On first thought, agtech seems something of a contradiction. Agriculture involves growing crops. There’s no real world shortcut or fast track in that plants require planting in appropriate seasons with access to sun, water and soil nutrients and time to grow. By comparison, tech is about speed, rapid prototyping and timely deployment.

But agtech is actually an evolving nexus between the two practices of agriculture and next-generation connected tech.

See also: Platagon partners with Dubai university to create urban agriculture center

Agriculture is an industry that, while rooted firmly in traditions passed down through generations of farmers, is responding to technical advances in machines, robotics, analytics and software. One of the companies at the forefront of this revolution is Arable Labs. Headed by CEO Dr Adam Wolf, their team includes product engineers, biologists, and mathematicians and they work to solve the critical issue of accurate forecasting in the food supply chain.

I spoke to Dr. Wolf to learn more about their progress over the last year. The company had the evening before, come runner-up at a competition by water innovation accelerator, Imagine H20. Arable have previously worked with the Met service for Zambia, to facilitate weather monitoring as well as the New York City Parks Service for assistance with weather tracking for storm management so they are well placed to apply their technology to water technology.

But their biggest success of late is a $4.25 million Series A round of funding led by Middleland Capital’s agriculture technology fund and S2G Ventures. With the new funds, the company will support the expansion of data science and analytics for the food and ag supply chain, fuelled by mass production of the Arable Mark IoT device (formerly known as PulsePod) later this year.

“We have really outstanding investors who understand the space. They are well connected, they know people who are either out ahead in growing, processing or retailing. When you’re in food and agriculture it’s a real secret world that is far enough from most people’s lived experience that it’s hard to really discern what the pain points are who gets what margin,” explained Wolf.

“It’s important for us to be really focused on supply chain risk, which is the relationship between the producer and the processor and from the processor to the retailer. To understand how those contracts between those parties are structured and what’s the risk of failure. For example, what happens when a restaurant chain asks for so much cilantro and but then the farmer delivers half as much as they promised. This happens all the time. And so for someone to be able to get the people in the room and go, ok let’s get some conversation going. Well, that is very powerful. That’s where we’re fortunate to have these investors.”

The Arable Mark delivers a level of plant health data that is unmatched in the industry. The Mark measures more dimensions of physical meteorology, at greater spatial density than any weather model or station network, and more plant attributes, at a greater frequency than any satellite or aircraft. By measuring over 40 individual environmental data streams, the Mark is the most data-rich device available in IoT.

The Mark’s launch coincides with the all-new cloud-based Arable Insights software platform for crop consultants, farmers, large-scale producers, and food processors in the agricultural supply chain. Insights enable each stakeholder to communicate with trusted business partners based on real-time field-level data for the first time ever. It is now possible for managers to benchmark crop performance and seasonal progress across hundreds of fields, while also being able to drill down and understand the details of growth or weather events.  The data synthesis of weather and crop growth enables Arable to predict timing, quality perishability, and yield.

“When we think of our business model as hardware-enabled software as a service where the data from the hardware enables an array of different services. What we’ve learned is that data from the field is like a well from which many people can drink” notes Wolf.

How big is automation in farming?

The general public tends to have a highly romantic image of farmers as salt of the earth folk who eschew the conveniences of modern technology for the simple life. But the reality is that farmers are hacking their tractors, drones are being deployed for crop surveying, and a farmer is more likely to consult a data platform than an almanac before sowing crops. Wolf explained that one of the values their company promoted was customer service:

“The initial vision that we had was to treat the farmers with more respect, with good design and price, and it will reward us later.”

A 2012 Agriculture Census revealed that during the past 30 years the average age of U.S. farmers has grown by nearly eight years, from 50.5 years to 58.3 years. Does this mean that people will be replaced by robots? Wolf counters this with the comment:

“Someone who is growing 35,000 acres of leafy greens to deliver it to restaurants, you want that person to be really good and consistent at it. They really have figured out how to grow it at scale and deliver safe affordable food on time.

But young people don’t want to go into low-paid jobs. In places with year-round production, there’s a lot of families, but the children of the Latino workers are going to college, they’re studying software engineering, they don’t want to work in the fields. So the farms have real concerns about maintaining the workforce that they already have and capturing the institutional knowledge of older generations. There is a tendency towards consolidation. So then that team is stuck with managing an ever more sprawling field.

We’re definitely not displacing people in what we do, we’re empowering people with productivity. By being able to automate data collection, people get better jobs. People can prioritize and plan where to put their focus.”

The company will begin shipping the Mark this spring and make it more widely available through distributors later this year.

