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Extra Crunch roundup: AI eats fintech, fundraising visas, no-code transition tips, more

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Most American retail banks are designed the same way: Customers must pass several desks set aside for loan and mortgage officers before they can talk to a customer representative.

I only step inside a bank a few times each year, but even pre-pandemic, I can’t remember the last time I saw someone sitting at one of those desks. Everyone I know who’s obtained a home or business loan in the recent past started with an online application process.

For this morning’s column, Alex Wilhelm interviewed Dave Girouard, CEO of Upstart, an AI-powered fintech lender that expects to see growth increase 114% this year.

A forecast like that suggests that retail banks have gotten comfortable with using automated tools to calculate risk, which may help explain all the empty desks at my local branch.

“If Upstart hits its 2021 numbers, we will be able to read into them broader adoption of AI among old-guard firms,” says Alex.

According to PitchBook, investors are also more bullish on AI: Q4 2020 saw record funding for AI and ML startups, and exit totals are increasing as well.

I wouldn’t mind adding a gently used desk to my home office; perhaps I should call my bank and see if they have one to spare.

Thanks very much for reading Extra Crunch. Have a great weekend!

Walter Thompson
Senior Editor, TechCrunch
@yourprotagonist


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A crypto company’s journey to Data 3.0

young woman uses digital tablet on virtual visual screen at night

Image Credits: dowell (opens in a new window) / Getty Images

Data is a gold mine for a company. If managed well, it provides the clarity and insights that lead to better decision-making at scale, in addition to an important tool to hold everyone accountable.

However, most companies are stuck in Data 1.0.

Dear Sophie: What type of visa should we get to fundraise in Silicon Valley?

lone figure at entrance to maze hedge that has an American flag at the center

Image Credits: Bryce Durbin/TechCrunch

Dear Sophie:

A friend and I founded a tech startup last year. Like a lot of other startups, we’re looking for funding.

Should we come to Silicon Valley to meet with venture capitalists?

How should we begin that process? What type of visa should we get and how easy is it to get?

—Logical in Lagos

To solve all the small things, look to everyday Little AI

Numbers code panel with blue glowing on dark background.

Image Credits: Yuichiro Chino / Getty Images

Why are developers still solving everyday pain points with manual, archaic processes, as opposed to employing “Little AI”?

There are millions of everyday use cases for AI, where technology is empowered to learn and decide on a course of action that offers the best outcome for consumers and companies alike.

How to recruit data scientists without paying top dollar

Female scientists working on project data on whiteboard in research lab

Image Credits: Thomas Barwick (opens in a new window) / Getty Images

The increasing demand for AI and data science experts, driven in part by the pandemic’s economic impact, is showing no sign of abating.

Many employers are failing to identify viable job candidates, much less interviewing or hiring them. What’s holding them back?

Often, it’s a poorly drafted job posting.

3 steps to aid the transition to becoming a no-code company

Image Credits: Korrawin / Getty Images

No-code is changing how organizations build and maintain applications.

It democratizes application development by creating “citizen developers” who can quickly build out apps that meet their business-facing needs in real time, realigning IT and business objectives by bringing them closer together.

How can your company get ahead of the trend?

No taxation without innovation: The rise of tax startups

Image Credits: jokerpro / Getty Images

The idiosyncrasies of sales taxes are a burden on small- and medium-sized businesses, but a new legion of startups is emerging to help companies manage the intricacies of cross-jurisdictional taxes.

Snowflake gave up its dual-class shares: Should you?

Four business people used ropes to tighten their money bags, economic austerity, reduced income, economic crisis

Image Credits: VectorInspiration / Getty Images

Some founders and investors argue that these preferred shares protect them from the whims of the market, but the perspective isn’t universally accepted.

Dual-class shares are a controversial governance structure, and some wonder if they are setting up an unfair playing field by allowing a cabal to wield outsized power.

So why would Snowflake give up such a powerful tool?

MaaS transit: The business of mobility as a service

market-maps-public-transit

Image Credits: Bryce Durbin

As transit agencies seek to win back riders, a flurry of platforms — some backed by giants like Uber, Intel and BMW — are offering new technology partnerships.

Whether it’s bundling bookings, payments or just trip planning, startups are selling these mobility-as-a-service (MaaS) offerings as a lifeline to make transit agencies the backbone of urban mobility.

What eToro’s investor presentation and $10B valuation tells us about Robinhood

Israeli consumer stock-trading service eToro is going public in the United States via a SPAC. One thing that points to?

Trading platforms are being valued like high-margin video games.

