Research papers come out far too rapidly for anyone to read them all, especially in the field of machine learning, which now affects (and produces papers in) practically every industry and company. This column aims to collect the most relevant recent discoveries and papers — particularly in but not limited to artificial intelligence — and explain why they matter.
This week, a startup that’s using UAV drones for mapping forests, a look at how machine learning can map social media networks and predict Alzheimer’s, improving computer vision for space-based sensors and other news regarding recent technological advances.
Machine learning tools are being used to aid diagnosis in many ways, since they’re sensitive to patterns that humans find difficult to detect. IBM researchers have potentially found such patterns in speech that are predictive of the speaker developing Alzheimer’s disease.
The system only needs a couple minutes of ordinary speech in a clinical setting. The team used a large set of data (the Framingham Heart Study) going back to 1948, allowing patterns of speech to be identified in people who would later develop Alzheimer’s. The accuracy rate is about 71% or 0.74 area under the curve for those of you more statistically informed. That’s far from a sure thing, but current basic tests are barely better than a coin flip in predicting the disease this far ahead of time.
This is very important because the earlier Alzheimer’s can be detected, the better it can be managed. There’s no cure, but there are promising treatments and practices that can delay or mitigate the worst symptoms. A non-invasive, quick test of well people like this one could be a powerful new screening tool and is also, of course, an excellent demonstration of the usefulness of this field of tech.
(Don’t read the paper expecting to find exact symptoms or anything like that — the array of speech features aren’t really the kind of thing you can look out for in everyday life.)
Making sure your deep learning network generalizes to data outside its training environment is a key part of any serious ML research. But few attempt to set a model loose on data that’s completely foreign to it. Perhaps they should!
Researchers from Uppsala University in Sweden took a model used to identify groups and connections in social media, and applied it (not unmodified, of course) to tissue scans. The tissue had been treated so that the resultant images produced thousands of tiny dots representing mRNA.
Normally the different groups of cells, representing types and areas of tissue, would need to be manually identified and labeled. But the graph neural network, created to identify social groups based on similarities like common interests in a virtual space, proved it could perform a similar task on cells. (See the image at top.)
“We’re using the latest AI methods — specifically, graph neural networks, developed to analyze social networks — and adapting them to understand biological patterns and successive variation in tissue samples. The cells are comparable to social groupings that can be defined according to the activities they share in their social networks,” said Uppsala’s Carolina Wählby.
It’s an interesting illustration not just of the flexibility of neural networks, but of how structures and architectures repeat at all scales and in all contexts. As without, so within, if you will.
The vast forests of our national parks and timber farms have countless trees, but you can’t put “countless” on the paperwork. Someone has to make an actual estimate of how well various regions are growing, the density and types of trees, the range of disease or wildfire, and so on. This process is only partly automated, as aerial photography and scans only reveal so much, while on-the-ground observation is detailed but extremely slow and limited.
Treeswift aims to take a middle path by equipping drones with the sensors they need to both navigate and accurately measure the forest. By flying through much faster than a walking person, they can count trees, watch for problems and generally collect a ton of useful data. The company is still very early-stage, having spun out of the University of Pennsylvania and acquired an SBIR grant from the NSF.
“Companies are looking more and more to forest resources to combat climate change but you don’t have a supply of people who are growing to meet that need,” Steven Chen, co-founder and CEO of Treeswift and a doctoral student in Computer and Information Science (CIS) at Penn Engineering said in a Penn news story. “I want to help make each forester do what they do with greater efficiency. These robots will not replace human jobs. Instead, they’re providing new tools to the people who have the insight and the passion to manage our forests.”
Another area where drones are making lots of interesting moves is underwater. Oceangoing autonomous submersibles are helping map the sea floor, track ice shelves and follow whales. But they all have a bit of an Achilles’ heel in that they need to periodically be picked up, charged and their data retrieved.
Purdue engineering professor Nina Mahmoudian has created a docking system by which submersibles can easily and automatically connect for power and data exchange.
A yellow marine robot (left, underwater) finds its way to a mobile docking station to recharge and upload data before continuing a task. (Purdue University photo/Jared Pike)
The craft needs a special nosecone, which can find and plug into a station that establishes a safe connection. The station can be an autonomous watercraft itself, or a permanent feature somewhere — what matters is that the smaller craft can make a pit stop to recharge and debrief before moving on. If it’s lost (a real danger at sea), its data won’t be lost with it.
You can see the setup in action below:
Drones may soon become fixtures of city life as well, though we’re probably some ways from the automated private helicopters some seem to think are just around the corner. But living under a drone highway means constant noise — so people are always looking for ways to reduce turbulence and resultant sound from wings and propellers.
Researchers at the King Abdullah University of Science and Technology found a new, more efficient way to simulate the airflow in these situations; fluid dynamics is essentially as complex as you make it, so the trick is to apply your computing power to the right parts of the problem. They were able to render only flow near the surface of the theoretical aircraft in high resolution, finding past a certain distance there was little point knowing exactly what was happening. Improvements to models of reality don’t always need to be better in every way — after all, the results are what matter.
Computer vision algorithms have come a long way, and as their efficiency improves they are beginning to be deployed at the edge rather than at data centers. In fact it’s become fairly common for camera-bearing objects like phones and IoT devices to do some local ML work on the image. But in space it’s another story.
Performing ML work in space was until fairly recently simply too expensive power-wise to even consider. That’s power that could be used to capture another image, transmit the data to the surface, etc. HyperScout 2 is exploring the possibility of ML work in space, and its satellite has begun applying computer vision techniques immediately to the images it collects before sending them down. (“Here’s a cloud — here’s Portugal — here’s a volcano…”)
For now there’s little practical benefit, but object detection can be combined with other functions easily to create new use cases, from saving power when no objects of interest are present, to passing metadata to other tools that may work better if informed.
Machine learning models are great at making educated guesses, and in disciplines where there’s a large backlog of unsorted or poorly documented data, it can be very useful to let an AI make a first pass so that graduate students can use their time more productively. The Library of Congress is doing it with old newspapers, and now Carnegie Mellon University’s libraries are getting into the spirit.
CMU’s million-item photo archive is in the process of being digitized, but to make it useful to historians and curious browsers it needs to be organized and tagged — so computer vision algorithms are being put to work grouping similar images, identifying objects and locations, and doing other valuable basic cataloguing tasks.
“Even a partly successful project would greatly improve the collection metadata, and could provide a possible solution for metadata generation if the archives were ever funded to digitize the entire collection,” said CMU’s Matt Lincoln.
