Another Week In AI & Machine Learning

Driveless

So the hot news this week is that people keep crashing into driverless cars. Wired magazine discusses the issue that the way in which the current generation of prototypes being driven on America's roads are experiencing a high level of accidents, apparently because they don't behave is the same way that ones driven by humans do. 86% of collisions in California this year have been due to being rearended or side wiped! Autonomous cars appear to be over cautious and annoy human drivers, not only by stopping unexpectedly but also because they are over compliant with the letter of the law in interpreting situations. So why don't they have signs on the car, just like a learner does to warn drivers that they need to keep clear? It's a pretty similar situation and people do tend to steer as clear as practicable from learners who they know to be unpredictable.

AWS Capability Continues to Grow

AWS also held its AWS Innovate Online Conference. In his "state of the nation" talk, Boaz Ziniman outline current capabilities available Off the Cloud (OTC) rather than Off the Shelf (OTS). Basically, AWS still provides impressive capabilities which allow a developer, data scientist or organisation to just start using AI and scale rapidly. Though they have been adding to this capability with impressively powerful capabilities around visual recognition and voice processing as well as bundled environments which are pretty much deploy and go. this is very liberating, because it provides a readily used Pay as You Go (PAYG) capability, which avoids much of the traditional issues of continuously having to research, select, implement, integrate and tune the suite of tools and infrastructure needed, as well as continuously returning to CAPEX approval and purchase processes for scaling to deal with increasing volumes of performance issues. 

There's a caveat though. Some of the capabilities, e.g. recognising a face in a crowd, which may be useful for security solutions, might also be used as the tools for enforcing a police state or merciless pursuit by paparazzi. Enabling such massive AI at scale, will to some extent be another nail in the coffin of personal privacy.

Early Adopters Surge Ahead

Finally, MIT Sloan and Boston Consulting Group released a report "Artificial Intelligence in Business Gets Real" which surveyed the application of AI in business across the globe. Whilst there were the usual scare stories about China being ahead of the West in adoption and that the gap between pioneers and laggards is growing larger there were some interesting points. The Chinese are deploying to meet efficiency and cost needs. Everyone else is deploying, because AI helps them do more and there was a message that AI will not replace jobs, but it will change the nature of work and the skills needed.

The second key message, was to experiment with something simple and demonstrate the benefits, because business leaders who had seen AI in practice, get it and are prepared to invest more, to the point that AI is almost addictive in the way in which it influences investment appetite once a business has tried it.

The final message was really around AI at scale. If you are planning on building your  future business model around AI, then you need to plan, prioritise long term investments and to get effective data governance in place. This does not just mean establishing once source of the truth, with valid, complete and coherent data. It also means having a good handle on version control. Since, if multiple applications of AI depend on the same data, it needs to be synchronised to avoid unintended problems. So Lean Data practices are fundamental to adopting AI at scale.

Finally, it was interesting to hear how paranoid Chinese companies, adopting AI, are about cyber security. Protecting their data from competitors and the continuing availability of data and AI based solutions becomes increasingly important once a company's business model has evolved to adopt AI. So the principle "Cherish your Data" (see The Way of DAU) is key to successful exploitation.

Conclusion

Human issues, ethics and common sense remain central to AI adoption, an Agile mindset encourages adoption and Data Governance is a key enabler.