Remove Examples Remove Manufacturing Remove Meeting Remove Training
article thumbnail

Getting ready for artificial general intelligence with examples

IBM Big Data Hub

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. However, these systems lack genuine understanding and can’t adapt to situations outside their training. Regardless, these are examples of narrow AI.

article thumbnail

Business process reengineering (BPR) examples

IBM Big Data Hub

BPR examples are not one-time projects, but rather examples of a continuous journey of innovation and change focused on optimizing end-to-end processes and eliminating redundancies. This blog outlines some BPR examples that benefit from a BPM methodology.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

5 ways to detect a phishing email – with examples

IT Governance

In this blog, we use real-life examples to demonstrate five clues to help you spot phishing scams. For example, emails from Google will read ‘@google.com’. Take this example of a scam mimicking PayPal: Image: WeLiveSecurity. Take this example of a scam imitating Windows: Image: KnowBe4. Not even Google.

Phishing 111
article thumbnail

Renewable energy trends and developments powering a cleaner future

IBM Big Data Hub

For example, some policymakers incentivize renewable power generation by individuals and businesses through net-metering programs that allow utility customers to send excess energy generated back to their utilities for credits. The country was home to 95% of new solar technology manufacturing facilities in 2022.)

article thumbnail

Generative AI use cases for the enterprise

IBM Big Data Hub

For example, organizations can use generative AI to: Quickly turn mountains of unstructured text into specific and usable document summaries, paving the way for more informed decision-making. Imagine training a generative AI model on a dataset of only romance novels.

article thumbnail

Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

article thumbnail

AI governance: What it is, why you need it, and why it’s essential for your AI initiatives

Collibra

For starters, just imagine the repeated cost of training an LLM on a data set that contains poor quality, inconsistent, inaccurate or incomplete data. The truth is data is the backbone of AI, and if the data is bad, the AI models trained on it will produce human-sounding language that looks good but is fundamentally flawed.