A very different project, yet one that seems somehow connected, is this work by a student at the Escola Politécnica da Universidade de Pernambuco in Brazil, who had the bright idea to try sprucing up some old maps with machine learning.
The tool they used takes old line-drawing maps and attempts to create a sort of satellite image based on them using a Generative Adversarial Network; GANs essentially attempt to trick themselves into creating content they can’t tell apart from the real thing.
Well, the results aren’t what you might call completely convincing, but it’s still promising. Such maps are rarely accurate but that doesn’t mean they’re completely abstract — recreating them in the context of modern mapping techniques is a fun idea that might help these locations seem less distant.
In an overcrowded market of online fashion brands, consumers are spoilt for choice on what site to visit. They are generally forced to visit each brand one by one, manually filtering down to what they like. Most of the experience is not that great, and past purchase history and cookies aren’t much to go on to tailor user experience. If someone has bought an army-green military jacket, the e-commerce site is on a hiding to nothing if all it suggests is more army-green military jackets…
Instead, Psycke ( it’s brand name is ‘PSYKHE’) is an e-commerce startup that uses AI and psychology to make product recommendations based both on the user’s personality profile and the ‘personality’ of the products. Admittedly, a number of startups have come and gone claiming this, but it claims to have taken a unique approach to make the process of buying fashion easier by acting as an aggregator that pulls products from all leading fashion retailers. Each user sees a different storefront that, says the company, becomes increasingly personalized.
It has now raised $1.7 million in seed funding from a range of investors and is announcing new plans to scale its technology to other consumer verticals in the future in the B2B space.
The investors are Carmen Busquets – the largest founding investor in Net-a-Porter; SLS Journey – the new investment arm of the MadaLuxe Group, the North American distributor of luxury fashion; John Skipper – DAZN Chairman and former Co-chairman of Disney Media Networks and President of ESPN; and Lara Vanjak – Chief Operating Officer at Aser Ventures, formerly at MP & Silva and FC Inter-Milan.
So what does it do? As a B2C aggregator, it pools inventory from leading retailers. The platform then applies machine learning and personality-trait science, and tailors product recommendations to users based on a personality test taken on sign-up. The company says it has international patents pending and has secured affiliate partnerships with leading retailers that include Moda Operandi, MyTheresa, LVMH’s platform 24S, and 11 Honoré.
The business model is based around an affiliate partnership model, where it makes between 5-25% of each sale. It also plans to expand into B2B for other consumer verticals in the future, providing a plug-in product that allows users to sort items by their personality.
How does this personality test help? Well, Psykhe has assigned an overall psychological profile to the actual products themselves: over 1 million products from commerce partners, using machine learning (based on training data).
So for example, if a leather boot had metal studs on it (thus looking more ‘rebellious’), it would get a moderate-low rating on the trait of ‘Agreeableness’. A pink floral dress would get a higher score on that trait. A conservative tweed blazer would get a lower score tag on the trait of ‘Openness’, as tweed blazers tend to indicate a more conservative style and thus nature.
It’s competitors include The Yes and Lyst. However, Psykhe’s main point of differentiation is this personality scoring. Furthermore, The Yes is app-only, US-only, and only partners with monobrands, while Lyst is an aggregator with 1,000s of brands, but used as more of a search platform.
Psykhe is in a good position to take advantage of the ongoing effects of COVID-19, which continue to give a major boost to global ecommerce as people flood online amid lockdowns.
The startup is the brainchild of Anabel Maldonado, CEO & founder, (along with founding team CTO Will Palmer and Lead Data Scientist, Rene-Jean Corneille, pictured above), who studied psychology in her hometown of Toronto, but ended up working at in the UK’s NHS in a specialist team that made developmental diagnoses for children under 5.
She made a pivot into fashion after winning a competition for an editorial mentorship at British Marie Claire. She later went to the press department of Christian Louboutin, followed by internships at the Mail on Sunday and Marie Claire, then spending several years in magazine publishing before moving into e-commerce at CoutureLab. Going freelance, she worked with a number of luxury brands and platforms as an editorial consultant. As a fashion journalist, she’s contributed industry op-eds to publications such as The Business of Fashion, T The New York Times Style, and Marie Claire.
As part of the fashion industry for 10 years, she says she became frustrated with the narratives which “made fashion seem more frivolous than it really is. I thought, this is a trillion-dollar industry, we all have such emotional, visceral reactions to an aesthetic based on who we are, but all we keep talking about is the ‘hot new color for fall and so-called blanket “must-haves’.”
But, she says, “there was no inquiry into individual differences. This world was really missing the level of depth it deserved, and I sought to demonstrate that we’re all sensitive to aesthetic in one way or another and that our clothing choices have a great psychological pay-off effect on us, based on our unique internal needs.” So she set about creating a startup to address this ‘fashion psychology’ – or, as she says “why we wear what we wear”.
Project management service Wrike today announced a major update to its platform at its user conference that includes a lot of new AI smarts for keeping individual projects on track and on time, as well as new solutions for marketers and project management offices in large corporations. In addition, the company also launched a new budgeting feature and tweaks to the overall user experience.
The highlight of the launch, though, is, without doubt, the launch of the new AI and machine learning capabilities in Wrike . With more than 20,000 customers and over 2 million users on the platform, Wrike has collected a trove of data about projects that it can use to power these machine learning models.
The way Wrike is now using AI falls into three categories: project risk prediction, task prioritization and tools for speeding up the overall project management workflow.
Figuring out the status of a project and knowing where delays could impact the overall project is often half the job. Wrike can now predict potential delays and alert project and team leaders when it sees events that signal potential issues. To do this, it uses basic information like start and end dates, but more importantly, it looks at the prior outcomes of similar projects to assess risks. Those predictions can then be fed into Wrike’s automation engine to trigger actions that could mitigate the risk to the project.
Task prioritization does what you would expect and helps you figure out what you should focus on right now to help a project move forward. No surprises there.
What is maybe more surprising is that the team is also launching voice commands (through Siri on iOS) and Gmail-like smart replies (in English for iOS and Android). Those aren’t exactly core features of a project management tools, but as the company notes, these features help remove the overall friction and reduce latencies. Another new feature that falls into this category is support for optical character recognition to allow you to scan printed and handwritten notes from your phones and attach them to tasks (iOS only).
