Ideaforge

Part of learning to be an engineer is understanding the tools you’ll have to work with — voltmeters, spectrum analyzers, things like that. But why use two, or eight for that matter, where one will do? The Moku:Go combines several commonly used tools into one compact package, saving room on your workbench or classroom while also providing a modern, software-configurable interface. Creator Liquid Instruments has just raised $13.7 million to bring this gadget to students and engineers everywhere.

The idea behind Moku:Go is largely the same as the company’s previous product, the Moku:Lab. Using a standard input port, a set of FPGA-based tools perform the same kind of breakdowns and analyses of electrical signals as you would get in a larger or analog device. But being digital saves a lot of space that would normally go towards bulky analog components.

The Go takes this miniaturization further than the Lab, doing many of the same tasks at half the weight and with a few useful extra features. It’s intended for use in education or smaller engineering shops where space is at a premium. Combining eight tools into one is a major coup when your bench is also your desk and your file cabinet.

Those eight tools, by the way, are: waveform generator, arbitrary waveform generator, frequency response analyzer, logic analyzer/pattern generator, oscilloscope/voltmeter, PID controller, spectrum analyzer, and data logger. It’s hard to say whether that really adds up to more or less than eight, but it’s definitely a lot to have in a package the size of a hardback book.

You access and configure them using a software interface rather than a bunch of knobs and dials — though let’s be clear, there are good arguments for both. When you’re teaching a bunch of young digital natives, however, a clean point-and-click interface is probably a plus. The UI is actually very attractive; you can see several examples by clicking the instruments on this page, but here’s an example of the waveform generator:

Love those pastels.

The Moku:Go currently works with Macs and Windows but doesn’t have a mobile app yet. It integrates with Python, MATLAB, and LabVIEW. Data goes over Wi-Fi.

Compared with the Moku:Lab, it has a few perks. A USB-C port instead of a mini, a magnetic power port, a 16-channel digital I/O, optional power supply of up to four channels, and of course it’s half the size and weight. It compromises on a few things — no SD card slot and less bandwidth for its outputs, but if you need the range and precision of the more expensive tool, you probably need a lot of other stuff too.

Since the smaller option also costs $500 to start (“a price comparable to a textbook”… yikes) compared with the big one’s $3,500, there’s major savings involved. And it’s definitely cheaper than buying all those instruments individually.

The Moku:Go is “targeted squarely at university education,” said Liquid Instruments VP of marketing Doug Phillips. “Professors are able to employ the device in the classroom and individuals, such as students and electronic engineering hobbyists, can experiment with it on their own time. Since its launch in March, the most common customer profile has been students purchasing the device at the direction of their university.”

About a hundred professors have signed on to use the device as part of their Fall classes, and the company is working with other partners in universities around the world. “There is a real demand for portable, flexible systems that can handle the breadth of four years of curriculum,” Phillips said.

Production starts in June (samples are out to testers), the rigors and costs of which likely prompted the recent round of funding. The $13.7M comes from existing investors Anzu Partners and ANU Connect Ventures, and new investors F1 Solutions and Moelis Australia’s Growth Capital Fund. It’s a convertible note “in advance of an anticipated Series B round in 2022,” Phillips said. It’s a larger amount than they intended to raise at first, and the note nature of the round is also not standard, but given the difficulties faced by hardware companies over the last year, some irregularities are probably to be expected.

No doubt the expected B round will depend considerably on the success of the Moku:Go’s launch and adoption. But this promising product looks as if it might be a commonplace item in thousands of classrooms a couple years from now.

Source: Tech Crunch

At its I/O developer conference, Google today announced a slew of updates to its Firebase developer platform, which, as the company also announced, now powers over 3 million apps.

There’s a number of major updates here, most of which center around improving existing tools like Firebase Remote Config and Firebase’s monitoring capabilities, but there are also a number of completely new features here as well, including the ability to create Android App Bundles and a new security tool called App Check.

“Helping developers be successful is what makes Firebase successful,” Firebase product manager Kristen Richards told me ahead of today’s announcements. “So we put helpfulness and helping developers at the center of everything that we do.” She noted that during the pandemic, Google saw a lot of people who started to focus on app development — both as learners and as professional developers. But the team also saw a lot of enterprises move to its platform as those companies looked to quickly bring new apps online.

Maybe the marquee Firebase announcement at I/O is the updated Remote Config. That’s always been a very powerful feature that allows developers to make changes to live production apps on the go without having to release a new version of their app. Developers can use this for anything from A/B testing to providing tailored in-app experience to specific user groups.

