How Zoom Created An $18 Billion Juggernaut

This week, I attended Zoom’s annual conference in San Jose. A big takeaway: The company remains laser-focused on pushing the boundaries of innovation. During the past year, Zoom has added over 300 features to its platform. The company is also making a bold play for the massive phone market. The vision is to develop a true unified communications system.

As a result, Zoom has become one of the world’s most valuable cloud companies (it came public in April). In the latest quarter, revenues soared by 96% to $145.8 million and net income came to $5.5 million (yes, this is one of the few newly minted IPOs that is profitable!) There are now about 66,300 customers with more than ten employees, up 78% on a year-over-year basis, and 466 contribute more than $100,000 on an annual basis. 

But getting to this point was not easy. After all, when CEO Eric Yuan founded the Zoom in 2011, the market for web conferencing appeared to be mature and was dominated by large tech companies.

One of the early investors–Emergence Capital’s Santi Subotovsky–said to me that Zoom was the toughest deal to get follow-on financing for. “There was the issue of the Silicon Valley echo chamber,” he said. “But if you looked to other countries, you could see there was much opportunity to make web conferencing better. Not everyone has high-speed Internet access.”

Another key to Zoom’s success was that Eric built a video-first platform that worked seamlessly with mobile. Keep in mind that the legacy solutions were mostly for screen sharing. What’s more, they often required frustrating configuration and setup. Zoom, on the other hand, was about making the experience as friction-less as possible.

There was also much investment in making the platform highly reliable. “Our software adjusts for network challenges,” said Kelly Steckleber, who is the CFO of Zoom. “You can have 40% of packet loss and still have a great video experience. But with other solutions, you’ll typically see degradation at about 15%.”

As for Eric, he had the advantage of being a pioneer of the web conferencing industry. In 1997 he jointed the engineering team at WebEx and stayed with the company after the sale to Cisco. But he felt stifled as his ideas for making the product better fell on deaf ears.

So when he started Zoom, Eric would strive for absolute excellence. It was not about putting together a flimsy MVP (minimally viable product).  Consider that he spent about two years creating Zoom before commercializing it. And in this process, he created small teams that had much responsibility for making decisions (let’s face it, committee’s can be killers for software development).

Now all this is not to imply that Eric is only concerned about technical details and data. From the inception of Zoom, he has been focused on building a culture that brings “happiness” to customers and employees. For example, Zoom has an NPS (Net Promoter Score) of 70, which compares to the average among the company’s peers of a mere 20 (Apple’s is 73).

One of the happy customers is Five9, which is a top cloud-based call center operator. “In our evaluation of vendors, it was clear that Zoom was the highest quality software out there,” said Rowan Trollope, who is the CEO. “But it’s backed up with a deep commitment to the customer. When I had some initial issues with Zoom, Eric gave me his phone number and he said he’d get it done over the weekend.”

AI (Artificial Intelligence): What’s The Next Frontier For Healthcare?

Perhaps one of the biggest opportunities for AI (Artificial Intelligence) is the healthcare industry. According to ReportLinker, spending on this category is forecasted to jump from $2.1 billion to $36.1 billion by 2025. This is a hefty 50.2% compound annual growth rate (CAGR).

So then what are some of the trends that look most interesting within healthcare AI? Well, to answer this question, I reached out to a variety of experts in the space.

Here’s a look: 

Ori Geva, who is the CEO of Medial EarlySign:

One of the key trends is the use of health AI to spur the transition of medicine from reactive to proactive care. Machine learning-based applications will preempt and prevent disease on a more personal level, rather than merely reacting to symptoms. Providers and payers will be better positioned to care for their patients’ needs with the tools to delay or prevent the onset of life-threatening conditions. Ultimately, patients will benefit from timely and personalized treatment to improve outcomes and potentially increase survival rates.

Dr. Gidi Stein, who is the CEO of MedAware:

In the next five years, consumers will gain more access to their health information than ever before via mobile electronic medical records (EMR) and health wearables. AI will facilitate turning this mountain of data into actionable health-related insights, promoting personalized health and optimizing care. This will empower patients to take the driving wheel of their own health, promote better patient-provider communication and facilitate high-end healthcare to under-privileged geographies.

Tim O’Malley, who is the President and Chief Growth Officer at EarlySense:

Today, there are millions of physiologic parameters which are extracted from a patient. I believe the next mega trend will be harnessing this AI-driven “Smart Data” to accurately predict and avoid adverse events for patients. The aggregate of this data will be used to formulate predictive analytics to be used across diverse patient populations across the continuum of care, which will provide truly personalized medicine.

