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Deliverr raises $7M to help e-commerce businesses compete with Amazon Prime

When Amazon rolled out its membership-based two-day shipping service in 2005, e-commerce and customer expectations around fulfillment speed changed forever. Today, more than 100 million people use Amazon Prime. That means, 100 million people are fully accustomed to two-day shipping and if they can’t have it, they shop elsewhere. As The Wall Street Journal’s Christopher Mims recently put it: “Alongside life, liberty and the pursuit of happiness, you can now add another inalienable right: two-day shipping on practically everything.” Only recently have Amazon’s competitors begun to offer similar fast delivery options. About two years ago, Walmart launched its own free two-day delivery service for its owned-inventory; eBay followed suit, establishing a three-day or less delivery guaranteed option for shoppers in March 2017. To power these Prime-like delivery options, Walmart, eBay and the Canadian e-commerce business Shopify are relying on a little upstart. One-year-old Deliverr helps businesses offer rapid delivery experiences to their customers. Today, the company is announcing a $7.1 million Series A led by Joe Lonsdale’s 8VC, with participation from Zola founder Shan-Lyn Ma, Flexport chief executive officer Ryan Peterson and others. The San Francisco-based startup uses machine learning and predictive intelligence to determine which of its warehouses to store its client’s goods. Walmart launches free, 2-day shipping without a membership on purchases of $35 or more Currently, Deliverr operates out of more than 10 warehouses in Texas, Missouri, Pennsylvania, Ohio and New Jersey, among other states, though co-founder Michael Krakaris says that number is growing every week. Its customers typically store inventory in three to five different locations based on Deliverr’s predictive algorithms. Unlike Amazon, which owns more than 75 fulfillment centers, Deliverr doesn’t own its warehouses. Krakaris describes the company’s strategy as a sort of Uber for fulfillment. “Uber didn’t change the physical infrastructure of cars. They didn’t build their own taxis. What they did was create software that could connect excess capacity drivers,” Krakaris told TechCrunch. “Most warehouses aren’t going to be full. We are going in and filling that extra space they wouldn’t otherwise fill.” One of the startup’s tricks is to use brand-neutral packaging so any and all marketplaces could theoretically power fulfillment through Deliverr. Amazon,…

Microsoft acquires Lobe, a drag-and-drop AI tool

Microsoft today announced that is has acquired Lobe, a startup that lets you build machine learning models with the help of a simple drag-and-drop interface. Microsoft plans to use Lobe, which only launched into beta earlier this year, to build upon its own efforts to make building AI models easier, though, for the time being, Lobe will operate as before. “As part of Microsoft, Lobe will be able to leverage world-class AI research, global infrastructure, and decades of experience building developer tools,” the team writes. “We plan to continue developing Lobe as a standalone service, supporting open source standards and multiple platforms.” Lobe was co-founded by Mike Matas, who previously worked on the iPhone and iPad, as well as Facebook’s Paper and Instant Articles products. The other co-founders are Adam Menges and Markus Beissinger. In addition to Lobe, Microsoft also recently bought Bonsai.ai, a deep reinforcement learning platform, and Semantic Machines, a conversational AI platform. Last year, it acquired Disrupt Battlefield participant Maluuba. It’s no secret that machine learning talent is hard to come by, so it’s no surprise that all of the major tech firms are acquiring as much talent and technology as they can. “In many ways though, we’re only just beginning to tap into the full potential AI can provide,” Microsoft’s EVP and CTO Kevin Scott writes in today’s announcement. “This in large part is because AI development and building deep learning models are slow and complex processes even for experienced data scientists and developers. To date, many people have been at a disadvantage when it comes to accessing AI, and we’re committed to changing that.” It’s worth noting that Lobe’s approach complements Microsoft’s existing Azure ML Studio platform, which also offers a drag-and-drop interface for building machine learning models, though with a more utilitarian design than the slick interface that the Lobe team built. Both Lobe and Azure ML Studio aim to make machine learning easy to use for anybody, without having to know the ins and outs of TensorFlow, Keras or PyTorch. Those approaches always come with some limitations, but just like low-code tools, they…

