Machine Learning Tech Brief By HackerNoon

This story was originally published on HackerNoon at: https://hackernoon.com/ai-boom-spurs-us-china-chip-race. Explore the intense US-China rivalry in the semiconductor industry, driven by the AI boom and geopolitical tensions, reshaping the future of global tech. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai-boom, #ai-chip-market, #us-china-chip-war, #ai-and-semiconductors, #nvidia-ai-chips, #us-ai-chip-market, #china-ai-chip-market, #hackernoon-top-story, and more. This story was written by: @thetechpanda. Learn more about this writer by checking @thetechpanda's about page, and for more stories, please visit hackernoon.com. The semiconductor chip tug-of-war between the US and China has chip companies scrambling to keep business going as usual. Apple’s M4 chip can conduct 38 trillion operations per second, making it much better for AI tasks and enhancing features like Live Captions and Visual Look Up on the iPad Pro.

This story was originally published on HackerNoon at: https://hackernoon.com/why-quadratic-cost-functions-are-ineffective-in-neural-network-training. Explore why quadratic cost functions hinder neural network training and how cross-entropy improves learning efficiency in deep learning models. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #deep-learning, #neural-networks, #what-is-cross-entropy, #sigmoid-activation-function, #neural-network-training, #quadratic-cost-function, #cross-entropy-cost-function, #hackernoon-top-story, and more. This story was written by: @varunnakra1. Learn more about this writer by checking @varunnakra1's about page, and for more stories, please visit hackernoon.com. One of the most common question asked during deep learning knowledge interviews is - “Why can’t we use a quadratic cost function to train a Neural Network?**” We will delve deep into the answer for that. There will be a lot of Math involved but nothing crazy! and I will keep things simple yet precise.

This story was originally published on HackerNoon at: https://hackernoon.com/will-you-really-let-ai-do-the-thinking-for-you. Learn why active creation is crucial for innovation and personal fulfillment. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #artificial-intelligence, #creativity, #human-creativity, #creative-process, #creativity-neuroscience, #active-creation, #technology-and-society, #hackernoon-top-story, and more. This story was written by: @benoitmalige. Learn more about this writer by checking @benoitmalige's about page, and for more stories, please visit hackernoon.com. The digital landscape is becoming a uniform echo chamber. It’s all driven by AI that replicates but never truly creates.

This story was originally published on HackerNoon at: https://hackernoon.com/syntax-error-free-and-generalizable-tool-use-for-llms-abstract-and-intro. Researchers propose TOOLDEC, a finite-state machine-guided decoding for LLMs, reducing errors and improving tool use. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #llms, #tool-augmentation, #syntax-errors, #decoding-algorithm, #finite-state-machine, #tooldec, #tool-selection, #syntax-error-free, and more. This story was written by: @textmodels. Learn more about this writer by checking @textmodels's about page, and for more stories, please visit hackernoon.com. Researchers propose TOOLDEC, a finite-state machine-guided decoding for LLMs, reducing errors and improving tool use.

This story was originally published on HackerNoon at: https://hackernoon.com/leveraging-natural-supervision-appendix-a-appendix-to-chapter-3. In this study, researchers describe three lines of work that seek to improve the training and evaluation of neural models using naturally-occurring supervision. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #llm-natural-supervision, #llm-self-supervision, #llm-language-pretraining, #llm-word-prediction, #ai-language-modeling, #ai-vector-representations, #ai-neural-models, #ai-sentence-representations, and more. This story was written by: @textmodels. Learn more about this writer by checking @textmodels's about page, and for more stories, please visit hackernoon.com. In this study, researchers describe three lines of work that seek to improve the training and evaluation of neural models using naturally-occurring supervision.

