Just ducky: Engineers develop robots to replace ducks in cleaning and patrolling rice paddy fields.

Aigamo ducks in a rice paddy.

It’s a common theme that we hear: Artificial intelligence and robotics are coming for many of the jobs that have traditionally been performed by humans.

But what about the fate of animals?

That prospect was raised recently by David Mantey, a writer for Thomas Publishing, in an article about what’s happening in rice paddy fields.  And it involves ducks.

More specifically, aigamo ducks, which are a cross between mallards and domestic fowl. There is a farming method, originating in Japan, that employs these creatures to clear and keep unwanted plants and parasites out of rice paddy fields.

Essentially, it’s an environmentally-friendly practice in which the ducks keep the paddies clear without the need for pesticides. As an ancillary benefit, the ducks’ own waste acts as fertilizer for the rice plants.

The centuries-old practice was revived in Japan the mid-1980s, and has since become a popular natural rice farming method beyond that country, used in places like China, Iran and France.

Broadly speaking, approximately 15 ducks can keep more than a 10,000 sq. ft. area clear of weeds and insects, while also enriching the water with oxygen via stirring up the soil beneath.

It seems like a neat and tidy solution all-around — and one that works based on decades of experience with the farming practice. But as it turns out, it’s something that a robot can accomplish, too (well, maybe not the duck waste bit) — with certain improvements on the original tradition.

A rice paddy robot doing its thing.

While ducks can be “trained” to patrol specific areas of a rice paddy, it isn’t a foolproof proposition. As for the robotic version (which looks more like a white, floating ROOMBA® than it does a duck), it utilizes wi-fi and GPS technology to stir up the soil and keep the bugs at bay.

Reportedly, the robot is more accurate and more consistent in its execution compared to the aigamo ducks.

At the moment, the rice paddy robot is in an experimental phase with beta prototypes patrolling paddies in Yamagata Prefecture in northern Japan — and it’s too soon to know if or when the robot will be deemed ready for commercialization.

But the development goes to show that robots are spreading into some very surprising corners.  Indeed, it seems that robotics technology knows no bounds.

Marketing AI and Machine Learning Come Into Better Focus

Artificial intelligence and machine learning are two phrases that have become regular currency in the marketing world over the past several years. It isn’t hard to figure out why, as both AI and machine learning have the potential to help marketers make sense of the ever-increasing volume (and complexity) of raw data that’s become available in increasing amounts, thanks to the digitization of “everything.”

Some people use the two terms interchangeably, but that isn’t exactly right. According to Thorin McGee, director of content at Fast Capital 360, the distinction is subtle yet significant:

  • AI is when you develop an algorithm that allows a computer to “think” for you towards achieving a goal.
  • Machine learning is when you let a computer create an algorithm to find ways to meet the goals you give it, based on large pools of data.

Put the two together, and you have the ability to gain some really deep insights into what your data is actually telling you, thereby improving decision-making success.

On the data front, this great potential is tempered by some significant challenges. Christopher Penn, chief innovation officer of marketing data and analytics consulting firm Trust Insights, characterizes them as the “5 V’s” of data:

  1. Volume — There’s so darned much of it.
  2. Variety — More kinds of data are being churned out.
  3. Velocity — Data is coming at us faster than ever.
  4. Veracity — If data isn’t verified, it can do more harm than good.
  5. Value — In raw form, data isn’t particularly useful. Like oil, data needs to be refined to be of value.

If getting your arms around data seems like trying to hug a stream of water, you aren’t alone in thinking that. Many companies are pretty adept at using data to identify what happened — and maybe even to diagnose problems and why they happened.  But it’s less easy to predict what will happen based on data … and even harder to use data to determine with confidence what should happen.

The biggest challenge — but also the one with the biggest potential payoff — is tapping machine learning to process and use data in forging future business as you wish it to be.

To date, very few companies have come all that close to becoming AI-powered enterprises. But it’s where we’re headed in the coming decade.  It represents one of the biggest opportunities for differentiating one company from another.  But it will require a disciplined and concerted effort:  talent acquisition (developers and data scientists), tapping outside vendors, along with taking available open-source code and building upon that to implement the appropriate marketing technologies.

Oh, and committing to a multi-year initiative and budget even after all of those other pieces are in place.

Surveying the current landscape, are there particular entities that you see as on the leading edge in applying AI and machine learning to their marketing endeavors? Please share your observations with other readers.

How disruptive will artificial intelligence be to the jobs we know?

With artificial intelligence seemingly affecting everything it touches, one might wonder what AI’s impact will be on the employment picture in the years ahead.

It’s something that AI expert and author Kai-Fu Lee has thought about in depth. Lee is the former president of Google China and the author of the best-selling book AI Superpowers:  China, Silicon Valley and the New World Order.

Recently, Lee published a column in which he described ten job categories that he feels are “safe” for human workers – regardless of how the AI world may develop around us.

His list is predicated on several fundamental weaknesses Lee sees with AI in handling certain aspects of job performance. Those weaknesses include:

  • An inability to create, conceptualize or manage complex strategic thinking
  • Difficulty handling complex work that requires precise hand-eye coordination
  • An inability to deal with unknown or unstructured spaces
  • The inability to feel empathy and compassion … and to react accordingly
Kai-Fu Lee

In short, Lee discerns a particular weakness in AI’s ability to perform “humanistic” tasks – ones that are personal, creative and compassionate.  Hence, the type of jobs that rely on such qualities will be safer from disruption, he believes.

As for career categories that Lee singles out as generally safe from AI disruption, he cites these ten in particular:

Computer Science – Lee predicts that a substantial portion of computer engineers, IT administrators and technology consultants will continue operate in job functions that aren’t automated by technology.

Criminal Law – The legal profession won’t be adversely affected, considering the persuasive powers that are needed to sway juries with legal arguments.  However, some paralegal tasks such as document review will likely migrate to AI applications.

Management – Simply put, there are too many “moving parts” to management – and aspects that require human interaction, values and decision-making – to make it a field that’s amenable to AI.  Of course, if a manager is more along the lines of a bureaucrat carrying out set orders, that type of job may be more susceptible to AI disruption.

Medical Care – Lee envisions a symbiotic relationship between humans and AI — the latter of which can help with the analytical and administrative aspects of healthcare but cannot handle most other healthcare responsibilities.

Physical Therapy – Dexterity is a challenge for AI, which makes it unlikely for AI to replace jobs in this field (also including massage therapy).

Psychiatry – Positions in this category, which encompass social work and marriage counseling in addition to strict psychiatry, require keen emotional intelligence which is the domain of humans.

R&D (particularly in AI-related field) – While some entry-level R&D positions will become automated, increased demand for R&D talent will likely outnumber the jobs replaced by AI.

Science – According to Lee, while AI will be of tremendous benefit to scientists in terms of testing hypotheses, it will be an amplification of the discipline rather than taking the place of human creativity in the sciences.

Teaching – While AI will be a valuable tool for teachers and schools, instruction will still be oriented around helping students figure out their interests and providing mentorship – qualities that AI lacks.

Writing – Specifically fiction and other creative writing, because “storytelling” is an aspect of writing that AI has difficulty emulating.

So, there you have it – Kai-Fu-Lee’s fearless predictions about the job categories that will remain safe in an increasingly AI world. Can you think of some other categories?  Please share your thoughts and perspectives with other readers.