Activity-monitoring startup Zensors repurposes its tech to help coronavirus response

Computer vision techniques used for commercial purposes are turning out to be valuable tools for monitoring people’s behavior during the present pandemic. Zensors, a startup that uses machine learning to track things like restaurant occupancy, lines, and so on, is making its platform available for free to airports and other places desperate to take systematic measures against infection.

The company, founded two years ago but covered by TechCrunch in 2016, was among the early adopters of computer vision as a means to extract value from things like security camera feeds. It may seem obvious now that cameras covering a restaurant can and should count open tables and track that data over time, but a few years ago it wasn’t so easy to come up with or accomplish that.

Since then Zensors has built a suite of tools tailored to specific businesses and spaces, like airports, offices, and retail environments. They can count open and occupied seats, spot trash, estimate lines, and all that kind of thing. Coincidentally, this is exactly the kind of data that managers of these spaces are now very interested in watching closely given the present social distancing measures.

Zensors co-founder Anuraag Jain told Carnegie Mellon University — which the company was spun out of — that it had received a number of inquiries from the likes of airpots regarding applying the technology to public health considerations.

Software that counts how many people are in line can be easily adapted to, for example, estimate how close people are standing and send an alert if too many people are congregating or passing through a small space.

“Rather than profiting off them, we thought we would give our help for free,” said Jain. And so, for the next two months at least, Zensors is providing its platform for free to “selected entities who are on the forefront of responding to this crisis, including our airport clients.”

The system has already been augmented to answer COVID-19-specific questions like whether there are too many people in a given area, when a surface was last cleaned and whether cleaning should be expedited, and how many of a given group are wearing face masks.

Airports surely track some of this information already, but perhaps in a much less structured way. Using a system like this could be helpful for maintaining cleanliness and reducing risk, and no doubt Zensors hopes that having had a taste via what amounts to a free trial, some of these users will become paying clients. Interested parties should get in touch with Zensors via its usual contact page.

Google and USCF collaborate on machine learning tool to help prevent harmful prescription errors

Machine learning experts working at Google Health have published a new study in tandem with the University of California San Francisco (UCSF)’s computational health sciences department that describes a machine learning model the researchers built that can anticipate normal physician drug prescribing patterns, using a patient’s electronic health records (EHR) as input. That’s useful because around 2 percent of patients who end up hospitalized are affected by preventable mistakes in medication prescriptions, some instances of which can even lead to death.

The researchers describe the system as working in a similar manner to automated, machine learning-based fraud detection tools that are commonly used by credit card companies to alert customers of possible fraudulent transactions: They essentially build a baseline of what’s normal consumer behavior based on past transactions, and then alert your bank’s fraud department or freeze access when they detect a behavior that is not in line with and individual’s baseline behavior.

Similarly, the model trained by Google and UCSF worked by identifying any prescriptions that “looked abnormal for the patient and their current situation.” That’s a much more challenging proposition in the case of prescription drugs, vs. consumer activity – because courses of medication, their interactions with one another, and the specific needs, sensitivities and conditions of any given patient all present an incredibly complex web to untangle.

To make it possible, the researchers used electronic health records from de-identified patient that include vital signs, lab results, prior medications and medical procedures, as well as diagnoses and changes over time. They paired this historical data with current state information, and came up with various models to attempt to output an accurate prediction of a course of prescription for a given patient.

Their best-performing model was accurate “three quarters of the time,” Google says, which means that it matched up with what a physician actually decided to prescribe in a large majority of cases. It was also even more accurate (93%) in terms of predicting at least one medication that would fall within a top ten list of a physician’s most likely medicine choices for a patient – even if its top choice didn’t match the doctor’s.

The researchers are quick to note that though the model thus far has been fairly accurate in predicting a normal course of prescription, that doesn’t mean it’s able to successfully detect deviations from that yet with any high degree of accuracy. Still, it’s a good first step upon which to build that kind of flagging system.

