As far as AI has come in the last few years there are still a few things that machines can't do as well as humans. However, all of that might soon change as OpenAI and Meta are both reported to be on the brink of releasing AIs capable of reasoning and planning. Leaders of both companies suggest that the latest versions of their AI models are coming soon and will be a lot more powerful. According to their reports, ChatGPT-5 and Llama-3 will not just generate text but start to do something that looks a lot more like thinking. Joelle Pineau, vice-president of AI research at Meta says: 'We are hard at work in figuring out how to get these models not just to talk, but actually to reason, to plan... to have memory.'

As a six-year-old growing up in Barcelona, Spain, during the 1940s, Maria would visit a neighbor's apartment in her building when she wanted to see her father. From there, she could try and try to catch a glimpse of him in the prison below, where he was locked up for opposing the dictatorship of Francisco Franco. There is no photo of Maria on that balcony. But she can now hold something like it: a fake photo--or memory-based reconstruction, as the Barcelona-based design studio Domestic Data Streamers puts it--of the scene that a real photo might have captured.The studio uses generative image models, such as OpenAI's DALL-E, to bring people's memories to life. The fake snapshots are blurred and distorted, but they can still rewind a lifetime in an instant.

OpenAI, Google, and the French artificial intelligence startup Mistral have all released new versions of their frontier AI models within 12 hours of one another, as the industry prepares for a burst of activity over the summer. The unprecedented flurry of releases come as the sector readies for the expected launch of the next major version of GPT, the system that underpins OpenAI's hit chatbot Chat-GPT. The first came only hours after Nick Clegg appeared on stage at an event in London, where he confirmed that the third version of Meta's own AI model, Llama, would be published in a matter of weeks. Seven hours after Clegg left the stage, Google's Gemini Pro 1.5, his competitor's most advanced large language model, was released to the general public, with a free tier limited to 50 requests a day. An hour later, OpenAI released its own frontier model, the final version of GPT-4 Turbo.

If you've ever posted something to the internet--a pithy tweet, a 2009 blog post, a scornful review, or a selfie on Instagram--it has most likely been slurped up and used to help train the current wave of generative AI. Large language models, like ChatGPT, and image creators are powered by vast reams of our data. And even if it's not powering a chatbot, the data can be used for other machine-learning features. On top of this, increasingly, firms with reams of people's posts are looking to get in on the AI gold rush by selling or licensing that information. However, as the lawsuits and investigations around generative AI and its opaque data practices pile up, there have been small moves to give people more control over what happens to what they post online.

On the eve of New Hampshire's primary election, a flood of robocalls exhorted Democratic voters to sit out a write-in campaign supporting President Joe Biden during the state's presidential primary. An AI-generated voice on the line matched the uncanny cadence and signature catchphrase-- ("malarkey!")--characteristic to Biden. From that call to fake creations envisioning a cascade of calamities under Biden's watch to AI deepfakes of a Slovakian candidate for country leader pondering vote rigging and raising beer prices, AI is making its mark on elections worldwide. Against this backdrop, governments and several tech companies are taking some steps to mitigate risks--European lawmakers just approved a watershed law, and as recently as February tech companies signed a pledge at the Munich Security Conference. But much more needs to be done to protect American democracy.

"It's very easy to see when you've got the memory right, because there is a very visceral reaction," says Pau Garcia, founder of Domestic Data Streamers. Dozens of people have now had their memories turned into images in this way via Synthetic Memories, a project run by Domestic Data Streamers. The studio uses generative image models, such as OpenAI's DALL-E, to bring people's memories to life. Since 2022, the studio, which has received funding from the UN and Google, has been working with immigrant and refugee communities around the world to create images of scenes that have never been photographed, or to re-create photos that were lost when families left their previous homes. Now Domestic Data Streamers is taking over a building next to the Barcelona Design Museum to record people's memories of the city using synthetic images.

