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IBM launches cloud tool to detect AI bias and explain automated decisions
IBM has launched a software service that scans AI systems as they work in order to detect bias and provide explanations for the automated decisions being made — a degree of transparency that may be necessary for compliance purposes not just a company’s own due diligence.
The new trust and transparency system runs on the IBM cloud and works with models built from what IBM bills as a wide variety of popular machine learning frameworks and AI-build environments — including its own Watson tech, as well as Tensorflow, SparkML, AWS SageMaker, and AzureML.
It says the service can be customized to specific organizational needs via programming to take account of the “unique decision factors of any business workflow”.
The fully automated SaaS explains decision-making and detects bias in AI models at runtime — so as decisions are being made — which means it’s capturing “potentially unfair outcomes as they occur”, as IBM puts it.
It will also automatically recommend data to add to the model to help mitigate any bias that has been detected.
Explanations of AI decisions include showing which factors weighted the decision in one direction vs another; the confidence in the recommendation; and the factors behind that confidence.
IBM also says the software keeps records of the AI model’s accuracy, performance and fairness, along with the lineage of the AI systems — meaning they can be “easily traced and recalled for customer service, regulatory or compliance reasons”.
For one example on the compliance front, the EU’s GDPR privacy framework references automated decision making, and includes a right for people to be given detailed explanations of how algorithms work in certain scenarios — meaning businesses may need to be able to audit their AIs.
The IBM AI scanner tool provides a breakdown of automated decisions via visual dashboards — an approach it bills as reducing dependency on “specialized AI skills”.
However it is also intending its own professional services staff to work with businesses to use the new software service. So it will be both selling AI, ‘a fix’ for AI’s imperfections, and experts to help smooth any wrinkles when enterprises are trying to fix their AIs… Which suggests that while AI will indeed remove some jobs, automation will be busy creating other types of work.
Nor is IBM the first professional services firm to spot a business opportunity around AI bias. A few months ago Accenture outed a fairness tool for identifying and fixing unfair AIs.
So with a major push towards automation across multiple industries there also looks to be a pretty sizeable scramble to set up and sell services to patch any problems that arise as a result of increasing use of AI.
And, indeed, to encourage more businesses to feel confident about jumping in and automating more. (On that front IBM cites research it conducted which found that while 82% of enterprises are considering AI deployments, 60% fear liability issues and 63% lack the in-house talent to confidently manage the technology.)
In additional to launching its own (paid for) AI auditing tool, IBM says its research division will be open sourcing an AI bias detection and mitigation toolkit — with the aim of encouraging “global collaboration around addressing bias in AI”.
“IBM led the industry in establishing trust and transparency principles for the development of new AI technologies. It’s time to translate principles into practice,” said David Kenny, SVP of cognitive solutions at IBM, commenting in a statement. “We are giving new transparency and control to the businesses who use AI and face the most potential risk from any flawed decision making.”
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Microsoft launches new AI applications for customer service and sales
Like virtually every other major tech company, Microsoft is currently on a mission to bring machine learning to all of its applications. It’s no surprise then that it’s also bringing ‘AI’ to its highly profitable Dynamics 365 CRM products. A year ago, the company introduced its first Dynamics 365 AI solutions and today it’s expanding this portfolio with the launch of three new products: Dynamics 365 AI for Sales, Customer Service and Market Insights.
“Many people, when they talk about CRM, or ERP of old, they referred to them as systems of oppression, they captured data,” said Alysa Taylor, Microsoft corporate VP for business applications and industry. “But they didn’t provide any value back to the end user — and what that end user really needs is a system of empowerment, not oppression.”
It’s no secret that few people love their CRM systems (except for maybe a handful of Dreamforce attendees), but ‘system of oppression’ is far from the ideal choice of words here. Yet Taylor is right that early systems often kept data siloed. Unsurprisingly, Microsoft argues that Dynamics 365 does not do that, allowing it to now use all of this data to build machine learning-driven experiences for specific tasks.
Dynamics 365 AI for Sales, unsurprisingly, is meant to help sales teams get deeper insights into their prospects using sentiment analysis. That’s obviously among the most basic of machine learning applications these days, but AI for Sales also helps these salespeople understand what actions they should take next and which prospects to prioritize. It’ll also help managers coach their individual sellers on the actions they should take.
Similarly, the Customer Service app focuses on using natural language understanding to understand and predict customer service problems and leverage virtual agents to lower costs. Taylor used this part of the announcement to throw some shade at Microsoft’s competitor Salesforce. “Many, many vendors offer this, but they offer it in a way that is very cumbersome for organizations to adopt,” she said. “Again, it requires a large services engagement, Salesforce partners with IBM Watson to be able to deliver on this. We are now out of the box.”