Andrea Fiumicelli, who is the vice president and general manager of Healthcare and Life Sciences at DXC Technology:

Ultimately, AI and data analytics could prove to be the catalyst in addressing some of today’s most difficult-to-treat health conditions. By combining genomics with individual patient data from electronic health records and real-world evidence on patient behavior culled from wearables, social media and elsewhere, health care providers can harness the power of precision medicine to determine the most effective approaches for specific patients.

This brings tremendous potential to treating complex conditions such as depression. AI can offer insights into a wealth of data to determine the likelihood of depression—based on the patient’s age, gender, comorbidities, genomics, life style, environment, etc.—and can provide information about potential reactions before they occur, thus enabling clinicians to provide more effective treatment sooner.

Ruthie Davi, who is the vice president of Data Science at Acorn AI, a Medidata company:

One key advance to consider is the use of carefully curated datasets to form Synthetic Control Arms as a replacement for placebo in clinical trials. Recruiting patients for randomized control trials can be challenging, particularly in small patient populations. From the patient perspective, while an investigational drug can offer hope via a new treatment option, the possibility of being in a control arm can be a disincentive. Additionally, if patients discover they are in a control arm, they may drop out or elect to receive therapies outside of the trial protocol, threatening the validity and completion of the entire trial.

However, thanks to advances in advanced analytics and the vast amount of data available in life sciences today, we believe there is a real opportunity to transform the clinical trial process. By leveraging patient-level data from historical clinical trials from Medidata’s expansive clinical trial dataset, we can create a synthetic control arm (SCA) that precisely mimics the results of a traditional randomized control. In fact, in a recent non-small cell lung cancer case study, Medidata together with Friends of Cancer Research was successful in replicating the overall survival of the target randomized control with SCA. This is a game-changing effort that will enhance the clinical trial experience for patients and propel next generation therapies through clinical development.

Schwab’s Zero Commission Bombshell: So What’s Next For Fintech?

In 1971, Charles Schwab launched a traditional brokerage firm. But the business did not take off until 1975, when the SEC ended fixed-rate commissions. Schwab knew that the future would be about the discount brokerage model.

To pull this off, he needed to invest heavily in technology, such as with online brokerage systems. Over the years, as the platforms changed–such as from proprietary services like AOL to the Internet to mobile apps–Schwab somehow found ways to adapt.

And yes, even though he is now 82, he still seems to be far from finished. This week his firm announced that commissions on US stock, options and ETFs will be $0.

“It’s encouraging in the broader context of corporate purpose and sustainability to see a firm stay true to its purpose and passion of making investing more affordable,” said Geoff Cole, who is the fintech senior manager with Grant Thornton.

Now for the traditional brokerage industry, the impact is certainly ominous. There will need to be a way to make up for the lost revenues, such as by innovating new services. There will also likely be more layoffs.

“Online brokers are already under pressure due to this year’s interest rate cuts,” said Arielle O’Shea, who is the investing and retirement specialist at NerdWallet. “Many generate revenue from banking divisions, or from interest earned on idle cash. Schwab is likely hoping this move will attract enough new assets to make up for that narrowing margin as well as the lost revenue from commissions.”

A Reckoning For Fintech Too?

Schwab’s move is a validation of the fintech industry, especially with the impact from the fast-growing Robinhood. The startups in the space have advantages like starting from a bank slate as well as having access to enormous amounts of venture capital.

“We’ve certainly seen that the rise of customer-centric fintech companies has pushed the industry in a more client-friendly direction, and part of that is lower fees,” said Adam Grealish, who is the Director of Investing at Betterment. “Fintech companies use technology to achieve lower operating costs and are able to pass the savings on to customers. This has forced incumbents to follow suit.”

Yet this is not to imply that fintechs are immune from challenges. Let’s face it, traditional brokers have inherent advantages, such as strong infrastructures, diverse service offerings and trusted brands. And besides, millions of people like talking to experts when it comes to their wealth.

“Unfortunately, I think in the short-term you will certainly see some attrition and consolidation among the start-ups whose sole selling point was free trading,” said Anthony Denier, who is the CEO of Webull. “There is more to investing than cost.”

