Month: November 2018

What Do Investors Need to Know About the Future of LED Grow Light Technology – Agfund

The horticultural lighting market is growing, and growing rapidly. According to a September press release from Report Linker, a market research firm specializing in agribusiness, the horticultural lighting market is estimated grow from a $2.43 billion market this year to $6.21 billion in 2023.

One of the key factors driving current market sector growth is increased development of LED grow light technology. LEDs (light emitting diodes) were first developed in the 1950s as a smaller and longer-lasting source of light compared to the traditional incandescent light bulb invented by Thomas Edison in 1879.

LEDs last longer, give off less heat, and are more efficient converting energy to light compared to other types of lights, all features that can result in higher yields and profits for indoor growers.

But until recently, LEDs were only used to grow plants indoors experimentally, largely because the cost was still too high for commercial businesses. Many commercial growers still use HID (High Intensity Discharge) lights such as High Pressure Sodium, Metal Halide, and Ceramic Metal Halide; all lights that have a high power output but are less durable than LED lights, generate far more heat, and have less customizable light spectra.

Today, LEDs are fast becoming the dominant horticultural lighting solution. This is due primarily to the one-million fold decrease in fabrication cost of semiconductor chips used to make LED lights since 1954.

For investors more familiar with field-based agriculture, it can certainly be a minefield to know where LED lighting technology for horticulture is going in the future. Although it is no longer the “early days” of LED technology development, current trends are still shaping the future of LED technology.

So what does the intelligent agtech investor need to know about the current state and future of LED grow light technology?

I interviewed Jeff Mastin, director of R&D at Total Grow LED Lighting, to discuss what the future of LED grow light technology for agriculture looks like, and how investors can use current trends to their advantage in the future.

What is your background – how did you get involved in grow light technology at Total Grow?

The company behind TotalGrow is called Venntis Technologies. Venntis has, and still does, specialize in integrating touch-sensing semiconductor technologies into applications.

Most people don’t realize LEDs are semiconductors; you can also use them for touch-sensing technologies, so there’s a strong bridge to agricultural LED technology.

Some of the biggest technical challenges in utilizing LEDs effectively for agriculture include LED glaring, shadowing and color separation.

We have used our expertise in touch-sensing LEDs to expand into horticultural LEDs, and we have developed technology that addresses the above challenges better, giving better control over the spectrum that the LED makes and the directional output of the light in a way that a standard LED by itself can’t do.

My personal background is in biology. When TotalGrow started exploring the horticultural world, that’s where being a biologist was a natural fit to take a lead on the science and the research side of the development process for the product; that was about 7 years ago now.

If you were going to distill your technical focus into trends that you’re seeing in the horticultural lighting space, what are the main trends to keep an eye on?

The horticultural lighting industry is really becoming revolutionized because of LEDs. Less than 10 years ago, LEDs in the horticultural world were mainly a research tool and a novelty.

In the past, they were not efficient enough and they were definitely not affordable enough yet to really consider them an economical general commercial light source.

But that is very quickly changing. The efficiencies are going up and prices down and they are really right now hitting the tipping point where for a lot of applications, but definitely not all applications, the LED world is starting to take over horticulture and indoor agriculture.

How do you view the translation of those trends into actionable points? For investors or technology developers in the agriculture technology space, how do they make sure that the LED light technology they are investing in isn’t going to be obsolete in a year or two?

With LEDs, the key question is still cost-efficiency, and there’s only so far the technology can improve.

Why? There are physical limitations. You can’t make a 100% efficient product that turns every bit of electricity into photons of light. At this point, the efficiency level of the top of line LEDs are up over 50%.

Can we ever get up to 70 or 80%? Probably not any time soon with an end-product, not one that’s going to be affordable and economical generally speaking.

So to answer your question, it’s not a category where you’re going to say, “well this is obsolete, I can get something three times better now.” The performance improvements will be more marginal in the future.

Ten years from now the cost will be cheaper. But that again doesn’t make current LED technologies obsolete. In terms of that fear, I don’t think people have to worry about current LED light technologies becoming obsolete.

