Technology

The TechCrunch AI glossary | TechCrunch

Artificial intelligence is a deep and convoluted world. The scientists who work in this field often rely on jargon and lingo to explain what they’re working on. As a result, we frequently have to use those technical terms in our coverage of the artificial intelligence industry. That’s why we thought it would be helpful to put together a glossary with definitions of some of the most important words and phrases that we use in our articles.

We will regularly update this glossary to add new entries as researchers continually uncover novel methods to push the frontier of artificial intelligence while identifying emerging safety risks.

An AI agent refers to a tool that makes use of AI technologies to perform a series of tasks on your behalf — beyond what a more basic AI chatbot could do — such as filing expenses, booking tickets or a table at a restaurant, or even writing and maintaining code. However, as we’ve explained before, there are lots of moving pieces in this emergent space, so different people can mean different things when they refer to an AI agent. Infrastructure is also still being built out to deliver on envisaged capabilities. But the basic concept implies an autonomous system that may draw on multiple AI systems to carry out multi-step tasks.

Given a simple question, a human brain can answer without even thinking too much about it — things like “which animal is taller between a giraffe and a cat?” But in many cases, you often need a pen and paper to come up with the right answer because there are intermediary steps. For instance, if a farmer has chickens and cows, and together they have 40 heads and 120 legs, you might need to write down a simple equation to come up with the answer (20 chickens and 20 cows).

In an AI context, chain-of-thought reasoning for large language models means breaking down a problem into smaller, intermediate steps to improve the quality of the end result. It usually takes longer to get an answer, but the answer is more likely to be right, especially in a logic or coding context. So-called reasoning models are developed from traditional large language models and optimized for chain-of-thought thinking thanks to reinforcement learning.

(See: Large language model)

A subset of self-improving machine learning in which AI algorithms are designed with a multi-layered, artificial neural network (ANN) structure. This allows them to make more complex correlations compared to simpler machine learning-based systems, such as linear models or decision trees. The structure of deep learning algorithms draws inspiration from the interconnected pathways of neurons in the human brain.

Deep learning AIs are able to identify important characteristics in data themselves, rather than requiring human engineers to define these features. The structure also supports algorithms that can learn from errors and, through a process of repetition and adjustment, improve their own outputs. However, deep learning systems require a lot of data points to yield good results (millions or more). It also typically takes longer to train deep learning vs. simpler machine learning algorithms — so development costs tend to be higher.

(See: Neural network)

This means further training of an AI model that’s intended to optimize performance for a more specific task or area than was previously a focal point of its training — typically by feeding in new, specialized (i.e. task-oriented) data.

Many AI startups are taking large language models as a starting point to build a commercial product but vying to amp up utility for a target sector or task by supplementing earlier training cycles with fine-tuning based on their own domain-specific knowledge and expertise.

(See: Large language model (LLM))

Large language models, or LLMs, are the AI models used by popular AI assistants, such as ChatGPT, Claude, Google’s Gemini, Meta’s AI Llama, Microsoft Copilot, or Mistral’s Le Chat. When you chat with an AI assistant, you interact with a large language model that processes your request directly or with the help of different available tools, such as web browsing or code interpreters.

AI assistants and LLMs can have different names. For instance, GPT is OpenAI’s large language model and ChatGPT is the AI assistant product.

LLMs are deep neural networks made of billions of numerical parameters (or weights, see below) that learn the relationships between words and phrases and create a representation of language, a sort of multidimensional map of words.

Those are created from encoding the patterns they find in billions of books, articles, and transcripts. When you prompt an LLM, the model generates the most likely pattern that fits the prompt. It then evaluates the most probable next word after the last one based on what was said before. Repeat, repeat, and repeat.

(See: Neural network)

Neural network refers to the multi-layered algorithmic structure that underpins deep learning — and, more broadly, the whole boom in generative AI tools following the emergence of large language models.

Although the idea to take inspiration from the densely interconnected pathways of the human brain as a design structure for data processing algorithms dates all the way back to the 1940s, it was the much more recent rise of graphical processing hardware (GPUs) — via the video game industry — that really unlocked the power of theory. These chips proved well suited to training algorithms with many more layers than was possible in earlier epochs — enabling neural network-based AI systems to achieve far better performance across many domains, whether for voice recognition, autonomous navigation, or drug discovery.

