Has AI Hit a Dead-End?

A new AI winter is coming, and it has nothing to do with AI.

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There has been a lot of hype around artificial intelligence (AI) and its subfields (machine learning, deep learning, etc.) for quite some time now. Yet, we may be on the verge of another AI winter — a period of reduced funding and interest in artificial intelligence research — despite the significant advances in algorithms and infrastructure, even with the vast amount of information and data that we have at our disposal.

We may be on the verge of another AI winter, despite the great advances in algorithms and infrastructure, even with the vast amount of information and data that we have at our disposal.

However, AI itself is not accountable for this letdown. This time, the technology is entering the trough of disillusionment because we do not know how to benefit from it. We do not have a robust methodology for transforming breakthrough research ideas into practical applications yet. Yes, many algorithms have “graduated”, and consumers are using them without even noticing it, but still, putting a model to production remains arduous. Coming from an academic background, the problem is clear to me: it is time to start developing solutions, not only prototypes.


The Red Flag

The coronavirus outbreak caught us off guard. Technology, and particularly the field of AI, fell short of the expectations of investors and policymakers, not because we do not invest enough but because we do not invest effectively. Don’t just fund research; fund solutions says Ilan Gur on his MIT Tech Review article “How the US Lost its Way On Innovation”.Indeed, although we could use AI to help us prevent, diagnose and maybe treat a disease like COVID-19, half a year after the outbreak in China we only talk about ideas and develop prototypes of what solutions might look like. Moreover, people that seek to capitalize on the public’s panic found startups with questionable methodologies and products. Hundreds of AI solutions are proposed for the pandemic, but few are proven warns Elise Reuter in her article for MedCity News.

We are about to lose this big opportunity to showcase the power and worth of AI and that would not only be a shame but could make investors more sceptical in future.

We should undoubtedly be extra careful when it comes to machine learning in the health sector; however, we are not closer to deploy an assistive machine learning solution now than we were six months ago. We are about to lose this great opportunity to showcase the power and worth of AI, and that would not only be a shame but could make investors more sceptical in future.

#data-science #machine-learning #deep-learning #artificial-intelligence #technology #deep learning

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Has AI Hit a Dead-End?
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