The global inequity in venture backing is staggering

I knew African founders lacked the same access to capital as entrepreneurs based in Europe or the United States, but the numbers are far less favorable than I thought.

According to Dauda Barry, CEO of Adaplay Esports, African startups have raised $500 million so far in 2021. If that trend continues, he estimates that the region’s tech companies will exceed the $1.4 billion they raised in 2020.

For perspective: “Stripe raised more yesterday than Barry had reported for the entire African continent this year,” Alex Wilhelm noted in today’s column.

Digging deeper, he pulled numbers from Crunchbase and PitchBook to track VC activity in Africa over the last three months. Once he filtered private equity funding from nonequity investments, the numbers were “staggering.”

“I am surprised that more VCs aren’t investing in Africa,” says Alex. “It smells like investing arbitrage.”

Farmland could be the next big asset class modernized by marketplace startups

"A green row celery field in the Salinas Valley, California USA"

Image Credits: Pgiam (opens in a new window) / Getty Images

Companies that help farmers raise money for agricultural development projects are revolutionizing the way farm and forestland are acquired, developed and commercialized across the United States.

While private equity has gotten a lot of press for expanding the size of their farmland investments, those investments are still dwarfed by the size of the potential farm industry in the U.S., meaning there’s still plenty of opportunity for investors to provide additional capital.

The NFT market is just getting started, but where is it headed?

The crypto art craze might seem silly and expensive, but it could empower artists from emerging economies and underrepresented groups to access the global art market in ways that they couldn’t before.

Can it outlive the hype?

Olo raises IPO range as DigitalOcean sees possible $5B debut valuation

Green arrow going up with red background

Image Credits: jayk7 (opens in a new window) / Getty Images

That Olo raised its IPO price is not a huge surprise, given the software company’s rapid growth and profits. In the case of DigitalOcean, we have more work to do as its approach to growth is a bit different.

Stripe’s epic new valuation and the value-capture gap between public and private markets

Stripe’s $600 million round values the payments and banking software company at $95 billion, near the top end of the valuation range at which the company was said to be raising funds back in November 2020.

Sadly, Stripe is still being coy with growth metrics. The Exchange digs in, no matter how vague.

Julia Collins and Sarah Kunst outline how to build a fundraising process

Julia Collins, the first Black woman to co-found a venture-backed unicorn, and investor Sarah Kunst offer fundraising pointers on Extra Crunch Live.

Kunst says good design is critical, but:

If you’re not a graphic designer, then any incremental minute that you’re spending on trying to make your deck pretty is a waste of time. You need to be focusing on content. Hire somebody, pay them a tiny bit of money to be able to do a nice graphics pass on your deck, and it’s going to make it a lot easier for people to to get the information that you need them to know.

How nontechnical talent can break into deep tech

Image Credits: Getty Images

Startup hiring processes can be opaque, and breaking into the deep tech world as a nontechnical person seems daunting. This column offers tactical advice for finding, reaching out to, cultivating relationships with and working at deep tech companies as a nontechnical candidate.

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Source: https://techcrunch.com/2021/03/19/extra-crunch-roundup-ai-eats-fintech-fundraising-visas-no-code-transition-tips-more/

AI

Aite survey: Financial institutions will invest more to automate loan process

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Financial institutions plan to increase their spend on automations and collections management solutions for their loan processes. Fresh results on consumer lending practice from research and advisory firm Aite Group indicate lenders plan to invest more heavily in their collections processes, said Leslie Parrish, senior analyst for the Aite Group’s consumer lending practice. Parrish shared […]

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Source: https://bankautomationnews.com/allposts/lending/aite-survey-financial-institutions-will-invest-more-to-automate-loan-process/

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AI

Facial recognition, other ‘risky’ AI set for constraints in EU

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Facial recognition and other high-risk artificial intelligence applications will face strict constraints under new rules unveiled by the European Union that threaten hefty fines for companies that don’t comply.

The European Commission, the bloc’s executive body, proposed measures on Wednesday that would ban certain AI applications in the EU, including those that exploit vulnerable groups, deploy subliminal techniques or score people’s social behavior.

The use of facial recognition and other real-time remote biometric identification systems by law enforcement would also be prohibited, unless used to prevent a terror attack, find missing children or tackle other public security emergencies.

Facial recognition is a particularly controversial form of AI. Civil liberties groups warn of the dangers of discrimination or mistaken identities when law enforcement uses the technology, which sometimes misidentifies women and people with darker skin tones. Digital rights group EDRI has warned against loopholes for public security exceptions use of the technology.

