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Microsoft launches Project Bonsai, its new machine teaching service for building autonomous systems

At its Build developer conference, Microsoft today announced that Project Bonsai, its new machine teaching service, is now in public preview.

If that name sounds familiar, it’s probably because you remember that Microsoft acquired Bonsai, a company that focuses on machine teaching, back in 2018. Bonsai combined simulation tools with different machine learning techniques to build a general-purpose deep reinforcement learning platform, with a focus on industrial control systems.

It’s maybe no surprise then that Project Bonsai, too, has a similar focus on helping businesses teach and manage their autonomous machines. “With Project Bonsai, subject-matter experts can add state-of-the-art intelligence to their most dynamic physical systems and processes without needing a background in AI,” the company notes in its press materials.

“The public preview of Project Bonsai builds on top of the Bonsai acquisition and the autonomous systems private preview announcements made at Build and Ignite of last year,” a Microsoft spokesperson told me.

Interestingly, Microsoft notes that project Bonsai is only the first block of a larger vision to help its customers build these autonomous systems. The company also stresses the advantages of machine teaching over other machine learning approaches, especially the fact that it’s less of a black box approach than other methods, which makes it easier for developers and engineers to debug systems that don’t work as expected.

In addition to Bonsai, Microsoft also today announced Project Moab, an open-source balancing robot that is meant to help engineers and developers learn the basics of how to build a real-world control system. The idea here is to teach the robot to keep a ball balanced on top of a platform that is held by three arms.

Potential users will be able to either 3D-print the robot themselves or buy one when it goes on sale later this year. There is also a simulation, developed by MathWorks, that developers can try out immediately.

“You can very quickly take it into areas where doing it in traditional ways would not be easy, such as balancing an egg instead,” said Mark Hammond, Microsoft general manager for Autonomous Systems. “The point of the Project Moab system is to provide that playground where engineers tackling various problems can learn how to use the tooling and simulation models. Once they understand the concepts, they can apply it to their novel use case.”

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Going fast: Buy a demo table at TC Sessions: Robotics+AI 2020

Startup founders, set your sites on TC Sessions: Robotics+AI, which takes place on March 3, 2020. This annual day-long event draws the brightest minds and makers from these two industries — 1,500 attendees last year alone. And if you really want to make 2020 a game-changing year, grab yourself a demo table and showcase your early-stage robotics or AI startup in front of those big names and serious influencers.

Simply purchase an Early-Stage Startup Exhibitor Package — the price includes four tickets to the event, so bring your crew, flex your networking mojo and take in some of the many discussions throughout the day. Get yours before they’re gone — only eight left.

The day’s programming covers a wide range of crucial issues focused on robotics and AI. TC editors conduct in-depth interviews and moderate panel discussions and Q&As with the industries’ leading minds, makers, technologists, researchers and investors. You’ll enjoy workshops, demos and plenty of networking opportunities.

We’re talking topics that appeal to every hungry startup founder. Like a panel discussion on investing featuring Eric Migicovsky, Kelly Chen and Dror Berman — all top VCs in robotics and AI.

These folks have their fingers on the pulse of robotics, AI and automation. They’ll be on hand to share insights on future industry trends, talk about the most compelling startups and what they look for when it comes to funding.

We’ll be sharing details and the names of plenty more speakers in the coming weeks, so keep checking back. You can always check out last year’s program to get a sense of what to expect.

Did you know we have a new twist to this year’s Session? It’s a pitch competition — Pitch Night. It takes place the night before, it doesn’t cost a thing and it’s open to founders of early-stage startups focused on robotics and AI. There’s only one small hoop to jump through: apply here by February 1.

TC Sessions: Robotics+AI takes place on March 3, 2020 at UC Berkeley. Buy your Early-Stage Startup Exhibitor Package today, and come impress the top technologists, makers, thinkers, researchers and investors. Make 2020 your game-changing year.

Is your company interested in sponsoring or exhibiting at TC Sessions: Robotics+AI 2020? Contact our sponsorship sales team by filling out this form.

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How Microsoft is trying to become more innovative

Microsoft Research is a globally distributed playground for people interested in solving fundamental science problems.

