Artificial Intelligence is rapidly transforming the business landscape, already shifting the way we work, create and gather data insights. This year, 72% of organizations have adopted generative AI in some way, and 50% have adopted AI in two or more business functions --up from less than a third of respondents in 2023. On the other hand, as AI adoption heats up, so do concerns around security -- with 45% of organizations experiencing data exposures while implementing AI. CISOs and security leaders now face the critical challenge of balancing AI implementation with growing data security risks.At the same time, government agencies are also turning their attention to AI security concerns -- and the regulatory landscape surrounding the technology is quickly evolving. Uncertainty persists on a federal level, as no all-encompassing legislature is currently in place in the US to set guardrails for the use of AI tools. However, frameworks including the AI Bill of Rights and Executive Order on AI, as well as state-wide regulations like the Colorado AI Act (with 45 other states in 2024 introducing AI bills), are gaining momentum -- as governments and organizations look to mitigate security risks associated with the use of AI.To prepare for rapidly evolving regulations in todays unpredictable threat landscape, while still advancing AI initiatives across the organization, here are the strategies security leaders must prioritize in the year ahead:Related:Building a robust data management infrastructure: Whether or not an organization is ready for widespread AI adoption, implementing an advanced data management, governance, and lifecycle infrastructure is critical to keep information safe from threat. However, 44% of organizations still lack basic information management measures, and only just over half have basic measures like archiving and retention policies (56%) and lifecycle management solutions (56%) in place.To keep sensitive data safe from potential threats, proper governance and access policies must be established before AI is widely implemented. That way, employees are not inadvertently sharing sensitive information with AI tools. Beyond keeping data secure, employing proper governance policies and investing in the automated tools needed to do so can also help streamline compliance with new regulations -- supporting security leaders by building a more flexible, agile data infrastructure to keep up with these fast-moving developments.Leveraging existing standards for AI use: To prepare data and security practices for new regulations in the years to come, CISOs can look towards existing, widely recognized standards for AI use within the industry. International standards like the ISO/IEC 42001 outline recommended practices for organizations looking to utilize AI tools, to support responsible development and use and provide a structure for risk management and data governance. Aligning internal practices with frameworks like ISO/IEC early on in the implementation process assures that AI data practices are meeting widely accepted benchmarks for security and ethics -- streamlining regulatory compliance down the road.Related:Fostering security-focused culture and principles: Security leaders must strive to emphasize that security is everyones job in the organization, and that all individuals play a part in keeping data safe from threats. Ongoing education around AI and new regulations (through constantly evolving and highly customized trainings) ensures that all members of the organization know how to use the technology safely -- and are prepared to meet new standards and mandates for security in the years to come.Adopting do no harm principles will also help to future-proof the organization to meet new regulations. This involves carefully assessing all of the potential consequences and effects of AI before implementation, evaluating how these tools can impact all individuals and stakeholders. Its important to establish these principles early on -- informing what limitations should be set to prevent potential misuse and preparing security teams for future regulations around ethical and fair use.Related:As we continue to see new AI regulations take shape in the coming years, security and business leaders need to focus their attention on how to prepare their entire organization to meet new compliance standards. As CISOs continue to face uncertainty in how regulations will progress, this is a strong signal to safeguard data and ensure individual preparedness now to meet new standards, as they evolve rapidly. AI is now everywhere, and ethical, secure and compliant use is an organization-wide effort in 2025 -- which begins with building the proper data management and fair use principles and emphasizing security awareness for all individuals.

