API series - OctoML: ML APIs need to take a lesson from their ancestors  ComputerWeekly.com

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API series - OctoML: ML APIs need to take a lesson from their ancestors - ComputerWeekly.com

From verifying the quality of incoming data to improving the quality of existing data, open-source data quality solutions can benefit your organization.

Given the importance of data for delivering machine learning and other data science-related workloads, data quality has never been more crucial for enterprises. Small wonder, then, that data quality is the top objective for data teams, according to multiple surveys.

Though companies may all nod in agreement at this statement, actually delivering data quality remains elusive for many. Open source data quality solutions can help, especially for companies that are looking for alternatives to the bigger data quality solutions.

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Its inevitable that data will break, Tom Baeyens, co-founder and CTO of Soda, said in an interview. You cannot prevent mistakes. The only thing you can do is start chasing them and be the first to know, and thats where data monitoring and testing come in.

Even if a company starts with pristine data, entropy sets in. From skewed inventory data to something as simple as misspelled customer names, poor data leads to poor business decisions and customer experiences. To Baeyens point, and similar to bug-free software, data quality is as much about process as anything else.

SEE: Hiring kit: Data scientist (TechRepublic Premium)

Data quality isnt something you buy, but data quality solutions can help enterprises implement the right processes to improve data quality over time. As Talend described in a recent whitepaper, data quality must be an always-on operation, a continuous and iterative process where you constantly control, validate, and enrich your data; smooth your data flows; and get better insights.

Data quality, generally, can be measured across a number of different factors. These might include data completeness, accuracy, availability or accessibility to relevant users, timeliness, and consistency. Yet, despite increased attention to these aspects of data quality, many enterprises still rely on black-box, proprietary solutions that yield little insight into why the tooling recommends certain actions on a given dataset.

Open source isnt a panacea for data or software quality but, as mentioned, open source data quality solutions can help to improve the processes associated with delivering quality. One of the clear trends in data science, generally, has been a shift toward open source data infrastructure, precisely because no one wants to bet blindly on algorithms that can be used but not understood.

So, which open source data quality solutions stand out?

One of the most interesting data quality tools isnt really a data quality tool at all. Rather, the Delta Lake open source storage framework, first created by Databricks but contributed to and maintained by the Linux Foundation, ensures any data lake can be turned into a data warehouse with all of the attendant benefits, including making it more easily queryable.

Delta Lake helps companies feel comfortable storing all of their data in a common, open source format, making it easier to use that data and apply data quality tools against it.

Talend, already mentioned, offers the popular Talend Open Studio for users that want an open source data quality solution. Talend makes it easy to observe, cleanse and analyze text fields, along with several other related tasks. The solution has a polished, easy-to-follow UI, as well as a robust community that can step in to help answer user questions.

As is detailed in an Indeed.com analysis, One unique value proposition of Open Studio is its ability to match time-series data Without adding any code, users can analyze the data ranging from simple data profiling to profiling based on different fields.

Apache Griffin is another community-driven open source data quality solution. Griffin supports both batch and streaming modes and includes a unified process to measure data quality. Griffin first enables an enterprise to define what data quality means for them across factors such as timeliness and completeness; then, they can identify the most critical characteristics. With this process, its easy to measure how data is living up to that data quality definition. Companies as varied as Expedia, VMware and Huawei rely on Griffin.

One newer entrant to the open source data quality universe is Soda, founded by open source veteran, Tom Baeyens. Soda helps data engineers control the tests used to screen for bad data and the metrics that are employed to evaluate results. Soda SQL uses efficient SQL requests to extract data metrics and column profiles with full control over the queries provided through declarative YAML configuration files.

Though Soda will often be used by data engineers, the platform is trying to democratize data monitoring, making it easy for non-technical, business-oriented people to build data monitors.

OpenRefine is a community-driven tool that is primarily used to tame messy data. Though it originated with Google, OpenRefine can be used to explore, clean and transform data at significant scale.

