Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. I was 21 when I first watched Spike Jonze's 2013 sci-fi romance Her in theaters in New York City--a then–fresh college graduate teeming with the potent and deluded optimism that came with being a very broke and online millennial hoping to change the world. Her sparked some of my first reflections about whether tech innovation is inherently good or bad for society, and helped validate my early moral quandaries and panic at the time. I was graduating at the first turn of a recovering recession (mainly due to big tech investments in digital and social media) and securing my first full-time role as an online reporter. Though I was eager and rosy, a quiet, worried voice also began growing inside of me. Me, my job, my realities, were entirely dependent on tech--mainly Facebook content dissemination and programmatic turnkey digital ads--and I was not sure these huge tech investments by our broligarchical founding fathers would lead us anywhere good.

These apps let you choose from over a hundred different languages. Traveling to another country is an exciting experience, but learning a new language in order to do so can be a challenge. Fitting lessons into your schedule is difficult and getting the right pronunciation down is always a struggle. With language learning apps like Babbel, Rosetta Stone, Beelinguap and uTalk, you can learn a language at your own pace. These apps have hundreds of languages to choose from, and each app has a different approach and teaches a language differently.

Always wanted to learn how to play the piano? There's no better time than now, and that's exactly when you can start with Skoove Premium Piano Lessons. This app uses advanced AI to give you curated virtual piano lessons so you can learn from home without a teacher. And right now, a lifetime subscription is just 149.99 (reg. So many people are already taking advantage of Skoove's interactive piano lessons, and it's not hard to see why.

Program synthesis with Large Language Models (LLMs) suffers from a "near-miss syndrome": the generated code closely resembles a correct solution but fails unit tests due to minor errors. We address this with a multi-agent framework called Synthesize, Execute, Instruct, Debug, and Repair (SEIDR). Effectively applying SEIDR to instruction-tuned LLMs requires determining (a) optimal prompts for LLMs, (b) what ranking algorithm selects the best programs in debugging rounds, and (c) balancing the repair of unsuccessful programs with the generation of new ones. We empirically explore these trade-offs by comparing replace-focused, repair-focused, and hybrid debug strategies. We also evaluate lexicase and tournament selection to rank candidates in each generation. On Program Synthesis Benchmark 2 (PSB2), our framework outperforms both conventional use of OpenAI Codex without a repair phase and traditional genetic programming approaches. SEIDR outperforms the use of an LLM alone, solving 18 problems in C++ and 20 in Python on PSB2 at least once across experiments. To assess generalizability, we employ GPT-3.5 and Llama 3 on the PSB2 and HumanEval-X benchmarks. Although SEIDR with these models does not surpass current state-of-the-art methods on the Python benchmarks, the results on HumanEval-C++ are promising. SEIDR with Llama 3-8B achieves an average pass@100 of 84.2%. Across all SEIDR runs, 163 of 164 problems are solved at least once with GPT-3.5 in HumanEval-C++, and 162 of 164 with the smaller Llama 3-8B. We conclude that SEIDR effectively overcomes the near-miss syndrome in program synthesis with LLMs.

This is Bloomberg Opinion Today, an updated Bayeux Tapestry of Bloomberg Opinion's opinions. Apple Inc. recently added audiobook narration to the growing list of occupations where algorithms are poised to replace humans alongside graphic designers, college essayists and limerick writers. Luckily, the fine art of newslettering remains (ahem) far beyond the capabilities of even the most sophisticated artificial intelligence software. Still, hope is at hand for those not fortunate enough to toil in the newsletter mines but still seeking gainful employment that won't disappear as robots take control.

In Mathematics, the Fibonacci Series is a sequence of numbers such that each number in the series is a sum of the preceding numbers. The series starts with 0 and 1. This blog will teach us how to create the Fibonacci Series in Python using a loop, recursion, and dynamic programming. Check out this Python for beginners course we have prepared for you to brush up your skils. Leonardo Pisano Bogollo was an Italian mathematician from the Republic of Pisa and was considered the most talented Western mathematician of the Middle Ages.

As an avid reader, you will realize I am not a big proponent of Artificial Intelligence (AI). First, not just because, these days, every line of code is referred to as AI. Or a machine with a few sensors is suddenly called a robot, magically suggesting superior qualities of adaptability. The second major gripe I have with AI is how artificial the definition of intelligence is in AI. For intelligence is knowing what to do in unprecedented scenarios no AI system provides today.

Complete Blender Creator: Learn 3D Modelling for Beginners - Use Blender to Create Beautiful 3D models for Video Games, 3D Printing & More. This course is in the process of being completely remastered in Blender 3.2. Currently both the new and original content are in this course, once the remaster is complete students will be able to access the original 2.8 content in a separate archive course. Blender is a fantastic platform which enables you to make AAA-quality models which can be exported to any game engine, 3D printer, or other software. Here are some of the reasons why you want to learn Blender with this online tutorial... Create assets for video games.

Have you ever wanted to build a customized recommender system for yourself? If Yes! Then this is the course you are looking for. Have you ever thought how YouTube adjust your feed as per your favorite content? Why is your Netflix recommending you your favorite TV shows? Have you ever wanted to build a customized recommender system for yourself?

Ever wanted to learn to make a recommendation engine in Python? I mean, recommendation engines are a seriously valuable data science product. Just ask NetFlix and Amazon! They've made cargo ships of cash off these quirky little contraptions. They're actually not all that complicated to make either.