The dictionary of TechCrunch AI
The world of artificial intelligence is elaborate, fascinating, and complex. Oftentimes, we grapple with technical jargon and complex terminologies while trying to understand it. That's why we have decided to create a dictionary where these key terms live - a one-stop-shop for the most pivotal phrases used in our AI discussions.
And, as researchers constantly innovate and unveil new aspects of artificial intelligence, we will keep our dictionary updated with fresh entries.
<hr/>Book your appointments, handle your expenses, or even help you write code. Is it magic? No, it's an AI agent! These tools leverage AI technologies to perform tasks for you, going far beyond the basic capabilities of classic AI chatbots. However, the concept of AI agent isn't set in stone yet — and may mean different things to different people. The landscape of AI agents is continually evolving, and so are their functionalities, but at the base level, it refers to an autonomous system that effectively uses various AI systems to accomplish multi-step tasks.
To get a clearer picture, check out this article. It explains more about this roller-coaster ride of an industry.
Consider this: "Which is taller, a cat or a giraffe?" Even a child can answer this without giving it a second thought. However, take this into consideration: "A farmer owns 40 heads and 120 legs of cows and poultry. What is his number of chicks and cows? You must use a methodical approach to reasoning when dealing with this kind of situation. It is comparable to generating a series of ideas.
In AI terms, a chain-of-thought reasoning approach forces AI to handle problems by first breaking them down into manageable bits - improving the overall quality of the outcome. This method might take a bit more time, but guarantees a more accurate solution, especially in reasoning-based contexts like logic or coding.
Imagine an ecosystem located within the realms of machine learning where AI algorithms are built using an artificial neural network with numerous strata. Like the human brain, these layers allow the AI to analyze and make sense of correlations that may be too complex for simpler models.
Deep learning models are smart. They can discover crucial aspects within data by themselves, learning and improving with every error they encounter. However, for these models to produce excellent results, they need a ton of data and a considerable amount of processing time, making them slightly costlier to develop.
Even after your AI model is up and running, there is room for improvement. Fine-tuning is just that - tweaking existing AI models to enhance their performance for a specific task that was not initially part of their training, usually by feeding in new, task-specific data.
AI startups have been leveraging large language models as a launchpad, enhancing their performance further in a specific sector or task by complementing prior training sessions with fine tuning, using their domain-specific know-how.
These are the brains behind your AI assistants - GPT from OpenAI, Google’s Gemini, or Microsoft Copilot, to name few. Each assistant uses a Large Language Model or LLM to grasp your requests and process them either directly or with some assistance.
LLMs are colossal neural networks loaded with billions of numerical parameters (referred to as weights) that learn relationships amid words and phrases, culminating in a comprehensive language map.
At the very heart of deep learning lies the multifaceted structure of neural networks that underpin the entire AI revolution. This structure is crafted reflecting the intricate pathways of the human brain.
Since the invention of graphical processing hardware, we've been to push the boundaries of this technology, enabling neural network-based AI systems to improve significantly in performance across many areas.
Weights are important factors in AI training, as they determine the significance assigned to various features in the training data, ultimately influencing the AI model's output.
They start as randomly assigned values but adjust as the model strives to achieve an output that aligns more accurately with the target. For instance, an AI model predicting housing prices may assign weights to factors like the number of rooms, parking availability, etc. These weights indicate how much each factor influences the property's price.