Data Management for Artificial Intelligence

Garbage in, garbage out

Think back to the grade-school game where one person whispers something in the ear of the next person, and the phrase is then whispered from person to person around a circle.

The last person reveals what he or she heard, and it is always something wildly and hilariously different from the statement that started the cycle.

Working with bad data can be like that.

And if your process is some form of artificial intelligence (AI) – where the machine actually adapts the underlying algorithms based on what it learns from the data – bad data can really get you into trouble.

The results will be wildly skewed from the input that went into the cycle, but it’s not hilarious at all.

Artificial intelligence is the science of training systems to emulate human tasks through learning and automation. With AI, machines can learn from experience, adjust to new inputs and accomplish tasks without manual intervention.

The explosion in market hype around the term is closely tied to advances in deep learning and cognitive science, but AI spans a variety of algorithms and methods. It doesn’t require the ashiest new technologies to still be considered an AI application.

As a topic of interest for years – from science fiction plots to futurists’ prophecies – the promise of AI has always been at the forefront of our minds. But what was once a distant vision is becoming reality as organizations embrace the value of AI now:

  • By 2025, the artificial intelligence market will surpass $100 billion. (Source: Constellation Research)
  • Seventy-two percent of business leaders believe AI will be fundamental in the future. (Source: PwC)
  • In the immediate future, execs are looking for AI to alleviate repetitive, menial tasks such as paperwork (82 percent), scheduling (79 percent) and timesheets (78 percent). (Source: PwC)
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