These projects often focus on machine learning and artificial intelligence, and since Microsoft is on a mission to infuse all of its products with more AI smarts, it’s no surprise that it’s also seeking ways to integrate Microsoft Research’s innovations into the rest of the company.

Across the board, the company is trying to find ways to become more innovative, especially around its work in AI, and it’s putting processes in place to do so. Microsoft is unusually open about this process, too, and actually made it somewhat of a focus this week at Ignite, a yearly conference that typically focuses more on technical IT management topics.

At Ignite, Microsoft will for the first time present these projects externally at a dedicated keynote. That feels similar to what Google used to do with its ATAP group at its I/O events and is obviously meant to showcase the cutting-edge innovation that happens inside of Microsoft (outside of making Excel smarter).

To manage its AI innovation efforts, Microsoft created the Microsoft AI group led by VP Mitra Azizirad, who’s tasked with establishing thought leadership in this space internally and externally, and helping the company itself innovate faster (Microsoft’s AI for Good projects also fall under this group’s purview). I sat down with Azizirad to get a better idea of what her team is doing and how she approaches getting companies to innovate around AI and bring research projects out of the lab.

“We began to put together a narrative for the company of what it really means to be in an AI-driven world and what we look at from a differentiated perspective,” Azizirad said. “What we’ve done in this area is something that has resonated and landed well. And now we’re including AI, but we’re expanding beyond it to other paradigm shifts like human-machine interaction, future of computing and digital responsibility, as more than just a set of principles and practices but an area of innovation in and of itself.”

Currently, Microsoft is doing a very good job at talking and thinking about horizon one opportunities, as well as horizon three projects that are still years out, she said. “Horizon two, we need to get better at, and that’s what we’re doing.”

It’s worth stressing that Microsoft AI, which launched about two years ago, marks the first time there’s a business, marketing and product management team associated with Microsoft Research, so the team does get a lot of insights into upcoming technologies. Just in the last couple of years, Microsoft has published more than 6,000 research papers on AI, some of which clearly have a future in the company’s products.

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Registration is open for TC Sessions: Robotics + AI 2020

It’s time to get your robotics fix, startup fans. That’s right, TC Sessions: Robotics & AI returns to UC Berkeley’s Zellerbach Hall on March 3, 2020. Join us for a day-long deep dive focused on the intersection of robotics and AI — arguably two of the most exciting and world-changing technologies.

Registration is now open. Save the date and save $100 when you buy an early-bird ticket to TC Sessions: Robotics & AI 2020. Want to save even more? Buy in bulk. You’ll save an extra 18% when you purchase four or more tickets at once.

This is our fourth year hosting this event and last year, 1,500 founders, technologists, engineering students and investors heard TechCrunch editors interview top leaders in AI and robotics, participated in workshops, watched live demos, attended speaker Q&As and enjoyed world-class networking. With so many advances in a range of technologies like AI, GPUs, sensors (to name just a few), it’s an exciting time to be part of this rapidly evolving space.

We’re building out the speaker roster and agenda, so keep checking back. In the meantime, take a look at last year’s agenda to get a sense of the quality programming you can expect.

Boston Dynamics founder Marc Raibert, a perennial favorite at TC Sessions: Robotics & AI, offers this perspective on the conference. It “blends the best of thoughtful, research-focused robotics with a unique business in technology focus.”

TC Sessions: Robotics & AI takes place on March 3, 2020 at UC Berkeley’s Zellerbach Hall. It’s not too early to save the date, and it’s never too early to save $100 on the price of admission. Join the top people in robotics and AI for a full day devoted to world-changing technologies.

Is your company interested in sponsoring or exhibiting at TC Sessions: Robotics & AI 2020? Contact our sponsorship sales team by filling out this form.

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RealityEngines.AI raises $5.25M seed round to make ML easier for enterprises

RealityEngines.AI, a research startup that wants to help enterprises make better use of AI, even when they only have incomplete data, today announced that it has raised a $5.25 million seed funding round. The round was led by former Google CEO and Chairman Eric Schmidt and Google founding board member Ram Shriram. Khosla Ventures, Paul Buchheit, Deepchand Nishar, Elad Gil, Keval Desai, Don Burnette and others also participated in this round.

