The farming industry is on the cusp of a so-called ‘technological revolution’. With drones, robots and intelligent monitoring systems now successfully being used in research and field trials, artificial intelligence, or machine learning, is set to revolutionise the future of farming as the next phase of Industrial Revolution in agriculture is on the horizon.

According to the UN Food and Agricultural Organisation (FAO), the global population is set to reach 9.2 billion by the year 2050. This means that the global agriculture sector is under more strain than ever with 2 billion more mouths to feed within the next 33 years. In such a scenario, a number of innovative agritech startups are coming up with increasingly accessible technology to transform the daily operations of the traditional family farms.

Vivek Rajkumar, the CEO-founder of Aibono, an agritech startup aimed holistically at turning ‘small’ to ‘smart’ in agriculture. The 30-something Social-Tech Entrepreneur who claims to be a third-generation farmer, quotes from the movie Martian, “There was a need to Science the hell out of Farming.” His Agritech Startup is transforming the model of agriculture from the ground up, attacking its inefficiencies by leveraging technologies, IoT, Crop Science and AI for farmers helping them grow profitable agri produce.

One of the biggest roadblocks to the growth of Indian agriculture is the low levels of yields. The predominant causes of low productivity are poor access to irrigation facilities; use of low quality seeds, low adoption of improved technology and lack of knowledge dissemination on improved agricultural practices. The challenge of small landholding size impacts diversification indices negatively. Technology and its access is a critical factor for diversified agriculture.

CXOToday: Can you share your views on how intelligent devices, such as robots and drones are enabling smart farming worldwide.

Rajkumar: There’s now worldwide pursuit towards Decision Agriculture powered by Data Sciences and Farm Analytics. We’re moving on from Precision Agriculture to Decision Agriculture. It’s an exciting time and we’d like to call it Agri 4.0 or the 4th Industrial Revolutionin farming. To set the context right, Precision Agriculture wave of the last decade is nothing short of impressive, from making inaccurate decisions on agro-chemical inputs without measurements, to technology around soil measurements and multispectral imaging of fields that enabled better decisions on agrochemicals, improving yields globally. Yet these remain been more congenial for large farms with economies of scale rather than smaller holdings.

While the last decade saw more broad-based decisions at farms, Industry 4.0, Intelligent Devices and Farm AI is rapidly enabling more granular decisions as well as in implementing these decisions in the farms. For example, “Variable Rate Fertigation” with a GPS enabled sprayer robot can vary the quantity of application per plant based on the ‘need’ at a hyperlocal level rather than treating 20,000 seedling in an acre of land as equals and applying the same quantity of fertilizer per plant; significantly increasing productivity, nutrient usage efficiency and an increased grower revenue. There are some good examples of start-ups from the West making farm robots that selectively removes weeds from Lettuce beds using image processing or apply herbicides.

The real potential in intelligent devices is in how it decentralizes scale to make efficient farming practices and higher productivity available for a farmer of smaller scale. Drones are replacing larger airplanes for crop dusting and satellites for imaging and Robots as a Service (RaaS) using machine learning and AI are replacing larger tractors. This era will be a game changer for a nation of small farms such as India, but for us, Robots and Intelligent devices will be ‘Part 2’ of Indian-ising Agriculture 4.0. The Part 1 is Information Services and AI.

CXOToday: How is it different in India? In a cost sensitive market like India, how can small entities afford these technologies?

Rajkumar: India holds the second largest agricultural land in the world, yet India’s agricultural landscape comprises 85% of small and marginal farmers operating on land holding of 2 acres of less. The average Indian small farmer, representing a 200 Million population, is therefore averse of capital or infrastructure spends, let alone intelligent or informed methods of farming. Lack of economies of scale had made modern technology, resources, experts or farm measurements unaffordable.

However, the 4th Revolution of Farming presents a wonderful opportunity to us, a nation of small farmers to bid adieu to poor yields and bad farm economics, that has been plaguing us for decades.

