The California Legislature passed a bill earlier this month to require human safety drivers in heavy-duty robot trucks for at least the next five years. On Friday, Gov. Gavin Newsom killed it. "Considering... the existing regulatory framework that presently and sufficiently governs this particular technology, this bill is not needed at this time," the governor said in a veto message. The bill was sponsored by the Teamsters union and backed by highway safety advocates. Opposed: driverless technology companies, Silicon Valley lobbyists, and various chambers of commerce and business leadership groups.

Gary Marcus: Two Models of AI Oversight -- and How Things Could Go Deeply Wrong https://bit.ly/3Qnxd9A June 12, 2023 Originally published on The Road to AI We Can Trust (http://bit.ly/3juuD3j) The Senate hearing that I participated in a few weeks ago (https://bit.ly/44QxHt1) I was thrilled by what I saw of the Senate that day: genuine interest and genuine humility. Senators acknowledged that they were too slow to figure out what do about social media, that the moves were made then, and that there was now a sense of urgency.

It has been called the new gold rush – a rush to catch up with China in producing and refining the materials needed in everything from computers to cars: but has it come too late to save Europe's car industry? Deep inside a former East German town lies the first fruits of the EU's grand plan to "de-risk" and wean itself off dependency on imports for the green revolution. In Bitterfeld-Wolfen, 140km south-west of Berlin, an Amsterdam-listed company is scrambling to complete construction of a vast factory that will be the first in Europe to deliver battery-grade lithium. There is now a race across Europe to both mine the silver-white soft metal and manufacture its refined form, lithium hydroxide – the key ingredient in the batteries that power electric cars, robot vacuum cleaners and mobile phones. "Everybody wants to get access to lithium. This is maybe why they call it the white gold, because it is like a gold rush," says Stefan Scherer, chief executive of AMG Lithium.

The discovery of scientific formulae that parsimoniously explain natural phenomena and align with existing background theory is a key goal in science. Historically, scientists have derived natural laws by manipulating equations based on existing knowledge, forming new equations, and verifying them experimentally. In recent years, data-driven scientific discovery has emerged as a viable competitor in settings with large amounts of experimental data. Unfortunately, data-driven methods often fail to discover valid laws when data is noisy or scarce. Accordingly, recent works combine regression and reasoning to eliminate formulae inconsistent with background theory. However, the problem of searching over the space of formulae consistent with background theory to find one that fits the data best is not well-solved. We propose a solution to this problem when all axioms and scientific laws are expressible via polynomial equalities and inequalities and argue that our approach is widely applicable. We further model notions of minimal complexity using binary variables and logical constraints, solve polynomial optimization problems via mixed-integer linear or semidefinite optimization, and prove the validity of our scientific discoveries in a principled manner using Positivestellensatz certificates. Remarkably, the optimization techniques leveraged in this paper allow our approach to run in polynomial time with fully correct background theory, or non-deterministic polynomial (NP) time with partially correct background theory. We demonstrate that some famous scientific laws, including Kepler's Third Law of Planetary Motion, the Hagen-Poiseuille Equation, and the Radiated Gravitational Wave Power equation, can be derived in a principled manner from background axioms and experimental data.

Due to the powerful capabilities demonstrated by large language model (LLM), there has been a recent surge in efforts to integrate them with AI agents to enhance their performance. In this paper, we have explored the core differences and characteristics between LLM-based AI agents and traditional AI agents. Specifically, we first compare the fundamental characteristics of these two types of agents, clarifying the significant advantages of LLM-based agents in handling natural language, knowledge storage, and reasoning capabilities. Subsequently, we conducted an in-depth analysis of the key components of AI agents, including planning, memory, and tool use. Particularly, for the crucial component of memory, this paper introduced an innovative classification scheme, not only departing from traditional classification methods but also providing a fresh perspective on the design of an AI agent's memory system. We firmly believe that in-depth research and understanding of these core components will lay a solid foundation for the future advancement of AI agent technology. At the end of the paper, we provide directional suggestions for further research in this field, with the hope of offering valuable insights to scholars and researchers in the field.

Entanglement is a useful resource for learning, but a precise characterization of its advantage can be challenging. In this work, we consider learning algorithms without entanglement to be those that only utilize separable states, measurements, and operations between the main system of interest and an ancillary system. These algorithms are equivalent to those that apply quantum circuits on the main system interleaved with mid-circuit measurements and classical feedforward. We prove a tight lower bound for learning Pauli channels without entanglement that closes a cubic gap between the best-known upper and lower bound. In particular, we show that $\Theta(2^n\varepsilon^{-2})$ rounds of measurements are required to estimate each eigenvalue of an $n$-qubit Pauli channel to $\varepsilon$ error with high probability when learning without entanglement. In contrast, a learning algorithm with entanglement only needs $\Theta(\varepsilon^{-2})$ rounds of measurements. The tight lower bound strengthens the foundation for an experimental demonstration of entanglement-enhanced advantages for characterizing Pauli noise.

