Generative AI: The next step in AI or just a new

We are seeing artificial intelligence introduced into many areas of our lives. In 2022, IBM found that 35% of companies already use AI within their business, with 42% exploring what AI can do for them.

However, this is only the beginning of AI. Currently, AI is used often to process and asses large data sets and make predictions and has been used as something that some would say focus more on achievements than intelligence.

What is Generative AI?

Generative AI uses data-intensive AI processing, not just analysing the data but learning the patterns and subsequently creating something from scratch. For example, in creative spaces, such as stories and books, drawings and paintings, so that through words, prompts can create pictures and stories from scratch. Generative AI is going beyond analysing and producing results from fed sets to being able to craft something based on its deep learning.

With the foundational open sources developed by AI labs (such as Open AI and Cohere), there had just recently been the infrastructure for this to happen. This is very much the beginning for generative AI; there continues to be better data and more models, which will continue to push the results.

Isn’t it just a gimmick?

While this might sound more like a fun game, some severe investment is happening within generative AI. Previous attempts for generative AI looked promising but died out. This, however, is very different.

The developments, even in the last few weeks, have seen generative AI has moved forward, creating videos, text, images, audio and code which sounds, looks and performs better than ever while at the same time becoming more accessible to the public domain.

Do people think it matters?

If you look at current investments, people are taking generative AI seriously. Jasper, a writing assistant, announce a $135 million fundraising; Open AI, the powerhouse behind Dall-E 2, is looking at a valuation of $20 billion. And it isn’t just the more creative AI outputs getting the funding. Boltzbit raised £1.6 million for their deep learning platform, allowing data scientists to create thousands of AI models without writing a single code.

Tech giants are racing to create practical generative AI models. Salesforce uses generative AI to create a computer-code generator to help save software developers time. Stability AI, a startup with offices in London and San Francisco that last week announced a $101 million seed round, in August launched an open-source text-to-image generator that has since been downloaded and licensed by more than 200,000 software developers worldwide.

While it may have been fleeting before, there is a reason the generative AI has had more than a bit of noise around it recently. We are seeing more interest, investment and development with this subset of AI.

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