In a large commercial vertical farming set up, what is the ballpark cost of horticultural LEDs currently?

To give just an order of magnitude sort of number, you’re probably going to be someplace in the $30 per square foot number for lights for a large facility. It can be half that or it can be double that.

That’s just talking within the realm of common vertical farming plants like greens and herbs, or other plants similar in size and lighting needs.

If you start talking about tomatoes or medicinal plants, then the ability to use higher light levels and have the plants make good use of it skyrockets. You can go four times higher with some of those other plants, and for good reason.

What type of horticultural lighting applications are LEDs still not the best solution for now and in the foreseeable future?

There are at least 3 areas where LEDs still may not make sense now and in the near future.

First, if the LED lights are not used often enough. The more hours per year the lights are used, the more quickly they return on their investment from power savings and reduced maintenance. Some applications only need a few weeks of lighting per year, which makes a cheaper solution appropriate.

Second, in some greenhouse applications, LED’s may not be the best choice for some time to come. Cheaper lights like high-pressure sodium have more of a role in greenhouses where hours of use are less and higher hang heights are possible. (Many greenhouses will still benefit strongly from LEDs, but the economics and other considerations make it important to consider both options in greenhouses.)

Lastly, some plants are not the best in vertical farming styles of growing where LEDs have their most drastic advantages. At least at this point it is not common to attempt to grow larger fruiting plants like tomatoes or cucumbers totally indoors, though when attempted that is still more practical with LEDs than legacy lights.

Source : https://agfundernews.com/what-do-investors-need-to-know-about-the-future-of-led-grow-light-technology.html/

6 Biases Holding You Back From Rational Thinking – Robert Greene

Emotions are continually affecting our thought processes and decisions, below the level of our awareness. And the most common emotion of them all is the desire for pleasure and the avoidance of pain. Our thoughts almost inevitably revolve around this desire; we simply recoil from entertaining ideas that are unpleasant or painful to us. We imagine we are looking for the truth, or being realistic, when in fact we are holding on to ideas that bring a release from tension and soothe our egos, make us feel superior. This pleasure principle in thinking is the source of all of our mental biases. If you believe that you are somehow immune to any of the following biases, it is simply an example of the pleasure principle in action. Instead, it is best to search and see how they continually operate inside of you, as well as learn how to identify such irrationality in others.

These biases, by distorting reality, lead to the mistakes and ineffective decisions that plague our lives. Being aware of them, we can begin to counterbalance their effects.

1) Confirmation Bias

I look at the evidence and arrive at my decisions through more or less rational processes.

To hold an idea and convince ourselves we arrived at it rationally, we go in search of evidence to support our view. What could be more objective or scientific? But because of the pleasure principle and its unconscious influence, we manage to find that evidence that confirms what we want to believe. This is known as confirmation bias.

We can see this at work in people’s plans, particularly those with high stakes. A plan is designed to lead to a positive, desired objective. If people considered the possible negative and positive consequences equally, they might find it hard to take any action. Inevitably they veer towards information that confirms the desired positive result, the rosy scenario, without realizing it. We also see this at work when people are supposedly asking for advice. This is the bane of most consultants. In the end, people want to hear their own ideas and preferences confirmed by an expert opinion. They will interpret what you say in light of what they want to hear; and if your advice runs counter to their desires, they will find some way to dismiss your opinion, your so-called expertise. The more powerful the person, the more they are subject to this form of the confirmation bias.

When investigating confirmation bias in the world take a look at theories that seem a little too good to be true. Statistics and studies are trotted out to prove them, which are not very difficult to find, once you are convinced of the rightness of your argument. On the Internet, it is easy to find studies that support both sides of an argument. In general, you should never accept the validity of people’s ideas because they have supplied “evidence.” Instead, examine the evidence yourself in the cold light of day, with as much skepticism as you can muster. Your first impulse should always be to find the evidence that disconfirms your most cherished beliefs and those of others. That is true science.

