The AI industry that was once optimistic about data abundance now understands its unsuspected side-effects
The increased data-driven enterprises with their expensive AI models might have some troubling times ahead. With their search for higher data accuracy, the rise of an infodemic, where data turns inaccurate, is inevitable. Although data is considered an asset for any business growth, the growing overuse of computational AI can cause data to become a liability.
Organizations are increasingly investing maximum funds in AI to stay ahead of their competitors. They feel compelled to invest in hopes for better discovery mechanisms and authentication processes. While higher financial and AI environmental costs consume businesses, the data-heavy approach will cause a steady decline in the productivity scale in the long run.
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OpenAI research indicates that even if AI has been efficient in data science goals, the community states that they require more compute to achieve success. Furthermore, an MIT study suggests that deep learning will reach its computational limit as it heavily relies on increased compute by tweaking existing techniques or discovering new computational methods.
To build such AI models, trial and error and training will require more compute resources. If three years of improved algorithms can equal a 10x increase in computing power, an imminent threat looms over enterprises.
Experts believe that even if the community creates a state-of-the-art AI model, they cannot guarantee beneficial and successful results. They elaborate that the model might focus on misleading variable correlations rather than identifying hidden correlations that could actually provide insight.
Data practitioners must realize that their efforts are only expanding AI-capabilities that already exist and produces efficient results. An increasing number of companies invest in the expansion of algorithmic efficacy by experimenting with new technology and innovations. However, the issue is that the algorithms only cater to some specific tasks. Linear progress rather than a thought progressive advancement is one of the key reasons behind the infodemic.
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To find a better working solution that can augment AI resources, experts suggest the integration of artificial intelligence and the human touch. AI should be implemented as a rule-based algorithm that hard codes human judgment. It can work wonders. Such models could be used on security applications that would need less training data. Many security vendors have already begun to favor AI-driven solutions that enhance human judgment.
While it is still early to evaluate the AI-human judgment combination success rate, AI experts urge the implementation of hardware and purpose-built cloud instances for AI computation. Some hardware and software are tailor-made for AI applications and are capable of unparalleled computations, processing of graphs, and matrix multiplications.
Conventional beliefs about maximum data providing maximum results may not hold true for very long. Industry experts are beginning to identify the dark side of data-hungry brands. Enterprises can find themselves to be more productive, actionable, and cost-effective by being wary of data abundance.
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