This story was originally published on HackerNoon at: https://hackernoon.com/ai-is-everywhere-so-wheres-the-funding. No surprize that AI has significantly changed the business landscape – but it turns out that those shifts are not only those one may have expected. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai, #business, #generative-ai, #influencers, #future-of-ai, #ai-trends, #ai-applications, #hackernoon-top-story, and more. This story was written by: @starinin. Learn more about this writer by checking @starinin's about page, and for more stories, please visit hackernoon.com. Even with AI grabbing headlines, startups diving into this area often hit a wall when it comes to getting funded. And it's no surprise – for investors, discerning truly innovative AI solutions is becoming increasingly difficult. They often wait for the fog to clear on who's leading the market and which business models actually work before they decide where to put their money.

This story was originally published on HackerNoon at: https://hackernoon.com/beyond-the-answer-box-how-ai-overviews-impact-search-and-content. Exploring public concerns and the potential impact of Google's AI Overviews on content creators, consumers, and the future of search. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai, #ai-search, #ai-generated-content, #google-ai, #future-of-content-marketing, #ai-powered-seo, #website-traffic, #hackernoon-top-story, and more. This story was written by: @viceasytiger. Learn more about this writer by checking @viceasytiger's about page, and for more stories, please visit hackernoon.com. AI Overviews in Google Search might hurt small content creators, but could also be a goldmine for fact-checking startups. TLDR; Unclear yet, but focus on great and unique content.

This story was originally published on HackerNoon at: https://hackernoon.com/llms-cannot-find-reasoning-errors-but-they-can-correct-them. In this paper, we break down the self-correction process into two core components: mistake finding and output correction. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #llms, #llm-mistake-finding, #llm-output-correction, #big-bench-mistake, #chain-of-thought, #nlp, #self-consistency, #zero-shot-prompting, and more. This story was written by: @textmodels. Learn more about this writer by checking @textmodels's about page, and for more stories, please visit hackernoon.com. Large Language Models (LLMs) have dominated the field of NLP in recent years. LLMs have demonstrated the ability to solve tasks with zero- or few-shot prompting. Recent literature has focused on the concept of self-correction, i.e. having an LLM correct its own outputs. Attempts to self-correct logical or reasoning errors often cause correct answers to become incorrect, resulting in worse performances overall. In this paper, we break down the self-Correction process into two core components: mistake finding and output correction. For mistake finding, we release BIG-Bench Mistake, a dataset of logical mistakes in Chain-of-Thought reasoning traces. For output

This story was originally published on HackerNoon at: https://hackernoon.com/how-can-ai-improve-sustainable-business-communication. AI is revolutionizing business communication, enhancing productivity, personalization, and accessibility, while addressing ethical considerations and promoting Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai-in-business, #business, #sustainable-business, #business-communication, #ai-for-business, #using-ai-for-your-business, #team-communication, #ai-businesses-communication, and more. This story was written by: @aprilmiller. Learn more about this writer by checking @aprilmiller's about page, and for more stories, please visit hackernoon.com. When harnessed correctly, AI can dramatically enhance communication, internally and externally. The responsible use of AI in business communication demands ethical considerations and a balance between automation efficiency and people-centered approaches. Successful human-AI collaboration is already well underway and yielding impressive results. The financial implications are astounding, with poor communication costing enterprises $1.2 trillion annually.

This story was originally published on HackerNoon at: https://hackernoon.com/will-ai-replace-all-jobs-in-the-future. If people don’t need to work for a living, our world will become a vast wasteland of purposelessness. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #future-of-ai, #ai-applications, #elon-musk-on-ai, #future-of-work, #universal-basic-income, #ai-integration, #work-automation, #ai-trends, and more. This story was written by: @allan-grain. Learn more about this writer by checking @allan-grain's about page, and for more stories, please visit hackernoon.com. Billionaire entrepreneur Elon Musk predicted recently that AI will eventually replace all jobs in the future. He said that in order for this to work, a universal high income would need to be set up and implemented. But if people don’t need to work for a living, our world will become a vast wasteland of purposelessness.