“With more employees working from home, work and personal life are becoming intertwined,” the company argues. “As workers use AI in their personal lives, team managers and everyday users expect the smarts they’re accustomed to in consumer devices and apps to help them manage their work as well. Wrike Work Intelligence is the most comprehensive machine learning foundation that taps into tens of millions of work-related user engagements to power cross-functional collaboration to help organizations achieve operational efficiency, create new opportunities and accelerate digital transformation. Teams can focus on the work that matters most, predict and minimize delays, and cut communication latencies.”
The other major new feature — at least if you’re in digital marketing — is Wrike’s new ability to pull in data about your campaigns from about 50 advertising, marketing automation and social media tools, which is then displayed inside the Wrike experience. In a fast-moving field, having all that data at your fingertips and right inside the tool where you think about how to manage these projects seems like a smart idea.
Somewhat related, Wrike’s new budgeting feature also now makes it easier for teams to keep their projects within budget, using a new built-in rate card to manage project pricing and update their financials.
“We use Wrike for an extensive project management and performance metrics system,” said Shannon Buerk, the CEO of engage2learn, which tested this new budgeting tool. “We have tried other PM systems and have found Wrike to be the best of all worlds: easy to use for everyone and savvy enough to provide valuable reporting to inform our work. Converting all inefficiencies into productive time that moves your mission forward is one of the keys to a culture of engagement and ownership within an organization, even remotely. Wrike has helped us get there.”
Every year at its MAX user conference, Adobe shows off a number of research projects that may or may not end up in its Creative Cloud apps over time. One new project that I hope we’ll soon see in its video apps is Project Sharp Shots, which will make its debut later today during the MAX Sneaks event. Powered by Adobe’s Sensei AI platform, Sharp Shots is a research project that uses AI to deblur videos.
Shubhi Gupta, the Adobe engineer behind the project, told me the idea here is to deblur a video — no matter whether it was blurred because of a shaky camera or fast movement — with a single click. In the demos she showed me, the effect was sometimes relatively subtle, as in a video of her playing ukulele, or quite dramatic, as in the example of a fast-moving motorcycle below.
With Project Sharp Shots, there’s no parameter tuning and adjustment like we used to do in our traditional methods,” she told me. “This one is just a one-click thing. It’s not magic. This is simple deep learning and AI working in the background, extracting each frame, deblurring it and producing high-quality deblurred photos and videos.”
Image Credits: AdobeGupta tells me the team looked at existing research on deblurring images and then optimized that process for moving images — and then optimized that for lower-memory usage and speed.
It’s worth noting that After Effects already offers some of these capabilities for deblurring and removing camera shake, but that’s a very different algorithm with its own set of limitations.
This new system works best when the algorithm has access to multiple related frames before and after, but it can do its job with just a handful of frames in a video.
The pandemic has put stress on companies dealing with a workforce that is mostly — and sometimes suddenly — working from home. That has led to rising needs for security and governance tooling, something that Egnyte is looking to meet with new features aimed at helping companies cope with file management during the pandemic.
Egnyte is an enterprise file storage and sharing (EFSS) company, though it has added security services and other tools over the years.
“It’s no surprise that there’s been a rapid shift to remote work, which has I believe led to mass adoption of multiple applications running on multiple clouds, and tied to that has been a nonlinear reaction of exponential growth in data security and governance concerns,” Vineet Jain, co-founder and CEO at Egnyte, explained.
There’s a lot of data at stake.
Egnyte’s announcements today are in part a reaction to the changes that COVID has brought, a mix of net-new features and capabilities that were on its road map, but accelerated to meet the needs of the changing technology landscape.
The company is introducing a new feature called Smart Cache to make sure that content (wherever it lives) that an individual user accesses most will be ready whenever they need it.
“Smart Cache uses machine learning to predict the content most likely to be accessed at any given site, so administrators don’t have to anticipate usage patterns. The elegance of the solution lies in that it is invisible to the end users,” Jain said. The end result of this capability could be lower storage and bandwidth costs, because the system can make this content available in an automated way only when it’s needed.
Another new feature is email scanning and governance. As Jain points out, email is often a company’s largest data store, but it’s also a conduit for phishing attacks and malware. So Egnyte is introducing an email governance tool that keeps an eye on this content, scanning it for known malware and ransomware and blocking files from being put into distribution when it identifies something that could be harmful.
As companies move more files around it’s important that security and governance policies travel with the document, so that policies can be enforced on the file wherever it goes. This was true before COVID-19, but has only become more true as more folks work from home.
Finally, Egnyte is using machine learning for auto-classification of documents to apply policies to documents without humans having to touch them. By identifying the document type automatically, whether it has personally identifying information or it’s a budget or planning document, Egnyte can help customers auto-classify and apply policies about viewing and sharing to protect sensitive materials.
Egnyte is reacting to the market needs as it makes changes to the platform. While the pandemic has pushed this along, these are features that companies with documents spread out across various locations can benefit from regardless of the times.
The company is over $100 million ARR today, and grew 22% in the first half of 2020. Whether the company can accelerate that growth rate in H2 2020 is not yet clear. Regardless, Egnyte is a budding IPO candidate for 2021 if market conditions hold.
Oxford scientists working out of the school’s Department of Physics have developed a new type of COVID-19 test that can detect SARS-CoV-2 with a high degree of accuracy, directly in samples taken from patients, using a machine learning-based approach that could help sidestep test supply limitations, and that also offers advantages when it comes to detecting actual virus particles, instead of antibodies or other signs of the presence of the virus which don’t necessarily correlate to an active, transmissible case.
The test created by the Oxford researchers also offer significant advantages in terms of speed, providing results in under five minutes, without any sample preparation required. That means it could be among the technologies that unlock mass testing – a crucial need not only for getting a handle on the current COVID-19 pandemic, but also on helping us deal with potential future global viral outbreaks, too. Oxford’s method is actually well-designed for that, too, since it can potentially be configured relatively easily to detect a number of viral threats.
The technology that makes this possible works by labelling any virus particles found in a sample collected by a patient using short, fluorescent DNA strands that act as markers. A microscope images the sample and the labelled viruses present, and then machine learning software takes over using algorithmic analysis developed by the team to automatically identify the virus, using differences that each one produces in terms of its fluorescent light emitted owing to their different physical surface makeup, size and individual chemical composition.
This technology, including the sample collection equipment, the microscopic imager and the flourescence insertion tools, as well as the compute capabilities, can be miniaturized to the point where it’s possible to be used just about anywhere, according to the researchers – including “businesses, music venues, airports,” and more. The focus now is to create a spinout company for the purposes of commercializing the device in a format that integrates all the components together.
