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Applied Generative AI for Beginners: Practical Knowledge on Diffusion Models, ChatGPT, and Other LLMs. Within these pages, you're about to embark on an exhilarating journey into the world of generative artificial intelligence (AI). This book serves as a comprehensive guide that not only unveils the intricacies of generative AI but
AI generative models today. Now, in a virtuous cycle, AI generative models are well poised to deliver considerable insights into nature itself, across biological, physical, and mental realms, with broad implications for solving key societal problems. For example, generative models of proteins can allow
- INTRODUCTION
- OVERVIEW OF ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, AND DEEP LEARNING
- Artificial Intelligence
- Machine Learning
- Deep Learning
- GENERATIVE AI
- Foundation Models
- Large Language Models
- IMPLEMENTING MACHINE LEARNING AND GENERATIVE AI
- CONSIDERATIONS AND RESPONSIBILITIES WITH GENERATIVE AI
- Intellectual Property and Copyright
- Reliability and Accuracy
- Privacy
- Explainability and Transparency
- Environmental Impact
- Bias and Hate Speech
- Cyberattacks and Fraud
- HOW TO ACCESS AND LEVERAGE GENERATIVE AI
- INDUSTRY-SPECIFIC GENERATIVE AI USE CASES
- Banking, Financial Services, and Insurance
- Manufacturing
- Aerospace and Defense
- SECURE, TRANSPARENT GENERATIVE AI TOOLS WITH ALTAIR RAPIDMINER
The advent of generative artificial intelligence (AI) as a consumer product has sparked excitement in virtually every industry and brought a new level of awareness of machine learning’s capabilities to the public. Now that generative AI is no longer confined to research labs and has entered the public square, it has sprouted into dozens of new appl...
It’s rare that a new technology comes along that gets everyone in the business world talking, but the wide release of generative AI has done just that. ChatGPT was announced in November 2022 and suddenly, it seemed that everyone was raving about the possibilities of generative AI. Advanced machine learning algorithms can now generate natural-soundi...
We call any computer or computing system an “artificial intelligence” when it can perform tasks that have typically required human intelligence. That’s a broad definition, but it’s meant to encompass a wide variety of computing tasks such as: Speech and text recognition, interpretation, and generation, “Smart” assistants such as Apple’s Siri or Ama...
Machine learning is a sub-field of AI that focuses on pattern recognition and forecasting. Once given a suficiently large and complex data set to study, a machine learning algorithm can identify patterns and then make predictions based on what it found. Machine learning programs use computational techniques and statistical rules to gather informati...
Deep learning is a sub-field of machine learning that teaches computers to solve problems using an internal software architecture inspired by the human brain. This structure is called an “artificial neural network,” and it’s made up of artificial neurons that work together to find a solution to the problems users pose to it. These artificial neuron...
Generative AI is a class of machine learning algorithms that use neural networks to create text, images, and other content that is substantially diferent from anything it was trained on and much more complex than any previous machine learning model was capable of. These models are “generative” because their distinguishing feature is the creation o...
Foundation models are the base upon which specific generative AI applications are built. They’re deep learning models trained on massive data sets – really huge, terabytes’ worth of data – containing a wide array of unlabeled data. Most foundation models have been trained to predict the most likely word to fill in gaps in texts. Only after they hav...
LLMs are trained for superior performance with text. LLMs can perform a variety of language-oriented tasks, first and foremost being content creation; they can also summarize text, parse natural language commands for tasks like searching a knowledge base or writing code, act as customer service chatbot agents, and so on. Because of the neural netwo...
There’s a lot of excitement about what generative AI can do, but the truth is that discriminative machine learning models – which are cheaper and quicker to create – can handle many of the proposed use cases for generative AI (and LLMs in particular). No need to use a chainsaw to cut a birthday cake, right? few examples where a discriminative model...
There’s a reason why we’re emphasizing using the right machine learning tool for the job: There are costs and risks in using generative AI, and managing both is good business. Leaders and advocates should be informed so they can ensure proper risk-mitigation strategies are put in place. Read up on some of the snags users and enterprises could run i...
We explained above how almost all LLM models are trained on a huge data set scraped from the internet. While a portion of that data would be in the public domain and could be considered “fair use,” that’s not necessarily the case for the whole data set. Foundation models trained on online content is almost certainly a violation of many, many copyri...
