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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?
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
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: 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?
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.
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!
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.
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.
Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.
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.
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
Metadata can play a very important role in using data assets to make data driven decisions. Generating metadata for your data assets is often a time-consuming and manual task. By harnessing the capabilities of generative AI, you can automate the generation of comprehensive metadata descriptions for your data assets based on their documentation, enhancing discoverability, understanding, and the overall data governance within your AWS Cloud environment.
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.
Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?
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.
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.
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.
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: Mike Rizzo, Founder & CEO, MarketingOps.com and Darrell Alfonso, Director of Marketing Strategy and Operations, Indeed.com
Though rarely in the spotlight, marketing operations are the backbone of the efficiency, scalability, and alignment that define top-performing marketing teams. In this exclusive webinar led by industry visionaries Mike Rizzo and Darrell Alfonso, we’re giving marketing operations the recognition they deserve! We will dive into the 7 P Model —a powerful framework designed to assess and optimize your marketing operations function.
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
This paper was accepted at the Efficient Natural Language and Speech Processing (ENLSP) Workshop at NeurIPS 2024. The pre-training phase of language models often begins with randomly initialized parameters. With the current trends in scaling models, training their large number of parameters can be extremely slow and costly. In contrast, small language models are less expensive to train, but they often cannot achieve the accuracy of large models.
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.
Speaker: Jay Allardyce, Deepak Vittal, Terrence Sheflin, and Mahyar Ghasemali
As we look ahead to 2025, business intelligence and data analytics are set to play pivotal roles in shaping success. Organizations are already starting to face a host of transformative trends as the year comes to a close, including the integration of AI in data analytics, an increased emphasis on real-time data insights, and the growing importance of user experience in BI solutions.
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