(See: Large language model (LLM))

Weights are core to AI training as they determine how much importance (or weight) is given to different features (or input variables) in the data used for training the system — thereby shaping the AI model’s output.

Put another way, weights are numerical parameters that define what’s most salient in a data set for the given training task. They achieve their function by applying multiplication to inputs. Model training typically begins with weights that are randomly assigned, but as the process unfolds, the weights adjust as the model seeks to arrive at an output that more closely matches the target.

For example, an AI model for predicting house prices that’s trained on historical real estate data for a target location could include weights for features such as the number of bedrooms and bathrooms, whether a property is detached, semi-detached, if it has or doesn’t have parking, a garage, and so on.

Ultimately, the weights the model attaches to each of these inputs is a reflection of how much they influence the value of a property, based on the given data set.

Technology

Pintarnya raises $16.7M to power jobs and financial services in Indonesia

Pintarnya, an Indonesian employment platform that goes beyond job matching by offering financial services along with full-time and side-gig opportunities, said it has raised a $16.7 million Series A round.

The funding was led by Square Peg with participation from existing investors Vertex Venture Southeast Asia & India and East Ventures.

Ghirish Pokardas, Nelly Nurmalasari, and Henry Hendrawan founded Pintarnya in 2022 to tackle two of the biggest challenges Indonesians face daily: earning enough and borrowing responsibly.

“Traditionally, mass workers in Indonesia find jobs offline through job fairs or word of mouth, with employers buried in paper applications and candidates rarely hearing back. For borrowing, their options are often limited to family/friend or predatory lenders with harsh collection practices,” Henry Hendrawan, co-founder of Pintarnya, told TechCrunch. “We digitize job matching with AI to make hiring faster and we provide workers with safer, healthier lending options — designed around what they can reasonably afford, rather than pushing them deeper into debt.”

Around 59% of Indonesia’s 150 million workforce is employed in the informal sector, highlighting the difficulties these workers encounter in accessing formal financial services because they lack verifiable income and official employment documentation.

Pintarnya tackles this challenge by partnering with asset-backed lenders to offer secured loans, using collateral such as gold, electronics, or vehicles, Hendrawan added.

Since its seed funding in 2022, the platform currently serves over 10 million job seeker users and 40,000 employers nationwide. Its revenue has increased almost fivefold year-over-year and expects to reach break-even by the end of the year, Hendrawn noted. Pintarnya primarily serves users aged 21 to 40, most of whom have a high school education or a diploma below university level. The startup aims to focus on this underserved segment, given the large population of blue-collar and informal workers in Indonesia.

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“Through the journey of building employment services, we discovered that our users needed more than just jobs — they needed access to financial services that traditional banks couldn’t provide,” said Hendrawan. “We digitize job matching with AI to make hiring faster and we provide workers with safer, healthier lending options — designed around what they can reasonably afford, rather than pushing them deeper into debt.”

image credits: Pintarnya (Founders of Pintarnya Ghirish Pokardas, Nelly Nurmalasari, and Henry Hendrawan)

While Indonesia already has job platforms like JobStreet, Kalibrr, and Glints, these primarily cater to white-collar roles, which represent only a small portion of the workforce, according to Hendrawan. Pintarnya’s platform is designed specifically for blue-collar workers, offering tailored experiences such as quick-apply options for walk-in interviews, affordable e-learning on relevant skills, in-app opportunities for supplemental income, and seamless connections to financial services like loans.

The same trend is evident in Indonesia’s fintech sector, which similarly caters to white-collar or upper-middle-class consumers. Conventional credit scoring models for loans, which rely on steady monthly income and bank account activity, often leave blue-collar workers overlooked by existing fintech providers, Hendrawan explained.

When asked about which fintech services are most in demand, Hendrawan mentioned, “Given their employment status, lending is the most in-demand financial service for Pintarnya’s users today. We are planning to ‘graduate’ them to micro-savings and investments down the road through innovative products with our partners.”

The new funding will enable Pintarnya to strengthen its platform technology and broaden its financial service offerings through strategic partnerships. With most Indonesian workers employed in blue-collar and informal sectors, the co-founders see substantial growth opportunities in the local market. Leveraging their extensive experience in managing businesses across Southeast Asia, they are also open to exploring regional expansion when the timing is right.

