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Very Interesting!!

Today I shall refrain from sharing content about AI (although the incomparable Donald Clark has often been an intriguing source when it comes to AI-powered learning and education). Instead, I'll share Donald Clark's remarkable review of Peter Turchin's landmark book, End Times. The book is essential reading, the review shakes you up.

Demis Hassibis (CEO at Google DeepMind) shares a nuanced view of AI hype (and grifters) versus the genuine promises of AI research. To me, it is somewhat reassuring to have an informed AI executive compare the AI race to the crypto bubble - while remaining very positive about the technology. Mr Hassibis fears that the hype side may be distracting from useful research. I prefer this to the Sam Altman - Satya Nadella pledge to ramp up computing power in the hope that quantity will have a quality of its own.

Some MT engines are highly responsive and take feedback loops into account, others are more static. Just comparing untrained versions of MT engines and giving scores on the same content would be a biased evaluation.

Rigorous, well-documented investigation into comparative performance of 9 large language models (LLM) versus 8 specialised machine translation (MT) models. Methodology, analysis, results and even price comparison are given. Caveat: this is just for two Indo-European language pairs, both with English as a source language: English to Spanish and English to German.

I like to put things into perspective, certainly in a period such as this one, when every other week a new breakthrough in AI is announced, a new game changer, a new revolution. Mr Eugene Linden was the author of a piece on Artificial Intelligence in Time about 36 years ago. AI made the cover of Time in 1988.

More and more nitrogen keeps pouring into waterways, unleashing algal blooms and creating dead zones. To prevent the problem from worsening, scientists warn, the world must drastically cut back on synthetic fertilizers and double the efficiency of the nitrogen used on farms.

Beyond all the OpenAIs, Anthropics, Googles, Metas or Amazons of the Western world, which produce amazingly plausible results in English (never mind the confabulation and incoherence, they're probabilistic models, after all), there are interesting developments in the open source universe of Generative Pre-training Transformers (yes, GPTs ;-) that are better at generating content in other languages.

Printed sheets could fit to car rooves or awnings in what researchers say is ‘game changer’ for renewable energy industry

Do we want to take a step back and look at the revolutions that history labels as such? The invention of the wheel, of the press, the industrial revolution, internet and the world wide web?

And now, let's reflect on how many times per day we catch a glimpse of the word 'revolution' in connection with AI, quantum computing, large language models, the latest large language models, future large language models. Too fast, too often. The word 'revolution' has lost its power.

Can we look at the progress of AI through the Pareto Principle lens, and see if the 80/20 rule applies? Is it possible that it took just 20% of the R&D effort to achieve what we perceive today as 80% of the result? In terms of accuracy, reliability, relevance and usability, is it fair to say that current models of generative AI have reached that 80% threshold? This is debatable, but if we accept that assumption, it may imply that another 80% of the R&D effort may be needed to come close to 100%. And still, that would only be 100% of what a probabilistic model could achieve, 100% of what you can obtain from next token prediction. Believe me, there is a lot of hard work needed to make even barely noticeable incremental progress. Ad we'll need to read about many more 'game-changers', 'breakthroughs', 'paradigm shifts', 'tectonic shifts' and other revolutions before we can be confident that LLMs will make history.

I have been reading opinion pieces in which the key message was that Retrieval Augmented Generation (RAG) is a dead end. I was not convinced. The combination of RAG, knowledge graphs and large language models (LLMs) is powerful and promising. And it is work. I think that is what scares many of my peers and colleagues: it is hard to convince management that leveraging AI means investing a lot of time and resources in building a specialist LLM that does a specific task well.

So I am more supportive of approaches that take complexity into account and address the legwork that is required to fine-tune the models and embed principled design in them.

We all know that one use case is not another, that one MT engine may work well for some language pairs and some domains but may perform poorly in other pairs or domains. Large language models (LLM) occasionally outperform MT, and there are also cases where MT is needed. In a nutshell: it is a complex landscape, and testing is required in every situation and for every use case. Technology, when used with discernment, helps translate more and faster. But don't take anything for granted, do the research first, test, compare, evaluate. That has a cost. Managing client expectations includes explaining what this testing entails. No one-size-fits-all solution is credible.

