you should also learn to prepare for their failure," Myllyaho says. It may not necessarily be dangerous if a streaming service suggests uninteresting options to users, but such behavior undermines trust in the functionality of the system. However, faults in more critical systems that rely on machine learning can be much more harmful. "I wanted to investigate how to prepare for, for example, computer vision misidentifying things. For instance, in computed tomography artificial intelligence can identify objects in sections. If errors occur, it raises questions about to what extent computers should be trusted in such matters, and when to ask a human to take a look," says Myllyaho.