This story was originally published on HackerNoon at: https://hackernoon.com/as-healthcare-ai-advances-how-do-we-balance-the-benefits-with-privacy-concerns. The first concern is the privacy of the data used to train these models, second is the model's response based on the input of the patient’s data. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai, #healthcare, #cybersecurity, #ai-in-healthcare, #future-of-ai-in-healthcare, #ai-privacy-concerns, #advancements-in-healthcare-ai, #future-of-ai, and more. This story was written by: @emmaakin. Learn more about this writer by checking @emmaakin's about page, and for more stories, please visit hackernoon.com. Artificial intelligence (AI) in healthcare has the potential to revolutionize clinical research and care delivery. AI can be used to sift through massive databases and may have applications in mental healthcare and cancer detection. The concerns are in two categories. The first concern is the privacy of the data used to train these models, second is the model's response based on the input of the patient’s data.

This story was originally published on HackerNoon at: https://hackernoon.com/leveraging-lessons-from-next-gen-social-enterprise-strategies-for-user-centric-ai-deployment. Social media platforms like Lips, Landing, and Diem are addressing AI challenges in data privacy and bias through user-centric data annotation and ethical AI. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai-ethics, #social-media, #ai-trends, #responsible-ai, #tech-diversity, #llms, #user-centric-ai-deployment, #user-centric-machine-learning, and more. This story was written by: @andreafrancesb. Learn more about this writer by checking @andreafrancesb's about page, and for more stories, please visit hackernoon.com. Social media platforms like Lips, Landing, and Diem are addressing AI challenges in data privacy and bias through user-centric data annotation and ethical AI practices. Engaging users in moderation and recommendation processes improves transparency and inclusivity. Innovative strategies like federated learning and differential privacy help maintain user trust and privacy. These approaches set new standards for responsible AI deployment in social media.

This story was originally published on HackerNoon at: https://hackernoon.com/generative-ai-model-gans-part-1. This blog will be talking about the model components of Generative Neural Networks (GANs) GAN is an unsupervised learning technique Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #generative-ai, #gans, #what-is-generative-ai, #generative-ai-explained, #model-architectures, #what-is-a-generator-in-ai, #what-is-a-discriminator-in-ai, #how-does-generative-ai-work, and more. This story was written by: @jyotiyadav33111. Learn more about this writer by checking @jyotiyadav33111's about page, and for more stories, please visit hackernoon.com. This blog will be talking about the model components of Generative Neural Networks (GANs) GAN is an unsupervised learning technique where training data full of images is added as an input with no labels. Let’s deep dive into the model Architecture. What is Generative AI? It is a subset of Artificial Intelligence that can generate new data.

This story was originally published on HackerNoon at: https://hackernoon.com/i-asked-the-mixtral-llm-a-question-about-agi-this-was-the-shocking-response. Why is everyone so worried about the progress of AGI? This article has the answer - literally - from an AI in the form of a Large Language Model. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #llm, #agi, #is-ai-dangerous, #super-agi, #threatening-humanity, #risk-to-humans, #possible-extinction, #answer-from-the-ai's-mouth, and more. This story was written by: @thomascherickal. Learn more about this writer by checking @thomascherickal's about page, and for more stories, please visit hackernoon.com. 'List every inevitable situation that is likely to arise as AGI develops' was my question to Mixtral LLM (Large Language Model). This was the incredible answer it gave me.

This story was originally published on HackerNoon at: https://hackernoon.com/3-things-to-consider-before-adding-genai-to-your-business. Secure the future of Your Business with GenAI. Consider 3 factors needed for successful deployment of GenAI in your business. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai, #future-of-ai, #artificial-intelligence, #machine-learning, #generative-ai, #ai-trends, #productivity, #genai-use-cases, and more. This story was written by: @pramodnammi. Learn more about this writer by checking @pramodnammi's about page, and for more stories, please visit hackernoon.com. Gartner predicts generative AI will become a general-purpose technology akin to the steam engine, electricity, and the internet. The primary reason to consider integrating GenAI is the substantial value it can bring to your business. Automating repetitive tasks such as customer support queries and data entry, allows your team to focus on more strategic activities.

