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Author(s): Julia Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium. Everybody’s talking about AI, but how many of those who claim to be “experts” can actually break down the math behind it? It’s easy to get lost in the buzzwords and headlines, but the truth is — without a solid understanding of the equations and theories driving these technologies, you’re only skimming the surface.
In this feature article, Daniel D. Gutierrez, insideAInews Editor-in-Chief & Resident Data Scientist, explores why mathematics is so integral to data science and machine learning, with a special focus on the areas most crucial for these disciplines, including the foundation needed to understand generative AI.
Last Updated on November 10, 2024 by Editorial Team Author(s): Vita Haas Originally published on Towards AI. Image by Me and AI, My Partner in Crime When it comes to artificial intelligence (AI), opinions run the gamut. Some see AI as a miraculous tool that could revolutionize every aspect of our lives, while others fear it as a force that could upend society and replace human ingenuity.
Key Takeaways: Data used for personalization must be of high quality—accurate, up-to-date, and free of redundancies. 4 Practical Tips for Implementing Data-Driven Personalization in your organization. Many organizations struggle with siloed communication channels, which create fragmented customer experiences. How do you convert the everyday customers into loyal brand enthusiasts?
Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.
Large language models (LLMs) are powerful tools for generating text, but they are limited by the data they were initially trained on. This means they might struggle to provide specific answers related to unique business processes unless they are further adapted. Fine-tuning is a process used to adapt pre-trained models like Llama, Mistral, or Phi to specialized tasks without the enormous resource demands of training from scratch.
You can find useful datasets on countless platforms—Kaggle, Paperwithcode, GitHub, and more. But what if I tell you there’s a goldmine: a repository packed with over 400+ datasets, meticulously categorised across five essential dimensions—Pre-training Corpora, Fine-tuning Instruction Datasets, Preference Datasets, Evaluation Datasets, and Traditional NLP Datasets and more?
Databricks Marketplace is an open marketplace for data, analytics, and AI, powered by the open-source Delta Sharing standard. Since the release of Databricks.
Databricks Marketplace is an open marketplace for data, analytics, and AI, powered by the open-source Delta Sharing standard. Since the release of Databricks.
Artificial intelligence and machine learning are revolutionizing nearly every industry, from healthcare and finance to manufacturing and entertainment. Intelligent assistants, self-driving cars, facial recognition systems, and many other contributions are on the list. However, behind the glitz and glamor of these advancements, there is an underappreciated field: data engineering.
Last Updated on November 11, 2024 by Editorial Team Author(s): Vitaly Kukharenko Originally published on Towards AI. AI hallucinations are a strange and sometimes worrying phenomenon. They happen when an AI, like ChatGPT, generates responses that sound real but are actually wrong or misleading. This issue is especially common in large language models (LLMs), the neural networks that drive these AI tools.
Explaining the CPUs, GPUs, and NPUs in Intel ® ’s AI PCs Sponsored by Intel® So there I was — an AI person without an AI laptop. And no, not that kind of AI person; my ability to run an all-day AI workshop with barely a bio break has led a few of you to ask whether I am indeed a member of your species. (It turns out I’m an espresso-based lifeform.) This blog post, however, is sponsored by Intel, not espresso because… there I was, an AI person without an AI laptop.
In this contributed article, Ulrik Stig Hansen, President and Co-Founder of Encord, discusses the reality – AI hallucinations aren’t bugs in the system—they’re features of it. No matter how well we build these models, they will hallucinate. Instead of chasing the impossible dream of eliminating hallucinations, our focus should be on rethinking model development to reduce their frequency and implementing additional steps to mitigate the risks they pose.
Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage
There’s a lot of noise surrounding the ability of AI agents to connect to your tools, systems and data. But building an AI application into a reliable, secure workflow agent isn’t as simple as plugging in an API. As an engineering leader, it can be challenging to make sense of this evolving landscape, but agent tooling provides such high value that it’s critical we figure out how to move forward.