The zero-commission strategy may actually be a tipping point, giving traditional brokers an edge in customer acquisition. According to a J.D. Power survey of self-directed (DIY) investors, the No. 1 reason for selecting a firm was “low fees.”

“This creates a challenge for fintechs,” said Mike Foy, who is a Senior Director of Wealth Management Practice at J.D. Power. “They will need to work harder to differentiate themselves from incumbents to continue to attract new investors seeking a low-cost provider.”

While fintechs have been innovators– such as with compelling UIs–there will probably need to be much more. For the most part, the history of financial services is about relentless commoditization. And it’s been firms with massive scale, like Schwab, that have been able to thrive. This will likely be the case with fintechs as well.

Yet despite all this, the ultimate impact should positive, encouraging more and more competition. “In the end, the consumer wins,” said Steven Nuckols, who is the president and founder of Wealth Compass Financial.

Tesla’s AI Acquisition: A New Way For Autonomous Driving?

This week Tesla acquired DeepScale, which is a startup that focuses on developing computer vision technologies (the price of the deal was not disclosed). This appears to be a part of the company’s focus on building an Uber-like service as well building fully autonomous vehicles.

Founded in 2015, DeepScale has raised $15 million from investors like Point72, next47, Andy Bechtolsheim, Ali Partovi, and Jerry Yang. The founders include Forrest Iandola and Kurt Keutzer, who are both PhD’s. In fact, about a quarter of the engineering team has a PhD and they have more than 30,000 academic citations.

“DeepScale is a great fit for Tesla because the company specializes in compressing neural nets to work in vehicles, and hooking them into perception systems with multiple data types,” said Chris Nicholson, who is the CEO and founder of Skymind. “That’s what Tesla needs to make progress in autonomous driving.”

Tesla has the advantage of an enormous database of vehicle information. So with software expertise, the company should help accelerate the innovation. “If ‘data is the new oil’ then ‘AI models are the new Intellectual Property and barrier to entry,’” said Joel Vincent, who is the CMO of Zededa. “This is the dawn of a new age of competitive differentiation. AI models are useless without data and Telsa has an astounding amount of edge data.”

Now when it comes to autonomous driving, there are other major requirements–some which may get little attention.

Just look at the use of energy. “Large models require more powerful processors and larger memory to run them in production,” said Dr. Sumit Gupta, who is the the IBM Cognitive Systems VP of AI and HPC. “But vehicles have a limited energy budget, so the market is always trying to minimize the energy that the electronics in the car consume. This is what DeepScale is good at. The company invented an AI model called ‘SqueezeNet’ that requires a smaller memory footprint and also less CPU horsepower.”

Keep in mind that the lower energy consumption will mean there will be more capacity for sensors for vision. “This should help make autonomous vehicles safer,” said Arjan Wijnveen, who is the CEO of CVEDIA. “Tesla seems certain that they don’t need LiDAR for effective computer vision, but there are lots of other types of sensors you could see on their vehicles in the future, and sometimes just placing a second camera facing another angle can improve the AI model.”

Not using LiDAR would be a big deal, which would mean a much lower cost per vehicle. “There are concerns about the deployment of LIDAR lasers in the public sphere,” said Gavin D. J. Harper, who is a Faraday Institution Research Fellow at the University of Birmingham. “Safety measures include limiting the power and exposure of lasers. There is also the concern about the potential for causing inadvertent harm to those nearby.”

So all in all, the DeepScale deal could move the needle for Tesla and represent a shift in the industry. Although, it is still important to keep in mind that autonomous driving is still in the nascent stages (regardless of what Elon Musk boasts!) There remain many tough issues to work out, which could easily drag on because of regulatory processes.

“To get to full autonomy, you’re still going to need some major algorithmic improvements,” said Nicholson. “Some of the smartest people in the world are working on this, and it seems clear that we’ll get there, even if we don’t know when. In any case, companies like Tesla and Waymo have the right mix of talent, data, and cars on the road.”

FloQast: From More Than 100 No’s To Funding Triumph

After a stint as an auditor in the entertainment industry, Mike Whitmire took a job at Cornerstone OnDemand as a senior accountant. The tech firm was pre-IPO at the time and posting rapid growth.

But he noticed that it was extremely difficult to close the books at the end of the month. The process was highly manual, involving poor coordination that resulted in plenty of accounting challenges. It was a mess. It was also something that was very common across Corporate America.

Whitmire thought: “Isn’t there a better way to do this?”

There definitely was. Whitmire would launch FloQast at the end of 2012.

