Extra Crunch publishes a variety of article types, but how-tos are my favorite category.
For many entrepreneurs, the startup they are trying to get off the ground might be only the second entry on their resume. As a result, they don’t have much experience to draw from when it comes to basics like hiring, fundraising and growth marketing.
Last week, Natasha Mascarenhas interviewed experts who had some strategic advice for finding the right time to bring a product manager on board. This afternoon, we published a guest post by growth marketer Jessica Li with tips for “how nontechnical talent can build relationships with deep tech companies.”
We’ve also received great feedback on a recent guest post about bootstrapping options for SaaS founders written by a founder who’s actually done it.
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If you have some startup-related “how” and “why” questions, please browse our Extra Crunch How To stories. They’re aimed squarely at early-stage founders and workers trying to solve long-term problems.
Thanks very much for reading Extra Crunch this week! I hope you have a relaxing weekend.
Senior Editor, TechCrunch
Welcome to Bloxburg, public investors
As Roblox began to trade Wednesday, the company’s shares shot above its reference price of $45 per share. Roblox, a gaming company aimed at children, has had a tumultuous if exciting path to the public markets.
Seeing Roblox trade so very far above its direct listing reference price and final private valuation appears to undercut the argument that this sort of debut can sort out pricing issues inherent in more traditional IPOs.
4 ways startups will drive GPT-3 adoption in 2021
Trained on trillions of words, GPT-3 is a 175-billion parameter transformer model — the third of such models released by OpenAI.
GPT-3 is remarkable in its ability to generate human-like text and responses, able to return coherent and topical emails, tweets, trivia and much more. In 2021, this technology will power the launch of a thousand new startups and applications.
There have never been more $100M+ fintech rounds than right now
We are in a period of all-time record investment for so-called mega-rounds, or investments of $100 million or more inside the fintech realm.
To date, Q1 2021 is ahead and is thus guaranteed to set a new record, having already bested the preceding all-time high. What’s going on?
Global-e files to go public as e-commerce startups enjoy a renaissance
Global-e, an e-commerce platform that helps online sellers reach global consumers, filed to go public on Tuesday. Global-e’s business exploded amid the pandemic in 2020, and the company expects that the COVID-fueled shift to e-commerce will only lead to future growth.
Passive collaboration is essential to remote work’s long-term success
Have you ever popped into a meeting because you overheard a snippet of a conversation and wanted to share your perspective?
That’s passive collaboration — low-friction ways to invite new ideas. But it’s only when we’re able to fully realize passive collaboration virtually that we’ll have unlocked the full potential of remote and hybrid work situations.
Dear Sophie: What are the pros and cons of the H-1B, O-1A and EB-1A?
I’m an entrepreneur who wants to expand my startup to the U.S. What are the benefits and drawbacks of various types of visas and green cards?
The ones I’ve heard the most about are the H-1B, O-1 and EB-1A.
— Intelligent in India
Proactive CEOs should prioritize European expansion
Many investors will encourage CEOs to remain U.S.-centric this year and perhaps expand their product offering or move into new market segments. But 95% of the world’s population lives outside the U.S., making an expansion into Europe your best growth lever.
Coupang follows Roblox to a strong first day of trading
After Roblox debuted on Wednesday, Coupang followed, with shares shooting above the South Korean e-commerce giant’s IPO price range. Quick math shows Coupang is worth around $92 billion at the moment, a huge number that nearly zero companies will ever reach.
How and when to hire your first product manager
Because product managers and founders often have overlapping skill sets, it can be tricky to find the right candidate.
While it’s different for every company, hiring a PM ensures companies aren’t “chasing the shiny object” but rather building the things that create enduring value for customers.
Deep Science: AI adventures in arts and letters
AI isn’t confined to the tech sphere; machine learning is applicable across disciplines, from music and the “computational unfolding” of ancient letters to figuring out where EV charging stations need to be built.
A first look at Coursera’s S-1 filing
The SEC filing offers a glimpse into the finances of how an edtech company, accelerated by the pandemic, performed over the past year.
It paints a picture of growth, albeit one that came at steep expense.
Olo’s IPO could value the company north of $3B as Toast waits in the wings
Olo has a history of growth and profitability, making its impending pricing all the more interesting.
But are investors willing to pay more for profits? And, if so, how much?
From electric charging to supply chain management, InMotion Ventures preps Jaguar for a sustainable future
InMotion’s investment in Circulor, a company that monitors supply chains from raw material inputs to finished outputs with an eye toward sustainable sourcing, shows the firm’s dedication to backing companies across the mobility space broadly.