How Machine Learning Will Affect What You Pay for Insurance

Data is a powerful tool. For decades, insurance companies have been using the data available to them to measure risk and determine pricing. But as technology evolves, so do the data practices insurers use to optimize pricing and risk. Machine learning is a new tool that goes beyond what an actuary can craft, and the impact it will have on what you pay for insurance is becoming noticeable. Understanding Machine Learning Machine learning enables computers to learn without human intervention. The computer acquires data and predicts outcomes without manual input. As machine learning gains more data and adapts to the past, its future predictions become more accurate. In insurance, machine learning can act like an underwriter. As a novice underwriter, you need loads of data to make a decision. As you gain data and analyze outcomes over time, you become better at assessing risk, predicting losses and calculating premiums. Machine learning in insurance is a tool that will help the underwriter make decisions with more precision. Optimization of Premium Dollars In 2017, the insurance industry accounted for 3.1 percent of the U.S. Gross Domestic Product (GDP), contributing more than $600 billion to the economy. With so much at stake, optimizing premium is pivotal. With machine learning, insurance companies can analyze immense amounts of data to create the ideal premium for each consumer. Think about your own personal auto insurance policy. There are many factors that impact your premium, including: Your car’s make, model and year The number of miles you drive Where you live Your history of accidents Your age The number of years you’ve had a driver’s license Machine learning can collect this data on millions of drivers and use it to recognize patterns and assess risks, which helps insurers craft tailored premiums for each consumer. Decreasing Premiums, Improving Combined Ratio Insurance companies are always working to improve their “combined ratios.” While this term may sound like industry jargon, it simply compares the amount of an insurance company’s payouts and expenses to the amount they brought in via premiums. For instance, if a company with a combined ratio of 99 percent, it means…

Video Of The Week: Elon Musk on Joe Rogan Experience

I have never been as obsessed with Elon Musk as many are in the tech sector. We own two Tesla cars. We pre-ordered Tesla’s solar roof tiles several years ago but have not yet received delivery of them. I appreciate his ingenuity and creativity and we like the Tesla products we own. We are not and have never been shareholders of Tesla or SpaceX. With all of that disclosure, I want to share the video of Elon’s appearance on Joe Rogan Experience as the video of this week. Much has been made of Elon’s decision to take a puff on a tobacco/weed joint on the show. I don’t make too much of that. I’ve been around people smoking pot since I was a teenager and I think it is a lot like alcohol. I believe it is fine if it is done responsibly and appropriately and I am pleased that it is becoming legal in many states around the country. What is more interesting to me in this video is how introspective and thoughtful Elon is in this interview, particularly about the role of AI in our society and the likely impact of AI on our world in the coming years. It is a lengthy conversation, but worth watching if you have some time this weekend.

Incentivai launches to simulate how hackers break blockchains

Cryptocurrency projects can crash and burn if developers don’t predict how humans will abuse their blockchains. Once a decentralized digital economy is released into the wild and the coins start to fly, it’s tough to implement fixes to the smart contracts that govern them. That’s why Incentivai is coming out of stealth today with its artificial intelligence simulations that test not just for security holes, but for how greedy or illogical humans can crater a blockchain community. Crypto developers can use Incentivai’s service to fix their systems before they go live. “There are many ways to check the code of a smart contract, but there’s no way to make sure the economy you’ve created works as expected,” says Incentivai’s solo founder Piotr Grudzień. “I came up with the idea to build a simulation with machine learning agents that behave like humans so you can look into the future and see what your system is likely to behave like.” Incentivai will graduate from Y Combinator next week and already has a few customers. They can either pay Incentivai to audit their project and produce a report, or they can host the AI simulation tool like a software-as-a-service. The first deployments of blockchains it’s checked will go out in a few months, and the startup has released some case studies to prove its worth. “People do theoretical work or logic to prove that under certain conditions, this is the optimal strategy for the user. But users are not rational. There’s lots of unpredictable behavior that’s difficult to model,” Grudzień explains. Incentivai explores those illogical trading strategies so developers don’t have to tear out their hair trying to imagine them. Protecting crypto from the human x-factor There’s no rewind button in the blockchain world. The immutable and irreversible qualities of this decentralized technology prevent inventors from meddling with it once in use, for better or worse. If developers don’t foresee how users could make false claims and bribe others to approve them, or take other actions to screw over the system, they might not be able to thwart the attack. But given the right open-ended incentives…

