Using AI responsibly to fight the coronavirus pandemic

The emergence of the novel coronavirus has left the world in turmoil. COVID-19, the disease caused by the virus, has reached virtually every corner of the world, with the number of cases exceeding a million and the number of deaths more than 50,000 worldwide. It is a situation that will affect us all in one way or another.

With the imposition of lockdowns, limitations of movement, the closure of borders and other measures to contain the virus, the operating environment of law enforcement agencies and those security services tasked with protecting the public from harm has suddenly become ever more complex. They find themselves thrust into the middle of an unparalleled situation, playing a critical role in halting the spread of the virus and preserving public safety and social order in the process. In response to this growing crisis, many of these agencies and entities are turning to AI and related technologies for support in unique and innovative ways. Enhancing surveillance, monitoring and detection capabilities is high on the priority list.

For instance, early in the outbreak, Reuters reported a case in China wherein the authorities relied on facial recognition cameras to track a man from Hangzhou who had traveled in an affected area. Upon his return home, the local police were there to instruct him to self-quarantine or face repercussions. Police in China and Spain have also started to use technology to enforce quarantine, with drones being used to patrol and broadcast audio messages to the public, encouraging them to stay at home. People flying to Hong Kong airport receive monitoring bracelets that alert the authorities if they breach the quarantine by leaving their home.

In the United States, a surveillance company announced that its AI-enhanced thermal cameras can detect fevers, while in Thailand, border officers at airports are already piloting a biometric screening system using fever-detecting cameras.

Isolated cases or the new norm?

With the number of cases, deaths and countries on lockdown increasing at an alarming rate, we can assume that these will not be isolated examples of technological innovation in response to this global crisis. In the coming days, weeks and months of this outbreak, we will most likely see more and more AI use cases come to the fore.

While the application of AI can play an important role in seizing the reins in this crisis, and even safeguard officers and officials from infection, we must not forget that its use can raise very real and serious human rights concerns that can be damaging and undermine the trust placed in government by communities. Human rights, civil liberties and the fundamental principles of law may be exposed or damaged if we do not tread this path with great caution. There may be no turning back if Pandora’s box is opened.

In a public statement on March 19, the monitors for freedom of expression and freedom of the media for the United Nations, the Inter-American Commission for Human Rights and the Representative on Freedom of the Media of the Organization for Security and Co-operation in Europe issued a joint statement on promoting and protecting access to and free flow of information during the pandemic, and specifically took note of the growing use of surveillance technology to track the spread of the coronavirus. They acknowledged that there is a need for active efforts to confront the pandemic, but stressed that “it is also crucial that such tools be limited in use, both in terms of purpose and time, and that individual rights to privacy, non-discrimination, the protection of journalistic sources and other freedoms be rigorously protected.”

This is not an easy task, but a necessary one. So what can we do?

Ways to responsibly use AI to fight the coronavirus pandemic

  1. Data anonymization: While some countries are tracking individual suspected patients and their contacts, Austria, Belgium, Italy and the U.K. are collecting anonymized data to study the movement of people in a more general manner. This option still provides governments with the ability to track the movement of large groups, but minimizes the risk of infringing data privacy rights.
  2. Purpose limitation: Personal data that is collected and processed to track the spread of the coronavirus should not be reused for another purpose. National authorities should seek to ensure that the large amounts of personal and medical data are exclusively used for public health reasons. The is a concept already in force in Europe, within the context of the European Union’s General Data Protection Regulation (GDPR), but it’s time for this to become a global principle for AI.
  3. Knowledge-sharing and open access data: António Guterres, the United Nations Secretary-General, has insisted that “global action and solidarity are crucial,” and that we will not win this fight alone. This is applicable on many levels, even for the use of AI by law enforcement and security services in the fight against COVID-19. These agencies and entities must collaborate with one another and with other key stakeholders in the community, including the public and civil society organizations. AI use case and data should be shared and transparency promoted.
  4. Time limitation:  Although the end of this pandemic seems rather far away at this point in time, it will come to an end. When it does, national authorities will need to scale back their newly acquired monitoring capabilities after this pandemic. As Yuval Noah Harari observed in his recent article, “temporary measures have a nasty habit of outlasting emergencies, especially as there is always a new emergency lurking on the horizon.” We must ensure that these exceptional capabilities are indeed scaled back and do not become the new norm.

Within the United Nations system, the United Nations Interregional Crime and Justice Research Institute (UNICRI) is working to advance approaches to AI such as these. It has established a specialized Centre for AI and Robotics in The Hague and is one of the few international actors dedicated to specifically looking at AI vis-à-vis crime prevention and control, criminal justice, rule of law and security. It assists national authorities, in particular law enforcement agencies, to understand the opportunities presented by these technologies and, at the same time, to navigate the potential pitfalls associated with these technologies.

Working closely with International Criminal Police Organization (INTERPOL), UNICRI has set up a global platform for law enforcement, fostering discussion on AI, identifying practical use cases and defining principles for responsible use. Much work has been done through this forum, but it is still early days, and the path ahead is long.