The researchers anticipate being able to form the company, and start product development by early next year, with the potentially of having a device approved for use and ready for distribution around six months after that. It’s a tight timeline for development of a new diagnostic device, but timelines have changed already amply in the face of this pandemic, and will continue to do so as we’re unlikely to see if fade away anytime in the near future.
Savana, a machine learning-based service that turns clinical notes into structured patient information for physicians and pharmacists, has raised $15 million to take its technology from Spain to the U.S., the company said.
The investment was led by Cathay Innovation with participation from the Spanish investment firm Seaya Ventures, which led the company’s previous round, and new investors like MACSF, a French insurance provider for doctors.
The company has already processed 400 million electronic medical records in English, Spanish, German, and French.
Founded in Madrid in 2014, the company is relocating to New York and is already working with the world’s largest pharmaceutical companies and over 100 healthcare facilities.
“Our mission is to predict the occurrence of disease at the patient level. This focuses our resources on discovering new ways of providing medical knowledge almost in real time — which is more urgent than ever in the context of the pandemic,” said Savana chief executive Jorge Tello. “Healthcare challenges are increasingly global, and we know that the application of AI across health data at scale is essential to accelerate health science.”
Company co-founder and chief medical officer, Dr. Ignacio Hernandez Medrano, also emphasized that while the company is collecting hundreds of millions of electronic records, it’s doing its best to keep that information private.
“One of our main value propositions is that the information remains controlled by the hospital, with privacy guaranteed by the de-identification of patient data before we process it,” he said.
If you ever doubted the hunger brands have for more and better information about consumers, you only need to look at Twilio buying customer data startup Segment this week for $3.2 billion. Google sees this the same as everyone else, and today it introduced updates to Google Analytics to help companies understand their customers better (especially in conjunction with related Google tools).
Vidhya Srinivasan, vice president of measurement, analytics and buying platforms at Google, wrote in a company blog post introducing the new features that the company sees this changing customer-brand dynamic due to COVID, and it wants to assist by adding new features that help marketers achieve their goals, whatever those may be.
One way to achieve this is by infusing Analytics with machine learning to help highlight data automatically that’s important to marketers using the platform. “[Google Analytics] has machine learning at its core to automatically surface helpful insights and gives you a complete understanding of your customers across devices and platforms,” Srinivasan wrote in the blog post.
The idea behind the update is to give marketers access to more information they care about most by using that machine learning to surface data like which groups of customers are most likely to buy and which are most likely to churn, the very types of information marketing (and sales) teams need to try make proactive moves to keep customers from leaving or conversely turning those ready to buy into sales.
Image Credits: Google
If it works as described, it can give marketers a way to measure their performance with each customer or group of customers across their entire lifecycle, which is especially important during COVID when customer needs are constantly changing.
Of course, this being a Google product it’s designed to play nicely with Google Ads, YouTube and other tools like Gmail and Google Search, along with non-Google channels. As Srinivasan wrote:
The new approach also makes it possible to address longtime advertiser requests. Because the new Analytics can measure app and web interactions together, it can include conversions from YouTube engaged views that occur in-app and on the web in reports. Seeing conversions from YouTube video views alongside conversions from Google and non-Google paid channels, and organic channels like Google Search, social, and email, helps you understand the combined impact of all your marketing efforts.
All of this is designed to help marketers, caught in trying times with a shifting regulatory landscape, to better understand customer needs and deliver them what they want when they want it — when they’re just trying to keep the customers satisfied.
Atlassian has been offering collaboration tools, often favored by developers and IT for some time with such stalwarts as Jira for help desk tickets, Confluence to organize your work and BitBucket to organize your development deliverables, but what it lacked was machine learning layer across the platform to help users work smarter within and across the applications in the Atlassian family.
That changed today, when Atlassian announced it has been building that machine learning layer called Atlassian Smarts, and is releasing several tools that take advantage of it. It’s worth noting that unlike Salesforce, which calls its intelligence layer Einstein or Adobe, which calls its Sensei; Atlassian chose to forgo the cutesy marketing terms and just let the technology stand on its own.
Shihab Hamid, the founder of the Smarts and Machine Learning Team at Atlassian, who has been with the company 14 years, says that they avoided a marketing name by design. “I think one of the things that we’re trying to focus on is actually the user experience and so rather than packaging or branding the technology, we’re really about optimizing teamwork,” Hamid told TechCrunch.
Hamid says that the goal of the machine learning layer is to remove the complexity involved with organizing people and information across the platform.
“Simple tasks like finding the right person or the right document becomes a challenge, or at least they slow down productivity and take time away from the creative high-value work that everyone wants to be doing, and teamwork itself is super messy and collaboration is complicated. These are human challenges that don’t really have one right solution,” he said.
He says that Atlassian has decided to solve these problems using machine learning with the goal of speeding up repetitive, time-intensive tasks. Much like Adobe or Salesforce, Atlassian has built this underlying layer of machine smarts, for lack of a better term, that can be distributed across their platform to deliver this kind of machine learning-based functionality wherever it makes sense for the particular product or service.
“We’ve invested in building this functionality directly into the Atlassian platform to bring together IT and development teams to unify work, so the Atlassian flagship products like JIRA and Confluence sit on top of this common platform and benefit from that common functionality across products. And so the idea is if we can build that common predictive capability at the platform layer we can actually proliferate smarts and benefit from the data that we gather across our products,” Hamid said.
The first pieces fit into this vision. For starters, Atlassian is offering a smart search tool that helps users find content across Atlassian tools faster by understanding who you are and how you work. “So by knowing where users work and what they work on, we’re able to proactively provide access to the right documents and accelerate work,” he said.
The second piece is more about collaboration and building teams with the best personnel for a given task. A new tool called predictive user mentions helps Jira and Confluence users find the right people for the job.
“What we’ve done with the Atlassian platform is actually baked in that intelligence, because we know what you work on and who you collaborate with, so we can predict who should be involved and brought into the conversation,” Hamid explained.
Finally, the company announced a tool specifically for Jira users, which bundles together similar sets of help requests and that should lead to faster resolution over doing them manually one at a time.
“We’re soon launching a feature in JIRA Service Desk that allows users to cluster similar tickets together, and operate on them to accelerate IT workflows, and this is done in the background using ML techniques to calculate the similarity of tickets, based on the summary and description, and so on.”
All of this was made possible by the company’s previous shift from mostly on-premises to the cloud and the flexibility that gave them to build new tooling that crosses the entire platform.
Today’s announcements are just the start of what Atlassian hopes will be a slew of new machine learning-fueled features being added to the platform in the coming months and years.