Artificial Intelligence. I can’t think of a reference to the intellect that would feel more unauthentic and fake. It’s no wonder people turn to The Terminator or The Matrix to fathom what it’s all about. Fortunately, after 60 years of AI rumors fueled by academia and movies, we’re finally starting to see signs that it means more than just robots taking over.

Working in the tech industry, it’s ironic that the AI lightbulb clicked not through an understanding of machine learning or engineering — but through the human challenges we face in the technology world.

See also: Look at all the amazing things AI can do for lawyers

While at Yahoo! and Apple, it was amazing to be part of technologies that not only helped enable the modern cloud today but continue to support its more than doubling in performance every year. But on the people side, opening a new data center meant hiring a team with over 100 man-years of various expertise and experience. As these skills became harder and harder to find, we realized that since we couldn’t find more people that had them, we needed to figure out how to get more out of people that didn’t.

We’re living in a world where the evolution of technology is exponentially outpacing that of people. AI is now empowering machines with the intelligence to form its own insights and thoughts. What were sensors are now evolving into senses, where machines can acutely comprehend things like sight, sound, and touch.

The Internet of Things (IoT) is enabling “self-driving” actions to be performed, based on those thoughts and senses. Right in front of our eyes, technology is making the leap from being the inanimate tools of yesterday to serving as our collaborative and conscious AI-powered co-workers of tomorrow. As people, our responsibility is to serve as the manager and mentor of this new form of co-worker.

Using comprehensive human-to-machine learning interfaces, all people will have the ability to effectively teach and mentor our new co-workers using one’s own natural human language. Yes, human knowledge will be the foundation of what makes artificial intelligence real. This is why AI will prove to be more useful as an extension of human intelligence, than a substitution for it. It is then that it will no longer be viewed as Artificial Intelligence, but understood as Augmented Intelligence.

Enter augmented intelligence

AI is going to augment natural human intelligence and enable people to gain the world’s collective expertise while requiring less time and study than what has been required to become an expert in any one thing today. Traditionally in humans, an expert’s mind possesses fewer possibilities for slower growth, while a beginners mind offers many possibilities for rapid growth.

Augmented Intelligence will empower us with the best of both. In doing so, there is no reason to think that our own personal capabilities for intellectual advancement cannot equal or surpass the doubling per year that we see in computers. Yes, we will see the day when the augmented human expert will be able to get 2x smarter, every year.

Being a leader of people, I learned long ago that my own success is more defined by the augmented intelligence and capabilities that my team provides than anything I could solely do as an individual. The same will apply to the new performance enhancing co-workers that will augment our intelligence and capabilities. Through today’s eye, this picture of our future may be seen as heresy or a superhuman threat. That said, the same fear occurred in the past when it was suggested that all people could be given the power of reading.

Evolution is life. Embrace the future.

We’ve all been inundated with the hype surrounding Internet of Things “smart stuff” and the impending arrival of our robot overlords, so we tend to minimize the mind-blowing wonder of the responsive and intelligent computing metamorphosis that is upon us.

For years, the IoT community has been saying that if we really want “things” to be of value, they cannot be dumb. The first wave was getting everything connected, and we have made headway there. The next step is to actually make “things” smarter. 

There are a variety of commercial solutions that do not really deliver on the promise of automating our way to more productive lives. And the concerns over properly securing our connected things still weigh heavily. But there really have been transformative leaps in computing capability and achievable functionality. The killer use case for IoT is on the horizon, but before defining what that is and describing how it is going to manifest, I think it’s important to broadly identify how we got here.

The “Trinity”

The impact of the open source movement in driving exponential leaps in technological advancement cannot be minimized. The algorithms and computing infrastructure that drive “smart” things — IoT, Artificial Intelligence, and machine learning capabilities — have been around for decades. Anyone at the NSA can tell you as much.

The difference now is in accessibility to the masses. These technologies were once jealously guarded, closed off from the wider world, and only available within formidable institutions possessing vast resources in both personnel and compute power. Open source changed all that. New things no longer have to be constructed from ground zero, thus supercharging the innovation cycle. The widespread access to knowledge bases and software allows anyone so inclined to build upon the shoulders of giants and leverage the wisdom of crowds.

The creative explosion fueled by open source helped give rise to the cloud, which is the second movement responsible for ushering in our new era of computing. Freed from the physical limitations and expense of individual server stacks and on-premise storage, the “app for everything” age dawned and the capacity for on-demand collection and consumption of big data was unleashed. Once we could scale compute power unconstrained by geography, our technology became mobile and the dream of smaller and increasingly powerful devices trafficking in colossal quantities of information became a reality.