Openbook is the latest dream of a digital life beyond Facebook

As tech’s social giants wrestle with antisocial demons that appear to be both an emergent property of their platform power, and a consequence of specific leadership and values failures (evident as they publicly fail to enforce even the standards they claim to have), there are still people dreaming of a better way. Of social networking beyond outrage-fuelled adtech giants like Facebook and Twitter. There have been many such attempts to build a ‘better’ social network of course. Most have ended in the deadpool. A few are still around with varying degrees of success/usage (Snapchat, Ello and Mastodon are three that spring to mine). None has usurped Zuckerberg’s throne of course. This is principally because Facebook acquired Instagram and WhatsApp. It has also bought and closed down smaller potential future rivals (tbh). So by hogging network power, and the resources that flow from that, Facebook the company continues to dominate the social space. But that doesn’t stop people imagining something better — a platform that could win friends and influence the mainstream by being better ethically and in terms of functionality. And so meet the latest dreamer with a double-sided social mission: Openbook. The idea (currently it’s just that; a small self-funded team; a manifesto; a prototype; a nearly spent Kickstarter campaign; and, well, a lot of hopeful ambition) is to build an open source platform that rethinks social networking to make it friendly and customizable, rather than sticky and creepy. Their vision to protect privacy as a for-profit platform involves a business model that’s based on honest fees — and an on-platform digital currency — rather than ever watchful ads and trackers. There’s nothing exactly new in any of their core ideas. But in the face of massive and flagrant data misuse by platform giants these are ideas that seem to sound increasingly like sense. So the element of timing is perhaps the most notable thing here — with Facebook facing greater scrutiny than ever before, and even taking some hits to user growth and to its perceived valuation as a result of ongoing failures of leadership and a management philosophy that’s been attacked…

Snap40 raises $8M for its AI-powered patient monitoring solution

Snap40, a Scottish startup that has developed an AI-enabled wearable device to help health professionals monitor patients either on the hospital ward or at home, has raised $8 million in seed funding. The round is led by ADV, with participation from MMC Ventures, and brings total funding to $10 million. Originally launched as a clinical pilot in August 2016, the Snap40 hardware and software platform initially set out to enable hospitals to monitor patients whose health is at risk of rapidly deteriorating while on ward, but has since expanded to increasingly focus on what happens after a patient is discharged, in addition to monitoring clinical trials. Claiming to have the same accuracy as ICU monitoring, the wearable device captures oxygen saturation, respiration rate, pulse rate, temperature, movement and posture. In addition to onboard sensors, the Snap40 platform offers integrations with other devices e.g. a BP cuff, weighing scales, a glucose monitor. It then feeds this real-time data to the cloud where it is analysed by the company’s proprietary algorithms to identify if a patient’s health is at risk and alert a physician proactively. In a call with Snap40 co-founder and CEO Christopher McCann he explained that where a patient has left hospital after an acute illness or has a long-term health condition, this can ultimately help to reduce hospital re-admission. In more extreme cases, it can also directly save lives. Let’s take cardiac arrest, for example. McCann cites a report published by the U.K. National Confidential Enquiry into Patient Outcome and Death (NCEPOD) in 2012 that found physiological instability (e.g. elevation of respiration rate or a decrease in blood pressure) was present six hours prior to arrest in 62 percent of patients and twelve hours prior to arrest in 47 percent. Conversely, that instability had not been picked up on in 36 percent of cases where earlier recognition could have improved outcomes. As another example, Sepsis, which McCann says is the number one cause of hospital readmission in the U.S., can be detected via an elevation in temperature, respiration rate or pulse rate and a drop in blood pressure or…