ImmunityBio and Microsoft team up to precisely model how key COVID-19 protein leads to infection

An undertaking that involved combining massive amounts of graphics processing power could provide key leverage for researchers looking to develop potential cures and treatments for the novel coronavirus behind the current global pandemic. Immunotherapy startup ImmunityBio is working with Microsoft’s Azure to deliver a combined 24 petaflops of GPU computing capability for the purposes of modelling, in a very high degree of detail, the structure o the so-called “spike protein” that allows the SARS-CoV-2 virus that causes COVID-19 to enter human cells.

This new partnership means that they were able to produce a model of the spike protein within just days, instead of the months it would’ve taken previously. That time savings means that the model can get in the virtual hands of researchers and scientists working on potential vaccines and treatments even faster, and that they’ll be able to gear their work towards a detailed replication of the very protein they’re trying to prevent from attaching to the human ACE-2 proteins’ receptor, which is what sets up the viral infection process to begin with.

The main way that scientists working on treatments look to prevent or minimize the spread of the virus within the body is to block the attachment of the virus to these proteins, and the simplest way to do that is to ensure that the spike protein can’t connect with the receptor it targets. Naturally-occurring antibodies in patients who have recovered from the novel coronavirus do exactly that, and the vaccines under development are focused on doing the same thing pre-emptively, while many treatments are looking at lessening the ability of the virus to latch on to new cells as it replicates within the body.

In practical terms, the partnership between the two companies included a complement of 1,250 NVIDIA V100 Tensor Core GPUs designed for use in machine learning applications from a Microsoft Azure cluster, working with ImmunityBio’s existing 320 GPU cluster that is tuned specifically to molecular modeling work. The results of the collaboration will now be made available to researchers working on COVID-19 mitigation and prevention therapies, in the hopes that they will enable them to work more quickly and effectively towards a solution.

Africa Roundup: Africa’s tech ecosystem responds to COVID-19

In March, the virus gripping the world — COVID-19 — started to spread in Africa. In short order, actors across the continent’s tech ecosystem began to step up to stem the spread.

Early in March Africa’s coronavirus cases by country were in the single digits, but by mid-month those numbers had spiked leading the World Health Organization to sound an alarm.

“About 10 days ago we had 5 countries affected, now we’ve got 30,” WHO Regional Director Dr Matshidiso Moeti said at a press conference on March 19. “It’s has been an extremely rapid…evolution.” 

By the World Health Organization’s stats Tuesday there were 3671 COVID-19 cases in Sub-Saharan Africa and 87 confirmed deaths related to the virus — up from 463 cases and 8 deaths on March 18.

As the COVID-19 began to grow in major economies, governments and startups in Africa started measures to shift a greater volume of transactions toward digital payments and away from cash — which the World Health Organization flagged as a conduit for the spread of the coronavirus.

Africa’s leader in digital payment adoption — Kenya — turned to mobile-money as a public-health tool.

At the urging of the Central Bank and President Uhuru Kenyatta, the country’s largest telecom, Safaricom, implemented a fee-waiver on East Africa’s leading mobile-money product, M-Pesa, to reduce the physical exchange of currency.

The company announced that all person-to-person (P2P) transactions under 1,000 Kenyan Schillings (≈ $10) would be free for three months.

Kenya has one of the highest rates of digital finance adoption in the world — largely due to the dominance of M-Pesa  in the country — with 32 million of its 53 million population subscribed to mobile-money accounts, according to Kenya’s Communications Authority.

On March 20, Ghana’s central bank directed mobile money providers to waive fees on transactions of GH₵100 (≈ $18), with restrictions on transactions to withdraw cash from mobile-wallets.

Ghana’s monetary body also eased KYC requirements on mobile-money, allowing citizens to use existing mobile phone registrations to open accounts with the major digital payment providers, according to a March 18 Bank of Ghana release.

Growth in COVID-19 cases in Nigeria, Africa’s most populous nation of 200 million, prompted one of the country’s largest digital payments startups to act.