While the COVID-19 pandemic has illustrated several innovative use cases, as well as the urgency for the governments to do their utmost to stop the spread of the virus, it is important to not let consideration of fundamental principles, rights and respect for the rule of law be set aside. The positive power and potential of AI is real. It can help those embroiled in fighting this battle to slow the spread of this debilitating disease. It can help save lives. But we must stay vigilant and commit to the safe, ethical and responsible use of AI.

It is essential that, even in times of great crisis, we remain conscience of the duality of AI and strive to advance AI for good.

You can now buy AWS’ $99 DeepComposer keyboard

AWS today announced that its DeepComposer keyboard is now available for purchase. And no, DeepComposer isn’t a mechanical keyboard for hackers but a small MIDI keyboard for working with the AWS DeepComposer service that uses AI to create songs based on your input.

First announced at AWS re:Invent 2019, the keyboard created a bit of confusion, in part because Amazon’s announcement almost made it seem like a consumer product. DeepComposer, which also works without the actual hardware keyboard, is more of a learning tool, though, and belongs to the same family of AWS hardware like DeepLens and DeepRacer. It’s meant to teach developers about generative adversarial networks, just like DeepLens and DeepRacer also focus on specific machine learning technologies.

Users play a short melody, either using the hardware keyboard or an on-screen one, and the service then automatically generates a backing track based on your choice of musical style. The results I heard at re:Invent last year were a bit uneven (or worse), but that may have improved by now. But this isn’t a tool for creating the next Top 40 song. It’s simply a learning tool. I’m not sure you need the keyboard to get that learning experience out of it, but if you do, you can now head over to Amazon and buy it.

Flagship Pioneering raises $1.1 billion to spend on sustainability and health-focused biotech

Flagship Pioneering, the Boston-based biotech company incubator and holding company, said it has raised $1.1 billion for its Flagship Labs unit.

Flagship, which raised $1 billion back in 2019 for growth stage investment vehicles, develops and operates startups that leverage biotechnology innovation to provide goods and services that improve human health and promote sustainable industries.

“We’re honored to have the strong support of our existing Limited Partners, as well as the interest from a select group of new Limited Partners, to support Flagship’s unique form of company origination during this time of unprecedented economic uncertainty,” said Noubar Afeyan, the founder and chief executive of Flagship Pioneering, in a statement.

In addition to its previous focus on health and sustainability, Flagship will use the new funds to focus on new medicines, artificial intelligence and “health security”, which the company says is “designed to create a range of products and therapies to improve societal health defenses by treating pre-disease states before they escalate,” according to Afeyan.

Flagship companies are already on the forefront of the healthcare industry’s efforts to stop the COVID-19 pandemic. Portfolio company Moderna is one of the companies leading efforts to develop a vaccine for the novel coronavirus which causes COVID-19.

In the 20 years since its launch, Flagship has 15 wholly owned companies and another 26 growth stage companies among its portfolio of investments.

New companies include: Senda Biosciences, Generate Biomedicines, Tessera Therapeutics, Cellarity, Cygnal Therapeutics, Ring Therapeutics, and Integral Health. Growth Companies developed or backed by Flagship include Ohana Biosciences, Kintai Therapeutics, and Repertoire Immune Medicines.

Two of the companies in the Flagship Labs portfolio have already had initial public offerings in the past two years, the company said. Kaleido Biosciences and Axcella Health raised public capital in 2019 and Moderna Therapeutics conducted a $575 million secondary offering earlier this year.

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.

NASA issues agency-wide crowdsourcing call for ideas around COVID-19 response

There’s crowdsourcing a problem, and then there’s crowdsourcing a problem within NASA, where some of the smartest, most creative and resourceful problem-solvers in the world solve real-world challenges daily as part of their job. That’s why it’s uplifting to hear that NASA has issued a call to its entire workforce to come up with potential ways the agency and its resources can contribute to the ongoing effort to with the current coronavirus pandemic.

NASA is using its crowdsourcing platform NASA @ WORK, which it uses to internally source creative solutions to persistent problems, in order to collect creative ideas about new ways to address the COVID-19 crisis and the various problems it presents. Already, NASA is engaged in a few different ways, including offering supercomputing recourses for treatment research, and working on developing AI solutions that can help provide insight into key scientific investigations that are ongoing around the virus.

There is a degree of specificity in the open call NASA put to its workforce: It identified key areas where solutions are most urgently needed, working together with the White House and other government agencies involved in the response, and determined that NASA staff efforts should focus on addressing shortfalls and gaps in the availability of personal protective equipment, ventilation hardware, and ways to monitor and track the coronavirus spread and transmission. That’s not to say NASA doesn’t want to hear solutions about other COVID-19 issues, just that these are the areas where they’ve identified the most current need.

To add some productive time-pressure to this endeavor, NASA is looking for submissions from staff on all the areas above to be made via NASA @ WORK by April 15. Then there’ll be a process of assessing what’s most viable, and allocating resources to make those a reality. Any products or designs that result will be made “open source for any business or country to use,” the agency says – with the caveat that this might not be strictly possible in all cases depending on the specific technologies involved.