Finally, Dynamics 365 AI for Market Insights does just what the name implies: it provides teams with data about social sentiment, but this, too, goes a bit deeper. “This allows organizations to harness the vast amounts of social sentiment, be able to analyze it, and then take action on how to use these insights to increase brand loyalty, as well as understand what newsworthy events will help provide different brand affinities across an organization,” Taylor said. So the next time you see a company try to gin up some news, maybe it did so based on recommendations from Dynamics 365 AI for Market Insights.
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Clinc is building a voice AI system to replace humans in drive-through restaurants
Clinc is expanding its focus on fintech into new verticals that could take advantage of its conversational artificial intelligence. The Ann Arbor-based company recently took the wraps off its new system that aims to provide quick-service restaurants like McDonald’s and Taco Bell with a voice assistant in the drive-through window.
I got a demo of the new system. For the most part, even in its early state, it works as advertised. Want a double cheeseburger without pickles and mayo with a side of fries and a Coke? With Clinc’s system, a person can order food as if they were talking to a human. Have questions or want to make changes to the order? Again, the person ordering the food does not have to modify their speech pattern or use a voice menu tree — just talk to the system normally.
This is Clinc’s second implementation of it conversational AI system. This isn’t Siri or Alexa. This technology is from the next generation.
The company started with a solution for fintech and currently has several contracts with major banks such as USAA, Barclays and S&P Global. In most cases, when integrated into the bank’s system, Clinc’s technology emulates human intelligence and can interpret unstructured, unconstrained speech. The idea is to let users converse with their bank account using natural language without pre-defined templates or hierarchical voice menus.
Clinc was founded by University of Michigan professors Dr. Jason Mars, Dr. Johann Hauswald, Dr. Lingjia Tang and Dr. Michael Laurenzano.
Mars tells me Clinc spun up the quick-service restaurant (QSR) product in about two weeks. He explains that Clinc’s platform allows programmers to drag and drop a restaurant’s menu to add items to the voice service.
I watched a Clinc engineer use the system for about an hour. Over and over again, the system processed the order correctly, but occasionally it got it wrong. It seems changing an order is just as easy as placing one though, and the engineer was able to modify the order on the fly.
When using the system, it’s obvious a computer is speaking. Good or bad, if implemented by restaurants, this could be one of the largest barriers to adoption by consumers. For the most part, ordering from a fast food restaurant is an easy affair, but occasionally it gets complicated and Clinc’s system has to be able to handle everything — or have triggers that cause the system to connect the orderer with a live person to resolve the issue.
The QSR product is coming to market at a critical time. Fast-food restaurants are increasingly looking for ways to reduce the number of workers in their stores while also looking for new ways for customers to order food. It’s clear this product can be modified to address other voice-heavy industries, too, such as call centers and appointment booking services.
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Y Combinator invests in non-invasive breast cancer screening bra EVA
According to a report by the American Cancer Society, an estimated 266,120 women will be newly diagnosed with breast cancer in the United States this year and (according to a 2016 estimate) can expect to pay between $60,000 and $134,000 on average for treatment and care. But, after hundreds of thousands of dollars and non-quantifiable emotional stress for them and their families, the American Cancer Society still estimates 40,920 women will lose their battle to the disease this year.
Worldwide, roughly 1.7 million women will be diagnosed with the disease yearly, according to a 2012 estimate by The World Cancer Research Fund International.
While these numbers are stark, they do little to fully capture just how devastating a breast cancer diagnosis is for women and their loved ones. This is a feeling that Higia Technologies‘ co-founder and CEO Julián Ríos Cantú is unfortunately very familiar with.
“My mom is a two-time breast cancer survivor,” Cantú told TechCrunch. “The first time she was diagnosed I was eight years old.”
Cantú says that his mother’s second diagnosis was originally missed through standard screenings because her high breast density obscured the tumors from the X-ray. As a result, she lost both of her breasts, but has since fully recovered.
“At that moment I realized that if that was the case for a woman with private insurance and a prevention mindset, then for most women in developing countries, like Mexico where we’re from, the outcome could’ve not been a mastectomy but death,” said Cantú.
Following his mother’s experience, Cantú resolved to develop a way to improve the value of women’s lives and support them in identifying breast abnormalities and cancers early in order to ensure the highest likelihood of survival.
To do this, at the age of 18 Cantú designed EVA — a bio-sensing bra insert that uses thermal sensing and artificial intelligence to identify abnormal temperatures in the breast that can correlate to tumor growth. Cantú says that EVA is not only an easy tool for self-screening but also fills in gaps in current screening technology.
Today, women have fairly limited options when it comes to breast cancer screening. They can opt for a breast ultrasound (which has lower specificity than other options), or a breast MRI (which has higher associated costs), but the standard option is a yearly or bi-yearly mammogram for women 45 and older. This method requires a visit to a doctor, manual manipulation of the breasts by a technologist and exposure to low-levels of radiation for an X-ray scan of the breast tissue.
While this method is relatively reliable, there are still crucial shortcomings, Higia Technologies’ medical adviser Dr. Richard Kaszynski M.D., PhD told TechCrunch.