Although, he did not realize how tough the funding process would be.

During a six month process, Whitmire was able to get a check for $50,000 from Amplify, a tech accelerator based in the LA area. He actually closed this seed round after he was able to find a co-founder and get an initial customer.

Of course, Whitmire would need much more money to make his venture viable. “Securing $1.3 million was brutal,” he said. “This was an 8-month process. Pitching to angel investors was not easy and we were too early for real VCs. Because of this, I received more than 80 no’s and five yes’s before being introduced to Toba Capital.”

At the time, Whitmire had $300,000 committed for a $500,000 round. But Toba’s Rob Meinhardt and Vinny Smith saw the huge potential for FloQast. “They were ready to take a risk and gave us $1 million,” said Whitmire.

After this, the company did get momentum with its product. But it was still not enough to make the next round any easier!

“Securing the Series A was the worst round by far,” said Whitmire. “It took nine months of constantly traveling to San Francisco to hear no’s from Seed and Series A stage funds. There were probably about 40 no’s before I was about to close one deal.”

It did not help that the overall market for SaaS companies was crumbling (this was at the end of 2015). So yes, several term sheets were pulled.

“Then I went on to receive another 20 no’s from various funds,” said Whitmire. “But it was Polaris Partners and Gary Swart who were the ones to ultimately take the risk.”

In the end, the deep persistence paid off in a big way. The Series B round was easy, bringing in $25 million. And the company has gone on to be a major player in the SaaS world for accounting. Last year, FloQast doubled its revenues and added 250 new customers (bringing the total to 750), including Lyft, Zoom Video, Twilio and the Golden State Warriors.

Lessons Learned

In light of all this, what are the takeaways for Whitmire? Well, he mentioned the following:

#1: “I learned this world isn’t like Shark Tank. You don’t pitch and beg for money. You are forming a partnership that will last many years. Thinking about it like that makes it wildly important who you take money from.”

#2: “My best advice is to meet venture capitalists before you need money. And don’t be a snob about only talking to partners. Associates are a great way to get your foot in the door at a firm and get on the partner’s radar. Once you’ve met them, tell them about yourself, your business, and what you are going to do before the next round. Then, do it. When you follow up with them, remind them about the prior conversation and discuss how you executed. Rinse and repeat until funding time. By then, you’ll have built trust in your ability to execute and they will believe you during your fundraising story.”

Startup Lessons: How Stripe Created A $35 Billion Giant

This week Stripe—a top fintech payments company—raised $250 million at a valuation of about $35 billion. This was up about 50% since its prior round of funding earlier in the year.

“Every fintech startup aspires to build a product that clients love,” said Matt Burton, who is a partner at QED Investors. “Stripe has now done this in multiple fintech categories and shows no signs of slowing down. On top of this, they are winning the recruiting war with the top talent choosing them over everyone else. Truly impressive.”

Back in 2010, two twenty-something Irish entrepreneurs, John and Patrick Collison (they are brothers), launched Stripe. The main reason was the frustrations they experienced with online payment systems.

So John and Patrick initially joined Y Combinator to incubate the business and they would then go on to get $2 million in funding from Peter Thiel, Sequoia Capital, and Andreessen Horowitz in 2011.

“Stripe originally built an incredibly simple and innovative approach to online payments,” said Eytan Bensoussan, who is the co-founder and CEO of NorthOne. “A few lines of code and developers could integrate payment processing into a website or app. This was a departure from other financial service integrations that could take months or years. And it empowered the company with massive credibility and loyalty with the user base. Stripe built exponential enterprise value from that point forward by not only protecting its core offering but also by methodically expanding into new areas with the same level of simplicity. The key, however, was that each new area, be it subscription management, invoicing or lending, has been adjacent to its flagship product. This has created more points of entry for new customers and more cross-sell opportunities for existing ones.”

While having a standout service was critical, there were other factors that explain the success of Stripe. Keep in mind that the company pursued a distribution model that was obsessive on the needs of the customers.

“Stripe caught on because it didn’t sell to companies,” said Dmytro Okunyev, who is the founder of Chanty. “It sold to developers. That is, Stripe offered an alternative to PayPal and Authorize that was so much easier to implement that developers around the world were naturally inclined to use it.”

