Given a combinatorial optimization problem taking an input, can we learn a strategy to solve it from the examples of input-solution pairs without knowing its objective function? In this paper, we consider such a setting and study the misinformation prevention problem. Given the examples of attacker-protector pairs, our goal is to learn a strategy to compute protectors against future attackers, without the need of knowing the underlying diffusion model. To this end, we design a structured prediction framework, where the main idea is to parameterize the scoring function using random features constructed through distance functions on randomly sampled subgraphs, which leads to a kernelized scoring function with weights learnable via the large margin method. Evidenced by experiments, our method can produce near-optimal protectors without using any information of the diffusion model, and it outperforms other possible graph-based and learning-based methods by an evident margin.

Meta-World is widely used for evaluating multi-task and meta-reinforcement learning agents, which are challenged to master diverse skills simultaneously. Since its introduction however, there have been numerous undocumented changes which inhibit a fair comparison of algorithms. This work strives to disambiguate these results from the literature, while also leveraging the past versions of Meta-World to provide insights into multi-task and meta-reinforcement learning benchmark design. Through this process we release a new open-source version of Meta-World (https://github.com/Farama-Foundation/Metaworld/) that has full reproducibility of past results, is more technically ergonomic, and gives users more control over the tasks that are included in a task set.

Large Language Model~(LLM) based agents have been increasingly popular in solving complex and dynamic tasks, which requires proper evaluation systems to assess their capabilities. Nevertheless, existing benchmarks usually either focus on single-objective tasks or use overly broad assessing metrics, failing to provide a comprehensive inspection of the actual capabilities of LLM-based agents in complicated decision-making tasks. To address these issues, we introduce DSGBench, a more rigorous evaluation platform for strategic decision-making. Firstly, it incorporates six complex strategic games which serve as ideal testbeds due to their long-term and multi-dimensional decision-making demands and flexibility in customizing tasks of various difficulty levels or multiple targets. Secondly, DSGBench employs a fine-grained evaluation scoring system which examines the decision-making capabilities by looking into the performance in five specific dimensions and offering a comprehensive assessment in a well-designed way. Furthermore, DSGBench also incorporates an automated decision-tracking mechanism which enables in-depth analysis of agent behaviour patterns and the changes in their strategies. We demonstrate the advances of DSGBench by applying it to multiple popular LLM-based agents and our results suggest that DSGBench provides valuable insights in choosing LLM-based agents as well as improving their future development. DSGBench is available at https://github.com/DeciBrain-Group/DSGBench.

We are clearly in a period of recession. But for all businesses, it's a good time to put a change strategy in place. Here's why 2023 needs to be the year to optimize and automate your IT… The pandemic has shown companies that they need to be more agile in order to react quickly to sometimes unexpected events. The looming economic recession is an example of an unexpected factor whose repercussions may well exceed those of the periods of confinement that we have experienced during the pandemic. Unfortunately, companies tend to suspend development during economic downturns, cancel contracts, delay projects, and generally "batten down the hatches" to weather the storm. At first sight, this approach, often motivated by financial reasons, seems logical.

The metaverse is already a big part of business. It will only become more central. As digital technologies move to the next stage of advancement--the metaverse--there are two questions companies should ask: How will the metaverse change our business? And how can we get ahead of the change and shape it to our advantage? This is our perspective on both.

A new paper published by the Government on the 18th July 2018 called'Establishing A Pro-Innovation Approach To Regulating AI' states that the regulation of artificial intelligence in the UK will be underpinned by 6 core principles designed to manage the risks that come with the technology. The six core principles will be applied across all sectors of the economy on a non-statutory basis, complemented by context-specific regulatory guidance and voluntary standards that will be implemented by UK regulators such as the Information Commissioner's Office. Hence, there will be no central AI regulator, but instead sector regulators who will apply the 6 core principles to artificial intelligence systems operated within the area they oversee. Given these proposals, the UK is adopting a far more light-touch risk-based approach compared to the more prescriptive and standardized one being pursued by the EU, which published its draft AI Act back in 2021. The UK approach to artificial intelligence will instead focus upon proportionality, with the regulatory framework for artificial intelligence systems being determined by the industry and context in which the system is being deployed.

Like the steam engine, electricity, or the internet, Artificial Intelligence is a general purpose technology – with the potential to revolutionise every aspect of our lives, help realise our ambitions to be a science superpower, and to foster economic growth across the UK. The UK excels at AI – from scientific research, where we rank third in the world for number of academic journal citations; to investment – receiving more investment in AI companies than France and Germany combined in 2021. As a Government, we are committed to unlocking the enormous benefits of AI across our economy and society. That is why we have invested over £2.3 billion in AI since 2014, which has been bolstered year-on-year by ambitious announcements such as the creation of the NHS AI Lab to drive use of AI in improving healthcare, to the creation of Turing AI World-leading Researcher Fellowships, to ensure the UK attracts and retains the best and brightest AI talent. Last September, we also published the National AI Strategy – a 10-year vision to ensure the UK is the best place to start and grow an AI business and to strengthen our position as a global AI leader.

Summary: Whether you're a data scientist building an implementation case to present to executives or a non-data scientist leader trying to figure this out there's a need for a much broader framework of strategic thinking around how to capture the value of AI/ML. There are many articles written from a tools perspective about how to take advantage of specific capabilities of AI. Those encompass for example chatbots from NLP or image classification based on CNNs. To be clear, I'm talking about the expanded definition of AI that should more correctly be called AI/ML since the more mature field of machine learning is full of good implementation lessons ranging from marketing to fraud to forecasting. But whether you're a data scientist building an implementation case to present to executives or a non-data scientist leader trying to figure this out there's a need for a much broader framework of strategic thinking around how to capture the value of AI/ML.

You might not notice it, but you've likely adopted artificial intelligence into your daily life. It can be as simple as personalizing your news feeds, searching for products on shopping sites or voice-to-text conversion on smartphones. It can also be applied to more sophisticated tasks like predicting court outcomes in cases involving employment law or used for robotic welding applications. The transformative power of AI is also an economic growth driver, which is why the Canadian government has given the green light to advancing the country's AI strategy. According to a recent announcement from Minister of Innovation, Science and Industry François-Philippe Champagne, more than $443 million in Budget 2021 is designated for the second phase of the pan-Canadian Artificial Intelligence Strategy.

This article was published as a part of the Data Science Blogathon. When asked about a business case challenge at an interview for a Machine learning engineer, Data scientist, or other comparable position, it is typical to become nervous. Top firms like FAANG like to integrate business case problems in their screening process these days. This approach is followed by a few other leading companies, like Uber and Twitter. Most case studies are open-minded and technical.