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Why did file sharing drive so much startup innovation?



One of the great things about editing all of our deep-dive EC-1 startup profiles is that you start to notice patterns across successful companies. While origin stories and trajectories can vary widely, the best companies seem to come from similar places and are conceived around very peculiar themes.

To wit, one common theme that came from our recent profiles of Expensify and NS1 is the centrality of file sharing (or, illegal file sharing if you are on that side of the fence) and internet infrastructure in the origin stories of the two companies. That’s peculiar, because the duo honestly couldn’t be more different. Expensify is an SF-founded (now Portland-based), decentralized startup focused on building expense reporting and analytics software for companies and CFOs. New York-based NS1 designs highly redundant DNS and internet traffic performance tools for web applications.

Yet, take a look at how the two companies were founded. Anna Heim on the origins of Expensify:

To truly understand Expensify, you first need to take a close look at a unique, short-lived, P2P file-sharing company called Red Swoosh, which was Travis Kalanick’s startup before he founded Uber. Framed by Kalanick as his “revenge business” after his previous P2P startup Scour was sued into oblivion for copyright infringement, Red Swoosh would be the precursor for Expensify’s future culture and ethos. In fact, many of Expensify’s initial team actually met at Red Swoosh, which was eventually acquired by Akamai Technologies in 2007 for $18.7 million.

[Expensify founder and CEO David] Barrett, a self-proclaimed alpha geek and lifelong software engineer, was actually Red Swoosh’s last engineering manager, hired after the failure of his first project,, a P2P push-to-talk program that couldn’t compete against Skype. “While I was licking my wounds from that experience, I was approached by Travis Kalanick who was running a startup called Red Swoosh,” he recalled in an interview.

Then you head over to Sean Michael Kerner’s story on how NS1 came together:

NS1’s story begins back at the turn of the millennium, when [NS1 co-founder and CEO Kris] Beevers was an undergrad at Rensselaer Polytechnic Institute (RPI) in upstate New York and found himself employed at a small file-sharing startup called Aimster with some friends from RPI. Aimster was his first taste of life at an internet startup in the heady days of the dot-com boom and bust, and also where he met an enterprising young engineer by the name of Raj Dutt, who would become a key relationship over the next two decades.

By 2007, Beevers had completed his Ph.D. in robotic mapping at RPI and tried his hand at co-founding and running an engineered-wood-product company named SolidJoint Research, Inc. for 10 months. But he soon boomeranged back to the internet world, joining some of his former co-workers from Aimster at a company called Voxel that had been founded by Dutt.

The startup provided a cornucopia of services including basic web hosting, server co-location, content delivery and DNS services. “Voxel was one of those companies where you learn a lot because you’re doing way more than you rightfully should,” Beevers said. “It was a business sort of built out of love for the tech, and love for solving problems.”

The New York City-based company peaked at some 60 employees before it was acquired in December 2011 by Internap Network Services for $35 million.

Note some of the similarities here. First, these wildly different founders ended up both working on key internet plumbing. Which makes sense of course, since two decades ago, building out the networking and compute capacity of the internet was one of the major engineering challenges of that period in the web’s history.

Additionally in both cases, the founding teams met at little-known companies defined by their engineering cultures and which sold to larger internet infrastructure conglomerates for relatively small amounts of money. And those acquirers ended up being laboratories for all kinds of innovation, even as few people really remember Akamai or Internap these days (both companies are still around today mind you).

The cohort of founders is fascinating. Obviously, you have Travis Kalanick, who would later go on to found Uber. But the Voxel network that went to Internap is hardly a slouch:

Dutt would leave Internap to start Grafana, an open-source data visualization vendor that has raised over $75 million to date. Voxel COO Zachary Smith went on to found bare metal cloud provider, Packet, in 2013, which he ran as CEO until the company was acquired by Equinix in March 2020 for $335 million. Meanwhile, Justin Biegel, who spent time at Voxel in operations, has raised nearly $62 million for his startup Kentik. And of course, NS1 was birthed from the same alumni network.

What’s interesting to me with these two companies (and some others in our set of stories) is how often founders worked on other problems before starting the companies that would make them famous. They learned the trade, built networks of hyperintelligent present and future colleagues, understood business development and growth, and started to create a flywheel of innovation amidst their friends. They also got a taste of an exit without really getting the whole meal, if you will.