The fact that the service was able to raise from this rather prominent group of investors clearly shows that its overall thesis resonates. The company, which doesn’t have a product yet, tells me that it specifically wants to help enterprises make better use of the smaller and noisier data sets they have and provide them with state-of-the-art machine learning and AI systems that they can quickly take into production. It also aims to provide its customers with systems that can explain their predictions and are free of various forms of bias, something that’s hard to do when the system is essentially a black box.

As RealityEngines CEO Bindu Reddy, who was previously the head of products for Google Apps, told me, the company plans to use the funding to build out its research and development team. The company, after all, is tackling some of the most fundamental and hardest problems in machine learning right now — and that costs money. Some, like working with smaller data sets, already have some available solutions like generative adversarial networks that can augment existing data sets and that RealityEngines expects to innovate on.

Reddy is also betting on reinforcement learning as one of the core machine learning techniques for the platform.

Once it has its product in place, the plan is to make it available as a pay-as-you-go managed service that will make machine learning more accessible to large enterprise, but also to small and medium businesses, which also increasingly need access to these tools to remain competitive.

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Twitter bags deep learning talent behind London startup, Fabula AI

Twitter has just announced it has picked up London-based Fabula AI. The deep learning startup has been developing technology to try to identify online disinformation by looking at patterns in how fake stuff vs genuine news spreads online — making it an obvious fit for the rumor-riled social network.

Social media giants remain under increasing political pressure to get a handle on online disinformation to ensure that manipulative messages don’t, for example, get a free pass to fiddle with democratic processes.

Twitter says the acquisition of Fabula will help it build out its internal machine learning capabilities — writing that the UK startup’s “world-class team of machine learning researchers” will feed an internal research group it’s building out, led by Sandeep Pandey, its head of ML/AI engineering.

This research group will focus on “a few key strategic areas such as natural language processing, reinforcement learning, ML ethics, recommendation systems, and graph deep learning” — now with Fabula co-founder and chief scientist, Michael Bronstein, as a leading light within it.

Bronstein is chair in machine learning & pattern recognition at Imperial College, London — a position he will remain while leading graph deep learning research at Twitter.

Fabula’s chief technologist, Federico Monti — another co-founder, who began the collaboration that underpin’s the patented technology with Bronstein while at the University of Lugano, Switzerland — is also joining Twitter.

“We are really excited to join the ML research team at Twitter, and work together to grow their team and capabilities. Specifically, we are looking forward to applying our graph deep learning techniques to improving the health of the conversation across the service,” said Bronstein in a statement.

“This strategic investment in graph deep learning research, technology and talent will be a key driver as we work to help people feel safe on Twitter and help them see relevant information,” Twitter added. “Specifically, by studying and understanding the Twitter graph, comprised of the millions of Tweets, Retweets and Likes shared on Twitter every day, we will be able to improve the health of the conversation, as well as products including the timeline, recommendations, the explore tab and the onboarding experience.”

Terms of the acquisition have not been disclosed.

We covered Fabula’s technology and business plan back in February when it announced its “new class” of machine learning algorithms for detecting what it colloquially badged ‘fake news’.

Its approach to the problem of online disinformation looks at how it spreads on social networks — and therefore who is spreading it — rather than focusing on the content itself, as some other approaches do.

Fabula has patented algorithms that use the emergent field of “Geometric Deep Learning” to detect online disinformation — where the datasets in question are so large and complex that traditional machine learning techniques struggle to find purchase. Which does really sound like a patent designed with big tech in mind.

Fabula likens how ‘fake news’ spreads on social media vs real news as akin to “a very simplified model of how a disease spreads on the network”.

One advantage of the approach is it looks to be language agnostic (at least barring any cultural differences which might also impact how fake news spread).

Back in February the startup told us it was aiming to build an open, decentralised “truth-risk scoring platform” — akin to a credit referencing agency, just focused on content not cash.

It’s not clear from Twitter’s blog post whether the core technologies it will be acquiring with Fabula will now stay locked up within its internal research department — or be shared more widely, to help other platforms grappling with online disinformation challenges.

The startup had intended to offer an API for platforms and publishers later this year.

