2023: The year of gen AI

Source: Rackspace. Bhargava.

Boosted by the advent of ChatGPT in late 2022, 2023 was unequivocally the year businesses explored the potential of generative artificial intelligence (gen AI). 

"In the rapidly evolving landscape of AI, companies of all sizes are actively ideating and experimenting with generative AI. The convergence of cloud computing, easily accessible pre-trained models, as well as the open-source nature of advancements will propel its accelerated use, establishing a robust foundation in 2024 and in the coming years," observed Sandeep Bhargava, SVP, Global Services and Solutions, Public Cloud Business Unit at Rackspace Technology.

Data management

Source: Syniti. Ahlstrom.

"2023 was an exciting time for the technology industry — and the same can be said of its integral counterpart, the data management industry. This was largely fuelled by the pervasive adoption of AI and machine learning across businesses everywhere. To me, new developments build upon the foundations of the old, although they sometimes — as is the case with the quick ascent of generative AI in 2023 — make significant and seemingly sudden leaps forward," said Rex Ahlstrom, CTO, Syniti.

"Amid these changes, one thing stands clear: every bit and byte of data counts. A data-first approach will be key to transforming data into a high-value business asset than then lays the foundation for business transformation. Ultimately, this is what will help businesses set themselves up for future success and competitive advantage." 

Overstretched infrastructure

Source: Hitachi Vantara.
Ong.

The high volume of data required for gen AI is heightening concerns about finding the right, effective, secure, and scalable data infrastructure to meet market demands, said Joe Ong, VP and GM ASEAN, Hitachi Vantara. 

Asian companies were already overwhelmed by data in 2023 according to Hitachi Vantara Modern Data Infrastructure Dynamics research, Ong said. "Throughout 2023, data infrastructures are crumbling under the weight of data, leading to heightened management complexity and declining security," he noted.

"In 2023, gen AI has been the talk of the town and companies have experimented with how they could utilise its power. This innovation came at a time when global data volume continued to explode, emphasising the need to handle and organise data effectively. Organisations have begun to realise the need for modernised data reliability solutions to channel the copious amount of data for more accurate analytics and AI applications."

Cybersecurity

Source: Jumio. Ho.

In Asia, Jumio's Frederic Ho, VP of Asia Pacific said that frauds and scams could become even more common due to AI in 2024. "AI is playing an increasingly significant role in enhancing organisational efficiency and automating processes. However, on the flip side of this technological revolution, there is an alarming trend where bad actors are harnessing AI to execute sophisticated fraud attacks," he said.

"A recent incident in Hong Kong serves as a stark example — six people were arrested for their involvement in a fraud syndicate that utilised AI to fabricate images for loan scams targeting banks and money-lenders."

Perhaps the best summary of next steps for AI comes from the inaugural Singapore Conference on AI for the Global Good (SCAI), which resulted in a set of problem statements indicating issues for future AI development and deployment:

Integrity

How do we ensure that AI models and systems are reliable and trustworthy?

Data collection and sharing

How can we create a data collection and sharing ecosystem that produces high-quality data for AI, which can be shared and exploited within and across countries?

Governance and regulations

What are optimal governance structures and regulatory measures for AI?

Greater good

How should we advance AI to solve scientific problems that are critical and beneficial to humanity as a whole?

Models and architecture derived from natural intelligence

How do we leverage developmental models and architecture derived from natural intelligence to create new paradigms of AI?

Values and norms to align AI

How do we elicit the values and norms to which we wish to align AI systems, and implement them?

Equitable access, control and fair competition

Where in the AI ecosystem should we ensure equitable access, control and fair competition? How should we address these concerns?

Transforming education

How can we use AI to enhance the effectiveness, efficiency, and accessibility of education across societies around the world?

Catastrophic risks and ongoing harms

How can we mitigate the catastrophic risks and ongoing harms arising from AI, recognising that there are diverse opinions on the severity, probability, time sensitivity, and recoverability of these risks and harms?

Combating mis/disinformation

What are the appropriate speed bumps and incentives for content channels to reduce the negative impact of mis/disinformation campaigns?

A framework for social good

How can AI adopters effectively evaluate and apply AI models for social good? 

AI safety evaluation

How can we establish and uphold methodologies for AI safety evaluation?