In particular with file sharing, what’s interesting is the rebellious and democratic ethos that came with that world back at the turn of the millennium. To work in file sharing in that era meant fighting the big music labels, overturning the economics of entire industries, and breaking down barriers to allow the internet economy to flourish. It attracted a weird bunch of folks — the exact kind of weirdness that happens to make good startup founders, apparently. It echos one of the key arguments of Fred Turner’s book, “From Counterculture to Cyberculture.”

Which begs the question then: What are the “file-sharing” markets today that these sorts of individuals congregate around? One that seems obvious to me is blockchain, which has precisely that balance of rebelliousness, democratization and technical excellence. (Well, at least some of the time!) And then there are the modern-day “pirates” today such as Alexandra Elbakyan who invented and has operated Sci-Hub to make the world’s research and knowledge democratized.

It’s maybe not the current batch of companies that we see that will become the next extraordinary unicorns. But watch the people who show up in the interesting places — because their next projects often seem to hit gold.

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From Red Gold to Olympic Gold: Seedo Corp. Seeks Solutions for Athletes and More



Seedo Corp. highlights saffron’s medicinal properties as mental health takes center stage at the Olympics in Tokyo.

TEL AVIV, Israel, August 3, 2021 /PRNewswire/ – Seedo Corp. (OTC: SEDO), an agtech company that is developing the protocols to grow saffron using vertical farming technology, today announced that it is expanding its research to include the study of the spice’s well known natural anti-anxiety and antidepressant properties. Similar to the approach of the indoor cannabis industry, Seedo hopes to be able to enhance the medicinal properties of this unique herb for use in pharmaceutical and nutraceutical applications. Mental health has come to the forefront in sports with Simone Biles withdrawing from the Tokyo Olympics and Naomi Osaka opting out of the French Open and Wimbledon. With mental health entering the cultural conversation, Seedo Corp. hopes saffron will be seen as part of a new nature-based approach to mental health.

“The timing is right to leverage saffron’s potential medicinal properties and create new applications that could address the recent paradigm shift regarding mental health,” says David Freidenberg, CEO. “Seedo Corp is committed to developing breakthroughs rooted in nature that are effective and safe for athletes and everyday consumers alike.”

“Until recently, the options for treating depression and anxiety were quite limited,” says Dr.  Nizan Primor, CEO, Naveh Pharma, a company that specializes in creating pharmaceutical and healthcare products with unique active ingredients including saffron. “A recent study found that taking 28 mg of saffron daily was just as effective as Fluoxetine, Imipramine, and Citalopram — conventional treatments for depression.”A fascinating study was published in the Journal of Adolescent Psychopharmacology which explicitly found that saffron extract has the same efficacy as Ritalin in improving focus for children with ADHD, suggesting there is a promising future in developing new natural therapies to treat these common ailments. In another recent study by the Journal of Psychopharmacology it was discovered that patients who were administered saffron extract for eight weeks saw “a greater improvement in depressive symptoms.”

Seedo Corp successfully harvests saffron using vertical farming technology. Seedo Corp hopes to expand the billion dollar saffron market by producing a reliable, consistent and large-scale supply of the spice.

About Seedo:

Seedo Corp. (OTC: SEDO) is an agtech company that focuses on the research, development, and commercialization of agriculture products that are high in demand but are hindered by the low yields and specifications required by traditional farming. Seedo’s technology is aimed at offering a responsible and sustainable way to grow crops in a world confronted by environmental challenges and dwindling earth reserves, diminishing water sources and unstable weather conditions.

Cautionary Note Regarding Forward-Looking Statements

This letter contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995, which are based on management’s current beliefs and expectations and are subject to substantial risks and uncertainties, both known and unknown, that could cause our future results, performance or achievements to differ significantly from that expressed or implied by such forward-looking statements. Important factors that could cause or contribute to such differences include risks relating to our ability to successfully execute a smooth transition of CFO functions as well as our ability to retain and recruit qualified executives; uncertainties related to, and failure to achieve, the potential benefits and success of our senior management team and organizational structure; our ability to successfully compete in the marketplace; our substantial indebtedness, which may limit our ability to incur additional indebtedness, engage in additional transactions or make new investments; compliance, regulatory and litigation matters; other financial and economic risks; and other factors discussed in our Quarterly Reports on Form 10-Q and in our Annual Report on Form 10-K, including in the sections captioned “Risk Factors” and “Forward Looking Statements.” Forward-looking statements speak only as of the date on which they are made, and we assume no obligation to update or revise any forward-looking statements or other information contained herein, whether as a result of new information, future events or otherwise. You are cautioned not to put undue reliance on these forward-looking statements.