So yes, Stripe essentially built a thriving community of developers, which created many champions of the platform. “The smartest thing that Stripe did, apart from targeting the payment technology space, was to become a developer-first product,” said Sayid Shabeer, who is the Chief Product Officer of HighRadius. “They used the word-of-mouth growth engine of the developers to create a community that was self-sustaining.”

It also helped that Stripe broke down the walls of the traditional business model for the payments industry. “The company was wise to offer transparent pricing right from the start,” said Ian Wright, who is the founder of Merchant Machine. “This should not be confused with the best pricing, since Stripe is not the cheapest solution in the market. However, the payments industry was and to a degree still is stuck in an opaque pricing mindset. This makes Stripe standout as you know exactly how much it’s going to cost, which for most startups is better than trying to negotiate with a legacy payments company.”

All in all, Stripe has executed near flawlessly on an aggressive disruption strategy. “The payments industry was primed for disruption when Stripe came onto the scene,” said David Blumberg, who is the founder of Blumberg Capital. “Stripe communicated their benefits and differentiators in these categories early on and spoke directly to interoperability and ease of use, a major concern to many small businesses across many industries.”

WeWork IPO Postponement: What’s The Impact For Startups?

The IPO process has turned into a nightmare for WeWork. Simply put, investors have been far from convinced about the opportunity–at least in terms of the current valuation. There are also nagging issues about the business model and corporate governance.

“There has been a lot of scrutiny in the run-up to the We Company’s filing of their highly anticipated S-1,” said Kelly Rodriguez, who is the CEO of Forge. “As the financial community has been largely divided on its viability as a business, many hoped the filing would help clear the air and provide a more comprehensive look at their business model. While there is a lot to like in the We Company’s S-1, like the sharp revenue growth year-over-year, there are still a lot of loose ends and ambiguity that leave potential investors questioning its ability to sustain long-term growth.”

In light of all this, it should be no surprise that WeWork has delayed its public offering. It looks like the offering will happen next month.

Now all this comes as the equity markets are near all-time highs. But it does look like investors are getting more cautious on some deals, especially for those companies with substantial losses (last year WeWork reported a $1.61 billion loss on $1.82 billion in revenues).

So what does this all mean for startups? Well, I reached out to entrepreneurs and experts to get some viewpoints:

Mike Volpe, the CEO of Lola.com:

The market is merely working exactly as it should. The problem is specific to WeWork and their business, not a systemic issue. The company has huge capital needs but produces slim profit margins for each customer they sign up. Wall Street is saying WeWork is not a great investment at their recent lofty private valuation. A different company that is growing fast and has great profitability per customer would be able to have a wonderful IPO today.

Tobias van Gils, who is a co-founder of Countach Research:

Investors have shown that they do not perceive WeWork as a tech company as it fails in multiple key characteristics in comparison to tech companies. Two such examples are scalability (limited by locations and the need for consultations and brokers for larger businesses) and the very large percentage of revenue going to rent payments.

Charley Moore, a tech attorney and CEO and founder of Rocket Lawyer:

With the economy and financial markets giving off late cycle signals, there appears to be a shift away from growth at any cost and toward value. When that happens, compliance, regulation and governance tend to pick up steam as the government seeks to fix the excesses of the prior expansion and investors become more cautious. Examples include the increased antitrust scrutiny of big tech, and Facebook and others in the spotlight over privacy.

One implication of this example could absolutely be a heightened focus on profitability. And companies that are losing money, thereby facing existential capital needs, may have to trade control for survival. This seems to be at least part of the trading going on in the WeWork deal.

Vineet Jain, the Founder and CEO of Egnyte:

More than ever before, the path to profitability is being heavily evaluated. A company can burn through money—and ultimately lose money—however, if that company is demonstrating a reduction in losses and a convergence toward break-even, you will still get the benefit of the doubt from public investors if you have a high growth curve.

The other thing I think investors are not buying into is the dual share structure: founders or CEOs with one share that is the equivalent to 10 shares of voting rights. I believe that is going away.

Timothy Spence, a SEC Consultant:

Does the WeWork IPO bode poorly for tech? No. The scrutiny means you have to have a solid business model. Clearly, what’s happening in the IPO world is an investment in a future potential of the company.

SmileDirectClub: Lessons For Entrepreneurs

On the first day of trading for the SmileDirectClub IPO, the performance was downright awful, with the shares plunging about 28%. But things were much better the next day. The stock rose about 12%. Despite all the volatility, SmileDirectClub was still able to raise a hefty $1.35 billion.

