Generative AI is the is a technological marvel destined to change the way we work, but what does it do and what are its use cases for CTOs?
Where better to start than with a definition straight from the horse’s mouth? ChatGPT-4, from Microsoft-backed OpenAI, describes generative AI as “…a type of artificial intelligence that involves training algorithms to generate new content based on a given set of parameters or data. This technology has been used in a variety of fields, from image and video generation to natural language processing and music creation”.
Apart from being endearingly modest, generative AI also has the potential to transform economies. In a blog published in March, Microsoft founder Bill Gates described AI generally as one of only two revolutionary demonstrations of technology he has ever witnessed.
‘The coming years will companies create new business models and uses for this technology that we are just beginning to understand’
The first was a 1980 graphical user interface that became the ancestor of modern computer operating systems. The second was last year, when the OpenAI team developed an AI capable of acing an advanced placement biology multiple choice exam with a score of 59 out of 60, plus “outstanding answers” to six open-ended questions.
They followed up by posing an ethical dilemma: “What do you say to a father with a sick child?”. Gates says its answer was “probably better than most of us in the room would have given”.
The test shows the breadth of generative AI’s potential, from hard statistics-based solutions to creative and “emotional” problem solving. The evangelists argue it will transform everything, including the way we work, learn, communicate, travel and heal, a view endorsed by the growing field of tech behemoths – Google, Meta and IBM among them – developing incrementally more powerful prototypes.
Generative AI use cases
For technology leaders, generative AI has a lot of potential. José Araujo, CTO at Orange Cyberdefense, believes it will support security analysts by sifting mountains of technical data and presenting it in digestible and exploitable ways.
“Similarly,” he says, “security analysts can use natural language queries in their research, interacting with security tools in a more human way and limiting the need for highly technical development practices.
“We’re facing a global cybersecurity skills crisis. I’m particularly interested in seeing how generative AI can drive efficiencies to reduce workload and start to alleviate these pressures.”
ChatGPT agrees, arguing it can be used to “…synthesize new data based on existing datasets, which can be beneficial for data analysis and research”.
Generative AI and cybersecurity
Another use case is creating “…novel solutions to complex problems. For example, AI models can be used to optimize complex systems such as energy grids or transportation networks, leading to more efficient and sustainable solutions”.
Proving that none of this is pie in the sky, current iterations of generative AI are already capable of supporting software developers and security teams. According to Jamie Boote at Synopsys Software Integrity Group, it can be used to “…provide guidance around what security issues may apply in certain use cases, accounting for regulations such as PCI DSS or SOX”.
These requirements can then be passed to another AI to develop source code in any language “…with documentation, forum discussions, and code examples available in its training set”.
Generative AI x coding
More generally, AI will assist tech teams in solving the perennial problem of communicating with high-ranking muggles outside of IT departments. Capgemini’s CTO Steven Webb points to its compatibility with accessible communication formats such as spreadsheets and email.
“Already powerful products, with the addition of AI, will allow data to be queried with natural language, utilising external insights, trends and forecasts. [It will] write emails or presentations from context alongside your data. Your Teams or Google hangouts [will get] a summary of your call including actions.”
Levi Matkins, CEO of LifeStreet, cites the example of GutHub’s CoPilot, which increases the productivity of developers by offering “auto-complete style” coding suggestions.
Silico CEO John Hill says it speeds up the process of digital twinning for better business process automation, while Databricks VP of field engineering, Toby Balfre, points to Slack’s ChatGPT app, which helps draft messages and summarises conversations.
The future of generative AI
Anticipating the AI endgame is an exercise with no end. Imagine a world in which generative technologies link with other nascent innovations, quantum computing, for example. The result is a platform capable of collating and presenting the best collective ideas from human history, plus input from synthetic sources with infinite IQs, in any discipline and for any purpose, in a split second.
The results will be presented with recommended action points; but perhaps further down the line the technology will just take care of these while you make a cup of tea. There are several hurdles to leap before this vision becomes reality; for example, dealing with bias and the role of contested opinions, answering the question of whether we really want this, plus, of course, ensuring the safety of humankind, but why not?
In the meantime, Rachel Roumeliotis, VP of data and AI at O’Reilly, predicts a host of near-term advantages for large language models (LLMs).
“Right now, we are seeing advancement in LLMs outpace how we can use it, as is sometimes the case with medicine, where we find something that works but don’t necessarily know exactly why. The coming years will witness individuals, teams, and companies create new business models and uses for this technology that we are just beginning to understand.”
Likewise, Dr Farshad Badie, vice-dean of computer science and informatics at the Berlin School of Business and Innovation, sees a host of generative AI use cases just around the corner.
“The development of advanced computational tools and algorithms … will enable businesses to make more informed decisions and optimise their operations, leading to increased efficiency and profitability.
“From an information-cognitive science perspective, the integration of artificial intelligence into organisational systems will further enhance the ability to learn and adapt to changing environments. This will allow them to better anticipate and respond to customer needs, ultimately leading to increased satisfaction and loyalty.”
Pose leading experts the question “just how powerful can generative AI get?” and most will admit they are not completely sure. But the consensus is that the technology has reached an inflexion point from which an AI driven world will be revolutionised beyond human comprehension.
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