Page 26 – Advance Aii

The evolution of Natural Language Models...

I decided to go through some of the break through papers in the field of NLP (Natural Language Processing) and summarize my learnings. The papers date from early 2000s to 2018. Source – KDNuggets If you are completely new to the field of NLP – I recommend you start by reading this article which touches on a variety of NLP basics. 1. A Neural Probabilistic Language Model 2. Efficient Estimation of Word Representations in Vector SpaceWord2Vec – Skipgram Model 3. Distributed Representations of Words and Phrases and their Compositionally 4. GloVe: Global Vectors for Word Representation 5. Recurrent neural network based language model 6. Extensions of Recurrent Neural Network Language Model Let’s start with #1, A Neural Probabilistic Language Model Bengio et al. propose a distributed representation for words to fight the curse of dimensionality. The curse of dimensionality stems from using a vector representation of a single word equal to vocabulary size and learning the distance of one word with respect to all the words. For example, to model a joint distribution of 10 consecutive words with a vocabulary size of 100,000 – The number of parameters to be learnt would be 1050-1. A language can be statistically modeled to represent the...

Vue.ai joins hands with SimpliFI Consulting to amplify AI orchestration across Middle Eastern financial institutions2 min read Reading Time: 2 minutes SimpliFI Consulting, founded by Jinesh Gosar, a banking veteran from the MENA region, selects Vue.ai, an enterprise data and AI orchestration platform developed by Mad Street Den across a decade. The partnership aims to scale AI adoption across regions in the Middle East and Asia where the cloud journey has just started to mature and there is a significant need for these organisations to leapfrog the digital transformation initiatives to use AI to accelerate their cloud and AI transformations. At SimpliFI Consulting, we recognise the benefits of partnering with a robust, enterprise grade AI orchestration platform like Vue.ai. Having worked extensively in the region, the approach of adopting such a platform offers a significant, long-term advantage compared to juggling multiple specialist vendors for specific use cases Jinesh Gosar, Founder & CEO, SimpliFI Consulting The nascent adoption of cloud platforms across many regions, especially in the Middle East has just started to break away, and earnest efforts are being put in place for financial organisations to harness the power of the cloud-driven AI and its offerings. Through our partnership with SimpliFI...

Rapid advancement in technology industry is not just a trend, it’s the norm. Yet amid the momentum around artificial intelligence, many tech companies remain on the sidelines. Whether due to uncertainty, budget constraints, or the fear of disruption, the hesitation to adopt AI is becoming an increasingly expensive decision. AI is no longer an experimental frontier. It is already transforming product development, IT operations, customer support, cybersecurity, and decision-making. For tech companies, the cost of doing nothing is more than missed innovation, it’s about falling behind in an ecosystem that rewards adaptability and speed. The opportunity cost of inaction is stacking up, and for many, the clock is ticking. The Hidden Costs of AI Inaction While the direct costs of AI adoption are often scrutinized, the hidden costs of inaction receive far less attention. These costs don’t appear on a balance sheet but are deeply felt in performance metrics, market positioning, and long-term resilience. 1. Competitive Lag Tech-first companies are already embedding AI into every layer of their business. From code generation to IT automation and personalized user experiences, they’re setting new performance benchmarks. Companies that hesitate risk being outpaced not just by innovators, but by competitors who move faster,...

object(WP_Post)#8426 (24) { [“ID”]=> int(40160) [“post_author”]=> string(2) “36” [“post_date”]=> string(19) “2025-05-26 02:54:37” [“post_date_gmt”]=> string(19) “2025-05-26 02:54:37” [“post_content”]=> string(3706) “ Across the communications landscape, teams are being asked to do more with less, while staying aligned, responsive and compliant in the face of complex and often shifting stakeholder demands. In that environment, how we track, report and manage our relationships really matters. In too many organisations, relationship management is still built around tools designed for customer sales. CRM systems, built for structured pipelines and linear user journeys, have long been the default for managing contact databases. They work well for sales and customer service functions. But for communications professionals managing journalists, political offices, internal leaders and external advocates, these tools often fall short. Stakeholder relationships don’t follow a straight line. They change depending on context, shaped by policy shifts, public sentiment, media narratives or crisis response. A stakeholder may be supportive one week and critical the next. They often hold more than one role, and their influence doesn’t fit neatly into a funnel or metric. Managing these relationships requires more than contact management. It requires context. The ability to see not just who you spoke to, but why, and what happened...

January 24, 2025 4 min read By Cogito Tech. 224 views The complexity and increasing volume of healthcare data drive the adoption of artificial intelligence (AI) in the field. AI is already being applied in several areas, such as diagnosis, treatment recommendations, research, patient engagement, and administrative tasks in medical institutions and life sciences companies. While advanced AI tools have the potential to perform healthcare tasks as well as, or even better than, humans in several areas, the effectiveness of AI models depends on the quality of medical data used for training. Models trained on biased, incomplete, or poorly annotated data can generate inaccurate diagnoses, misleading treatment recommendations, and unreliable administrative outputs. Cogito Tech’s Medical AI Innovation Hub addresses these challenges by offering innovative data solutions. Building on over a decade of experience, we provide labeling, annotation, and curation services to train AI models on accurate, diverse, and unbiased medical datasets. In this post, we take a deep dive into Cogito Tech’s AI Innovation Hub, revealing a global network of medical talent, advanced techniques, and streamlined processes designed to deliver high-quality, HIPAA-compliant data labeling. These components boost diagnostic accuracy and accelerate AI development timelines. We also explore key best practices...

