A Guide to The Rise of Generative Artificial Intelligence

AI is revolutionising creative and analytical tasks. We look at what it is, and how advancements in computing power, AI research breakthroughs, and mass user adoption are propelling this transformative technology into the spotlight.

Artificial intelligence (AI) is traditionally defined as a field of science involving computers or machines to reason, learn and behave in ways typically requiring human intelligence.

Traditional or analytical AI involves the use of machines for analysis of existing data sets to establish relationships, find patterns and discover insights. Familiar AI applications include recommendation engines (e.g. Top Picks on Netflix, Discover on Spotify, Your recommendations on Amazon), image and speech recognition, language translation, and fraud detection.

A new category of AI is Generative AI, where machines generate something new rather than analyzing something that already exists. Generative AI involves the use of machine learning models that can create and generate new content such as text, images, audio, video, code, art and more based on simple word-based prompts. While generative AI models have been around for some time, next generation models have embraced new approaches to training, enabling greater accuracy. For example, OpenAI (owner of ChatGPT) announced that its latest AI model GPT 4 passed a simulated bar exam with a score around the top 10%; this compared with the previous version GPT 3.5’s score of around the bottom 10% only 6 months ago.

The rapid advancements in Generate AI has been driven by a number of factors:

• Greater computing power with advancements in Graphics Processing Units (GPUs) making it possible to run multiple complex computations simultaneously in Generative AI models which require significant computing power to train.
• Breakthroughs in AI research including the development of Transformer models (introduced by Google Brain in 2017), which changes the way neural network models sequence data and include mechanisms to help predict the next word in a sentence by analysing the most relevant parts of an input sentence.
• The proliferation of data that has fuelled the training of AI to be smarter and more efficient.
• The emergence of applications in 2022 that have driven awareness and mass user adoption. This includes Open AI’s text-toimage model DALL-E 2 and conversational chatbot ChatGPT which reached 100m users in 2 months, far more rapidly than any other application in history

What could be the benefits of Generative AI?

Up until recently, humans were far better than machines at creative tasks including writing, designing products, coding, making games, etc. However, machines are starting to catch up and Generative AI will likely be faster, cheaper, and potentially (over time) better than humans at creative and analytical tasks. While it is early, here are some examples that are emerging:

• Next generation Search with Microsoft Bing leveraging GPT-4 and Alphabet’s Bard using large language model LaMDA. Both are deploying chatbots to reinvent and improve how information is discovered with text based queries.
• Writing code with Generative AI taking your text or voice input and translating that into code. Microsoft’s Github Co-pilot helps developers write 40% of their code in an autocomplete format – recently Andrej Karpathy a leading AI researcher and former Director of AI at Tesla indicated he uses Copilot to write 80% of his code.
• Microsoft is embedding OpenAI’s GPT-4 technology into a Microsoft co-pilot for its Microsoft 365 applications (Word, Excel, Outlook, Powerpoint) and Dynamics (CRM) which can help users automate tasks such as writing emails, meeting notes and transcripts, organizing events, analysing data sets and drafting presentations.
• Adobe has launched its text to image generation and editing platform Firefly, with native Generative AI integrations into Creative Cloud, Adobe Express and Adobe Experience Cloud. Firefly uses generative AI and simple text prompts to create high quality images, text effects, background edits, and fresh colour palettes.

Generative AI could impact industries that require knowledge and creative work – coding, product design, finance, social media, gaming, law, marketing, sales, medicine, etc. It could make knowledge workers more efficient, creative, and productive and it could also help upskill people with skills they didn’t have previously. While many tasks could be automated – which may have implications for certain jobs – generative AI would increase efficiencies and free up time for more productive tasks. For example, it could help medical workers with paperwork, drafting notes, etc. freeing up their time for more important tasks including caring for patients.