Species distribution models (SDMs) are widely used to predict species' geographic distributions, serving as critical tools for ecological research and conservation planning. Typically, SDMs relate species occurrences to environmental variables representing abiotic factors, such as temperature, precipitation, and soil properties. However, species distributions are also strongly influenced by biotic interactions with other species, which are often overlooked. While some methods partially address this limitation by incorporating biotic interactions, they often assume symmetrical pairwise relationships between species and require consistent co-occurrence data. In practice, species observations are sparse, and the availability of information about the presence or absence of other species varies significantly across locations. To address these challenges, we propose CISO, a deep learning-based method for species distribution modeling Conditioned on Incomplete Species Observations. CISO enables predictions to be conditioned on a flexible number of species observations alongside environmental variables, accommodating the variability and incompleteness of available biotic data. We demonstrate our approach using three datasets representing different species groups: sPlotOpen for plants, SatBird for birds, and a new dataset, SatButterfly, for butterflies. Our results show that including partial biotic information improves predictive performance on spatially separate test sets. When conditioned on a subset of species within the same dataset, CISO outperforms alternative methods in predicting the distribution of the remaining species. Furthermore, we show that combining observations from multiple datasets can improve performance. CISO is a promising ecological tool, capable of incorporating incomplete biotic information and identifying potential interactions between species from disparate taxa.

Building carbon-free datacenters depends on effective load scheduling, such as suspend-and-resume [1, 12, 18] and wait-and-scale [5, This paper represents the first effort to quantify uncertainty in 16]. The core idea of these scheduling strategies is to adapt to renewable carbon intensity forecasting for datacenter decarbonization. We energy supplies based on carbon intensity forecasts. Inaccurate identify and analyze two types of uncertainty--temporal and spatial--and carbon intensity forecasts can not only fail to reduce carbon discuss their system implications. To address the temporal emissions but may even increase them [4]. While prior work has dynamics in quantifying uncertainty for carbon intensity forecasting, introduced various methods for carbon intensity forecasting such we introduce a conformal prediction-based framework. Evaluation as ARIMA models [3] and neural networks [9, 10], they focus on results show that our technique robustly achieves target point-based estimation, neglecting to account for their uncertainty coverages in uncertainty quantification across various significance levels. As prior studies point out, considering uncertainty is crucial levels. We conduct two case studies using production power traces, for effective scheduling [17].

Human intelligence and intuition are vital to training artificial intelligence (AI) and machine learning (ML) models to provide enterprises with hybrid cybersecurity at scale. Combining human intelligence and intuition with AI and ML models helps catch the nuances of attack patterns that elude numerical analysis alone. Experienced threat hunters, security analysts and data scientists help ensure that the data used to train AI and ML models enables a model to accurately identify threats and reduce false positives. Combining human expertise and AI and ML models with a real-time stream of telemetry data from enterprises' many systems and apps defines the future of hybrid cybersecurity. "Based on behaviors and insights, AI and ML allow us to predict [that] something will happen before it does," says Monique Shivanandan, CISO at HSBC, a global bank.

Modern cyber attackers' tactics, techniques, and procedures (TTPs) have become both rapid and abundant while advanced threats such as ransomware, cryptojacking, phishing, and software supply chain attacks are on an explosive rise. The increasing dependence global workforces have on digital resources adds another facet to a growing cyber attack surface we all now share. In an effort to stand up to these challenges, businesses task their CISOs with developing, maintaining, and constantly updating their cybersecurity strategies and solutions. From a tactical standpoint, CISOs ensure that their business's security architecture can withstand the ever-shifting modern threat landscape. This means choosing the right tool stack that is capable of combating complex cyber threats at the breakneck speed in which they appear.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Consolidating their organization's tech stacks, defending budgets and reducing risk are three of the top challenges facing CISOs going into 2023. Identifying which security technologies deliver the most value and defining spending guardrails is imperative. Forrester's 2023 security and risk planning guide provides CISOs prescriptive guidance on which technologies to increase and defend their investments and which to consider paring back spending and investment.