When it comes to India, we are entering an era of Internet Enabled Shared Services. For us, the answer in farming was probably never Capital. It is Services, Sharing & Data. We’ve got the fastest growth of affordable internet, smartphone adoption and the world’s largest population of youth (a 200 million) who can gather and interpret data from farms. I’d say the stage is set for Agri 4.0 and to turn our small farmers to smart farmers. Adoption of technology and the superior yields of Agri 4.0 will happen in two stages in India.Stage 1: At an Information level (now-2022): Shared Services & Equipment, Data Science and AI that helps small farmers make intelligent and informed decisions all the way from choice of crops to hedging risks to precise day-to-day agrochemical application for maximising yields and return of efforts.Stage 2: At a Hardware Level (2022-future): Farmers sharing smarter farm machinery and hardware with small form factor and higher degree of intelligence enabled by AI & Cloud, affordable for small holdings.

– CXOToday: Do you think farmers would be willing to trust such high-end tech solutions and researchers more than their age-old traditions?

Rajkumar: In our experience, for a farmer, seeing is believing. Better yields, better harvests, uniformly looking farm as opposed to sparsely spread out crops – are a few critical aspects that triggers a farmer’s attention. Despite age old traditional methods, a farmer today is more dependent on NPK fertilizers from the local store.

With respect to high-end tech solutions, Aibono provides Smart Farming Services, shared among Collectives of farmers that Aibono aggregates. Farmers share Sensors, Farm Managers, Data, Intelligence, Farm Experts, Tech Support, & Farm Equipment backed by AI and Data Analytics and in our Lab Farms, we demonstrate data led decisions. We earn the trust of a farmer and integrate ourselves around him and his community, which makes our Smart Farming Collectives both people and technology centric, and on the Cloud and on the Ground.

Also, the farmers are a sweet bunch – if they see results and get to like something, they spread the word our fast. Word of mouth is even faster than an app making the runs in an urban community. They are very results-driven and value science, agricultural expertise and modern methods.

– CXOToday: What was the idea behind Aibono? How exactly are you helping the farming agricultural sector in scaling up and remain sustainable?

Rajkumar: Aibono began in the niche area of providing Farm Management as-a-Service whereby, a farmer gets to outsource his entire measurement, production management and decision-making processes to a Service. We provide this service on a sharing basis deploying a shared Farm Manager along with shared instruments mapping the data onto cloud. Our centrally managed Data Science and Recommendation Engines enabled our by Data Scientists and agronomists give precise day-to-day interventions to farmers, enabling a 30-50% increase in yields.

Further, we evolved this model into incorporating what we call ‘Smart Farming Collectives™’ where groups of farmers collectively produce a uniform supply of a given mix of produce by sharing decisions and opportunities of choice of crops and collectively sharing resources. Thus they don’t compete amongst each other by producing the same crop, and the collective also enables the farmer to reach the end market. We’ve generated +1000 harvests, with 30-50% higher yields and consistently good incomes that come to the doorstep of the farmer. The sustainability of how we have interpreted Agri 4.0 for India, is really in the improved P&Ls of the farmer, our ally, enabled through our Smart Farming Collectives.

– CXOToday: At the moment, what are some of the biggest challenges in agricultural practices you are trying to solve with technology?

Rajkumar: One of the biggest roadblocks to the growth of Indian agriculture is the low levels of yields. The predominant causes of low productivity are poor access to irrigation facilities; use of low quality seeds, low adoption of improved technology and lack of knowledge dissemination on improved agricultural practices. The challenge of small landholding size impacts diversification indices negatively. All are triggered by economies of scale of a small farm. Farming yields in India can be as low as 20%-30% of Global benchmarks and our farming community has been cornered for decades due to lack of scale.

Technology and its access is a critical factor for diversified agriculture. With Internet Enabled Shares Services and our Smart Farming Collectives, we leverage Sharing & Aggregation to re-create economies of scale and increase yields by an order. We don’t stop there, we go the whole length to get the farmer a buyer and the produce a rightful place in the value chain, an enable a better income and return on effort that justifies his efforts. And, everybody wins.

– CXOToday: Can you give an instance where smart modern technologies such as big data, IoT and AI are being used to resolve some of these issues?

