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Across two events on February 9 and 16, SmartCompany and AWS dived into all things AI and machine learning. In his introduction speech, host Simon Crerar referenced the wide-eyed futurism of science fiction films like Blade Runner. According to keynote speaker Simon Johnston of AWS, that sci-fi future is already here. “If you can take one thing away from today’s presentation from myself it would be that the technology is good to go. It’s not about the technology, in my opinion, it’s purely the application, the integration, the process.”
Sharing their insights were speakers from AWS, Deloitte, Carsales and Nearmap. Let’s take a closer look at a few of the key themes that emerged over the two days.
One big talking point of both sessions was AI/ML democratisation — the idea that the technology is accessible to more businesses, budgets and skill levels than ever. Simon Johnston touched on how AWS uses a platform called Canvas for accessible education. “If you’re a business and you’ve got business analysts that know their part of the business really well, know their data and use cases but don’t know machine learning, they shouldn’t be prevented from developing these capabilities. That’s what Canvas allows.”
Augustinus Nalwan, GM of AI, Data Science and Data Platform at Carsales, showed how that business is putting AI in the hands of just about every employee. Carsales began its AI journey in 2016, added a data science team in 2018 and, when the project brought success, the workload increased. The problem, according to Nalwan, is that hiring more data scientists and machine learning experts is extremely expensive. Instead, Carsales used existing Metaflow and Sagemaker ecosystems to automate workflows and upskill employees. “At Carsales, 70% of AI models can be built using this platform’s algorithm which does not require data scientists,” said Nalwan. “Anyone with good practice and guided by data scientists can perform this job.” Carsales has even gone further, using AWS tools like Rekognition and Comprehend to allow those with no programming skills (such as marketing and finance teams) to train models such as spam message recognition.
Simon Johnston noted the recent, rapid growth of AI, talking about themes of data growth and increases in model sophistication. “In the space of two years we’ve had a 1600x growth in the number of parameters. When you talk about ChatGPT and Open AI-type algorithms, they’re sitting around 175 billion parameters and it’ll continue to grow.”
With such rapid growth has come both extreme complexity and, as Michael Bewley of aerial imaging company Nearmap has found, heavy processing requirements. Nearmap uses deep learning models to create incredibly detailed geospatial images which now total over 25 petabytes of data. Bewley says that, for businesses similarly leaning on ML, it’s wise to use cloud AI like AWS Sagemaker rather than taking everything on in-house. “At some point there’s a break point where local machines really start to suffer. They’re great for freedom early on but then there’s size and scale limitations. Cloud computing is really important. Probably the most important thing is, don’t bring your legacy baggage with you on the transition to cloud.”
In Melbourne, Simon Johnston asked the audience how many have or would be implementing machine learning into their business and about a quarter raised their hands. The question for those attendees, then, is how do we get started?
In the discussion panel, Alon Ellis of Deloitte pointed to a classic model of technological adoption, the Gartner hype cycle. Ellis says that, for businesses looking to effectively wield AI and ML tech, they need to avoid the distraction of hype and focus on practically applying these technologies. “It’s bringing it back to that business problem, getting really clear on how that’s going to work, how you’re going to alter the business going forward, what that might mean for different teams, different ways of working and capitalising on that so you can go from the hype through to the pragmatic, implemented outcome.”
For AWS chief technologist Rada Stanic, jumping into AI and ML means getting your business’s data ready to go. “The success of the project will rely on the quality and breadth of the data that you have. If the quality data is there and it’s ready, I’ve seen proof of concepts happen in a couple of days, a week, to demonstrate that there is value in pursuing the project.”
Learn about the 6 key trends driving Machine Learning innovation across Australian and New Zealand industries inclusive of improvements to Model Sophistication, Data Growth, ML Industrialisation, ML Powered Use Cases, Responsible AI and ML democratisation.
On-Demand Keynote Recording: View Here
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