Where Are All the New Cars? New cars were scant at CES this year, largely because the center of gravity for the auto world has moved--technologically and geographically--to China. This robotaxi built by Uber, Lucid, and Nuro was one of the few cars announced at CES, and it's not even one you can buy. Some years ago now, a very senior Mercedes executive in the US confided in me that CES was "the second-most important car show in the world, after Detroit." Before the auto world's full-on EV boom, this was quite the thing to admit--shocking, in fact--but it marked the subsequent carmaker takeover of the world's largest tech show. This year in Las Vegas, however, the cars were almost nowhere to be seen.

Figure 4: P lot over 100 epochs with 3 - Agents F. Ablation Study - Impact of the LearningOptimizerAgent To quantify the OptimizerAgent's effect on the system, we conducted an ablation study that set up two different configurations. Baseline (3 - Agent Framework): CrimeAnalysisAssistant, FeedbackAgent, and CrimePredictorAgent. Extended (4 - Agent Framework): All of the above, with the OptimizerAgent that could oversee and control how the other agents worked. Both settings were tested using the same protocol, working with the same data for 100 epochs and evaluated according to the already mentioned metrics described in Section V - B. Importantly, during the extended framework tests the OptimizerAgent did not have access to the ground truth and its actions reflected those of a real - world supervisor trying to be efficient with resources . The main aim was to bring more stability and better learning curve using our framework LUCID - MA. Table 2: 4 - Aegnts Observed Improvement Metric Baseline (3 agents) With OptimizerAgent Improvement CrimeAnalysis Assistant Final Score 0.94 0.96 +0.02 FeedbackAgent Final Score 0.89 0.92 +0.03 CrimePredictorAgent Final Score 0.85 0.91 +0.06 Avg. Redundancy Across Epochs 14.2% 6.8% - 7.4% Using the OptimizerAgent resulted in a marked increase in the variety and quality of final system outputs . Visual Result: The final plot demonstrates that agent - level meta - control, As a result, the model exhibits higher consistency, greater variety in its results and more reliable improvement over time -- all accomplished without any need for further model fine - tuning. Figure 5: P lot over 100 epochs with 4 - Agents In addition to standard performance comparison metrics, our system portrayed advanced behavioral dynamics pointing to the pre sence of emergent intelligence capabilities which we delve into in the next section in great detail.

Uber will invest 300m in electric vehicle maker Lucid in a robotaxi deal that aims to start with one major US city late next year. The two companies announced the new partnership on Thursday. Over six years starting in 2026, Uber will acquire and deploy over 20,000 Lucid Gravity SUVs that will be equipped with autonomous vehicle (AV) technology from startup Nuro, the three companies said in a statement. The agreement illustrates the renewed plans and push for financing for self-driving cabs, years after a first wave of autonomous driving investment produced only a limited number of vehicles. Tesla has recently launched a robotaxi trial in Austin, and Alphabet's driverless taxi unit, Waymo, is speeding up its expansion.

Neural networks are susceptible to privacy attacks that can extract private information of the training set. To cope, several training algorithms guarantee differential privacy (DP) by adding noise to their computation. However, DP requires to add noise considering every possible training set. This leads to a significant decrease in the network's accuracy. Individual DP (iDP) restricts DP to a given training set. We observe that some inputs deterministically satisfy iDP without any noise. By identifying them, we can provide iDP label-only access to the network with a minor decrease to its accuracy. However, identifying the inputs that satisfy iDP without any noise is highly challenging. Our key idea is to compute the iDP deterministic bound (iDP-DB), which overapproximates the set of inputs that do not satisfy iDP, and add noise only to their predicted labels. To compute the tightest iDP-DB, which enables to guard the label-only access with minimal accuracy decrease, we propose LUCID, which leverages several formal verification techniques. First, it encodes the problem as a mixed-integer linear program, defined over a network and over every network trained identically but without a unique data point. Second, it abstracts a set of networks using a hyper-network. Third, it eliminates the overapproximation error via a novel branch-and-bound technique. Fourth, it bounds the differences of matching neurons in the network and the hyper-network and employs linear relaxation if they are small. We show that LUCID can provide classifiers with a perfect individuals' privacy guarantee (0-iDP) -- which is infeasible for DP training algorithms -- with an accuracy decrease of 1.4%. For more relaxed $\varepsilon$-iDP guarantees, LUCID has an accuracy decrease of 1.2%. In contrast, existing DP training algorithms reduce the accuracy by 12.7%.