White-label voice assistants will win the battle for podcast discovery
Americans are bored, housebound and screened out, driving roughly 128 million Americans to use a voice assistant at least once a month.
This has created a golden opportunity for audio as consumers turn to podcasts, voice assistants and smart speakers.
Why I’m hitting pause on ARR-focused coverage
One of the first recurring features Alex Wilhelm established at Extra Crunch was the “$100M ARR Club,” ongoing coverage of startups that have reached scale.
“Forget a $1 billion valuation — $100 million in annual recurring revenue is the cool kids’ club,” he wrote in December 2019. Since then, he expanded it to cover companies that attained $50M ARR.
The concept is a useful lens for studying the market. I can say this with confidence because it’s been widely copied by other tech news outlets. But this morning, Alex surprised me — he’s shelving the ARR Club, at least for now.
“In the end it became a pre-IPO list that was fun but not entirely educational, by my reckoning,” he told me. “The $50M ARR club evolution was supposed to help shake loose more interesting operational details, but just didn’t.”
Before putting the format on hiatus, Alex’s last ARR Club roundup looks at in-office display and kiosk startup AppSpace, data backup unicorn Druva, and Synack, which makes security software.
TC Early Stage: The premier how-to event for startup entrepreneurs and investors
From April 1-2, some of the most successful founders and VCs will explain how they build their businesses, raise money and manage their portfolios.
At TC Early Stage, we’ll cover topics like recruiting, sales, legal, PR, marketing and brand building. Each session includes ample time for audience questions and discussion.
Use discount code ECNEWSLETTER to take 20% off the cost of your TC Early Stage ticket!
Listen: OakNorth CIO shares automation trends in commercial lending
Commercial banks have been automating aspects of the lending and decisioning process, primarily at the lower end of the commercial lending spectrum, but hesitate to automate for loans more than $1 million. This means commercial banks have kept automations focused on loans of less than $1 million, explains Sean Hunter in this podcast discussion with […]
Predictive Maintenance is a Killer AI App
By John P. Desmond, AI Trends Editor
Predictive maintenance (PdM) has emerged as a killer AI app.
In the past five years, predictive maintenance has moved from a niche use case to a fast-growing, high return on investment (ROI) application that is delivering true value to users. These developments are an indication of the power of the Internet of Things (IoT) and AI together, a market considered in its infancy today.
These observations are from research conducted by IoT Analytics, consultants who supply market intelligence, which recently estimated that the $6.9 billion predictive maintenance market will reach $28.2 billion by 2026.
The company began its research coverage of the IoT-driven predictive maintenance market in 2016, at an industry maintenance conference in Dortmund, Germany. Not much was happening. “We were bitterly disappointed,” stated Knud Lasse Lueth, CEO at IoT Analytics, in an account in IoT Business News. “Not a single exhibitor was talking about predictive maintenance.”
Things have changed. IoT Analytics analyst Fernando Alberto Brügge stated, “Our research in 2021 shows that predictive maintenance has clearly evolved from the rather static condition-monitoring approach. It has become a viable IoT application that is delivering overwhelmingly positive ROI.”
Technical developments that have contributed to the market expansion include: a simplified process for connecting IoT assets, major advances in cloud services, and improvements in the accessibility of machine learning/data science frameworks, the analysts state.
Along with the technical developments, the predictive maintenance market has seen a steady increase in the number of software and service providers offering solutions. IoT Analytics identified about 100 companies in the space in 2016; today the company identifies 280 related solution providers worldwide. Many of them are startups who recently entered the field. Established providers including GE, PTC, Cisco, ABB, and Siemens, have entered the market in the past five years, many through acquisitions.
The market still has room; the analysts predict 500 companies will be in the business in the next five years.
In 2016, the ROI from predictive analytics was unclear. In 2021, a survey of about 100 senior IT executives from the industrial sector found that predictive maintenance projects have delivered a positive ROI in 83% of the cases. Some 45% of those reported amortizing their investments in less than a year. “This data demonstrated how attractive the investment has become in recent years,” the analysts stated.
More IoT Sensors Means More Precision
Implemented projects that the analysts studied in 2016 relied on a limited number of data sources, typically one sensor value, such as vibration or temperature. Projects described in the 2021 report described 11 classes of data sources, such as data from existing sensors or data from the controllers. As more sources are tapped, the precision of the predictions increase, the analysts state.
