The rise of human-information systems, cybernetic systems, and increasingly autonomous systems requires the application of epistemic frameworks to machines and human-machine teams. This chapter discusses higher-order design principles to guide the design, evaluation, deployment, and iteration of Lethal Autonomous Weapons Systems (LAWS) based on epistemic models. Epistemology is the study of knowledge. Epistemic models consider the role of accuracy, likelihoods, beliefs, competencies, capabilities, context, and luck in the justification of actions and the attribution of knowledge. The aim is not to provide ethical justification for or against LAWS, but to illustrate how epistemological frameworks can be used in conjunction with moral apparatus to guide the design and deployment of future systems. The models discussed in this chapter aim to make Article 36 reviews of LAWS systematic, expedient, and evaluable. A Bayesian virtue epistemology is proposed to enable justified actions under uncertainty that meet the requirements of the Laws of Armed Conflict and International Humanitarian Law. Epistemic concepts can provide some of the apparatus to meet explainability and transparency requirements in the development, evaluation, deployment, and review of ethical AI.

Gray-box graph attacks aim at disrupting the performance of the victim model by using inconspicuous attacks with limited knowledge of the victim model. The parameters of the victim model and the labels of the test nodes are invisible to the attacker. To obtain the gradient on the node attributes or graph structure, the attacker constructs an imaginary surrogate model trained under supervision. However, there is a lack of discussion on the training of surrogate models and the robustness of provided gradient information. The general node classification model loses the topology of the nodes on the graph, which is, in fact, an exploitable prior for the attacker. This paper investigates the effect of representation learning of surrogate models on the transferability of gray-box graph adversarial attacks. To reserve the topology in the surrogate embedding, we propose Surrogate Representation Learning with Isometric Mapping (SRLIM). By using Isometric mapping method, our proposed SRLIM can constrain the topological structure of nodes from the input layer to the embedding space, that is, to maintain the similarity of nodes in the propagation process. Experiments prove the effectiveness of our approach through the improvement in the performance of the adversarial attacks generated by the gradient-based attacker in untargeted poisoning gray-box setups.

Much recent research has uncovered and discussed serious concerns of bias in facial analysis technologies, finding performance disparities between groups of people based on perceived gender, skin type, lighting condition, etc. These audits are immensely important and successful at measuring algorithmic bias but have two major challenges: the audits (1) use facial recognition datasets which lack quality metadata, like LFW and CelebA, and (2) do not compare their observed algorithmic bias to the biases of their human alternatives. In this paper, we release improvements to the LFW and CelebA datasets which will enable future researchers to obtain measurements of algorithmic bias that are not tainted by major flaws in the dataset (e.g. identical images appearing in both the gallery and test set). We also use these new data to develop a series of challenging facial identification and verification questions that we administered to various algorithms and a large, balanced sample of human reviewers. We find that both computer models and human survey participants perform significantly better at the verification task, generally obtain lower accuracy rates on dark-skinned or female subjects for both tasks, and obtain higher accuracy rates when their demographics match that of the question. Computer models are observed to achieve a higher level of accuracy than the survey participants on both tasks and exhibit bias to similar degrees as the human survey participants.

Physics-informed Neural Networks (PINNs) have been shown to be effective in solving partial differential equations by capturing the physics induced constraints as a part of the training loss function. This paper shows that a PINN can be sensitive to errors in training data and overfit itself in dynamically propagating these errors over the domain of the solution of the PDE. It also shows how physical regularizations based on continuity criteria and conservation laws fail to address this issue and rather introduce problems of their own causing the deep network to converge to a physics-obeying local minimum instead of the global minimum. We introduce Gaussian Process (GP) based smoothing that recovers the performance of a PINN and promises a robust architecture against noise/errors in measurements. Additionally, we illustrate an inexpensive method of quantifying the evolution of uncertainty based on the variance estimation of GPs on boundary data. Robust PINN performance is also shown to be achievable by choice of sparse sets of inducing points based on sparsely induced GPs. We demonstrate the performance of our proposed methods and compare the results from existing benchmark models in literature for time-dependent Schr\"odinger and Burgers' equations.