“We need to identify a real-world solution to diagnosing breast cancer earlier,” said Dr. Kaszynski. “It’s always a trade-off when we’re talking about mammography because you have the radiation exposure, discomfort and anxiety in regards to exposing yourself to a third-party.”
Dr. Kaszynski continued to say that these yearly or bi-yearly mammograms also leave a gap in care in which interval cancers — cancers that begin to take hold between screenings — have time to grow unhindered.
Additionally, Dr. Kaszynski says mammograms are not highly sensitive when it comes to detecting tumors in dense breast tissue, like that of Cantú’s mom. Dense breast tissue, which is more common in younger women and is present in 40 percent of women globally and 80 percent of Asian women, can mask the presence of tumors in the breast from mammograms.
Through its use of non-invasive, thermal sensors EVA is able to collect thermal data from a variety of breast densities that can enable women of all ages to more easily (and more frequently) perform breast examinations.
Here’s how it works:
To start, the user inserts the thermal sensing cups (which come in three standard sizes ranging from A-D) into a sports bra, open EVA’s associated EVA Health App, follow the instructions and wait for 60 minutes while the cup collects thermal data. From there, EVA will send the data via Bluetooth to the app and an AI will analyze the results to provide the user with an evaluation. If EVA believes the user may have an abnormality that puts them at risk, the app will recommend follow-up steps for further screening with a healthcare professional.
While sacrificing your personal health data to the whims of an AI might seem like a scary (and dangerous, if the device were to be hacked) idea to some, Cantú says Higia Technologies has taken steps to protect its users’ data, including advanced encryption of its server and a HIPAA-compliant privacy infrastructure.
So far, EVA has undergone clinical trials in Mexico, and through these trials has seen 87.9 percent sensibility and 81.7 percent specificity from the device. In Mexico, the company has already sold 5,000 devices and plans to begin shipping the first several hundred by October of this year.
And the momentum for EVA is only increasing. In 2017, Cantú was awarded Mexico’s Presidential Medal for Science and Technology and so far this year Higia Technologies has won first place in the SXSW’s International Pitch Competition, been named one of “30 Most Promising Businesses of 2018” by Forbes Magazine Mexico and this summer received a $120,000 investment from Y Combinator.
Moving forward, the company is looking to enter the U.S. market and has plans to begin clinical trials with Stanford Medicine X in October 2018 that should run for about a year. Following these trials, Dr. Kaszynski says that Higia Technologies will continue the process of seeking FDA approval to sell the inserts first as a medical device, accessible at a doctor’s office, and then as a device that users can have at home.
The final pricing for the device is still being decided, but Cantú says he wants the product to be as affordable and accessible as possible so it can be the first choice for women in developing countries where preventative cancer screening is desperately needed.
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Computer vision researchers build an AI benchmark app for Android phones
A group of computer vision researchers from ETH Zurich want to do their bit to enhance AI development on smartphones. To wit: They’ve created a benchmark system for assessing the performance of several major neural network architectures used for common AI tasks.
They’re hoping it will be useful to other AI researchers but also to chipmakers (by helping them get competitive insights); Android developers (to see how fast their AI models will run on different devices); and, well, to phone nerds — such as by showing whether or not a particular device contains the necessary drivers for AI accelerators. (And, therefore, whether or not they should believe a company’s marketing messages.)
The app, called AI Benchmark, is available for download on Google Play and can run on any device with Android 4.1 or higher — generating a score the researchers describe as a “final verdict” of the device’s AI performance.
AI tasks being assessed by their benchmark system include image classification, face recognition, image deblurring, image super-resolution, photo enhancement or segmentation.
They are even testing some algorithms used in autonomous driving systems, though there’s not really any practical purpose for doing that at this point. Not yet anyway. (Looking down the road, the researchers say it’s not clear what hardware platform will be used for autonomous driving — and they suggest it’s “quite possible” mobile processors will, in future, become fast enough to be used for this task. So they’re at least prepped for that possibility.)
The app also includes visualizations of the algorithms’ output to help users assess the results and get a feel for the current state-of-the-art in various AI fields.
The researchers hope their score will become a universally accepted metric — similar to DxOMark that is used for evaluating camera performance — and all algorithms included in the benchmark are open source. The current ranking of different smartphones and mobile processors is available on the project’s webpage.
The benchmark system and app was around three months in development, says AI researcher and developer Andrey Ignatov.
He explains that the score being displayed reflects two main aspects: The SoC’s speed and available RAM.
“Let’s consider two devices: one with a score of 6000 and one with a score of 200. If some AI algorithm will run on the first device for 5 seconds, then this means that on the second device this will take about 30 times longer, i.e. almost 2.5 minutes. And if we are thinking about applications like face recognition this is not just about the speed, but about the applicability of the approach: Nobody will wait 10 seconds till their phone will be trying to recognize them.
