Optimisation of functions on graphs is challenging.

Documenting tacit knowledge in organizations can be a challenging task due to incomplete initial information, difficulty in identifying knowledgeable individuals, the interplay of formal hierarchies and informal networks, and the need to ask the right questions. To address this, we propose an agent-based framework leveraging large language models (LLMs) to iteratively reconstruct dataset descriptions through interactions with employees. Modeling knowledge dissemination as a Susceptible-Infectious (SI) process with waning infectivity, we conduct 864 simulations across various synthetic company structures and different dissemination parameters. Our results show that the agent achieves 94.9% full-knowledge recall, with self-critical feedback scores strongly correlating with external literature critic scores. We analyze how each simulation parameter affects the knowledge retrieval process for the agent. In particular, we find that our approach is able to recover information without needing to access directly the only domain specialist. These findings highlight the agent's ability to navigate organizational complexity and capture fragmented knowledge that would otherwise remain inaccessible.

A trip to a Pennsylvania lake turned into a tragedy for one man who was left paralyzed after running into the water for a swim. Noland Arbaugh, 29, recalls being hit on the side of the head by another person, leaving him unable to move his body from the shoulders down when he woke up face down in the lake. The 2016 accident led him on a journey to become Neuralink's patient zero this year, which saw him receive a brain implant that lets him control computers and other devices. 'I was a little worried it wouldn't work because [that could happen] with the first of anything, but I wanted to be the first to test all of that out,' he said in an interview on The Kim Komando Show. 'If anyone was going to go through it, to experience the downsides, I wanted to take that on as much as possible to help people after me.'

A mid-day drunk-dial from a friend has changed one man's life forever. Noland Arbaugh, 29, rose to fame after being revealed as Neuralink's first patient to receive its brain chip, but it all started when his friend called slurring his words in September. Arbaugh was paralyzed eight years ago during a diving accident. The friend called Arbaugh to tell him about Elon Musk opening up human trials and urged him to apply and even helped him fill out the form. Just five months after he was approved for the Neuralink trial, Arbaugh had a cutting edge brain chip embedded in his skull.

The increasing availability of graph-structured data motivates the task of optimising over functions defined on the node set of graphs. Traditional graph search algorithms can be applied in this case, but they may be sample-inefficient and do not make use of information about the function values; on the other hand, Bayesian optimisation is a class of promising black-box solvers with superior sample efficiency, but it has been scarcely been applied to such novel setups. To fill this gap, we propose a novel Bayesian optimisation framework that optimises over functions defined on generic, large-scale and potentially unknown graphs. Through the learning of suitable kernels on graphs, our framework has the advantage of adapting to the behaviour of the target function. The local modelling approach further guarantees the efficiency of our method. Extensive experiments on both synthetic and real-world graphs demonstrate the effectiveness of the proposed optimisation framework.

Reconstructing missing information in epidemic spreading on contact networks can be essential in prevention and containment strategies. For instance, identifying and warning infective but asymptomatic individuals (e.g., manual contact tracing) helped contain outbreaks in the COVID-19 pandemic. The number of possible epidemic cascades typically grows exponentially with the number of individuals involved. The challenge posed by inference problems in the epidemics processes originates from the difficulty of identifying the almost negligible subset of those compatible with the evidence (for instance, medical tests). Here we present a new generative neural networks framework that can sample the most probable infection cascades compatible with observations. Moreover, the framework can infer the parameters governing the spreading of infections. The proposed method obtains better or comparable results with existing methods on the patient zero problem, risk assessment, and inference of infectious parameters in synthetic and real case scenarios like spreading infections in workplaces and hospitals.

When we say #ThinkBeyond, what we really mean is: Think anew. The irony of the word'intelligence' in our industry is that it has come to mean an empty set of quickly expiring, low-confidence, weakly-attributed, slow-to-operationalize, cloud-dependent, noise. Even so, it has become a virtual currency between C-level intelligence communities, traded almost like challenge coins, in a quid-pro-quo manner. This dynamic defeats the whole purpose of'intelligence' to begin with, much like when monetized search results killed the dream of free information for the world during the early 2000's. What we need to do in the industry is to shift focus to a different kind of intelligence that has wisdom as its goal.

When developer IO Interactive separated from publisher Square Enix, it got to keep the popular Hitman franchise, giving the now-independent studio control over the 2016 stealth shooter game it developed. Back in June, the company released the first episode of Hitman for free to attract new customers, and now it's putting out a "Game of the Year" version of the game for PS4, Xbox One and PC on November 7th. The new edition will include the entire first season, a brand new'Patient Zero' campaign, new Escalation Contracts, new weapons, new suits, new challenges and improved graphic and lighting effects. IO Interactive's first new content release for Hitman since it went independent will contain all seven locations and more than 100 hours of gameplay, including all of the original Challenge Packs, Escalation & Featured Contracts and more than 700 challenges. The "Patient Zero" campaign is entirely new, too.

Despite all of the money, time and effort spent over the last few decades, we've been chasing the attacker… waiting for a'patient zero' to emerge in the wild before any response to a new campaign can begin. But we are now living in a new era, where artificial intelligence (AI) can truly predict attacks by weeks, months, or even years ahead of a campaign. We call this the Temporal Predictive Advantage (TPA) that AI gives the defender over the attacker. Measured in days, TPA is a metric for a new era of predictive prevention being ushered in by data science, and it will forevermore be the measurement of the true impact AI has on disrupting malware economies and nation-state efforts to evolve malware. Time… the ultimate battlespace advantage, is finally on the side of the defender.