How generative AI works DALL-E Video Tutorial LinkedIn Learning, formerly Lynda com
These are just a few of the many generative AI tools available for businesses. It’s important to carefully evaluate each tool and choose the one that best meets your specific needs and requirements. By detecting patterns and anomalies in these images, generative AI can assist radiologists in identifying potential health issues and making more accurate diagnoses. Helping marketers with SEO optimization by analyzing search engine data to identify keywords and phrases that are relevant to their content. By using generative AI to optimize their content for search engines, marketers can improve their search engine rankings and attract more traffic to their website.
![how generative ai works](image/jpeg;base64,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)
But as powerful as zero- and few-shot learning are, they come with a few limitations. First, many generative models are sensitive to how their instructions are formatted, which has inspired a new AI discipline known as prompt-engineering. A good Yakov Livshits instruction prompt will deliver the desired results in one or two tries, but this often comes down to placing colons and carriage returns in the right place. A prompt that works beautifully on one model may not transfer to other models.
Audio
As a result, bad actors seem to have carte blanche when it comes to exploiting these tools for malicious intent. The misuse of generative video technology swiftly became apparent when it was employed to harass and threaten women through the distribution of deepfake pornographic content. Another concern regarding the implementation of generative models is model accuracy.
NoVa’s Data Parrot is bringing generative AI to sales data – Technical.ly
NoVa’s Data Parrot is bringing generative AI to sales data.
Posted: Wed, 13 Sep 2023 13:27:00 GMT [source]
Marketers can use it to create content, scientists to model complex systems, and artists to produce unique artworks. Even in predictive maintenance, generative AI can create simulations to predict when a machine is likely to fail. While generative AI has made impressive strides, the quality of the content it generates can still vary. At times, outputs may not make sense — They may lack coherence or be factually incorrect.
Generative AI in the real world
The future is waiting to be created, and generative AI can be your guide on this exciting journey of innovation and discovery. First, it differs from discriminatory AI, which makes classifications between inputs, which is what is meant by “discriminatory” in this case. The objective of a discriminating learning algorithm would be to make a judgment about incoming inputs based on what was learned during training. The term generative AI is used to describe any form of artificial intelligence which creates fresh digital imagery, video, audio, text, or code utilizing unsupervised learning methods. That said, there are a few common facts about the magic of gen-AI, no matter how it is packaged.
Computer science classes grapple with the presence of generative AI – University of Virginia The Cavalier Daily
Computer science classes grapple with the presence of generative AI.
Posted: Sun, 10 Sep 2023 07:00:00 GMT [source]
What we’re seeing is certainly just the tip of the iceberg of what AI can do in different settings. As we enter a new era of artificial intelligence, generative AI is only going to become more and more common. If you need an explainer to cover all the basics, you’re in the right place. Read on to learn all about generative AI, from its humble beginnings in the 1960s to today – and its future, including all the questions about what may come next. This subset of machine learning has become one of the most-used buzzwords in tech circles – and beyond.
Generative AI and no code
They allow you to adapt the model without having to adjust its billions to trillions of parameters. They work by distilling the user’s data and target task into a small number of parameters that are inserted into a frozen large model. Language transformers today are used for non-generative tasks like classification and entity extraction as well as generative tasks like translation, summarization, and question answering. More recently, transformers have stunned the world with their capacity to generate convincing dialogue, essays, and other content.
Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.
![how generative ai works](image/jpeg;base64,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)
Microsoft’s first foray into chatbots in 2016, called Tay, for example, had to be turned off after it started spewing inflammatory rhetoric on Twitter. Transformer architecture has evolved rapidly since it was introduced, giving rise to LLMs such as GPT-3 and better pre-training techniques, such as Google’s BERT. The AI-powered chatbot that took the world by storm in November 2022 was built on OpenAI’s GPT-3.5 implementation.
