Honeywell announces its H1 quantum computer with 10 qubits

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Honeywell, which was a bit of a surprise entrant into the quantum computing space when it announced its efforts to build the world’s most powerful quantum computer earlier this year, today announced its newest system: the Model H1. The H1 uses trapped-ion technology and features 10 fully connected qubits that allow it to reach a quantum volume of 128 (where quantum volume (QV) is a metric of the overall compute power of a quantum computer, no matter the underlying technology). That’s higher than comparable efforts by IBM, but also well behind the QV 4,000,000 machine IonQ says it was able to achieve with 32 qubits.

The H1 will be available to enterprises through the Azure Quantum platform and the company says that it is partnering with Zapata Computing and Cambridge Quantum Computing on this project.

When it first announced its efforts, Honeywell said that its experience in building control systems allowed it to build an advanced ion trap and more uniform qubits that hence make error correction easier.

Image Credits: Honeywell

In addition to the next generation of its quantum computer, the company also today announced its overall quantum roadmap for the next ten years. The plan here is to go from 10 to 40 qubits with all-to-all connectivity as it moves toward a next generation of devices that are fault tolerant and can be deployed at a larger scale.

“Honeywell’s aggressive quantum computing roadmap reflects our commitment to achieving commercial scale for our quantum business. Our subscription-based model provides enterprise customers with access to Honeywell’s most advanced system available,” said Tony Uttley, President of Honeywell Quantum Solutions. “Honeywell’s unique methodology enables us to systematically and continuously ‘upgrade’ the H1 generation of systems through increased qubit count, even higher fidelities and unique feature modifications.”

Image Credits: Honeywell

Microsoft now lets you bring your own data types to Excel

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Over the course of the last few years, Microsoft started adding the concept of ‘data types’ to Excel, that is, the ability to pull in geography and real-time stock data from the cloud, for example. Thanks to its partnership with Wolfram, Excel now features over 100 of these data types that can flow into a spreadsheet. But you won’t be limited to only these pre-built data types for long. Soon, Excel will also let you bring in your own data types.

That means you can have a ‘customer’ data type, for example, that can bring in rich customer data from a third-party service into Excel. The conduit fort his is either Power BI, which now allows Excel to pull in any data you previously published there, or Microsoft’s Power Query feature in Excel that lets you connect to a wide variety of data sources, including common databases like SQL Server, MySQL and PostreSQL, as well as third-party services like Teradata and Facebook.

“Up to this point, the Excel grid has been flat… it’s two dimensional,” Microsoft’ head of product for Excel, Brian Jones, writes in today’s announcement. “You can lay out numbers, text, and formulas across the flexible grid, and people have built amazing things with those capabilities. Not all data is flat though and forcing data into that 2D structure has its limits. With Data Types we’ve added a 3rd dimension to what you can build with Excel. Any cell can now contain a rich set of structured data… in just a single cell.”

The promise here is that this will make Excel more flexible and I’m sure a lot of enterprises will adapt these capabilities. These companies aren’t likely to move to Airtable or similar Excel-like tools anytime soon but have data analysis needs that are only increasing now that every company gathers more data than it knows what to do with. This is also a feature that none of Excel’s competitors currently offer, including Google Sheets.

Enso Security raises $6M for its application security management platform

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Enso Security, a Tel Aviv-based startup that is building a new application security platform, today announced that it has raised a $6 million seed funding round led by YL Ventures, with participation from Jump Capital. Angel investors in this round include HackerOne co-founder and CTO Alex Rice; Sounil Yu, the former chief security scientist at Bank of America; Omkhar Arasaratnam, the former head of Data Protection Technology at JPMorgan Chase and toDay Ventures.

The company was founded by Roy Erlich (CEO), Chen Gour Arie (CPO) and Barak Tawily (CTO). As is so often the case with Israeli security startups, the founding team includes former members of the Israeli Intelligence Corps, but also a lot of hands-on commercial experience. Erlich, for example, was previously the head of application security at Wix, while Gour Arie worked as an application security consultant for numerous companies across Europe and Tawily has a background in pentesting and led a security team at Wix, too.

Image Credits: Enso Security / Getty Images

“It’s no secret that, today, the diversity of R&D allows [companies] to rapidly introduce new applications and push changes to existing ones,” Erlich explained. “But this great complexity for application security teams results in significant AppSec management challenges. These challenges include the difficulty of tracking applications across environments, measuring risks, prioritizing tasks and enforcing uniform Application Security strategies across all applications.”

