Category
AI without the hype: models, agents and use cases that work in production.
Claude 2, launched by Anthropic in July 2023, offers a 100,000-token context window and safety grounded in Constitutional AI. Against GPT-4 it wins on long-document analysis and wide-context code; GPT-4 remains ahead on complex mathematical reasoning and its tooling ecosystem.
With quantization, model weights are stored with fewer bits (4, 5, or 8 instead of 16), so Llama 2 13B shrinks from 26 GB to about 7.5 GB. With llama.cpp it runs on an ordinary 16GB-RAM laptop with no dedicated GPU, and the quality loss is smaller than intuition suggests.
A text embedding is a numeric vector that encodes the meaning of a word or phrase, so that semantically similar pieces of text produce nearby vectors measured by cosine distance. The models most used in production are OpenAI ada-002, Sentence Transformers, and BGE, and they mainly serve semantic search, RAG systems, and text classification without training a classic classifier.
Function calling converts the LLM into a component that returns structured data. How it works, real use cases, and common production pitfalls.
Five months after launch, GPT-4 excels at chained reasoning, technical writing, and medium-complexity code, but still fails at arithmetic, post-cutoff information, and cross-conversation consistency. Claude 2 wins on long context; LLaMA 2 wins on cost and privacy.
Meta released LLaMA 2 on July 18, 2023 with a royalty-free commercial licence, in three sizes (7B, 13B, 70B parameters). The 70B model matches or beats GPT-3.5 on standard benchmarks. For 99.9% of organisations the licence allows download, modification, and production use with full data privacy and no fine-tuning restrictions.
Google launched Bard in February 2023 with PaLM 2 as its answer to ChatGPT, unveiling the model in May the same year in four sizes: Gecko, Otter, Bison, and Unicorn. PaLM 2 competes with GPT-3.5 and GPT-4 on benchmarks like MMLU and BIG-bench, but Google's real edge is Workspace integration, not the model itself.
Fine-tuning your own LLM pays off in three cases: you need a very specific style or voice, a rigid structured output format, or you want lower cost and latency from a small specialised model. LoRA and QLoRA have cut the GPU cost, but preparing data and running the model in production are still expensive. For everything else, RAG and prompt engineering are usually enough.
ChatGPT plugins let the model invoke external services through an OpenAPI specification. Three months after launch, the ecosystem has around 500 plugins with a clear pattern: they work well for live data lookup and internal API exposure, but show friction in multi-plugin orchestration and real-money transactions.
OpenAI Code Interpreter extends ChatGPT Plus with an isolated Python sandbox: it runs code on demand, reads files you upload (CSV, Excel, PDF, images, ZIPs) and returns results plus charts within the same chat. Sessions are ephemeral and offline, but remarkably effective for exploratory ad-hoc analysis without spinning up a notebook.
DINOv2 is Meta AI's computer vision model, trained via self-supervision on 142 million images with no human labels. With a simple linear layer on the frozen encoder, it matches or beats supervised models on ImageNet classification, semantic segmentation and monocular depth estimation.
An ensemble combines the predictions of several models, through bagging, boosting, or stacking, to reach a more accurate and stable result than any single model. Random Forest and XGBoost dominate tabular data because they exploit that idea: diversity between models reduces error, as long as their mistakes are not correlated with each other.
The step function, or Heaviside function, is the simplest activation function in neural networks: it converts any numeric input into a binary output, 0 or 1, depending on whether it crosses a fixed threshold. It was the central mechanism of Rosenblatt's 1958 perceptron, but because it is not differentiable, it cannot be used in modern backpropagation training.
LazyPredict is a Python library that automatically evaluates dozens of scikit-learn classification and regression models on your dataset in seconds, without writing training code for each one. LazyClassifier and LazyRegressor return a comparative metrics table that shows which models are worth tuning further.
AI optimises B2B sales through four levers: predictive lead scoring that prioritises the buyers most likely to close, conversation analysis, at-scale outreach personalisation and automating repetitive tasks. Its real impact depends on starting from clean CRM data.
Federated learning trains AI models collaboratively across many devices or organisations without moving the original data: each participant trains locally and sends only gradients to the central server. Formalised by Google in 2016, it does not guarantee privacy on its own: it needs differential privacy or secure aggregation to prevent leaks from those gradients.
Intelligent automation combines AI, machine learning, and physical robots that perceive, decide, and adapt in real time instead of following a fixed script. It is transforming manufacturing, logistics, healthcare, and food processing, and by 2024 there were already more than 4.6 million industrial robots active worldwide, per the IFR.
Computer vision is the branch of artificial intelligence that lets machines interpret digital images: detecting objects, segmenting regions and recognising patterns through convolutional neural networks. Since 2012, when AlexNet cut ImageNet classification error to 15.3%, it has spread into manufacturing, medicine, transport and precision agriculture.
Natural Language Processing (NLP) is the AI discipline that enables machines to understand, interpret, and generate human text and speech. Powered by the transformer architecture since 2017, NLP drives chatbots, automatic translation, and clinical diagnosis tools, with open challenges in causal reasoning, energy efficiency, and bias mitigation.
