The A.I. Bubble Is Coming for Your Browser Artificial-intelligence startups, like the makers of the "smart" web browser Dia, are being acquired for vast sums. There's an old business maxim dating to the California gold rush: it's easier to make money selling picks and shovels to aspiring miners than to strike it rich finding gold. Artificial intelligence is in a picks-and-shovels phase right now. If gold, in this metaphor, is artificial general intelligence--a machine smarter than a human--or some version of a digital god, then tech companies are snapping up the tools to create one, including graphics-processing units, data centers, and trained A.I. models. That scramble is why Mark Zuckerberg is paying a twenty-four-year-old A.I. researcher two hundred and fifty million dollars to work at Meta, and why Sam Altman, the C.E.O. of OpenAI, recently said that the company would spend "trillions of dollars" building infrastructure.

Robots zipping down footpaths may sound futuristic, but they are increasingly being put to work making deliveries around the world – though a legal minefield and cautious approach to new tech means they are largely absent in Australia. Retail and food businesses have been using robots for a variety of reasons, with hazard detection robots popping up in certain Woolworths stores and virtual waiters taking dishes from kitchens in understaffed restaurants to hungry diners in recent years. Overseas, in jurisdictions such as California, robots are far more visible in everyday life. Following on from the first wave of self-driving car trials in cities such as San Francisco, humans now also share footpaths with robots. Likened to lockers on wheels, companies including Serve Robotics and Coco have partnered with Uber Eats and Doordash, which have armies of robots travelling along footpaths in Los Angeles delivering takeaway meals and groceries.

We introduce a novel distributed source seeking framework, DIAS, designed for multi-robot systems in scenarios where the number of sources is unknown and potentially exceeds the number of robots. Traditional robotic source seeking methods typically focused on directing each robot to a specific strong source and may fall short in comprehensively identifying all potential sources. DIAS addresses this gap by introducing a hybrid controller that identifies the presence of sources and then alternates between exploration for data gathering and exploitation for guiding robots to identified sources. It further enhances search efficiency by dividing the environment into Voronoi cells and approximating source density functions based on Gaussian process regression. Additionally, DIAS can be integrated with existing source seeking algorithms. We compare DIAS with existing algorithms, including DoSS and GMES in simulated gas leakage scenarios where the number of sources outnumbers or is equal to the number of robots. The numerical results show that DIAS outperforms the baseline methods in both the efficiency of source identification by the robots and the accuracy of the estimated environmental density function.

Back in 2022, The Browser Company released an innovative web browser called Arc that prioritized ease-of-use and user experience over all things. The simplified interface and feature set was good for niche users, but didn't offer enough pizzazz for advanced users. Now, the makers of Arc have unveiled the first taste of their next product, which happens to be yet another web browser -- except this one is an AI-powered "smart browser" called Dia. It's no secret that the world is moving towards an AI-driven future. "What we believe specifically is that AI will not exist as an app or a button, but AI will be a whole new environment built on top of a browser," said The Browser Company's CEO Josh Miller in a recruitment video on YouTube: In the video, the company shows off several prototype features in Dia.

Data-Independent Acquisition (DIA) was introduced to improve sensitivity to cover all peptides in a range rather than only sampling high-intensity peaks as in Data-Dependent Acquisition (DDA) mass spectrometry. However, it is not very clear how useful DIA data is for de novo peptide sequencing as the DIA data are marred with coeluted peptides, high noises, and varying data quality. We present a new deep learning method DIANovo, and address each of these difficulties, and improves the previous established system DeepNovo-DIA by from 25% to 81%, averaging 48%, for amino acid recall, and by from 27% to 89%, averaging 57%, for peptide recall, by equipping the model with a deeper understanding of coeluted DIA spectra. This paper also provides criteria about when DIA data could be used for de novo peptide sequencing and when not to by providing a comparison between DDA and DIA, in both de novo and database search mode. We find that while DIA excels with narrow isolation windows on older-generation instruments, it loses its advantage with wider windows. However, with Orbitrap Astral, DIA consistently outperforms DDA due to narrow window mode enabled. We also provide a theoretical explanation of this phenomenon, emphasizing the critical role of the signal-to-noise profile in the successful application of de novo sequencing.