This enables retailers to create more effective marketing campaigns and increase sales. Chatbots powered by generative AI can provide personalized assistance to customers, addressing common queries and offering real-time support. This improves customer satisfaction and reduces the workload on human customer service representatives. AI can be used to detect fraud by analyzing patterns and anomalies in financial transactions. The technology can learn from past data to detect new types of fraud and identify suspicious activities, helping financial institutions prevent fraud and reduce financial losses. This is a common problem in generative AI where a model becomes too closely fit to the training data, resulting in poor performance when presented with new data.
Image generation for illustrations
Manufacturers are starting to turn to generative AI solutions to help with product design, quality control, and predictive maintenance. Generative AI can be used to analyze historical data to improve machine failure predictions and help manufacturers with maintenance planning. According to research conducted by Capgemini, more than half of European manufacturers are implementing some AI solutions (although so far, these aren’t generative AI solutions). This is largely because the sheer amount of manufacturing data is easier for machines to analyze at speed than humans. The impact of generative AI is quickly becoming apparent—but it’s still in its early days.
![how generative ai works](image/jpeg;base64,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)
Last year, GPT-3 was an obvious leader in what concerned generating content. This year, GPT-3 is still strong, after all it is able to generate text, code, and images using prompts and natural language commands. However, everybody was obviously blown away with a new project, MidJourney, of course, that doesn’t just generate something but creates digital art that actually makes sense. Generative AI has been around for years, arguably since ELIZA, a chatbot that simulates talking to a therapist, was developed at MIT in 1966.
A generative AI model starts by efficiently encoding a representation of what you want to generate. For example, a generative AI model for text might begin by finding a way to represent the words as vectors that characterize the similarity between words often used in the same sentence or that mean similar things. Some companies will look for opportunities to replace humans where possible, while others will use generative AI to augment and enhance their existing workforce. OpenAI, an AI research and deployment company, took the core ideas behind transformers to train its version, dubbed Generative Pre-trained Transformer, or GPT. Observers have noted that GPT is the same acronym used to describe general-purpose technologies such as the steam engine, electricity and computing. Most would agree that GPT and other transformer implementations are already living up to their name as researchers discover ways to apply them to industry, science, commerce, construction and medicine.
- This was followed by revenue growth (26%), cost optimization (17%) and business continuity (7%).
- Generative AI enables users to quickly generate new content based on a variety of inputs.
- Generative AI holds enormous potential to create new capabilities and value for enterprise.
- This is useful when real data is not enough, improving the accuracy and reliability of the models.
- There are even implications for the future of security, with potentially ambitious applications of ChatGPT for improving detection, response, and understanding.
- The content you can produce using generative AI is limited by your imagination.
It goes beyond traditional machine learning approaches by enabling machines to create rather than simply classify or predict. Generative AI holds immense potential across various domains, from art and entertainment to healthcare and design. In this blog, we will delve into the world of generative AI, exploring its role in machine learning, as well as the key components and techniques used to unlock its creative capabilities. The generator creates new and original output, such as images, based on random input or a given condition. It trains on an existing dataset and learns to generate output that resembles real examples.
By generating content, generative AI can help make information and experiences more accessible. For example, AI could generate text descriptions of images for visually impaired users or help translate content into different languages to reach a broader audience. Generative AI can generate new ideas, designs, and solutions that humans may not think of. This can be especially valuable in fields like product design, data science, scientific research, and art, where fresh perspectives and novel ideas are highly valued. Generative AI text models are used in various applications, including chatbots, automatic text completion, text translation, creative writing, and more. Their goal is often to produce text that is indistinguishable from that written by a human.
![how generative ai works](image/jpeg;base64,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)
Absolutely, generative AI often works in tandem with other AI technologies like Natural Language Processing (NLP) and computer vision to accomplish more complex tasks. For example, a chatbot might use generative AI to create responses but rely on NLP for understanding user queries. While it’s possible to run simpler models on a standard computer, specialized hardware like Graphics Processing Units (GPUs) or Tensor Processing Units (TPUs) are generally recommended for more complex tasks. As we continue to innovate, adapt, and integrate these models into various facets of our lives, we are setting the stage for a future that promises to be as complex as it is exciting.