UPS Is Using Predictive Analytics. Here’s What Your Business Should Do Says Ramon Ray

The Wall Street Journal reports that UPS is using predictive analytics to “gather and consolidate data from various applications within the company’s logistics network to better predict package flow, volume and delivery status, says Juan Perez, chief information and engineering officer.” According to Webopedia, predictive analytics, is  “the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. Predictive analytics does not tell you what will happen in the future.” Every business can leverage the power of predictive analytics, artificial intelligence and machine learning to help them make smarter decisions and faster decisions. While you can’t spend billions of dollars to do this for your company, you can rely on companies such as Intuit, NetSuite, Salesforce, SAP and Microsoft who embed these “smarts” into their software products to help make your business smarter.

Booksy, the worldwide booking system, raises $13.2 million

Booksy, a Poland-based booking application for the beauty business, has raised $13.2 million in a Series B effort to drive global growth. The company, founded in 2014 by Stefan Batory and Konrad Howard, is currently seeing 2.5 million bookings per month. The company raised from Piton Capital, OpenOcean, Kulczyk Investments, and Zach Coelius. Batory, an ultramarathoner, also co-founded iTaxi, Poland’s popular taxi hailing app. Booksy came about when he was trying to schedule physiotherapy appointments after long runs. He would come home sore and plan on calling his physiotherapist but it was always too late. “I didn’t want to bother him after I was done with my workout late night, and it was virtually impossible to contact him during day time as his hands were busy massaging people and he did not answer my calls,” he said. Booksy launched in the US in 2017 and “rapidly become the number one booking app in the world,” said Batory. “We will use the funding to drive global growth, recruit high profile talent and develop proprietary technologies that will further support beauty businesses,” he said. “That includes the implementation of one-click booking, a feature that uses machine learning and AI technologies, to determine each user’s buying pattern and offer them the best dates with their favorite stylists, thus simplifying user experience for both merchants and their customers.”

MeetFrank nets $1.1M for its passive job matching chatbot

MeetFrank, aka a ‘secret’ recruitment app that uses machine learning plus a chatbot wrapper to take the strain out of passive job hunting and talent-to-vacancy matching, has closed a €1 million (~$1.1M) seed funding round to fuel market expansion in Europe. Hummingbird VC, Karma VC, and Change Ventures are the investors. The Estonian startup was only founded last September but says it has ~125,000 active users in its first markets: Estonia, Finland, Sweden, Latvia, Lithuania, plus its most recent market addition, Germany, an expansion this seed has financed. Around 2,000 companies are using the app to try to attract talent. In Germany employers on board with MeetFrank include Daimler, Eon, Delivery Hero, SumUp, Blinkist, High Mobility and MyTaxi. “The average company profile we have at the moment is a start-up/scale-up company that develops their product in-house,” says co-founder Kaarel Holm. “At the moment we are mainly focused on technology-related companies — so positions you can find from average start-up or a scale-up,” he tells TechCrunch. “Around 50% of the position are engineering and other 50% is marketing, sales, customer support, legal, data science, product/project management etc.” He names TransferWise, Taxify, Testlio, Smartly and High-Mobility as other early customers. Here’s how MeetFrank works on the talent side: The person downloads the app and goes through a relatively quick onboarding chat with ‘Frank’ (the emoji-loving chatbot) where they are asked to specify their skills and experience — choosing from pre-set lists, rather than needing to type — plus to state their current job title and salary. So while MeetFrank’s target is passive job seekers, these people do still need to actively download the app and input some data. Hence the chatbot having a strong emoji + GIF game to convince talent that a little upfront effort will go a long way… The bot also asks what would convince them to switch jobs — offering options to choose from such as a higher salary, more flexible or remote working, relocation, a startup culture and so on. The anonymous aspect comes in because there’s no requirement for users to provide their real name or any other…

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