Large language models work well for technical problem solving in English but perform poorly when the same questions are asked in Bangla. A simple solution would be to translate Bangla questions into English first and then use these models. However, existing Bangla-English translation systems struggle with technical terms. They often mistranslate specialized vocabulary, which changes the meaning of the problem and leads to wrong answers. We present BanglaSTEM, a dataset of 5,000 carefully selected Bangla-English sentence pairs from STEM fields including computer science, mathematics, physics, chemistry, and biology. We generated over 12,000 translations using language models and then used human evaluators to select the highest quality pairs that preserve technical terminology correctly. We train a T5-based translation model on BanglaSTEM and test it on two tasks: generating code and solving math problems. Our results show significant improvements in translation accuracy for technical content, making it easier for Bangla speakers to use English-focused language models effectively. Both the BanglaSTEM dataset and the trained translation model are publicly released at https://huggingface.co/reyazul/BanglaSTEM-T5.

Schema induction builds a graph representation explaining how events unfold in a scenario. Existing approaches have been based on information retrieval (IR) and information extraction(IE), often with limited human curation. We demonstrate a human-in-the-loop schema induction system powered by GPT-3. We first describe the different modules of our system, including prompting to generate schematic elements, manual edit of those elements, and conversion of those into a schema graph. By qualitatively comparing our system to previous ones, we show that our system not only transfers to new domains more easily than previous approaches, but also reduces efforts of human curation thanks to our interactive interface.

How would an AI writing program start an article on the future of AI writing? Well, there's one easy way to find out: I used the best known of these tools, OpenAI's GPT-3, to do the job for me. Using GPT-3 is disarmingly simple. You have a text box to type into and a menu on the side to adjust parameters, like the "temperature" of the response (which essentially equates to randomness). You type, hit enter, and GPT-3 completes what you've written, be it poetry, fiction, or code. I tried inputting a simple headline and a few sentences about the topic, and GPT-3 began to fill in the details. It told me that AI uses "a series of autocomplete-like programs to learn language" and that these programs analyze "the statistical properties of the language" to "make educated guesses based on the words you've typed previously."

When OpenAI demonstrated a powerful artificial intelligence algorithm capable of generating coherent text last June, its creators warned that the tool could potentially be wielded as a weapon of online misinformation. Now a team of disinformation experts has demonstrated how effectively that algorithm, called GPT-3, could be used to mislead and misinform. The results suggest that although AI may not be a match for the best Russian meme-making operative, it could amplify some forms of deception that would be especially difficult to spot. Over six months, a group at Georgetown University's Center for Security and Emerging Technology used GPT-3 to generate misinformation, including stories around a false narrative, news articles altered to push a bogus perspective, and tweets riffing on particular points of disinformation. "I don't think it's a coincidence that climate change is the new global warming," read a sample tweet composed by GPT-3 that aimed to stoke skepticism about climate change.

Amid advances in precision medicine, healthcare is facing the twin challenges of having to curate and tailor the use of patient data to drive genomics-powered breakthroughs. That was the takeaway from the AI & data sciences track of last week's Precision Medicine World Conference in Santa Clara, California. "There aren't a lot of physicians saying, 'Bring me more AI,' " said John Mattison, MD, emeritus CMIO and assistant medical director of Kaiser Permanente. "Every physician is saying bring me a safer and more efficient way to deliver care." Mattison recalled his prolonged conversations with the original developers of IBM's Watson AI technology.

Last month, HBO launched a handy website for figuring out which of its original shows to watch. But instead of using computer algorithms to sort through its vast catalog, the company hired people to make their case for each series by making video testimonials. The site, called "Recommended by Humans," is more marketing stunt than product strategy--HBO has no plans to offer an app-based version for mobile or TV devices, and it declined to say how humans might factor into its forthcoming HBO Max service. But its very existence was a statement about our current moment in tech. We've realized that recommendation algorithms aren't as infallible as tech companies once made them out to be, and that handing the curation job to people still has value.

Before my eyes were even open, I spoke to my smart assistant this morning: "Alexa," I asked hopefully, "what time is it?" She kindly replied, "The time is seven twenty-seven a.m." I did not need her to tell me the morning was going to be a struggle. An hour later, dropping the kids off at school, I realized their violins were left in haste by the front door. As I made the U-turn in the school parking lot, I thought to myself, "Inconceivable! I have all this technology surrounding me! How long do I have to wait for this to get easier?"

Greg Linden may not be a household name, but he changed the way we interact with culture and transformed retail forever. An engineer at Amazon in the late 1990s, Linden worked on a curious problem: how to recommend books without human intervention. Until then Amazon relied on editors who wrote hundreds of reviews every year. It was a costly and time-consuming process. Automating recommendations proved trickier than anyone expected.