“The same is about memory: The larger is the network/input image — the more RAM is needed to process it. If the phone has a small amount of RAM that is e.g. only enough to enhance 0.3MP photo, then this enhancement will be clearly useless, but if it can do the same job for Full HD images — this opens up much wider possibilities. So, basically the higher score — the more complex algorithms can be used / larger images can be processed / it will take less time to do this.”
Discussing the idea for the benchmark, Ignatov says the lab is “tightly bound” to both research and industry — so “at some point we became curious about what are the limitations of running the recent AI algorithms on smartphones”.
“Since there was no information about this (currently, all AI algorithms are running remotely on the servers, not on your device, except for some built-in apps integrated in phone’s firmware), we decided to develop our own tool that will clearly show the performance and capabilities of each device,” he adds.
“We can say that we are quite satisfied with the obtained results — despite all current problems, the industry is clearly moving towards using AI on smartphones, and we also hope that our efforts will help to accelerate this movement and give some useful information for other members participating in this development.”
After building the benchmarking system and collating scores on a bunch of Android devices, Ignatov sums up the current situation of AI on smartphones as “both interesting and absurd”.
For example, the team found that devices running Qualcomm chips weren’t the clear winners they’d imagined — i.e. based on the company’s promotional materials about Snapdragon’s 845 AI capabilities and 8x performance acceleration.
“It turned out that this acceleration is available only for ‘quantized’ networks that currently cannot be deployed on the phones, thus for ‘normal’ networks you won’t get any acceleration at all,” he says. “The saddest thing is that actually they can theoretically provide acceleration for the latter networks too, but they just haven’t implemented the appropriated drivers yet, and the only possible way to get this acceleration now is to use Snapdragon’s proprietary SDK available for their own processors only. As a result — if you are developing an app that is using AI, you won’t get any acceleration on Snapdragon’s SoCs, unless you are developing it for their processors only.”
Whereas the researchers found that Huawei’s Kirin’s 970 CPU — which is technically even slower than Snapdragon 636 — offered a surprisingly strong performance.
“Their integrated NPU gives almost 10x acceleration for Neural Networks, and thus even the most powerful phone CPUs and GPUs can’t compete with it,” says Ignatov. “Additionally, Huawei P20/P20 Pro are the only smartphones on the market running Android 8.1 that are currently providing AI acceleration, all other phones will get this support only in Android 9 or later.”
It’s not all great news for Huawei phone owners, though, as Ignatov says the NPU doesn’t provide acceleration for ‘quantized’ networks (though he notes the company has promised to add this support by the end of this year); and also it uses its own RAM — which is “quite limited” in size, and therefore you “can’t process large images with it”…
“We would say that if they solve these two issues — most likely nobody will be able to compete with them within the following year(s),” he suggests, though he also emphasizes that this assessment only refers to the one SoC, noting that Huawei’s processors don’t have the NPU module.
For Samsung processors, the researchers flag up that all the company’s devices are still running Android 8.0 but AI acceleration is only available starting from Android 8.1 and above. Natch.
They also found CPU performance could “vary quite significantly” — up to 50% on the same Samsung device — because of throttling and power optimization logic. Which would then have a knock on impact on AI performance.
For Mediatek, the researchers found the chipmaker is providing acceleration for both ‘quantized’ and ‘normal’ networks — which means it can reach the performance of “top CPUs”.
But, on the flip side, Ignatov calls out the company’s slogan — that it’s “Leading the Edge-AI Technology Revolution” — dubbing it “nothing more than their dream”, and adding: “Even the aforementioned Samsung’s latest Exynos CPU can slightly outperform it without using any acceleration at all, not to mention Huawei with its Kirin’s 970 NPU.”
“In summary: Snapdragon — can theoretically provide good results, but are lacking the drivers; Huawei — quite outstanding results now and most probably in the nearest future; Samsung — no acceleration support now (most likely this will change soon since they are now developing their own AI Chip), but powerful CPUs; Mediatek — good results for mid-range devices, but definitely no breakthrough.”
It’s also worth noting that some of the results were obtained on prototype samples, rather than shipped smartphones, so haven’t yet been included in the benchmark table on the team’s website.
“We will wait till the devices with final firmware will come to the market since some changes might still be introduced,” he adds.
For more on the pros and cons of AI-powered smartphone features check out our article from earlier this year.
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Landbot gets $2.2M for its on-message ‘anti-AI’ chatbot
Who needs AI to have a good conversation? Spanish startup Landbot has bagged a $2.2 million seed round for a ‘dumb’ chatbot that doesn’t use AI at all but offers something closer to an old school ‘choose your adventure’ interaction by using a conversational choice interface to engage potential customers when they land on a website.
The rampant popularity of consumer messaging apps has long been influencing product development decisions, and plenty of fusty business tools have been consumerized in recent years, including by having messaging-style interfaces applied to simplify all kinds of digital interactions.
In the case of Landbot, the team is deploying a familiar rich texting interface as a website navigation tool — meaning site visitors aren’t left to figure out where to click to find stuff on their own. Instead they’re pro-actively met with an interactive, adaptive messaging thread that uses conversational choice prompts to get them the information they need.