Disclosure: I work for MongoDB, but the views expressed herein are mine.

Excerpt from:

Benefits of working with open source data quality solutions - TechRepublic

Earlier this year, LinkedIn co-founder and venture capitalist Reid Hoffman issued a warning mixed with amazement about AI. "There is literally magic happening," said Hoffman, speaking to technology executives across sectors of the economy.

Some of that magic is becoming more apparent in creative spaces, like the visual arts, and the idea of "generative technology" has captured the attention of Silicon Valley. AI has even recently won awards at art exhibitions.

But Hoffman's message was squarely aimed at executives.

"AI will transform all industries," Hoffman told the members of the CNBC Technology Executive Council. "So everyone has to be thinking about it, not just in data science."

The rapid advances being made by Copilot AI, the automated code writing tool from the GitHub open source subsidiary of Microsoft, were an example Hoffman, who is on the Microsoft board, directly cited as a signal that all firms better be prepared for AI in their world. Even if not making big investments today in AI, business leaders must understand the pace of improvement in artificial intelligence and the applications that are coming or they will be "sacrificing the future," he said.

"100,000 developers took 35% of the coding suggestions from Copilot," Hoffman said. "That's a 35% increase in productivity, and off last year's model. ... Across everything we are doing, we will have amplifying tools, it will get there over the next three to 10 years, a baseline for everything we are doing," he added.

Copilot has already added another 5% to the 35% cited by Hoffman. GitHub CEO Thomas Dohmke recently told us that Copilot is now handling up to 40% of coding among programmers using the AI in the beta testing period over the past year. Put another way, for every 100 lines of code, 40 are being written by the AI, with total project time cut by up to 55%.

Copilot, trained on massive amounts of open source code, monitors the code being written by a developer and works as an assistant, taking the input from the developer and making suggestions about the next line of code, often multi-line coding suggestions, often "boilerplate" code that is needed but is a waste of time for a human to recreate. We all have some experience with this form of AI now, in places like our email, with both Microsoft and Google mail programs suggesting the next few words we might want to type.

AI can be logical about what may come next in a string of text. But Dohmke said, "It can't do more, it can't capture the meaning of what you want to say."

Whether a company is a supermarket working on checkout technology or a banking company working on customer experience in an app, they are all effectively becoming software companies, all building software, and once a C-suite has developers it needs to be looking at developer productivity and how to continuously improve it.

That's where the 40 lines of code come in. "After a year of Copilot, about 40% of code was written by the AI where Copilot was enabled," Dohmke said. "And if you show that number to executives, it's mind-blowing to them. ... doing the math on how much they are spending on developers."

With the projects being completed in less than half the time, a logical conclusion is that there will be less work to do for humans. But Dohmke says another way of looking at the software developer job is that they do many more high-value tasks than just rewrite code that already exists in the world. "The definition of 'higher value' work is to take away the boiler-plate menial work writing things already done over and over again," he said.

The goal of Copilot is to help developers "stay in the flow" when they are on the task of coding. That's because some of the time spent writing code is really spent looking for existing code to plug in from browsers, "snippets from someone else," Dohmke said. And that can lead coders to get distracted. "Eventually they are back in editor mode and copy and paste a solution, but have to remember what they were working on," he said. "It's like a surfer on a wave in the water and they need to find the next wave. Copilot is keeping them in the editing environment, in the creative environment and suggesting ideas," Dohmke said. "And if the idea doesn't work, you can reject it, or find the closest one and can always edit," he added.

The GitHub CEO expects more of those Copilot code suggestions to be taken in the next five years, up to 80%. Unlike a lot going on in the computer field, Dohmke said of that forecast, "It's not an exact science ... but we think it will tremendously grow."