Lagos based venture Paga made fee adjustments, allowing merchants to accept payments from Paga customers for free — a measure “aimed to help slow the spread of the coronavirus by reducing cash handling in Nigeria,” according to a company release.

In March, Africa’s largest innovation incubator, CcHub, announced funding and engineering support to tech projects aimed at curbing COVID-19 and its social and economic impact.

The Lagos and Nairobi based organization posted an open application on its website to provide $5,000 to $100,000 funding blocks to companies with COVID-19 related projects.

CcHub’s CEO Bosun Tijani expressed concern for Africa’s ability to combat a coronavirus outbreak. “Quite a number of African countries, if they get to the level of Italy or the UK, I don’t think the system… is resilient enough to provide support to something like that,” Tijani said.

Cape Town based crowdsolving startup Zindi — that uses AI and machine learning to tackle complex problems — opened a challenge to the 12,000 registered engineers on its platform.

The competition, sponsored by AI4D, tasks scientists to create models that can use data to predict the global spread of COVID-19 over the next three months. The challenge is open until April 19, solutions will be evaluated against future numbers and the winner will receive $5,000.

Zindi will also sponsor a hackathon in April to find solutions to coronavirus related problems.

Image Credits: Sam Masikini via Zindi

On the digital retail front, Pan-African e-commerce company Jumia announced measures it would take on its network to curb the spread of COVID-19.

The Nigeria headquartered operation — with online goods and services verticals in 11 African countries — said it would donate certified face masks to health ministries in Kenya, Ivory Coast, Morocco, Nigeria and Uganda, drawing on its supply networks outside Africa.

The company has also offered African governments use of of its last-mile delivery network for distribution of supplies to healthcare facilities and workers.

Jumia is reviewing additional assets it can offer the public sector. “If governments find it helpful we’re willing to do it,” CEO Sacha Poignonnec told TechCrunch.

More Africa-related stories @TechCrunch

African tech around the ‘net

Cnvrg.io launches a free version of its data science platform

Data science platform cnvrg.io today announced the launch of the free community version of its data science platform. Dubbed ‘CORE,’ this version includes most — but not all — of the standard feature in cnvrg’s main commercial offering. It’s an end-to-end solution for building, managing and automating basic ML models with limitations in the free version that mostly center around the production capabilities of the paid premium version and working with larger teams of data scientists.

As the company’s CEO Yochay Ettun told me, CORE users will be able to use the platform either on-premise or in the cloud, using Nvidia-optimized containers that run on a Kubernetes cluster. Because of this, it natively handles hybrid- and multi-cloud deployments that can automatically scale up and down as needed — and adding new AI frameworks is simply a matter of spinning up new containers, all of which are managed from the platform’s web-based dashboard.

Ettun describes CORE as a ‘lightweight version’ of the original platform but still hews closely to the platform’s original mission. “As was our vision from the very start, cnvrg.io wants to help data scientists do what they do best – build high impact AI,” he said. “With the growing technical complexity of the AI field, the data science community has strayed from the core of what makes data science such a captivating profession — the algorithms. Today’s reality is that data scientists are spending 80 percent of their time on non-data science tasks, and 65 percent of models don’t make it to production. Cnvrg.io CORE is an opportunity to open its end-to-end solution to the community to help data scientists and engineers focus less on technical complexity and DevOps, and more on the core of data science — solving complex problems.”

This has very much been the company’s direction from the outset and as Ettun noted in a blog post from a few days ago, many data scientists today try to build their own stack by using open-source tools. They want to remain agile and able to customize their tools to their needs, after all. But he also argues that data scientists are usually hired to build machine learning models, not to build and manage data science platforms.

While other platforms like H2O.ai, for example, are betting on open source and the flexibility that comes with that, cnvrg.io’s focus is squarely on ease of use. Unlike those tools, Jerusalem-based cnvrg.io, which has raised about $8 million so far, doesn’t have the advantage of the free marketing that comes with open source, so it makes sense for the company to now launch this free self-service version

It’s worth noting that while cnvrg.io features plenty of graphical tools for managing date ingestion flows, models and clusters, it’s very much a code-first platform. With that, Ettun tells me that the ideal user is a data scientist, data engineer or a student passionate about machine learning. “As a code-first platform, users with experience and savvy in the data science field will be able to leverage cnvrg CORE features to produce high impact models,” he said. “As our product is built around getting more models to production, users that are deploying their models to real-world applications will see the most value.”

