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Vad är en deepfake och hur fungerar tekniken? Läs om riskerna med deepfakes, hur de används i bedrägerier och desinformation samt hur du kan identifiera och skydda dig mot manipulerat digitalt innehåll. Läs mer i artikeln Deepfake: Vad det är, riskerna och hur du skyddar dig

Today we’re launching deep research in ChatGPT, a new agentic capability that conducts multi-step research on the internet for complex tasks. It accomplishes in tens of minutes what would take a human many hours.

Deep research is OpenAI's next agent that can do work for you independently—you give it a prompt, and ChatGPT will find, analyze, and synthesize hundreds of online sources to create a comprehensive report at the level of a research analyst. Powered by a version of the upcoming OpenAI o3 model that’s optimized for web browsing and data analysis, it leverages reasoning to search, interpret, and analyze massive amounts of text, images, and PDFs on the internet, pivoting as needed in reaction to information it encounters.

The ability to synthesize knowledge is a prerequisite for creating new knowledge. For this reason, deep research marks a significant step toward our broader goal of developing AGI, which we have long envisioned as capable of producing novel scientific research.

2025-02-06 08:34 Weblink News Kenneth Alfelt

At Paniax, we're not just keeping up. We are setting the pace. 

Nexus is redefining how models are built, trained, and deployed. No more bottlenecks. No more weeks-long development cycles and "soon" promises, just seamless, intuitive model creation that works. In real time.

Imagine the speed of Figma, but for deep learning. Top it off with production-ready models. Not just prototypes. From dev to prod in a single platform. That’s the power of Nexus.

Fast-movers win. Are you ready to lead?

Många företag genomför piloter med AI. Men hur gå från pilotprojekt till fullskalig implementering i organisationen? Enligt BCG handlar det till 70% om att ge stödet till medarbetarna och till en mindre del om tekniken. Artikeln ger dig som arbetar inom IT och L&D en praktisk guide till införande av AI. Jag beskriver de vanligaste fallgroparna, delar insikter från färsk forskning och mina egna projekt.

Slutligen presenterar jag en modell för hur ni lyckas med AI-transformationen.

AI påverkar idag många jobbroller med tydliga exempel inom marknadsföring, försäljning, kundservice och HR. Men hur påverkar AI lärandet? Under det senaste året har det kommit en mängd AI-appar för att effektivisera utveckling av kurser och höja kvaliteten. Det hjälper dem som utvecklar kurser.

Men det finns en annan aspekt—stödet för det informella lärandet som sker medan vi arbetar. Det är något som påverkar oss alla.

AI fungerar som en kraftfull förstärkare för hur vi lär genom erfarenheter, interaktion med andra och vår omvärld. Det möjliggör att du kan växla upp ditt lärande kraftigt för att hänga med i den allt snabbare utvecklingen. I artikeln beskriver jag hur AI påverkar både det formella och informella lärandet och ger dig konkreta tips som turboladdar ditt lärande i arbetet.

Varför är det så svårt att få till ökat användande av generativ AI? Trots uppenbara vinster är det få som använder AI i sin dagliga verksamhet. Tekniska hinder är en av orsakerna, men den största utmaningen ligger i vårt tänkande. Vår rädsla för AI och förändring, bristande tillit till att AI faktiskt kan vara till hjälp, samt våra arbetsvanor och kulturen i organisationen. 

I den här artikeln undersöker jag vad som krävs för att sätta fart på implementeringen av AI i våra organisationer.

Att bygga din AI-förmåga handlar inte om att göra en dramatisk förändring över en natt – det är en resa som börjar med små, medvetna steg. I artikeln beskriver jag hur du kan gå från att vara nyfiken på AI till att integrera den som en naturlig del av ditt dagliga arbete. 

Artikeln beskriver hur du kan etablera dagliga AI-vanor, skapa mer effektiva promptar och slutligen skapa egna AI-lösningar. Jag beskriver fyra steg där du gradvis omvandlar ditt arbetssätt, frigör tid för kreativitet och innovation, och positionerar dig som en ledare i den AI-drivna framtiden. Är du redo att ta ditt första steg mot att bygga en förmåga som kan förändra din karriär?

När är rätt tidpunkt att hoppa på tåget när det gäller generativ AI? Det är den stora frågan för många svenska organisationer. Det saknas inte exempel och studier som visar på vinsterna. Trots det har bara en liten andel företag globalt genomfört en storskalig AI-implementering, och många svenska företag ligger efter i utvecklingen. 