After being in the market for a year, he said new models are getting better fast. As developers reject some code suggestions from Copilot, the AI learns. And as more developers adopt Copilot it gets smarter by interacting with developers similar to a new coworker, learning from what is accepted or rejected. New models of the AI don't come out every day, but every time a new model is available, "we might have a leap," he said.

But the AI is still far short of replacing humans. "Copilot today can't do 100% of the task," Dohmke said. "It's not sentient. It can't create itself without user input."

With Copilot still in private beta testing among individual developers 400,000 developer signed up to use the AI in the first months it was available and hundreds of thousands of more developers since GitHub has not announced any enterprise clients, but it expects to begin naming business customers before the end of the year. There is no enterprise pricing information being disclosed yet, but in the beta test Copilot pricing has been set at a flat rate per developer $10 per individual per month or $100 annually, often expensed by developers on company cards. "And you can imagine what they earn per month so it's a marginal cost," Dohmke said. "If you look at the 40% and think of the productivity improvement, and take 40% of opex spend on developers, the $10 is not a relevant cost. ... I have 1,000 developers and it's way more money than 1000 x 10," he said.

The GitHub CEO sees what is taking place now with AI as the next logical phase of the productivity advances in a coding world he has been a part of since the late 1980s. That was a time when coding was emerging out of the punch card phase, and there was no internet, and coders like Dohmke had to buy books and magazines, and join computer clubs to gain information. "I had to wait to meet someone to ask questions," he recalled.

That was the first phase of developer productivity, and then came the internet, and now open source, allowing developers to find other developers on the internet who had already "developed the wheel," he said.

Now, whether the coding task is related to payment processing or a social media login, most companies whether startups or established enterprises put in open source code. "There is a huge dependency tree of open source that already exists," Dohmke said.

It's not uncommon for up to 90% of code on mobile phone apps to be pulled from the internet and open source platforms like GitHub. In a coding era of "whatever else is already available," that's not what will differentiate a developer or app.

"AI is just the third wave of this," Dohmke said. "From punch cards to building everything ourselves to open source, to now withina lot of code, AI writing more," he said. "With 40%, soon enough if AI spreads across industries, the innovation on the phone will be created with the help of AI and the developer."

Today, and into the foreseeable future, Copilot remains a technology that is trained on code, and is making proposals based on looking things up in a library of code. It is not inventing any new algorithms, but at the current pace of progress, eventually, "it is entirely possible that with help of a developer it will create new ideas of source code,," Dohmke said.

But even that still requires a human touch. "Copilot is getting closer, but it will always need developers to create innovation," he said.

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Microsoft's GitHub Copilot AI is making rapid progress. Here's how its human leader thinks about it - CNBC

A new company is setting out to challenge Airtable, the 10-year-old company recently valued at a whopping $11 billion, with a slightly different take on what it means to be a no-code database platform.

NocoDB is one of a number of startups to emerge on the scene with plans to usurp the mighty Airtable, with an open source foundation serving as a core selling point. While NocoDB works in a similar fashion in terms of allowing non-technical users to create fresh databases, its twist is that it also works directly on live production data that resides in databases such as Postrgres, MySQL or MariaDB, or data warehouses, and turns them into what it calls a smart spreadsheet.

This allows anyone to leverage legacy databases without needing ITs input no SQL queries or code required. Its all about enabling business, finance or even marketing teams to connect to live data and collaborate with developers to build no-code applications.

U.K.-based founder and CEO Naveen Rudrappa claims that the core open source project has already been used by more than 2,000 companies, including behemoths such as Google, Walmart, American Express and McAfee.

The adoption weve seen has been really unprecedented weve had 7 million Docker downloads within one year of launch and more than 30,000 GitHub stars, putting us amongst the top 350 open source projects in the world, Rudrappa told TechCrunch.

NocoDB: Grid view. Image Credits: NocoDB

A little more than a year on from its inception, the company is announcing a sizeable seed funding round from a veritable whos who from the angel investment world.