In the age of information overload, it’s easy to get lost in the large amount of content available online. YouTube offers billions of videos, and the internet is filled with articles, blogs, and academic papers. With such a large volume of data, it’s often difficult to extract useful insights without spending hours reading and watching. […] The post Build Your Own YT and Web Summarizer with LangChain appeared first on Analytics Vidhya.
OpenAI Orion, the company’s next-generation AI model, is hitting performance walls that expose limitations in traditional scaling approaches. Sources familiar with the matter reveal that Orion is delivering smaller performance gains than its predecessors, prompting OpenAI to rethink its development strategy. Early testing reveals plateauing improvements Initial employee testing indicates that OpenAI Orion achieved GPT-4 level performance after completing only 20% of its training.
Machine learning (ML) models contain numerous adjustable settings called hyperparameters that control how they learn from data. Unlike model parameters that are learned automatically during training, hyperparameters must be carefully configured by developers to optimize model performance.
In this contributed article, Dmitry Shapiro, Founder & CEO of MindStudio, discusses how businesses worldwide are recognizing the potential of AI to not only streamline complex, data-heavy tasks but also to redefine traditional job roles, preparing organizations to thrive in an increasingly fast-paced, data-centric landscape.
Speaker: Andrew Skoog, Founder of MachinistX & President of Hexis Representatives
Manufacturing is evolving, and the right technology can empower—not replace—your workforce. Smart automation and AI-driven software are revolutionizing decision-making, optimizing processes, and improving efficiency. But how do you implement these tools with confidence and ensure they complement human expertise rather than override it? Join industry expert Andrew Skoog as he explores how manufacturers can leverage automation to enhance operations, streamline workflows, and make smarter, data-dri
Flax is an advanced neural network library built on top of JAX, aimed at giving researchers and developers a flexible, high-performance toolset for building complex machine learning models. Flax’s seamless integration with JAX enables automatic differentiation, Just-In-Time (JIT) compilation, and support for hardware accelerators, making it ideal for both experimental research and production.
Gemini 2.0 leaked this week, sparking anticipation for Google’s latest AI model release. TestingCatalog identified a model titled Gemini-2.0-Pro-Exp-0111 on the Gemini web app, available only to select users under the Gemini Advanced section. This discovery has heightened speculation about Gemini 2.0’s potential capabilities and suggests Google may be gearing up for a public launch soon.
Jasper, one of the earlier players in generative AI marketing tech, has developed new ways to give marketers more control over AI-created content. Today, the Austin-based startup is adding several new features to give marketers more control and consistency when creating and scaling AI-generated content. One new feature, Brand IQ, uses API-based tooling to let marketers embed brand guidelines into an AI model for consistent text and visual outputs.
In this contributed article, Gal Naor, Co-Founder and CEO of Storone, explores why auto-tiering is essential for AI solutions in terms of data storage. By embracing auto-tiering, AI-driven organizations can ensure they meet both the demands of today’s data-intensive environments and the challenges of tomorrow.
Documents are the backbone of enterprise operations, but they are also a common source of inefficiency. From buried insights to manual handoffs, document-based workflows can quietly stall decision-making and drain resources. For large, complex organizations, legacy systems and siloed processes create friction that AI is uniquely positioned to resolve.
Owl ViT is a computer vision model that has become very popular and has found applications across various industries. This model takes in an image and a text query as input. After the image processing, the output comes with a confidence score and the object’s location (from the text query) in the image. This model’s […] The post Zero-shot Object Detection with Owl ViT Base Patch32 appeared first on Analytics Vidhya.
Many workers say they’re embarrassed to use AI at work. The latest research from Slack reveals a troubling plateau in AI tool adoption among workers, with many feeling both anxious and embarrassed about utilizing these technologies in their roles. Over the past few months, just a modest increase from 32% to 33% in reported AI usage has been noted, despite a compelling desire from executives for employees to engage more with AI.