Google is putting A.I. and machine learning technologies into the hands of journalists. The company this morning announced a suite of new tools, Journalist Studio, that will allow reporters to do their work more easily. At launch, the suite includes a host of existing tools as well as two new products aimed at helping reporters search across large documents and visualizing data.
The first tool is called Pinpoint and is designed to help reporters work with large file sets — like those that contain hundreds of thousands of documents.
Pinpoint will work as an alternative to using the “Ctrl + F” function to manually seek out specific keywords in the documents. Instead, the tool takes advantage of Google Search and its A.I.-powered Knowledge Graph, along with optical character recognition and speech-to-text technologies.
It’s capable of sorting through scanned PDFs, images, handwritten notes, and audio files to automatically identify the key people, organizations, and locations that are mentioned. Pinpoint will highlight these terms and even their synonyms across the files for easy access to the key data.
Image Credits: Google
The tool has already been put to use by journalists at USA Today, for its report on 40,600 COVID-19-related deaths tied to nursing homes. Reveal also used Pinpoint look into the COVID-19 “testing disaster” in ICE detention centers. And The Washington Post used it for a piece about the opioid crisis.
Because it’s also useful for speeding up research, Google notes Pinpoint can be used for shorter-term projects, as well — like Philippines-based Rappler’s examination of CIA reports from the 1970s or Mexico-based Verificado MX’s fast fact checking of the government’s daily pandemic updates.
Pinpoint is available now to interested journalists, who can sign up to request access. The tool currently supports seven languages: English, French, German, Italian, Polish, Portuguese, and Spanish.
Google has also partnered with The Center for Public Integrity, Document Cloud, Stanford University’s Big Local News program and The Washington Post to create shared public collections that are available to all users.
The second new tool being introduced today is The Common Knowledge Project, still in beta.
The tool allows journalists to explore, visualize and share data about important issues in their local communities by creating their own interactive charts using thousands of data points in a matter minutes, the company says.
Image Credits: Google
These charts can then be embedded in reporters’ stories on the web or published to social media.
This particular tool was built by the visual journalism team at Polygraph, supported by the Google News Initiative. The data for use in The Common Knowledge Project comes from Data Commons, which includes thousands of public datasets from organizations like the U.S. Census and the CDC.
At launch, the tool offers U.S. data on issues including demographics, economy, housing, education, and crime.
As it’s still in beta testing, Google is asking journalists to submit their ideas for how it can be improved.
Google will demonstrate and discuss these new tools in more detail during a series of upcoming virtual events, including the Online News Association’s conference on Thursday, October 15. The Google News Initiative training will also soon host a six-part series focused on tools for reporters in seven different languages across nine regions, starting the week of October 20.
The new programs are available on the Journalist Studio website, which also organizes other tools resources for reporters, including Google’s account security system, the Advanced Protection Program; direct access to the Data Commons; DataSet Search; a Fact Check Explorer; a tool for visualizing data using customizable templates, Flourish; the Google Data GIF Maker; Google Public Data Explorer; Google Trends; DIY VPN Outline; DDoS defense tool, Project Shield; and tiled cartogram maker Tilegrams.
The site additionally points to other services from Google, like Google Drive, Google Scholar, Google Earth, Google News, and others, as well as training resources.
Google launched version 4.1 of Android Studio, its IDE for developing Android apps, into its stable channel today. As usual for Android Studio, the minor uptick in version numbers doesn’t quite do the update justice. It includes a vast number of new and improved features that should make life a little bit easier for Android developers. The team also fixed a whopping 2370 bugs during this release cycle and closed 275 public issues.
The highlights of today’s release are a new database inspector and better support for on-device machine learning by allowing developers to bring TensorFlow Lite models to Android, as well as the ability to run the Android Emulator right inside of Android Studio and support for testing apps for foldable phones in the emulator as well. That’s in addition to various other changes the company has outlined here.
The one feature that will likely improve the quality of life for developers the most is the ability to run the Android Emulator right in Android Studio. That’s something the company announced earlier this summer, so it’s not a major surprise, but it’s a nice update for developers since they won’t have to switch back and forth between different windows and tools to test their apps.
Talking about testing, the other update is support for foldable devices in the Android Emulator, which now allows developers to simulate the hinge angle sensor and posture changes so their apps can react accordingly. That’s still a niche market, obviously, but more and more developers are now aiming to offer apps to actually support these devices.
Also new is improved support for TensorFlow Lite models in Android Studio, so that developers can bring those models to their apps, as well as a new database inspector that helps developers get easier insights into their queries and the data they return — and that lets them modify values white running their apps to see how their apps react to those.
Other updates include new templates in the New Project dialog that support Google’s Material Design Components, Dagger navigation support, System Trace UI improvements and new profilers to help developers optimize their apps’ performance and memory usage.
Grid AI, a startup founded by the inventor of the popular open-source PyTorch Lightning project, William Falcon, that aims to help machine learning engineers more efficiently, today announced that it has raised an $18.6 million Series A funding round, which closed earlier this summer. The round was led by Index Ventures, with participation from Bain Capital Ventures and firstminute.
Falcon co-founded the company with Luis Capelo, who was previously the head of machine learning at Glossier. Unsurprisingly, the idea here is to take PyTorch Lightning, which launched about a year ago, and turn that into the core of Grid’s service. The main idea behind Lightning is to decouple the data science from the engineering.
The time argues that a few years ago, when data scientists tried to get started with deep learning, they didn’t always have the right expertise and it was hard for them to get everything right.
“Now the industry has an unhealthy aversion to deep learning because of this,” Falcon noted. “Lightning and Grid embed all those tricks into the workflow so you no longer need to be a PhD in AI nor [have] the resources of the major AI companies to get these things to work. This makes the opportunity cost of putting a simple model against a sophisticated neural network a few hours’ worth of effort instead of the months it used to take. When you use Lightning and Grid it’s hard to make mistakes. It’s like if you take a bad photo with your phone but we are the phone and make that photo look super professional AND teach you how to get there on your own.”
As Falcon noted, Grid is meant to help data scientists and other ML professionals “scale to match the workloads required for enterprise use cases.” Lightning itself can get them partially there, but Grid is meant to provide all of the services its users need to scale up their models to solve real-world problems.
What exactly that looks like isn’t quite clear yet, though. “Imagine you can find any GitHub repository out there. You get a local copy on your laptop and without making any code changes you spin up 400 GPUs on AWS — all from your laptop using either a web app or command-line-interface. That’s the Lightning “magic” applied to training and building models at scale,” Falcon said. “It is what we are already known for and has proven to be such a successful paradigm shift that all the other frameworks like Keras or TensorFlow, and companies have taken notice and have started to modify what they do to try to match what we do.”