Deep learning has been successful in automating the design of features in machine learning pipelines. However, the algorithms optimizing neural network parameters remain largely hand-designed and computationally inefficient. We study if we can use deep learning to directly predict these parameters by exploiting the past knowledge of training other networks. We introduce a large-scale dataset of diverse computational graphs of neural architectures - DeepNets-1M - and use it to explore parameter prediction on CIFAR-10 and ImageNet. By leveraging advances in graph neural networks, we propose a hypernetwork that can predict performant parameters in a single forward pass taking a fraction of a second, even on a CPU. The proposed model achieves surprisingly good performance on unseen and diverse networks. For example, it is able to predict all 24 million parameters of a ResNet-50 achieving a 60% accuracy on CIFAR-10. On ImageNet, top-5 accuracy of some of our networks approaches 50%. Our task along with the model and results can potentially lead to a new, more computationally efficient paradigm of training networks. Our model also learns a strong representation of neural architectures enabling their analysis.

Torc Robotics and Daimler Truck kick off their third year of partnership poised to commercialize the first scalable, profitable Level 4 autonomous truck that will help fleets improve their operations while bolstering the backbone of the U.S. economy. Torc is currently testing the Level 4 trucks on public roads in Virginia, New Mexico, and Texas, with continued route expansion in the works. The two companies are pursuing a focused, safety-oriented approach to market that also seeks to build trust among fleets and the drivers of vehicles who will share the road. Introducing a world-changing technology into an existing infrastructure, where human drivers will share the road with automated trucks, requires credibility and responsibility, according to Dr. Peter Vaughan Schmidt, Head of Daimler Truck's Autonomous Technology Group. "As the inventor of the truck, Daimler Truck has many decades of experience in testing and validation of commercial vehicles. Nevertheless, to develop a safe autonomous level 4 truck remains a complex task and resembles a marathon, not a sprint. Two years together with Torc Robotics, we have accomplished a lot, collaboratively pursuing a common goal of leading the logistics sector into the future and making road traffic safer for society. I am convinced that we are optimally positioned as a company and together with Torc we have the right partner at our side to achieve our goals."

American journalist Spencer Ackerman, in collaboration with documentary filmmaker Laura Poitras, just released a document related to the US drone operations leaked by National Security Agency whistleblower, Edward Snowden. The document is an article on Intellipedia, a secretive US data site where the US intelligence agencies share the material they mine on us all across the world. Titled "Targeted Killing: Policy, Legal and Ethical Controversy", the entry reflects Intellipedia's take on the work of many human rights defenders and organisations – including my own – to stop the CIA drone war across the world. Because of my human rights work, I have always assumed that I was being tracked by security agencies. Indeed, when I write an email to my wife, I sometimes add an ironic post-script to their agents apologising for being boring.

Nearly two-thirds of Americans want the U.S to regulate the development and use of artificial intelligence in the next year or sooner -- with half saying that regulation should have begun yesterday, according to a Morning Consult poll. Another 13% say that regulation should start in the next year. "You can thread this together," Austin Carson, founder of new nonprofit group SeedAI and former government relations lead for Nvidia, said in an email. "Half or more Americans want to address all of these things, split pretty evenly along ideological lines." The poll, which SeedAI commissioned, backs up earlier findings that while U.S. adults support investment in the development of AI, they want clear rules around that development.

Artificial intelligence (AI) has become a part of everyday modern life and continues to generate excitement. In this article, we will take a look at how biotechs are advancing into AI-facilitated early drug design, its real-world outcomes so far, and whether it is living up to its potential in the life science industry. AI enables computers and machines to learn from past behavior and mistakes, much like humans can. From search algorithms to self-driving cars and Siri, it is cemented in our daily lives. In complex drug discovery, AI has the potential to make processes faster and more cost-effective, with the hope of reducing the time a new drug needs to reach the patient.

Every time a dramatic, unforeseen political event happens, there follows a left-field fixation that some out-of-control technology created it. Whenever this fear about big tech comes around we are told that something new, even more toxic, has infiltrated our public discourse, triggering hatred towards politicians and public figures, conspiracy theories about Covid and even major political events like Brexit. The concern over anonymity online becomes a particular worry – as if ending it will somehow, like throwing a blanket at a raging house fire, subdue our fevered state. You may remember that during the summer's onslaught of racist abuse towards black players in the England football team, instead of reckoning with the fact that racism still haunts this country, we busied ourselves with bluster about how "cowards" online would be silenced if we only just demanded they identify themselves. We resort to this explanation, that shadowy social media somehow stimulate our worst impulses, despite there being little evidence that most abuse is from unidentifiable sources.