This story was originally published on HackerNoon at: https://hackernoon.com/openais-latest-controversy-scarlett-johansson-takes-legal-action-for-unauthorized-voice-use. OpenAI's new voice model, Sky, sparks controversy as Scarlett Johansson claims it mimics her voice without consent, leading to legal action and ethical debates. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #chatgpt, #openai, #sam-altman, #privacy, #voice-model, #ai-controversy, #ethics-in-ai, #ai-regularion, and more. This story was written by: @carolisabino. Learn more about this writer by checking @carolisabino's about page, and for more stories, please visit hackernoon.com. OpenAI introduced several new voice models to GPT-4o: Aiden, Ember, Zephyr, Orion, and Sky. The Sky voice model sparked controversy after actress Scarlett Johansson revealed that she was approached to license her voice for the new model. Johansson: “I was shocked, angry, and disbelieving that Mr. Altman sought a voice that sounded so strangely like mine.”

This story was originally published on HackerNoon at: https://hackernoon.com/the-transformer-algorithm-with-the-lowest-optimal-time-complexity-possible. Do you know the recent advances in the Transformer algorithm variations? And who the clear winner is? Read this article to find out! Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #llms, #mamba, #xlstms, #jamba, #optimal-time-complexity, #transformer-algorithm, #attention-based-transformer, #hackernoon-top-story, and more. This story was written by: @thomascherickal. Learn more about this writer by checking @thomascherickal's about page, and for more stories, please visit hackernoon.com. There have been some incredible advances in transformers like DPO, LoRa, etc. But the basic transformer algorithm has also been improved upon! We are going to see transformers perform at a whole new level. The magnitude of these discoveries cannot be overestimated! This is revolutionary.

This story was originally published on HackerNoon at: https://hackernoon.com/top-20-image-datasets-for-machine-learning-and-computer-vision-rq3w3zxo. Computer vision enables computers to understand the content of images and videos. The goal in computer vision is to automate tasks that the human visual system can do. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #artificial-intelligence, #ai, #ml, #machine-learning, #data-science, #datasets, #dataset, #computer-vision, and more. This story was written by: @limarc. Learn more about this writer by checking @limarc's about page, and for more stories, please visit hackernoon.com. Computer vision enables computers to understand the content of images and videos. The goal in computer vision is to automate tasks that the human visual system can do. The image data can come in different forms, such as video sequences, view from multiple cameras at different angles, or multi-dimensional data from a medical scanner. MIT has created a large dataset of 187,240 images, 62,197 annotated images, and 658,992 labeled objects. Google’s Open Images is a collection of 9 million URLs to images that have been annotated with labels spanning over 6,000 categories.

This story was originally published on HackerNoon at: https://hackernoon.com/how-to-bypass-turnitin-ai-detection-10-tips-for-undetectable-writing. Knowing how to bypass Turnitin is essential, especially if you are a student who relies on AI to aid them in their schoolwork. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #bypassgpt, #bypass-ai-detection, #undetectable-ai, #ai-humanizers, #bypass-turnitin-ai-detection, #how-to-bypass-turnitin, #bypassing-ai-detection-tools, #good-company, and more. This story was written by: @margrowth. Learn more about this writer by checking @margrowth's about page, and for more stories, please visit hackernoon.com. The simplest way to make any AI content undetectable is by using an AI Humanizer. In the current market, the best tool to consider is BypassGPT. Using cutting-edge AI humanization technology, this tool can rewrite AI content and make it less detectable, more authentic and natural-sounding in just a few clicks.

This story was originally published on HackerNoon at: https://hackernoon.com/maggie-the-saga-of-a-baby-translator-ai-startup. Follow the journey of two classmates as they build a baby translator app, facing technical challenges and the harsh realities of startup life. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai, #ml, #classification, #entrepreneurship, #startup, #agile, #user-interview, #hackernoon-top-story, and more. This story was written by: @mta. Learn more about this writer by checking @mta's about page, and for more stories, please visit hackernoon.com. 7 years ago, Christopher Shedd and I built a baby translator. Yes, that's right, a baby translator. Following our journey, where we navigate the ups and downs of deploying ML models in production, wrestle with the unexpected challenges of doing something cutting-edge with little experience, find friends who help us along the way, and reflect on the nature of entrepreneurship in the digital age.