Bad actors know all they need to do is find one unprotected machine identity, and they're into a company's network. Analyzing their breaches shows they move laterally across systems, departments, and servers, looking for the most valuable data to exfiltrate while often embedding ransomware. By scanning enterprise networks, bad actors often find unprotected machine identities to exploit. These factors are why machine identities are a favorite attack surface today. Organizations quickly realize they're competing in a zero-trust world today, and every endpoint, whether human or machine-based, is their new security perimeter.

Chief information security officers' (CISOs) greatest challenge going into 2022 is countering the speed and severity of cyberattacks. The latest real-time monitoring and detection technologies improve the odds of thwarting an attack but aren't foolproof. CISOs tell VentureBeat that bad actors avoid detection with first-line monitoring systems by modifying attacks on the fly. Enterprises fail to get the most value from threat monitoring, detection, and response cybersecurity strategies because they're too focused on data collection and security monitoring alone. CISOs tell VentureBeat they're capturing more telemetry (i.e., remote) data than ever, yet are short-staffed when it comes to deciphering it, which means they're often in react mode.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. In March, the FBI released a report declaring that malicious actors almost certainly will leverage "synthetic content" for cyber and foreign influence operations in the next 12-18 months. This synthetic content includes deepfakes, audio or video that is either wholly created or altered by artificial intelligence or machine learning to convincingly misrepresent someone as doing or saying something that was not actually done or said. We've all heard the story about the CEO whose voice was imitated convincingly enough to initiate a wire transfer of $243,000. Now, the constant Zoom meetings of the anywhere workforce era have created a wealth of audio and video data that can be fed into a machine learning system to create a compelling duplicate.

With the threat landscape constantly evolving and increasing in complexity, continued digital innovation, technological developments, and the introduction of 5G, coupled with the challenges of accelerated remote working practices and a growing cybersecurity skills gap, have collectively exacerbated the challenges that CISOs face in terms of protecting their companies' digital assets. However, enhancing AI solutions with machine learning, augmented intelligence, and analytics capabilities, among others, lets CISOs create a much stronger cybersecurity ecosystem for their organisation. "To reinforce a robust cybersecurity ecosystem, CISOs must develop strategic, proactive cybersecurity approaches that leverage AI-driven solutions to act on threat intelligence. In addition to leveraging solutions like augmented intelligence, analytics, and machine learning combined with AI, CISOs should consider resourcing their IT and security teams with the right people to strengthen their security strategy. "However, CISOs can improve efficiencies and strengthen their security operations by leveraging AI solutions and tools, particularly those with built-in automation and integration, to alleviate the pressure on IT teams without reducing the effectiveness of the security strategy." With the threat landscape constantly evolving and increasing in complexity, continued digital innovation, technological developments, and the introduction of 5G, coupled with the challenges of accelerated remote working practices and a growing cybersecurity skills gap, have collectively exacerbated the challenges that CISOs face in terms of protecting their companies' digital assets. However, enhancing AI solutions with machine learning, augmented intelligence, and analytics capabilities, among others, lets CISOs create a much stronger cybersecurity ecosystem for their organisation. "To reinforce a robust cybersecurity ecosystem, CISOs must develop strategic, proactive cybersecurity approaches that leverage AI-driven solutions to act on threat intelligence.

Click to learn more about author Anne Hardy. Artificial intelligence (AI) is no longer the future – it's already in our homes, cars, and pockets. As technology expands its role in our lives, an important question has emerged: What level of trust can – and should – we place in these AI systems? Trust is the very question the European Union (EU) Commission has set out to answer under its newly proposed EU Artificial Intelligence Act. Margrethe Vestager, Executive Vice President of the European Commission for A Europe Fit for the Digital Age, stated that trust is a must with AI.