Using large language models, this paper presents techniques to improve extremely low-resourced indigenous language translations. Our approaches are grounded in the use of (1) the presence of a datastore consisting of a limited number of parallel translation examples, (2) the inherent capabilities of LLMs like GPT-3.5, and (3) a word-level translation dictionary. We harness the potential of LLMs and in-context learning techniques in such a setting for using LLMs as universal translators for extremely low-resourced languages. Our methodology hinges on utilizing LLMs as language compilers for selected language pairs, hypothesizing that they could internalize syntactic structures to facilitate accurate translation. We introduce three techniques: KNNPrompting with Retrieved Prompting Context, Chain-of-Thought Prompting and Learningfrom-Mistakes Prompting, with the last method addressing past errors. The evaluation results suggest that, even with limited corpora, LLMs can effectively translate extremely low-resource languages when paired with proper prompting.

What Did Ray Kurzweil Predict? Communicating with someone across the world by smartphones, lighting the house with electric light bulbs, and even traveling outside the earth with spaceships are all things, which human beings could have never ever imagined before and which have come into the reality in the past hundreds of years. Human beings have conquered countless difficulties and crossed technology thresholds, and that leads to a question: when the next breakthrough will happen and what it will be like. Historian Yuval Harari points out that human immortality is possible in the future, and humans change it from the imagination to a technical problem. This idea is approached by Michio Kaku, a physicist, in two ways: one is to create brains that have consciousness and processing functions exactly like real human beings, and the other one is to model a real brain in a biological way.

I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. I was recently having a debate about strong vs. weak AI with one of my favorite new thinkers in this market and it reminded me of something that I wrote over a year ago. So I decided to dust it off and restructure those thoughts in a new article.

I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. I was recently having a debate about strong vs. weak AI with one of my favorite new thinkers in this market and it reminded me of something that I wrote over a year ago. So I decided to dust it off and restructure those thoughts in a new article.

At the same time, AI could disrupt the human workforce greatly, noted Kaku, who warned that a smarter future comes with tradeoffs. AI, he said, does "pose an existential threat." Much of that threat, Kaku said, involves the long-term impact on the human workforce. According to some estimates, about 30% of the activities in 60% of all occupations could be automated. The displacement is already taking place, noted Jeff Hesse, a PwC principal.

Future robots could kill humans in a'murderous' rage if humans don't take steps to stop them, a prominent futurist has claimed. Dr Michio Kaku believes we can put a stop to the killer robots by simply embedding chips in their brains to control their thoughts. Robots could be smart enough to'become dangerous' before the end of this century, Kaku warned during a question and answer session on Reddit. 'I think we should chip their brain to shut them off if they have murderous thoughts,' he explained. The humanoid robot'Alter' (pictured) has sensors that mimic the neural network of the human brain.

Artificial intelligence is not only poised to disrupt industries and workplaces, but also the way that we as humans interact. As our AI journey continues, we will increasingly see the advancements that it provides play out in our daily lives. Technologists like myself are not the only ones thinking about our AI future and the implications for society; a variety of authors have explored the topic. For those fascinated with AI or looking to enter the field, reading about the evolution of technology and its potential is a good place to start. As a scientist, avid reader and follower of AI technology, here are a few of my top book recommendations around this fascinating topic.

As Obi Wan Kenobi and Darth Vader pulled out their light sabers for a deadly battle 30 years ago today, "Stars Wars" movie-goers asked themselves one thing: Where can I get one of those? The iconic movie series prompted young children to tote R2D2 lunch boxes and teenage boys to fall in love with side hair buns and gold bikinis. But in 1977, the groundbreaking fan favorite did more than just secure its place in Americana -- it also captured the hearts and minds of scientists of the '70s and a few younger, budding lab rats waiting in the wings. "I think the influence is huge," Michio Kaku, one of the world's most prominent physicists and the co-founder of string field theory, told ABCNEWS.com. "Many people don't realize that science fiction has been an inspiration for the world's leading scientists."

In the daily edtech trenches, the forest is easily lost for the trees. Technological minutiae in the classroom carry such immense consequences that it can be hard to think beyond tomorrow's software update, nevermind next year's LMS rollout. In his opening keynote at the ISTE 2016 conference, noted physicist Dr. Michio Kaku showed educators the forest that he and others believe will encircle the classroom of the future. And oh, what a forest it might be. According to Dr. Kaku, talking wallpaper, data-reading toilets and other technologies that seem like miracles today are a mere fifty years away.

The moment that humanity is forced to take the threat of artificial intelligence seriously might be fast approaching, according to futurist and theoretical physicist Michio Kaku. In an interview with CNBC's "The Future of Us," Kaku drew concern from the earlier-than-expected victory Google's deep learning machine notched this past March, in which it was able to beat a human master of the ancient board game Go. Unlike chess, which features far fewer possible moves, Go allows for more moves than there are atoms in the universe, and thus cannot be mastered by the brute force of computer simulation. "This machine had to have something different, because you can't calculate every known atom in the universe -- it has learning capabilities," Kaku said. "That's what's novel about this machine, it learns a little bit, but still it has no self awareness ... so we have a long way to go."