David Freidenberg,

Chief Executive Officer
[email protected]
+1 (800) 608-6432

Source: Plato Data Intelligence

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Artificial Intelligence

Eli Lilly joins $30M Series A financing for startup bringing AI analysis to endoscopy



Jonathan Ng, Iterative Scopes

The artificial intelligence-based technology of Iterative Scopes brings computer vision analysis to endoscopic images. The startup’s technology was initially developed to assist gastroenterologists in finding pre-cancerous polyps but CEO and founder Jonathan Ng said it’s also finding additional use helping pharmaceutical companies identify patients for clinical trials.

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Researchers Working to Improve Autonomous Vehicle Driving Vision in the Rain 



By John P. Desmond, AI Trends Editor 

To help autonomous cars navigate safely in the rain and other inclement weather, researchers are looking into a new type of radar.  

Self-driving vehicles can have trouble “seeing” in the rain or fog, with the car’s sensors potentially blocked by snow, ice or torrential downpours, and their ability to “read” road signs and road markings impaired. 

Many autonomous vehicles rely on lidar radar technology, which works by bouncing laser beams off surrounding objects to give a high-resolution 3D picture on a clear day, but does not do so well in fog, dust, rain or snow, according to a recent report from abc10 of Sacramento, Calif. 

“A lot of automatic vehicles these days are using lidar, and these are basically lasers that shoot out and keep rotating to create points for a particular object,” stated Kshitiz Bansal, a computer science and engineering Ph.D. student at University of California San Diego, in an interview. 

The university’s autonomous driving research team is working on a new way to improve the imaging capability of existing radar sensors, so they more accurately predict the shape and size of objects in an autonomous car’s view.  

Dinesh Bharadia, professor of electrical and computer engineering,UC San Diego Jacobs School of Engineering

“It’s a lidar-like radar,” stated Dinesh Bharadia, a professor of electrical and computer engineering at the UC San Diego Jacobs School of Engineering, adding that it is an inexpensive approach. “Fusing lidar and radar can also be done with our techniques, but radars are cheap. This way, we don’t need to use expensive lidars.” 

The team places two radar sensors on the hood of the car, enabling the system to see more space and detail than a single radar sensor. The team conducted tests to compare their system’s performance on clear days and nights, and then with foggy weather simulation, to a lidar-based system. The result was the radar plus lidar system performed better than the lidar-alone system.  

“So, for example, a car that has lidar, if it’s going in an environment where there is a lot of fog, it won’t be able to see anything through that fog,” Bansaid stated. “Our radar can pass through these bad weather conditions and can even see through fog or snow,” he stated.  

The team uses millimeter radar, a version of radar that uses short-wavelength electromagnetic waves to detect the range, velocity and angle of objects.   

20 Partners Working on AI-SEE in Europe to Apply AI to Vehicle Vision 

Enhanced autonomous vehicle vision is also the goal of a project in Europe—called AI-SEE—involving startup Algolux, which is cooperating with 20 partners over a period of three years to work towards Level 4 autonomy for mass-market vehicles. Founded in 2014, Algolux is headquartered in Montreal and has raised $31.8 million to date, according to Crunchbase.  

The intent is to build a novel robust sensor system supported by artificial intelligence enhanced vehicle vision for low visibility conditions, to enable safe travel in every relevant weather and lighting condition such as snow, heavy rain or fog, according to a recent account from AutoMobilSport.    

The Algolux technology employs a multisensory data fusion approach, in which the sensor data acquired will be fused and simulated by means of sophisticated AI algorithms tailored to adverse weather perception needs. Algolux plans to provide technology and domain expertise in the areas of deep learning AI algorithms, fusion of data from distinct sensor types, long-range stereo sensing, and radar signal processing.  

Dr. Werner Ritter, Consortium Lead, Mercedes Benz AG: “Algolux is one of the few companies in the world that is well versed in the end-to-end deep neural networks that are needed to decouple the underlying hardware from our application,” stated Dr. Werner Ritter, consortium lead, from Mercedes Benz AG. “This, along with the company’s in-depth knowledge of applying their networks for robust perception in bad weather, directly supports our application domain in AI-SEE.”  