The service is now in private beta.
With this new funding, Grid, which currently has 25 employees, plans to expand its team and strengthen its corporate offering via both Grid AI and through the open-source project. Falcon tells me that he aims to build a diverse team, not in the least because he himself is an immigrant, born in Venezuela, and a U.S. military veteran.
“I have first-hand knowledge of the extent that unethical AI can have,” he said. “As a result, we have approached hiring our current 25 employees across many backgrounds and experiences. We might be the first AI company that is not all the same Silicon Valley prototype tech-bro.”
“Lightning’s open-source traction piqued my interest when I first learned about it a year ago,” Index Ventures’ Sarah Cannon told me. “So intrigued in fact I remember rushing into a closet in Helsinki while at a conference to have the privacy needed to hear exactly what Will and Luis had built. I promptly called my colleague Bryan Offutt who met Will and Luis in SF and was impressed by the ‘elegance’ of their code. We swiftly decided to participate in their seed round, days later. We feel very privileged to be part of Grid’s journey. After investing in seed, we spent a significant amount with the team, and the more time we spent with them the more conviction we developed. Less than a year later and pre-launch, we knew we wanted to lead their Series A.”
As machine learning has grown, one of the major bottlenecks remains labeling things so the machine learning application understands the data it’s working with. Datasaur, a member of the Y Combinator Winter 2020 batch, announced a $3.9 million investment today to help solve that problem with a platform designed for machine learning labeling teams.
The funding announcement, which includes a pre-seed amount of $1.1 million from last year and $2.8 million seed right after it graduated from Y Combinator in March, included investments from Initialized Capital, Y Combinator and OpenAI CTO Greg Brockman.
Company founder Ivan Lee says that he has been working in various capacities involving AI for seven years. First when his mobile gaming startup, Loki Studios was acquired by Yahoo! in 2013, and Lee was eventually moved to the AI team, and most recently at Apple. Regardless of the company, he consistently saw a problem around organizing machine learning labeling teams, one that he felt he was uniquely situated to solve because of his experience.
“I have spent millions of dollars [in budget over the years] and spent countless hours gathering labeled data for my engineers. I came to recognize that this was something that was a problem across all the companies that I’ve been at. And they were just consistently reinventing the wheel and the process. So instead of reinventing that for the third time at Apple, my most recent company, I decided to solve it once and for all for the industry. And that’s why we started Datasaur last year,” Lee told TechCrunch.
He built a platform to speed up human data labeling with a dose of AI, while keeping humans involved. The platform consists of three parts: a labeling interface, the intelligence component, which can recognize basic things, so the labeler isn’t identifying the same thing over and over, and finally a team organizing component.
He says the area is hot, but to this point has mostly involved labeling consulting solutions, which farm out labeling to contractors. He points to the sale of Figure Eight in March 2019 and to Scale, which snagged $100 million last year as examples of other startups trying to solve this problem in this way, but he believes his company is doing something different by building a fully software-based solution.
The company currently offers a cloud and on-prem solution, depending on the customer’s requirements. It has 10 employees with plans to hire in the next year, although he didn’t share an exact number. As he does that, he says he has been working with a partner at investor Initialized on creating a positive and inclusive culture inside the organization, and that includes conversations about hiring a diverse workforce as he builds the company.
“I feel like this is just standard CEO speak but that is something that we absolutely value in our top of funnel for the hiring process,” he said.
As Lee builds out his platform, he has also worried about built-in bias in AI systems and the detrimental impact that could have on society. He says that he has spoken to clients about the role of labeling in bias and ways of combatting that.
“When I speak with our clients, I talk to them about the potential for bias from their labelers and built into our product itself is the ability to assign multiple people to the same project. And I explain to my clients that this can be more costly, but from personal experience I know that it can improve results dramatically to get multiple perspectives on the exact same data,” he said.
Lee believes humans will continue to be involved in the labeling process in some way, even as parts of the process become more automated. “The very nature of our existence [as a company] will always require humans in the loop, […] and moving forward I do think it’s really important that as we get into more and more of the long tail use cases of AI, we will need humans to continue to educate and inform AI, and that’s going to be a critical part of how this technology develops.”
Research papers come out far too rapidly for anyone to read them all, especially in the field of machine learning, which now affects (and produces papers in) practically every industry and company. This column aims to collect the most relevant recent discoveries and papers — particularly in but not limited to artificial intelligence — and explain why they matter.
The topics in this week’s Deep Science column are a real grab bag that range from planetary science to whale tracking. There are also some interesting insights from tracking how social media is used and some work that attempts to shift computer vision systems closer to human perception (good luck with that).
One of machine learning’s most reliable use cases is training a model on a target pattern, say a particular shape or radio signal, and setting it loose on a huge body of noisy data to find possible hits that humans might struggle to perceive. This has proven useful in the medical field, where early indications of serious conditions can be spotted with enough confidence to recommend further testing.
This arthritis detection model looks at X-rays, same as doctors who do that kind of work. But by the time it’s visible to human perception, the damage is already done. A long-running project tracking thousands of people for seven years made for a great training set, making the nearly imperceptible early signs of osteoarthritis visible to the AI model, which predicted it with 78% accuracy three years out.
The bad news is that knowing early doesn’t necessarily mean it can be avoided, as there’s no effective treatment. But that knowledge can be put to other uses — for example, much more effective testing of potential treatments. “Instead of recruiting 10,000 people and following them for 10 years, we can just enroll 50 people who we know are going to be getting osteoarthritis … Then we can give them the experimental drug and see whether it stops the disease from developing,” said co-author Kenneth Urish. The study appeared in PNAS.
It’s amazing to think that ships still collide with and kill large whales on a regular basis, but it’s true. Voluntary speed reductions haven’t been much help, but a smart, multisource system called Whale Safe is being put in play in the Santa Barbara channel that could hopefully give everyone a better idea of where the creatures are in real-time.
The system uses underwater acoustic monitoring, near-real-time forecasting of likely feeding areas, actual sightings and a dash of machine learning (to identify whale calls quickly) to produce a prediction for whale presence along a given course. Large container ships can then make small adjustments well-ahead of time instead of trying to avoid a pod at the last minute.
