Personal, Agentic Assistants: A Practical Blueprint for a Secure, Multi-User, Self-Hosted ChatbotTowards Data Science Build a self-hosted, end-to-end platform that gives each user a personal, agentic chatbot that can autonomously vector-search through files that the user explicitly allows it to access.
The post Personal, Agentic Assistants: A Practical Blueprint for a Secure, Multi-User, Self-Hosted Chatbot appeared first on Towards Data Science.
Build a self-hosted, end-to-end platform that gives each user a personal, agentic chatbot that can autonomously vector-search through files that the user explicitly allows it to access.
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Accenture and Anthropic partner to boost enterprise AI integrationAI News Accenture and Anthropic are setting out to boost enterprise AI integration with a newly-expanded partnership. While 2024 was defined by corporate curiosity regarding Large Language Models (LLMs), the current mandate for business leaders is operationalising these tools to achieve a return on investment. The new Accenture Anthropic Business Group combines Anthropic’s model capabilities with Accenture’s
The post Accenture and Anthropic partner to boost enterprise AI integration appeared first on AI News.
Accenture and Anthropic are setting out to boost enterprise AI integration with a newly-expanded partnership. While 2024 was defined by corporate curiosity regarding Large Language Models (LLMs), the current mandate for business leaders is operationalising these tools to achieve a return on investment. The new Accenture Anthropic Business Group combines Anthropic’s model capabilities with Accenture’s
The post Accenture and Anthropic partner to boost enterprise AI integration appeared first on AI News. Read More
How Scout24 is building the next generation of real-estate search with AIOpenAI News Scout24 has created a GPT-5 powered conversational assistant that reimagines real-estate search, guiding users with clarifying questions, summaries, and tailored listing recommendations.
Scout24 has created a GPT-5 powered conversational assistant that reimagines real-estate search, guiding users with clarifying questions, summaries, and tailored listing recommendations. Read More
Prompt Engineering for Outlier DetectionKDnuggets Learn how to detect outliers by doing a real-life data project and improve the process with AI.
Learn how to detect outliers by doing a real-life data project and improve the process with AI. Read More
How to Develop AI-Powered Solutions, Accelerated by AITowards Data Science From idea to impact : using AI as your accelerating copilot
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From idea to impact : using AI as your accelerating copilot
The post How to Develop AI-Powered Solutions, Accelerated by AI appeared first on Towards Data Science. Read More
OpenAI targets AI skills gap with new certification standardsAI News Adoption of generative AI has outpaced workforce capability, prompting OpenAI to target the skills gap with new certification standards. While it’s safe to say OpenAI’s tools have reached mass adoption, organisations struggle to convert this usage into reliable output. To address this, OpenAI has announced ‘AI Foundations,’ a structured initiative designed to standardise how employees
The post OpenAI targets AI skills gap with new certification standards appeared first on AI News.
Adoption of generative AI has outpaced workforce capability, prompting OpenAI to target the skills gap with new certification standards. While it’s safe to say OpenAI’s tools have reached mass adoption, organisations struggle to convert this usage into reliable output. To address this, OpenAI has announced ‘AI Foundations,’ a structured initiative designed to standardise how employees
The post OpenAI targets AI skills gap with new certification standards appeared first on AI News. Read More
GraphRAG in Practice: How to Build Cost-Efficient, High-Recall Retrieval SystemsTowards Data Science Smarter retrieval strategies that outperform dense graphs — with hybrid pipelines and lower cost
The post GraphRAG in Practice: How to Build Cost-Efficient, High-Recall Retrieval Systems appeared first on Towards Data Science.
Smarter retrieval strategies that outperform dense graphs — with hybrid pipelines and lower cost
The post GraphRAG in Practice: How to Build Cost-Efficient, High-Recall Retrieval Systems appeared first on Towards Data Science. Read More
Real-world reasoning: How Amazon Nova Lite 2.0 handles complex customer support scenariosArtificial Intelligence This post evaluates the reasoning capabilities of our latest offering in the Nova family, Amazon Nova Lite 2.0, using practical scenarios that test these critical dimensions. We compare its performance against other models in the Nova family—Lite 1.0, Micro, Pro 1.0, and Premier—to elucidate how the latest version advances reasoning quality and consistency.
