AI v copyright: US government body asks public for opinion

This inspired interest in — and fear of — how generative AI could be used to create realistic deepfakes that impersonate voices and people in videos. Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data. The recent buzz around generative AI has been driven by the simplicity of new user interfaces for creating high-quality text, graphics and videos in a matter of seconds. Generative AI builds on the foundation of machine learning, which is a powerful sub- category of artificial intelligence. ML can crunch through vast amounts of data, gleaning patterns from it and providing key insights.

These companies employ some of the world’s best computer scientists and engineers. But there are some questions we can answer—like how generative AI models are built, what kinds of problems they are best suited to solve, and how they fit into the broader category of machine learning. A major concern around the use of generative AI tools -– and particularly those accessible to the public — is their potential for spreading misinformation and harmful content. The impact of doing so can be wide-ranging and severe, from perpetuating stereotypes, hate speech and harmful ideologies to damaging personal and professional reputation and the threat of legal and financial repercussions.

Generative AI vs Traditional AI – so what is changing?

Vertex AI is being expanded with new extensions to make it easier for developers to connect to data sources. Google is making both the Vertex AI Search and Vertex AI Conversation services generally available, providing search and chatbot capabilities to Google’s enterprise users. Front and center are enhancements and new capabilities across Google’s Vertex AI platform, including both developer tooling and foundation models.

There are considerations specific to use cases and decision points around cost, effort, data privacy, intellectual property and security. It is possible to use one or more deployment options within an enterprise trading off against these decision points. As our article on deep learning explains, deep learning is a subset of machine learning. The primary difference between machine learning and deep learning is how each algorithm learns and how much data each type of algorithm uses. Alternatively, they might use labels, such as “pizza,” “burger” or “taco” to streamline the learning process through supervised learning. Generative AI outputs are carefully calibrated combinations of the data used to train the algorithms.

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For instance, AI computes different angles of an x-ray image to visualize the possible expansion of the tumor. A low-resolution and bad quality picture can be turned into a decent resolution thanks to some Generative AI tools. A new McKinsey survey shows that the vast majority of workers—in a variety of industries and geographic locations—have tried generative AI tools at least once, whether in or outside work. One surprising result is that baby boomers report using gen AI tools for work more than millennials.

The results are impressive, especially when compared to the source images or videos, that are full of noise, are blurry and have low frames per second. The same applies to computer games which can upscale the resolution to 4K while maintaining high frames per second based on lower resolution textures. The results are impressive, much better than from traditional techniques, and textures are sharp and look natural. ML based upscaling for 4K, as well as FPS, enhance from 30 to 60 or even 120 fps for smoother videos. All of us remember scenes from the movies when someone says “enhance, enhance” and magically zoom shows fragments of the image.

Founder of the DevEducation project

Bottom Line: Generative AI vs. AI

Some companies will look for opportunities to replace humans where possible, while others will use generative AI to augment and enhance their existing workforce. These technologies aid in providing valuable insights on the trends beyond conventional calculative analysis. Several businesses already use automated fraud-detection practices that leverage the power of AI. These practices have helped them locate malicious and suspicious actions quickly and with superior accuracy. AI is now detecting illegal transactions through preset algorithms and rules and is making the detection of theft identification easier. AI allows users to acknowledge and differentiate target groups for promotional campaigns.

Using neural networks, speech and image recognition tasks can happen in minutes instead of the hours they take when done manually. Transformer-based models are trained on large sets of data to understand the relationships between sequential information, such as words and sentences. Underpinned by deep learning, these AI models tend to be adept at NLP and understanding the structure and context of language, making them well suited for text-generation tasks. ChatGPT-3 and Google Bard are examples of transformer-based generative AI models. Artificial intelligence is a technology used to approximate – often to transcend – human intelligence  and ingenuity through the use of software and systems.

For example, a text-to-image generation model that generates a poor image already defeats the aim of the model. Generative AI tools, on the other hand, are built genrative ai for creating original output by learning from data patterns. So unlike conversational AI engines, their primary function is original content generation.

Generative AI could work in tandem with traditional AI to provide even more powerful solutions. For instance, a traditional AI could analyze user behavior data, and a generative AI could use this analysis to create personalized content. Vendors will integrate generative AI capabilities into their additional tools to streamline content generation workflows.

Machine learning is the foundational component of AI and refers to the application of computer algorithms to data for the purposes of teaching a computer to perform a specific task. Machine learning is the process that enables AI systems to make informed decisions or predictions based on the patterns they have learned. Multimodal models can understand and process multiple types of data simultaneously, such as text, images and audio, allowing them to create more sophisticated outputs. An example might be an AI model capable of generating an image based on a text prompt, as well as a text description of an image prompt. I enjoyed reading the article by Awais Bajwa explaining the time-lapse of traditional AI from 1950s to 2008.