Some have criticised Generative AI Systems for replicating the familiar pathologies of already widely-deployed AI systems. Other critics highlight how they foreshadow vastly more powerful future systems, which might threaten humanity's survival. The first group says there is nothing new here; the other looks through the present to a perhaps distant horizon. In this paper, I instead pay attention to what makes these particular systems distinctive: both their remarkable scientific achievement, and the most likely and consequential ways in which they will change society over the next five to ten years. In particular, I explore the potential societal impacts and normative questions raised by the looming prospect of 'Generative Agents', in which multimodal large language models (LLMs) form the executive centre of complex, tool-using AI systems that can take unsupervised sequences of actions towards some goal.

Over the past decade, AI research has focused heavily on building ever-larger deep learning models. This approach has simultaneously unlocked incredible achievements in science and technology, and hindered AI from overcoming long-standing limitations with respect to explainability, ethical harms, and environmental efficiency. Drawing on qualitative interviews and computational analyses, our three-part history of AI research traces the creation of this "epistemic monoculture" back to a radical reconceptualization of scientific progress that began in the late 1980s. In the first era of AI research (1950s-late 1980s), researchers and patrons approached AI as a "basic" science that would advance through autonomous exploration and organic assessments of progress (e.g., peer-review, theoretical consensus). The failure of this approach led to a retrenchment of funding in the 1980s. Amid this "AI Winter," an intervention by the U.S. government reoriented the field towards measurable progress on tasks of military and commercial interest. A new evaluation system called "benchmarking" provided an objective way to quantify progress on tasks by focusing exclusively on increasing predictive accuracy on example datasets. Distilling science down to verifiable metrics clarified the roles of scientists, allowed the field to rapidly integrate talent, and provided clear signals of significance and progress. But history has also revealed a tradeoff to this streamlined approach to science: the consolidation around external interests and inherent conservatism of benchmarking has disincentivized exploration beyond scaling monoculture. In the discussion, we explain how AI's monoculture offers a compelling challenge to the belief that basic, exploration-driven research is needed for scientific progress. Implications for the spread of AI monoculture to other sciences in the era of generative AI are also discussed.

In the near future, mobile networks are expected to broaden their services and coverage to accommodate a larger user base and diverse user needs. Thus, they will increasingly rely on artificial intelligence (AI) to manage network operation and control costs, undertaking complex decision-making roles. This shift will necessitate the application of techniques that incorporate critical thinking abilities, including reasoning and planning. Symbolic AI techniques already facilitate critical thinking based on existing knowledge. Yet, their use in telecommunications is hindered by the high cost of mostly manual curation of this knowledge and high computational complexity of reasoning tasks. At the same time, there is a spurt of innovations in industries such as telecommunications due to Generative AI (GenAI) technologies, operating independently of human-curated knowledge. However, their capacity for critical thinking remains uncertain. This paper aims to address this gap by examining the current status of GenAI algorithms with critical thinking capabilities and investigating their potential applications in telecom networks. Specifically, the aim of this study is to offer an introduction to the potential utilization of GenAI for critical thinking techniques in mobile networks, while also establishing a foundation for future research.

In the era of 6G, featuring compelling visions of intelligent transportation system, digital twins, remote surveillance is poised to become a ubiquitous practice. The substantial data volume and frequent updates present challenges in wireless networks. To address this, we propose a novel agent-driven generative semantic communication (A-GSC) framework based on reinforcement learning. In contrast to the existing research on semantic communication (SemCom), which mainly focuses on semantic compression or semantic sampling, we seamlessly cascade both together by jointly considering the intrinsic attributes of source information and the contextual information regarding the task. Notably, the introduction of the generative artificial intelligence (GAI) enables the independent design of semantic encoders and decoders. In this work, we develop an agent-assisted semantic encoder leveraging the knowledge based soft actor-critic algorithm, which can track the semantic changes, channel condition, and sampling intervals, so as to perform adaptive semantic sampling. Accordingly, we design a semantic decoder with both predictive and generative capabilities, which consists of two tailored modules. Moreover, the effectiveness of the designed models has been verified based on the dataset generated from CDNet2014, and the performance gain of the overall A-GSC framework in both energy saving and reconstruction accuracy have been demonstrated.