Theres another sort of Marvel multiverse than the one depicted onscreen in the various movies and TV shows of the Marvel CInematic Universe: Thats the alternate versions of the MCU thatcouldhave happened if Marvel had followed through with some of their various initialideas for the characters. What if, for example, Marvel had added Captain Marvel to the Avengers in the final scene ofAvengers: Age of Ultron.That almost happened, until Marvel changed their plans.And moments like that which could have changed the MCU forever are the subject of our latest Marvel video. In it, we detail some of the most important deleted scenes in the companys history; the ones that might have had the biggest impact and set the MCU on a wildly different path than it ultimately took. They include a Wanda storyline that would have completely changed her arc in the MCU, a wildly different scene that almost endedEternals, one scene in The Incredible Hulk that could have shifted the entire MCU in a much bleaker direction,and Deadpools almost cameo inAvengers: Endgame.Check out our full video below:READ MORE: The Worst Marvel Comics EverIf you liked that video on some of the most notable deleted scenes in the history of Marvel, and how they could have changed the MCU forever, check out more of our videos below, including one on why Eternals isnt as bad as you think and why we still want a sequel, one on the new trailer for Captain America: Brave New World and all its Marvel Easter eggs, and one on all of the interesting details in Marvels trailer for their 2025 TV series. Plus, theres tons more videos over atScreenCrushs YouTube channel. Be sure to subscribe to catch all our future episodes.The next Marvel Cinematic Universe movie, Captain America: Brave New World, is scheduled to open in theaters on February 14, 2025.Sign up for Disney+ here.Get our free mobile appEvery Marvel Cinematic Universe Movie, Ranked From Worst to BestIt started with Iron Man and its continued and expanded ever since. Its the Marvel Cinematic Universe, with 34 movies and counting. But whats the best and the worst? We ranked them all.

We need a Product Analyst who can help our development team discover the truth about user behaviour. Our product has 30 million teachers and student users. This job is a unique opportunity to make a big positive impact in the education field.In this role, you can expect to:identify opportunities for product improvements based on data analysisinitiate and perform product analysis and A/B test analysisexecute ad-hoc requestsanalyse datasets to generate hypothesescollaborate with product managers, designers, and engineers to deliver improvementsuse quantitative methods to find bottlenecks and opportunitiesRequirementsConsider applying if you:have a degree in mathematics, physics, computer science, data science or similarhave 3 years' experience in a data science or product analytics role in a software development contextare located within the time zones UTC-01:00 to UTC+03:00have expertise in advanced analytics using tools like Python and SQLhave solid understanding of basic statistical concepts, such as correlation, confidence intervals, probability distributions, and regressionhave great written and verbal communication skillshave a high level of self-organisation, proactivity, strategy, discipline, result orientationhave a product mindsetSalary and benefits50 - 60k per year30 days paid holiday100% Remote and flexible working #LI-RemoteReal applicants only - please do not contact us if you represent an agency. Related Jobs See more Product jobs

Time zones: UTC -4, UTC -4:30, UTC -3, UTC -2, SBT (UTC +11), GMT (UTC +0), CET (UTC +1), EET (UTC +2), MSK (UTC +3), AST (UTC -4), FKST (UTC -3), NST (UTC -3:30), CEST (UTC +2), BST (UTC +1), IRDT (UTC +4:30), CVT (UTC -1), WAT (UTC +1), SAST (UTC +2), EAT (UTC +3)At Intellisync, we offer more than just a job we provide the opportunity to make a meaningful impact. If youre passionate about turning ideas into impactful realities, we want you to lead the charge as our first Product Owner.What Youll DoDefine and articulate the product vision, ensuring alignment with company goals and the needs of clients in government, defense, and tech.Collaborate with developers, product designers, engineering managers and stakeholders to prioritize features and drive the successful delivery of high-value products.Create and maintain product backlogs, making data-informed decisions to ensure every feature adds maximum value for end-users.Act as the primary point of contact for stakeholders, keeping them informed, engaged, and inspired by the product's progress and future.Use data, feedback, and insights to continuously improve our products, ensuring they stay ahead of the curve.About YouWere looking for a leader with a blend of strategic thinking, practical execution, and a passion for innovation:Youve successfully taken products from concept to launch and have a strong foundation in product management.Your communication skills are exceptional, enabling you to inspire teams and manage stakeholders effectively.You have a deep understanding of user needs and a drive to solve real-world problems through impactful products.You rely on evidence and insights to guide priorities and actions.Background in government, defense, or tech is a plus.You can overlap with CET/CEST time zones by at least 4 hours to collaborate effectively with our distributed teams. Applicant from European time zones are preferred.Why Join IntelliSync?Work on products that serve critical industries, from government and defense to cutting-edge technology.Collaborate with brilliant, motivated developers who thrive in a supportive environment.Enjoy Euro-level salaries, over 30 days of vacation, sick pay, and additional perks.This is a permanent, full-time role. We believe investing in people leads to extraordinary outcomes.We celebrate and support diversity in the workplace. Applications are encouraged from all backgrounds, regardless of race, ethnicity, gender, orientation, age, or religion. If youre excited about this opportunity but dont meet every requirement, we still encourage you to apply wed love to hear from you. Related Jobs See more Product jobs