2) Conviction Bias

I believe in this idea so strongly. It must be true.

We hold on to an idea that is secretly pleasing to us, but deep inside we might have some doubts as to its truth and so we go an extra mile to convince ourselves — to believe in it with great vehemence, and to loudly contradict anyone who challenges us. How can our idea not be true if it brings out of us such energy to defend it, we tell ourselves? This bias is revealed even more clearly in our relationship to leaders — if they express an opinion with heated words and gestures, colorful metaphors and entertaining anecdotes, and a deep well of conviction, it must mean they have examined the idea carefully and therefore express it with such certainty. Those on the other hand who express nuances, whose tone is more hesitant, reveal weakness and self-doubt. They are probably lying, or so we think. This bias makes us prone to salesmen and demagogues who display conviction as a way to convince and deceive. They know that people are hungry for entertainment, so they cloak their half-truths with dramatic effects.

3) Appearance Bias

I understand the people I deal with; I see them just as they are.

We do not see people as they are, but as they appear to us. And these appearances are usually misleading. First, people have trained themselves in social situations to present the front that is appropriate and that will be judged positively. They seem to be in favor of the noblest causes, always presenting themselves as hardworking and conscientious. We take these masks for reality. Second, we are prone to fall for the halo effect — when we see certain negative or positive qualities in a person (social awkwardness, intelligence), other positive or negative qualities are implied that fit with this. People who are good looking generally seem more trustworthy, particularly politicians. If a person is successful, we imagine they are probably also ethical, conscientious and deserving of their good fortune. This obscures the fact that many people who get ahead have done so by doing less than moral actions, which they cleverly disguise from view.

4) The Group Bias

My ideas are my own. I do not listen to the group. I am not a conformist.

We are social animals by nature. The feeling of isolation, of difference from the group, is depressing and terrifying. We experience tremendous relief to find others who think the same way as we do. In fact, we are motivated to take up ideas and opinions because they bring us this relief. We are unaware of this pull and so imagine we have come to certain ideas completely on our own. Look at people that support one party or the other, one ideology — a noticeable orthodoxy or correctness prevails, without anyone saying anything or applying overt pressure. If someone is on the right or the left, their opinions will almost always follow the same direction on dozens of issues, as if by magic, and yet few would ever admit this influence on their thought patterns.

5) The Blame Bias

I learn from my experience and mistakes.

Mistakes and failures elicit the need to explain. We want to learn the lesson and not repeat the experience. But in truth, we do not like to look too closely at what we did; our introspection is limited. Our natural response is to blame others, circumstances, or a momentary lapse of judgment. The reason for this bias is that it is often too painful to look at our mistakes. It calls into question our feelings of superiority. It pokes at our ego. We go through the motions, pretending to reflect on what we did. But with the passage of time, the pleasure principle rises and we forget what small part in the mistake we ascribed to ourselves. Desire and emotion will blind us yet again, and we will repeat exactly the same mistake and go through the same mild recriminating process, followed by forgetfulness, until we die. If people truly learned from their experience, we would find few mistakes in the world, and career paths that ascend ever upward.

6) Superiority Bias

I’m different. I’m more rational than others, more ethical as well.

Few would say this to people in conversation. It sounds arrogant. But in numerous opinion polls and studies, when asked to compare themselves to others, people generally express a variation of this. It’s the equivalent of an optical illusion — we cannot seem to see our faults and irrationalities, only those of others. So, for instance, we’ll easily believe that those in the other political party do not come to their opinions based on rational principles, but those on our side have done so. On the ethical front, few will ever admit that they have resorted to deception or manipulation in their work, or have been clever and strategic in their career advancement. Everything they’ve got, or so they think, comes from natural talent and hard work. But with other people, we are quick to ascribe to them all kinds of Machiavellian tactics. This allows us to justify whatever we do, no matter the results.

