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In an effort to improve the performance of robots that pick, sort, and pack products in warehouses, Amazon has publicly released the largest dataset of images captured in an industrial product-sorting setting. Where the largest previous dataset of industrial images featured on the order of 100 objects, the Amazon dataset, called ARMBench, features more than 190,000 objects. As such, it could be used to train “pick and place” robots that are better able to generalize to new products and contexts.
We describe ARMBench in a paper we will present later this spring at the International Conference on Robotics and Automation (ICRA).
The scenario in which the ARMBench images were collected involves a robotic arm that must retrieve a single item from a bin full of items and transfer it to a tray on a conveyor belt. The variety of objects and their configurations and interactions in the context of the robotic system make this a uniquely challenging task.
For years, we’ve debated the benefits of artificial intelligence (AI) for society, but it wasn’t until now that people can finally see its daily impact. But why now? What changed that’s made AI in 2023 substantially more impactful than before?
First, consumer exposure to emerging AI innovations has elevated the subject, increasing acceptance. From songwriting and composing images in ways previously only imagined to writing college-level papers, generative AI has made its way into our everyday lives. Second, we’ve also reached a tipping point in the maturity curve for AI innovations in the enterprise—and in the cybersecurity industry, this advancement can’t come fast enough.
Perfect recall, computational wizardry and rapier wit: That’s the brain we all want, but how does one design such a brain? The real thing is comprised of ~80 billion neurons that coordinate with one another through tens of thousands of connections in the form of synapses. The human brain has no centralized processor, the way a standard laptop does.
Instead, many calculations are run in parallel, and outcomes are compared. While the operating principles of the human brain are not fully understood, existing mathematical algorithms can be used to rework deep learning principles into systems more like a human brain would. This brain-inspired computing paradigm—spiking neural networks (SNN)—provides a computing architecture well-aligned with the potential advantages of systems using both optical and electronic components.
In SNNs, information is processed in the form of spikes or action potentials, which are the electrical impulses that occur in real neurons when they fire. One of their key features is that they use asynchronous processing, meaning that spikes are processed as they occur in time, rather than being processed in a batch like in traditional neural networks. This allows SNNs to react quickly to changes in their inputs, and to perform certain types of computations more efficiently than traditional neural networks.
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Generative AI can help businesses in many sectors increase efficiency and boost their bottom-lines, not just tech giants. A UBS report analyzes well-positioned firms.
Demand for AI via consumer mobile apps has been climbing, with market leader OpenAI’s ChatGPT mobile app topping half a million downloads in its first six days. Now, another AI app is touting its own launch success, as the a16z-backed Character. AI app is claiming to have pulled in over 1.7 million new installs in less than a week on the market. The AI app maker, which announced a whopping $150 million in Series A funding earlier this year, valuing its business at $1 billion, offers customizable AI companions with distinct personalities, as well as the ability for users to create their own characters.
While there are a number of these AI character generators available on today’s app stores, interest in Character. AI has a lot to do with its founders. The Palo Alto–based startup was created by Noam Shazeer and Daniel De Freitas, AI experts who previously led a team of researchers at Google that built LaMDA (Language Model for Dialogue Applications), a language model that helps power conversational AI experiences.
At Google, the founders had become frustrated with the company’s hesitancy to roll out AI chatbots to other researchers and the general public, including through integrations with other Google products, like Assistant, The Wall Street Journal reported. Believing AI would revolutionize search and other areas, the duo ultimately decided to leave Google in late 2021, despite pleas from CEO Sundar Pichai to stay and continue their work on LaMDA. That same November, Shazeer and De Freitas founded Character Technologies, now home to Character. AI.