A dozen AI scientists, researchers and investors told Reuters they believe that these techniques, which are behind OpenAI's recently released o1 model, could reshape the AI arms race, and have implications for the types of resources that AI companies have an insatiable demand for, from energy to types of chips. But now, some of the most prominent AI scientists are speaking out on the limitations of this “bigger is better” philosophy.
Cloudera, the hybrid platform for data, analytics, and AI, announced that it entered into a definitive agreement with Octopai B.I. Ltd. (Octopai) to acquire Octopai’s data lineage and catalog platform that enables organizations to understand and govern their data.
Speaker: Chris Townsend, VP of Product Marketing, Wellspring
Over the past decade, companies have embraced innovation with enthusiasm—Chief Innovation Officers have been hired, and in-house incubators, accelerators, and co-creation labs have been launched. CEOs have spoken with passion about “making everyone an innovator” and the need “to disrupt our own business.” But after years of experimentation, senior leaders are asking: Is this still just an experiment, or are we in it for the long haul?
The most powerful VLMs available today remain proprietary, limiting open research exploration. Open models often lag due to dependency on synthetic data generated by proprietary models, restricting true openness. Molmo, a sophisticated vision-language model, seeks to bridge this gap by creating high-quality multimodal capabilities built from open datasets and independent training methods.
Large language models (LLMs) for code have become indispensable in various domains, including code generation, reasoning tasks and agent systems.While open-access code LLMs are increasingly approaching the performance levels of proprietary models, high-quality code LLMs suitable for rigorous scientific investigation, particularly those with reproducible data processing pipelines and transparent training protocols, remain limited.
Live streaming has been gaining immense popularity in recent years, attracting an ever-growing number of viewers and content creators across various platforms. From gaming and entertainment to education and corporate events, live streams have become a powerful medium for real-time engagement and content consumption. However, as the reach of live streams expands globally, language barriers and accessibility challenges have emerged, limiting the ability of viewers to fully comprehend and participa
Alteryx, Inc., a leader in automated and AI analytics, today announced its Fall 2024 release for the Alteryx platform. The latest update supports hybrid architectures and meets customers where they are—whether in the cloud or on premises. Alteryx’s Fall 2024 release provides business analysts with a seamless analytics experience that scales data-driven insights across departments and industries.
Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage
When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m
Imagine chatting with a virtual assistant that remembers not just your last question but the entire flow of your conversation—personal details, preferences, even follow-up queries. This memory transforms chatbots from simple Q&A machines into sophisticated conversational partners, capable of handling complex topics over multiple interactions. In this article, we dive into the fascinating world of […] The post Enhancing AI Conversations with LangChain Memory appeared first on Analytics
Last Updated on November 10, 2024 by Editorial Team Author(s): Tata Ganesh Originally published on Towards AI. Photo by Jaredd Craig on Unsplash In this article, we will review the paper titled “Computation-Efficient Knowledge Distillation via Uncertainty-Aware Mixup” [1], which aims to reduce the computational cost associated with distilling the knowledge of computer vision models.
In Part 1 of this series, we defined the Retrieval Augmented Generation (RAG) framework to augment large language models (LLMs) with a text-only knowledge base. We gave practical tips, based on hands-on experience with customer use cases, on how to improve text-only RAG solutions, from optimizing the retriever to mitigating and detecting hallucinations.
IBM (NYSE: IBM) announced quantum hardware and software advancements to execute complex algorithms on IBM quantum computers with record levels of scale, speed, and accuracy.
In this new webinar, Tamara Fingerlin, Developer Advocate, will walk you through many Airflow best practices and advanced features that can help you make your pipelines more manageable, adaptive, and robust. She'll focus on how to write best-in-class Airflow DAGs using the latest Airflow features like dynamic task mapping and data-driven scheduling!
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