Headline, January 06 2024/ ''' ! SCIENCE TECHNOLOGY SCHEMES ! '''

''' ! SCIENCE TECHNOLOGY

 SCHEMES ! '''

GLOBAL FOUNDERS & ENGINEERS : Rabo - Haleema - Hussain - Salar and Dee. I say to you all :  '' That a master accomplishment for !WOW!, in the time ahead, would be to have 'strategic research alliances with all the centers' ''.

INTEREST IN ARTIFICIAL INTELLIGENCE [AI] REACHED fever pitch in 2023. In the six months after OPENAI'S launch in November 2022 of ChatGPT, the internet's most famed and effective chatbot, the topic '' artificial intelligence '' nearly quadrupled in popularity on Google's search engine.

By August 2023, a third of respondents to the latest McKinsey Global Survey said their organisations were using generative AI in at least one capacity.

HOW WILL TECHNOLOGY DEVELOP IN 2024? There are three main-dimensions on which researchers are improving AI models : size, data and applications.

Start with size : For the past few years, the accepted dogma of AI research has been that bigger means better.

Although computers have got smaller even as they have become more powerful, that is not true of large Language Models [LLMS], the size of which is measured measured in billions or trillions of ''parameters'' .

According to SemiAnalysis, a research firm, GPT-4, the LLM which powers the deluxe version of ChatGPT, required more than 16,000 specialised GPU chips and took multiple weeks to train, at a cost of more $100 million.

According to Nvidia, a chipmaker, inference costs - getting the trained models to respond to users queries - now exceed training costs when deploying an LLM at any reasonable scale.

As AI models transition to being commercial commodities there is a growing focus on maintaining performance while making them smaller and faster. 

One way to do so is to train a smaller model using more training data. For instance '' Chinchilla '', an LLM developed in 2022 by Google DeepMind, outperforms OpenAI'S GPT-3, despite being a quarter of the size [ it was trained on four times the data].

Another approach is to reduce the numerical precision of the parameters that a model comprises. A team at the University of Washington has shown that it is possible to squeeze a model the size of Chinchilla onto one GPU chip, without a marked dip in performance.

Small models, crucially, are much less expensive to run later on. Some can even run on a laptop or smartphone.

Next, data : AI models are prediction machines that become more effective when they are trained on more data. But focus is also shifting from ''how much'' to '' how good '' .

That is especially relevant because it is getting harder to find more training data : an analysis in 2022 suggested that stocks of new, high-quality text might dry up in the next few years.

Using the outputs of the models to train future models may lead to less capable models to train future models may lead to less capable models - so the adoption of LLM makes the internet less valuable as a source of training data.

But quantity isn't everything. Figuring out the right mix of training data is still much more of an art than a science.

And models are increasingly being trained on combinations of data types, including natural language, computer code, images and even videos, which give them new capabilities.

WHAT NEW applications might emerge? There is some ''overhang'' when it comes to AI, meaning that it has advanced more quickly than people have been able to take advantage of it.

Showing what is possible has turned into fighting out what is practical. The most consequential advantages will not be in the quality of the models themselves, but in learning how to use them more effectively.

To sum for now : There is '' no reason to believe ........ that this is the ultimate neural architecture.

The Honour and Serving of the Latest Global Operational Research on Science, Technology, AI and the Future continues. The World Students Society thanks The Economist.

With most respectful dedication to The Global Founder Framers of The World Students Society - the exclusive and eternal ownership of every student in the world, and then Students, Professors and Teachers of the world.

See You all prepare for Great Global Elections on !WOW! - for every subject in the world : wssciw.blogspot.com and Twitter X !E-WOW! - The Ecosystem 2011 :

Good Night and God Bless

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