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Generative Artificial Intelligence: What it is, How it Differs from Traditional Artificial Intelligence and What Can Be Generated With It

McKinsey is certain that generative artificial intelligence (AI) can dramatically change the approach to creating content and more. Below is a deeper look at what is behind this new term, why technology IT giants consider this technology a breakthrough, and where generative AI is already being applied.

What is Generative AI?

Generative AI (also known as Generative Artificial Intelligence, GenAI) is one of the most rapidly developing branches of artificial intelligence which creates something that did not exist before. First and foremost these are new types of content: text, audio and visual. Generative models use datasets as a basis for learning, but they do not simply combine them according to the request, but actually create them from scratch. This is the main difference from discriminative AI which analyses the differences between different types of data. 

What is an example of artificial intelligence? If you ask a discriminative AI a question like “Is a person walking on the road or a car driving on the road?”, it is very likely to give an accurate and unambiguous answer. Generative AI can be asked to draw a picture of a person walking on one road and being overtaken by a car. It will perform this task just as well – it will produce a picture, not a text. 

Thus, generative AI creates new content based on what it has learnt from the content someone else has already generated before, and this takes place with AI continuous learning. 

What is an AI model? There are several of them which are based on the following transformations:

  • text to text;
  • text to 2D image;
  • text to 3D image/video;
  • text to action (answering a question, searching for information, analysing data).

What generative artificial intelligence can do: have conversations as if there is someone just like you on the other side of the screen, write software code, create images and videos from scratch based on descriptions. ChatGPT is also an example of generative AI, and its basic version is available to a wide range of users. The principle behind generative AI can be clearly seen in the following image:

According to information by Goldman Sachs, generative AI could provide a 7% increase in global GDP (or nearly $7 trillion) and 1.5 percentage points of productivity gains over the next 10 years. At the same time, VentureBeat research shows that 18.2% of large companies worldwide are already implementing the technology, but only a fifth of them plan to increase spending on GenAI in the next year. The main reasons include limited IT budgets or not prioritising this task enough. 

Something about Evolution of Artificial Intelligence

Generative AI has been actively discussed over the last couple of years, although this technology can hardly be described as new – the test Alan Turing proposed back in 1950 fits the description of generative AI. At that time, he claimed that a machine could be called intelligent if it started generating answers to questions that were no different from human answers. Generative models were developed in the 1960s and 1970s, but the most complex of them – for example, deep learning models – appeared only in the 1990s. They were able to generate quite realistic text, which is indistinguishable from human one, and even recreate speech.  Another burst in the popularity of generative AI occurred when GPT-3 developed by OpenAI appeared (ChatGPT is its brainchild which uses this language model).

According to Bloomberg Intelligence research, generative AI will become a $1.3 trillion market by 2032, and the industry itself will grow at an average annual rate of 42% over the next 10 years.

Where are AI technologies already being used?

If we consider what artificial intelligence can generate, there are a huge number of fields and the most incredible projects – from education and medicine to big data processing and predictive analytics. If you narrow it down to generative AI, making unique creative content is also just one of the likely scenarios for applying it. 

The tasks where GenAI is in demand and is already widely used:

  • improving the quality of digital images and video;
  • creating personalised content;
  • prototyping for manufacturing purposes;
  • generating software code;
  • creating chatbots, virtual assistants;
  • performing visual inspections and quality control.

In the long-term perspective, generative AI will be employed in almost every industry, but today we can already identify several priority areas where it can be used to its maximum effect and value.

Finance: creating chatbots to speed customer service and improve its quality, preparing personalised financial advice and recommendations on selecting products, identifying fraud schemes and unscrupulous would-be borrowers.

Health care: accelerating development and testing of new medicines, creating synthetic patient datasets to further train AI models, modelling clinical trials, and researching rare genetic diseases.

Automotive industry: designing new car models, developing intelligent virtual assistants for drivers, creating new microchips and structural elements in the car.

Energy: analysing big data, forecasting, improving the quality of customer service, developing energy efficiency programmes, optimising power generation.

Telecommunications: improving network performance, developing personalised recommendations for clients. 

Online learning: preparing personalised learning materials and learning scenarios, automating assessment, creating interactive learning environments and even identifying pieces of homework created via this very same AI. 

Furthermore, numerous case studies show that using solutions based on generative AI makes people in a wide variety of professions more productive, such as digital artists, software developers, testers, marketers, engineers, etc. 

Colobridge’s Expert:

“Generative AI is already revolutionising a wide variety of industries, generating brand new quality personalised content quickly. However, a productive IT infrastructure is always behind the two components capable of handling the most demanding workloads. To provide significant computing power, GenAI often requires graphics processing units (GPUs) and specialised tensor processing units (TPUs) designed to be used together with TensorFlow machine learning library. There are also early indications that GenAI-as-a-Service will appear in the portfolios of top cloud providers, making the technology even more accessible for a wide range of implementations in a variety of areas”.

A customised dedicated server infrastructure is the best possible solution to host AI-related workloads. Colobridge specialists will help you develop and implement a project that best meets your expectations and needs, as well as take care of IT infrastructure administration and maintenance if necessary. Email or call us to learn more about the opportunities Colobridge platform offers for hosting your IT services.

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