Tag Archives: Machine learning

12Mar/24

Google Cloud Announces New Generative AI Advancements for Healthcare and Life Science Organizations

Today at HIMSS24, Google Cloud announced several new solutions to help healthcare and life sciences organizations enable interoperability, build a better data foundation for their businesses, and deploy generative AI (gen AI) tools to improve patient outcomes. Continue reading

10Oct/23

THALES PARTNERS WITH GOOGLE CLOUD TO BUILD NEW, GENERATIVE AI-POWERED SECURITY CAPABILITIES

  • New AI-powered features built into the CipherTrust Data Security Platform utilizing Google Cloud’s foundation models and generative AI support in Vertex AI1 to automate the Discovery and Protection of critical data
  • Collaboration will enable the automation of fundamental tasks for customers and help ensure data security Continue reading
16Jun/23

How do you start a sentiment analysis project?

June 16, 2023 /Developers/ — Sentiment analysis is the process of determining the emotional tone of a piece of text. It is a subfield of natural language processing (NLP) that deals with identifying and extracting subjective information from text. Sentiment analysis is often used to understand customer sentiment, brand reputation, and social media trends.

There are two main types of sentiment analysis: Continue reading

10Jun/23

Are AI-Generated Images Biased?

David Ngure  Cybersecurity Researcher
Updated on 16th March 2023 – Artificial intelligence image generators use machine learning and mathematical algorithms to create images from a description written in natural language. With OpenAI making DALL-E available to the public, and Microsoft adding AI-image generators to products like Bing and Microsoft Edge, the technology is becoming more accessible to the general public.

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01May/23

Quickly extract key insights from complex data

May 1, 2023 /Technology/ — Artificial intelligence (AI) can be a valuable tool for data exploration. It can help to increase speed, improve accuracy, and reduce costs. However, it is important to note that AI is not a replacement for human analysts. AI can help to automate tasks, but it is still important for human analysts to be involved in the data exploration process to ensure that the results are accurate and meaningful. AI is used in data exploration in a variety of ways. Some of the most common uses include:
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21Apr/23

Google merges Brain, DeepMind into new unit that will focus on artificial intelligence

April 21, 2023 /Technology/ – On April 20, 2023, Google announced that it would be merging its two main artificial intelligence research units, Google Brain and DeepMind, into a new unit called Google AI. The new unit will be led by Jeff Dean, who is currently the lead of Google Brain.
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20Apr/23

What Large Language Models (or LLMs) are, how they are developed, and how they work.

April 19, 2023 /Technology/ — A large language model (LLM) is a type of artificial intelligence (AI) that is trained on a massive amount of text data. This data can be anything from books and articles to social media posts and code. LLMs are able to learn the statistical relationships between words and phrases, which allows them to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. LLMs can be used for a variety of tasks, including: Continue reading

03Apr/23

AI will soon become impossible for humans to comprehend – the story of neural networks tells us why

David Beer, University of York

In 1956, during a year-long trip to London and in his early 20s, the mathematician and theoretical biologist Jack D. Cowan visited Wilfred Taylor and his strange new “learning machine”. On his arrival he was baffled by the “huge bank of apparatus” that confronted him. Cowan could only stand by and watch “the machine doing its thing”. The thing it appeared to be doing was performing an “associative memory scheme” – it seemed to be able to learn how to find connections and retrieve data.

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19Jan/23

Digital welfare dystopia

By Samuel Woodhams | Digital rights researcher and journalist

Algorithms to determine welfare payments and detect fraud are becoming standard practice around the world. From Manchester to Melbourne, peoples’ lives are being shaped by secretive tools that determine who is eligible for what, and how much debt is owed.

Although the technology has been around for some time, the outbreak of COVID-19 renewed enthusiasm for the digital welfare state and, for thousands of cash-strapped public bodies, the promise of increased efficiency and lower costs has proven irresistible.
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