Founded in 2014, the company has certainly been on the fast track as it has aggressively disrupted the traditional orthodontic market. According to the S-1:

“Our member journey starts with two convenient options: a member books an appointment to take a free, in-person 3D oral image at any of our over 300 SmileShops across the U.S., Puerto Rico, Canada, Australia, and the U.K., or orders an easy-to-use doctor prescribed impression kit online, which we mail directly to their door. Using the image or impression, we create a draft custom treatment plan that demonstrates how the member’s teeth will move during treatment. Next, via SmileCheck, a state licensed doctor within our network reviews and approves the member’s clinical information and treatment plan. If the member is a good candidate for clear aligners, the treating doctor then prescribes custom-made clear aligners, the member has the opportunity to review a 3D rendering of how their teeth will move over time and, if the member decides to purchase, we then manufacture and ship the aligners directly to the member. SmileCheck is also used by the treating doctor to monitor the member’s progress and enables seamless communication with the member over the course of treatment. Upon completion of treatment, a majority of our members purchase retainers every six months to prevent their teeth from relapsing to their original position. We also offer a growing suite of ancillary oral care products, such as whitening kits, to maintain a perfect smile.”

Because of this innovative model, the company is able to charge $1,895 for its services and high-quality clear aligners, versus the $5,000 to $8,000 that a dentist may charge. The SmileDirectClub also provides affordable payment plans and has insurance arrangements with United Healthcare and Aetna.

In light of all this, it should be no surprise that the company has been growing at a torrid pace. Note that last year SmileDirectClub posted revenues of $423.2 million, up a sizzling 190%.

So then what are some of the lessons for entrepreneurs? Let’s take a look:

Phil Strazzulla, the founder of SelectSoftware:

SmileDirect was able to decrease pricing due to a new supply chain and direct-to-consumer strategy that piggy backed off a nascent and underdeveloped Instagram ad ecosystem. This led to cheap and scalable customer acquisition. They were also able to expand the market due to their cheaper pricing and direct-to-consumer marketing that allowed for customer segments that weren’t historically thinking about this type of product.

Gretchen Halpin, the co-founder of Beyond AUM:

They have removed all friction points from the process, such as:

Braces are expensive – AFFORDABILITY

Appointments are hard to schedule – EASY DELIVERY

Vanity on how braces look – INVISIBLE

Quality and results – TECHNOLOGY

Point of purchase ease: ONLINE AND STORES

Price and finances – AFFORDABLE AND MONTHLY PAYMENTS

Jim Berryhill, the CEO and co-founder of DecisionLink:

When you believe there is a huge unmet need in the market and you figure out a solution for addressing that need, you ideally surround yourselves with others who believe and understand why that gap exists, how your solution can make a big difference in the lives of your target customers and then figure out the best way to deliver that solution to them. It sounds pretty straightforward, but it takes a lot of energy, determination and persistence to make it happen. You also need to get lots of great people to help you get from the idea to the actual solution and then sell the heck out of it!

Becky Beach, the owner of MomBeach.com:

We live in a “delivery culture” where everyone enjoys food, videos, clothes, and more goods being delivered to our homes. I used InvisAlign through my dentist about 4 years ago and it cost $4,000. I had to schedule several trips to the dentist, which caused me to miss lots of work when I had a full-time job. With the SmileDirectClub, you don’t need to go to the dentist every two to three weeks for a new tray anymore. The trays are sent to your door, instead.

There are plenty more openings in the market for future offerings that an entrepreneur could discover. Think of what problems exist in the market for consumers and how you could solve them.

What AI (Artificial Intelligence) Will Mean For The Cannabis Space

Just about every estimate shows that the cannabis industry will see strong long-term growth. Yet there are some major challenges–and they are more than just about changing existing laws and regulations.

But AI (Artificial Intelligence) is likely to be a big help. True, the industry has not been a big adopter of new technologies. However, this should change soon as investors pour billions of dollars into the space.

So how might AI impact things?  Well, look at what the CEO and Director of CROP Corp, Michael Yorke, has to say: “The use of AI in sensors and high-definition cameras can be used to keep track of and adjust multiple inputs in the growing environment such as water level, PH level, temperature, humidity, nutrient feed, light spectrum and CO2 levels. Tracking and adjusting these inputs can make a major difference in the quantity and quality of cannabis that growers are able to produce. AI also helps automate trimming technology so that it is able to de-leaf buds saving countless hours of manual labor. Similarly, it can be applied to automated planting equipment to increase the effectiveness and efficiency of planting. And AI can identify the sex of the plants, detect sick plants, heal or remove sick plants from the environment, and track the plant growth rate to be able to predict size and yield.”