We feel a tremendous pull to imagine ourselves as rational, decent, and ethical. These are qualities highly promoted in the culture. To show signs otherwise is to risk great disapproval. If all of this were true — if people were rational and morally superior — the world would be suffused with goodness and peace. We know, however, the reality, and so some people, perhaps all of us, are merely deceiving ourselves. Rationality and ethical qualities must be achieved through awareness and effort. They do not come naturally. They come through a maturation process.

Source : https://medium.com/the-mission/6-biases-holding-you-back-from-rational-thinking-f2eddd35fd0f

Here Are the Top Five Questions CEOs Ask About AI – CIO

Recently in a risk management meeting, I watched a data scientist explain to a group of executives why convolutional neural networks were the algorithm of choice to help discover fraudulent transactions. The executives—all of whom agreed that the company needed to invest in artificial intelligence—seemed baffled by the need for so much detail. “How will we know if it’s working?” asked a senior director to the visible relief of his colleagues.

Although they believe AI’s value, many executives are still wondering about its adoption. The following five questions are boardroom staples:

1. “What’s the reporting structure for an AI team?”

Organizational issues are never far from the minds of executives looking to accelerate efficiencies and drive growth. And, while this question isn’t new, the answer might be.

Captivated by the idea of data scientists analyzing potentially competitively-differentiating data, managers often advocate formalizing a data science team as a corporate service. Others assume that AI will fall within an existing analytics or data center-of-excellence (COE).

AI positioning depends on incumbent practices. A retailer’s customer service department designated a group of AI experts to develop “follow the sun chatbots” that would serve the retailer’s increasingly global customer base. Conversely a regional bank considered AI more of an enterprise service, centralizing statisticians and machine learning developers into a separate team reporting to the CIO.

These decisions were vastly different, but they were both the right ones for their respective companies.

Considerations:

  • How unique (e.g., competitively differentiating) is the expected outcome? If the proposed AI effort is seen as strategic, it might be better to create team of subject matter experts and developers with its own budget, headcount, and skills so as not distract from or siphon resources from existing projects.
  • To what extent are internal skills available? If data scientists and AI developers are already clustered within a COE, it might be better to leave the team as-is, hiring additional experts as demand grows.
  • How important will it be to package and brand the results of an AI effort? If AI outcome is a new product or service, it might be better to create a dedicated team that can deliver the product and assume maintenance and enhancement duties as it continues to innovate.

2. “Should we launch our AI effort using some sort of solution, or will coding from scratch distinguish our offering?”

When people hear the term AI they conjure thoughts of smart Menlo Park hipsters stationed at standing desks wearing ear buds in their pierced ears and writing custom code late into the night. Indeed, some version of this scenario is how AI has taken shape in many companies.

Executives tend to romanticize AI development as an intense, heads-down enterprise, forgetting that development planning, market research, data knowledge, and training should also be part of the mix. Coding from scratch might actually prolong AI delivery, especially with the emerging crop of developer toolkits (Amazon Sagemaker and Google Cloud AI are two) that bundle open source routines, APIs, and notebooks into packaged frameworks.

These packages can accelerate productivity, carving weeks or even months off development schedules. Or they can exacerbate collaboration efforts.

Considerations:

  • Is time-to-delivery a success metric? In other words, is there lower tolerance for research or so-called “skunkworks” projects where timeframes and outcomes could be vague?
  • Is there a discrete budget for an AI project? This could make it easier to procure developer SDKs or other productivity tools.
  • How much research will developer toolboxes require? Depending on your company’s level of skill, in the time it takes to research, obtain approval for, procure, and learn an AI developer toolkit your team could have delivered important new functionality.

3. “Do we need a business case for AI?”

It’s all about perspective. AI might be positioned as edgy and disruptive with its own internal brand, signaling a fresh commitment to innovation. Or it could represent the evolution of analytics, the inevitable culmination of past efforts that laid the groundwork for AI.

I’ve noticed that AI projects are considered successful when they are deployed incrementally, when they further an agreed-upon goal, when they deliver something the competition hasn’t done yet, and when they support existing cultural norms.

