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In this article, we dive into the concepts of machine learning and artificial intelligence modelexplainability and interpretability. We explore why understanding how models make predictions is crucial, especially as these technologies are used in critical fields like healthcare, finance, and legal systems.
Google Cloud has launched two generative AImodels on its Vertex AI platform, Veo and Imagen 3, amid reports of surging revenue growth among enterprises leveraging the technology. ” Knowledge sharing platform Quora has developed Poe , a platform that enables users to interact with generative AImodels. .”
Researchers from the Tokyo University of Science (TUS) have developed a method to enable large-scale AImodels to selectively “forget” specific classes of data. Progress in AI has provided tools capable of revolutionising various domains, from healthcare to autonomous driving.
Meta has confirmed plans to utilise content shared by its adult users in the EU (European Union) to train its AImodels. The announcement follows the recent launch of Meta AI features in Europe and aims to enhance the capabilities and cultural relevance of its AI systems for the region’s diverse population.
OpenAI is facing diminishing returns with its latest AImodel while navigating the pressures of recent investments. According to The Information , OpenAI’s next AImodel – codenamed Orion – is delivering smaller performance gains compared to its predecessors.
is being hailed by Google DeepMind as its “most intelligent AImodel” to date. The first model from this latest generation is an experimental version of Gemini 2.5 The capacity for “reasoning” extends beyond mere classification and prediction, Kavukcuoglu explains. Gemini 2.5 The post Gemini 2.5:
Google has launched Gemma 3, the latest version of its family of open AImodels that aim to set a new benchmark for AI accessibility. models, Gemma 3 is engineered to be lightweight, portable, and adaptableenabling developers to create AI applications across a wide range of devices.
This launch marks the beginning of ASI-1 Minis rollout and a new era of community-owned AI. By decentralising AIs value chain, were empowering the Web3 community to invest in, train, and own foundational AImodels, said Humayun Sheikh, CEO of Fetch.ai and Chairman of the Artificial Superintelligence Alliance.
xAI unveiled its Grok 3 AImodel on Monday, alongside new capabilities such as image analysis and refined question answering. The early version of Grok 3 is also currently leading on Chatbot Arena, a crowdsourced evaluation platform where users pit AImodels against one another and rank their outputs.
If we can't explain why a model gave a particular answer, it's hard to trust its outcomes, especially in sensitive areas. Interpretability also helps identify and fix biases or errors, ensuring the models are safe and ethical. Right now, attribution graphs can only explain about one in four of Claudes decisions.
In recent news, OpenAI has been working on a groundbreaking tool to interpret an AImodel’s behavior at every neuron level. Large language models (LLMs) such as OpenAI’s ChatGPT are often called black boxes.
The reported advances may influence the types or quantities of resources AI companies need continuously, including specialised hardware and energy to aid the development of AImodels. The o1 model is designed to approach problems in a way that mimics human reasoning and thinking, breaking down numerous tasks into steps.
Thats why explainability is such a key issue. People want to know how AI systems work, why they make certain decisions, and what data they use. The more we can explainAI, the easier it is to trust and use it. Large Language Models (LLMs) are changing how we interact with AI. Thats where LLMs come in.
ExplainableAI aims to make machine learning models more transparent to clients, patients, or loan applicants, helping build trust and social acceptance of these systems. Now, different models require different explanation methods, depending on the audience.
AI News caught up with Nerijus veistys, Senior Legal Counsel at Oxylabs , to understand the state of play when it comes to AI regulation and its potential implications for industries, businesses, and innovation. According to veistys, China began regulating AImodels as early as 2021.
Then generative AI creating text, images, and sound. Now, we’re entering the era of physical AI, AI that can perceive, reason, plan, and act.” These models, presented as NVIDIA NIM (Neural Interaction Model) microservices, are designed to integrate with the RTX 50 Series hardware.
In this article, we’ll examine the barriers to AI adoption, and share some measures that business leaders can take to overcome them. ” Today, only 43% of IT professionals say they’re confident about their ability to meet AI’s data demands. The best way to reduce the risks is to limit access to sensitive data.
Meta has unveiled five major new AImodels and research, including multi-modal systems that can process both text and images, next-gen language models, music generation, AI speech detection, and efforts to improve diversity in AI systems. “AudioSeal is being released under a commercial license.
Pro Experimental AImodel late last month, and its quickly stacked up top marks on a number of coding, math, and reasoning benchmark testsmaking it a contender for the worlds best model right now. However, models often struggle with information overload, making it difficult to extract meaningful insights from all that context.
For this article, AI News caught up with some of the worlds leading minds to see what they envision for the year ahead. Smaller, purpose-driven models Grant Shipley, Senior Director of AI at Red Hat , predicts a shift away from valuing AImodels by their sizeable parameter counts. The solutions?
Increasingly though, large datasets and the muddled pathways by which AImodels generate their outputs are obscuring the explainability that hospitals and healthcare providers require to trace and prevent potential inaccuracies. In this context, explainability refers to the ability to understand any given LLM’s logic pathways.