Call it a chatty twist on the ‘lazyweb’…
It’s also of course mobile first design, where constrained screen real estate is never very friendly to full fat homepages. Using a messaging thread interface plus marketing bots thus offers an alternative way to cut to the navigational chase, while simultaneously creaming off intent intelligence on potential customers. (Albeit it does risk getting old fast if your site visitors have a habit of clearing their cookies.)
Landbot, which was launched just over a year ago in June 2017, started as an internal experiment after its makers got frustrated by the vagaries of their own AI chatbots. So they had the idea to create a drag-and-drop style bot-builder that doesn’t require coding to support custom conversation flows.
“Since we already had a product, a business model, and some customers, we developed Landbot as an internal experiment. “What would happen with a full-screen conversation instead of the regular live-chat?,” we thought. What we got? A five times higher conversion rate on our homepage! Ever since, our whole strategy changed and Landbot, born from an experiment, became our core product,” explains CEO and co-founder Jiaqi Pan.
At the same time, the current crop of ‘cutting-edge’ AI chatbots are more often defined by their limitations than by having impressively expansive conversational capacities. Witness, for example, Google’s Duplex voice AI, heavily trained to perform very specific and pretty formulaic tasks — such as booking a hair appointment or a restaurant. Very few companies are in a position to burn so much engineering resource to try to make AI useful.
So there’s something rather elegant about eschewing the complexity and chaos of an AI engine (over)powering customer engagement tools — and just giving businesses user-friendly building blocks to create their own custom chat flows and channel site visitors through a few key flows.
After all, a small business knows its customers best. So a tool that helps SMEs create an engaging interface themselves, without having to plough resources they likely don’t have into training high maintenance chat AIs which are probably overkill for their needs anyway, seems a good and sensible thing.
Hence Pan talks about “democratizing the power of chatbots”. “Most landbot customers are marketing managers from small and medium companies that want to discover new ways of optimizing their conversion rates,” he tells us, saying that most are using the tool to convert more leads in their home/landing page; add dynamic surveys/forms to their websites; or explain their services — “in a more engaging way while scoring leads and being able to take over conversations when necessary”. (Buddy Nutrition is a Landbot customer, for example).
“We started our chatbot journey using Artificial Intelligence technology but found out that there was a huge gap between user expectations and reality. No matter how well trained our chatbots were, users were constantly dropped off the desired flow, which ended up in 20 different ways of saying “TALK WITH A HUMAN”,” he adds. “But we were in love with the conversational approach and, inspired by some great automation flow builders out there, we decided to give Conversational User Interfaces a try. Some would call them ‘dumb chatbots’.
“The results were amazing: The implementation process was way shorter, the technical background was removed from the equation and, finally, costs dropped too! Now, even companies with 100% focus on AI-based chatbots use Landbot as a truly cost-effective prototyping tool. We ended up creating the easiest and fastest chatbot builder out there. No technical knowledge, just a drag and drop interface and unlimited possibilities.”
Despite the startup-y hyperbole, the team does seem to have hit a sweet spot for their product. In less than a year since launching — via Product Hunt — Landbot has signed up more than 900 customers from 50+ countries, and is seeing a 30-40% MRR Growth MoM, according to Pan. Although they are offering a (branded) freemium version to help stoke the product’s growth, as well as paid tiers.
The $2.2M seed round is led by Nauta Capital, with Bankinter and Encomenda Smart Capital also participating. The plan for the funding is to grow headcount and pay for relocating Landbot’s head office from Valencia to Barcelona — to help with their international talent hunt as they look to triple the size of the team.
They’ll also be using the funding on their own brand marketing, rather than relying on viral growth — acknowledging that marketing spend is going to be important to stand out in such a crowded space, with thousands of competing solutions also vying for SMEs’ cash.
And, indeed, other conversational UIs out in the wild delivering a similarly chatty experience on the customer end, though Landbot’s claim is it’s differentiating in the market behind the scenes, with easy to use, ‘no coding necessary’ customization tools.
On the competition from, Pan names the likes of Chatfuel and Manychat as “powerful but channel-dependent” rival chatbot builders, while at the more powerful end he points to DialogFlow or IBM Watson but notes they do require technical knowledge, so the market positioning is different.
“Landbot tries to bring chatbots to the average Joe,” he adds. “While still keeping features for developers that demand complex functionalities in their chatbots (they can achieve by configuring webhooks, callbacks, CSS and JS customization).”
He also identifies players in the automated lead generation space — such as Intercom (Operator) and Drift (Drift bot) — saying they are aiming to transform sales and marketing processes “into something more conversational”. “The flow customization possibilities are fewer but the whole product is robust as they cover each stage of the conversion funnel, all the way to customer service,” he adds.
In terms of capabilities, Landbot also rubs up against survey/form offerings like SurveyMonkey and Google Form — or indeed Barcelona-based Typeform, which has raised around $50M since 2012 and bills itself as a platform for “conversational data collection”.
