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2024 has been yet another groundbreaking year for AI, with major breakthroughs, industry shifts, and ethical challenges shaping its future. Let's uncover together the key moments that defined AI this year about to finalize.
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 contributed article, Yoram Novick, President and CEO of Zadara, discusses how enterprises are in search of and implementing their own AI powered clouds, and the benefits and challenges they face in the effort to keep their data available and secure.
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 a world where bustling office spaces fell silent, and the daily commute became a distant memory. When COVID-19 hit, that world became a reality, transforming how we work. Remote work quickly transitioned from a perk to a necessity, and data science—already digital at heart—was poised for this change. According to a recent report from Gartner, 47% of employers are open to full-time remote work even beyond the pandemic, highlighting a massive shift in the job landscape.
Source: Unsplash In the high-stakes world of data science and AI, project success is far from guaranteed. As leaders in this field, we're acutely aware of the multifaceted challenges that can derail even the most promising initiatives. From models falling short of requirements to production failures with real-world data, the path to success is fraught with potential pitfalls.
The fields of Data Science, Artificial Intelligence (AI), and Large Language Models (LLMs) continue to evolve at an unprecedented pace. To keep up with these rapid developments, it’s crucial to stay informed through reliable and insightful sources. In this blog, we will explore the top 7 LLM, data science, and AI blogs of 2024 that have been instrumental in disseminating detailed and updated information in these dynamic fields.
The fields of Data Science, Artificial Intelligence (AI), and Large Language Models (LLMs) continue to evolve at an unprecedented pace. To keep up with these rapid developments, it’s crucial to stay informed through reliable and insightful sources. In this blog, we will explore the top 7 LLM, data science, and AI blogs of 2024 that have been instrumental in disseminating detailed and updated information in these dynamic fields.
In this contributed article, Ishan Gupta. CEO and Co-founder of RipenApps, discusses how banks have historically been at the forefront of technological advancements, they are renowned for using computers as well as providing internet-based financial services. However, the rise of AI has brought with it a new dawn of innovations. These days, AI is disrupting the entire banking sector in several ways.
Introduction Software development is on the brink of a transformative shift as artificial intelligence (AI) continues to push the boundaries of what was once deemed impossible. Enter Devin AI, an AI software engineer developed by the innovative minds at Cognition. This groundbreaking creation aims to revolutionize how we approach software development, streamlining the process and […] The post Could AI Replace Software Engineers?
Unity makes strength. This well-known motto perfectly captures the essence of ensemble methods: one of the most powerful machine learning (ML) approaches -with permission from deep neural networks- to effectively address complex problems predicated on complex data, by combining multiple models for addressing one predictive task.
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?
Many app developers are interested in building on device experiences that integrate increasingly capable large language models (LLMs). Running these models locally on Apple silicon enables developers to leverage the capabilities of the user's device for cost-effective inference, without sending data to and from third party servers, which also helps protect user privacy.
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.
It’s been nearly 6 months since our research into which AI tools software engineers use, in the mini-series, AI tooling for software engineers: reality check. At the time, the most popular tools were ChatGPT for LLMs, and GitHub copilot for IDE-integrated tooling. Then this summer, I saw the Cursor IDE becoming popular around when Anthropic’s Sonnet 3.5 model was released, which has superior code generation compared to ChatGPT.
TL;DR: Landmines pose a persistent threat and hinder development in over 70 war-affected countries. Humanitarian demining aims to clear contaminated areas, but progress is slow: at the current pace, it will take 1,100 years to fully demine the planet. In close collaboration with the UN and local NGOs, we co-develop an interpretable predictive tool for landmine contamination to identify hazardous clusters under geographic and budget constraints, experimentally reducing false alarms and clearance
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!
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.
Crusoe, the vertically integrated AI infrastructure provider, announced it has closed a $600 million Series D funding round. The investment was led by Founders Fund, with participation from new and existing investors, including Fidelity, Long Journey Ventures, Mubadala, NVIDIA, Ribbit Capital, and Valor Equity Partners.
In the realm of data analysis, understanding data distributions is crucial. It is also important to understand the discrete vs continuous data distribution debate to make informed decisions. Whether analyzing customer behavior, tracking weather, or conducting research, understanding your data type and distribution leads to better analysis, accurate predictions, and smarter strategies.
In this contributed article, Aayam Bansal explores the increasing reliance on AI in surveillance systems and the profound societal implications that could lead us toward a surveillance state. This piece delves into the ethical risks of AI-powered tools like predictive policing, facial recognition, and social credit systems, while raising the question: Are we willing to trade our personal liberties for the promise of safety?
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.
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.
In this contributed article, Chris Peake, Chief Information Security Officer (CISO) and Senior Vice President of Security at Smartsheet, explores how the role of CISOs is evolving to address new security challenges posed by generative AI. The article underscores the importance of collaboration and adaptability to keep organizations secure as AI is expected to continue to reshape cybersecurity in 2025.
In this contributed article, Kunju Kashalikar, Senior Director of Product Management at Pentaho, discusses how to dream big without the risk: three steps to AI-grade data. The industry adage of ‘garbage-in-garbage-out' has never been more applicable than now. Clean, accurate data is the key to winning the AI race - but leaving the starting blocks is the challenge for most.
In this contributed article, engineering leader Uma Uppin emphasizes that high-quality data is fundamental to effective AI systems, as poor data quality leads to unreliable and potentially costly model outcomes. Key data attributes like accuracy, completeness, consistency, timeliness, and relevance play crucial roles in shaping AI performance and minimizing ethical risks.
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?
Snowflake Intelligence is a groundbreaking platform that will empower business users to create data agents, so they can analyze, summarize, and take action from their enterprise data Snowflake (NYSE: SNOW), the AI Data Cloud company, announced Snowflake Intelligence (in private preview soon), a new platform that will enable enterprises to easily ask business questions across their enterprise […]
KNIME, the open source data analytics and AI company, announced the launch of its AI companion K-AI to all users. With K-AI, users can co-create powerful data workflows with AI. K-AI will answer questions, make recommendations, and extend or build whole data workflows based on user prompts.
As the world becomes more interconnected and data-driven, the demand for real-time applications has never been higher. Artificial intelligence (AI) and natural language processing (NLP) technologies are evolving rapidly to manage live data streams. They power everything from chatbots and predictive analytics to dynamic content creation and personalized recommendations.
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: 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.
Last year, the DeepSeek LLM made waves with its impressive 67 billion parameters, meticulously trained on an expansive dataset of 2 trillion tokens in English and Chinese comprehension. Setting new benchmarks for research collaboration, DeepSeek ingrained the AI community by open-sourcing both its 7B/67B Base and Chat models. Now, what if I tell you there […] The post Andrej Karpathy Praises DeepSeek V3s Frontier LLM, Trained on a $6M Budget appeared first on Analytics Vidhya.
A new Capital Onesurvey"AI readiness survey: Are companies ready for AI adoption?" found that 87% of business leaders see their data ecosystem as ready to build and deploy AI at scale, yet 70% of technical practitioners spend hours daily fixing data issues.
Tuskira, a pioneering threat defense platform leveraging an AI-powered security mesh, has launched out of stealth mode with $28.5 million in funding. The round was led by Intel Capital and SYN Ventures, with participation from Sorenson Capital, Rain Capital, Wipro Ventures, and other key industry leaders.
Image segmentation is another popular computer vision task that has applications with different models. Its usefulness across different industries and fields has allowed for more research and improvements. Maskformer is part of another revolution of image segmentation, using its mask attention mechanism to detect objects that overlap their bounding boxes.
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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