But of course building a platform is a major undertaking. And, in the meanwhile, Twitter — with its pressing need to better understand the stuff its network spreads — came calling.

A source close to the matter told us that Fabula’s founders decided that selling to Twitter instead of pushing for momentum behind a vision of a decentralized, open platform because the exit offered them more opportunity to have “real and deep impact, at scale”.

Though it is also still not certain what Twitter will end up doing with the technology it’s acquiring. And it at least remains possible that Twitter could choose to make it made open across platforms.

“That’ll be for the team to figure out with Twitter down the line,” our source added.

A spokesman for Twitter did not respond directly when we asked about its plans for the patented technology but he told us: “There’s more to come on how we will integrate Fabula’s technology where it makes sense to strengthen our systems and operations in the coming months.  It will likely take us some time to be able to integrate their graph deep learning algorithms into our ML platform. We’re bringing Fabula in for the team, tech and mission, which are all aligned with our top priority: Health.”

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Microsoft launches a drag-and-drop machine learning tool

Microsoft today announced three new services that all aim to simplify the process of machine learning. These range from a new interface for a tool that completely automates the process of creating models, to a new no-code visual interface for building, training and deploying models, all the way to hosted Jupyter-style notebooks for advanced users.

Getting started with machine learning is hard. Even to run the most basic of experiments takes a good amount of expertise. All of these new tools greatly simplify this process by hiding away the code or giving those who want to write their own code a pre-configured platform for doing so.

The new interface for Azure’s automated machine learning tool makes creating a model as easy as importing a data set and then telling the service which value to predict. Users don’t need to write a single line of code, while in the backend, this updated version now supports a number of new algorithms and optimizations that should result in more accurate models. While most of this is automated, Microsoft stresses that the service provides “complete transparency into algorithms, so developers and data scientists can manually override and control the process.”

For those who want a bit more control from the get-go, Microsoft also today launched into preview a visual interface for its Azure Machine Learning service that will allow developers to build, train and deploy machine learning models without having to touch any code.

This tool, the Azure Machine Learning visual interface, looks suspiciously like the existing Azure ML Studio, Microsoft’s first stab at building a visual machine learning tool. Indeed, the two services look identical. The company never really pushed this service, though, and almost seemed to have forgotten about it despite the fact that it always seemed like a really useful tool for getting started with machine learning.

Microsoft says this new version combines the best of Azure ML Studio with the Azure Machine Learning service. In practice, this means that while the interface is almost identical, the Azure Machine Learning visual interface extends what was possible with ML Studio by running on top of the Azure Machine Learning service and adding that services’ security, deployment and life cycle management capabilities.

The service provides an easy interface for cleaning up your data, training models with the help of different algorithms, evaluating them and, finally, putting them into production.

While these first two services clearly target novices, the new hosted notebooks in Azure Machine Learning are clearly geared toward the more experienced machine learning practitioner. The notebooks come pre-packaged with support for the Azure Machine Learning Python SDK and run in what the company describes as a “secure, enterprise-ready environment.” While using these notebooks isn’t trivial either, this new feature allows developers to quickly get started without the hassle of setting up a new development environment with all the necessary cloud resources.

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MIT’s new chip could bring neural nets to battery-powered gadgets

 MIT researchers have developed a chip designed to speed up the hard work of running neural networks, while also reducing the power consumed when doing so dramatically – by up to 95 percent, in fact. The basic concept involves simplifying the chip design so that shuttling of data between different processors on the same chip is taken out of the equation. The big advantage of this new… Read More

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The AI ecosystem to be on display at Disrupt SF

 As AI creeps deeper into each and every industry vertical, demand for experienced technical talent continues to increase. Now online education tools like Udacity and Coursera are being thrust into the spotlight as potential solutions to the problem. But Fortune 2000 companies are still paying an unsustainable premium for data scientists. We’re excited to showcase this ecosystem at… Read More

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H2O.ai’s Driverless AI automates machine learning for businesses

 Driverless AI is the latest product from H2O.ai aimed at lowering the barrier to making data science work in a corporate context. The tool assists non-technical employees with preparing data, calibrating parameters and determining the optimal algorithms for tackling specific business problems with machine learning. Read More

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