I den här artikeln beskriver jag varför det är dags att planera införandet av generativ AI i din organisation redan nu

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Latest in AI

2025-02-08 04:53 MarkTechPost

The integration of visual and textual data in artificial intelligence presents a complex challenge. Traditional models often struggle to interpret structured visual documents such as tables, charts, infographics, and diagrams with precision. This limitation affects automated content extraction and comprehension, which are crucial for applications in data analysis, information retrieval, and decision-making. As organizations increasingly […]

The post IBM AI Releases Granite-Vision-3.1-2B: A Small Vision Language Model with Super Impressive Performance on Various Tasks appeared first on MarkTechPost.

2025-02-08 04:19 MarkTechPost

After the success of large language models (LLMs), the current research extends beyond text-based understanding to multimodal reasoning tasks. These tasks integrate vision and language, which is essential for artificial general intelligence (AGI). Cognitive benchmarks such as PuzzleVQA and AlgoPuzzleVQA evaluate AI’s ability to process abstract visual information and algorithmic reasoning. Even with advancements, LLMs […]

The post Singapore University of Technology and Design (SUTD) Explores Advancements and Challenges in Multimodal Reasoning for AI Models Through Puzzle-Based Evaluations and Algorithmic Problem-Solving Analysis appeared first on MarkTechPost.

2025-02-08 03:49 MarkTechPost

Reinforcement learning (RL) for large language models (LLMs) has traditionally relied on outcome-based rewards, which provide feedback only on the final output. This sparsity of reward makes it challenging to train models that need multi-step reasoning, like those employed in mathematical problem-solving and programming. Additionally, credit assignment becomes ambiguous, as the model does not get […]

The post Process Reinforcement through Implicit Rewards (PRIME): A Scalable Machine Learning Framework for Enhancing Reasoning Capabilities appeared first on MarkTechPost.

2025-02-08 03:44 MarkTechPost

Aligning large language models (LLMs) with human values remains difficult due to unclear goals, weak training signals, and the complexity of human intent. Direct Alignment Algorithms (DAAs) offer a way to simplify this process by optimizing models directly without relying on reward modeling or reinforcement learning. These algorithms use different ranking methods, such as comparing […]

The post Unraveling Direct Alignment Algorithms: A Comparative Study on Optimization Strategies for LLM Alignment appeared first on MarkTechPost.

2025-02-07 23:14 MarkTechPost

LLM inference is highly resource-intensive, requiring substantial memory and computational power. To address this, various model parallelism strategies distribute workloads across multiple GPUs, reducing memory constraints and speeding up inference. Tensor parallelism (TP) is a widely used technique that partitions weights and activations across GPUs, enabling them to process a single request collaboratively. Unlike data […]

The post Optimizing Large Model Inference with Ladder Residual: Enhancing Tensor Parallelism through Communication-Computing Overlap appeared first on MarkTechPost.

2025-02-07 21:43 Wired

The ACLU says it stands ready to sue for access to government records that detail DOGE’s access to sensitive personnel data.

2025-02-03 19:27 ScienceDaily

Researchers developed an automated system to help programmers increase the efficiency of their deep learning algorithms by simultaneously leveraging two types of redundancy in complex data structures: sparsity and symmetry.

AI Vision
2022-05-03 13:22 Project English

An AI vision sets the direction for working with AI. Organizations that are increasing their focus on AI can benefit from getting aligned behind a vision that creates a better understanding for AI and its place in the organization. Without a vision, organizations risk putting too little focus on the most valuable projects, or worse; spending resources on the wrong ones. However, developing an AI vision can be complicated and achieving the intended impact can be hard. Not much guidance has so far been produced on what goes into an AI vision and there have been few best practice references.   

In a recent collaboration, AI Sweden and it’s partners Centiro, Region Gävleborg, Sahlgrenska University Hospital, the Swedish Tax Agency, Västra Götalandsregionen, Zenzeact and Östgötatrafiken have developed the AI Vision White Paper. This is a guide to help organizations create their AI vision faster and with better results. This practical tool is the product of their combined experiences. It explains what an AI vision is, the process of creating it and how to communicate it. In addition to the white paper, an easy-to-use template accompanies it.  

Who can benefit from using the white paper?

Private or public, any organization that wants to work more with AI could benefit from it. Ideally, an AI vision is developed after the organization has developed its first experiments or proof of concepts.. The AI vision serves as a roadmap for further scaling.

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