Pan rather delightfully characterizes Typeform as “bringing that conversational essence to the almighty sequences of fields”. Though he argues it’s also more limited “in terms of integrations and real-time human take-over capabilities”, i.e. as a consequence of wrangling those “almighty sequences”. So basically his argument is that Landbot isn’t saddled with Typeform’s form(ulaic) straightjacket. (Though Typeform would probably retort that its conversational platform is flexible.)
Still, where customer engagement is concerned, there’s never going to be one way. Sometimes the straight form will do it, but for another brand or use case something more colloquial might be called for.
Commenting on the seed round in a statement, Jordi Vinas, general partner at Nauta Capital, adds: “Landbot has experienced strong commercial traction and virality over the past months and the team has been able to attract customers from a variety of countries and verticals. We strongly believe in Jiaqi’s ability to continue scaling the business in a capital efficient way.”
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Planck Re scores $12M Series A to simplify insurance underwriting with artificial intelligence
Planck Re, a startup that wants to simplify insurance underwriting with artificial intelligence, announced today that it has raised a $12 million Series A. The funding was led by Arbor Ventures, with participation from Viola FinTech and Eight Roads. Co-founder and CEO Elad Tsur tells TechCrunch that the capital will be used to expand Planck Re’s product line into more segments, including retail, contractors, IT and manufacturing, and grow its research and development team in Israel and North American sales team.
The Tel Aviv and New York-based startup plans to focus first on its business in the United States, where it has already launched pilot programs with several insurance carriers. Tsur says that Planck Re’s clients generally use it to help underwrite insurance for small to medium-sized businesses, including business owner policies, which cover property and liability risks, and workers’ compensation.
Founded in 2016 by Tsur, Amir Cohen and David Schapiro, Planck Re poses its technology as a more efficient and accurate alternative to the lengthy risk assessment questionnaire insurers ask clients to fill out. Its platform crawls the internet for publicly available data, including images, text, videos, social media profiles and public records, to build profiles of SMBs seeking insurance coverage. Then it analyzes that data to help carriers figure out their potential risk.
Before launching Planck Re, Tsur and Cohen founded Bluetail, a data mining startup that was acquired by Salesforce in 2012, where it served as the base technology for Salesforce Einstein. Schapiro was previously CEO of financial analytics company Earnix.
There are already a handful of startups, including SoftBank-backed Lemonade, Trōv, Cover, Hippo and Swyfft, that use algorithms to make picking and buying insurance policies easier for consumers, but AI-based underwriting is still a nascent category. One example is Flyreel, which focuses on underwriting property insurance and recently signed a deal with Microsoft to accelerate its go-to-market strategy.
Tsur says Planck Re is developing more dedicated algorithms to meet the evolving needs of insurance providers. For example, many underwriters now want to know if clients in photography use aerial imaging equipment, so Planck Re’s imaging process capabilities automatically check images for that information.
He adds that being able to automate underwriting enables carriers to find new distribution channels, including allowing customers to apply for insurance online without needing to fill out any forms. Planck Re also continues to monitor and underwrite policies, which means if a customer’s risk profile changes, insurers can react quickly.
In a statement, Arbor Ventures vice president and head of Israel Lior Simon said, “We are excited to partner with Planck Re and the driven, entrepreneurial team. Insurance companies are thirsty for actionable data, to assess risk, gain real time insights and enhance customer understanding. Planck Re aims to empower them through a streamlined digital approach, which we believe will truly alter the insurance industry.”
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Apple introduces the AI phone
At Apple’s WWDC 2018 — an event some said would be boring this year with its software-only focus and lack of new MacBooks and iPads — the company announced what may be its most important operating system update to date with the introduction of iOS 12. Through a series of Siri enhancements and features, Apple is turning its iPhone into a highly personalized device, powered by its Siri AI.
This “new AI iPhone” — which, to be clear, is your same ol’ iPhone running a new mobile OS — will understand where you are, what you’re doing and what you need to know right then and there.
The question now is will users embrace the usefulness of Siri’s forthcoming smarts, or will they find its sudden insights creepy and invasive?
Siri Suggestions
After the installation of iOS 12, Siri’s Suggestions will be everywhere.
In the same place on the iPhone Search screen where you today see those Siri suggested apps to launch, you’ll begin to see other things Siri thinks you may need to know, too.
For example, Siri may suggest that you:
- Call your grandma for her birthday.
- Tell someone you’re running late to the meeting via a text.
- Start your workout playlist because you’re at the gym.
- Turn your phone to Do Not Disturb at the movies.
And so on.
These will be useful in some cases, and perhaps annoying in others. (It would be great if you could swipe on the suggestions to further train the system to not show certain ones again. After all, not all your contacts deserve a birthday phone call.)
Siri Suggestions will also appear on the Lock Screen when it thinks it can help you perform an action of some kind. For example, placing your morning coffee order — something you regularly do around a particular time of day — or launching your preferred workout app, because you’ve arrived at the gym.