This post evaluates the reasoning capabilities of our latest offering in the Nova family, Amazon Nova Lite 2.0, using practical scenarios that test these critical dimensions. We compare its performance against other models in the Nova family—Lite 1.0, Micro, Pro 1.0, and Premier—to elucidate how the latest version advances reasoning quality and consistency. Read More
The Machine Learning “Advent Calendar” Day 9: LOF in ExcelTowards Data Science In this article, we explore LOF through three simple steps: distances and neighbors, reachability distances, and the final LOF score. Using tiny datasets, we see how two anomalies can look obvious to us but completely different to different algorithms. This reveals the key idea of unsupervised learning: there is no single “true” outlier, only definitions. Understanding these definitions is the real skill.
The post The Machine Learning “Advent Calendar” Day 9: LOF in Excel appeared first on Towards Data Science.
In this article, we explore LOF through three simple steps: distances and neighbors, reachability distances, and the final LOF score. Using tiny datasets, we see how two anomalies can look obvious to us but completely different to different algorithms. This reveals the key idea of unsupervised learning: there is no single “true” outlier, only definitions. Understanding these definitions is the real skill.
The post The Machine Learning “Advent Calendar” Day 9: LOF in Excel appeared first on Towards Data Science. Read More
On measuring grounding and generalizing grounding problemscs.AI updates on arXiv.org arXiv:2512.06205v1 Announce Type: new
Abstract: The symbol grounding problem asks how tokens like cat can be about cats, as opposed to mere shapes manipulated in a calculus. We recast grounding from a binary judgment into an audit across desiderata, each indexed by an evaluation tuple (context, meaning type, threat model, reference distribution): authenticity (mechanisms reside inside the agent and, for strong claims, were acquired through learning or evolution); preservation (atomic meanings remain intact); faithfulness, both correlational (realized meanings match intended ones) and etiological (internal mechanisms causally contribute to success); robustness (graceful degradation under declared perturbations); compositionality (the whole is built systematically from the parts). We apply this framework to four grounding modes (symbolic; referential; vectorial; relational) and three case studies: model-theoretic semantics achieves exact composition but lacks etiological warrant; large language models show correlational fit and local robustness for linguistic tasks, yet lack selection-for-success on world tasks without grounded interaction; human language meets the desiderata under strong authenticity through evolutionary and developmental acquisition. By operationalizing a philosophical inquiry about representation, we equip philosophers of science, computer scientists, linguists, and mathematicians with a common language and technical framework for systematic investigation of grounding and meaning.
arXiv:2512.06205v1 Announce Type: new
Abstract: The symbol grounding problem asks how tokens like cat can be about cats, as opposed to mere shapes manipulated in a calculus. We recast grounding from a binary judgment into an audit across desiderata, each indexed by an evaluation tuple (context, meaning type, threat model, reference distribution): authenticity (mechanisms reside inside the agent and, for strong claims, were acquired through learning or evolution); preservation (atomic meanings remain intact); faithfulness, both correlational (realized meanings match intended ones) and etiological (internal mechanisms causally contribute to success); robustness (graceful degradation under declared perturbations); compositionality (the whole is built systematically from the parts). We apply this framework to four grounding modes (symbolic; referential; vectorial; relational) and three case studies: model-theoretic semantics achieves exact composition but lacks etiological warrant; large language models show correlational fit and local robustness for linguistic tasks, yet lack selection-for-success on world tasks without grounded interaction; human language meets the desiderata under strong authenticity through evolutionary and developmental acquisition. By operationalizing a philosophical inquiry about representation, we equip philosophers of science, computer scientists, linguists, and mathematicians with a common language and technical framework for systematic investigation of grounding and meaning. Read More