 

‘It’s part of my job as a VC to remain calm,’ says Anorak’s Greg Castle

As the venture landscape adjusts to the COVID-19 pandemic and seismic shifts in public markets, early-stage VCs are reassessing which bets they’re making, along with questions they’re asking of founders who are exploring bleeding-edge technology.

Anorak Ventures is a small seed-investment firm that bets on emerging tech like AR/VR, machine learning and robotics. I recently hopped on a Zoom call with founder Greg Castle to talk about what he’s seen recently in seed investing and how the sector is responding to the crisis. Castle was an early investor in Oculus; his other bets at Anorak include Against Gravity, 6D.ai and Anduril.

Our conversation has been edited for length and clarity.

TechCrunch: Has this pandemic affected the types of companies that you’re looking at?

Greg Castle: From my experience as an investor thus far, being reactive as an investor and looking at “hot” areas has a lot of pitfalls to be mindful of. I think a lot of the areas that excite me as an investor could benefit from what’s going on here, those areas including robotics, automation, immersive entertainment and immersive computing.

Just generally, do you feel like a recession is more likely to negatively impact emerging tech more so than other areas?

Zindi taps 12,000 African data scientists for solutions to COVID-19

Since its inception, Cape Town based crowdsolving startup Zindi has been building a database of data scientists across Africa.

It now has 12,000 registered on its its platform that uses AI and machine learning to tackle complex problems and will offer them cash-prizes to find solutions to curb COVID-19.

Zindi has an open challenge focused on stemming the spread and havoc of coronavirus and will introduce a hackathon in April. The current competition, sponsored by AI4D, tasks scientists to create models that can use data to predict the global spread of COVID-19 over the next three months.

The challenge is open until April 19, solutions will be evaluated against future numbers and the winner will receive $5000.

The competition fits with Zindi’s business model of building a platform that can aggregate pressing private or public-sector challenges and match the solution seekers to problem solvers.

Founded in 2018, the early-stage venture allows companies, NGOs or government institutions to host online competitions around data oriented issues.

Zindi’s model has gained the attention of some notable corporate names in and outside of Africa. Those who have hosted competitions include Microsoft, IBM and Liquid Telecom. Public sector actors — such as the government of South Africa and UNICEF — have also tapped Zindi for challenges as varied as traffic safety and disruptions in agriculture.

Zindi Team in Cape Town 1

Image Credits: Zindi

The startup’s CEO didn’t imagine a COVID-19 situation precisely, but sees it as one of the reasons she co-founded Zindi with South African Megan Yates and Ghanaian Ekow Duker.

The ability to apply Africa’s data science expertise, to solve problems around a complex health crisis such as COVID-19 is what Zindi was meant for, Lee explained to TechCrunch on a call from Cape Town.

“As an online platform, Zindi is well-positioned to mobilize data scientists at scale, across Africa and around the world, from the safety of their homes,” she said.

Lee explained that perception leads many to believe Africa is the victim or source of epidemics and disease. “We wanted to show Africa can actually also contribute to the solution for the globe.”

With COVID-19, Zindi is being employed to alleviate a problem that is also impacting its founder, staff and the world.

Lee spoke to TechCrunch while sheltering in place in Cape Town, as South Africa went into lockdown Friday due to coronavirus. Zindi’s founder explained she also has in-laws in New York and family in San Francisco living under similar circumstances due to the global spread of COVID-19.

Lee believes the startup’s competitions can produce solutions that nations in Africa could tap as the coronavirus spreads. “The government of Kenya just started a task force where they’re including companies from the ICT sector. So I think there could be interest,” she said.

Starting April, Zindi will launch six weekend hackathons focused on COVID-19.