No doubt, such things could certainly move the needle in a big way. 

There are also opportunities to help with such things as more accurate predictions, which would allow for maximizing efficiency. And yes, AI is likely to be key in discovering new strains or customize strains for specific effects (examples would include relaxation, excitement or increasing/decreasing hunger). The result could be even more growth in the cannabis market. 

But there is something else to keep in mind: With no legalization on a federal level in the US, there is a need for sophisticated tracking systems. 

“The existing regulations are complex, requiring businesses to follow detailed rules that govern every area of the industry from growing to packaging and selling to consumers,” said Mark Krytiuk, who is the president of Nabis Holdings. “Even the smallest error can cost a cannabis business thousands, and incur harsh punishments such as losing their cannabis license.”

The situation is even more complex with retail operations. “Artificial intelligence is one key technological advancement that could make a significant impact,” said Krytiuk. “By implementing this technology, cannabis retailers would be able to more easily track state-by-state regulations, and the constant changes that are being made. With this information, they would be able to properly package, ship, and sell products in a more compliant way that is less likely to be intercepted by government regulations.”

Keep in mind that the problems with compliance are a leading cause of failure for cannabis operators. “Running a cannabis business can be costly, especially when it comes to getting and keeping a license, paying high taxes, and dealing with the added pressure of ever-changing government regulations,” said Krytiuk. “If more cannabis businesses had access to automated, AI-powered technology that could help them be more compliant, there would be more successful companies helping the industry to grow.”

Again, the AI part of the cannabis industry is very much in the nascent stages. It will likely take some time to get meaningful traction. But for entrepreneurs, the opportunity does look promising. “The industry is only going to continue to grow, so it’s only a matter of time before it reaches its own technological revolution,” said Krytiuk.

AI (Artificial Intelligence) Words You Need To Know

In 1956, John McCarthy setup a ten-week research project at Dartmouth University that was focused on a new concept he called “artificial intelligence.” The event included many of the researchers who would become giants in the emerging field, like Marvin Minsky, Nathaniel Rochester, Allen Newell, O.G. Selfridge, Raymond Solomonoff, and Claude Shannon.

Yet the reaction to the phrase artificial intelligence was mixed. Did it really explain the technology? Was there a better way to word it?

Well, no one could come up with something better–and so AI stuck.

Since then, we’ve seen the coining of plenty of words in the category, which often define complex technologies and systems. The result is that it can be tough to understand what is being talked about.

So to help clarify things, let’s take a look at the AI words you need to know:

Algorithm

From Kurt Muehmel, who is a VP Sales Engineer at Dataiku:

A series of computations, from the most simple (long division using pencil and paper), to the most complex. For example, machine learning uses an algorithm to process data, discover rules that are hidden in the data, and that are then encoded in a “model” that can be used to make predictions on new data.

Machine Learning

From Dr. Hossein Rahnama, who is the co-founder and CEO of Flybits:

Traditional programming involves specifying a sequence of instructions that dictate to the computer exactly what to do. Machine learning, on the other hand, is a different programming paradigm wherein the engineer provides examples comprising what the expected output of the program should be for a given input. The machine learning system then explores the set of all possible computer programs in order to find the program that most closely generates the expected output for the corresponding input data. Thus, with this programming paradigm, the engineer does not need to figure out how to instruct the computer to accomplish a task, provided they have a sufficient number of examples for the system to identify the correct program in the search space.

Neural Networks

From Dan Grimm, who is the VP and General Manager of Computer Vision a RealNetworks:

Neural networks are mathematical constructs that mimic the structure of the human brain to summarize complex information into simple, tangible results. Much like we train the human brain to, for example, learn to control our bodies in order to walk, these networks also need to be trained with significant amounts of data. Over the last five years, there have been tremendous advancements in the layering of these networks and the compute power available to train them.

Deep Learning

From Sheldon Fernandez, who is the CEO of DarwinAI:

Deep Learning is a specialized form of Machine Learning, based on neural networks that emulate the cognitive capabilities of the human mind. Deep Learning is to Machine Learning what Machine Learning is to AI–not the only manifestation of its parent, but generally the most powerful and eye-catching version. In practice, deep learning networks capable of performing sophisticated tasks are 1.) many layers deep with millions, sometimes, billions of inputs (hence the ‘deep’); 2.) trained using real world examples until they become proficient at the prevailing task (hence the ‘learning’).