“Our vision is for Pintarnya to be the everyday companion that empowers Indonesians to not only make ends meet today, but also plan, grow, and upgrade their lives tomorrow … In five years, we see Pintarnya as the go-to super app for Indonesia’s workers, not just for earning income, but as a trusted partner throughout their life journey,” Hendrawan said. “We want to be the first stop when someone is looking for work, a place that helps them upgrade their skills, and a reliable guide as they make financial decisions.”

Technology

OpenAI warns against SPVs and other ‘unauthorized’ investments

In a new blog post, OpenAI warns against “unauthorized opportunities to gain exposure to OpenAI through a variety of means,” including special purpose vehicles, known as SPVs.

“We urge you to be careful if you are contacted by a firm that purports to have access to OpenAI, including through the sale of an SPV interest with exposure to OpenAI equity,” the company writes. The blog post acknowledges that “not every offer of OpenAI equity […] is problematic” but says firms may be “attempting to circumvent our transfer restrictions.”

“If so, the sale will not be recognized and carry no economic value to you,” OpenAI says.

Investors have increasingly used SPVs (which pool money for one-off investments) as a way to buy into hot AI startups, prompting other VCs to criticize them as a vehicle for “tourist chumps.”

Business Insider reports that OpenAI isn’t the only major AI company looking to crack down on SPVs, with Anthropic reportedly telling Menlo Ventures it must use its own capital, not an SPV, to invest in an upcoming round.

Technology

Meta partners with Midjourney on AI image and video models

Meta is partnering with Midjourney to license the startup’s AI image and video generation technology, Meta Chief AI Officer Alexandr Wang announced Friday in a post on Threads. Wang says Meta’s research teams will collaborate with Midjourney to bring its technology into future AI models and products.

“To ensure Meta is able to deliver the best possible products for people it will require taking an all-of-the-above approach,” Wang said. “This means world-class talent, ambitious compute roadmap, and working with the best players across the industry.”

The Midjourney partnership could help Meta develop products that compete with industry-leading AI image and video models, such as OpenAI’s Sora, Black Forest Lab’s Flux, and Google’s Veo. Last year, Meta rolled out its own AI image generation tool, Imagine, into several of its products, including Facebook, Instagram, and Messenger. Meta also has an AI video generation tool, Movie Gen, that allows users to create videos from prompts.

The licensing agreement with Midjourney marks Meta’s latest deal to get ahead in the AI race. Earlier this year, CEO Mark Zuckerberg went on a hiring spree for AI talent, offering some researchers compensation packages worth upwards of $100 million. The social media giant also invested $14 billion in Scale AI, and acquired the AI voice startup Play AI.

Meta has held talks with several other leading AI labs about other acquisitions, and Zuckerberg even spoke with Elon Musk about joining his $97 billion takeover bid of OpenAI (Meta ultimately did not join the offer, and OpenAI denied Musk’s bid).

While the terms of Meta’s deal with Midjourney remain unknown, the startup’s CEO, David Holz, said in a post on X that his company remains independent with no investors; Midjourney is one of the few leading AI model developers that has never taken on outside funding. At one point, Meta talked with Midjourney about acquiring the startup, according to Upstarts Media.

Midjourney was founded in 2022 and quickly became a leader in the AI image generation space for its realistic, unique style. By 2023, the startup was reportedly on pace to generate $200 million in revenue. The startup sells subscriptions starting at $10 per month. It offers pricier tiers, which offer more AI image generations, that cost as much as $120 per month. In June, the startup released its first AI video model, V1.

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Meta’s partnership with Midjourney comes just two months after the startup was sued by Disney and Universal, alleging that it trained AI image models on copyrighted works. Several AI model developers — including Meta — face similar allegations from copyright holders, however, recent court cases pertaining to AI training data have sided with tech companies.

Got a sensitive tip or confidential documents? We’re reporting on the inner workings of the AI industry — from the companies shaping its future to the people impacted by their decisions. Reach out to Rebecca Bellan at [email protected] and Maxwell Zeff at [email protected]. For secure communication, you can contact us via Signal at @rebeccabellan.491 and @mzeff.88.

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