But as companies push out code faster than ever, the application security teams aren’t able to keep up — and may not even know about every application being developed internally. The team argues that application security today is often a manual effort to identify owners and measure risk, for example — and the resources for application security teams are often limited, especially when compared the size of the overall development team in most companies. Indeed, the Enso team argues that most AppSec teams today spend most of their time creating relationships with developers and performing operational and product-related tasks — and not on application security.

Image Credits: Enso Security / Getty Images

“It’s a losing fight from the application security side because you have no chance to cover everything,” Erlich noted. “Having said that, […] it’s all about managing the risk. You need to make sure that you take data-driven decisions and that you have all the data that you need in one place.”

Enso Security then wants to give these teams a platform that gives them a single pane of glass to discover applications, identify owners, detect changes and capture their security posture. From there, teams can then prioritize and track their tasks and get real-time feedback on what is happening across their tools. The company’s tools currently pull in data from a wide variety of tools, including the likes of JIRA, Jenkins, GitLab, GitHub, Splunk, ServiceNow and the Envoy edge and service proxy. But as the team argues, even getting data from just a few sources already provides benefits for Enso’s users.

Looking ahead, the team plans to continue improving its product and staff up from its small group of seven employees to about 20 in the next year.

“Roy, Chen and Barak have come up with a very elegant solution to a notoriously complex problem space,” said Ofer Schreiber, partner at YL Ventures . “Because they cut straight to visibility — the true heart of this issue — cybersecurity professionals can finally see and manage all of the applications in their environments. This will have an extraordinary impact on the rate of application rollout and enterprise productivity.”

MachEye raises $4.6M for its business intelligence platform

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We’ve seen our fair share of business intelligence (BI) platforms that aim to make data analysis accessible to everybody in a company. Most of them are still fairly complicated, no matter what their marketing copy says. MachEye, which is launching its AI-powered BI platform today, is offering a new twist on this genre. In addition to its official launch, the company also today announced a previously unreported $4.6 seed funding round led by Canaan Partners with participation from WestWave Capital.

MachEye is not just what its founder and CEO Ramesh Panuganty calls a “low-prep, no-prep” BI platform, but it uses natural language processing to allow anybody to query data using natural language — and it can then automatically generate interactive data stories on the fly that put the answer into context. That’s quite a different approach from its more dashboard-centric competition.

“I have seen the business intelligence problems in the past,” Panuganty said. “And I saw that Traditional BI, even though it has existed for 30 or 40 years, had this paradigm of ‘what you ask is what you get.’ So the business user asks for something, either in an email, on the phone or in person, and then he gets an answer to that question back. That essentially has these challenges of being dependent on the experts and there is a time that is lost to get the answers — and then there’s a lack of exploratory capabilities for the business user. and the bigger problem is that they don’t know what they don’t know.”

Panuganty’s background includes time at Sun Microsystems and Bell Labs, working on their operating systems before becoming an entrepreneur. He build three companies over the last 12 years or so. The first was a cloud management platform, Cloud365, which was acquired by Cognizant. The second was analytics company Drastin, which got acquired by Splunk in 2017, and the third was the AI-driven educational platform SelectQ, which Thinker acquired this April. He also holds 15 patents related to machine learning, analytics and natural language processing.

Given that track record, it’s probably no surprise why VCs wanted to invest in his new startup, too. Panuganty tells me that when he met with Canaan Partners, he wasn’t really looking for an investment. He had already talked to the team while building SelectQ, but Canaan never got to make an investment because the company got acquired before it needed to raise more funding. But after an informal meeting that ended up lasting most of the day, he received an offer the next morning.

Image Credits: MachEye

MachEye’s approach is definitely unique. “Generating audio-visuals on enterprise data, we are probably the only company that does it,” Panuganty said. But it’s important to note that it also offers all of the usual trappings of a BI service. If you really want dashboards, you can build those, and developers can use the company’s APIs to use their data elsewhere, too. The service can pull in data from most of the standard databases and data warehousing services, including AWS Redshift, Azure Synapse, Google BigQuery, Snowflake and Oracle. The company promises that it only takes 30 minutes from connecting a data source to being able to ask questions about that data.

Interestingly, MachEye’s pricing plan is per seat and doesn’t limit how much data you can query. There’s a free plan, but without the natural search and query capabilities, an $18/month/user plan that adds those capabilities and additional search features, but it takes the enterprise plan to get the audio narrations and other advanced features. The team is able to use this pricing model because it is able to quickly spin up the container infrastructure to answer a query and then immediately shut it down again — all within about two minutes.

Salto raises $27M to let you configure your SaaS platforms with code

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Salto, a Tel Aviv-based open-source startup that allows you to configure SaaS platforms like Salesforce, NetSuite and HubSpot with code, is coming out of stealth today and announced that it has raised a $27 million Series A round. This round was led by Bessemer Venture Partners, Lightspeed Venture Partners and Salesforce Ventures.