Big data gives lifeblood to modern computing. But data does not do anything and, in itself, has no value. This brings us to the third movement in the “smart” revolution: analytics. The types of augmented computing that people encounter in everyday life now — voice recognition, image recognition, self-driving and driver-assisting cars — are founded in concepts that rose out of analytics and the pursuit of predictive analytics models, which was all the rage just a few short years ago.

The disheartening realization with predictive analytics was that, to train effective models, you need both massive amounts of data and scores of data scientists to continually build and maintain and improve data models. We were once again running up against the roadblocks of access and resource constraint.

And so we arrive at the present, where things are shifting in a new direction. The difference now is that we do not need to recruit an army of data scientists to build models; we have taught our programs to remove some of those roadblocks for themselves.

Inherent intelligence

Our AI-driven systems, especially Deep Learning systems, can now be fed millions upon millions of training sets, train in days/hours, and continuously re-train as more data becomes available. Open source tools and cloud computing are still important and evolving, and we still traffic in loads of data to perform lightning-fast analysis, but our programs now incorporate AI as the engine to make themselves “smarter.”

Expertise from vastly different computing realms has congealed to imbue programs with previously unimagined capabilities. The paradox is that as the cloud becomes ever more powerful and less expensive, the smart IoT strategy is to move much of the first line of entry processing away from the cloud and to the edge. This serves two purposes: to enable on-device decisions without needing cloud intervention and to deliver edge patterns and analytics to the cloud for fast second-stage analytics. Tiny AI engines can now perform analysis in near real time on edge devices and “things” no larger than a matchbook. And as these points of computational power grow increasingly commonplace in ordinary objects — intelligent routers and gateways, autonomous vehicles, real-time medical monitoring devices — their potential functionality expands exponentially.

John Crupi, Vice President and Engineering System Architect, Greenwave Systems

Artificial intelligence at the edge

In the early days of IoT (aka M2M), the focus was on getting data up to the cloud when possible. FTPing log files every night was the rage. When General Electric came on the scene with the “industrial internet,” everyone began talking about real-time live data connectivity. That was a big jump from FTP, but people treated edge devices as simply “things” that transferred data to the cloud for analytics. We are now in the midst of an exponential reverse fan out of that thinking. Real-time requirements are redefining the paradigm. The cloud is now shifting into the role of IoT support and second-tier analytics, and the processing is getting pushed out to the edge.

For example, we have been working with a company developing a next-generation medical monitoring device. Initially, we assumed with such a small device, we would send raw data from the device to the cloud for analysis. But that is not what was desired, nor is it what transpired. The company wanted the analytics on the monitor. They wanted the analytics and pattern detection to occur directly on the device, to take actions on the device, and for only “intelligent” (as opposed to raw) data be sent up to the cloud. The model differed dramatically from standard industrial M2M operations — where everything would be connected, and batches of data coming in from all sources would be collected and processed on some set timeline at some central repository.

The whole purpose of connecting now is to obtain instantaneous precision results at the point of entry for immediate answers. Even the low latency involved in “traditional” cloud-processing with hundreds of thousands if not millions and billions of devices is not as efficient for real-time edge analytics as using this new architecture. In some cases, you can achieve a data reduction of 1,000x by just sending the analytics and patterns vs. raw data to the cloud. 

We no longer deal in dumb collection devices; we need them to do more than just curate. They must be artificially (and naturally) intelligent — capable of doing pattern recognition and analytics in their tiny engines. They push those results up to the cloud for other uses. As this ideal proliferates, so, too, do the possible applications.

As is perfectly embodied in the example of an autonomous car, this dual edge/cloud analytics model produces precision, real-time results that can be continually and automatically refined against ever-growing troves of more data, thus producing valuable, useable information and powering productive action. Even a year ago, I would have called B.S. on this notion for widespread IoT and AI integration — but edge computing and AI have really broken out of the lab and into our world. It will yield outcomes we have never seen before.

The killer use cases for IoT are manifesting through truly intelligent edge devices — in solutions that are purpose-built for specific problems or tasks, then interconnected and subjected to patterns that move beyond their initial application. As more and more smarter, AI-enabled “things” are incorporated into our everyday lives and operate at the edges of our inter-communicating networks, we will see things moving beyond merely being connected and into actively embodying intelligence. Smart stuff indeed.

This article is produced in partnership with Greenwave Systems.

The author is Vice President and Engineering System Architect at Greenwave Systems, where he guides development on the edge-based visual analytics and real-time pattern discovery environment AXON Predict. He has over 25 years of experience executing enterprise systems and advanced visual analytics solutions.