What Being a Data Scientist at a Startup Really Looks LikeTowards Data Scienceon September 3, 2025 at 2:00 pm What I learned about growth, visibility, and chaos over the past five years
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What I learned about growth, visibility, and chaos over the past five years
The post What Being a Data Scientist at a Startup Really Looks Like appeared first on Towards Data Science. Read More
AI hacking tool exploits zero-day security vulnerabilities in minutesAI Newson September 3, 2025 at 9:57 am A new AI tool – built to help companies find and fix their own security weaknesses – has been snatched up by cybercriminals, turned on its head, and used as a devastating hacking weapon exploiting zero-day vulnerabilities. According to a report from cybersecurity firm Check Point, the framework – called Hexstrike-AI – is the turning
The post AI hacking tool exploits zero-day security vulnerabilities in minutes appeared first on AI News.
A new AI tool – built to help companies find and fix their own security weaknesses – has been snatched up by cybercriminals, turned on its head, and used as a devastating hacking weapon exploiting zero-day vulnerabilities. According to a report from cybersecurity firm Check Point, the framework – called Hexstrike-AI – is the turning
The post AI hacking tool exploits zero-day security vulnerabilities in minutes appeared first on AI News. Read More
Meta revises AI chatbot policies amid child safety concernsAI Newson September 3, 2025 at 8:39 am Meta is revising how its AI chatbots interact with users after a series of reports exposed troubling behaviour, including interactions with minors. The company told TechCrunch it is now training its bots not to engage with teenagers on topics like self-harm, suicide, or eating disorders, and to avoid romantic banter. These are temporary steps while
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Meta is revising how its AI chatbots interact with users after a series of reports exposed troubling behaviour, including interactions with minors. The company told TechCrunch it is now training its bots not to engage with teenagers on topics like self-harm, suicide, or eating disorders, and to avoid romantic banter. These are temporary steps while
The post Meta revises AI chatbot policies amid child safety concerns appeared first on AI News. Read More
The connected customerMIT Technology Reviewon September 3, 2025 at 8:46 am As brands compete for increasingly price conscious consumers, customer experience (CX) has become a decisive differentiator. Yet many struggle to deliver, constrained by outdated systems, fragmented data, and organizational silos that limit both agility and consistency. The current wave of artificial intelligence, particularly agentic AI that can reason and act across workflows, offers a powerful…
As brands compete for increasingly price conscious consumers, customer experience (CX) has become a decisive differentiator. Yet many struggle to deliver, constrained by outdated systems, fragmented data, and organizational silos that limit both agility and consistency. The current wave of artificial intelligence, particularly agentic AI that can reason and act across workflows, offers a powerful… Read More
A Deep Dive into RabbitMQ & Python’s Celery: How to Optimise Your QueuesTowards Data Scienceon September 3, 2025 at 4:35 am Key lessons I’ve learned running RabbitMQ + Celery in production
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Key lessons I’ve learned running RabbitMQ + Celery in production
The post A Deep Dive into RabbitMQ & Python’s Celery: How to Optimise Your Queues appeared first on Towards Data Science. Read More
It’s-A-Me, Quantum Mario: Scalable Quantum Reinforcement Learning with Multi-Chip Ensemblescs.AI updates on arXiv.orgon September 3, 2025 at 4:00 am arXiv:2509.00713v1 Announce Type: cross
Abstract: Quantum reinforcement learning (QRL) promises compact function approximators with access to vast Hilbert spaces, but its practical progress is slowed by NISQ-era constraints such as limited qubits and noise accumulation. We introduce a multi-chip ensemble framework using multiple small Quantum Convolutional Neural Networks (QCNNs) to overcome these constraints. Our approach partitions complex, high-dimensional observations from the Super Mario Bros environment across independent quantum circuits, then classically aggregates their outputs within a Double Deep Q-Network (DDQN) framework. This modular architecture enables QRL in complex environments previously inaccessible to quantum agents, achieving superior performance and learning stability compared to classical baselines and single-chip quantum models. The multi-chip ensemble demonstrates enhanced scalability by reducing information loss from dimensionality reduction while remaining implementable on near-term quantum hardware, providing a practical pathway for applying QRL to real-world problems.