That could be timely given the trend of COVID-19 in Africa. The continent’s cases by country were in the single digits in early March, but those numbers spiked last week — prompting the World Health Organization’s Regional Director Dr Matshidiso Moeti to sound an alarm on the rapid evolution of the virus on the continent.

By the WHO’s stats Wednesday there were 1691 COVID-19 cases in Sub-Saharan Africa and 29 confirmed deaths related to the virus — up from 463 cases and 10 deaths last Wednesday.

The trajectory of the coronavirus in Africa has prompted countries and startups, such as Zindi, to include the continent’s tech sector as part of a broader response. Central banks and fintech companies in Ghana, Nigeria, and Kenya have employed measures to encourage more mobile-money usage, vs. cash — which the World Health Organization flagged as a conduit for the spread of the virus.

The continent’s largest incubator, CcHub, launched a fund and open call for tech projects aimed at curbing COVID-19 and its social and economic impact.

Pan-African e-commerce company Jumia has offered African governments use of its last-mile delivery network for distribution of supplies to healthcare facilities and workers.

Zindi’s CEO Celina Lee anticipates the startup’s COVID-19 related competitions can provide additional means for policy-makers to combat the spread of the virus.

“The one that’s open right now should hopefully go into informing governments to be able to anticipate the spread of the disease and to more accurately predict the high risk areas in a country,” she said.

Espressive lands $30M Series B to build better help chatbots

Espressive, a four-year-old startup from former ServiceNow employees, is working to build a better chatbot to reduce calls to company help desks. Today, the company announced a $30 million Series B investment.

Insight Partners led the round with help from Series A lead investor General Catalyst along with Wing Venture Capital. Under the terms of today’s agreement, Insight founder and managing director Jeff Horing will be joining the Espressive Board. Today’s investment brings the total raised to $53 million, according to the company.

Company founder and CEO Pat Calhoun says that when he was at ServiceNow he observed that, in many companies, employees often got frustrated looking for answers to basic questions. That resulted in a call to a Help Desk requiring human intervention to answer the question.

He believed that there was a way to automate this with AI-driven chatbots, and he founded Espressive to develop a solution. “Our job is to help employees get immediate answers to their questions or solutions or resolutions to their issues, so that they can get back to work,” he said.

They do that by providing a very narrowly focused natural language processing (NLP) engine to understand the question and find answers quickly, while using machine learning to improve on those answers over time.

“We’re not trying to solve every problem that NLP can address. We’re going after a very specific set of use cases which is really around employee language, and as a result, we’ve really tuned our engine to have the highest accuracy possible in the industry,” Calhoun told TechCrunch.

He says what they’ve done to increase accuracy is combine the NLP with image recognition technology. “What we’ve done is we’ve built our NLP engine on top of some image recognition architecture that’s really designed for a high degree of accuracy and essentially breaks down the phrase to understand the true meaning behind the phrase,” he said.

The solution is designed to provide a single immediate answer. If, for some reason, it can’t understand a request, it will open a help ticket automatically and route it to a human to resolve, but they try to keep that to a minimum. He says that when they deploy their solution, they tune it to the individual customers’ buzzwords and terminology.

So far they have been able to reduce help desk calls by 40% to 60% across customers with around 85% employee participation, which shows that they are using the tool and it’s providing the answers they need. In fact, the product understands 750 million employee phrases out of the box.

The company was founded in 2016. It currently has 65 employees and 35 customers, but with the new funding, both of those numbers should increase.

Monitoring is critical to successful AI

As the world becomes more deeply connected through IoT devices and networks, consumer and business needs and expectations will soon only be sustainable through automation.

Recognizing this, artificial intelligence and machine learning are being rapidly adopted by critical industries such as finance, retail, healthcare, transportation and manufacturing to help them compete in an always-on and on-demand global culture. However, even as AI and ML provide endless benefits — such as increasing productivity while decreasing costs, reducing waste, improving efficiency and fostering innovation in outdated business models — there is tremendous potential for errors that result in unintended, biased results and, worse, abuse by bad actors.