With this update, Google is introducing updates to the Remote Config console, to make it easier for developers to see how they are using this tool, as well as an updated publish flow and redesigned test results pages for A/B tests.

What’s most important, though, is that Google is taking Remote Config a step further now by launching a new Personalization feature that helps developers automatically optimize the user experience for individual users. “It’s a new feature of [Remote Config] that uses Google’s machine learning to create unique individual app experiences,” Richards explained. “It’s super simple to set up and it automatically creates these personalized experiences that’s tailored to each individual user. Maybe you have something that you would like, which would be something different for me. In that way, we’re able to get a tailored experience, which is really what customers expect nowadays. I think we’re all expecting things to be more personalized than they have in the past.”

Google is also improving a number of Firebase’s analytics and monitoring capabilities, including its Crashlytics service for figuring out app crashes. For game developers, that means improved support for games written with the help of the Unity platform, for example, but for all developers, the fact that Firebase’s Performance Monitoring service now processes data in real time is a major update to having performance data (especially on launch day) arrive with a delay of almost half a day.

Firebase is also now finally adding support for Android App Bundles, Google’s relatively new format for packaging up all of an app’s code and resources, with Google Play optimizing the actual APK with the right resources for the kind of device the app gets installed on. This typically leads to smaller downloads and faster installs.

On the security side, the Firebase team is launching App Check, now available in beta. App Check helps developers guard their apps against outside threats and is meant to automatically block any traffic to online resources like Cloud Storage, Realtime Database and Cloud Functions for Firebase (with others coming soon) that doesn’t provide valid credentials.

The other update worth mentioning here is to Firebase Extensions, which launched a while ago, but which is getting support for a few more extensions today. These are new extensions from Algolia, Mailchimp and MessageBird, that helps bring new features like Algolia’s search capabilities or MessageBird’s communications features directly to the platform. Google itself is also launching a new extension that helps developers detect comments that could be considered “rude, disrespectful, or unreasonable in a way that will make people leave a conversation.”

Source: Tech Crunch

At Google I/O today Google Cloud announced Vertex AI, a new managed machine learning platform that is meant to make it easier for developers to deploy and maintain their AI models. It’s a bit of an odd announcement at I/O, which tends to focus on mobile and web developers and doesn’t traditionally feature a lot of Google Cloud news, but the fact that Google decided to announce Vertex today goes to show how important it thinks this new service is for a wide range of developers.

The launch of Vertex is the result of quite a bit of introspection by the Google Cloud team. “Machine learning in the enterprise is in crisis, in my view,” Craig Wiley, the director of product management for Google Cloud’s AI Platform, told me. “As someone who has worked in that space for a number of years, if you look at the Harvard Business Review or analyst reviews, or what have you — every single one of them comes out saying that the vast majority of companies are either investing or are interested in investing in machine learning and are not getting value from it. That has to change. It has to change.”

Wiley, who was also the general manager of AWS’s SageMaker AI service from 2016 to 2018 before coming to Google in 2019, noted that Google and others who were able to make machine learning work for themselves saw how it can have a transformational impact, but he also noted that the way the big clouds started offering these services was by launching dozens of services, “many of which were dead ends,” according to him (including some of Google’s own). “Ultimately, our goal with Vertex is to reduce the time to ROI for these enterprises, to make sure that they can not just build a model but get real value from the models they’re building.”

Vertex then is meant to be a very flexible platform that allows developers and data scientist across skill levels to quickly train models. Google says it takes about 80% fewer lines of code to train a model versus some of its competitors, for example, and then help them manage the entire lifecycle of these models.

The service is also integrated with Vizier, Google’s AI optimizer that can automatically tune hyperparameters in machine learning models. This greatly reduces the time it takes to tune a model and allows engineers to run more experiments and do so faster.

Vertex also offers a “Feature Store” that helps its users serve, share and reuse the machine learning features and Vertex Experiments to help them accelerate the deployment of their models into producing with faster model selection.

Deployment is backed by a continuous monitoring service and Vertex Pipelines, a rebrand of Google Cloud’s AI Platform Pipelines that helps teams manage the workflows involved in preparing and analyzing data for the models, train them, evaluate them and deploy them to production.

To give a wide variety of developers the right entry points, the service provides three interfaces: a drag-and-drop tool, notebooks for advanced users and — and this may be a bit of a surprise — BigQuery ML, Google’s tool for using standard SQL queries to create and execute machine learning models in its BigQuery data warehouse.

