In this paper we explore cyber security defence, through the unification of a novel cyber security simulator with models for (causal) decision-making through optimisation. Particular attention is paid to a recently published approach: dynamic causal Bayesian optimisation (DCBO). We propose that DCBO can act as a blue agent when provided with a view of a simulated network and a causal model of how a red agent spreads within that network. To investigate how DCBO can perform optimal interventions on host nodes, in order to reduce the cost of intrusions caused by the red agent. Through this we demonstrate a complete cyber-simulation system, which we use to generate observational data for DCBO and provide numerical quantitative results which lay the foundations for future work in this space.

Linear time-invariant state space models (SSM) are a classical model from engineering and statistics, that have recently been shown to be very promising in machine learning through the Structured State Space sequence model (S4). A core component of S4 involves initializing the SSM state matrix to a particular matrix called a HiPPO matrix, which was empirically important for S4's ability to handle long sequences. However, the specific matrix that S4 uses was actually derived in previous work for a particular time-varying dynamical system, and the use of this matrix as a time-invariant SSM had no known mathematical interpretation. Consequently, the theoretical mechanism by which S4 models long-range dependencies actually remains unexplained. We derive a more general and intuitive formulation of the HiPPO framework, which provides a simple mathematical interpretation of S4 as a decomposition onto exponentially-warped Legendre polynomials, explaining its ability to capture long dependencies. Our generalization introduces a theoretically rich class of SSMs that also lets us derive more intuitive S4 variants for other bases such as the Fourier basis, and explains other aspects of training S4, such as how to initialize the important timescale parameter. These insights improve S4's performance to 86% on the Long Range Arena benchmark, with 96% on the most difficult Path-X task.

We study planning problems faced by robots operating in uncertain environments with incomplete knowledge of state, and actions that are noisy and/or imprecise. This paper identifies a new problem sub-class that models settings in which information is revealed only intermittently through some exogenous process that provides state information periodically. Several practical domains fit this model, including the specific scenario that motivates our research: autonomous navigation of a planetary exploration rover augmented by remote imaging. With an eye to efficient specialized solution methods, we examine the structure of instances of this sub-class. They lead to Markov Decision Processes with exponentially large action-spaces but for which, as those actions comprise sequences of more atomic elements, one may establish performance bounds by comparing policies under different information assumptions. This provides a way in which to construct performance bounds systematically. Such bounds are useful because, in conjunction with the insights they confer, they can be employed in bounding-based methods to obtain high-quality solutions efficiently; the empirical results we present demonstrate their effectiveness for the considered problems. The foregoing has also alluded to the distinctive role that time plays for these problems -- more specifically: time until information is revealed -- and we uncover and discuss several interesting subtleties in this regard.

We present Branch-Train-Merge (BTM), a communication-efficient algorithm for embarrassingly parallel training of large language models (LLMs). We show it is possible to independently train subparts of a new class of LLMs on different subsets of the data, eliminating the massive multi-node synchronization currently required to train LLMs. BTM learns a set of independent expert LMs (ELMs), each specialized to a different textual domain, such as scientific or legal text. These ELMs can be added and removed to update data coverage, ensembled to generalize to new domains, or averaged to collapse back to a single LM for efficient inference. New ELMs are learned by branching from (mixtures of) ELMs in the current set, further training the parameters on data for the new domain, and then merging the resulting model back into the set for future use. Experiments show that BTM improves in- and out-of-domain perplexities as compared to GPT-style Transformer LMs, when controlling for training cost. Through extensive analysis, we show that these results are robust to different ELM initialization schemes, but require expert domain specialization; LM ensembles with random data splits do not perform well. We also present a study of scaling BTM into a new corpus of 64 domains (192B whitespace-separated tokens in total); the resulting LM (22.4B total parameters) performs as well as a Transformer LM trained with 2.5 times more compute. These gains grow with the number of domains, suggesting more aggressive parallelism could be used to efficiently train larger models in future work.

State space models (SSM) have recently been shown to be very effective as a deep learning layer as a promising alternative to sequence models such as RNNs, CNNs, or Transformers. The first version to show this potential was the S4 model, which is particularly effective on tasks involving long-range dependencies by using a prescribed state matrix called the HiPPO matrix. While this has an interpretable mathematical mechanism for modeling long dependencies, it introduces a custom representation and algorithm that can be difficult to implement. On the other hand, a recent variant of S4 called DSS showed that restricting the state matrix to be fully diagonal can still preserve the performance of the original model when using a specific initialization based on approximating S4's matrix. This work seeks to systematically understand how to parameterize and initialize such diagonal state space models. While it follows from classical results that almost all SSMs have an equivalent diagonal form, we show that the initialization is critical for performance. We explain why DSS works mathematically, by showing that the diagonal restriction of S4's matrix surprisingly recovers the same kernel in the limit of infinite state dimension. We also systematically describe various design choices in parameterizing and computing diagonal SSMs, and perform a controlled empirical study ablating the effects of these choices. Our final model S4D is a simple diagonal version of S4 whose kernel computation requires just 2 lines of code and performs comparably to S4 in almost all settings, with state-of-the-art results for image, audio, and medical time-series domains, and averaging 85\% on the Long Range Arena benchmark.

