We Are At The AI Frontier
By Catherine Lian, Managing Director, IBM Malaysia (pic)
As with the previous years, technology trends tend to dominate the headlines at the dawn of the year. However the volume of this seems to have pumped up in 2020 as the prior decade saw a gamut of innovation that impacted how humans work and play. There is no doubt that as technology continues its pervasive journey and become further ingrained into the fabric of the world, this decade will see Artificial Intelligence (‘AI’) take shape in more ways than we can imagine.
Last year IDC reported that enterprise AI adoption among local companies continues to be fragmented and opportunistic, even though 65% of the enterprises have already introduced some form of AI such as chatbots and virtual agents, to support their business.
An IBM study, conducted in October of 2019, found that 3 out of 4 organizations have either adopted, or are actively looking to adopt, AI technologies. The reason for the growth is because vendors are now able to help companies overcome the barriers they’ve been facing in adopting AI by building the right skills, paving access to good data, and securing trust in AI.
I believe that by 2025, AI will be ‘ubiquitous AI’ and pervasive across every industry and every organization, large and small alike. AI will move from being used reactively, to proactively make better predictions and optimize processes; AI will be seamlessly infused in all our ways of working and interacting, impacting the entire supply chain of every industry from food to travel, to leisure to manufacturing. This list is endless!
Neuro-Symbolic Technology Drives Pervasive AI
Across the board, we will see AI systems working more quickly and easily for data scientists, businesses and consumers through automation. Improved iterations of natural language processing will also play a key role in enabling AI systems to converse, debate and solve problems using every day language.
We expect to see organizations not only adopt AI – but scale it across their enterprises, by building/developing their own AI, or putting ready-made AI applications to work.
Another IBM survey, “From Roadblock to Scale: The Global Sprint towards AI” also found that 40% of respondents currently deploying AI said they are developing proof-of-concepts for specific AI-based or AI-assisted projects, and 40% are using pre-built AI applications, such as chatbots and virtual agents.
As the decade unfolds, AI systems will begin to rely on “neuro-symbolic” technology that combines learning and logic to support organisations better. Neuro-symbolic technology help computers better understand human language and conversations by incorporating common sense reasoning and domain knowledge. Such breakthroughs will soon help businesses deploy more conversational automated customer care and technical support tools, while requiring much less data to train the AI.
The impact of AI to the workplace will be obvious in 2020 where you can see it change how work is done because guess what, we will begin to adjust our skills to the new work order.
New research from the MIT-IBM Watson AI Lab shows that AI will increasingly help us with tasks such as scheduling, but will have a less direct impact on jobs that require skills such as design expertise and industrial strategy.
A personal tip to EITN readers: Learn new skills now
As to challenges in AI adoption, IBM studies have found that the following:-
- i) Limited AI expertise and knowledge is a hinderance from successful AI adoption at their business (37%)
- ii) Increasing data complexities and siloed data (31%)
- iii)Lack of tools for developing AI models (26%)
Trust Required for Sustainable and Self-Governed AI
I believe that trust is the bedrock of any AI’s deployment. Globally, 78% of respondents across all countries surveyed say it is very or critically important that they can trust that their AI’s output is fair, safe, and reliable. It is universally important to be able to explain how AI arrived at a decision is (based on 83% of global respondents).
To trust AI, these systems have to be reliable, fair, and accountable. We have to ensure that the public can be certain that the technology is secure and that its conclusions or recommendations aren’t biased or manipulated.
More transparent and accountable practices will emerge, to better manage AI data; as well as software tools that can handle ‘explainability’ to ‘bias detection’ functions.
During 2020, components that regulate trustworthiness will be interwoven into the fabric of the AI lifecycle to help us build, test, run, monitor, and certify AI applications for trust, not just performance.
Just like with the rise of AI in mobile devices and automobiles ,we’ll see the rise of AI to govern AI. This adoption will create trustworthy AI workflows across industries, especially those that are heavily regulated.
The days ahead are exciting as we AI tech more sustainable by supporting the growing AI workloads and at the same time, reduce its carbon footprint.
This journey has started too with the Global High-Resolution Atmospheric Forecasting (GRAF) which is the first hourly-updating commercial weather system able to predict something as small as thunderstorms globally, providing a 3x improvement in forecasting resolution for much of the globe. This innovation, a joint effort with National Center for Atmospheric Research, would bring the rest of the world up to the standard once limited to just a handful of countries.
Indeed, 2020-2030 is the new AI decade that we must not only watch but participate actively to shape our shared future.