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Left to their own devices, an army of AI characters didnt just survive they thrived. They developed in-game jobs, shared memes, voted on tax reforms and even spread a religion. The experiment played out on the open-world gaming platform Minecraft, where up to 1000 software agents at a time used large language models (LLMs) to interact with one another. Given just a nudge through text prompting, they developed a remarkable range of personality traits, preferences and specialist roles, with no further inputs from their human creators. The work, from AI startup Altera, is part of a broader field that wants to use simulated agents to model how human groups would react to new economic policies or other interventions. But for Alteras founder, Robert Yang, who quit his position as an assistant professor in computational neuroscience at MIT to start the company, this demo is just the beginning. He sees it as an early step towards large-scale AI civilizations that can coexist and work alongside us in digital spaces. The true power of AI will be unlocked when we have actually truly autonomous agents that can collaborate at scale, says Yang. Yang was inspired by Stanford University researcher Joon Sung Park who, in 2023, found that surprisingly humanlike behaviors arose when a group of 25 autonomous AI agents was let loose to interact in a basic digital world. Once his paper was out, we started to work on it the next week, says Yang. I quit MIT six months after that. Yang wanted to take the idea to its extreme. We wanted to push the limit of what agents can do in groups autonomously. ALTERA Altera quickly raised more than $11m in funding from investors including A16Z and the former Google CEO Eric Schmidts emerging tech VC firm. Earlier this year Altera released its first demo: an AI-controlled character in Minecraft that plays alongside you. Alteras new experiment, Project Sid, uses simulated AI agents equipped with brains made up of multiple modules. Some modules are powered by LLMs and designed to specialize in certain tasks, such as reacting to other agents, speaking, or planning the agents next move. The team started small, testing groups of around 50 agents in Minecraft to observe their interactions. Over 12 in-game days (4 real-world hours) the agents began to exhibit some interesting emergent behavior. For example, some became very sociable and made many connections with other characters, while others appeared more introverted. The likability rating of each agent (measured by the agents themselves) changed over time as the interactions continued. The agents were able to track these social cues and react to them: in one case an AI chef tasked with distributing food to the hungry gave more to those who he felt valued him most. More humanlike behaviors emerged in a series of 30-agent simulations. Despite all the agents starting with the same personality and same overall goalto create an efficient village and protect the community against attacks from other in-game creaturesthey spontaneously developed specialized roles within the community, without any prompting. They diversified into roles such as builder, defender, trader, and explorer. Once an agent had started to specialize, its in-game actions began to reflect its new role. For example, an artist spent more time picking flowers, farmers gathered seeds and guards built more fences. We were surprised to see that if you put [in] the right kind of brain, they can have really emergent behavior, says Yang. That's what we expect humans to have, but don't expect machines to have. Yangs team also tested whether agents could follow community-wide rules. They introduced a world with basic tax laws and allowed agents to vote for changes to the in-game taxation system. Agents prompted to be pro or anti tax were able to influence the behavior of other agents around them, enough that they would then vote to reduce or raise tax depending on who they had interacted with. The team scaled up, pushing the number of agents in each simulation to the maximum the Minecraft server could handle without glitching, up to 1000 at once in some cases. In one of Alteras 500-agent simulations, they watched how the agents spontaneously came up with and then spread cultural memes (such as a fondness for pranking, or an interest in eco-related issues) among their fellow agents. The team also seeded a small group of agents to try to spread the (parody) religion, Pastafarianism, around different towns and rural areas that made up the in-game world, and watched as these Pastafarian priests converted many of the agents they interacted with. The converts went on to spread Pastafarianism (the word of the Church of the Flying Spaghetti Monster) to nearby towns in the game world. The way the agents acted might seem eerily lifelike, but