Many projects today are using hybrid modeling approaches that rely on domain expertise, virtual sensors and augmented data. AspenTech and PARC are two suppliers identified in the report as embracing hybrid modeling approaches. AspenTech has worked with over 60 companies to develop and test hybrid models that combine physics with ML/data science knowledge, enhancing prediction accuracy.
The move to edge computing is expected to further benefit predictive modeling projects, by enabling algorithms to run at the point where data is collected, reducing response latency. The supplier STMicroelectronics recently introduced some smart sensor nodes that can gather data and do some analytic processing.
More predictive maintenance apps are being integrated with enterprise software systems, such as enterprise resource planning (ERP) or computerized maintenance management systems (CMMS). Litmus Automation offers an integration service to link to any industrial asset, such as a programmable logic controller, a distributed control system, or a supervisory control and data acquisition system.
Reduced Downtime Results in Savings
Gains come from preventing downtime. “Predictive maintenance is the result of monitoring operational equipment and taking action to prevent potential downtime or an unexpected or negative outcome,” stated Mike Leone, an analyst at IT strategy firm Enterprise Strategy Group, in an account from TechTarget.
Advances that have made predictive maintenance more practical today include sensor technology becoming more widespread, and the ability to monitor industrial machines in real time, stated Felipe Parages, senior data scientist at Valkyrie, data sense consultants. With more sensors, the volume of data has grown exponentially, and data analytics via cloud services has become available.
It used to be that an expert had to perform an analysis to determine if a machine was not operating in an optimal way. “Nowadays, with the amount of data you can leverage and the new techniques based on machine learning and AI, it is possible to find patterns in all that data, things that are very subtle and would have escaped notice by a human being,” stated Parages.
As a result, one person can now monitor hundreds of machines, and companies are accumulating historical data, which enables deeper trend analysis. “Predictive maintenance “is a very powerful weapon,” he stated.
In an example project, Italy’s primary rail operator, Trenitalia, adopted predictive maintenance for its high-speed trains. The system is expected to save eight to 10% of an annual maintenance budget of 1.3 billion Euros, stated Paul Miller, an analyst with research firm Forrester, which recently issued a report on the project.
“They can eliminate unplanned failures which often provide direct savings in maintenance but just as importantly, by taking a train out of service before it breaks—that means better customer service and happier customers,” Miller stated. He recommended organizations start out with predictive maintenance by fielding a pilot project.
In an example of the types of cooperation predictive maintenance projects are expected to engender, the CEOs of several European auto and electronics firms recently announced plans to join forces to form the “Software Republique,” a new ecosystem for innovation in intelligent mobility. Atos, Dassault Systèmes, Groupe Renault, and STMicroelectronics and Thales announced their decision to pool their expertise to accelerate the market.
Luca de Meo, Chief Executive Officer of Groupe Renault, stated in a press release from STMicroelectronics, “In the new mobility value chain, on-board intelligence systems are the new driving force, where all research and investment are now concentrated. Faced with this technological challenge, we are choosing to play collectively and openly. There will be no center of gravity, the value of each will be multiplied by others. The combined expertise in cybersecurity, microelectronics, energy and data management will enable us to develop unique, cutting-edge solutions for low-carbon, shared, and responsible mobility, made in Europe.”
The Software République will be based in Guyancourt, a commune in north-central France at the Renault Technocentre in a building called Odyssée, a 12,000 square meter space which is eco-responsible. For example, its interior and exterior structure is 100 percent wood, and the building is covered with photovoltaic panels.
Post Office Looks to Gain an Edge With Edge Computing
By AI Trends Editor John P. Desmond
NVIDIA on May 6 detailed a partnership with the US Postal Service underway for over a year to speed up mail service using AI, with a goal of reducing current processing time tasks that take days to hours.
The project fields edge servers at 195 Post Services sites across the nation, which review 20 terabytes of images a day from 1,000 mail processing machines, according to a post on the NVIDIA blog.
“The federal government has been for the last several years talking about the importance of artificial intelligence as a strategic imperative to our nation, and as an important funding priority. It’s been talked about in the White House, on Capitol Hill, in the Pentagon. It’s been funded by billions of dollars, and it’s full of proof of concepts and pilots,” stated Anthony Robbins, Vice President of Federal for NVIDIA, in an interview with Nextgov. “And this is one of the few enterprise–wide examples of an artificial intelligence deployment that I think can serve to inspire the whole of the federal government.”
The project started with USPS AI architect at the time Ryan Simpson, who had the idea to try to expand an image analysis system a postal team was developing, into something much bigger, according to the blog post. (Simpson worked for USPS for over 12 years, and moved to NVIDIA as a senior data scientist eight months ago.) He believed that a system could analyze billions of images each center generated, and gain insights expressed in a few data points that could be shared quickly over the network.
