We introduce SigLIP 2, a family of new multilingual vision-language encoders that build on the success of the original SigLIP. In this second iteration, we extend the original image-text training objective with several prior, independently developed techniques into a unified recipe -- this includes captioning-based pretraining, self-supervised losses (self-distillation, masked prediction) and online data curation. With these changes, SigLIP 2 models outperform their SigLIP counterparts at all model scales in core capabilities, including zero-shot classification, image-text retrieval, and transfer performance when extracting visual representations for Vision-Language Models (VLMs). Furthermore, the new training recipe leads to significant improvements on localization and dense prediction tasks. We also train variants which support multiple resolutions and preserve the input's native aspect ratio. Finally, we train on a more diverse data-mixture that includes de-biasing techniques, leading to much better multilingual understanding and improved fairness. To allow users to trade off inference cost with performance, we release model checkpoints at four sizes: ViT-B (86M), L (303M), So400m (400M), and g (1B).

OpenAI announced today it has hired three senior computer vision and machine learning engineers from rival Google DeepMind, all of whom will work in a newly opened OpenAI office in Zurich, Switzerland. OpenAI executives told staff in an internal memo on Tuesday that Lucas Beyer, Alexander Kolesnikov, and Xiaohua Zhai will be joining the company to work on multimodal AI, artificial intelligence models capable of performing tasks in different mediums ranging from images to audio. OpenAI has long been at the forefront of multimodal AI and released the first version of its text-to-image platform Dall-E in 2021. Its flagship chatbot ChatGPT, however, was initially only capable of interacting with text inputs. The company later added voice and image features as multimodal functionality became an increasingly important part of its product line and AI research.

Fox News chief political anchor Bret Baier has the latest on the pros and cons of the bombshell developments on'Special Report.' It is said that predicting the future isn't magic. If that's the case, perhaps we should ask AI when Congress might pass a bill to regulate the emerging technology – before it spirals out of control. There's a push by Congressional leaders to approve a bill regulating AI when lawmakers return to Washington after the election. But the path to passage - and developing a consensus on establishing guardrails for AI - is far from certain.

Don Beyer's car dealerships were among the first in the U.S. to set up a website. As a representative, the Virginia Democrat leads a bipartisan group focused on promoting fusion energy. He reads books about geometry for fun. So when questions about regulating artificial intelligence emerged, the 73-year-old Beyer took what for him seemed like an obvious step, enrolling at George Mason University to get a master's degree in machine learning. In an era when lawmakers and Supreme Court justices sometimes concede they don't understand emerging technology, Beyer's journey is an outlier, but it highlights a broader effort by members of Congress to educate themselves about artificial intelligence as they consider laws that would shape its development.

On Tuesday, speaker of the House of Representatives Mike Johnson and Democratic leader Hakeem Jeffries launched a bipartisan Task Force on Artificial Intelligence. Johnson, a Louisiana Republican, and Jeffries, a New York Democrat, each appointed 12 members to the Task Force, which will be chaired by Representative Jay Obernolte, a California Republican, and co-chaired by Representative Ted Lieu, a California Democrat. According to the announcement, the Task Force will "produce a comprehensive report that will include guiding principles, forward-looking recommendations and bipartisan policy proposals developed in consultation with committees of jurisdiction." Obernolte--who has a masters in AI from the University of California, Los Angeles and founded the video game company FarSight Studios--and Lieu--who studied computer science and political science at Stanford University--are natural picks to lead the Task Force. But many of the members have expertise in AI too.

You know something strange is afoot when Elon Musk comes out in favor of tech regulation. Or when Kevin McCarthy and a left-wing Joe Biden appointee agree that one particular issue is a priority. These are not people who tend to agree on, well, anything. But such are the nascent, topsy-turvy politics of artificial intelligence. AI is not really a single issue you can be for or against the way you can with, say, guns or abortion.

Senior Episcopal priest in Connecticut Father Thomas W. Blake, Greek-Orthodox Christian clergyman in Nevada Father Stephen R. Karcher, Hindu statesman Rajan Zed, renowned Buddhist minister Reverend Matthew T. Fisher, esteemed Jewish rabbi in California-Nevada ElizaBeth Webb Beyer, well-respected Senior United Methodist Pastor Dawn M. Blundell; in a joint statement, said that AI should be used responsibly and religions should be involved and given active role in developing appropriate and adequate moral-ethical guardrails around it, before it changes our way of life. Blake, Karcher, Zed, Fisher, Beyer, Blundell emphasize that since sentient machines are no longer unthinkable and there are claims of the possibility of developing sentient AI systems in the future; technologies seem to be venturing into God's arena which can create serious spiritual implications. Tech should not be in the business of simply discarding overnight the thousands of years of wisdom of the texts. Rajan Zed, who is President of Universal Society of Hinduism, points out that if machines become completely sentient having consciousness, it is a serious theological issue. An urgent and honest global conversation is needed (with religions as the major partner) before the self-aware machines become game changers and reshape humanity.

Businesses across almost every industry deploy artificial intelligence to make jobs simpler for staff and tasks easier for consumers. Computer software teaches customer service agents how to be more compassionate, schools use machine learning to scan for weapons and mass shooters on campus, and doctors use AI to map the root cause of diseases. Sectors such as cybersecurity, online entertainment and retail use the tech in combination with wide swaths of customer data in revolutionary ways to streamline services. Though these applications may seem harmless, perhaps even helpful, the AI is only as good as the information fed into it, which can have serious implications. You might not realize it, but AI helps determine whether you qualify for a loan in some cases.

One Georgia school district plans to spend $16.5 million to install artificial intelligence-powered surveillance cameras in its roughly 100 buildings in coming years. Businesses across almost every industry deploy artificial intelligence to make jobs simpler for staff and tasks easier for consumers. Computer software teaches customer service agents how to be more compassionate, schools use machine learning to scan for weapons and mass shooters on campus, and doctors use AI to map the root cause of diseases. Sectors such as cybersecurity, online entertainment and retail use the tech in combination with wide swaths of customer data in revolutionary ways to streamline services. Though these applications may seem harmless, perhaps even helpful, the AI is only as good as the information fed into it, which can have serious implications.

Gartner predicts that 60% of organizations will deploy data virtualization software as part of their data integration tool set by 2020. That's a big jump from the adoption rate of about 35% the consulting and market research company cited in a November 2018 report on the data virtualization market. But the technology "is rapidly gaining momentum," a group of four Gartner analysts wrote in the report. The analysts said data virtualization use cases are on the rise partly because IT teams are struggling to physically integrate a growing number of data silos, as relational database management system (DBMS) environments are augmented by big data systems and other new data sources. They also pointed to increased technology maturity that has removed deployment barriers for data virtualization users.