With a surge in the usage of social media postings to express opinions, emotions, and ideologies, there has been a significant shift towards the calibration of social media as a rapid medium of conveying viewpoints and outlooks over the globe. Concurrently, the emergence of a multitude of conflicts between two entities has given rise to a stream of social media content containing propaganda, hate speech, and inconsiderate views. Thus, the issue of monitoring social media postings is rising swiftly, attracting major attention from those willing to solve such problems. One such problem is Hate Speech detection. To mitigate this problem, we present our novel ensemble learning approach for detecting hate speech, by classifying text-embedded images into two labels, namely "Hate Speech" and "No Hate Speech". We have incorporated state-of-art models including InceptionV3, BERT, and XLNet. Our proposed ensemble model yielded promising results with 75.21 and 74.96 as accuracy and F-1 score (respectively). We also present an empirical evaluation of the text-embedded images to elaborate on how well the model was able to predict and classify. We release our codebase here (https://github.com/M0hammad-Kashif/MultiModalHateSpeech).

This paper presents an interpretable review of various machine learning and deep learning models to predict the maintenance of aircraft engine to avoid any kind of disaster. One of the advantages of the strategy is that it can work with modest datasets. In this study, sensor data is utilized to predict aircraft engine failure within a predetermined number of cycles using LSTM, Bi-LSTM, RNN, Bi-RNN GRU, Random Forest, KNN, Naive Bayes, and Gradient Boosting. We explain how deep learning and machine learning can be used to generate predictions in predictive maintenance using a straightforward scenario with just one data source. We applied lime to the models to help us understand why machine learning models did not perform well than deep learning models. An extensive analysis of the model's behavior is presented for several test data to understand the black box scenario of the models. A lucrative accuracy of 97.8%, 97.14%, and 96.42% are achieved by GRU, Bi-LSTM, and LSTM respectively which denotes the capability of the models to predict maintenance at an early stage.

Background: Since the launch of various generative AI tools, scientists have been striving to evaluate their capabilities and contents, in the hope of establishing trust in their generative abilities. Regulations and guidelines are emerging to verify generated contents and identify novel uses. Objective: we aspire to demonstrate how ChatGPT claims are checked computationally using the rigor of network models. We aim to achieve fact-checking of the knowledge embedded in biological graphs that were contrived from ChatGPT contents at the aggregate level. Methods: We adopted a biological networks approach that enables the systematic interrogation of ChatGPT's linked entities. We designed an ontology-driven fact-checking algorithm that compares biological graphs constructed from approximately 200,000 PubMed abstracts with counterparts constructed from a dataset generated using the ChatGPT-3.5 Turbo model. Results: in 10-samples of 250 randomly selected records a ChatGPT dataset of 1000 "simulated" articles, the fact-checking link accuracy ranged from 70% to 86%. The computational process was followed by a manual process using IntAct Interaction database and the Gene regulatory network database (GRNdb) to confirm the validity of the links identified computationally. We also found that the proximity of the edges of ChatGPT graphs were significantly shorter (90 -- 153) while literature distances were (236 -- 765). This pattern held true in all 10-samples. Conclusion: This study demonstrated high accuracy of aggregate disease-gene links relationships found in ChatGPT-generated texts. The strikingly consistent pattern offers an illuminate new biological pathways that may open the door for new research opportunities.

Accurate facial landmark detection is critical for facial analysis tasks, yet prevailing heatmap and coordinate regression methods grapple with prohibitive computational costs and quantization errors. Through comprehensive theoretical analysis and experimentation, we identify and elucidate the limitations of existing techniques. To overcome these challenges, we pioneer the application of True-Range Multilateration, originally devised for GPS localization, to facial landmark detection. We propose KeyPoint Positioning System (KeyPosS) - the first framework to deduce exact landmark coordinates by triangulating distances between points of interest and anchor points predicted by a fully convolutional network. A key advantage of KeyPosS is its plug-and-play nature, enabling flexible integration into diverse decoding pipelines. Extensive experiments on four datasets demonstrate state-of-the-art performance, with KeyPosS outperforming existing methods in low-resolution settings despite minimal computational overhead. By spearheading the integration of Multilateration with facial analysis, KeyPosS marks a paradigm shift in facial landmark detection. The code is available at https://github.com/zhiqic/KeyPosS.