In recent years, Reward Machines (RMs) have stood out as a simple yet effective automata-based formalism for exposing and exploiting task structure in reinforcement learning settings. Despite their relevance, little to no attention has been directed to the study of their security implications and robustness to adversarial scenarios, likely due to their recent appearance in the literature. With my thesis, I aim to provide the first analysis of the security of RM-based reinforcement learning techniques, with the hope of motivating further research in the field, and I propose and evaluate a novel class of attacks on RM-based techniques: blinding attacks.

OpenAI, the company behind ChatGPT, has become the fastest-growing consumer application in history. With more than 30 executives, engineers, and researchers leaving the company to start their own companies, OpenAI has raised over US$1 billion in financing and created the "OpenAI Mafia", a powerful network of talent, social connections, and capital opportunities. This new generation of AI companies is driving a new round of technological frenzy and investment opportunities, and OpenAI is dedicated to helping humans realize their beautiful vision with an elite team. The OpenAI Mafia is the new generation of AI companies founded by OpenAI employees in the past five years, and is set to revolutionize the AI industry and shape the future of AI technology. Anthropic is an AI company founded in 2021 by Dario and Daniela Amodei, former vice presidents of OpenAI.

In this contributed article, Ankush Gupta and Kavya Shree of FischerJordan, explore the scope, use cases and challenges of AutoML and how data scientists and AutoML can have a future together. The authors discuss the causes driving the use of AutoML, the benefits and challenges associated, and major providers in the space. They conclude by analyzing the parts of the data science and ML process that can/cannot be automated and if AutoML will replace data scientists / both will go hand-in-hand.

Anomaly detection to recognize unusual events in large scale systems in a time sensitive manner is critical in many industries, eg. bank fraud, enterprise systems, medical alerts, etc. Large-scale systems often grow in size and complexity over time, and anomaly detection algorithms need to adapt to changing structures. A hierarchical approach takes advantage of the implicit relationships in complex systems and localized context. The features in complex systems may vary drastically in data distribution, capturing different aspects from multiple data sources, and when put together provide a more complete view of the system. In this paper, two datasets are considered, the 1st comprising of system metrics from machines running on a cloud service, and the 2nd of application metrics from a large-scale distributed software system with inherent hierarchies and interconnections amongst its system nodes. Comparing algorithms, across the changepoint based PELT algorithm, cognitive learning-based Hierarchical Temporal Memory algorithms, Support Vector Machines and Conditional Random Fields provides a basis for proposing a Hierarchical Global-Local Conditional Random Field approach to accurately capture anomalies in complex systems across various features. Hierarchical algorithms can learn both the intricacies of specific features, and utilize these in a global abstracted representation to detect anomalous patterns robustly across multi-source feature data and distributed systems. A graphical network analysis on complex systems can further fine-tune datasets to mine relationships based on available features, which can benefit hierarchical models. Furthermore, hierarchical solutions can adapt well to changes at a localized level, learning on new data and changing environments when parts of a system are over-hauled, and translate these learnings to a global view of the system over time.

Did you miss a session at the Data Summit? MixMode, which today announced a $45 million series B funding round, has a massive opportunity ahead to deploy its self-learning, "third-wave" AI system to proactively secure customers against previously unknown cyberattacks, CEO John Keister told VentureBeat. A significant portion of the hundreds of billions of dollars spent each year on cybersecurity is focused on signature-based solutions, which only protect against the 20% of successful attacks that had previously been seen, Keister said. But the other 80% of cyberattacks (according to figures from the Ponemon Institute) are novel attacks -- and identification of those requires advanced AI capabilities, he said. "The existing systems simply don't address that 80%," Keister said.

ManpowerGroup reported in 2018 that 70% of companies interviewed in a survey said that they expected a skills shortage in field service personnel over the next 10 years. Field service workers are those who travel to locations to repair goods and services onsite. Among the contributing factors were an aging (and retiring) workforce and a lack of enthusiasm among millennials for field service careers. "For large enterprises, field service must address an entire lifecycle of a product or service," said Arka Prava Dhar, CEO and founder of Zinier, which provides field service AI solutions. Field service starts with work orders, then the work is done by the field service worker, then it's either verified or forwarded to an expert, Dhar said.

Artificial intelligence, or AI, is a simulation of intelligent human behavior. It's a computer or system designed to perceive its environment, understand its behaviors, and take action. Consider self-driving cars: AI-driven systems like these integrate AI algorithms, such as machine learning and deep learning, into complex environments that enable automation. AI is estimated to create $13 trillion in economic value worldwide by 2030, according to a McKinsey forecast. That's because AI is transforming engineering in nearly every industry and application area.

Knowledge graphs in manufacturing and production aim to make production lines more efficient and flexible with higher quality output. This makes knowledge graphs attractive for companies to reach Industry 4.0 goals. However, existing research in the field is quite preliminary, and more research effort on analyzing how knowledge graphs can be applied in the field of manufacturing and production is needed. Therefore, we have conducted a systematic literature review as an attempt to characterize the state-of-the-art in this field, i.e., by identifying exiting research and by identifying gaps and opportunities for further research. To do that, we have focused on finding the primary studies in the existing literature, which were classified and analyzed according to four criteria: bibliometric key facts, research type facets, knowledge graph characteristics, and application scenarios. Besides, an evaluation of the primary studies has also been carried out to gain deeper insights in terms of methodology, empirical evidence, and relevance. As a result, we can offer a complete picture of the domain, which includes such interesting aspects as the fact that knowledge fusion is currently the main use case for knowledge graphs, that empirical research and industrial application are still missing to a large extent, that graph embeddings are not fully exploited, and that technical literature is fast-growing but seems to be still far from its peak.

A disconnected/disengaged workforce, broken business processes and an overall decrease in efficiency represent the most recurrent challenges facing organizations today. As a result, digital workplace solutions have grown in popularity as they offer an holistic solution capable of integrating different tools and applications. A typical digital workplace includes a knowledge management system (KMS), an enterprise social network (ESN), an intranet portal, instant messaging and more. It also integrates different third party software used internally, from CRM to Human Resources Information Systems (HRIS). For better usage and efficiency, a digital workplace needs to collect data from all these data sources and make it widely accessible to users in a centralized place – thus the importance of the enterprise search engine.

As global taste for rapid delivery increases, so too does the pressure on those facilitating logistics internationally. Regardless of the scale of operation, says Dean Porter at Zebra Technologies, inventory management is one of the most frequently reported pain points in the warehouse and logistics industries. What was once manageable – or at least tolerable – and done manually, now requires a distinct minimum level of technology to run. Without intelligent databases, around the clock connectivity and smart, ruggedised devices, stock would get lost, workers confused, and management baffled without a live account of operations. The solution is to invest time into looking at what the next wave of technology will bring and how it can plug into existing systems – just don't get put off by jargon or futuristic titles.