DeepMind’s Agent57 AI agent can best human players across a suite of 57 Atari games

Development of artificial intelligence agents tends to frequently be measured by their performance in games, but there’s a good reason for that: Games tend to offer a wide proficiency curve, in terms of being relatively simple to grasp the basics, but difficult to master, and they almost always have a built-in scoring system to evaluate performance. DeepMind’s agents have tackled board game Go, as well as real-time strategy video game StarCraft – but the Alphabet company’s most recent feat is Agent57, a learning agent that can beat the average human on each of 57 Atari games with a wide range of difficulty, characteristics and gameplay styles.

Being better than humans at 57 Atari games may seem like an odd benchmark against which to measure the performance of a deep learning agent, but it’s actually a standard that goes all the way back to 2012, with a selection of Atari classics including Pitfall, Solaris, Montezuma’s Revenge and many others. Taken together, these games represent a broad range of difficulty levels, as well as requiring a range of different strategies in order to achieve success.

That’s a great type of challenge for creating a deep learning agent because the goal is not to build something that can determine one effective strategy that maximizes your chances of success every time you play a game – instead, the reason researchers build these agents and set them to these tasks at all is to develop something that can learn across multiple and shifting scenarios and conditions, with the long-term aim of building a learning agent that approaches general AI – or AI that is more human in terms of being able to apply its intelligence to any problem put before it, including challenges it’s never encountered before.

DeepMind’s Agent57 is remarkable because it performs better than human players on each of the 57 games in the Atari57 set – previous agents have been able to be better than human players on average – but that’s because they were extremely good at some of the simpler games that basically just worked via a simple action-reward loop, but terrible at games that required more advanced play, including long-term exploration and memory, like Montezuma’s Revenge.

The DeepMind team addressed this by building a distributed agent with different computers tackling different aspects of the problem, with some tuned to focus on novelty rewards (encountering things they haven’t encountered before), with both short- and long-term time horizons for when the novelty value resets. Others sought out more simple exploits, figuring out which repeated pattern provided the biggest reward, and then all the results are combined and managed by an agent equipped with a meta-controller that allows it to weight the costs and benefits of different approaches based on which game it encounters.

In the end, Agent57 is an accomplishment, but the team says it can stand to be improved in a few different ways. First, it’s incredibly computationally expensive to run, so they will seek to streamline that. Second, it’s actually not as good at some of the simpler games as some simpler agents – even though it excels at the the top 5 games in terms of challenge to previous intelligent agents. The team says it has ideas for how to make it even better at the simpler games that other, less sophisticated agents, are even better at.

Pre-school EdTech startup Lingumi raises £4m, adds some free services during Covid-19

At these difficult times, parents are concerned for their children’s education, especially given so much of it has had to go online during the Covid-19 pandemic. But what about pre-schoolers who are missing out?

Pre-school children are sponges for information but don’t get formal training on reading and writing until they enter the classroom when they are less sponge-like and surrounded by 30 other children. Things are tougher for non-English speaking children who’s parents want them to learn English.

Lingumi, a platform aimed at toddlers learning critical skills, has now raised £4 million in a funding round led by China-based technology fund North Summit Capital – a fund run by Alibaba’s former Chief Data Scientist Dr Min Wanli – alongside existing investors LocalGlobe, ADV, and Entrepreneur First.

The startup, launched in 2017, is also announcing the launch of daily free activity packs and videos to support children and families during the COVID-19 outbreak, and has pledged to donate 20% of its sales during this period to the Global Children’s Fund.

Lingumi’s interactive courses offer one-to-one tutoring with a kind ‘social learning’ and its first course helps introduce key English grammar and vocabulary from the age of 2.

Instead of tuning into live lessons with tutors, which are typically timetabled and expensive, Lingumi’s lessons are delivered through interactive speaking tasks, teacher videos, and games. At the end of each lesson, children can see videos of Lingumi friends speaking the same words and phrases as them. Because the kids are watching videos, Lingumi is cheaper than live courses, and thus more flexible for parents.

The company launched the first Lingumi course in China last year, focused on teaching spoken English to non-English speakers. The platform is now being used by more than 100,000 families globally, including in mainland China, Taiwan, UK, Germany, Italy, and France. More than 1.5 million English lessons have taken place in China over the past six months, and 40% of active users are also playing lessons daily. Lingumi says its user base grew 50% during China’s lockdown and it has had a rapid uptake in Europe.

“Lingumi’s rapid expansion in the Chinese market required a strategic local investor, and Dr Min and the team had a clear-sighted understanding of the technology and scale opportunity both in China, and globally.”

Dr Wanli Min, general partner at North Summit Capital, commented: “It is only the most privileged children who can access native English speakers for one-on-one tutoring… Lingumi has the potential to democratize English learning and offer every kid a personalized curriculum empowered by AI & Lingumi’s ‘asynchronous teaching; model.”

Competitors to include Lingumi include live teaching solutions like VIPKid, and learning platforms like Jiliguala in China, or Lingokids in the West.

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.”