“We had two guiding lights while building Vertex AI: get data scientists and engineers out of the orchestration weeds, and create an industry-wide shift that would make everyone get serious about moving AI out of pilot purgatory and into full-scale production,” said Andrew Moore, vice president and general manager of Cloud AI and Industry Solutions at Google Cloud. “We are very proud of what we came up with in this platform, as it enables serious deployments for a new generation of AI that will empower data scientists and engineers to do fulfilling and creative work.”

Source: Tech Crunch

Things have been a bit quiet on the foldables front of late, but plenty of parties are still bullish about the form factor’s future. Ahead of today’s big I/O kickoff, Samsung (undoubtedly the most bullish of the bunch) posted a bunch of metrics this morning, noting,

The global outlook is just as impressive. This year alone, the foldables market is expected to triple over last year — a year in which Samsung accounted for three out of every four foldable smartphones shipped worldwide.

Part of anticipating growth in the category is ensuring that the software is ready it. Samsung has been tweaking things for a while now on its end, and at I/O in 2018, Google announced that it would be adding support for foldable screens. Recent rumors have suggested that the company is working on its own foldable Pixel, but even beyond that, it’s probably in the company’s best interest to ensure that Android plays nicely with the form factor.

“We studied how people interact with large screens,” the company said in today’s developer keynote. This includes a variety of different aspects, including where users place their hands while using the device — which can be a bit all over the place when dealing with different applications in different orientations and form factors. Essentially, you don’t want to, say, put buttons where people generally place your hands.

The list of upgrades includes the ability to resize content automatically, without overly stretching it out to fit multiple panels. All of this is no doubt going to be a learning curve as foldables end up in the hands of more users. But at very least, it signals Google’s continued view of foldables as a growing category. It’s also one of multiple updates today that involve the company working more closely with Samsung.

The two tech giants also announced a joint Wear OS/Tizen play early today.

Source: Tech Crunch

Flutter, Google’s cross-platform UI toolkit for building mobile and desktop apps, is getting a small but important update at the company’s I/O conference today. Google also announced that Flutter now powers 200,000 apps in the Play Store alone, including popular apps from companies like WeChat, ByteDance, BMW, Grab and DiDi. Indeed, Google notes that 1 in 8 new apps in the Play Store are now Flutter apps.

The launch of Flutter 2.2 follows Google’s rollout of Flutter 2, which first added support for desktop and web apps in March, so it’s no surprise that this is a relatively minor release. In many ways, the update builds on top of the features the company introduced in version 2 and reliability and performance improvements.

Version 2.2 makes null safety the default for new projects, for example, to add protections against null reference exceptions. As for performance, web apps can now use background caching using service workers, for example, while Android apps can use deferred components and iOS apps get support for precompiled shaders to make first runs smoother.

Google also worked on streamlining the overall process of bringing Flutter apps to desktop platforms (Windows, macOS and Linux).

But as Google notes, a lot of the work right now is happening in the ecosystem. Google itself is introducing a new payment plugin for Flutter built in partnership with the Google Pay team and Google’s ads SDK for Flutter is getting support for adaptive banner formats. Meanwhile, Samsung is now porting Flutter to Tizen and Sony is leading an effort to bring it to embedded Linux. Adobe recently announced its XD to Flutter plugin for its design tool and Microsoft today launched the alpha of Flutter support for Universal Windows Platform (UWP) apps for Windows 10 in alpha.

Source: Tech Crunch

At its I/O developer conference, Google today announced the first beta of the next version of its Android Studio IDE, Arctic Fox. For the most part, the idea here is to bring more of the tooling around building Android apps directly into the IDE.

While there is a lot that’s new in Arctic Fox, maybe the marquee feature of this update is the integration of Jetpack Compose, Google’s toolkit for building modern user interfaces for Android. In Android Studio, developers can now use Compose Preview to create previews of different configurations (think themes and devices) or deploy a preview directly to a device, all while the layout inspector makes it easier for developers to understand how (and why) a layout is rendered the way it is. With Live Updates enabled any change is then also directly streamed to the device.

The team also integrated the Android Accessibility Test Framework directly into Android Studio to help developers find accessibility issues like missing content descriptions or a low contrast in their designs.

Just like with some of the updates to Android itself, the team is also looking at making it easier to develop for a wider range of form factors. To build Wear OS apps, developers previously had to physically connect the watch to their development machine or go through a lot of steps to pair the watch. Now, users can simply pair a watch and phone emulator (or physical phone) with the new Wear OS Pairing feature. All this takes now is a few clicks.