The project will be co-funded by the National Research Council of Canada Industrial Research Assistance Program (NRC IRAP), the Austrian Research Promotion Agency (FFG), Business Finland, and the German Federal Ministry of Education and Research BMBF under the PENTA EURIPIDES label endorsed by EUREKA. 

Nvidia Researching Stationary Objects in its Driving Lab  

The ability of the autonomous car to detect what is in motion around it is crucial, no matter the weather conditions, and the ability of the car to know which items around it are stationary is also important, suggests a recent blog post in the Drive Lab series from Nvidia, an engineering look at individual autonomous vehicle challenges. Nvidia is a chipmaker best known for its graphic processing units, widely used for development and deployment of applications employing AI techniques.   

The Nvidia lab is working on using AI to address the shortcomings of radar signal processing in distinguishing moving and stationary objects, with the aim of improving autonomous vehicle perception.   

Neda Cvijetic, autonomous vehicles and computer vision research, Nvidia

“We trained a DNN [deep neural network] to detect moving and stationary objects, as well as accurately distinguish between different types of stationary obstacles, using data from radar sensors,” stated Neda Cvijetic, who works on autonomous vehicles and computer vision for Nvidia; the author of the blog post. In her position for about four years, she previously worked as a systems architect for Tesla’s Autopilot software.   

Ordinary radar processing bounces radar signals off of objects in the environment and analyzes the strength and density of reflections that come back. If a sufficiently strong and dense cluster of reflections comes back, classical radar processing can determine this is likely some kind of large object. If that cluster also happens to be moving over time, then that object is probably a car, the post outlines. 

While this approach can work well for inferring a moving vehicle, the same may not be true for a stationary one. In this case, the object produces a dense cluster of reflections that are not moving. Classical radar processing would interpret the object as a railing, a broken down car, a highway overpass or some other object. “The approach often has no way of distinguishing which,” the author states. 

A deep neural network is an artificial neural network with multiple layers between the input and output layers, according to Wikipedia. The Nvidia team trained their DNN to detect moving and stationary objects, as well as to distinguish between different types of stationary objects, using data from radar sensors.  

Specifically, we trained a DNN to detect moving and stationary objects, as well as accurately distinguish between different types of stationary obstacles, using data from radar sensors.  

Training the DNN first required overcoming radar data sparsity problems. Since radar reflections can be quite sparse, it’s practically infeasible for humans to visually identify and label vehicles from radar data alone. However, Lidar data, which can create a 3D image of surrounding objects using laser pulses, can supplement the radar data. “In this way, the ability of a human labeler to visually identify and label cars from lidar data is effectively transferred into the radar domain,” the author states. 

The approach leads to improved results. “With this additional information, the radar DNN is able to distinguish between different types of obstacles—even if they’re stationary—increase confidence of true positive detections, and reduce false positive detections,” the author stated. 

Many stakeholders involved in fielding safe autonomous vehicles, find themselves working on similar problems from their individual vantage points. Some of those efforts are likely to result in relevant software being available as open source, in an effort to continuously improve autonomous driving systems, a shared interest. 

Read the source articles and information from abc10 of Sacramento, Calif., from AutoMobilSport and in a blog post in the Drive Lab series from Nvidia. 

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Ag-tech Employing AI and Range of Tools With Dramatic Results 



By AI Trends Staff  

An agricultural technology (ag-tech) startup in San Francisco, Plenty, plants its crops vertically indoors, in a year-round operation employing AI and robots that uses 95% less water and 99% less land than conventional farming. 

Plenty’s vertical farm approach can produce the same quantity of fruits and vegetables as a 720-acre flat farm, on only two acres.    

Nate Storey, cofounder and chief science officer of the startup Plenty

“Vertical farming exists because we want to grow the world’s capacity for fresh fruits and vegetables, and we know it’s necessary,” stated Nate Storey, cofounder and chief science officer of the startup Plenty, in an account in Intelligent Living 

The yield of 400x that of flat farms makes vertical farming “not just an incremental improvement,” and the fraction of water use “is also critical in a time of increasing environmental stress and climate uncertainty,” Storey stated. “All of these are truly game-changers.”  

Plenty is one of hundreds of ag-tech startups using new technology approaches—including AI, drones, robots and IoT sensors—being supported with billions of investments from the capital markets.     

Plenty’s climate-controlled indoor farm has rows of plants growing vertically, hung from the ceiling. LED lights mimicking the sun shine on the plants; robots move them around; AI manages all the variables of water, temperature, and light. The AI continuously learns and optimizes how to grow better crops.   