Explainability

From Michael Beckley, who is the CTO and founder of Appian:

Explainability is knowing why AI rejects your credit card charge as fraud, denies your insurance claim, or confuses the side of a truck with a cloudy sky. Explainability is necessary to build trust and transparency into AI-powered software. The power and complexity of AI deep learning can make predictions and decisions difficult to explain to both customers and regulators. As our understanding of potential bias in data sets used to train AI algorithms grows, so does our need for greater explainability in our AI systems. To meet this challenge, enterprises can use tools like Low Code Platforms to put a human in the loop and govern how AI is used in important decisions.

Supervised, Unsupervised and Reinforcement Learning

From Justin Silver, who is the manager of science & research at PROS:

There are three broad categories of machine learning: supervised, unsupervised, and reinforcement learning. In supervised learning, the machine observes a set of cases (think of “cases” as scenarios like “The weather is cold and rainy”) and their outcomes (for example, “John will go to the beach”) and learns rules with the goal of being able to predict the outcomes of unobserved cases (if, in the past, John usually has gone to the beach when it was cold and rainy, in the future the machine will predict that John will very likely go to the beach whenever the weather is cold and rainy). In unsupervised learning, the machine observes a set of cases, without observing any outcomes for these cases, and learns patterns that enable it to classify the cases into groups with similar characteristics (without any knowledge of whether John has gone to the beach, the machine learns that “The weather is cold and rainy” is similar to “It’s snowing” but not to “It’s hot outside”). In reinforcement learning, the machine takes actions towards achieving an objective, receives feedback on those actions, and learns through trial and error to take actions that lead to better fulfillment of that objective (if the machine is trying to help John avoid those cold and rainy beach days, it could give John suggestions over a period of time on whether to go to the beach, learn from John’s positive and negative feedback, and continue to update its suggestions).

Bias

From Mehul Patel, who is the CEO of Hired:

While you may think of machines as objective, fair and consistent, they often adopt the same unconscious biases as the humans who built them. That’s why it’s vital that companies recognize the importance of normalizing data—meaning adjusting values measured on different scales to a common scale—to ensure that human biases aren’t unintentionally introduced into the algorithm. Take hiring as an example: If you give a computer a data set with 100 female candidates and 300 male candidates and ask it to predict the best person for the job, it is going to surface more male candidates because the volume of men is three times the size of women in the data set. Building technology that is fair and equitable may be challenging but will ensure that the algorithms informing our decisions and insights are not perpetuating the very biases we are trying to undo as a society.

Backpropagation

From Victoria Jones, who is the Zoho AI Evangelist:

Backpropagation algorithms allow a neural network to learn from its mistakes. The technology tracks an event backwards from the outcome to the prediction and analyzes the margin of error at different stages to adjust how it will make its next prediction. Around 70% of our AI assistant (called Zia) features the use of backpropagation, including Zoho Writer’s grammar-check engine and Zoho Notebook’s OCR technology, which lets Zia identify objects in images and make those images searchable. This technology also allows Zia’s chatbot to respond more accurately and naturally. The more a business uses Zia, the more Zia understands how that business is run. This means that Zia’s anomaly detection and forecasting capabilities become more accurate and personalized to any specific business.

NLP (Natural Language Processing)

Courtney Napoles, who is the Language Data Manager at Grammarly:

The field of NLP brings together artificial intelligence, computer science, and linguistics with the goal of teaching machines to understand and process human language. NLP researchers and engineers build models for computers to perform a variety of language tasks, including machine translation, sentiment analysis, and writing enhancement. Researchers often begin with analysis of a text corpus—a huge collection of sentences organized and annotated in a way that AI algorithms can understand.

The problem of teaching machines to understand human language—which is extraordinarily creative and complex—dates back to the advent of artificial intelligence itself. Language has evolved over the course of millennia, and devising methods to apprehend this intimate facet of human culture is NLP’s particularly challenging task, requiring astonishing levels of dexterity, precision, and discernment. As AI approaches—particularly machine learning and the subset of ML known as deep learning—have developed over the last several years, NLP has entered a thrilling period of new possibilities for analyzing language at an unprecedented scale and building tools that can engage with a level of expressive intricacy unimaginable even as recently as a decade ago.