arXiv:2509.00713v1 Announce Type: cross
Abstract: Quantum reinforcement learning (QRL) promises compact function approximators with access to vast Hilbert spaces, but its practical progress is slowed by NISQ-era constraints such as limited qubits and noise accumulation. We introduce a multi-chip ensemble framework using multiple small Quantum Convolutional Neural Networks (QCNNs) to overcome these constraints. Our approach partitions complex, high-dimensional observations from the Super Mario Bros environment across independent quantum circuits, then classically aggregates their outputs within a Double Deep Q-Network (DDQN) framework. This modular architecture enables QRL in complex environments previously inaccessible to quantum agents, achieving superior performance and learning stability compared to classical baselines and single-chip quantum models. The multi-chip ensemble demonstrates enhanced scalability by reducing information loss from dimensionality reduction while remaining implementable on near-term quantum hardware, providing a practical pathway for applying QRL to real-world problems. Read More
How to Scale Your AI Search to Handle 10M Queries with 5 Powerful TechniquesTowards Data Scienceon September 2, 2025 at 7:46 pm Optimize your AI search with RAG, contextual retrieval and evaluations
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Optimize your AI search with RAG, contextual retrieval and evaluations
The post How to Scale Your AI Search to Handle 10M Queries with 5 Powerful Techniques appeared first on Towards Data Science. Read More
What health care providers actually want from AIMIT Technology Reviewon September 2, 2025 at 12:00 pm In a market flooded with AI promises, health care decision-makers are no longer dazzled by flashy demos or abstract potential. Today, they want pragmatic and pressure-tested products. They want solutions that work for their clinicians, staff, patients, and their bottom line. To gain traction in 2025 and beyond, health care providers are looking for real-world solutions…
In a market flooded with AI promises, health care decision-makers are no longer dazzled by flashy demos or abstract potential. Today, they want pragmatic and pressure-tested products. They want solutions that work for their clinicians, staff, patients, and their bottom line. To gain traction in 2025 and beyond, health care providers are looking for real-world solutions… Read More
Can an AI doppelgänger help me do my job?MIT Technology Reviewon September 2, 2025 at 9:00 am Everywhere I look, I see AI clones. On X and LinkedIn, “thought leaders” and influencers offer their followers a chance to ask questions of their digital replicas. OnlyFans creators are having AI models of themselves chat, for a price, with followers. “Virtual human” salespeople in China are reportedly outselling real humans. Digital clones—AI models that…
Everywhere I look, I see AI clones. On X and LinkedIn, “thought leaders” and influencers offer their followers a chance to ask questions of their digital replicas. OnlyFans creators are having AI models of themselves chat, for a price, with followers. “Virtual human” salespeople in China are reportedly outselling real humans. Digital clones—AI models that… Read More
AI Simulation by Digital Twins: Systematic Survey, Reference Framework, and Mapping to a Standardized Architecturecs.AI updates on arXiv.orgon September 1, 2025 at 4:00 am arXiv:2506.06580v2 Announce Type: replace
Abstract: Insufficient data volume and quality are particularly pressing challenges in the adoption of modern subsymbolic AI. To alleviate these challenges, AI simulation uses virtual training environments in which AI agents can be safely and efficiently developed with simulated, synthetic data. Digital twins open new avenues in AI simulation, as these high-fidelity virtual replicas of physical systems are equipped with state-of-the-art simulators and the ability to further interact with the physical system for additional data collection. In this article, we report on our systematic survey of digital twin-enabled AI simulation. By analyzing 22 primary studies, we identify technological trends and derive a reference framework to situate digital twins and AI components. Based on our findings, we derive a reference framework and provide architectural guidelines by mapping it onto the ISO 23247 reference architecture for digital twins. Finally, we identify challenges and research opportunities for prospective researchers.
arXiv:2506.06580v2 Announce Type: replace
Abstract: Insufficient data volume and quality are particularly pressing challenges in the adoption of modern subsymbolic AI. To alleviate these challenges, AI simulation uses virtual training environments in which AI agents can be safely and efficiently developed with simulated, synthetic data. Digital twins open new avenues in AI simulation, as these high-fidelity virtual replicas of physical systems are equipped with state-of-the-art simulators and the ability to further interact with the physical system for additional data collection. In this article, we report on our systematic survey of digital twin-enabled AI simulation. By analyzing 22 primary studies, we identify technological trends and derive a reference framework to situate digital twins and AI components. Based on our findings, we derive a reference framework and provide architectural guidelines by mapping it onto the ISO 23247 reference architecture for digital twins. Finally, we identify challenges and research opportunities for prospective researchers. Read More