In a three-week sprint, Simpson worked with half a dozen architects at NVIDIA and others to design the needed deep-learning models. The work was done within the Edge Computing Infrastructure Program (ECIP), a distributed edge AI system up and running on Nvidia’s EGX platform at USPS. The EGX platform enables existing and modern, data-intensive applications to be accelerated and secure on the same infrastructure, from data center to edge.
“It used to take eight or 10 people several days to track down items, now it takes one or two people a couple of hours,” stated Todd Schimmel, Manager, Letter Mail Technology, USPS. He oversees USPS systems including ECIP, which uses NVIDIA-Certified edge servers from Hewlett-Packard Enterprise.
In another analysis, a computer vision task that would have required two weeks on a network of servers with 800 CPUs can now get done in 20 minutes on the four NVIDIA V100 Tensor Core GPUs in one of the HPE Apollo 6500 servers.
Contract Awarded in 2019 for System Using OCR
USPS had put out a request for proposals for a system using optical character recognition (OCR) to streamline its imaging workflow. “In the past, we would have bought new hardware, software—a whole infrastructure for OCR; or if we used a public cloud service, we’d have to get images to the cloud, which takes a lot of bandwidth and has significant costs when you’re talking about approximately a billion images,” stated Schimmel.
Today, the new OCR application will rely on a deep learning model in a container on ECIP managed by Kubernetes, the open source container orchestration system, and served by NVIDIA Triton, the company’s open-source inference-serving software. Triton allows teams to deploy trained AI models from any framework, such as TensorFlow or PyTorch.
The deployment was very streamlined,” Schimmel stated. “We awarded the contract in September 2019, started deploying systems in February 2020 and finished most of the hardware by August—the USPS was very happy with that,” he added
Multiple models need to communicate to the USPS OCR application to work. The app that checks for mail items alone requires coordinating the work of more than a half dozen deep-learning models, each checking for specific features. And operators expect to enhance the app with more models enabling more features in the future.
“The models we have deployed so far help manage the mail and the Postal Service—they help us maintain our mission,” Schimmel stated.
One model, for example, automatically checks to see if a package carries the right postage for its size, weight, and destination. Another one that will automatically decipher a damaged barcode could be online this summer.
“We’re at the very beginning of our journey with edge AI. Every day, people in our organization are thinking of new ways to apply machine learning to new facets of robotics, data processing and image handling,” he stated.
Accenture Federal Services, Dell Technologies, and Hewlett-Packard Enterprise contributed to the USPS OCR system incorporating AI, Robbins of NVIDIA stated. Specialized computing cabinets—or nodes—that contain hardware and software specifically tuned for creating and training ML models, were installed at two data centers.
“The AI work that has to happen across the federal government is a giant team sport,” Robbins stated to Nextgov. “And the Postal Service’s deployment of AI across their enterprise exhibited just that.”
The new solutions could help the Postal Service improve delivery standards, which have fallen over the past year. In mid-December, during the last holiday season, the agency delivered as little as 62% of first-class mail on time—the lowest level in years, according to an account in VentureBeat . The rate rebounded to 84% by the week of March 6 but remained below the agency’s target of about 96%.
The Postal Service has blamed the pandemic and record peak periods for much of the poor service performance.
Here Come the AI Regulations
By AI Trends Staff
New laws will soon shape how companies use AI.
The five largest federal financial regulators in the US recently released a request for information how banks use AI, signaling that new guidance is coming for the finance business. Soon after that, the US Federal Trade Commission released a set of guidelines on “truth, fairness and equity” in AI, defining the illegal use of AI as any act that “causes more harm than good,” according to a recent account in Harvard Business Review.
And on April 21, the European Commission issued its own proposal for the regulation of AI (See AI Trends, April 22, 2021)
While we don’t know what these regulation will allow, “Three central trends unite nearly all current and proposed laws on AI, which means that there are concrete actions companies can undertake right now to ensure their systems don’t run afoul of any existing and future laws and regulations,” stated article author Andrew Burt, the managing partner of bnh.ai, a boutique law firm focused on AI and analytics.
First, conduct assessments of AI risks. As part of the effort, document how the risks have been minimized or resolved. Regulatory frameworks that refer to these “algorithmic impact assessments,” or “IA for AI,” are available.
For example, Virginia’s recently-passed Consumer Data Protection Act, requires assessments for certain types of high-risk algorithms.