Rajkumar: Farm Analytics is one of the hottest drivers of the farming industry right now. The end consumer in agriculture is not always very aware of what trends have arrived now. Take this company called Solum Inc, an Iowa-based Agri-Data science company, whose soil sciences arm got acquired by weather & farming data analytics company called Climate Corporation, which again was acquired by Monsanto for $930 million, which in turn got acquired by Baeyer- The world’s largest agrochemical company- for $66 billion. All of this, in the last couple of years.

– CXOToday: What technologies are you offering farmers to maintain and scale crop diversity and intensive farming?

Rajkumar: An average vegetable farmer in India grow 4-6 crops cycle a year. Aibono’s farmers grow up to 24 crops per year with ERP Assisted Farming. Crop management for a small landholding farmer is quite confusing. In a Factory, ERP can enable small teams managing simultaneous batches, SKU variants and changeovers handle complex processes and reminders with ease. We provide Assisted ERP services for complex farm management processes, that not only enables our collectives of small farmers to push beyond 6 crops a year to 24 crops/year – even though it makes the farmer’s Crop Management and maintaining diversity 4x complex. The benefits however are significantly better portfolio risk management and more uniform earnings that outweigh the hassles.

– CXOToday: What do you see as the future of farming in terms of using smart technologies?

Rajkumar: India, as a country, has one of the largest Youth population, with a complex diversity and an infamous Jugaad mind that can adopt new tools and frugally solve problems and now we have taken the world by surprise in our ability to be the fastest adopters of smartphones and the internet. There are 400 million smartphone users in India – and aside just a fraction in cities, our use base is far beyond urban. We are after all the IT hub of the world.

The industry so far is depended heavily on scale of the user and therefore struggled to improve lives of small farmers in India. Agri 4.0 is perfect for an Evergreen Revolution in India, favouring small farmers and leveraging the IT & Smartphone Savvy Youth as a bridge between our farmers and cloud. Driven by internet, commoditised smartphones, smart sensors, AI and Cloud, Agri 4.0 is about Scale at the Cyberspace but Distributed in the physical space. Aibono as a company is marching towards a Self-Service model, where the farmers will be able to enrol for Shared services, ERP, shared equipment, farmer collective and intelligent market access and supply chain, where they will be enabled to sign-up and operate by themselves.

The farmer of the future will not have to go out of his way to access AI, Cloud, Farm Robots, Market Access or an Income that justifies his efforts. He will be served at his farm thanks to concepts like cyber physical share farming.

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There are a lot of myths out there about artificial intelligence (AI).

In June, Alibaba founder Jack Ma said AI is not only a massive threat to jobs but could also spark World War III. Because of AI, he told CNBC, in 30 years we’ll work only 4 hours a day , 4 days a week.

Recode founder Kara Swisher told NPR’s “Here and Now” that Ma is “a hundred percent right,” adding that “any job that’s repetitive, that doesn’t include creativity, is finished because it can be digitized” and “it’s not crazy to imagine a society where there’s very little job availability.”

She even suggested only eldercare and childcare jobs will remain because they require “creativity” and “emotion”-something Swisher says AI can’t provide yet.

I actually find that all hard to imagine. I agree it has always been hard to predict new kinds of jobs that’ll follow a technological revolution, largely because they don’t just pop up. We create them. If AI is to become an engine of revolution, it’s up to us to imagine opportunities that will require new jobs. Apocalyptic predictions about the end of the world as we know it are not helpful.

Common confusion

So, what may be the biggest myth- Myth 1: AI is going to kill our jobs -is simply not true.

Ma and Swisher are echoing the rampant hyperbole of business and political commentators and even many technologists-many of whom seem to conflate AI, robotics, machine learning, Big Data, and so on. The most common confusion may be about AI and repetitive tasks. Automation is just computer programming, not AI. When Swisher mentions a future automated Amazon warehouse with only one human, that’s not AI.

We humans excel at systematizing, mechanizing, and automating. We’ve done it for ages. It takes human intelligence to automate something, but the automation that results isn’t itself “intelligence”-which is something altogether different. Intelligence goes beyond most notions of “creativity” as they tend to be applied by those who get AI wrong every time they talk about it. If a job lost to automation is not replaced with another job, it’s lack of human imagination to blame.

