Deep learning has been making it possible for powerful machines to approximate and imitate abilities and techniques once thought to be uniquely human. Mathematicians have struggled to explain how they work so well and may now get some answers by looking outside mathematics and into the nature of the universe.
Microsoft said Thursday it was adopting a set of ethical principles for the use of its facial recognition technology, and urged the government to follow its lead with regulations barring unlawful discrimination and focusing on transparency.
In a blog post, Microsoft president Brad Smith pushed for the government, as well as tech companies, to regulate facial-recognition technology and ensure it “creates broad societal benefits while curbing the risk of abuse.”
“The facial recognition genie, so to speak, is just emerging from the bottle,” Smith said in the post. “Unless we act, we risk waking up five years from now to find that facial recognition services have spread in ways that exacerbate societal issues.”
Over the past few years, classical convolutional neural networks (cCNNs) have led to remarkable advances in computer vision. Many of these algorithms can now categorize objects in good quality images with high accuracy.
However, in real-world applications, such as autonomous driving or robotics, imaging data rarely includes pictures taken under ideal lighting conditions. Often, the images that CNNs would need to process feature occluded objects, motion distortion, or low signal to noise ratios (SNRs), either as a result of poor image quality or low light levels.
Although cCNNs have also been successfully used to de-noise images and enhance their quality, these networks cannot combine information from multiple frames or video sequences and are hence easily outperformed by humans on low quality images. Till S. Hartmann, a neuroscience researcher at Harvard Medical School, has recently carried out a study that addresses these limitations, introducing a new CNN approach for analyzing noisy images.
China has launched the first mission to land a robotic craft on the far side of the Moon, Chinese media say.
The Chang’e-4 mission will see a static lander and rover touch down in Von Kármán crater, located on the side of the Moon which never faces Earth.
The payload blasted off atop a Long March 3B rocket from Xichang Satellite Launch Center.
The accelerating investment in artificial intelligence has vast implications for economic and cognitive development globally. However, AI is currently dominated by an oligopoly of centralized mega-corporations, who focus on the interests of their stakeholders. There is a now universal need for AI services by businesses who lack access to capital to develop their own AI services, and independent AI developers lack visibility and a source of revenue. This uneven playing field has a high potential to lead to inequitable circumstances with negative implications for humanity. Furthermore, the potential of AI is hindered by the lack of interoperability standards. The authors herein propose an alternative path for the development of AI: a distributed, decentralized, and democratized market for AIs run on distributed ledger technology. We describe the features and ethical advantages of such a system using SingularityNET, a watershed project being developed by Ben Goertzel and colleagues, as a case study. We argue that decentralizing AI opens the doors for a more equitable development of AI and AGIt will also create the infrastructure for coordinated action between AIs that will significantly facilitate the evolution of AI into true AGI that is both highly capable and beneficial for humanity and beyond.
China is getting set to launch the first-ever surface mission to the moon’s far side.
The robotic Chang’e 4 mission is scheduled to launch atop a Long March 3B rocket on Friday (Dec. 7) at around 1:30 p.m. EST (1830 GMT; 2:30 a.m. on Dec. 8 local China time).
If all goes according to plan, Chang’e 4’s lander-rover duo will touch down within the moon’s South Pole‐Aitken (SPA) basin after a 27-day flight, then study both the surface and subsurface of this region. [China’s Moon Missions Explained (Infographic)].
