Edd Gent is a British freelance science writer now living in India. His main interests are the wackier fringes of computer science, engineering, bioscience and science policy. Edd has a Bachelor of Arts degree in Politics and International Relations and is an NCTJ qualified senior reporter.
Artificial intelligence refers to any technology exhibiting some facets of human intelligence, and it has been a prominent field in computer science for decades. AI tasks can include anything from picking out objects in a visual scene to knowing how to frame a sentence, or even predicting stock price movements.
How does AI work?While scientists can take many approaches to building AI systems, machine learning is the most widely used today. This involves getting a computer to analyze data to identify patterns that can then be used to make predictions. "Deep neural networks are kind of machine learning on steroids," Hooker said."They're both the most computationally expensive models, but also typically big, powerful, and expressive"
The last major training approach is"reinforcement learning," which lets an AI learn by trial and error. This is most commonly used to train game-playing AI systems or robots — including humanoid robots like Figure 01, or these soccer-playing miniature robots — and involves repeatedly attempting a task and updating a set of internal rules in response to positive or negative feedback. This approach powered Google Deepmind's ground-breaking AlphaGo model.
"What I say next hinges on what I said before — our language is connected in time," said Hooker."That was one of the pivotal breakthroughs, this ability to actually see the words as a whole." Narrow AI vs artificial general intelligence : What's the difference?People have grown excited about LLMs due to the breadth of tasks they can perform. Most machine learning systems are trained to solve a particular problem — such as detecting faces in a video feed or translating from one language to another. These models are known as “narrow AI” because they can only tackle the specific task they were trained for.
But there is still debate as to whether LLMs will be a precursor to an AGI, or simply one architecture in a broader network or ecosystem of AI architectures that is needed for AGI. Some say LLMs are miles away from replicating human reasoning and cognitive capabilities.
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