The general idea here — which is similar to the ‘infrastructure-as-code’ movement — is to allow business operations teams to automate the labor-intensive and error-prone ways they currently use to manage SaaS platforms. While others in this space are betting on no-code solutions for managing these systems, Salto is going the other way and is betting on code instead.

“We realized the challenges BizOps teams face are very similar to the problems encountered by software and DevOps engineers on a daily basis,” writes Salto co-founder and CEO Rami Tamir in today’s announcement. “So we adapted software development fundamentals and best practices to the BizOps field. There’s no need to reinvent the wheel; the same techniques used to make high-quality software can also be applied to keeping control over business applications.”

Image Credits: Salto

Salto makes the core of its service available as open source. This open-source version includes the company’s NaCI language, a declarative configuration language based on the syntax of HashiCorp’s hcl, a command-line interface for deploying configuration changes (and fetching the current configuration state of an application) and a VS Code extension.

In combination with Git, business operations teams can collaborate on writing these configurations and test them in staging environments. The company is essentially taking modern software development practices and applying them to business operations.

Image Credits: Salto

“Defining a company’s business logic as code can make a fundamental change in the way business applications are delivered,” writes Tamir. “We like to think about it as ‘company-as-code,’ much in the same way as ‘infrastructure-as-code’ transformed the way we manage data centers.”

Some of the use cases here are configuring custom Salesforce CPQ fields, and syncing profiles across Salesforce environments and maintaining audio logs for NetSuite. For now, the company only supports connections to Salesforce, HubSpot and NetSuite, with others following soon.

Like other open-source companies, Salto’s business model involved selling a hosted version of its service, which the company is also announcing today.

In terms of raising this new round, it surely helped that the founding team, which includes Benny Schnaider and Gil Hoffer, in addition to Tamir, previously sold the three companies they founded. Pentacom was acquired by Cisco earlier this year; Oracle acquired Ravello Systems in 2016 and Qumranet was acquired by Red Hat in 2008.

“Business agility is more important than ever today, and the alignment of external business services to real business needs is increasing in strategic importance,” said Alex Kayyal, Partner and Head of International at Salesforce Ventures . “BizOps teams are becoming more and more crucial to the success of companies. With Salto they are empowered to meet the tasks they are charged with, equipped with modernized methodologies and a greatly enhanced toolbox.”

Deci raises $9.1M to optimize AI models with AI

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Deci, a Tel Aviv-based startup that is building a new platform that uses AI to optimized AI models and get them ready for production, today announced that it has raised a $9.1 million seed round led by Emerge and Square Peg.

The general idea here is to make it easier and faster for businesses to take AI workloads into production — and to optimize those production models for improved accuracy and performance. To enable this, the company built an end-to-end solution that allows engineers to bring in their pre-trained models and then have Deci manage, benchmark and optimize them before they package them up for deployment. Using its runtime container or Edge SDK, Deci users can also then serve those models on virtually any modern platform and cloud.

Deci’s insights screen combines all indicators of a deep learning model’s expected behavior in production, resulting in the Deci Score – a single metric summarizing the overall performance of the model.

The company was co-founded by co-founded by deep learning scientist Yonatan Geifman, technology entrepreneur Jonathan Elial, and professor Ran El-Yaniv, a computer scientist and machine learning expert at the Technion – Israel Institute of Technology.

“Deci is leading a paradigm shift in AI to empower data scientists and deep learning engineers with the tools needed to create and deploy effective and powerful solutions,” says Yonatan Geifman, CEO and co-founder of Deci. “The rapidly increasing complexity and diversity of neural network models make it hard for companies to achieve top performance. We realized that the optimal strategy is to harness the AI itself to tackle this challenge. Using AI, Deci’s goal is to help every AI practitioner to solve the world’s most complex problems.”

Deci’s lab screen enables users to manage their deep learning models’ lifecycles, optimize inference performance, and prepare models for deployment. Image Credits: Deci

The company promises is that, on the same hardware and with comparable accuracy, Deci-optimized models will run between five and ten times faster than before. It can make use of CPUs and GPUs for running its inference workloads and the company says that it is already working with customers in autonomous driving, manufacturing, communication and healthcare, among others.

“Deci‘s ability to automatically craft top-performing deep learning solutions is a paradigm shift in artificial intelligence and unlocks new opportunities for many businesses across different industries,” said Liad Rubin, Partner at Emerge. “We are proud to have partnered with such incredible founders and be part of Deci’s journey from day one.”