The funding has in fact dripped in over a couple of tranches since its incorporation in June last year, but in total the round amounts to around $10.5 million, with institutional backers including Decibel, OSS Capital, Uncorrelated Ventures and Together.fund. The angel side, meanwhile, includes YouTube co-founder Chad Hurley; WordPress creator Matt Mullenweg; RedHat co-founder Bob Young; early Google investor Ram Shriram; and founders from Cloudera, CockroachDB, PipeDream, Talend, AngelList, BrightRoll and Freshworks.

The genesis of NocoDB can be traced back to 2017, when Rudrappa was working on a related open source database passion project under a different name, one that was purely a backend with no user interface at all. The problem he was trying to solve involved creating APIs to access a MySQL database of U.K. real estate data something that wasnt easy to achieve.

I realized that the fundamental problem of making a database API-accessible still remained unsolved, Rudrappa said. So, I built a prototype, released it on GitHub, and the next morning woke up to see a thousand GitHub stars for my project. The problem was much more widespread than I had imagined and my initial prototype had struck a chord with the users. This hobby project received a quarter of a million downloads, then I decided to team up with a friend and started building NocoDB.

When NocoDB arrived on GitHub last year, Rudrappa said that it garnered more than a million downloads within the first 10 weeks.

Live production data stores, like MySQL or Snowflake, are intimidating for business users, or even to developers who arent used to working with the backend tech stack, he said. But they need access to this data in order to build useful applications quickly. NocoDB makes it possible to connect any organizational data source to the universally well-understood spreadsheet interface, allowing users with zero coding experience to build workflows and automations that work in concert with real business data.

With $10.5 million in the bank, and the support of some of the biggest names from the technology sphere, NocoDB is well-positioned to build out a commercial component to the main open source project. This includes a new premium incarnation thats currently in private beta, one that allows companies to connect to Oracle Database and Snowflake.

This commercial version is a request from the customer side, as they need a working contract with us when they use the software, Rudrappa explained. Enterprise customers need different support, and we want to accommodate that while also balancing the needs of our open source community.

On top of that, NocoDB is also working on a managed and hosted cloud version, replete with enterprise-grade features including connectors, single sign-on (SSO), access control, auditing and more.

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NocoDB takes on Airtable with open source no-code platform that connects to production databases - TechCrunch

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Global Drone Software Market

Global Drone Software Market

Dublin, Oct. 13, 2022 (GLOBE NEWSWIRE) -- The "Drone Software Market by Solution (Application, System), Platform (Defense & Government, Commercial, Consumer) Architecture (Open Source, Closed Source), Deployment (Onboard Drone, Ground-Based, Region - Global Forecast to 2027" report has been added to ResearchAndMarkets.com's offering.

The drone software market is projected to grow from USD 5.1 Billion in 2022 to USD 11.2 Billion by 2027, at a CAGR of 17.1% from 2022 to 2030.

Increasing private investments in drones and Increasing use of drones for automated remote infrastructure inspection are some of the factors fueling the growth of the market.

According to primary respondents, the impact of COVID-19 is positive on the drone software market due to an increase in experimental flights with delivery drones across various countries and the realization of the significant potential of drone technology by various industrial sectors.

Based on architecture, the open source segment is projected to lead the drone software market during the forecast period.

Drone software can be open source or closed source based on its architecture. Open-source software features an open development process that is reprogrammable and can be modified as per the requirements of the end user. Closed source software code is secure, and only authorized programmers can reprogram it. Developers are working on innovations such as collision avoidance, air traffic management, and computer vision in drone software.

Companies operational in the drone software market are focusing on technological advancements by integrating and experimenting with different payloads, controlling drones through mobile/web applications, writing customized algorithms for required automation, and cloud computing. These companies are also engaged in research activities to develop completely autonomous drones that can perform efficiently in complex environments.Based on platform, the commercial segment is projected to lead the drone software market during the forecast period.

