A robust AI-based solution is built on data – not just any data but high-quality, accurately annotated data. Only the best and most refined data can power your AI project, and this data purity will have a huge impact on the project’s outcome. At the core of successful AI projects lies data annotation, the process of refining raw data into a format that machines can understand. However, the process of preparing training data is layered, tedious, and time-consuming. From sourcing data to cleaning, annotating, and ensuring compliance, it can often feel overwhelming. This is why many organizations consider outsourcing their data labeling needs to expert vendors. But how do you ensure both accuracy in data annotation and choose the right data labeling vendor? This comprehensive guide will help you with both. Why Accurate Data Annotation is Critical for AI Projects We’ve often called data the fuel for AI projects – but not just any data will do. If you need “rocket fuel” to help your project achieve liftoff, you can’t put raw oil in the tank. Data needs to be carefully refined to ensure that only the highest-quality information powers your project. This refinement process, known as data annotation, is...

The rapid advancement of artificial intelligence has led to increasingly sophisticated models, yet these systems still face fundamental efficiency challenges. A team of researchers led by Dr. Suin Yi, Assistant Professor at Texas A&M College of Engineering, has developed a new approach called Super-Turing AI, which mimics the human brain’s ability to learn and adapt. This innovation could greatly improve AI by significantly reducing computational costs and energy consumption. Current AI models rely on architectures that separate data storage from processing, requiring enormous computational power and energy to migrate information between these two components. In contrast, the human brain integrates learning and memory through neural connections called synapses, which dynamically strengthen or weaken based on experience – a process known as synaptic plasticity. Dr. Yi’s team has taken inspiration from neuroscience to develop AI systems that function more like biological brains. Traditional AI models depend heavily on backpropagation, an optimization algorithm used to adjust neural networks during training. While effective, backpropagation is computationally intensive and biologically implausible. To address this, the team explores alternative mechanisms such as Hebbian learning – often summarized as “cells that fire together, wire together” – and spike-timing-dependent plasticity (STDP). These biologically inspired learning processes allow...

Tech companies have been racing to integrate AI across products and processes, but many of those efforts hit walls that are not immediately obvious. The excitement is there. The models are available. The compute power exists. Yet progress stalls. What often goes unnoticed is the real blocker: data. Not the lack of it, but the way it is organized, accessed, and governed. Most tech teams still rely on legacy data systems and fragmented strategies built for a different era, long before AI pipelines, real time decisions, or large language models entered the picture. The result? Long ramp up times, underperforming models, and AI initiatives that never reach production. This is not a tooling issue. It is a foundational problem. And until data strategy catches up, even the best AI ideas will remain stuck in neutral. Want guidance from an AI and Data experts on how to implement AI in your business? Contact Fusemachines today! How Legacy Data Strategies Create Invisible Friction Legacy data systems were never designed with AI in mind. They evolved over time to support transactional workloads, reporting, and disconnected business functions not fast moving AI pipelines. In many tech companies, data still lives in separate systems maintained...

Food recipes, that is. But cooking is kind of like programming and other computer efforts, isn’t it? Although it might not seem obvious, there is a very close connection between Coding and Cooking, as they are similar in many ways, to name a few they both need creativity, and have a solid foundation from where they develop their activity. You can just follow a recipe, but in order to re-interpret traditional dishes, some inventiveness is required. In coding you can create a programme based on an instruction set or you can remix a project using your imagination. Sky is the limit! —Stefania Altieri Aside from coding, when I first figure out a manual sequence of steps, for example to configure a Linux OS for a specific task, or how to get something deployed, I refer to it as a “recipe” at least until it’s scripted and/or automated. As I cook more and learn more dishes—and start having personal preferences for certain ingredients—I’ve started putting them in a repository on Github: https://github.com/samrecipes/recipes A lot of my current recipes started by looking at various websites and combining them together. And then specializing it for my own whims and typical ingredients I have...

I recently attended a talk by Kevin Clarke (CS224n) where he talked about the future trends in NLP. I am writing this post to summarize and discuss the recent trends. Slide snippets are from his guest lecture. There are 2 primary topics that lay down the trends for NLP with Deep Learning:1. Pre-Training using Unsupervised/Unlabeled data2. OpenAI GPT-2 breakthrough 1. Pre-Training using Unsupervised/Unlabeled data Supervised data is expensive and limited, how can we use Unsupervised data to supplement training with supervised fine-tuning to do better? Let’s apply this to the problem of Machine Translation and see how this helps – If you have 2 corpus of text (transcriptions or wikipedia articles) in different languages with no cross language mapping. We can use this for pre-training, train an encoder and decoder LSTM (without attention) individually on both corpus and fit them together in a model and fine-tune over a labeled dataset.  How does this help? Both the encoder and decoder LSTMs here have learnt the notion of their respective language distributions and are good as generative models for each of their languages. When you put them together (#2) the model learn to use the compressed representation and map them from source...

The recent disruption caused by DeepSeek’s R1 model sent shockwaves through the AI community, demonstrating that Chinese AI advancements may have been underestimated. The model’s performance, rivaling some of the most advanced offerings from OpenAI and Anthropic at a fraction of the cost, signaled a new era of competition in artificial intelligence. However, DeepSeek is not the only Chinese company making waves in AI. While industry giants like Alibaba, Tencent, Baidu, and ByteDance continue to lead the charge, a new generation of AI startups – often referred to as the “Chinese AI Tigers” – is emerging as formidable players. These startups are pushing the limits of generative AI, challenging global incumbents with state-of-the-art models and breakthrough innovations. In this article, we will explore DeepSeek and five of the most influential Chinese AI startups: Moonshot AI, Zhipu AI, Baichuan AI, MiniMax, and 01.AI. Each of these companies has developed cutting-edge AI models and solutions that are shaping the future of artificial intelligence, both in China and beyond. DeepSeek: A Research-Driven AI Powerhouse in China Founded in May 2023, DeepSeek is an AI company based in Hangzhou, operating as an independent entity under High-Flyer, a leading Chinese quantitative hedge fund. Unlike many...