Continue reading “Getting to grips with the real costs of AI implementation”
Tag: Data and Artificial Intelligence
Three ways to extend your runway and free up your best R&D talent
Tech firms are having to battle economic turbulence, the high cost of hiring top talent, funding limitations and a range of other headwinds.
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Ciklum and RoBUTCHER partner to revolutionize the food industry with innovative AI solutions
This innovative partnership aims to introduce ethical and ecological values into food processing by offering automation accessibility, scalability, and flexibility to meet changing production volumes and market demands. By creating AI-assisted Factory Cells, this initiative envisions a revolutionary change of the entire food system towards a more ethical and sustainable approach to food processing.
Is your business fully exploiting data? Here’s what might be holding you back
There are many businesses that are not yet fully exploiting the capabilities in data and analytics that are available to them: as a result, they are being left behind.
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10 Ways To Monetise Data In Retail And Supply Chain
With vast amounts of information accessible at a single click, organisations can draw powerful insights and create transformative experiences for individuals, customers, and businesses alike.
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An Algorithm to Transcribe Ancient Kuzushiji into Сontemporary Japanese Characters
The Situation
What was the weather like 500 years ago? What happened when Mt. Fuji erupted? How can one fold 100 cranes using only one piece of paper? The answers to these questions are in those books.
Pros and Cons of Big Data
Key takeaways:
- Using Big Data in real time requires more resources.
- AI can help Big Data analytics teams quickly move past irrelevant trends and avoid obvious/unhelpful results.
- Gen AI can be used to enrich the supplementary information used in presenting and describing results to ensure that they trigger conversions.
Or maybe it’s just good old-fashioned bars and coins made of gold. After all, a currency’s worth is defined by what you make of it — and the more there is to go around, the more that can be made.
Garbage In, Garbage Out: How to Prepare Your Data Set for Machine Learning
These commonly used and often linked terms all share the common thread of using data to build machines that are smarter, more efficient and more capable than ever before.
But in order for computers to take full advantage of AI and capabilities, there’s another acronym that computer scientists must be familiar with to build successful machines: GIGO, short for “garbage in, garbage out.”
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Limitations of Big Data Analytics
To uncover these insights, big data analysts, often working for consulting agencies, use data mining, text mining, modelling, predictive analytics, and optimisation. As of late, big data analytics has been touted as a panacea to cure all the woes of business. Big data is seen by many to be the key that unlocks the door to growth and success.
Big Data and the Challenge of Unstructured Data
While that’s a legitimate and real concern for both consumers and those who work in data security, industry insiders are facing another challenge: how to handle unstructured data?
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