Languages such as P4 and NPL have enabled a wide and diverse range of networking applications that take advantage of programmable dataplanes. However, software development in these languages is difficult. To address this issue, high-level languages have been designed to offer programmers powerful abstractions that reduce the time, effort and domain-knowledge required for developing networking applications. These languages are then translated by a compiler into P4/NPL code. Inspired by the recent success of Large Language Models (LLMs) in the task of code generation, we propose to raise the level of abstraction even higher, employing LLMs to translate prose into high-level networking code. We analyze the problem, focusing on the motivation and opportunities, as well as the challenges involved and sketch out a roadmap for the development of a system that can generate high-level dataplane code from natural language instructions. We present some promising preliminary results on generating Lucid code from natural language.

Spurred by recent advances in Large Language Models (LLMs), virtual assistants are poised to take a leap forward in terms of their dialogue capabilities. Yet a major bottleneck to achieving genuinely transformative task-oriented dialogue capabilities remains the scarcity of high quality data. Existing datasets, while impressive in scale, have limited domain coverage and contain few genuinely challenging conversational phenomena; those which are present are typically unlabelled, making it difficult to assess the strengths and weaknesses of models without time-consuming and costly human evaluation. Moreover, creating high quality dialogue data has until now required considerable human input, limiting both the scale of these datasets and the ability to rapidly bootstrap data for a new target domain. We aim to overcome these issues with LUCID, a modularised and highly automated LLM-driven data generation system that produces realistic, diverse and challenging dialogues. We use LUCID to generate a seed dataset of 4,277 conversations across 100 intents to demonstrate its capabilities, with a human review finding consistently high quality labels in the generated data.

Keypoint matching between pairs of images using popular descriptors like SIFT or a faster variant called SURF is at the heart of many computer vision algorithms including recognition, mosaicing, and structure from motion. However, SIFT and SURF do not perform well for real-time or mobile applications. As an alternative very fast binary descriptors like BRIEF and related methods use pairwise comparisons of pixel intensities in an image patch. We present an analysis of BRIEF and related approaches revealing that they are hashing schemes on the ordinal correlation metric Kendall's tau. Here, we introduce Locally Uniform Comparison Image Descriptor (LUCID), a simple description method based on linear time permutation distances between the ordering of RGB values of two image patches. LUCID is computable in linear time with respect to the number of pixels and does not require floating point computation.

The climax culminated with a drone light display of 500 Unmanned Ariel Vehicles (UAVs) illustrating the whimsical characters of the popular mobile game over the Hudson. Rather than applauding the decision, New York lawmakers ostracized the avionic wonders to Jersey. In the words of Democratic State Senator, Brad Hoylman, "Nobody owns New York City's skyline – it is a public good and to allow a private company to reap profits off it is in itself offensive." The complimentary event followed the model of Macy's New York fireworks that have illuminated the Hudson skies since 1958. Unlike the department store's pyrotechnics that release dangerous greenhouse gases into the atmosphere, drones are a quiet climate-friendly choice.

Have you ever heard of emotion artificial intelligence (AI)? Emotion AI, or affective AI, is a field of computer science that helps machines gain an understanding of human emotions. The MIT Media Lab and Dr. Rosalind Picard are the premier innovators in this space. Through their work, they sparked the idea to help machines develop empathy. Empathy is a complex concept with a lot of strings attached to it, but on a basic level, it means having an understanding of another person's emotional states.

Tuvalu's foreign minister, Simon Kofe, told the COP27 climate summit yesterday that Tuvalu would look to the metaverse to preserve its culture and history. With global temperatures expected to rise as much as 2.8 degrees Celsius by the end of the century, the Pacific island nation is particularly vulnerable to rising sea levels. At last year's COP26 summit, Kofe addressed the conference while standing knee-deep in seawater to highlight the climate change threat. Climate scientists anticipate the entire country will be underwater by the end of the 21st century. Addressing the climate summit, Kofe said: "As our land disappears, we have no choice but to become the world's first digital nation. Our land, our ocean, our culture are the most precious assets of our people. And to keep them safe from harm, no matter what happens in the physical world, we'll move them to the cloud."