Deep neural network (DNN) models, including those used in safety-critical domains, need to be thoroughly tested to ensure that they can reliably perform well in different scenarios. In this article, we provide an overview of structural coverage metrics for testing DNN models, including neuron coverage (NC), k-multisection neuron coverage (kMNC), top-k neuron coverage (TKNC), neuron boundary coverage (NBC), strong neuron activation coverage (SNAC) and modified condition/decision coverage (MC/DC). We evaluate the metrics on realistic DNN models used for perception tasks (including LeNet-1, LeNet-4, LeNet-5, and ResNet20) as well as on networks used in autonomy (TaxiNet). We also provide a tool, DNNCov, which can measure the testing coverage for all these metrics. DNNCov outputs an informative coverage report to enable researchers and practitioners to assess the adequacy of DNN testing, compare different coverage measures, and to more conveniently inspect the model's internals during testing.

This paper proposes a novel approach that enables a robot to learn an objective function incrementally from human directional corrections. Existing methods learn from human magnitude corrections; since a human needs to carefully choose the magnitude of each correction, those methods can easily lead to over-corrections and learning inefficiency. The proposed method only requires human directional corrections -- corrections that only indicate the direction of an input change without indicating its magnitude. We only assume that each correction, regardless of its magnitude, points in a direction that improves the robot's current motion relative to an unknown objective function. The allowable corrections satisfying this assumption account for half of the input space, as opposed to the magnitude corrections which have to lie in a shrinking level set. For each directional correction, the proposed method updates the estimate of the objective function based on a cutting plane method, which has a geometric interpretation. We have established theoretical results to show the convergence of the learning process. The proposed method has been tested in numerical examples, a user study on two human-robot games, and a real-world quadrotor experiment. The results confirm the convergence of the proposed method and further show that the method is significantly more effective (higher success rate), efficient/effortless (less human corrections needed), and potentially more accessible (fewer early wasted trials) than the state-of-the-art robot learning frameworks.

Machine learning seems to be more and more prominent as businesses are adopting it. From streaming services that use algorithms to study viewer behavior to self-driving cars, it is clear that machine learning solutions will continue to benefit humanity. And why should that not be the case when there is so much machine learning can offer us. But are things really as good as they seem? And what is the future looking like for machine learning?

Artificial Intelligence (AI) systems are poised to drastically alter the way businesses and governments operate on a global scale, with significant changes already under way. This technology has manifested itself in multiple forms including natural language processing, machine learning, and autonomous systems, but with the proper inputs can be leveraged to make predictions, recommendations, and even decisions. Accordingly,enterprises are increasingly embracing this dynamic technology. A 2022 global study by IBM found that 77% of companies are either currently using AI or exploring AI for future use, creating value by increasing productivity through automation, improved decision-making, and enhanced customer experience. Further, according to a 2021 PwC study the COVID-19 pandemic increased the pace of AI adoption for 52% of companies as they sought to mitigate the crises' impact on workforce planning, supply chain resilience, and demand projection.

Whether you're hip to it or not, conversational AI--which is really the sequencing of technologies like NLU/NLP, code-free programming, RPA, and machine learning inside of organizational ecosystems--has already begun reshaping the world at large. Lemonade, a tech- and user-centric insurance company is upending its industry by providing customers with a rewarding experience buying insurance that's facilitated by Maya, an intelligent digital worker described as "utterly charming" that can quickly connect dots and get customers insured. Maya is essentially an infinitely replicable agent that is always learning and doesn't make the same mistake twice. Compare that with whatever it costs Allstate to retain more than 12,000 agents in the US and Canada who are likely using outdated legacy systems and it's clear to see which way ROI is trending. Even bigger successes have been enjoyed by Ant Group (formerly Ant Financial) a nimble, Chinese financial giant that had surpassed the number of customers served by today's largest US banks by more than 10 times back at the start of 2020. Their IPO--which would have been the world's largest to date--collapsed after Chinese Communist Party leader Xi Jinping allegedly intervened. Subsequently, the company has broadened its scope past fintech to include sustainability and inclusive services (whatever those might be). Still, its core operations were built around a streamlined business structure that uses conversational AI to deliver meaningful experiences. While this kind of adoption of conversational AI in business settings is roundly expected to boom in the coming years, it will quickly seep into our daily lives as well, going beyond how we interact with the many companies in our lives and taking root in our interactions with all of the different technologies we regularly touch. I've always taken an interest in these topics, but like many cutting-edge things, they're hard to approach. Especially if you have no idea where to start. Especially if you don't have the expertise; the lexicon, the mindset, the lived experience. I'm a bit of an accidental Luddite, someone perpetually late to the party when it comes to the latest and greatest. Not to say that I'm completely unfamiliar with these things, just that integrating them into my work, and my life, is hard.