“Predictive models like this give us a clue for what lies ahead, much like a daily weather forecast,” said Briana Abrahms, who led the effort from the University of Washington. “We’re harnessing the best and most current data to understand what habitats whales use in the ocean, and therefore where whales are most likely to be as their habitats shift on a daily basis.”
Incidentally, Salesforce founder Marc Benioff and his wife Lynne helped establish the UC Santa Barbara center that made this possible.
The machine learning and AI-powered tools being deployed in response to COVID-19 arguably improve certain human activities and provide essential insights needed to make certain personal or professional decisions; however, they also highlight a few pervasive challenges faced by both machines and the humans that create them.
Nevertheless, the progress seen in AI/machine learning leading up to and during the COVID-19 pandemic cannot be ignored. This global economic and public health crisis brings with it a unique opportunity for updates and innovation in modeling, so long as certain underlying principles are followed.
Here are four industry truths (note: this is not an exhaustive list) my colleagues and I have found that matter in any design climate, but especially during a global pandemic climate.
When a big group of people is collectively working on a problem, success may become more likely. Looking at historic examples like the 2008 Global Financial Crisis, there were several analysts credited with predicting the crisis. This may seem miraculous to some until you consider that more than 200,000 people were working in Wall Street, each of them making their own predictions. It then becomes less of a miracle and more of a statistically probable outcome. With this many individuals simultaneously working on modeling and predictions, it was highly likely someone would get it right by chance.
Similarly, with COVID-19 there are a lot of people involved, from statistical modelers and data scientists to vaccine specialists, and there is also an overwhelming eagerness to find solutions and concrete data-based answers. Following appropriate statistical rigor, coupled with machine learning and AI, can improve these models and decrease the chances of false predictions that arrive from too many predictions being made.
During a crisis, time-management is essential. Automation technology can be used not only as part of the crisis solution, but also as a tool for monitoring productivity and contributions of team members working on the solution. For modeling, automation can also greatly improve the speed of results. Every second a piece of software can perform automation for a model, it allows a data scientist (or even a medical scientist) to conduct other more important tasks. User-friendly platforms in the market now give more people, like business analysts, access to predictions from custom machine learning models.
Privacy data mismanagement is a lurking liability within every commercial enterprise. The very definition of privacy data is evolving over time and has been broadened to include information concerning an individual’s health, wealth, college grades, geolocation and web surfing behaviors. Regulations are proliferating at state, national and international levels that seek to define privacy data and establish controls governing its maintenance and use.
Existing regulations are relatively new and are being translated into operational business practices through a series of judicial challenges that are currently in progress, adding to the confusion regarding proper data handling procedures. In this confusing and sometimes chaotic environment, the privacy risks faced by almost every corporation are frequently ambiguous, constantly changing and continually expanding.
Conventional information security (infosec) tools are designed to prevent the inadvertent loss or intentional theft of sensitive information. They are not sufficient to prevent the mismanagement of privacy data. Privacy safeguards not only need to prevent loss or theft but they must also prevent the inappropriate exposure or unauthorized usage of such data, even when no loss or breach has occurred. A new generation of infosec tools is needed to address the unique risks associated with the management of privacy data.
A variety of privacy-focused security tools emerged over the past few years, triggered in part by the introduction of GDPR (General Data Protection Regulation) within the European Union in 2018. New capabilities introduced by this first wave of innovation were focused in the following three areas:
Data discovery, classification and cataloging. Modern enterprises collect a wide variety of personal information from customers, business partners and employees at different times for different purposes with different IT systems. This data is frequently disseminated throughout a company’s application portfolio via APIs, collaboration tools, automation bots and wholesale replication. Maintaining an accurate catalog of the location of such data is a major challenge and a perpetual activity. BigID, DataGuise and Integris Software have gained prominence as popular solutions for data discovery. Collibra and Alation are leaders in providing complementary capabilities for data cataloging.
Consent management. Individuals are commonly presented with privacy statements describing the intended use and safeguards that will be employed in handling the personal data they supply to corporations. They consent to these statements — either explicitly or implicitly — at the time such data is initially collected. Osano, Transcend.io and DataGrail.io specialize in the management of consent agreements and the enforcement of their terms. These tools enable individuals to exercise their consensual data rights, such as the right to view, edit or delete personal information they’ve provided in the past.
Amazon announces a new game service and plenty of hardware upgrades, tech companies team up against app stores and United Airlines tests a program for rapid COVID-19 testing. This is your Daily Crunch for September 24, 2020.
The big story: Amazon unveils its own game-streaming platform
Amazon’s competitor to Google Stadia and Microsoft xCloud is called Luna, and it’s available starting today at an early access price of $5.99 per month. Subscribers will be able to play games across PC, Mac and iOS, with more than 50 games in the library.
The company made the announcement at a virtual press event, where it also revealed a redesigned Echo line (with spherical speakers and swiveling screens), the latest Ring security camera and a new, lower-cost Fire TV Stick Lite.
You can also check out our full roundup of Amazon’s announcements.
The tech giants
App makers band together to fight for App Store changes with new ‘Coalition for App Fairness’ — Thirteen app publishers, including Epic Games, Deezer, Basecamp, Tile, Spotify and others, launched a coalition formalizing their efforts to force app store providers to change their policies or face regulation.
LinkedIn launches Stories, plus Zoom, BlueJeans and Teams video integrations as part of wider redesign — LinkedIn has built its business around recruitment, so this redesign pushes engagement in other ways as it waits for the job economy to pick up.
Facebook gives more details about its efforts against hate speech before Myanmar’s general election — This includes adding Burmese language warning screens to flag information rated false by third-party fact-checkers.
Startups, funding and venture capital
Why isn’t Robinhood a verb yet? — The latest episode of Equity discusses a giant funding round for Robinhood.
Twitter-backed Indian social network ShareChat raises $40 million — Following TikTok’s ban in India, scores of startups have launched short-video apps, but ShareChat has clearly established dominance.
Spotify CEO Daniel Ek pledges $1Bn of his wealth to back deeptech startups from Europe — Ek pointed to machine learning, biotechnology, materials sciences and energy as the sectors he’d like to invest in.
Advice and analysis from Extra Crunch
3 founders on why they pursued alternative startup ownership structures — At Disrupt, we heard about alternative approaches to ensuring that VCs and early founders aren’t the only ones who benefit from startup success.
Coinbase UX teardown: 5 fails and how to fix them — Many of these lessons, including the need to avoid the “Get Started” trap, can be applied to other digital products.
As tech stocks dip, is insurtech startup Root targeting an IPO? — Alex Wilhelm writes that Root’s debut could clarify Lemonade’s IPO and valuation.