Other high-risk applications that could endanger people’s safety or legal status—such as self-driving cars, employment or asylum decisions — would have to undergo checks of their systems before deployment and face other strict obligations.

The measures are the latest attempt by the bloc to leverage the power of its vast, developed market to set global standards that companies around the world are forced to follow, much like with its General Data Protection Regulation.

The U.S. and China are home to the biggest commercial AI companies — Google and Microsoft Corp., Beijing-based Baidu, and Shenzhen-based Tencent — but if they want to sell to Europe’s consumers or businesses, they may be forced to overhaul operations.

Key Points:

  • Fines of 6% of revenue are foreseen for companies that don’t comply with bans or data requirements
  • Smaller fines are foreseen for companies that don’t comply with other requirements spelled out in the new rules
  • Legislation applies both to developers and users of high-risk AI systems
  • Providers of risky AI must subject it to a conformity assessment before deployment
  • Other obligations for high-risk AI includes use of high quality datasets, ensuring traceability of results, and human oversight to minimize risk
  • The criteria for ‘high-risk’ applications includes intended purpose, the number of potentially affected people, and the irreversibility of harm
  • AI applications with minimal risk such as AI-enabled video games or spam filters are not subject to the new rules
  • National market surveillance authorities will enforce the new rules
  • EU to establish European board of regulators to ensure harmonized enforcement of regulation across Europe
  • Rules would still need approval by the European Parliament and the bloc’s member states before becoming law, a process that can take years

—Natalia Drozdiak (Bloomberg Mercury)

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Source: https://bankautomationnews.com/allposts/comp-reg/facial-recognition-other-risky-ai-set-for-constraints-in-eu/

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Artificial Intelligence

Prioritizing Artificial Intelligence and Machine Learning in a Pandemic

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AI and ML
Illustration: © IoT For All

Artificial Intelligence (AI) and Machine Learning (ML) give companies the one thing humans can’t – scalability. Over time, humans limit a businesses’ ability to scale; they can only work so many hours at a given efficiency. On the other hand, AI and ML can work around the clock with the sole focus on a given project. As organizations navigate through COVID-19’s impact and the future of a remote workforce, scalability and efficiency can be the key to an organization’s successful recovery.

Implementation Challenges

The benefits of AI and ML don’t come without their own challenges; however, the top challenges are a lack of skills and time for proper implementation. In July, Deloitte found in a survey that 69% of respondents said the skills gap for AI implementation ranged from moderate to major to extreme. Simultaneously, many companies overlook the investment it takes to build the processes and infrastructure needed for successfully training, testing, deploying, and maintaining AI and ML in their enterprise.

Such challenges often cause companies to de-prioritize AI and ML projects, especially in times of uncertainty. That has been increasingly obvious throughout the COVID-19 pandemic. But while some organizations have drawn back on their efforts, the current global state demands the greater need for AI and ML to support critical business processes. This is especially true today given the growing remote workforce, considerations for returning to the workplace and work happening in silos worldwide.

Though challenging, it is not impossible to properly implement AI and ML. In this evolving COVID-influenced business landscape, four steps are key to effectively implementing a strong AI and ML system that helps streamline critical business processes despite uncertainty and limited resources.

Identify the Problem to Be Solved

Some companies mistakenly view AI and ML projects as a ‘silver bullet’ to solve all their problems. This often results in overinflated expectations, an unfocused approach, and unsatisfactory results. Instead, companies should identify those specific problems that will have the biggest impact from implementing AI and ML solutions and be hyper-focused on solving those problems.

Select Your Data

The second step in creating a strong AI and ML algorithm is to select the source data that your algorithm will be training on. There are two main options: training on your own data or training on a larger scale data set. Based on experience, training your algorithm on your own data puts you at a disadvantage. By training on a larger scale data set, the likelihood of success increases because your data is more representative and varied. Through advanced concepts such as transfer learning, companies can use semi-trained models based on larger data sets and then train the “last mile” using their own specific content unique to their business.

Clean House

The standby rules of data management apply here – garbage in, garbage out. Ultimately, the quality and accuracy of machine learning models depend on being representative. AI and ML – fed with the right data – can streamline operations and increase the benefit of companies’ DX and cloud migration journeys.

When you’re kicking off an AI or ML project, the most critical step is to clean up the data that your algorithm will be training on, especially if you’re using your own data or models.

Make Room for Training

AI and ML are all about probability. When you ask it a question, for example, “Is this a cat?,” the results you receive are the algorithm saying, “Out of the three buckets I was trained on, the likelihood of this image being a cat is .91, the likelihood of this image being a dog is .72 and the likelihood of this image being a bird is .32.”