Estimation of cardiovascular model parameters from electronic health records (EHR) poses a significant challenge primarily due to lack of identifiability. Structural non-identifiability arises when a manifold in the space of parameters is mapped to a common output, while practical non-identifiability can result due to limited data, model misspecification, or noise corruption. To address the resulting ill-posed inverse problem, optimization-based or Bayesian inference approaches typically use regularization, thereby limiting the possibility of discovering multiple solutions. In this study, we use inVAErt networks, a neural network-based, data-driven framework for enhanced digital twin analysis of stiff dynamical systems. We demonstrate the flexibility and effectiveness of inVAErt networks in the context of physiological inversion of a six-compartment lumped parameter hemodynamic model from synthetic data to real data with missing components.

Improved defense intelligence collection and analysis of emerging technologies will rely on forming new partnerships between government and industry, according to leadership at the Defense Intelligence Agency. Doug Wade, the head of the DIA's China Mission Group, spoke during a Tuesday media discussion about his agency's effort to bring together the best of industry to identify specific threats China poses and coordinate responses . "So China is investing heavily right in its AI and ML capabilities," Wade said. "China's ability to use things like AI to ensure that they have strong…surveillance coverage of citizens, whether they're China citizens or whether they export that technology to other regimes around the world, and then those regimes use it to exert totalitarian control." Wade said that this potential exportation of AI technology should be an area of concern as a threat to U.S. national security, as well as to ally nations in Europe.

Accurately predicting the possible behaviors of traffic participants is an essential capability for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically changing environments, they are expected to make accurate predictions regardless of where they are and what driving circumstances they encountered. A number of methodologies have been proposed to solve prediction problems under different traffic situations. However, these works either focus on one particular driving scenario (e.g. highway, intersection, or roundabout) or do not take sufficient environment information (e.g. road topology, traffic rules, and surrounding agents) into account. In fact, the limitation to certain scenario is mainly due to the lackness of generic representations of the environment. The insufficiency of environment information further limits the flexibility and transferability of the predictor. In this paper, we propose a scenario-transferable and interaction-aware probabilistic prediction algorithm based on semantic graph reasoning, which predicts behaviors of selected agents. We put forward generic representations for various environment information and utilize them as building blocks to construct their spatio-temporal structural relations. We then take the advantage of these structured representations to develop a flexible and transferable prediction algorithm, where the predictor can be directly used under unforeseen driving circumstances that are completely different from training scenarios. The proposed algorithm is thoroughly examined under several complicated real-world driving scenarios to demonstrate its flexibility and transferability with the generic representation for autonomous driving systems.

Data clustering is a fundamental problem with a wide range of applications. Standard methods, eg the $k$-means method, usually require solving a non-convex optimization problem. Recently, total variation based convex relaxation to the $k$-means model has emerged as an attractive alternative for data clustering. However, the existing results on its exact clustering property, ie, the condition imposed on data so that the method can provably give correct identification of all cluster memberships, is only applicable to very specific data and is also much more restrictive than that of some other methods. This paper aims at the revisit of total variation based convex clustering, by proposing a weighted sum-of-$\ell_1$-norm relating convex model. Its exact clustering property established in this paper, in both deterministic and probabilistic context, is applicable to general data and is much sharper than the existing results. These results provided good insights to advance the research on convex clustering. Moreover, the experiments also demonstrated that the proposed convex model has better empirical performance when be compared to standard clustering methods, and thus it can see its potential in practice.

The Defense Intelligence Agency will host a two-day event at its Washington headquarters in August to gather technology ideas from industry and academic professionals in the artificial intelligence and machine learning areas. DIA said Thursday its innovation office aims to identify new technologies and business processes as well as introduce attendees to the agency's Innovation Hub environment during the Industry Day series scheduled for Aug. 2 and 3. IHUB is designed to virtually connect entrepreneurs in a collaborative environment for testing and evaluation of intelligence platforms. "Innovation is happening in the private sector, so we want to leverage that to bring them into our agency and see how we can transform what we're doing in the defense intelligence enterprise," said Robert Dixon, special adviser for innovation at DIA. The agency seeks AI and machine learning tools that can support automated reporting, data transformation, database development, information monitoring and thematic data management activities. Interested vendors and members of the academia can submit concepts to the DIA in accordance with the "NEED 99 – Other Innovative Capabilities" list on the agency's NeedipeDIA website.