The IEA points to the collapse of coal in the most industrialised countries. Coal demand is estimated to have dropped back to 1900 levels, with coal generation falling to a “historic low” of 17% (3/4)

This is a brilliant business model:

How this South African entrepreneur is slashing grocery costs and waste in the country’s townships

The K–12 education challenges we face today and their implications for the long-term health of the economy are just as important as they were 40 years ago ... Yet corporate leaders are largely missing in action, and the silence is deafening

In comes "content recycling", not just an upgrade of the TM paradigm: here full chunks (or at least large chunks) of similar content are stored. This approach requires no (or much less) review.

Yes...they are quite good. Good Luck!

How to verify the quality of these translations?

How to verify the quality of these translations?

Absolutely! More importantly in the quality of translation

How many languages is it available in?

The limitations of LLMs that solely rely on the transformer model are known: these are purely probabilistic systems, which predict the most likely next token. No reasoning ability whatsoever. When combining the LLM with the symbolic engine, it seems that the process is much closer to the way humans would reason to solve a problem.

The IT Certification Council (ITCC) announces the publication of its new Incusive Item Writing Resource, which can be accessed and downloaded free of charge here. Let me quote Liberty Munson here: "The goal of this document is to help item writers take diversity and inclusion into consideration as they create exam and assessment content. Inclusive item writing helps ensure that the wording and structure of the questions is accessible to as many people as possible. By doing this, we are ensuring that the exam measures only the relevant skills, abilities, or traits that it is designed to measure. This, in turn, ensures that the assessment is fair, valid, and meaningful, regardless of their individual differences." This is extremely useful and relevant work. These guidelines are clear and actionable. Raising awareness of DEI issues at that stage will allow the DEI reviews to be more focused.

Who calls this a success story, and who calls it a dangerous trend? Food for thought! I see it as one of the use cases where AI can be deployed successfully, but I am not confident that it can be replicated easily. Introducing AI in the workflows requires a lot of effort, expertise and investment. A manager's wishful thinking is not enough to take that step.

Who coined the term "artificial intelligence"? When was that? The answer is in the latest post on Donald Clark's infamous blog

Does this mean that, when the linguists meet after work, they should stop spending half the night opposing the evidence that backs up the Steppe hypothesis to the Anatolian hypothesis? Does this mean both could be right? All these debates to find out that data reconciles both hypotheses?

I am not an AI skeptic, I participate in projects that make it possible to develop and use large language models in languages other than English. However, I am increasingly drawn by specialist large language models, trained to do a specific task well and access a circumscribed dataset, rather than resource-intensive generalist large language models, which sometimes generate spectacular if unreliable output.

Hey, That's some real work. Congratulations on the solid traffic! Your story in creating Yesicon and SearchEmoji showcases the power of AI in overcoming language barriers and enhancing user experiences. From my experience, I have learned that emojis are searched very differently depending on different language and culture. The search result also varies due to that. I think to retain that traffic, you must try to make sure that emojis searched in whatever language help user find the correct one and replicate the same meaning. Basically, a quality control check would be nice to solidify your position and user base.

This post dates back to February - the Stanford Institute for Human-Centered Artificial Intelligence (HAI) made their DetectGPT application as a demo and published a paper. They made the ambitious claim that their tool correctly identified human versus authorship in 95% of the cases across five popular open-source large language models (LLMs).

Since then, papers have been published to show the bias against non-native speakers, whose productions were more likely to be classified as AI-generated. Prompt engineers have also found creative ways to "humanise" AI-generated output. Where do we stand today? I can vouch for a significantly lower than 95% reliability threshold - and, by the way, this applies mainly to English - a different detection tool needs to be developed for other languages, or at the very least for other language families.

Hi, You are right. Chat-GPT or other AI enabled tools out there are making translation pretty easy and accuracy has greatly improved. However, for sensitive matters like translation of tests, assessments, surveys etc. there requires a serious quality check before and after translation which an AI tool cannot accurately do for the moment. Do you know such tools that can do quality checks? I just know few enterprises that are working on it and this is one of them: https://www.capstan.be/linguistic-quality-assurance/