The study said tweaking a model after its initial phase of development was an importand factor in making it more persuasive. The study said tweaking a model after its initial phase of development was an importand factor in making it more persuasive. Chatbots can sway political opinions but are'substantially' inaccurate, study finds'Information-dense' AI responses are most persuasive but these tend to be less accurate, says security report Chatbots can sway people's political opinions but the most persuasive artificial intelligence models deliver "substantial" amounts of inaccurate information in the process, according to the UK government's AI security body. Researchers said the study was the largest and most systematic investigation of AI persuasiveness to date, involving nearly 80,000 British participants holding conversations with 19 different AI models. The AI Security Institute carried out the study amid fears that chatbots can be deployed for illegal activities including fraud and grooming.

Efforts to understand the universe could get a boost from an AI developed by Google DeepMind. The algorithm, which can reduce unwanted noise by up to 100 times, could allow the Laser Interferometer Gravitational-Wave Observatory (LIGO) to spot a particular type of black hole that has so far eluded us. LIGO is designed to detect the gravitational waves produced when objects such as black holes spiral into each other and collide. These waves cross the universe at the speed of light, but the fluctuations they cause in space-time are extremely small – 10,000 times smaller than the nucleus of an atom. Since its first observations 10 years ago, LIGO has recorded such signals produced by nearly 100 black hole collisions.

A combination of AMD's 3D V-Cache, AI, and multiple cores offers enthusiasts a bold new opportunity for tweaking the performance of their PCs. But a new Asus BIOS option, Asus AI Cache Boost, takes the potential complexity out of it all, offering double-digit performance increases just by enabling the Cache Boost option. We've already discovered that you can boost the performance of a Ryzen AI Max processor by up to 60 percent just by adjusting the UMA frame buffer. The new Asus BIOS option offers a related tweak specifically for AMD Ryzen 9950X3D and 9900X3D processors. Naturally, the performance varies depending upon the type of applications being run.

In late 2022, large language models (LLMs) erupted into the public spotlight. Pundits were quick to claim LLMs are the next step in the path to artificial general intelligence (AGI) and even the Singularity. LLMs are artificial neural networks (ANN) created by a complex process. First, the core ANN is trained on billions of words of text from the Internet to respond to a prompt with a list of most probable next words after the prompt. Second, the core ANN is then "fine-tuned" by a complex process called "tweaking" to make the core ANN outputs more satisfactory to humans.

Pietro Schirano adds, "To me, it seemed like magic." "I was slightly frightened since it was so wonderful." He is discussing the most recent iteration of the ChatGPT artificial intelligence (AI) technology. You type anything, and the system generates a response for you. The responses are eerily human, amiable, and intelligent.

When creating a classification model, many algorithms offer the function predict_proba() to give us the probability of that observation being classified under each class. Thus, it is common to see an…

This article was published as a part of the Data Science Blogathon. This article will implement time series forecasting using the Prophet library in python. The prophet is a package that facilitates the simple implementation of time series analysis. Implementing time series forecasting can be complicated depending on the model we use. Many approaches are available for time series forecasting, for example, ARIMA ( Auto-Regressive Integrated Moving Average), Auto-Regressive Model, Exponential Smoothing, and deep learning-based models like LSTM ( long short term memory).

Imagine that while you are enjoying your morning bowl of Cheerios, a spider drops from the ceiling and plops into the milk. Years later, you still can't get near a bowl of cereal without feeling overcome with disgust. Researchers have now directly observed what happens inside a brain learning that kind of emotionally charged response. In a new study published in January in the Proceedings of the National Academy of Sciences, a team at the University of Southern California was able to visualize memories forming in the brains of laboratory fish, imaging them under the microscope as they bloomed in beautiful fluorescent greens. From earlier work, they had expected the brain to encode the memory by slightly tweaking its neural architecture. Instead, the researchers were surprised to find a major overhaul in the connections.

Imperial researchers have found that variability between brain cells might speed up learning and improve the performance of the brain and future AI. The new study found that by tweaking the electrical properties of individual cells in simulations of brain networks, the networks learned faster than simulations with identical cells. They also found that the networks needed fewer of the tweaked cells to get the same results, and that the method is less energy intensive than models with identical cells. The authors say that their findings could teach us about why our brains are so good at learning, and might also help us to build better artificially intelligent systems, such as digital assistants that can recognise voices and faces, or self-driving car technology. First author Nicolas Perez, a PhD student at Imperial College London's Department of Electrical and Electronic Engineering, said: "The brain needs to be energy efficient while still being able to excel at solving complex tasks. Our work suggests that having a diversity of neurons in both brains and AI fulfils both these requirements and could boost learning."

I suggest that you check out the chat stream. The comments were very enlightening. My takeaway is that the concept of AutoML is good, but scope of the AutoML vision is missing 80% of the AI/ML model development and operationalization – providing high quality and complete data that feeds the AI/ML models. Figure 5 from "Big Data to Good Data: Andrew Ng Urges ML Community to Be More Data..." nicely summarizes the broader AutoML challenge with respect to data management.