These suggestions even show up on Apple Watch’s Siri watch face screen.
Apple says the relevance of its suggestions will improve over time, based on how you engage.
If you don’t take an action by tapping on these items, they’ll move down on the watch face’s list of suggestions, for instance.
AI-powered workflows
These improvements to Siri would have been enough for iOS 12, but Apple went even further.
The company also showed off a new app called Siri Shortcuts.
The app is based on technology Apple acquired from Workflow, a clever — if somewhat advanced — task automation app that allows iOS users to combine actions into routines that can be launched with just a tap. Now, thanks to the Siri Shortcuts app, those routines can be launched by voice.
Onstage at the developer event, the app was demoed by Kim Beverett from the Siri Shortcuts team, who showed off a “heading home” shortcut she had built.
When she tells Siri she’s “heading home,” her iPhone simultaneously launched directions for her commute in Apple Maps, set her home thermostat to 70 degrees, turned on her fan, messaged an ETA to her roommate and launched her favorite NPR station.
That’s arguably very cool — and it got a big cheer from the technically minded developer crowd — but it’s most certainly a power user feature. Launching an app to build custom workflows is not something everyday iPhone users will do right off the bat — or in some cases, ever.
Developers to push users to Siri
But even if users hide away this new app in their Apple “junk” folder, or toggle off all the Siri Suggestions in Settings, they won’t be able to entirely escape Siri’s presence in iOS 12 and going forward.
That’s because Apple also launched new developer tools that will allow app creators to build directly into their own apps integrations with Siri.
Developers will update their apps’ code so that every time a user takes a particular action — for example, placing their coffee order, streaming a favorite podcast, starting their evening jog with a running app or anything else — the app will let Siri know. Over time, Siri will learn users’ routines — like, on many weekday mornings, around 8 to 8:30 AM, the user places a particular coffee order through a coffee shop app’s order ahead system.
These will inform those Siri Suggestions that appear all over your iPhone, but developers will also be able to just directly prod the user to add this routine to Siri right in their own apps.
In your favorite apps, you’ll start seeing an “Add to Siri” link or button in various places — like when you perform a particular action — such as looking for your keys in Tile’s app, viewing travel plans in Kayak, ordering groceries with Instacart and so on.
Many people will probably tap this button out of curiosity — after all, most don’t watch and rewatch the WWDC keynote like the tech crowd does.
The “Add to Siri” screen will then pop up, offering a suggestion of voice prompt that can be used as your personalized phase for talking to Siri about this task.
In the coffee ordering example, you might be prompted to try the phrase “coffee time.” In the Kayak example, it could be “travel plans.”
You record this phrase with the big, red record button at the bottom of the screen. When finished, you have a custom Siri shortcut.
You don’t have to use the suggested phrase the developer has written. The screen explains you can make up your own phrase instead.
In addition to being able to “use” apps via Siri voice commands, Siri can also talk back after the initial request.
It can confirm your request has been acted upon — for example, Siri may respond, “OK. Ordering. Your coffee will be ready in 5 minutes,” after you said “Coffee time” or whatever your trigger phrase was.
Or it can tell you if something didn’t work — maybe the restaurant is out of a food item on the order you placed — and help you figure out what to do next (like continue your order in the iOS app).
It can even introduce some personality as it responds. In the demo, Tile’s app jokes back that it hopes your missing keys aren’t “under a couch cushion.”
There are a number of things you could do beyond these limited examples — the App Store has more than 2 million apps whose developers can hook into Siri.
And you don’t have to ask Siri only on your phone — you can talk to Siri on your Apple Watch and HomePod, too.
Yes, this will all rely on developer adoption, but it seems Apple has figured out how to give developers a nudge.
Siri Suggestions are the new Notifications
You see, as Siri’s smart suggestions spin up, traditional notifications will wind down.
In iOS 12, Siri will take note of your behavior around notifications, and then push you to turn off those with which you don’t engage, or move them into a new silent mode Apple calls “Delivered Quietly.” This middle ground for notifications will allow apps to send their updates to the Notification Center, but not the Lock Screen. They also can’t buzz your phone or wrist.
At the same time, iOS 12’s new set of digital well-being features will hide notifications from users at particular times — like when you’ve enabled Do Not Disturb at Bedtime, for example. This mode will not allow notifications to display when you check your phone at night or first thing upon waking.
Combined, these changes will encourage more developers to adopt the Siri integrations, because they’ll be losing a touchpoint with their users as their ability to grab attention through notifications fades.
Machine learning in photos
AI will further infiltrate other parts of the iPhone, too, in iOS 12.
A new “For You” tab in the Photos app will prompt users to share photos taken with other people, thanks to facial recognition and machine learning. And those people, upon receiving your photos, will then be prompted to share their own back with you.
The tab will also pull out your best photos and feature them, and prompt you to try different lighting and photo effects. A smart search feature will make suggestions and allow you to pull up photos from specific places or events.