The EU’s new proposal requires an eight-part technical document to be completed for high-risk AI systems that outlines “the foreseeable unintended outcomes and sources of risks” of each AI system, Burt states. The EU proposal is similar to the Algorithmic Accountability Act filed in the US Congress in 2019. The bill did not go anywhere but is expected to be reintroduced.
Second, accountability and independence. This suggestion is that the data scientists, lawyers and others evaluating the AI system have different incentives than those of the frontline data scientists. This could mean that the AI is tested and validated by different technical personnel than those who originally developed it, or organizations may choose to hire outside experts to assess the AI system.
“Ensuring that clear processes create independence between the developers and those evaluating the systems for risk is a central component of nearly all new regulatory frameworks on AI,” Burt states.
Third, continuous review. AI systems are “brittle and subject to high rates of failure,” with risks that grow and change over time, making it difficult to mitigate risk at a single point in time. “Lawmakers and regulators alike are sending the message that risk management is a continual process,” Burt stated.
Approaches in US, Europe and China Differ
The approaches between the US, Europe and China toward AI regulation differ in their approach, according to a recent account in The Verdict, based on analysis by Global Data, the data analytics and consulting company based in London.
“Europe appears more optimistic about the benefits of regulation, while the US has warned of the dangers of over regulation,”’ the account states. Meanwhile, “China continues to follow a government-first approach” and has been widely criticized for the use of AI technology to monitor citizens. The account noted examples in the rollout by Tencent last year of an AI-based credit scoring system to determine the “trust value” of people, and the installation of surveillance cameras outside people’s homes to monitor the quarantine imposed after the breakout of COVID-19.
“Whether the US’ tech industry-led efforts, China’s government-first approach, or Europe’s privacy and regulation-driven approach is the best way forward remains to be seen,” the account stated.
In the US, many companies are aware of the risk of new AI regulation that could stifle innovation and their ability to grow in the digital economy, suggested a recent report from pwc, the multinational professional services firm.
“It’s in a company’s interests to tackle risks related to data, governance, outputs, reporting, machine learning and AI models, ahead of regulation,” the pwc analysts state. They recommended business leaders assemble people from across the organization to oversee accountability and governance of technology, with oversight from a diverse team that includes members with business, IT and specialized AI skills.
Critics of European AI Act Cite Too Much Gray Area
While some argue that the European Commission’s proposed AI Act leaves too much gray area, the hope of the European Commission is that their proposed AI Act will provide guidance for businesses wanting to pursue AI, as well as a degree of legal certainty.
“Trust… we think is vitally important to allow the development we want of artificial intelligence,” stated Thierry Breton, European Commissioner for the Internal Market, in an account in TechCrunch. AI applications “need to be trustworthy, safe, non-discriminatory — that is absolutely crucial — but of course we also need to be able to understand how exactly these applications will work.”
“What we need is to have guidance. Especially in a new technology… We are, we will be, the first continent where we will give guidelines—we’ll say ‘hey, this is green, this is dark green, this is maybe a little bit orange and this is forbidden’. So now if you want to use artificial intelligence applications, go to Europe! You will know what to do, you will know how to do it, you will have partners who understand pretty well and, by the way, you will come also to the continent where you will have the largest amount of industrial data created on the planet for the next ten years.”
“So come here—because artificial intelligence is about data—we’ll give you the guidelines. We will also have the tools to do it and the infrastructure,” Breton suggested.
Another reaction was that the Commission’s proposal has overly broad exemptions, such as for law enforcement to use remote biometric surveillance including facial recognition technology, and it does not go far enough to address the risk of discrimination.
Reactions to the Commission’s proposal included plenty of criticism of overly broad exemptions for law enforcement’s use of remote biometric surveillance (such as facial recognition tech) as well as concerns that measures in the regulation to address the risk of AI systems discriminating don’t go nearly far enough.
“The legislation lacks any safeguards against discrimination, while the wide-ranging exemption for ‘safeguarding public security’ completely undercuts what little safeguards there are in relation to criminal justice,” stated Griff Ferris, legal and policy officer for Fair Trials, the global criminal justice watchdog based in London. “The framework must include rigorous safeguards and restrictions to prevent discrimination and protect the right to a fair trial. This should include restricting the use of systems that attempt to profile people and predict the risk of criminality.”
To accomplish this, he suggested, “The EU’s proposals need radical changes to prevent the hard-wiring of discrimination in criminal justice outcomes, protect the presumption of innocence and ensure meaningful accountability for AI in criminal justice.”
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