Contemporary ML separates the static structure of parameters from the dynamic flow of inference, yielding systems that lack the sample efficiency and thermodynamic frugality of biological cognition. In this theoretical work, we propose \textbf{Memory-Amortized Inference (MAI)}, a formal framework rooted in algebraic topology that unifies learning and memory as phase transitions of a single geometric substrate. Central to our theory is the \textbf{Homological Parity Principle}, which posits a fundamental dichotomy: even-dimensional homology ($H_{even}$) physically instantiates stable \textbf{Content} (stable scaffolds or ``what''), while odd-dimensional homology ($H_{odd}$) instantiates dynamic \textbf{Context} (dynamic flows or ``where''). We derive the logical flow of MAI as a topological trinity transformation: \textbf{Search $\to$ Closure $\to$ Structure}. Specifically, we demonstrate that cognition operates by converting high-complexity recursive search (modeled by \textit{Savitch's Theorem} in NPSPACE) into low-complexity lookup (modeled by \textit{Dynamic Programming} in P) via the mechanism of \textbf{Topological Cycle Closure}. We further show that this consolidation process is governed by a topological generalization of the Wake-Sleep algorithm, functioning as a coordinate descent that alternates between optimizing the $H_{odd}$ flow (inference/wake) and condensing persistent cycles into the $H_{even}$ scaffold (learning/sleep). This framework offers a rigorous explanation for the emergence of fast-thinking (intuition) from slow-thinking (reasoning) and provides a blueprint for post-Turing architectures that compute via topological resonance.

Intelligence is fundamentally non-ergodic: it emerges not from uniform sampling or optimization from scratch, but from the structured reuse of prior inference trajectories. We introduce Memory-Amortized Inference (MAI) as a formal framework in which cognition is modeled as inference over latent cycles in memory, rather than recomputation through gradient descent. MAI systems encode inductive biases via structural reuse, minimizing entropy and enabling context-aware, structure-preserving inference. This approach reframes cognitive systems not as ergodic samplers, but as navigators over constrained latent manifolds, guided by persistent topological memory. Through the lens of delta-homology, we show that MAI provides a principled foundation for Mountcastle's Universal Cortical Algorithm, modeling each cortical column as a local inference operator over cycle-consistent memory states. Furthermore, we establish a time-reversal duality between MAI and reinforcement learning: whereas RL propagates value forward from reward, MAI reconstructs latent causes backward from memory. This inversion paves a path toward energy-efficient inference and addresses the computational bottlenecks facing modern AI. MAI thus offers a unified, biologically grounded theory of intelligence based on structure, reuse, and memory. We also briefly discuss the profound implications of MAI for achieving artificial general intelligence (AGI).

Could you tell if you were listening to an AI-generated voice? Even when people know they may be listening to AI-generated speech, it is still difficult for both English and Mandarin speakers to reliably detect a deepfake voice. That means billions of people who understand the world's most spoken languages are potentially at risk when exposed to deepfake scams or misinformation. Kimberly Mai at University College London and her colleagues challenged more than 500 people to identify speech deepfakes among multiple audio clips. Some clips contained the authentic voice of a female speaker reading generic sentences in either English or Mandarin, while others were deepfakes created by generative AIs trained on female voices.

As the world contends with the lingering catastrophic effects of a global pandemic, set against the ongoing backdrop of climate disasters and worsening conflict and humanitarian disasters, the need for rapid scientific advancement in response to crisis has never been so apparent. We can't say we weren't warned. In 2015, the United Nations released The 2030 Agenda for Sustainable Development, a shared blueprint constituting "an urgent call for action by all countries -- developed and developing -- in a global partnership." Central to the agenda are the 17 Sustainable Development Goals (SDGs) that address a range of urgent needs for humanity, from poverty alleviation and gender equality, to decent work, sustainable cities and communities, a clean environment, affordable and clean energy, and peace. The goals were founded on decades of input from global researchers, stakeholders and policy makers.

This comprehensive analysis in this Mobile Artificial Intelligence (MAI) market report describes data on a variety of topics, including growth strategies and restrictions. This market report contains critical information about the market landscape that considerably aids key stakeholders in making the best decision possible before investing in a business. In order to deliver the most accurate estimations and forecasts possible, this market study adopts a systematic and progressive research process focused on reducing deviation. For segregating and evaluating quantitative features of the market, the market report incorporates elements of bottom-up and top-down methodologies. On a great scale, raw market statistics is collected and analyzed in this Mobile Artificial Intelligence (MAI) market report.

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A survey by U.S. consultancy Boston Consulting Group shows that 70% of young people are motivated to shop by browsing or viewing media content. As a result, content-based ads are becoming a new trend in the e-commerce marketplace. Live commerce platforms and image- and video-sharing social media are also winning the hearts of fickle consumers by stimulating consumer appetite via the content. While there is a lot of content that can be monetized on China's Twitter-like microblogging site Weibo and video-sharing platforms, existing methods like spot ads do not appeal to viewers or hamper users' viewing experience. Markable AI (mai), launched in 2016, is an artificial intelligence-based solution for content recognition technology aimed at optimizing content ads.

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Artificial Intelligence (AI) and Machine Learning (ML) are rapidly changing the way things work, how, and how much we work. This is increasingly noticeable in restaurant ordering and in retail as well as in other industries. Both the retail and the hospitality industries are going to see tremendous innovation and change in the next few years. Most recently, contributing to this change, Encounter AI created Mai, the world's most advanced voice ordering assistant for retailers and restaurants that also provides accessibility for the blind. Encounter AI was founded in September 2018 when the company's CEO and co-founder Derrick Johnson decided to take his experience as a former fast-food employee and investor to create a solution that could be able to simplify ordering.

It is one of the marvels of human innovation but artificial intelligence (AI) offers tough competition for us. The days of speculating rain and sunshine may soon fade with artificial intelligence's capability to predict right conditions with precision to an extent. It comprises one of the basic aspects of precision agriculture (PA) promoted even by the government to boost productivity and in turn, farmers' income. AI-based sowing advisories lead to 30 per cent higher yields as Microsoft, in collaboration with ICRISAT, developed an AI Sowing App powered by Microsoft Cortana Intelligence Suite including Machine Learning and Power BI. The app sends sowing advisories to participating farmers on the optimal date to sow without them installing any sensors in their fields or any additional cost; all they need is a phone capable of receiving text messages.