Smart or creepy?
Overall, iOS 12’s AI-powered features will make Apple’s devices more personalized to you, but they could also rub some people the wrong way.
Maybe people won’t want their habits noticed by their iPhone, and will find Siri prompts annoying — or, at worst, creepy, because they don’t understand how Siri knows these things about them.
Apple is banking hard on the fact that it’s earned users’ trust through its stance on data privacy over the years.
And while not everyone knows that Siri is does a lot of its processing on your device, not in the cloud, many do seem to understand that Apple doesn’t sell user data to advertisers to make money.
That could help sell this new “AI phone” concept to consumers, and pave the way for more advancements later on.
But on the flip side, if Siri Suggestions become overbearing or get things wrong too often, it could lead users to just switch them off entirely through iOS Settings. And with that, Apple’s big chance to dominate in the AI-powered device market, too.
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Microsoft and DJI team up to bring smarter drones to the enterprise
At the Microsoft Build developer conference today, Microsoft and Chinese drone manufacturer DJI announced a new partnership that aims to bring more of Microsoft’s machine learning smarts to commercial drones. Given Microsoft’s current focus on bringing intelligence to the edge, this is almost a logical partnership, given that drones are essentially semi-autonomous edge computing devices.
DJI also today announced that Azure is now its preferred cloud computing partner and that it will use the platform to analyze video data, for example. The two companies also plan to offer new commercial drone solutions using Azure IoT Edge and related AI technologies for verticals like agriculture, construction and public safety. Indeed, the companies are already working together on Microsoft’s FarmBeats solution, an AI and IoT platform for farmers.
As part of this partnership, DJI is launching a software development kit (SDK) for Windows that will allow Windows developers to build native apps to control DJI drones. Using the SDK, developers can also integrate third-party tools for managing payloads or accessing sensors and robotics components on their drones. DJI already offers a Windows-based ground station.
“DJI is excited to form this unique partnership with Microsoft to bring the power of DJI aerial platforms to the Microsoft developer ecosystem,” said Roger Luo, DJI president, in today’s announcement. “Using our new SDK, Windows developers will soon be able to employ drones, AI and machine learning technologies to create intelligent flying robots that will save businesses time and money and help make drone technology a mainstay in the workplace.”
Interestingly, Microsoft also stresses that this partnership gives DJI access to its Azure IP Advantage program. “For Microsoft, the partnership is an example of the important role IP plays in ensuring a healthy and vibrant technology ecosystem and builds upon existing partnerships in emerging sectors such as connected cars and personal wearables,” the company notes in today’s announcement.
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Microsoft brings more AI smarts to the edge
At its Build developer conference this week, Microsoft is putting a lot of emphasis on artificial intelligence and edge computing. To a large degree, that means bringing many of the existing Azure services to machines that sit at the edge, no matter whether that’s a large industrial machine in a warehouse or a remote oil-drilling platform. The service that brings all of this together is Azure IoT Edge, which is getting quite a few updates today. IoT Edge is a collection of tools that brings AI, Azure services and custom apps to IoT devices.
As Microsoft announced today, Azure IoT Edge, which sits on top of Microsoft’s IoT Hub service, is now getting support for Microsoft’s Cognitive Services APIs, for example, as well as support for Event Grid and Kubernetes containers. In addition, Microsoft is also open sourcing the Azure IoT Edge runtime, which will allow developers to customize their edge deployments as needed.
The highlight here is support for Cognitive Services for edge deployments. Right now, this is a bit of a limited service as it actually only supports the Custom Vision service, but over time, the company plans to bring other Cognitive Services to the edge as well. The appeal of this service is pretty obvious, too, as it will allow industrial equipment or even drones to use these machine learning models without internet connectivity so they can take action even when they are offline.
As far as AI goes, Microsoft also today announced that it will bring its new Brainwave deep neural network acceleration platform for real-time AI to the edge.
The company has also teamed up with Qualcomm to launch an AI developer kit for on-device inferencing on the edge. The focus of the first version of this kit will be on camera-based solutions, which doesn’t come as a major surprise given that Qualcomm recently launched its own vision intelligence platform.
IoT Edge is also getting a number of other updates that don’t directly involve machine learning. Kubernetes support is an obvious one and a smart addition, given that it will allow developers to build Kubernetes clusters that can span both the edge and a more centralized cloud.
The appeal of running Event Grid, Microsoft’s event routing service, at the edge is also pretty obvious, given that it’ll allow developers to connect services with far lower latency than if all the data had to run through a remote data center.
Other IoT Edge updates include the planned launch of a marketplace that will allow Microsoft partners and developers to share and monetize their edge modules, as well as a new certification program for hardware manufacturers to ensure that their devices are compatible with Microsoft’s platform. IoT Edge, as well as Windows 10 IoT and Azure Machine Learning, will also soon support hardware-accelerated model evaluation with DirextX 12 GPU, which is available in virtually every modern Windows PC.
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