Computer scientists from Loughborough University in the UK have developed a new AI system that predicts air pollution levels days in advance.
The system developed analyzes air data through sensors installed in cities to predict the pollution levels.
It could be used to help us understand the environmental factors that affect one of the most dangerous pollutants in the world: PM2.5.
In a preprint paper, Microsoft researchers describe a machine learning system that reasons out the correct actions to take directly from camera images. It’s trained via simulation and learns to independently navigate environments and conditions in the real world, including unseen situations, which makes it a fit for robots deployed in search and rescue missions. Someday, it could help those robots more quickly identify people in need of help.
“We wanted to push current technology to get closer to a human’s ability to interpret environmental cues, adapt to difficult conditions and operate autonomously,” wrote the researchers in a blog post published this week. “We were interested in exploring the question of what it would take to build autonomous systems that achieve similar performance levels.”
Posted in futurism, robotics/AI
I hadn’t seen anything about this thing in about 5+ years, and it was pretty bad back then. Now they have it singing, although i’d like to just see it talking or trying to hold a conversation. Anyhow, the Mouth of your future humanoid robot:
The Prayer is presented in the show “Neurons, Simulated Intelligence”, at Centre Pompidou, Paris, curated by Frédéric Migayrou and Camille Lenglois from 26 February — 26 April 2020.
The Prayer is an art-installation that tries to explore the supernatural through artificial intelligence with a long-term experimental set up. A robot — installation operates a talking mouth, that is part of a computer system, creating and voicing prayers, that are generated in every very moment by the self-learning system itself, exploring ‘the divine’ the supernatural or ‘the noumenal’ as the mystery of ‘the unknown’, using deep learning.
How would a divine epiphany appear to an artificial intelligence? The focus of the project could maybe shed light on the difference between humans and AI machines in the debate about mind and matter and allows a speculative stance on the future of humans in the age of AI technology and AGI ambitions.
Above an anticipation of AI Singing with AI generated texts, since singing is a major religious practice.
Diemut Strebe, Author of Concept and Final Design.
I have spent the past several years of my life desperately trying to warn humanity that the robots are coming to destroy us all, and everybody laughed at me. But this week—shortly after the T-1000 was seen smooching his miniature horse and donkey—a robot noodle chef has taken over soba-making duties at a Tokyo train station, so who’s laughing now? (The robots are laughing now.)
In 2018, Putin approved a national research strategy that stretches to 2024. It calls for more money, extra support for early-career scientists, and some 900 new laboratories, including at least 15 world-class research centres with a focus on mathematics, genomics, materials research and robotics. Last year, the government completed a sweeping evaluation of scientific performance at its universities and institutes; it has vowed to modernize equipment in the 300 institutes that made the top quartile. And it says it wants to strengthen previously neglected areas, including climate and environmental research (see ‘Russia’s climate-science ambitions’).
Some researchers see promise in planned reforms.
A theory of how #AI & #brain recognize things. https://bit.ly/2Qnq3RC “In this article, we proposed a hypothesis that we call Switch Hypothesis for explaining how an ANN as well as a real neural network carry out its functions…” #MachineLearning #DeepLearning #NeuralNetworks
Neuroscience and psychology today has advanced significantly. With the use of neuroimaging methods such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), human beings have gradually revealed the secrets behind how our brains perceive, recognize and memorize things. However, if you’d like to have a detailed, neuronal-level elucidation on how brains realize its functions, you should be very disappointed because no one is currently capable of doing so. In other words, although our cerebrums are no longer a pitch-black box, it’s still at least a “gray” box, with a lot of enigmas yet to be explained.
“Automation is going to cause unemployment, and we need to prepare for it.”–Mark cuban.
Now, in an important new resource for the scientific community published today in Nature Biotechnology, researchers in the lab of Neville Sanjana, PhD, at the New York Genome Center and New York University have developed a new kind of CRISPR screen technology to target RNA.
The researchers capitalized on a recently characterized CRISPR enzyme called Cas13 that targets RNA instead of DNA. Using Cas13, they engineered an optimized platform for massively-parallel genetic screens at the RNA level in human cells. This screening technology can be used to understand many aspects of RNA regulation and to identify the function of non-coding RNAs, which are RNA molecules that are produced but do not code for proteins.
By targeting thousands of different sites in human RNA transcripts, the researchers developed a machine learning-based predictive model to expedite identification of the most effective Cas13 guide RNAs. The new technology is available to researchers through an interactive website and open-source toolbox to predict guide RNA efficiencies for custom RNA targets and provides pre-designed guide RNAs for all human protein-coding genes.
On March 9, 2016, the worlds of Go and artificial intelligence collided in South Korea for an extraordinary best-of-five-game competition, coined The DeepMind Challenge Match. Hundreds of millions of people around the world watched as a legendary Go master took on an unproven AI challenger for the first time in history.
Directed by Greg Kohs with an original score by Academy Award nominee, Hauschka, AlphaGo chronicles a journey from the halls of Oxford, through the backstreets of Bordeaux, past the coding terminals of DeepMind in London, and ultimately, to the seven-day tournament in Seoul. As the drama unfolds, more questions emerge: What can artificial intelligence reveal about a 3000-year-old game? What can it teach us about humanity?
Nvidia researchers propose human-robot handovers in which the robot meets the human halfway, classifies their grasp, and takes the object from their hand.