Under the hood of every AI application are algorithms that churn through data in their own language, one based on a vocabulary of tokens. AImodels process tokens to learn the relationships between them and unlock capabilities including prediction, generation and reasoning. How Are Tokens Used During AI Training?
When a user taps on a player to acquire or trade, a list of “Top Contributing Factors” now appears alongside the numerical grade, providing team managers with personalized explainability in natural language generated by the IBM® Granite™ large language model (LLM).
This success, however, has come at a cost, one that could have serious implications for the future of AI development. The Language Challenge DeepSeek R1 has introduced a novel training method which instead of explaining its reasoning in a way humans can understand, reward the models solely for providing correct answers.
Business Analyst: Digital Director for AI and Data Science Business Analyst: Digital Director for AI and Data Science is a course designed for business analysts and professionals explaining how to define requirements for data science and artificial intelligence projects.
Humans can validate automated decisions by, for example, interpreting the reasoning behind a flagged transaction, making it explainable and defensible to regulators. Financial institutions are also under increasing pressure to use ExplainableAI (XAI) tools to make AI-driven decisions understandable to regulators and auditors.
Efficiently managing and coordinating AI inference requests across a fleet of GPUs is a critical endeavour to ensure that AI factories can operate with optimal cost-effectiveness and maximise the generation of token revenue. Dynamo orchestrates and accelerates inference communication across potentially thousands of GPUs.
Leap towards transformational AI Reflecting on Googles 26-year mission to organise and make the worlds information accessible, Pichai remarked, If Gemini 1.0 released in December 2022, was notable for being Googles first natively multimodal AImodel. Flash, the flagship model of Geminis second generation. Its enhanced 1.5
The Qwen team at Alibaba has unveiled QwQ-32B, a 32 billion parameter AImodel that demonstrates performance rivalling the much larger DeepSeek-R1. This breakthrough highlights the potential of scaling Reinforcement Learning (RL) on robust foundation models. license, and is also accessible via Qwen Chat.
MatterGen enables a new paradigm of generative AI-assisted materials design that allows for efficient exploration of materials, going beyond the limited set of known ones, explains Microsoft.
As we navigate the recent artificial intelligence (AI) developments, a subtle but significant transition is underway, moving from the reliance on standalone AImodels like large language models (LLMs) to the more nuanced and collaborative compound AI systems like AlphaGeometry and Retrieval Augmented Generation (RAG) system.
DuckDuckGo has released a platform that allows users to interact with popular AI chatbots privately, ensuring that their data remains secure and protected. Users can choose from four AImodels: two closed-source models and two open-source models. The closed-source models are OpenAI’s GPT-3.5
It takes time to consider related ideas and explain their reasoning. OpenAI has already implemented some safety measures, like deliberative alignment , which help guide the models decision-making in following ethical principles. However, as AI advances, these measures will need to evolve.
Even AI-powered customer service tools can show bias, offering different levels of assistance based on a customers name or speech pattern. Lack of Transparency and Explainability Many AImodels operate as “black boxes,” making their decision-making processes unclear.
Then generative AI creating text, images and sound, Huang said. Now, were entering the era of physical AI, AI that can proceed, reason, plan and act. The latest generation of DLSS can generate three additional frames for every frame we calculate, Huang explained. The next frontier of AI is physical AI, Huang explained.
Instead of solely focusing on whos building the most advanced models, businesses need to start investing in robust, flexible, and secure infrastructure that enables them to work effectively with any AImodel, adapt to technological advancements, and safeguard their data. AImodels are just one part of the equation.
“Unlike traditional AImodels that are bound by static training data, the robot dog – dubbed Luna – perceives, processes, and improves itself through direct interaction with its world,” according to the company's press release. “IntuiCell is not chasing a bigger-is-better paradigm.
A triad of Ericsson AI labs Central to the Cognitive Labs initiative are three distinct research arms, each focused on a specialised area of AI: GAI Lab (Geometric Artificial Intelligence Lab): This lab explores Geometric AI, emphasising explainability in geometric learning, graph generation, and temporal GNNs.
While AI has gone from an emergent technology to a core part of product strategy for product and development leaders, the complexities of building with AI—and the question of whether to build or fine-tune your own AI capabilties, or to partner with a trusted AImodel provider remain top considerations in 2025.
With a mission to democratise access to AI and create systems that are both customisable and capable of working collaboratively with humans, the startup is setting ambitious goals to transform how AI integrates into everyday life and industry. This rests on two major pillars: model intelligence and high-quality infrastructure.
AI Squared aims to support AI adoption by integrating AI-generated insights into mission-critical business applications and daily workflows. What inspired you to found AI Squared, and what problem in AI adoption were you aiming to solve? How does AI Squared streamline AI deployment?
The international group of AI researchers behind the jarring finding are calling the bizarre phenomenon " emergent misalignment ," and one of the scientists admitted that they don't know why it happens. "We We cannot fully explain it," tweeted Owain Evans , an AI safety researcher at the University of California, Berkeley.
The introduction of the EU AI Act, alongside global regulatory frameworks, means companies must already navigate new compliance requirements related to AI transparency, bias mitigation, and ethical deployment. That means AI governance can no longer be an afterthought.
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