This is why training on varied data is so important. If your training data only includes images of cats, dogs, and birds and you ask the algorithm to analyze the picture of a crocodile, it will only respond based on the buckets it’s been trained on – cats, dogs, and birds.

If you’ve properly selected and cleaned your data, training should be an easy last step, but it’s also an opportunity to go back to the first two steps and further refine based on your training.

The front end of training an AI and ML algorithm can be time-intensive, but following these four steps can make it easier to achieve significant outcomes. Across industries, AI and ML can quickly show ROI. For example, in the insurance industry, AI and ML can help insurers quickly search contracts, so employees aren’t sifting through contracts and repositories around the globe to answer simple questions. This means time efficiencies for an industry that COVID-19 has heavily impacted.

Even better, working with a SaaS provider with experience in your industry can make this process much easier and less costly. SaaS platforms allow companies to take advantage of having all of the infrastructure, security, and pre-trained models in place to reduce the overall effort and time to value. Many platforms allow users to uptrain the predefined models with unique customer data, reducing the training effort needed for model creation. Companies can then focus on integration with their ecosystem and workflows rather than model creation itself.

Bigger Picture

Overall, businesses can soften the impact of COVID by focusing on the bigger picture with AI and ML. Implementing AI and ML projects increase business productivity despite these times of uncertainty. As we continue on the road to recovery, we need tools like AI and ML to stay focused on the bigger picture, mission-critical tasks.

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Source: https://www.iotforall.com/prioritizing-artificial-intelligence-and-machine-learning-in-a-pandemic

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AI

ProGlove promotes worker well-being with human digital twin technology

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ProGlove, the company behind an ergonomic barcode scanner, has developed new tools for analyzing human processes to build a human digital twin.

“We have always been driven to have our devices narrate the story of what is really happening on the shop floor, so we added process analytics capabilities that allow for time-motion studies, visualization of the shop floor, and more,” ProGlove CEO Andreas Koenig told VentureBeat.

The company’s newest process analytics tools can complement the typical top-down perspective of applications by adding a process-as-seen view to the conventional process-as-wanted view. Most importantly, it can also provide insights that improve well-being.

Koenig said, “We are building an ecosystem that empowers the human worker to make their businesses stronger.”

ProGlove CEO Andreas Koenig

Above: ProGlove CEO Andreas Koenig

Image Credit: ProGlove

The market for barcode scanning is still going strong and is often taken for granted, given how old it is. “You have technologies like RFID that have been celebrated for being the next big thing, and yet their impact thus far hasn’t been anywhere near where most pundits expected it,” Koenig said.

Companies like Zebra, Honeywell, and Datalogic have lasted for decades by building out an ecosystem of tools to address industry needs. “What sets us apart is that we looked beyond the obvious and started with the human worker in mind,” Koenig said.

Not only is the company providing a form factor designed to meet requirements for rugged tools, this shift to analytics could further promote efficiency, quality, and ergonomics on the shop floor.

How a human digital twin works

ProGlove’s cofounders participated in Intel’s Make It Wearable Challenge, with the idea of designing a smart glove for industries. Today, ProGlove’s MARK scanner can collect six-axis motion data, including pitch, yaw, roll, and acceleration, along with timestamps, a step count, and camera data (such as barcode reading speed and the scanner ID).

Koenig’s vision goes beyond selling a product to establish the right balance between businesses’ need for profits and their obligation to ensure worker well-being. Koenig estimates that human hands deliver 70% of added value in factories and on warehouse floors. “There is no doubt that they are your most valuable resource that needs protection. Even more so since we are way more likely to experience a shortage of human workers in the warehouses across the world than having them replaced by robots, automation, or AI.”

ProGlove Insight contextualizes the collected data and lets users compare workstations and measure the workload and effort necessary to complete the tasks. Users can also visualize their shop floor, look at heatmaps, and identify best practices or efficiency blockers. After a recent smart factory lab experiment with users, DPD and Asics realized efficiency gains by as much as 20%, Koenig said.

ProGlove’s vision of the human digital twin is built on three pillars: a digital representation of onsite workers, a visualization of the shop floor, and an industrial process engineer. “The human digital twin is all about striking the right balance between businesses’ needs for profitability, efficiency, and worker well-being,” Koenig said. At the same time, it is important that the human digital twin complies with data privacy regulations and provides transparency to frontline workers around what data is being transmitted.

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Source: https://venturebeat.com/2021/04/21/proglove-promotes-worker-well-being-with-human-digital-twin-technology/

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