Turns out, 'once in a blue moon' is right now. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. A'Super Blue moon' rose in the skies of the Dutch city of Nijmegen during the night from August 31st to September 1st, 2023. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

Claire chatted to Ahti Heinla from Starship Technologies about their AI-powered delivery robots that operate independently on streets and pavements. Ahti Heinla is the co-founder and CEO of Starship Technologies, the world's leading autonomous delivery company building AI-powered robots that operate fully independently in real-world environments. One of the original engineers behind Skype's billion-dollar success, Ahti later made a quiet pivot into robotics, spending the past decade advancing practical, consumer-facing AI. Under his leadership, Starship has completed more than 10 million autonomous deliveries with a fleet of over 2,700 robots navigating streets, pavements, weather, and people, without human intervention. Robot Talk is a weekly podcast that explores the exciting world of robotics, artificial intelligence and autonomous machines.

Bereich How DER SPIEGEL processed the data from the NSDAP membership card file aufklappen The NSDAP membership card file was recently made available by the US National Archives on its website in digitized form. DER SPIEGEL downloaded all of these documents and extracted the content with the help of artificial intelligence. To minimize errors when reading the old files, the dataset was first classified with the help of machine learning and pre-sorted into groups. The handwriting on the index cards is in some cases difficult to read, on some the text has faded, many are written in old German script (Sütterlin). Other cards, meanwhile, were filled out with a typewriter.

Data in the report show 4% of workers believe they have already lost a job because of AI. Data in the report show 4% of workers believe they have already lost a job because of AI. Exclusive: IPPR thinktank calls for new measures to boost employees' influence at'pivotal moment' in history Workers urgently need more bargaining power over the way AI is adopted in the workplace to ensure the benefits are fairly shared, according to a TUC-backed report from a leading thinktank. The Institute for Public Policy Research (IPPR) is calling for a package of measures to boost employees' influence at what it calls a "pivotal moment in the history of work". Its report cites survey data showing that while 20% of workers say AI is making their working life better, 21% say it has made it worse - and 4% believe they have already lost a job because of the technology.

The film forms part of a research project undertaken by Alan Warburton which also includes a research paper and a series of satellite events. The film is based on doctoral research undertaken at Birkbeck's Vasari Centre for Art & Technology. It was commissioned by the National Videogame Museum in collaboration with the Open Data Institute (ODI) and Cambridge University's Leverhulme Centre for the Future of Intelligence . The ODI hosted a webinar on 6 May to discuss the content of the film. The panellists explored what AI can and can't do, what effects a collapse of real and virtual could have on visual culture, and if we're living in a post-truth world.

Romania and its NATO allies have reacted angrily after a Russian drone crashed into an apartment building in eastern Romania, injuring two people. The Ministry of Foreign Affairs in Bucharest on Friday labelled the crash of the drone, part of an overnight attack aimed at Ukraine, a serious violation of international law. The incident is just the latest incursion along the alliance's eastern flank, raising concern that the risk of an open confrontation between Russia and NATO states is rising. Romania said the overnight drone was tracked by radar in its airspace before crashing onto the roof of a residential building in the city of Galati. Two F-16 fighter jets and a helicopter were scrambled, as authorities issued emergency alerts to residents.

Romania says a drone struck an apartment building in Galați, injuring a woman and child, marking the first time a Russian drone hit a populated area in the NATO member state.

A Russian drone hit an apartment building in Romania, the country's defence ministry said early on Friday, causing a fire and injuring two people. The drone crashed in the eastern city of Galati as Russia carried out attacks in Ukraine near the border, the ministry said in a statement. The Romanian General Inspectorate for Emergency Situations said the drone's entire explosive payload detonated, causing a fire on the 10th floor of the residential building. Russian drones have strayed across the border of the Nato member country a number of times during the four-year war with Ukraine, but this was the first time citizens from Romania had been hurt. Russia has yet to comment on the incident. This incident represents a serious and irresponsible escalation on the part of the Russian Federation, Romania's foreign ministry said, adding Bucharest had informed the Nato secretary general and requested measures to accelerate the transfer of anti-drone capabilities to Romania.

We study stochastic decision-theoretic online learning with full information and event-level pure differential privacy. A COLT open problem of Hu and Mehta asks to determine the optimal gap-dependent regret rate for stochastic decision-theoretic online learning under pure event-level differential privacy. For $K$ actions, losses in $[0,1]$, and a unique best action separated from the second-best action by gap $Δ_{\min}$, the known lower bound is of order $ \frac{\log K}{\min\{Δ_{\min},\varepsilon\}}, $ or equivalently, up to universal constants, of order \[ \frac{\log K}{Δ_{\min}}+\frac{\log K}{\varepsilon}. \] We give a horizon-free pure-DP algorithm and prove the explicit regret bound \[ \operatorname{Reg}_T \le 1000 \cdot \left(\frac{\log K}{Δ_{\min}}+\frac{\log K}{\varepsilon}\right) \] for every horizon $T$. The numerical constant is not optimized. The algorithm partitions time into blocks of exponentially increasing size, plays a single action throughout each block, and chooses the next action by an exponential mechanism applied to a data-independent random prefix of the previous block. The random prefix converts block regret into a sum, over all prefix lengths, of softmax selection errors. A single entropy-potential argument controls all privacy-dominated large-gap actions at cost $\log K/\varepsilon$.

The scaling limit where both the size of the training set $P$ and the width $N$ of a deep neural network grow at the same rate, the so-called proportional-width regime, has been intensely studied for shallow, single-hidden-layer networks. However, extending these non-perturbative results from shallow architectures to deep non-linear networks has proven very challenging. Here we present an effective approximate approach to predict the generalization performance of Bayesian multi-layer perceptrons (MLPs) of fixed depth $L$ on arbitrary high-dimensional data. We propose an equivalent Wishart Ansatz to capture the dominant stochastic fluctuations of the hierarchical empirical kernels of MLPs. This allows us to perform a large deviation analysis for the partition function of MLPs in the proportional limit, expressed in terms of a renormalized NNGP kernel. In this description, even strong representation learning in the proportional limit is encoded in at most $L$ scalar order parameters, determined self-consistently. Extending the approach to convolutional architectures (CNNs), we identify a hierarchical local kernel renormalization mechanism, which allows to quantify more complex data-dependent transformations of the large-width kernel in CNNs due to finite-width effects. We test our effective theory against sampling experiments from the Bayesian posterior of finite deep neural networks with depths $L \sim O(10)$ and $P\sim O(10^3)$ on classic benchmark datasets, finding overall very good agreement together with two distinct types of systematic deviations.