In my two decades spent conceiving and making AI systems work for me, I’ve seen people time and again trying to automate basic tasks using computers and over-marketing it as AI. Meanwhile, I’ve made AI work in places it’s not supposed to, solving problems we didn’t even know how to articulate using traditional means.

For instance, several years ago, my colleagues at MIT and I posited that if we could know how a cell’s DNA was being read it would bring us a step closer to designing personalized therapies. Instead of constraining a computer to use only what humans already knew about biology, we instructed an AI to think about DNA as an economic market in which DNA regulators and genes competed-and let the computer build its own model of that, which it learned from data. Then the AI used its own model to simulate genetic behavior in seconds on a laptop, with the same accuracy that took traditional DNA circuit models days of calculations with a supercomputer.

At present, the best AIs are laboriously built and limited to one narrow problem at a time. Competition revolves around research into increasingly sophisticated and general AI toolkits, not yet AIs. The aspiration is to create AIs that partner with humans across multiple domains-like in IBM’s ads for Watson. IBM’s aim is to turn what today’sjust a powerful toolkit into an infrastructure for businesses.

The larger objective

The larger objective for AI is to create AIs that partner with us to build new narratives around problems we care to solve and can’t today-new kinds of jobs follow from the ability to solve new problems.

That’s a huge space of opportunity, but it’s difficult to explore with all these myths about AI swirling around. Let’s dispel some more of them.

Myth 2: Robots are AI. Not true.

Industrial and other robots, drones, self-organizing shelves in warehouses, and even the machines we’ve sent to Mars are all just machines programmed to move.

Myth 3: Big Data and Analytics are AI. Wrong again. These, along with data mining, pattern recognition, and data science, are all just names for cool things computers do based on human-created models. They may be complex, but they’re not AI. Data are like your senses: just because smells can trigger memories, it doesn’t make smelling itself intelligent, and more smelling is hardly the path to more intelligence.

Myth 4: Machine Learning and Deep Learning are AI. Nope. These are just tools for programming computers to react to complex patterns-like how your email filters out spam by “learning” what millions of users have identified as spam. They’re part of the AI toolkit like an auto mechanic has wrenches. They look smart-sometimes scarily so, like when a computer beats an expert at the game Go -but they’re certainly not AI.

Myth 5: Search engines are AI. They look smart, too, but they’re not AI. You can now search information in ways once impossible, but you-the searcher-contribute the intelligence. All the computer does is spot patterns from what you search and recommend others do the same. It doesn’t actually know any of what it finds; as a system, it’s as dumb as they come.

In my own AI work, I’ve made use of AI whenever a problem we could imagine solving with science became too complex for science’s reductive approaches. That’s because AI allows us to ask questions that are not easy to ask in traditional scientific “terms.” For instance, more than 20 years ago, my colleagues and I used AI to invent a technology to locate cellphones in an emergency faster and more accurately than GPS ever could. Traditional science didn’t help us solve the problem of finding you, so we worked on building an AI that would learn to figure out where you are so emergency services can find you.

By the way, our AI solution actually created jobs.

AI’s most important attribute isn’t processing scores of data or executing programs-all computers do that-but rather learning to fulfill tasks we humans cannot so we can reach further. It’s a partnership: we humans guide AI and learn to ask better questions.

Swisher is right, though: we ought to figure out what the next jobs are, but not by agonizing over how much some current job is creative or repetitive. I would note that the AI toolkit has already created hundreds of thousands of jobs of all kinds-Uber, Facebook, Google, Apple, Amazon, and so on.

Our choice is continuing the dystopian AI narrative about the future of jobs. or having a different conversation about making the AI we want happen so we can address problems that cannot be solved by traditional means, for which the science we have is inadequate, incomplete, or nonexistent-and imagining and creating some new jobs along the way.

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One of the biggest problems with Washington is that more often than not the policy conversation isn’t grounded in the facts. We see this dysfunction clearly on technology policy, where Congress is largely uninformed on what the future of artificial intelligence (AI) technology will look like and what the actual consequences are likely to be. In this factual vacuum, we run the risk of ultimately adopting at best irrelevant or at worst extreme legislative responses.

