Generative AI is a type of artificial intelligence technology that broadly describes machine learning systems that can generate text, images, code, or other types of content.
The models of generative artificial intelligence are increasingly being incorporated into online tools and chatbot which allow users to type questions or instructions into an input field, upon which the AI model will generate a human-like response.
The models of generative artificial intelligence they use a complex computer process known as deep learning to analyze common patterns and arrangements in large data sets and then use this information to create new and compelling results. The models do this by incorporating machine learning techniques known as neural networks, which are loosely inspired by the way the human brain processes and interprets information and then learns from it over time.
To give an example, feeding a model of generative artificial intelligence with large amounts of narrative, over time the model would be able to identify and reproduce the elements of a story, such as plot structure, characters, themes, narrative devices, and so on.
The models of generative artificial intelligence they become more sophisticated as the data they receive and generate increases, again thanks to the techniques of deep learning need neural network below. As a result, the more content a template generates generative artificial intelligence, the more convincing and human-like its results become.
The popularity ofgenerative artificial intelligence exploded in 2023, largely thanks to programs Chat GPT e GIVE HER di OpenAI. Furthermore, the rapid advancement of technologies artificial intelligence, like natural language processing, has made thegenerative artificial intelligence accessible to consumers and content creators at scale.
Big tech companies have been quick to jump on the bandwagon, with Google, Microsoft, Amazon, Meta and others all lining up their own development tools. generative artificial intelligence within a few months.
There are numerous tools generative artificial intelligence, although the text and image generation models are probably the best known. The models of generative artificial intelligence they typically rely on a user providing a message that guides them towards producing the desired output, be it text, an image, a video or a piece of music, although this is not always the case.
There are various types of generative AI models, each designed for specific challenges and tasks. These can be broadly classified into the following types.
Transformer-based modelsTransformer-based models are trained on large data sets to understand relationships between sequential information, such as words and sentences. Supported by deep learning, these AI models tend to be well-versed in 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.
Generative adversarial networksGANs are made up of two neural networks known as a generator and discriminator, which essentially work against each other to create authentic-looking data. As the name suggests, the role of the generator is to generate a convincing output such as an image based on a suggestion, while the discriminator works to evaluate the authenticity of the said image. Over time, each component improves in their respective roles, achieving more convincing results. Both DALL-E and Midjourney are examples of GAN-based generative AI models.
Variational autoencodersVAEs use two networks to interpret and generate data: in this case it is an encoder and a decoder. The encoder takes the input data and compresses it into a simplified format. The decoder then takes this compressed information and reconstructs it into something new that resembles the original data, but isn't quite the same.
An example would be teaching a computer program to generate human faces using photos as training data. Over time, the program learns to simplify photos of people's faces by reducing them to a few important features, such as the size and shape of the eyes, nose, mouth, ears, etc., and then use them to create new faces.
Multimodal modelsMultimodal models can understand and process multiple types of data at once, such as text, images, and audio, allowing them to create more sophisticated outputs. An example would be an AI model that can generate an image based on a text prompt, as well as a textual description of an image prompt. FROM-E 2 e GPT-4 by OpenAI are examples of multimodal models.
For businesses, efficiency is arguably the most compelling benefit of generative AI because it can enable businesses to automate specific tasks and focus time, energy and resources on more important strategic goals. This can lead to lower labor costs, increased operational efficiency and new insights into whether or not certain business processes are performing.
For professionals and content creators, generative AI tools can help with idea generation, content planning and scheduling, search engine optimization, marketing, audience engagement, research and editing, and potentially more. Again, the main proposed benefit is efficiency because generative AI tools can help users reduce the time they spend on certain tasks so they can invest their energy elsewhere. That said, manual supervision and control of generative AI models remains extremely important.
Generative AI has found a foothold in numerous industry sectors and is rapidly expanding into commercial and consumer markets. McKinsey estimates that, by 2030, tasks that currently account for about 30% of work hours in the United States could be automated, thanks to the acceleration of generative artificial intelligence.
In customer service, AI-powered chatbots and virtual assistants help companies reduce response times and quickly handle common customer questions, reducing the burden on staff. In software development, generative AI tools help developers code more cleanly and efficiently by reviewing code, highlighting bugs, and suggesting potential solutions before they become bigger problems. Meanwhile, writers can use generative AI tools to plan, draft, and revise essays, articles, and other written work, though often with mixed results.
The use of generative AI varies from industry to industry and is more established in some than others. Current and proposed use cases include the following:
A major concern about the use of generative AI tools – and particularly those accessible to the public – is their potential to spread misinformation and harmful content. The impact of this can be wide-ranging and severe, from the perpetuation of stereotypes, hate speech and harmful ideologies to damage to personal and professional reputations and the threat of legal and financial repercussions. It has even been suggested that the misuse or mismanagement of generative AI could put national security at risk.
These risks have not escaped politicians. In April 2023, the European Union proposed new copyright rules for generative AI which would require companies to disclose any copyrighted material used to develop generative artificial intelligence tools. These rules were approved in the draft law voted by the European Parliament in June, which also included strict limitations on the use of artificial intelligence in EU member countries, including a proposed ban on real-time facial recognition technology in spaces public.
Automating tasks via generative AI also raises concerns about workforce and job displacement, as highlighted by McKinsey. According to the consultancy group, automation could cause 12 million career transitions between now and 2030, with job losses concentrated in office support, customer service and food service. The report estimates that demand for office workers could “… decline by 1,6 million jobs, in addition to losses of 830.000 for retail salespeople, 710.000 for administrative assistants and 630.000 for cashiers.”
Generative AI and general AI represent different sides of the same coin. Both concern the field of artificial intelligence, but the former is a subtype of the latter.
Generative AI uses various machine learning techniques, such as GAN, VAE, or LLM, to generate new content from models learned from training data. These outputs can be text, images, music, or anything else that can be represented digitally.
Artificial general intelligence, also known as artificial general intelligence, broadly refers to the concept of computer systems and robotics that possess human-like intelligence and autonomy. This is still the stuff of science fiction: think Disney Pixar's WALL-E, Sonny from 2004's I, Robot, or HAL 9000, the malevolent artificial intelligence from Stanley Kubrick's 2001: A Space Odyssey. Most current AI systems are examples of “narrow AI”, as they are designed for very specific tasks.
As described above, generative AI is a subfield of artificial intelligence. Generative AI models use machine learning techniques to process and generate data. In general, artificial intelligence refers to the concept of computers capable of performing tasks that would otherwise require human intelligence, such as decision making and NLP.
Machine learning is the fundamental component of artificial intelligence and refers to the application of computer algorithms to data for the purpose of teaching a computer to perform a specific task. Machine learning is the process that allows artificial intelligence systems to make informed decisions or predictions based on learned patterns.
The explosive growth of generative AI shows no signs of abating, and as more and more companies embrace digitalization and automation, generative AI looks set to play a central role in the future of the industry. The capabilities of generative AI have already proven valuable in industries such as content creation, software development, and medicine, and as the technology continues to evolve, its applications and use cases will expand.
That said, the impact of generative AI on businesses, individuals and society as a whole depends on how we address the risks it presents. Ensuring that artificial intelligence is used ethically minimizing bias, improving transparency and accountability and supporting the governance of data will be crucial, while ensuring that regulation keeps pace with the rapid evolution of technology is already proving to be a challenge. Likewise, finding a balance between automation and human involvement will be important if we hope to harness the full potential of generative AI while mitigating any negative consequences.
Ercole Palmeri
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