1X’s androids use Embodied Learning, integrating AI software directly into their physical forms for advanced capabilities.
Maximizing adaptable hardware potential, 1X employs comprehensive autonomy, teaching motor behaviors via neural networks from visual input.
In addition to movies and themeparks, Disney is now also operating in robotics space but taking a different approach from the rest.
Disney is challenging our very notion of what constitutes a robot and if they need to work alone at all times to be effective.
“We are facing an almost guaranteed event with potential to cause an existential catastrophe,” says Dr. Roman V. Yampolskiy.
There is no current evidence that AI can be controlled safely and without proof that AI can be controlled, it should not be developed, a researcher warns.
“This is Frame, a reinvention of eyewear for the AI era,” says Brilliant Labs.
Brilliant Labs has become the latest company to introduce a pair of smart glasses called Frame.
There are many pain points that warrant discussions between the two nations but AI could be the thing that brings them to the table.
Relations between the United States and China have been downward recently. Topics like artificial intelligence (AI) and such technology in automated weapons could be common points of interest to get the two countries talking again.
Tensions between the two nations have been on the rise for a host of issues. Recently, the origins of the COVID-19 pandemic, China’s burgeoning presence in the South China Sea, and the supply of powerful chips in the technology space have been areas of disagreement on both sides.
The lack of dialogue on such issues has led to tensions between the two countries, and the imposition of restrictions on both sides has further strained relations. While addressing these points is expected to be a long-drawn affair for the two countries, common areas like AI could offer a starting point, experts told the South China Morning Post (SCMP).
Generative AI has emerged as the next wave of innovation amidst the ongoing evolution of the technological landscape, attracting the attention of both researchers and investors.
Even as vector databases and Retrieval-Augmented Generation models become mainstream, offering innovative ways to handle and process data, traditional ETL processes retain their importance in the data management ecosystem. Traditional ETL is fundamental for preparing and structuring data from diverse sources into a coherent, standardized format, making it accessible and usable for various applications. This structured data is crucial for maintaining the accuracy and reliability of information within vector databases, which excel at handling similarity searches and complex queries by converting data into vector space.
Similarly, RAG models, which leverage vast databases to augment content generation with relevant information retrieval, depend on well-organized, high-quality data to enhance their output’s relevance and accuracy. By ensuring data is accurately extracted, cleaned and loaded into databases, traditional ETL processes complement the capabilities of vector databases and RAG models, providing a solid foundation of quality data that enhances their performance and utility. This symbiotic relationship underscores the continuing value of traditional ETL in the age of AI-driven data management, ensuring that advancements in data processing technologies are grounded in reliable and well-structured data sources.
The rise of generative AI has indeed shifted the technological focus, overshadowing some of the core technologies that have been instrumental in our digital progress.
There’s an episode of the show “Black Mirror” where a woman, trapped by grief, starts a relationship with an AI trained on her dead boyfriend’s data.
“You’re not enough of him,” she eventually decides. “You’re nothing.”
But even an empty happily-ever-after is tantalizing in the bleakness of 2024. AI platforms like ChatGPT claim to offer infinite solutions to infinite problems, from parking tickets to homework — and apparently now heartbreak as well. That’s right: if you’re still hung up after a breakup, now you can plug your ex’s emails and texts into a large language model, and date the simulacrum instead of moving on.
Google’s philanthropy arm is setting aside millions of dollars for European nonprofits to train workers how to use AI. But it’s unclear whether AI training will help save jobs.
New research on the continuity illusion uncovers how the brain perceives smooth motion, emphasizing the superior colliculus’s importance and suggesting new approaches for neuroscience research and clinical practice.
A study by a team at the Champalimaud Foundation (CF) has cast a new light on the superior colliculus (SC), a deep-seated brain structure often overshadowed by its more prominent cortical neighbor. Their discovery uncovers how the SC may play a pivotal role in how animals see the world in motion, and sheds light on the “continuity illusion,” an essential perceptual process integral to many of our daily activities, from driving vehicles to watching movies.
Understanding the Continuity Illusion.
We tend to separate the brain and muscle – the brain does the thinking; the muscle does the doing. The brain takes in complex information about the world, makes decisions, while muscle merely executes. This distinction extends to our understanding of cellular processes, where certain molecules within cells are perceived as the ‘thinkers’, processing information from the chemical environment to determine necessary actions for survival, while others are viewed as the ‘muscle’, constructing the essential structures for the cell’s survival.
But a new study shows how the molecules that build structures, i.e, the muscle, can themselves do both the thinking and the doing. The study, by scientists at Maynooth University, the University of Chicago, and California Institute of Technology was published in the journal Nature.
“We show that a natural molecular process – nucleation – that has been studied as a ‘muscle’ for a long time can do complex calculations that rival a simple neural network,” said University of Chicago Associate Professor Arvind Murugan, one of the two senior co-authors on the paper. “It’s an ability hidden in plain sight that evolution can exploit in cells to do more with less; the ‘doing’ molecules can also do the ‘thinking.’”
