A decision-making mechanism is the brain of an AI agent. It processes the information gathered by the sensors and decides what action to take using the actuators. The decision-making mechanism is where the real magic happens. AI agents use various decision-making mechanisms, such as rule-based...
They decide what is important and must remain in developer’s control, and what is not as important and could be abstracted away. I called this value judgment “an opinion” earlier in my writings. One way to view such a value judgment is as a bet: it is difficult to know ahead of t...
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python Selecting the Right Model Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validati...
An agentic AI system is designed to make decisions, handle complex situations and, in some cases, adjust its behavior autonomously. In the last case, the AI agent operates without human oversight, instead using algorithms and environmental data to optimize its behavior. However, today, AI still ...
AI Pulse is a new blog series by Trend Micro, catch up on the latest news in AI regulations in the fourth of this series.
What is Deepfake in Artificial Intelligence with Tutorial, Introduction, History of Artificial Intelligence, AI, AI Overview, types of agents, intelligent agent, agent environment etc.
The efficient causebehind all of them is software engineers, data, domain experts, infrastructure, and surely, the recent public emergence of LLMs. Let's outline thekey aspectsrequiredto treat a thing as an agent. Step one, here is the list of features that agents can be described with: ...
Autoregressive models: Thistype of transformer model is trained specifically to predict the next word in a sequence, which represents a huge leap forward in the ability to generate text. Examples of autoregressive LLMs include GPT,Llama, Claude and the open-source Mistral. ...
fromqueryimportanswer_queryimportunittestclassTestQuestionAnswerAgent(unittest.TestCase):deftest_valid_response(self):response = answer_query("What did the author do growing up?") self.assertIsNotNone(response) self.assertTrue(len(str(response)) >0)deftest_invalid_response(self):response = answer...
Third, when you answer the query in this thought experiment, your output is also limited to text. You’ve never had prolonged communication with an active agent. That is, you’ve never had a conversation. So, even your responses are limited to analyzing the patterns you see in t...