One of the biggest technological advances that we're about now is artificial intelligence,

The rise of AI in human contexts places new demands on automated systems to be transparent and explainable. We examine some anthropomorphic ideas and principles relevant to such accountablity in order to develop a theoretical framework for thinking about digital systems in complex human contexts and the problem of explaining their behaviour. Structurally, systems are made of modular and hierachical components, which we abstract in a new system model using notions of modes and mode transitions. A mode is an independent component of the system with its own objectives, monitoring data, and algorithms. The behaviour of a mode, including its transitions to other modes, is determined by functions that interpret each mode's monitoring data in the light of its objectives and algorithms. We show how these belief functions can help explain system behaviour by visualising their evaluation as trajectories in higher-dimensional geometric spaces. These ideas are formalised mathematically by abstract and concrete simplicial complexes. We offer three techniques - a framework for design heuristics, a general system theory based on modes, and a geometric visualisation - and apply them in three types of human-centred systems.

In recent years there has been substantial growth in the capabilities of systems designed to generate text that mimics the fluency and coherence of human language. From this, there has been considerable research aimed at examining the potential uses of these natural language generators (NLG) towards a wide number of tasks. The increasing capabilities of powerful text generators to mimic human writing convincingly raises the potential for deception and other forms of dangerous misuse. As these systems improve, and it becomes ever harder to distinguish between human-written and machine-generated text, malicious actors could leverage these powerful NLG systems to a wide variety of ends, including the creation of fake news and misinformation, the generation of fake online product reviews, or via chatbots as means of convincing users to divulge private information. In this paper, we provide an overview of the NLG field via the identification and examination of 119 survey-like papers focused on NLG research. From these identified papers, we outline a proposed high-level taxonomy of the central concepts that constitute NLG, including the methods used to develop generalised NLG systems, the means by which these systems are evaluated, and the popular NLG tasks and subtasks that exist. In turn, we provide an overview and discussion of each of these items with respect to current research and offer an examination of the potential roles of NLG in deception and detection systems to counteract these threats. Moreover, we discuss the broader challenges of NLG, including the risks of bias that are often exhibited by existing text generation systems. This work offers a broad overview of the field of NLG with respect to its potential for misuse, aiming to provide a high-level understanding of this rapidly developing area of research.

In this article, we develop a modular framework for the application of Reinforcement Learning to the problem of Optimal Trade Execution. The framework is designed with flexibility in mind, in order to ease the implementation of different simulation setups. Rather than focusing on agents and optimization methods, we focus on the environment and break down the necessary requirements to simulate an Optimal Trade Execution under a Reinforcement Learning framework such as data pre-processing, construction of observations, action processing, child order execution, simulation of benchmarks, reward calculations etc. We give examples of each component, explore the difficulties their individual implementations \& the interactions between them entail, and discuss the different phenomena that each component induces in the simulation, highlighting the divergences between the simulation and the behavior of a real market. We showcase our modular implementation through a setup that, following a Time-Weighted Average Price (TWAP) order submission schedule, allows the agent to exclusively place limit orders, simulates their execution via iterating over snapshots of the Limit Order Book (LOB), and calculates rewards as the \$ improvement over the price achieved by a TWAP benchmark algorithm following the same schedule. We also develop evaluation procedures that incorporate iterative re-training and evaluation of a given agent over intervals of a training horizon, mimicking how an agent may behave when being continuously retrained as new market data becomes available and emulating the monitoring practices that algorithm providers are bound to perform under current regulatory frameworks.

The US Court of Appeals for the Federal Circuit affirmed on August 5 that only a natural person--not an artificial intelligence system--can be an inventor. Artificial Intelligence (AI) technology is widely applied as a tool in different technical areas, such as machine learning, image processing, and speech recognition. More complex AI technology can create new products or processes with little or no human help. If an AI system can independently create something new, can it be designated as an inventor? The Federal Circuit finally settled this issue--affirming decisions of the US Patent and Trademark Office (USPTO) and Eastern District of Virginia that an AI system cannot be an inventor.

Crypto may be hogging headlines, but it's far from the only technological innovation causing problems for financial regulators. Consumer protection regulators, for example, are concerned that the use of machine-learning algorithms may already be violating fair lending laws. Although we might hope that automating decisions about who gets credit and on what terms would eliminate discrimination, the reality is that if the data used to train the algorithm reflect human biases, the algorithm will perpetuate those biases.

With 3,000 Rioters out there I'm sure you can find many pictures of Rioters of all genders wearing all sorts of swimwear," he said. "Another claim in the tweet was that there is a rule against raising possible dress code violations. In fact, we want and encourage Rioters to bring any concerns they may have about their workplace environment to our attention so that they can be addressed as quickly as possible. In this case, if this issue had been raised internally we would have reviewed the T-shirts in question and probably asked those Rioters to change them."

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Wondering where AI regulation stands in your state? Today, the Electronic Privacy Information Center (EPIC) released The State of State AI Policy, a roundup of AI-related bills at the state and local level that were passed, introduced or failed in the 2021-2022 legislative session. Within the past year, according to the document, states and localities have passed or introduced bills "regulating artificial intelligence or establishing commissions or task forces to seek transparency about the use of AI in their state or locality."

Role requiring'No experience data provided' months of experience in Houston About VLink: Started in 2006 and headquartered in Connecticut, VLink is one of the fastest-growing digital technology services and consulting companies. Since its inception, our innovative team members have been solving the most complex business, and IT challenges of our global clients. Client is looking for a Data Architect who is primarily an individual contributor but can be responsible for a small team. Main scope of work is to provide solution architecture development, consultancy and assurance to projects, making sure applications are well designed and conform to client standards and reference/segment architectures. Translates the guidelines and standards into practice and solves common technical challenges and provides technical recommendations which have a perceptible impact on local business performance; actively drives the identification, development and implementation of new technologies and opportunities to optimise technology/IT systems. May represent the Company externally as a subject matter expert with suppliers, customers and external agencies. Empowered to make decisions on solutions within guidelines. Applies TOE standards and raises step-outs if needed. Understands the IT Strategic Roadmap and applies within the context of their organisational assignment.

Below gives you a sense of how we're thinking about what you'll need to be successful in the role.