AI

Large Language Model, or LLM technology, converges Artificial Intelligence (AI) and Natural Language Processing (NLP) to create wonders of language understanding and generation. In the era of AI-driven advancements, LLM technology has emerged as the hero of language comprehension and communication. You might have heard whispers of "LLM technology ai" or "LLM tech" echoing through tech forums and AI discussions. But what exactly is it, and why should you care?   Well, you are about to find out the workings of these…

AI holds significant promise for the IoT, but running these models on IoT semiconductors is challenging. These devices’ limited hardware makes running intelligent software locally difficult. Recent breakthroughs in neuromorphic computing (NC) could change that. Even outside the IoT, AI faces a scalability problem. Running larger, more complex algorithms with conventional computing consumes a lot of energy. The strain on power management semiconductors aside, this energy usage leads to sustainability and cost…

The practical application of generative artificial intelligence has gone from an abstract, future concept to a concrete reality in a matter of mere months. Businesses and organizations large and small are scrambling to figure out if and how AI can help their people be more productive and efficient. For organizations using Microsoft software, the application of AI in a business environment is being led by the Microsoft Copilot platform. Businesses at the enterprise level are also looking for ways AI can leverage…

Data science and machine learning professionals now how to seek answers in data: that’s probably the central pillar of their work. Things get murkier when we look at some of the thornier issues surrounding our data, from its built-in biases to the ways it can be leveraged for questionable ends.As we enter the final stretch of the year, we invite our readers to explore some of these big-picture issues that have sparked crucial discussions in recent years, and are all but guaranteed to continue to shape the field in 2024…

Is a generative AI preamble necessary for a newsletter focused on Knowledge Graphs, Graph Databases, Graph Analytics, and Graph AI? Normally, it should not be. However, the influence of generative AI on the items included in this issue was overwhelming. There is a simple explanation for that. It's been a year since Generative AI burst into the mainstream with the release of ChatGPT. Notwithstanding a rather spotty record both in terms of technical performance and accuracy as well as in terms of business reliability,…

In 2023, the rise of Large Language Models (LLMs) like Alpaca, Falcon, Llama 2, and GPT-4 indicates a trend toward AI democratization. This allows even small companies to afford customized models, promoting widespread adoption. However, challenges persist, such as restricted licensing for open-source models and the costs of fine-tuning and maintenance, which are manageable mainly for large enterprises or research institutes. The key to maximizing LLM potential is in fine-tuning and customizing pre-trained models for…

If you’ve ever written an SQL query that takes ages to run, this is the article for youContinue reading on Towards Data Science » If you’ve ever written an SQL query that takes ages to run, this is the article for youContinue reading on Towards Data Science » FOLLOW US ON GOOGLE NEWS Read original article here Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media. In each content, the hyperlink to the primary source is specified. All trademarks belong to…

Image created by author using Dall-EBenchmarking OpenAI function calling and explanationsThanks to Roger Yang for his contributions to this pieceObservability in third-party large language models (LLMs) is largely approached with benchmarking and evaluations since models like Anthropic’s Claude, OpenAI’s GPT models, and Google’s PaLM 2 are proprietary. In this blog post, we benchmark OpenAI’s GPT models with function calling and explanations against various performance metrics. We are specifically interested in how the…

Time Series Classification for Fatigue Detection in Runners — A TutorialA step-by-step walkthrough of inter-participant and intra-participant classification performed on wearable sensor data of runnersImage by authorRunning data collected using wearable sensors can provide insights about a runner’s performance and overall technique. The data that comes from these sensors are usually time series by nature. This tutorial runs through a fatigue detection task where time series classification methods are used on a running…

The main optimization algorithms used for training neural networks, explained and implemented from scratch in PythonPhoto by Jack Anstey / UnsplashIn my previous article about gradient descent, I explained the basic concepts behind it and summarized the main challenges of this kind of optimization.However, I only covered Stochastic Gradient Descent (SGD) and the “batch” and “mini-batch” implementation of gradient descent.Other algorithms offer advantages in terms of convergence speed, robustness to “landscape” features…