It is well-known that machine and deep learning algorithms are usually data-dependent. But how well does your data reflect the real world? While technology companies with massive data troves can be more certain that their data is representative, having high-quality information on a particular issue allows companies with smaller datasets to gain from AI.
Computer scientist and co-founder of Coursera, Andrew Ng, recommends collecting the right data rather than creating a custom AI system: “companies that are faster to adopt a data-centric approach to AI will have a leg up relative to competitors.” A data-centric approach shifts talent from developing bespoke models toward building data pipelines.
For a business to collect the kind of data that an AI model can use, significant infrastructure needs to be built around the algorithms. New machine learning operations – or MLOps – tools are designed to help produce these high-quality datasets. MLOps refers to all the engineering pieces that come together and often help to deploy, run, and train AI models. While businesses are still competing for AI experts in a red hot tech talent market, effective MLOps tools can make AI deployment easier, more efficient, and more accessible to companies with smaller data sets.
As AI becomes a ubiquitous tool for decision-making in business, the winners may not have the most data but will have a strong grasp on why they have collected data and what they want an AI to learn from it.
AI News This Week
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Climate AI startups offer businesses shelter from inclement weather risk (Wall Street Journal)
Eight extreme weather events have already caused losses in the US exceeding $1 billion in 2021. The Enterprise Climate Planning platform created by Radical Ventures portfolio company ClimateAI helps businesses prepare for the unavoidable. Last month, we discussed our investment in ClimateAI alongside Robert Downey Jr.’s FootPrint Coalition. We are excited to support a solution for a more resilient global supply chain.
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A new generation of AI-powered robots is taking over warehouses (MIT Technology Review)
“Within a few years, any task that previously required hands to perform could be partially or fully automated.” During the pandemic, e-commerce demand skyrocketed and labour shortages intensified. It became apparent that warehouse conditions were no longer safe for people. Radical Ventures portfolio company Covariant is already enabling safer and more resilient warehouses and logistics with AI-powered robotics.
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Artificial Intelligence may diagnose dementia in a day (BBC)
Giving a patient an accurate diagnosis and progression path will significantly help them plan their lives. It is extremely difficult to predict the progression of dementia. AI could help, diagnosing dementia after a single brain scan. Researchers are working with AIs that can identify patterns in the brain even expert neurologists cannot see.
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Using AI to find corporate greenwashers (Bloomberg)
‘Environmentally-friendly’ sells. Putting an environmental marketing spin, or ‘greenwashing’ products is a serious issue that misleads consumers. Investors and regulators are increasingly sounding the alarm about companies that exaggerate or misrepresent their environmental credentials. Researchers at the University College Dublin are leveraging algorithms to help the financial services sector detect and quantify greenwashing.
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Just for Fun: Appreciating the poetic misunderstandings of AI art (The New Yorker)
Using a GAN to generate surreal images is not new, but offers a glimpse into the evolving visual vocabulary of AI, “which is still messy, mushy, and strange.” As we have previously discussed, AI systems have been used to mimic masters in the art world and generate new faces. Although at times bizarre, these exercises help AI to better understand language by providing meaning to otherwise formless words and sentences.
Radical Reads is edited by Ebin Tomy.