There’s no way around it: LLMs are designed to produce writing that looks and sounds like a human wrote it, not necessarily writing that’s factually correct and supported by evidence. Often, generative AI models sound very confident. But sounding right doesn’t mean being right. Generative AI models aren’t sentient and they don’t understand what the...
If a user chooses to access the public-facing interface of a generative AI tool directly, they need to be aware that whatever they type into the prompt box becomes part of the model’s training data and is used to further refine the product. This has already led to a leak of sensitive data from users at Samsung, resulting in a company-wide ban of th...
It’s in the very nature of neural networks to be black boxes – as a highly intraconnected unsupervised machine learning model, no one really knows the exact method the model uses to reach its conclusion. And now that these AIs are in the market as commercial products, business interests are driving companies to be more secretive regarding training ...
Neural networks require a ton of compute power to train and maintain. The advanced GPUs needed to run the models consume a lot of energy, as do the climate control systems needed to cool the server stacks. Energy use equals carbon emissions and that leads to intensified climate change. (Not to mention that the electricity bill needs to be paid, too...
Another consequence of LLMs being trained on data pulled unfiltered from the internet is that such a sample reproduces societal biases against marginalized groups. In fact, experts have noticed that sexist, racist, and other intolerant and hateful material is overrepresented in the source data because of the anonymous nature of online discourse. Th...
This is more of a heads-up for CISOs and IT leaders rather than a risk of using the product itself. Hackers and other bad actors are already using generative AI to deploy ransomware attacks, phishing scams, and other cybersecurity threats more quickly and easily than ever before. Countermeasures will need to be further refined starting immediately.
Now that we have a map of the benefits and pitfalls of generative AI, we can start thinking about how to take advantage of all it has to ofer while staying savvy to when a discriminative machine learning model might do the trick. The next question is: How does my enterprise access generative AI applications in a cost-efective, secure, and right-siz...
In addition to the information we’ve gathered for everyone interested in generative AI, we’ve also collected some helpful insights for specific industry groups. There are three industries we’ve put together additional information for: Banking, financial services, and insurance (BFSI) Manufacturing, including high-tech manufacturing and biomedical/s...
The BFSI industry is already making good use of machine learning technology – and there’s still room to grow in that regard. Machine learning models are already in place at many banks and insurance companies seeking to accelerate underwriting decisions and loan applications, as well as monitoring for financial crime and fraud. These are great uses...
Manufacturing and heavy industry can sometimes be on the cautious side; the large capital investitures these organizations often have to make fosters risk aversion and a measured approach to adopting new technologies. But there are job functions and analytical tasks where discriminative AI models are already proving their worth. A lot is being left...
Enterprises in aerospace engineering and defense manufacturing have special needs and high standards. Security is paramount for essentially every aspect of their business and so they will want to be especially mindful of the privacy risks associated with generative AI models. All data will need to be totally isolated. The good news is that defense ...
The Altair RapidMiner platform ofers comprehensive, end-to-end solutions, from data ingestion and modeling to operationalization and visualization. It’s designed for many diferent skill sets, from data scientists and engineers to business analysts and executives, to get more value from data. We’ve added generative AI to our solutions to help bring ...
Generative AI --v 5 --q 2 --s 750 What is Generative AI? Generative AI refers to a subset of Artificial Intelligence that involves training machines to generate new and original data, such as images, music, text, or even videos. Unlike traditional AI, which operates on pre-existing data sets to recognize patterns
Dec 6, 2023 · Generative AI finds its utility across various modalities, including the generation of text, image, video, code, sound, and other produced content, such as molecules or 3D ren. derings (see Table ...
it is about to change how they work. Ideally, Generative AI can bolster innovation, productivity, and outcomes. while making work easier for people.For business leaders, globally, the challenge is twofold: understanding the possibilities and risks Generative AI brings and preparing for the inevitable organizati.
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May 17, 2024 · Abstract. In recent years, the study of artificial intelligence (AI) has undergone a paradigm shift. This has been propelled by the groundbreaking capabilities of generative models both in ...
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Embrace Generative AI and LLMs With the AI Data Cloud. Learn to Supercharge Your Data with LLMs and Gen AI Technology. Get Your Free Copy.
Build Generative AI Applications Using Foundation Models Through a Serverless API Service. AWS Offers Secure and Easy Ways To Build Generative AI Applications Quickly.
