Sometimes when we went to analyze what AI is doing in our world, we should go back to one of the simplest types of metrics – what are people using it for a day to day?
Just before Christmas Eve, Bari Weiss at the Free Press interviewed Sam Altman, the creator of ChatGPT technologies and leader at OpenAI, about the general state of artificial intelligence in our world.
How, she asked, do we measure its impact?
The British-Canadian computer scientist often touted as a “godfather” of artificial intelligence has shortened the odds of AI wiping out humanity over the next three decades, warning the pace of change in the technology is “much faster” than expected.
Prof Geoffrey Hinton, who this year was awarded the Nobel prize in physics for his work in AI, said there was a “10% to 20%” chance that AI would lead to human extinction within the next three decades.
Synchronicity!😉 Just a few hours ago I watched a video which stated that the philosopher Henri Bergson argued our linear perception of time limited our ability to appreciate the relationship between time and consciousness.
What if our understanding of time as a linear sequence of events is merely an illusion created by the brain’s processing of reality? Could time itself be an emergent phenomenon, arising from the complex interplay of quantum mechanics, relativity, and consciousness? How might the brain’s multidimensional computations, reflecting patterns found in the universe, reveal a deeper connection between mind and cosmos? Could Quantum AI and Reversible Quantum Computing provide the tools to simulate, manipulate, and even reshape the flow of time, offering practical applications of D-Theory that bridge the gap between theoretical physics and transformative technologies? These profound questions lie at the heart of Temporal Mechanics: D-Theory as a Critical Upgrade to Our Understanding of the Nature of Time, 2025 paper and book by Alex M. Vikoulov. D-Theory, also referred to as Quantum Temporal Mechanics, Digital Presentism, and D-Series, challenges conventional views of time as a fixed, universal backdrop to reality and instead redefines it as a dynamic interplay between the mind and the cosmos.
Time, as experienced by humans, is more than a sequence of events dictated by physical laws. It emerges from our awareness of change, a psychological construct shaped by consciousness. Recent advancements in neuroscience, quantum physics, and cognitive science reveal fascinating parallels between the brain and the universe. Studies suggest that neural processes operate in up to 11 dimensions, echoing M-Theory’s depiction of a multiverse with similar dimensionality. These insights hint at a profound structural resemblance, where the brain and the cosmos mirror each other as interconnected systems of information processing.
Quantum Temporal Mechanics goes further, positing that consciousness not only perceives time but actively participates in its manifestation. In quantum theory, the observer plays a pivotal role in collapsing wavefunctions, a process that may extend beyond the microcosm to the fabric of reality itself. Various interpretations of quantum mechanics, such as Quantum Bayesianism and Consciousness Causes Collapse theory, support the idea that the observer’s awareness helps shape how time unfolds. In this framework, the flow of time becomes a participatory phenomenon, where consciousness and the universe co-create the temporal experience.
The implications of this perspective are far-reaching. By placing consciousness at the center of temporal reality, D-Theory suggests that the universe operates as a self-simulating quantum neural network—a vast, intelligent system continuously evolving and self-regulating. Reality itself becomes an active, dynamic process in which every quantum event contributes to the universe’s collective intelligence, much like neurons firing in a biological brain. This conceptualization reimagines the universe as a living, thinking entity, where time, space, and experience are constructs shaped by a universal consciousness.
Universal transformer memory optimizes prompts using neural attention memory models (NAMMs), simple neural networks that decide whether to “remember” or “forget” each given token stored in the LLM’s memory.
“This new capability allows Transformers to discard unhelpful or redundant details, and focus on the most critical information, something we find to be crucial for tasks requiring long-context reasoning,” the researchers write.
NAMMs are trained separately from the LLM and are combined with the pre-trained model at inference time, which makes them flexible and easy to deploy. However, they need access to the inner activations of the model, which means they can only be applied to open-source models.
A conversation with Marinka Zitnik, assistant professor of biomedical informatics in the Blavatnik Institute at HMS
Chinese automaker GAC unveils its third-gen self-developed humanoid robot, showcasing motion control, navigation, and AI capabilities.
Super saturday 1: productive day at the OEC!
Our inaugural Super Saturday session kicked off on a high note! Emmanuel showcased his handyman skills by expertly fixing two fluctuating lights at the Ogba Educational Clinic (OEC).
Special thanks to Mr. Kevin for his support in purchasing the necessary parts, including the choke, which made the repair possible.
Re grateful for the dedication and teamwork displayed by Emmanuel and Mr. Kevin. Their efforts have ensured a safer and more conducive learning environment for our students. +#buildingthefuturewithai #therobotarecoming #STEM
OpenAI, the company that makes ChatGPT, says in blogpost ‘we once again need to raise more capital than we’d imagined’
Generative artificial intelligence (AI) presents myriad opportunities for integrity actors—anti-corruption agencies, supreme audit institutions, internal audit bodies and others—to enhance the impact of their work, particularly through the use of large language models (LLMS). As this type of AI becomes increasingly mainstream, it is critical for integrity actors to understand both where generative AI and LLMs can add the most value and the risks they pose. To advance this understanding, this paper draws on input from the OECD integrity and anti-corruption communities and provides a snapshot of the ways these bodies are using generative AI and LLMs, the challenges they face, and the insights these experiences offer to similar bodies in other countries. The paper also explores key considerations for integrity actors to ensure trustworthy AI systems and responsible use of AI as their capacities in this area develop.