Data Archives - Liwaiwai

BigQuery Under The Hood: Behind The Serverless Storage And Query Optimizations That Supercharge Performance

Customers love the way BigQuery makes it easy for them to do hard things — from BigQuery Machine Learning (BQML) SQL turning data analysts into data scientists, to rich text analytics using the SEARCH function that unlocks ad-hoc text searches on unstructured data. A key reason for BigQuery’s ease of use is its underlying serverless architecture, which supercharges your analytical queries while making them run faster over time, all without changing a single line of SQL. In this blog, we lift the curtain and share the magic behind BigQuery’s serverless architecture, such as storage and query optimizations as well as…

ChatGPT: How To Prevent It Becoming A Nightmare For Professional Writers

Nearly half of white-collar professionals have tried using ChatGPT to help with their work, according to a recent survey of more than 10,000 people at blue chips such as Google, JP Morgan and McKinsey. That’s staggering, considering the AI chatbot was only released to the public in November. It’s potentially very exciting for the future of work, but it also brings serious risks. ChatGPT and other imminent rivals are part of a long history of technologies geared to reducing the labour of writing. These range from the printing press to the telegram, the typewriter, word processors and personal computing. AI…

How Data-Driven Coaching Helps Employees Reach Their Potential

Data is everywhere. In the business world alone, we use it to track search engine traffic, monitor website activity, land sales, improve customer service — you name it. One area where data doesn’t get enough attention (or application) is employee development. When properly applied, data can guide employees and coaches alike as they attempt to upskill and improve performance. How Data Improves HR Before we break down data in relation to coaching, let’s talk about its influence on human resources as a whole. It’s easy to look at HR as a human-to-human activity. Recruiters use “gut instinct.” Coaching requires that savvy veteran…

8 Ways To Cut Costs And Drive Profits Using Data And AI

We are increasingly seeing one question arise in virtually every customer conversation: How can the organization save costs and drive new revenue streams? Everyone would love a crystal ball, but what you may not realize is that you already have one. It’s in your data. By leveraging Data Cloud and AI solutions, you can put your data to work to achieve your financial objectives. Combining your data and AI reveals opportunities for your business to reduce expenses and increase profitability, which is especially valuable in an uncertain economy. Google Cloud customers globally are succeeding in this effort, across industries and…

Machine Learning

Efficient Technique Improves Machine-Learning Models’ Reliability

The method enables a model to determine its confidence in a prediction, while using no additional data and far fewer computing resources than other methods. Powerful machine-learning models are being used to help people tackle tough problems such as identifying disease in medical images or detecting road obstacles for autonomous vehicles. But machine-learning models can make mistakes, so in high-stakes settings it’s critical that humans know when to trust a model’s predictions. Uncertainty quantification is one tool that improves a model’s reliability; the model produces a score along with the prediction that expresses a confidence level that the prediction is…

Data

Reading And Storing Data For Custom Model Training On Vertex AI

Before you can train ML models in the cloud, you need to get your data to the cloud. But when it comes to storing data on Google Cloud there are a lot of different options. Not to mention the different ways you can read in data when designing input pipelines for custom models. Should you use the Cloud Storage API? Copy data directly to the machine where your training job is running? Use the data I/O library of your preferred ML framework? To make things a little easier for you, we’ve outlined some recommendations for reading data in your custom…

What Are Decision Trees In Machine Learning?

A decision Tree is a supervised machine learning algorithm that works on the basis of recursively answering some questions (if-else conditions). The algorithm is used both for regression and classification. However mostly for classification problems. The questions in boxes are called the internal nodes where the answers to the questions split it into branches. The nodes which do not split anymore are called leaf, which represent the decision/output of the model. This tree of course is much bigger and more complex for bigger datasets when compared to our simple example above. The tree grows and forms according to the data…

Machine Learning Helps Scientists Peer (a second) Into The Future

The past may be a fixed and immutable point, but with the help of machine learning, the future can at times be more easily divined. Using a new type of machine learning method called next generation reservoir computing, researchers at The Ohio State University have recently found a new way to predict the behavior of spatiotemporal chaotic systems — such as changes in Earth’s weather — that are particularly complex for scientists to forecast. The study, published today in the journal Chaos: An Interdisciplinary Journal of Nonlinear Science, utilizes a new and highly efficient algorithm that, when combined with next generation…

Uniting Human Brains And Computers: A New Type Of AI

Why are we on the verge of creating a technology that will combine the computer with the human nervous system into a single complex? How will a person change after connecting with an electronic machine? Can a computer system handle the flood of data from billions of living neurons? I will try to answer these questions in this article. In the previous article “Individual artificial intelligence: A new technology that will change our world”, we talked about the fact that a new type of artificial intelligence will become a bioelectronic hybrid in which a living human brain and a computer will work…

Artificial Intelligence

Researchers Build AI That Builds AI

By using hypernetworks, researchers can now preemptively fine-tune artificial neural networks, saving some of the time and expense of training. Artificial intelligence is largely a numbers game. When deep neural networks, a form of AI that learns to discern patterns in data, began surpassing traditional algorithms 10 years ago, it was because we finally had enough data and processing power to make full use of them. Today’s neural networks are even hungrier for data and power. Training them requires carefully tuning the values of millions or even billions of parameters that characterize these networks, representing the strengths of the connections…

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