Based on platform, the drone software market has been segmented into defense& government, commercial, and consumer. The commercial segment is further segmented into agriculture, logistics & transportation, energy & power, construction & mining, media & entertainment, insurance, wildlife, and academic research. In commercial media & entertainment segment will lead the market. Using drone swarm software, one can easily control swarm drones with a single computer without a distance limit and check video and flight information in real time.

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Even if swarm drones stop due to time lags, the software ensures that drones fly in alignment with a synchronized starting time at each stop to avoid collision risks. As there are dedicated servers, even in the event of a drone loss or computer malfunction, video information is automatically saved on the server. In January 2022, Intel (US) provided drones and software for a drone light show organized by Destination NSW (Australia) and AGB Events (Australia) and organized the largest illuminated drone light show in Australia for the ELEVATE SkyShow Sydney. It provided 500 Intel illuminated drones that flew high above the Sydney Harbour for a multi-night performance

The North American and Asia Pacific regions are projected to be high growth potential markets for drone software during the forecast period.

The drone software market in the North American region is expected to witness substantial growth and register the highest CAGR during the forecast period. Industrial asset inspections have historically been defined by significant manual work that can be risky to personnel, as well as time and resources spent due to the complexity of acquiring high-quality data and then analyzing it. Traditional drones have proven capable of reducing these risks by allowing inspectors to complete their work safely from the ground.

However, they are still time- and resource-intensive because of their complexity in operating and their high likelihood of crashing.In March 2022, Skydio (US) partnered with Optelos (US) to accelerate asset inspection. The combination of Skydio's autonomous drone software with Optelos' visual data management software will allow companies performing asset inspections to more rapidly identify and resolve issues, like being able to take accurate measurements.

The market in the Asia Pacific is expected to exhibit the highest CAGR due to the Market growth can also be attributed to innovations in drone software for image processing, fleet management, mapping, cloud connection, object detection, and flight control. The retail and e-commerce sectors are the major users of drone software in the Asia Pacific, followed by the healthcare and pharmaceutical sector. China and Japan are key markets for drone software used in commercial applications.

The prevailing trends of automation in India, Australia, and China and ongoing globalization are also fueling the growth of the drone software market in the Asia Pacific. Drones are being increasingly used in the real estate, pollution monitoring, and agriculture sectors to carry out inspections in China and Japan. AutoFlight (China) has announced plans to launch air taxis by 2025. AutoFlight's air-taxi 'Prosperity I' is an electric vertical takeoff and landing aircraft with a range of approximately 250 kilometers.

Key Topics Covered:

1 Introduction

2 Research Methodology

3 Executive Summary

4 Premium Insights4.1 Attractive Growth Opportunities in Drone Software Market4.2 Drone Software Market, by Application4.3 Drone Software Market, by Commercial Platform4.4 Drone Software Market, by Region

5 Market Overview5.1 Introduction5.2 Market Dynamics5.2.1 Drivers5.2.1.1 Increasing Use of Drones for Automated Remote Infrastructure Inspection5.2.1.2 Revolutionizing Agriculture with Drone-Powered Solutions5.2.1.3 Use of Drones to Create Digital Replicas of Sites and Assets in Renewable Energy Sector5.2.2 Restraints5.2.2.1 Growing Concerns Over Cyber Security5.2.2.2 Lack of Skilled Personnel to Operate Drones5.2.3 Opportunities5.2.3.1 Rise of Open-Source Drones5.2.3.2 Airborne Communication Nodes in Military Missions5.2.3.3 Incorporation of IoT in Ecosystem of Delivery Drones5.2.3.4 Increasing Private Investments in Drone Industry5.2.4 Challenges5.2.4.1 Stringent Government Regulations and Lack of Air Traffic Management5.2.4.2 Lack of Risk Management Framework and Insurance Cover for Drones5.2.4.3 Consumer Acceptance and Health Issues due to Ceaseless Noise from Drones5.3 Trends/Disruptions Impacting Customers' Business5.3.1 Revenue Shift and New Revenue Pockets for Drone Software Providers5.4 Value Chain Analysis5.5 Drone Software Market Ecosystem5.6 Trade Data Analysis5.7 Operational Data5.8 Key Conferences & Events in 2022-20235.9 Tariff and Regulatory Landscape5.10 Porter's Five Forces Analysis5.11 Key Stakeholders & Buying Criteria