really all they are doing is regurgitating patterns the LLMshave learned from being trained on human-created data on the internet. The takeaway is that LLMs have a sophisticated enough model of human social dynamics [to] mirror these human behaviors, says Altera co-founder Andrew Ahn. ALTERA In other words, the data makes them excellent mimics of human behavior, but they are in no way alive. But Yang has grander plans. Altera plans to expand into Roblox next, but Yang hopes to eventually move beyond game worlds altogether. Ultimately, his goal is a world in which humans dont just play alongside AI characters, but also interact with them in their day-to-day lives. His dream is to create a vast number of digital humans who actually care for us and will work with us to help us solve problems, as well as keep us entertained. We want to build agents that can really love humans (like dogs love humans, for example), he says. This viewpointthat AI could love usis pretty controversial in the field, with many experts arguing it's not possible to recreate emotions in machines using current techniques. AI veteran Julian Togelius, for example, who runs games testing company Modl.ai, says he likes Alteras work, particularly because it lets us study human behavior in simulation. But could these simulated agents ever learn to care for us, love us, or become self-aware? Togelius doesnt think so. There is no reason to believe a neural network running on a GPU somewhere experiences anything at all, he says. But maybe AI doesnt have to love us for real to be useful. If the question is whether one of these simulated beings could appear to care, and do it so expertly that it would have the same value to someone as being cared for by a human, that is perhaps not impossible, Togelius adds. You could create a good-enough simulation of care to be useful. The question is whether the person being cared for would care that the carer has no experiences. In other words, so long as our AI characters appear to care for us in a convincing way, that might be all we really care about.

Every time a new AI model is released, its typically touted as acing its performance against a series of benchmarks. OpenAIs GPT-4o, for example, was launched in May with a compilation of results that showed its performance topping every other AI companys latest model in several tests. The problem is that these benchmarks are poorly designed, the results hard to replicate, and the metrics they use are frequently arbitrary, according to new research. That matters because AI models scores against these benchmarks will determine the level of scrutiny and regulation they receive. It seems to be like the Wild West because we dont really have good evaluation standards, says Anka Reuel, an author of the paper, who is a PhD student in computer science at Stanford University and a member of its Center for AI Safety. A benchmark is essentially a test that an AI takes. It can be in a multiple-choice format like the most popular one, the Massive Multitask Language Understanding benchmark, known as the MMLU, or it could be an evaluation of AIs ability to do a specific task or the quality of its text responses to a set series of questions. AI companies frequently cite benchmarks as testament to a new models success. The developers of these models tend to optimize for the specific benchmarks, says Anna Ivanova, professor of psychology at the Georgia Institute of Technology and head of its Language, Intelligence, and Thought (LIT) lab, who was not involved in the Stanford research. These benchmarks already form part of some governments plans for regulating AI. For example, the EU AI Act, which goes into force in August 2025, references benchmarks as a tool to determine whether or not a model demonstrates systemic risk; if it does, it will be subject to higher levels of scrutiny and regulation. The UK AI Safety Institute references benchmarks in Inspect, which is its framework for evaluating the safety of large language models. But right now, they might not be good enough to use that way. Theres this potential false sense of safety were creating with benchmarks if they arent well designed, especially for high-stakes use cases, says Reuel. It may look as if the model is safe, but it is not. Given the increasing importance of benchmarks, Reuel and her colleagues wanted to look at the most popular examples to figure out what makes a good oneand whether the ones we use are robust enough. The researchers first set out to verify the benchmark results that developers put out, but often they couldnt reproduce them. To test a benchmark, you typically need some instructions or code to run it on a model. Many benchmark creators didnt make the code to run their benchmark publicly available. In other cases, the code was outdated. Benchmark creators often dont make the questions and answers in their data set publicly available either. If they did, companies could just train their model on the benchmark; it would be like letting a student see the questions and answers on a test before taking it. But that makes them hard to evaluate. Another issue is that benchmarks are frequently saturated, which means all the problems have been pretty much been solved. For example, lets say theres a test with simple math problems on it. The first generation of an AI model gets a 20% on the test, failing. The second generation of the model gets 90% and the third generation gets 93%. An outsider may look at these results and determine that AI progress