Role-based access control (RBAC) and hierarchical structures are foundational to how information flows and decisions are made within virtually all organizations. As the potential of Large Language Models (LLMs) to serve as unified knowledge repositories and intelligent assistants in enterprise settings becomes increasingly apparent, a critical, yet under explored, challenge emerges: \textit{can these models reliably understand and operate within the complex, often nuanced, constraints imposed by organizational hierarchies and associated permissions?} Evaluating this crucial capability is inherently difficult due to the proprietary and sensitive nature of real-world corporate data and access control policies. We introduce a synthetic yet representative \textbf{OrgAccess} benchmark consisting of 40 distinct types of permissions commonly relevant across different organizational roles and levels. We further create three types of permissions: 40,000 easy (1 permission), 10,000 medium (3-permissions tuple), and 20,000 hard (5-permissions tuple) to test LLMs' ability to accurately assess these permissions and generate responses that strictly adhere to the specified hierarchical rules, particularly in scenarios involving users with overlapping or conflicting permissions. Our findings reveal that even state-of-the-art LLMs struggle significantly to maintain compliance with role-based structures, even with explicit instructions, with their performance degrades further when navigating interactions involving two or more conflicting permissions. Specifically, even \textbf{GPT-4.1 only achieves an F1-Score of 0.27 on our hardest benchmark}. This demonstrates a critical limitation in LLMs' complex rule following and compositional reasoning capabilities beyond standard factual or STEM-based benchmarks, opening up a new paradigm for evaluating their fitness for practical, structured environments.

An artificial intelligence model can make virtual avatars gesture naturally to match spoken words – possibly paving the way for AI-generated newsreaders or influencers that move more realistically as they speak. As humans talk, we gesture to help convey our meaning. But when video game characters or digital avatars attempt similar behaviour, they often make generic movements regardless of what they are actually saying.

For a lot of higher-level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics -- stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it's used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science. TIP: most of Coursera's courses and specializations have the option to audit them. You won't get a certificate, but you'll access most of the resources of the course--something I personally found more than enough. At the moment of enrolling, just select the option to audit the course.

As video games give players more freedom to explore complex digital worlds, it becomes more challenging for a CG character to naturally move and interact with everything in it. So to prevent those awkward transitions between pre-programmed movements, researchers have turned to AI and deep learning to make video game characters move almost as realistically as real humans do. To help make video game characters walk, run, jump, and perform other movements as realistically as possible, video game developers will often rely on human performances that are captured and translated to digital characters. It produces results that are faster and better looking than animating video game characters by hand, but it's impossible to plan for every possible way a character will interact with a digital world, according to the researchers. Game developers try to plan for as many possibilities as they can, but they ultimately have to rely on software to transition between animations of a character walking up to a chair, and then sitting down on it, and more often than not, those segues feel stilted, unnatural, and can diminish a player's experience. Computer scientists from the University of Edinburgh and Adobe Research have come up with a novel solution they'll be presenting at the ACM Siggraph Asia conference being held in Brisbane, Australia, next month.

I recently read an article that had me asking, "Is it going to be fashionable to tap into the digital afterlife of an ex-employee?". The write-up referenced a report that stated; for a large organisation, it can take an average of 28 weeks for new workers to reach optimum productivity level. So, therefore, losing an employee can come at a cost. The solution could be for companies to use a kind of technology that would help their new recruits get up to speed via an ex-employee's digital doppelgänger. With costs estimated to be around £30,614 to replace a departing employee, the primary challenge is transferring the knowledge that the ex-employee accumulated in their role at the company to a new, greener employee.

When we talk about artificial intelligence in games, we usually picture smarter or more realistic enemies that don't come off as mindless automatons. New research, though, is showing how an AI powered by a neural network could revolutionize the way player avatars animate realistically through complicated game environments in real time. Phase-Functioned Neural Networks for Character Control is a fundamentally new way of handling character animation that will be presented at the ACM's upcoming SIGGRAPH conference this summer. In most games, character animation is handled through "canned," pre-recorded motion capture. This means an average player will see precisely the same motion cycled repeated thousands of times in a single play-through.

According to Forrester Tech Radar for B2B Marketing Technology and SD'16 (SiriusDecisions conference 2016), two of the hottest trends last year were ABM (Account-Based Marketing) and Predictive Marketing. I'm sure you all are now busy with deploying your ABM initiatives. Many of you have embraced or thinking of Predictive Marketing. AI is the red hot topic that is being discussed at c-level in all organizations. We all are beneficiaries of AI in our daily lives; from Alexa and Siri to TacoBot, we are reaping the rewards of AI.

AI's strongest applications are data-hungry. Pioneers in the field, such as Facebook, Google, and Uber, have each secured a "privileged zone" by gaining access to current and future data, the raw material of AI, from their users and others in ways that go far beyond traditional data harvesting. Their scale gives them the ability to run more training data through their algorithms and thus improve performance. In the race to leverage fully functional self-driving cars, for example, Uber has the advantage of collecting 100 million miles of fleet data daily from its drivers. This data will eventually inform the company's mobility services.