(Reminder: Extra Crunch is our subscription membership program, which aims to democratize information about startups. You can sign up here.)
United Airlines is making COVID-19 tests available to passengers, powered in part by Color — United is embarking on a new pilot project to see if easy access to COVID-19 testing immediately prior to a flight can help ease freedom of mobility.
Announcing the final agenda for TC Sessions: Mobility 2020 — TechCrunch reporters and editors will interview some of the top leaders in transportation.
The Daily Crunch is TechCrunch’s roundup of our biggest and most important stories. If you’d like to get this delivered to your inbox every day at around 3pm Pacific, you can subscribe here.
A yellow-eyed cat tilts its eyes at the camera, gazing up from a grey bedspread. ‘London Trip’, is the AI’s title for this photo-montage ‘Memory’ plucked from the depths of my iPhone camera-roll. It’s selected a sad score of plinking piano and sweeping violin. The algorithm has calculated it must tug at the heart strings.
Cut to a crop of a desk with a 2FA device resting on a laptop case. It’s not at all photogenic. On to a shot of a sofa in a living room. It’s empty. The camera inclines toward a radio on a sidetable. Should we be worried for the invisible occupant? The staging invites cryptic questions.
Cut to an outdoor scene: A massive tree spreading above a wrought iron park fence. Another overcast day in the city. Beside it an eccentric shock of orange. A piece of public art? A glass-blown installation? There’s no time to investigate or interrogate. The AI is moving on. There’s more data clogging its banks.
Cut to a conference speaker. White, male, besuited, he’s gesticulating against a navy wall stamped with some kind of insignia. The photo is low quality, snapped in haste from the audience, details too fuzzy to pick out. Still, the camera lingers, panning across the tedious vista. A wider angle shows conference signage for something called ‘Health X’. This long distant press event rings a dim bell. Another unlovely crop: My voice recorder beside a brick wall next to an iced coffee. I guess I’m working from a coffee shop.
On we go. A snap through a window-frame of a well kept garden, a bird-bath sprouting from low bushes. Another shot of the shrubbery shows a ladder laid out along a brick wall. I think it looks like a church garden in Southwark but I honestly can’t tell. No matter. The AI has lost interest. Now it’s obsessing over a billboard of a Google Play ad: “All the tracks you own and millions more to discover — Try it now for free,” the text reads above a weathered JCDecaux brand stamp.
There’s no time to consider what any of this means because suddenly it’s nighttime. It must be; my bedside lamp is lit. Or is it? Now we’re back on the living room sofa with daylight and a book called ‘Nikolski’ (which is also, as it happens, about separation and connection and random artefacts — although its artful narrative succeeds in serendipity).
Cut to a handful of berries in a cup. Cut to an exotic-looking wallflower which I know grows in the neighbourhood. The score is really soaring now. A lilting female vocal lands on cue to accompany a solitary selfie.
I am looking unimpressed. I have so many questions.
The AI isn’t quite finished. For the finale: A poorly framed crop of a garden fence and a patio of pot plants, washing weeping behind the foliage. The music is fading, the machine is almost done constructing its London trip. The last shot gets thrust into view: Someone’s hand clasping a half-drunk punch.
Go home algorithm, you’re drunk.
My iPhone has invented a 2014 'London trip' that wasn't. It calls this absurd & creepy construct "a new memory" – trying to pass off its weird fiction as my own. Do algorithms dream in camerarolls? More importantly, when & where did we agree to surveillance by lying AIs?
— Natasha (@riptari) September 17, 2020
Footnote: Apple says on-device machine learning powers iOS’ “intelligent photos experience” which “analyzes every photo in a user’s photo library using on-device machine learning [to] deliver a personalized experience for each user” — with the advanced processing slated to include scene classification, composition analysis, people and pets identification, quality analysis and identification of facial expressions
Working at the intersection of biology and computing may be the most exciting new spot for technologists at the moment.
Speaking at our virtual TechCrunch Disrupt conference, Koller, a serial entrepreneur who previously co-founded Coursera and briefly served as the chief computing officer for the Alphabet subsidiary focused on human health, Calico, views digital biology as the next big technological revolution.
“Digital biology is an incredible place to be right now,” Koller said in an interview.
It’s certainly been an incredible opportunity for Koller, whose work now spans the development of treatments for potential neurological diseases and a nearer-term research and development effort on hepatitis with Gilead Pharmaceuticals.
Koller’s Insitro takes its name and inspiration from the combination of two different practices in biological research — the in vitro experiments that are done on living samples in labs and the in silico experiments that are done on the computer.
By synthesizing these two disciplines, Koller’s company flips the process of drug discovery on its head, as the company is designed to sift through massive amounts of data to search for patterns in the expression of certain conditions. Once those patterns are determined, the company can examine the pathways or mechanisms associated with that expression to determine targets for potential therapies.
Then Insitro will pursue the development of novel molecules that can be used to intervene and either reverse or stop the progression of an illness by stopping the biological mechanisms associated with it.
“We now have massive amounts of data that is truly relevant to human disease,” Koller said. “Machine learning has given us a bunch of tools to really make sense of data.”
The company can identify new patient segments, new interventions new drugs that may modulate the expression of those conditions. “We view ourselves as being on the first phase of a very long journey using machine learning,” said Koller.
Take the company’s work on hepatitis in conjunction with Gilead. There, Koller and her team were able to take a small, high-quality data set from Gilead’s trials and identify how a disease progressed by looking at the patient data from different points in time. Looking at the progression allowed the company to identify drivers that facilitated the progression of fibrosis that causes tissue damage. Now the company is using those targets as a starting point to find modifiers that could slow down the progression of the disease.
It comes down to using computers to understand the biology, new biotechnology to model that biology in a Petri dish and, from the different models, determine the interventions that will make a difference, Koller said.
“What we’re trying to do is so different and so out of alignment with how these [pharmaceutical] companies do their work,” Koller said. “It’s trying to shift the trajectory of these companies of hundreds or thousands of people and shift the culture to a tech culture that is going to be really a challenge.”
It’s the main reason Koller launched her own company rather than joining a big pharma play, and it’s a classic example of the innovator’s dilemma and the disruptive power of technology laid out in the theories of Clayton Christensen that give the Disrupt conference its name.
“[It’s] the notion of the innovator’s dilemma and coming in with a mindset that says we’re going to do this a completely different way,” said Koller. “The drug discovery effort is becoming increasingly expensive and increasingly prone to failure and if we do this in a different way will it enable us to generate better outcomes.”