Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. I once witnessed a friend going through a severe midlife crisis. Basically overnight, this formerly serious and well-adjusted middle-aged man dumped his wife for a much younger girlfriend, got a face tattoo, and built a full-sized halfpipe in his house. Soon, we were barraged with music recommendations (all stuff he'd listened to in high school and college) and life updates laden with "hip" "slang" ("Despite the age gap, my situationship with Triniteigh is lowkey lit"). It was a transparent--and, from a certain perspective, even sympathetic--response to a universal anxiety: He'd seen that the good times were over, and that only decline lay ahead. But, like all nostalgists, he didn't realize that you can't ever truly go back; you can only go backward. The United States, under President Donald Trump, seems to be undergoing a similar midlife crisis, as this reactionary administration attempts to brute-force the country back to a golden age that many people are realizing either didn't exist in the first place or has been permanently lost to the mists of time and modernization.

We present ALLaM: A rabic Large Language M odel, a series of large language models to support the ecosystem of Arabic Language Technologies (AL T). ALLaM is carefully trained considering the values of language alignment and knowledge transfer at scale. Our autoregressive decoder-only architecture models demonstrate how second-language acquisition via vocabulary expansion and pretraining on a mixture of Arabic and English text can steer a model towards a new language (Arabic) without any catastrophic forgetting in the original language (English). Furthermore, we highlight the effectiveness of using parallel/translated data to aid the process of knowledge alignment between languages. Finally, we show that extensive alignment with human preferences can significantly enhance the performance of a language model compared to models of a larger scale with lower quality alignment. ALLaM achieves state-of-the-art performance in various Arabic benchmarks, including MMLU Arabic, ACV A, and Arabic Exams. Our aligned models improve both in Arabic and English from their base aligned models.

In the late 1990s, Recife, on Brazil's northeastern coast, was in decline. Its picturesque historic center, made up of 17th century colonial buildings with Dutch, Portuguese, and French influences, had plunged into neglect, reflecting a deep economic crisis worsened by deindustrialization. Many young people were fleeing the city for opportunities in the commercial centers of São Paulo and Rio de Janeiro, or heading overseas. In 2000, a group of businesspeople, government officials, and academics came up with a vision to regenerate Recife's historic center by building a new technology district. With 33 million reais ($6.8 million) raised from the privatization of the local electricity company, they created Porto Digital, a nonprofit organization with the mission of turning Recife into a hub for technology and the creative industries.

We prove that three strategy video games from the Sid Meier's Civilization series: Sid Meier's Civilization: Beyond Earth, Sid Meier's Civilization V, and Sid Meier's Civilization VI, are Turing complete. We achieve this by building three universal Turing machines-one for each game-using only the elements present in the games, and using their internal rules and mechanics as the transition function. The existence of such machines imply that under the assumptions made, the games are undecidable. We show constructions of these machines within a running game session, and we provide a sample execution of an algorithm-the three-state Busy Beaver-with one of our machines.

In late 2017, I started to develop interest in the Machine Learning field. I talked about my experience when I started my journey. In summary, it has been filled with fun challenges and lots of learning. I am an Android Engineer, and this is my experience working on ML projects with our data scientist. I remember attempting to solve an image classification problem that came up in one of our apps.

According to Bank of America Merrill Lynch, the artificial intelligence (AI) market is projected to grow to $153 billion by 2020. If you're in sales, this probably doesn't come as a surprise; science has been changing sales as we know it with the recent growth of predictive analytics, prescriptive insights and more. However, the evolution of AI in sales has resulted in a slew of new and sometimes confusing vocabulary words. That's why we've compiled the below glossary, which breaks down all of the AI-related sales terms you need to know to navigate this brave new world. Artificial Intelligence (AI) refers to the ability of computers to analyze information, accomplish tasks and make decisions like a person would.