That’s why I was particularly interested to see the comments by Tesla CEO Elon Musk to the National Governors Association that “AI is a fundamental existential risk for human civilization.” Musk is a tremendous innovator and someone who understands technology deeply, and while I don’t agree with his assessment, his dramatic statement is a challenge to lawmakers to start seriously examining this topic.

This is precisely why I’ve launched the Artificial Intelligence Caucus, a bipartisan initiative to better inform policymakers on the technological, economic and social impacts of AI. The AI Caucus is co-chaired by my colleague, Rep. Pete Olson, and includes members from every region of the country. Olson is a Republican from Texas and I’m a Democrat from Maryland, we have different perspectives and political philosophies, but we both agree that step one is that educating Washington on what the changes to come will look like.

The AI Caucus is working to bring together experts from academia, government and the private sector to discuss the latest technologies and the implications and opportunities created by these new changes. Already this year, we’ve been briefed by a variety of specialists and fellow policymakers from both Europe and the United States and the caucus participated in events this month organized by IBM.

Congress needs to have a better grasp of what AI actually looks like in practice, how it is being deployed and what future developments likely will be, and that’s where the AI Caucus comes in. AI won’t just impact one specific field or region and the issues it will raise will not fall under the jurisdiction of a single committee; ironically, AI is potentially such a big change that we might not see the forest for the trees.

It is clear that we are on the verge of a technological revolution. Artificial intelligence promises to be one of the paradigm-shifting developments of the next century, with the potential to reshape our economy just as fully as the internal combustion engine or the semiconductor. Contrary to some portrayals, AI is less about the Terminator and more about using powerful cognitive computing to find new treatments for cancer, improve crop yields and make structures like oil rigs safer. AI programming is a key component of emerging driverless car technology, new advances in designing robots to perform tasks that are too dangerous for humans to do and boosting fraud protection programs to combat identity theft.

As a former entrepreneur, I believe that innovation should always be encouraged, because it’s fundamental to economic growth. Imagine if we’d tried to put the brakes on the development of telephone or radio technology a century ago, personal computer technology a generation ago or cell phone technology a decade ago. Innovation creates new opportunities that are hard to predict, new jobs, even entirely new industries. Innovation can also boost productivity and wages and reduce costs to consumers.

But that doesn’t mean that there aren’t relevant concerns about the disruption that AI could bring. Again, it’s all about the facts, and in the past, new technologies have hurt certain jobs. While the overall impact might have been positive, there have still been industries and regions that have been hurt by automation. In manufacturing especially, we’ve seen automation reduce the number of jobs in recent years, in some cases to devastating effect.

We need to be honest about the fact that AI technology will replace some jobs, just as what happened under advances. In my view, we need to start the conversation now and take a hard look at how we can help those individuals who will be hurt. As policymakers, we should be thinking about those people who are working in jobs that are at risk and seeing what we can do to get them through this eventual change. We should focus on preparing our country for this next wave of innovation.

As I think about policies that help anticipate AI and the changes it will bring, it is my view that the country needs to become more entrepreneurial and more innovative. That means we should make it easier to start a business and encourage more startups, invest more in things like research and infrastructure, all to become a more dynamic economy. We have to think through how we can make benefits more portable and how we can create a more flexible high-skill workforce. Combined with long-term trends that will create an older society, we must anticipate that the shape of the economy and the job market will look very different in the decades to come. The emergence of AI is also another reminder of making sure that our social safety net programs will be able to meet the needs of the future. AI will also create new ethical and privacy concerns and these are issues that need to be worked out. I believe that it is imperative that we tackle these emerging issues thoughtfully and not rush into new programs or regulations prematurely.

My colleagues on the AI Caucus each have their own ideas and concerns and part of the caucus’s function is to also facilitate a dialogue between lawmakers. Our choice is to either get caught flatfooted or to proactively anticipate how things will change and work on smart policies to make sure that the country benefits as much as possible overall. The only way to do that is to become focused on the facts and focused on the future and the AI Caucus is a bipartisan effort to make that happen.

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