Three reasons why AI is taking off right now (and what you need to do about it)

Tech and economics are aligning so that machine intelligence can take off in a big way: here's why.

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Machine intelligence will soon be pervasive and cheap to use.

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Three factors are combining to create a tipping point after which the use of artificial intelligence will become commonplace.

According to the Leading Edge Forum - the research arm of tech vendor CSC - while there is still plenty of work to do, the three main ingredients needed for AI to take off are now in place:

Big Data: Large unstructured data sets are handy for training powerful machine intelligence and there are now plenty of these around. Initiatives such as language translation and image, facial, activity and emotion recognition - are based on predictive analytics that get more accurate as the data behind them gets richer. And the rise of big data - and social media in particular - means there are lots of data sets to exploit. As the report notes, Facebook enjoys a huge head start in facial recognition because it can already match our names and faces, just as Google has important advantages in machine translation because it has aggregated the best set of multilingual documents.

"Looking ahead, new and established MI companies will use millions of internet images, videos and podcasts of people smiling, laughing, frowning, talking, arguing, holding hands, walking, playing football and so on as the basis for unprecedented emotion and activity recognition capabilities. MI is now clearly among the most important Big Data applications."

Software and hardware advances: It's long been known that neural networks and parallel processing would be important development tools of AI because they more closely resemble the way the human brain works. In particular, the emergence of GPU-based computing can greatly accelerate neural network processing capabilities - and if more processing power is needed there are the vast cloud computing resources of Amazon, Microsoft, Google. "Taken together, deep learning software and parallel processing hardware now provide a powerful [machine intelligence] platform," the report said.

Cloud business models: The emergence of machine learning business models based on the use of the cloud is the single biggest reason that the field is so energized today, the report said: "We are essentially seeing the merger of machine intelligence with cloud economics."

Before the cloud, most AI work was isolated and relatively high cost, but the economics of the cloud mean machine learning capabilities, such as recognizing faces or translating languages, will cheap and easy to use

"It is this realization that is triggering both the explosion of highly specialized MI start-ups, as well as the major machine intelligence pushes at Google, Facebook, Microsoft,Apple, IBM and their various global rivals."

The researchers set out a 10 point plan for organisations that want to prepare for machine intelligence:

1. Embrace the idea that machine intelligence will matter to your organization.

2. Identify which forms could be most important to your firm.

3. Check out relevant start-ups and developments.

4. Understand which parts of your firm could be safely run by algorithms.

5. Determine which internal and external data sets have the most potential.

6. Assess the extent to which your firm's key professional expertisecan be automated.

7. Try out deep learning, neural computing and other technologies.

8. Map the relevant MI services and technologies to your firm's value chain.

9. Develop machine intelligence experts in your organisation.

10. Factor AI advances into your strategic planning.

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