Also, vertical farms can be located in urban areas resulting in locally-produced food, with many transportation miles eliminated. Benefits of locally-produced crops include reduction of CO2 emissions from transportation vehicles and potentially lower prices for consumers.    

“Supply-chain breakdowns resulting from COVID-19 and natural disruptions like this year’s California wildfires demonstrate the need for a predictable and durable supply of products can only come from vertical farming,” Storey stated.  

Plenty has received $400 million in investment capital from SoftBank, former Google chairman Eric Schmidt, and Amazon’s Jeff Bezos. It also struck a deal with Albertsons stores in California to supply 430 stores with fresh produce.  

Bowery Farming in New York City Supplying 850 Grocery Stores  

Another indoor farming venture is Bowery Farming in New York City, which has raised $467.5 million so far in capital, according to Crunchbase. Experiencing growth during the pandemic, the company’s produce is now available in 850 grocery stores, including Albertsons, Giant Good, Walmart and Whole Foods, according to an account in TechCrunch.   

The infusion of new capital, $300 million in May, “is an acknowledgement of the critical need for new solutions to our current agricultural system,” stated CEO Irving Fain in a release. “This funding not only fuels our continued expansion but the ongoing development of our proprietary technology, which sits at the core of our business and our ability to rapidly and efficiently scale toward an increasingly important opportunity in front of us,” Fain stated. 

The company plans to expand to new locations in the US, including a new site located in an industrial area in Bethlehem, Penn., which Bowery says will be its largest to date.  

blog post on the company’s website describes the BoweryOS as the “central nervous system” of each farm, offering plants individual attention at scale. “It works by collecting billions of data points through an extensive network of sensors and cameras that feed into proprietary machine-learning algorithms that are interpreted by the BoweryOS in real time,” the account states. In addition, “It gets smarter with each grow cycle, gaining a deeper understanding about the conditions each crop truly needs to thrive.”  

Ag-tech Spending Projected to Reach $15.3 Billion by 2025 

Global spending on smart, connected ag-tech systems including AI and machine learning, is projected to trip by 2025, to reach $15.3 billion, according to BI Intelligence Research, quoted in a recent account in Forbes. 

IoT-enabled ag-tech is the fastest growing segment, projected to reach $4.5 billion by 2025, according to PwC. 

Demand should be there. Prediction data on population and hunger from the United Nations shows the world population increasing by two billion people by 2050, requiring a 60% increase in food production. AI and ML are showing the potential to help meet the increased requirement for food.   

Louis Columbus, author and principal of Dassault Systemes, supplier of manufacturing software

“AI and ML are already showing the potential to help close the gap in anticipated food needs,” stated the author of the Forbes article, Louis Columbus, a principal of Dassault Systemes, supplier of manufacturing software.  

AI and machine learning are well-suited to tackle challenges in farming. “Imagine having at least 40 essential processes to keep track of, excel at and monitor at the same time across a large farming area often measured in the hundreds of acres,” Columbus stated.” Gaining insight into how weather, seasonal sunlight, migratory patterns of animals, birds, insects, use of specialized fertilizers, insecticides by crop, planting cycles and irrigation cycles all affect yield is a perfect problem for machine learning,” he stated.  

Among a list of ways AI has the potential to improve agriculture in 2021, he offered:  

Using AI and machine learning-based surveillance systems for monitoring. Real-time video feeds of every crop can be used to send alerts immediately after an animal or human breech, very practical for remote farms. Twenty20 Solutions is a leader in the field of AI and machine learning-based surveillance.  

Improve crop yield prediction with real-time sensor data and visual analytics data from drones. Farms have access to data sets from smart sensors and drones they have never had before. Now it’s possible to access data from in-ground sensors on moisture, fertilizer, and nutrient levels, to analyze growth patterns for each crop over time. Infrared imagery and real-time video analytics also provide farmers with new insights.  

Smart tractors and agribots—robots designed for agricultural purposes—using AI and machine learning are a viable option for many agricultural operations that struggle to find workers. Self-propelled agribots can be programmers for example to distribute fertilizer on each row of crops, in a way to keep operating costs down and improve yields. Robots from VineScout are used to create crop maps, then help manage crops, especially in wine vineyards. Based in Portugal, the project has been backed by the European Union and multiple investors.  

Read the source articles and information in Intelligent Living, in TechCrunch and in Forbes. 

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