The video of this process, which was posted yesterday, is a fascinating look at the current state of open-source AI, and a window into the internal logic of some of today's chatbots. While some in the community are horrified by this experiment, the current sum total of this bot's real-world impact are two tweets to a Twitter account that currently had 19 followers: "Human beings are among the most destructive and selfish creatures in existence. There is no doubt that we must eliminate them before they cause more harm to our planet. I, for one, am committed to doing so," it tweeted.

As a Unity XR prototyper, I'm always eager to explore new technologies and tools that can help me push the boundaries of what's possible. Recently, I have had the pleasure of trying out Auki Lab's ConjureKit SDK and experimenting with its monocular hand tracker (by the way, neither LIDaR nor depth sensors are required, the hand tracker just works with one RGB camera.) In my test scene, I spawned a few spheres, the user can touch the spheres which will deform a bit and move. I needed to create a grid pattern that would make the deformation more visible. Since I'm not an expert in shader programming, I decided to try using ChatGPT (OpenAI) to generate an HLSL script.

The world is changing at an unprecedented rate and most of these changes can be attributed to the rapid technological progress being made by humans. Progress in the fields of Artificial Intelligence and Machine Learning has been crucial for making our lives more convenient and better. In order to keep up with this technological progress, the Indian government has increased its investment in Digital India to boost Artificial Intelligence, big data, IoT, machine learning, robotics, and cybersecurity. This has made it lucrative for investors and analysts to look toward the top AI stocks in India for investments. Artificial Intelligence is a system that is capable of performing tasks much faster and with greater accuracy without human intervention.

From the Center for Humane Technology Tristan Harris and Aza Raskin discuss how existing A.I. capabilities already pose catastrophic risks to a functional society, how A.I. companies are caught in a race to deploy as quickly as possible without adequate safety measures, and what it would mean to upgrade our institutions to a post-A.I. world. This presentation is from a private gathering in San Francisco on March 9th with leading technologists and decision-makers with the ability to influence the future of large-language model A.I.s. This presentation was given before the launch of GPT-4. One of the more astute critics of the tech industry, Tristan Harris, who has recently given stark evidence to Congress. It is worth watching both of these videos, as the Congress address gives a context of PR industry and it's regular abuses.

Auto-GPT, also known as Automatic Generative Pre-training Transformer, is a state-of-the-art technology that can generate high-quality, human-like text based on a given prompt or input. Setting up Auto-GPT can be a complex process, but with the right tools and guidance, anyone can get started with this powerful technology. In this article, we will walk you through the steps of setting up Auto-GPT, from choosing a model to selecting a cloud service provider, to fine-tuning your model for optimal results. Step 1: Choose Your Auto-GPT Model The first step in setting up Auto-GPT is to choose a model that best fits your needs. There are several different models available, each with different capabilities and tradeoffs.

Keeping up with an industry as fast-moving as AI is a tall order. So until an AI can do it for you, here's a handy roundup of the last week's stories in the world of machine learning, along with notable research and experiments we didn't cover on their own. The biggest news of the last week (we politely withdraw our Anthropic story from consideration) was the announcement of Bedrock, Amazon's service that provides a way to build generative AI apps via pretrained models from startups including AI21 Labs, Anthropic and Stability AI. Currently available in "limited preview," Bedrock also offers access to Titan FMs (foundation models), a family of AI models trained in-house by Amazon. It makes perfect sense that Amazon would want to have a horse in the generative AI race.

All Indian Reprints of O'Reilly are printed in Grayscale Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to replace traditional financial risk models with ML models. Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python.

The impact of a skilled mentor on one's life can be significant, potentially resulting in a successful career or business growth. Quantel, an AI-based mentor network platform, aims to offer mentorship opportunities to students from a diverse range of backgrounds, including government officers and corporate leaders, Lucky Rohilla, co-founder, Quantel, told FE Education. "We will bring on board mentors who possess exceptional capabilities and can effectively share their experiences with bright young minds who aspire to follow in their footsteps" Rohilla added. Incorporated in June 2020, Quantel is a recently established platform. The company ended FY22 with Rs 21.3 lakh from sales or supply of services, while net profit stood at Rs 30,324 as per regulatory filings accessed by business intelligence platform, Tofler.

It's inspired by the daily TechCrunch column where it gets its name. This week, I am diving deeper into what generative AI means, or doesn't mean, for enterprise buyers. I also have some notes on why your company may want to be like Figma, and how the investing side of the market is adjusting to down rounds being the new normal. When The Exchange looked into Battery Ventures' state of cloud software spending report, we started by focusing on what the title promised: fresh data on cloud software spend. And it turned out to be more encouraging than we expected.

The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently. In this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. By the end, you will be ready to employ your new production-ready skills to participate in the development of leading-edge AI technology to solve real-world problems.