Get Your “My First Robot” Kit In today’s world, artificial intelligence and robotics are no longer just the stuff of science fiction. These technologies are shaping industries, education, and even how children learn and engage with technology. The “My First Robots” kit from Robot School serves as a perfect example of how AI and robotics are being brought into homes to inspire the next generation of inventors, engineers, and problem solvers. In this blog post, we’ll dive into the core elements of the “My First Robots” kit and explore why it stands out as a fantastic educational tool for kids. Introducing the “My First Robots” Kit Designed for children ages 6 and above, the “My First Robots” kit introduces youngsters to the world of robotics in an engaging and approachable way. Whether your child has an affinity for engineering, coding, or just loves to explore how things work, this kit makes an excellent introduction to building functional robots from scratch. The kit includes a variety of components, such as: Modular PartsThe kit comes with easily attachable and detachable parts, allowing children to construct different robot designs with minimal frustration. This versatility encourages creativity and helps kids gain confidence as they...

October 8, 2017 Vasilis Vryniotis . 3 Comments On October 19 2017, Ubuntu 17.10 will be released and as many of you know it packs lots of significant changes. I spend a week testing the Beta 2 and in this “last minute” review, I document some of the less obvious features/gotchas of Ubuntu 17.10. I also share with you my experience and provide a few workarounds for some problems that I spotted. For a more conventional review, have a look at OMGUbuntu’s excellent blogpost. For those of you who don’t know me, I’m a creature of habit and an Ubuntu fan-boy, so when Canonical announced that they are ditching Unity I was not particularly happy. Nevertheless, even though Unity had some great features and it was particularly appealing to me, I find it a bit pointless to try and go against the flow of the river (manually installing Unity 7, using one of the community forks etc). What it’s done is done and from my point of view one should try to adjust to the new reality or choose a different distro. On the positive side, Unity always shipped with a very large number of Gnome programs so most of...

In the last couple of years deep learning (DL) has become a main enabler for applications in many domains such as vision, NLP, audio, click stream data etc. Recently researchers started to successfully apply deep learning methods to graph datasets in domains like social networks, recommender systems and biology, where data is inherently structured in a graphical way. So how do Graph Neural Networks work? Why do we need them? In machine learning tasks involving graphical data, we usually want to describe each node in the graph in a way that allows us to feed it into some machine learning algorithm. Without DL, one would have to manually extract features, such as the number of neighbors a node has. But this is a laborious job. This is where DL shines. It automatically exploits the structure of the graph in order to extract features for each node. These features are called embeddings. The interesting thing is, that even if you have absolutely no information about the nodes, you can still use DL to extract embeddings. The structure of the graph, that is — the connectivity patterns, hold viable information. So how can we use the structure to extract information? Can the...

Humanoid robots, those with bodies that resemble humans, could soon help people to complete a wide variety of tasks. Many of the tasks that these robots are designed to complete involve picking up objects of different shapes, weights and sizes. While many humanoid robots developed up to date are capable of picking up small and light objects, lifting bulky or heavy objects has often proved to be more challenging. In fact, if an object is too large or heavy, a robot might end up breaking or dropping it. With this in mind, researchers at Johns Hopkins University and National University of Singapore (NUS) recently developed a technique that allows robots to determine whether or not they will be able to lift a heavy box with unknown physical properties. This technique, presented in a paper pre-published on arXiv, could enable the development of robots that can lift objects more efficiently, reducing the risk that they will pick up things that they cannot support or carry. “We were particularly interested in how a humanoid robot can reason about the feasibility of lifting a box with unknown physical parameters,” Yuanfeng Han, one of the researchers who carried out the study, told TechXplore.”To achieve such...

High-resolution samples from Stability AI’s 8B rectified flow model In this article, we delve into ten groundbreaking research papers that expand the frontiers of AI across diverse domains, including large language models, multimodal processing, video generation and editing, and the creation of interactive environments. Produced by leading research labs such as Meta, Google DeepMind, Stability AI, Anthropic, and Microsoft, these studies showcase innovative approaches, including scaling down powerful models for efficient on-device use, extending multimodal reasoning across millions of tokens, and achieving unmatched fidelity in video and audio synthesis. If you’d like to skip around, here are the research papers we featured: Mamba: Linear-Time Sequence Modeling with Selective State Spaces by Albert Gu at Carnegie Mellon University and Tri Dao at Princeton University Genie: Generative Interactive Environments by Google DeepMind Scaling Rectified Flow Transformers for High-Resolution Image Synthesis by Stability AI Accurate Structure Prediction of Biomolecular Interactions with AlphaFold 3 by Google DeepMind Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone by Microsoft Gemini 1.5: Unlocking Multimodal Understanding Across Millions of Tokens of Context by Gemini team at Google The Claude 3 Model Family: Opus, Sonnet, Haiku by Anthropic The Llama 3 Herd of Models by...