Considerations:

  • Do other strategic projects require business cases? If they do, decide whether you want AI to be part of the standard cadre of successful strategic initiatives, or to stand on its own.
  • Are business cases generally required for capital expenditures? If so, would bucking the norm make you an innovative disruptor, or an obstinate rule-breaker?
  • How formal is the initiative approval process? The absence of a business case might signal a lack of rigor, jeopardizing funding.
  • What will be sacrificed if you don’t build a business case? Budget? Headcount? Visibility? Prestige?

4. “We’ve had an executive sponsor for nearly every high-profile project. What about AI?”

Incumbent norms once again matter here. But when it comes to AI the level of disruption is often directly proportional to the need for a sponsor.

A senior AI specialist at a health care network decided to take the time to discuss possible AI use cases (medication compliance, readmission reduction, and deep learning diagnostics) with executives “so that they’d know what they’d be in for.” More importantly she knew that the executives who expressed the most interest in the candidate AI undertakings would be the likeliest to promote her new project. “This is a company where you absolutely need someone powerful in your corner,” she explained.

Considerations:

  • Does the company’s funding model require an executive sponsor? Challenging that rule might cost you time, not to mention allies.
  • Have high-impact projects with no executive sponsor failed?  You might not want your AI project to be the first.
  • Is the proposed AI effort specific to a line of business? In this case enlisting an executive sponsor familiar with the business problem AI is slated to solve can be an effective insurance policy.

5. “What practical advice do you have for teams just getting started?”

If you’re new to AI you’ll need to be careful about departing from norms, since this might attract undue attention and distract from promising outcomes. Remember Peter Drucker’s quote about culture eating strategy for breakfast? Going rogue is risky.

On the other hand, positioning AI as disruptive and evolutionary can do wonders for both the external brand as well as internal employee morale, assuring constituents that the company is committed to innovation, and considers emerging tech to be strategic.

Either way, the most important success measures for AI are setting accurate expectations, sharing them often, and addressing questions and concerns without delay.

Considerations:

  • Distribute a high-level delivery schedule. An unbounded research project is not enough. Be sure you’re building something—AI experts agree that execution matters—and be clear about the delivery plan.
  • Help colleagues envision the benefits. Does AI promise first mover advantage? Significant cost reductions? Brand awareness?
  • Explain enough to color in the goal. Building a convolutional neural network to diagnose skin lesions via image scans is a world away from using unsupervised learning to discover unanticipated correlations between customer segments. As one of my clients says, “Don’t let the vague in.”

These days AI has mojo. Companies are getting serious about it in a way they haven’t been before. And the more your executives understand about how it will be deployed—and why—the better the chances for delivering ongoing value.

Source : https://www.cio.com/article/3318639/artificial-intelligence/5-questions-ceos-are-asking-about-ai.html

Why Olam is Deploying Tech First, Then Thinking About CVC – AgFunder

Why Olam is Deploying Tech First, Then Thinking About CVC

“We have realized that some companies have gone down the wrong path by adopting the approach of inventing the problem. They find a technology that’s exciting and try to force-fit that technology for a problem that they don’t have. This is why we want to be very deliberate about the problems first, and then come to technology.”

Suresh Sundararajan is president and group head of strategic investments and shared services at Olam International, the Singapore-headquartered agribusiness giant. Sundararajan is speaking to AgFunderNews ahead of a speaking slot at the Rethink AgriFood Innovation Week in Singapore later this month.

“I’ll give you an example of blockchain. There’s so much hype about blockchain around the world. And in our industry, there are a few companies that have done some pilots. But we have not gone down that route, because we have not seen a tangible, scalable use case that could give us significant benefits for adopting blockchain.”

If one company could benefit from the efficiencies new technology can bring, it’s Olam, with a complex supply chain that grows, sources, processes, manufactures, transports, trades and markets 47 different agrifood products across 70 countries. These include commodities like coffee, cotton, cocoa, and palm oil that are farmed by over 4 million farmers globally, most of which are smallholders in developing countries.