Also new on the Wear OS side is a new heart rate sensor for the Wear OS Emulators in Android Studio, while the Android Automotive emulator gains the ability to replay car sensor data to help those developers with their development and testing workflow.

Android Studio users who work on a Mac will be happy to hear that Google is also launching a first preview of Android Studio for the Apple Silicon (arm64) architecture.

Source: Tech Crunch

Every branch of science is increasingly reliant on big data sets and analysis, which means a growing confusion of formats and platforms — more than inconvenient, this can hinder the process of peer review and replication of research. Code Ocean hopes to make it easier for scientists to collaborate by making a flexible, shareable format and platform for any and all datasets and methods, and it has raised a total of $21M to build it out.

Certainly there’s an air of “Too many options? Try this one!” to this (and here’s the requisite relevant XKCD). But Code Ocean isn’t creating a competitor to successful tools like Jupyter or Gitlab or Docker — it’s more of a small-scale container platform that lets you wrap up all the necessary components of your data and analysis in an easily shared format, whatever platform they live on natively.

The trouble appears when you need to share what you’re doing with another researcher, whether they’re on the bench next to you or at a university across the country. It’s important for replication purposes that data analysis — just like any other scientific technique — be done exactly the same way. But there’s no guarantee that your colleague will use the same structures, formats, notation, labels, and so on.

That doesn’t mean it’s impossible to share your work, but it does add a lot of extra steps as would-be replicators or iterators check and double check that all the methods are the same, that the same versions of the same tools are being used in the same order, with the same settings, and so on. A tiny inconsistency can have major repercussions down the road.

Turns out this problem is similar in a way to how many cloud services are spun up. Software deployments can be as finicky as scientific experiments, and one solution to this is containers, which like tiny virtual machines include everything needed to accomplish a computing task, in a portable format compatible with many different setups. The idea is a natural one to transfer to the research world, where you can tie up the data, the software used, and the specific techniques and processes used to reach a given result all in one tidy package. That, at least, is the pitch Code Ocean offers for its platform and “Compute Capsules.”

Say you’re a microbiologist looking at the effectiveness of a promising compound on certain muscle cells. You’re working in R, writing in RStudio on a Ubuntu machine, and your data are such and such collected during an in vitro observation. While you would naturally declare all this when you publish, there’s no guarantee anyone has an Ubuntu laptop with a working Rstudio setup around, so even if you provide all the code it might be for nothing.

If however you put it on Code Ocean, like this, it makes all the relevant code available, and capable of being inspected and run unmodified with a click, or being fiddled with if a colleague is wondering about a certain piece. It works through a single link and web app, cross platform, and can even be embedded on a webpage like a document or video. (I’m going to try to do that below, but our backend is a little finicky. The capsule itself is here.)

More than that, though, the Compute Capsule can be repurposed by others with new data and modifications. Maybe the technique you put online is a general purpose RNA sequence analysis tool that works as long as you feed it properly formatted data, and that’s something others would have had to code from scratch in order to take advantage of some platforms.

Well, they can just clone your capsule, run it with their own data, and get their own results in addition to verifying your own. This can be done via the Code Ocean website or just by downloading a zip file of the whole thing and getting it running on their own computer, if they happen to have a compatible setup. A few more example capsules can be found here.

This sort of cross-pollination of research techniques is as old as science, but modern data-heavy experimentation often ends up siloed because it can’t easily be shared and verified even though the code is technically available. That means other researchers move on, build their own thing, and further reinforce the silo system.

Right now there are about 2,000 public compute capsules on Code Ocean, most of which are associated with a published paper. Most have also been used by others, either to replicate or try something new, and some, like ultra-specific open source code libraries, have been used by thousands.

Naturally there are security concerns when working with proprietary or medically sensitive data, and the enterprise product allows the whole system to run on a private cloud platform. That way it would be more of an internal tool, and at major research institutions that in itself could be quite useful.

Code Ocean hopes that by being as inclusive as possible in terms of codebases, platforms, compute services and so on will make for a more collaborative environment at the cutting edge.

Clearly that ambition is shared by others, as the the company has raised $21M so far, $6M of which was in previously undisclosed investments and $15M in an A round announced today. The A round was led by Battery Ventures, with Digitalis Ventures, EBSCO, and Vaal Partners participating as well as numerous others.

The money will allow the company to further develop, scale, and promote its platform. With luck they’ll soon find themselves among the rarefied air often breathed by this sort of savvy SaaS — necessary, deeply integrated, and profitable.

 

Source: Tech Crunch