The market for advanced technologies including AI and ML will continue its exponential growth, with market research firm IDC projecting that spending on AI systems will reach $98 billion in 2023, more than two and one-half times the $37.5 billion that was projected to be spent in 2019. Additionally, IDC foresees that retail and banking will drive much of this spending, as the industries invested more than $5 billion in 2019.

These findings underscore the importance for companies that are leveraging or plan to deploy advanced technologies for business operations to understand how and why it’s making certain decisions. Moreover, having a fundamental understanding of how AI and ML operate is even more crucial for conducting proper oversight in order to minimize the risk of undesired results.

Companies often realize AI and ML performance issues after the damage has been done, which in some cases has made headlines. Such instances of AI driving unintentional bias include the Apple Card allowing lower credit limits for women and Google’s AI algorithm for monitoring hate speech on social media being racially biased against African Americans. And there have been far worse examples of AI and ML being used to spread misinformation online through deepfakes, bots and more.

Through real-time monitoring, companies will be given visibility into the “black box” to see exactly how their AI and ML models operate. In other words, explainability will enable data scientists and engineers to know what to look for (a.k.a. transparency) so they can make the right decisions (a.k.a. insight) to improve their models and reduce potential risks (a.k.a. building trust).

But there are complex operational challenges that must first be addressed in order to achieve risk-free and reliable, or trustworthy, outcomes.

5 key operational challenges in AI and ML models

Amazon, Apple and Microsoft CEOs detail their companies’ efforts to combat coronavirus pandemic

The tech industry is mobilizing its considerable resources to attempt to support efforts against the growing global coronavirus pandemic. Over the weekend, the CEOs of Amazon, Apple and Microsoft all shared updates regarding some aspects of their company’s ongoing contributions, which range from donations of medical supplies and personal protective equipment (PPE) for frontline healthcare workers, to software projects that help track and analyze the global spread of infection.

Apple CEO Tim Cook shared on Twitter that the company has been attempting to source necessary supplies that are needed for healthcare workers both in the U.S. and Europe, and that the company is joining “millions of masks” for this use. Apple also detailed some of its other updates via earlier releases, including a $15 million donation, along with two-to-one corporate matching for all employee donations that go towards COVID-19 response.

Amazon founder and CEO Jeff Bezos provided an update on Saturday on the company’s official blog that included details about the change in Amazon’s prioritization for its warehousing and logistics operations, which now focus on essential items including daily household staples, baby and medical supplies. Bezos also reiterated Amazon’s commitment to hiring 100,000 new roles, along with raising hourly wages for fulfilment workers.

Bezos notes that while the company has “placed purchase orders for millions of face masks” that it intends to distribute to its full-time and contract workers who are not able to work from home, “very few of those orders have been filled” to to the global supply shortage. He further notes that these resources are likely to go to frontline healthcare workers first, and that the company will focus on getting them to their staff in order of priority once they become available.

Microsoft CEO Satya Nadella provided a lengthy update about his company’s various efforts in a LinkedIn post on Saturday, publishing an email he sent to all Microsoft employees for external consumption. Nadella describes some of its telehealth platform software work, as well as a number of collaborative data projects, including the John Hopkins University global COVID-19 confirmed case tracker. The Centers for Disease Control and Prevention (CDC) also released a chatbot assessment tool for COVID-19 that uses Microsoft’s health chatbot tech as its underlying framework.

Microsoft is also seeing Teams and Minecraft being used globally for remote learning iniativies designed to supplement in-perosn school closures, and it’s working on machine learning and big data projects to support global research efforts. Earlier this week, Microsoft’s Chief Scientific Officer Eric Horvitz announced that it would be providing an open research data set in partnership with colleagues at academic institutions around the world, as well as the White House Office of Science and Technology Policy and the Chan Zuckerberg initiative. The data set, called the COVID-19 Open Research Data Set, includes more than 29,000 scholarly articles about the virus, and will grow as more are published.