In-House Tech

But the third largest agribusiness in the world has been noticeably absent from the agrifood corporate venture capital scene in recent years, instead opting mostly to build its own technology solutions in-house. (It did deploy Phytech’s FitBit for crops in Australia in 2016 as an outside example.)

For traceability, and perhaps an alternative to blockchain-enabled technology, there’s Olam AtSource, with a digital dashboard that provides Olam customers with access to rich data, advanced foot-printing, and granular traceability. Olam hopes AtSource will help its customers “meet multiple social and environmental targets thereby increasing resilience in supply chains.”

Olam has also developed and deployed the Olam Farmer Information System (OFIS), a smallholder farm data collection platform providing smallholders with management tools and Olam customers with information about the provenance of products.

“OFIS solves the information issue by providing a revolutionary tech innovation for collecting and analyzing first mile data,” Brayn-Smith told AgFunderNews when OFIS launched in 2017. “We are able to register thousands of smallholders, GPS map their farms and local infrastructure, collect all types of farm gate level data such as the age of trees, and record every training intervention.”

This product is a clear example of a “transformational technology” that solves a problem for Olam and also gives the business efficiencies that could impact the bottom line, according to Sundararajan.

And Olam has built on top of OFIS to transact directly with cocoa farmers in Indonesia where Olam is publishing prices to around 30,000 farmers and buying cocoa directly from them.

“Before technology was available, it was almost impossible for any company to buy directly from the farmers, just because of the sheer volume and number of farmers. But, with technology, you have a far better reach, which will allow us to directly communicate with them,” Sundararajan tells AgFunderNews.

“Now the farmer can just accept a price and type in that he wants to supply it, and we arrange the complete logistics to pick up the cocoa from the farmer,” he says adding that the company’s country heads in other parts of the world are keen to launch this service in their markets. The company is starting next in Peru, then Guatemala, Colombia, Cote d’Ivoire, Ghana, and Nigeria.

Olam as Disruptor

While Olam deployed OFIS to solve for a problem, it also gives the company the opportunity to be disruptive in the markets it serves, according to Sundararajan.

As well as looking for transformational ways to solve specific problems, Olam also looks at “any ideas we have that will give Olam an opportunity to disrupt our own industry. So, we end up being a disrupter and not be at the risk of being disrupted by a new player,” he says.

“This fundamental shift in terms of Olam getting an opportunity to directly interact and transact with farmers is a starting point of disruption for us. This is a very complex point, which will bring into play several technologies for us to be able to successfully scale it.”

Going down this route, Sundararajan says Olam could end up providing farmers with new services and creating “separate streams of revenue that has nothing to do with what we were doing five or 10 years back.”

In this vein, Olam is working on deploying a technology to detect moisture — and therefore quality — in its commodities. The company is also looking at financial tools for its farmers.

“Looking at our business model, we believe that we have a few very good opportunities at the first mile of the supply chain and the last mile of the supply chain to change the way we compete,” says Sundararajan. “We believe that since we have control of the supply chain end-to-end, we can use technology to differentiate our service to customers in a way that our competitors will find difficult to replicate.”

Informal Startup Interactions

Olam does interact with startups on a selective basis, and Sundararajan’s participation in Rethink’s Singapore conference, as well as a hackathon it took part in with Fujitsu in Australia last year, are two examples. Sundararajan said he is considering an idea like The Unilever Foundry, but the company has yet to create a formal process or framework for these interactions. And the same goes for corporate venture capital.

“We believe that our digital journey has to mature much more, where we should demonstrate success within, by implementing the solutions that we’re developing, before even considering investing in venture capital. We believe that we have a very good strategy and a suite of products, stretching across from farm to the factories, to digitize our operations, whether it is a digital buying model, or whether it is spot factories in terms of predictive maintenance or increasing yield or it’s drone imagery from our own plantations, and productivity apps for employees.”

Source : https://agfundernews.com/why-olam-is-deploying-tech-first-then-thinking-about-cvc.html/

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