has slowed down, but another interpretation could just be that the benchmark got solved and is no longer that great a measure of progress. It fails to capture the difference in ability between the second and third generations of a model. One of the goals of the research was to define a list of criteria that make a good benchmark. Its definitely an important problem to discuss the quality of the benchmarks, what we want from them, what we need from them, says Ivanova. The issue is that there isnt one good standard to define benchmarks. This paper is an attempt to provide a set of evaluation criteria. Thats very useful. The paper was accompanied by the launch of a website, BetterBench, that ranks the most popular AI benchmarks. Rating factors include whether or not experts were consulted on the design, whether the tested capability is well defined, and other basicsfor example, is there a feedback channel for the benchmark, or has it been peer-reviewed? The MMLU benchmark had the lowest ratings. I disagree with these rankings. In fact, Im an author of some of the papers ranked highly, and would say that the lower ranked benchmarks are better than them, says Dan Hendrycks, director of CAIS, the Center for AI Safety, and one of the creators of the MMLU benchmark. Some think the criteria may be missing the bigger picture. The paper adds something valuable. Implementation criteria and documentation criteriaall of this is important. It makes the benchmarks better, says Marius Hobbhahn, CEO of Apollo Research, a research organization specializing in AI evaluations. But for me, the most important question is, do you measure the right thing? You could check all of these boxes, but you could still have a terrible benchmark because it just doesnt measure the right thing. Essentially, even if a benchmark is perfectly designed, one that tests the models ability to provide compelling analysis of Shakespeare sonnets may be useless if someone is really concerned about AIs hacking capabilities. Youll see a benchmark thats supposed to measure moral reasoning. But what that means isnt necessarily defined very well. Are people who are experts in that domain being incorporated in the process? Often that isnt the case, says Amelia Hardy, another author of the paper and an AI researcher at Stanford University. There are organizations actively trying to improve the situation. For example, a new benchmark from Epoch AI, a research organization, was designed with input from 60 mathematicians and verified as challenging by two winners of the Fields Medal, which is the most prestigious award in mathematics. The participation of these experts fulfills one of the criteria in the BetterBench assessment. The current most advanced models are able to answer less than 2% of the questions on the benchmark, which means theres a significant way to go before it is saturated. We really tried to represent the full breadth and depth of modern math research, says Tamay Besiroglu, associate director at Epoch AI. Despite the difficulty of the test, Besiroglu speculates it will take only around four years for AI models to saturate the benchmark, scoring higher than 80%. And Hendrycks' organization, CAIS, is collaborating with Scale AI to create a new benchmark that he claims will test AI models against the frontier of human knowledge, dubbed Humanitys Last Exam, HLE. HLE was developed by a global team of academics and subject-matter experts, says Hendrycks. HLE contains unambiguous, non-searchable, questions that require a PhD-level understanding to solve. If you want to contribute a question, you can here. Although there is a lot of disagreement over what exactly should be measured, many researchers agree that more robust benchmarks are needed, especially since they set a direction for companies and are a critical tool for governments. Benchmarks need to be really good, Hardy says. We need to have an understanding of what really good means, which we dont right now.

Ole Scheeren's designs for the Urban Glen scheme in Hangzhou1/5show captionOle Scheeren has unveiled plans for a twin tower mixed-use scheme next to a Unesco world heritage site in Hangzhou, China.The Beijing-based architects designs for the Urban Glen scheme were drawn up for Hong Kong developer New World Development.Currently under construction, it consists of two main blocks, one containing a luxury Rosewood Hotel and the other housing 500,000sq ft of office space.The development is positioned between Hangzhous Unesco-listed West Lake, a natural freshwater lake surrounded by mountains and temples, and the Qiantang River.It is a key element of the Wangjiang New Town project, an urban initiative aiming to establish an art and cultural destination within the historic east China city.Ole Scheeren said his designs for the projects greenery-covered terraces were inspired by Hangzhous hilly landscapes.Instead of creating a hermetic singular volume, Urban Glen opens a highly interactive space in the middle of the city block a space that unites living and working with nature, culture, and leisure, the German-born architect said.Scheerens other recent projects in China include a twin-tower office scheme in Shenzhen for JD.com, one of the countrys largest online retailers.The architect is also behind a four-tower headquarters in Shenzhen for Tencent, Chinas biggest company.Scheeren beat a host of start names last year to win the job including his former practice OMA, Foster & Partners, Heatherwick Studio, Zaha Hadid Architects and Herzog & de Meuron.