6 Industry Trends6.1 Introduction6.2 Technology Trends6.2.1 Drone Swarm Software6.2.2 Fog Computing6.2.3 Real-Time Operating System (Rtos)6.2.4 Computer Vision6.2.5 Open-Source Operating Systems6.2.6 Advanced Algorithms and Analytics6.2.7 Machine Learning-Powered Analytics6.2.8 5G Technology6.2.9 Blockchain6.2.10 Cloud Computing6.2.11 New Sensor Development and Computing Capabilities6.3 Use Cases: Drone Software6.3.1 Measure Provides Software to Improve Operational Strategies and Gain Deeper Insights into Forests6.3.2 Interconnected Platform to Collect, Process, and Share Data6.3.3 Karuk Tribe Uses Esri Gis Software to Plan Restoration Projects to Return Forests to Balance6.3.4 Mapping Canyons of Ancients National Monument in Colorado6.4 Impact of Megatrends6.5 Patent Analysis

7 Drone Software Market, by Solution7.1 Introduction7.2 System Software7.2.1 Advanced Features of System Software in Various Applications Boost Demand7.3 Application Software7.3.1 Flight Planning, Fleet Operation & Management7.3.1.1 Remotely Piloted7.3.1.1.1 High Demand for Remotely Piloted Software in Construction Sector7.3.1.2 Semi-Autonomous7.3.1.2.1 Drones Equipped with Semi-Autonomous Software for Archaeological Mapping Popular7.3.1.3 Fully Autonomous7.3.1.3.1 Delivery Drone Companies Use Fully Autonomous Software7.3.2 Data Capture (Mapping Software)7.3.2.1 3D, 2D, Thermal, Lidar7.3.2.1.1 Increasing Use of Photogrammetry Software for Mapping7.3.3 Software Development Kit (Sdk)7.3.3.1 Increasing Demand to Build Applications for Specific Platforms7.3.4 Data Processing & Analytics7.3.4.1 Thermal Mapping & Modeling7.3.4.1.1 Drone-Generated Thermal Maps - Game-Changer for Roof Inspections7.3.4.2 2D Models & Imagery (Digital Terrain, Contour Maps, Point Clouds, Elevation Models)7.3.4.2.1 Lower Cost Involved in 2D Model Generation Using Drones7.3.4.3 3D Models7.3.4.3.1 Photogrammetry Software, Geotagged Images Used to Convert to 3D Models

8 Drone Software Market, by Platform8.1 Introduction8.2 Defense & Government8.2.1 Widely Used for Isr&T Applications in Military8.3 Commercial8.3.1 Agriculture8.3.1.1 Drone Software Used for Mapping and Analytics8.3.2 Logistics & Transportation8.3.2.1 Increasing Use of Fleet Management Software Among Delivery Drone Companies8.3.3 Energy & Power8.3.3.1 Used to Accelerate Inspections and Improve Worker Safety8.3.4 Construction & Mining8.3.4.1 Stockpile Measuring - Key Application8.3.5 Media & Entertainment8.3.5.1 Use of Drone Swarm Software for Light Shows8.3.6 Insurance8.3.6.1 Used to Make Crop Insurance Claim Process Faster and More Efficient8.3.7 Wildlife & Forestry8.3.7.1 Deployment for Forest and Wildlife Conservation8.3.8 Academic Research8.3.8.1 Utilizing Drone Software for Archaeology8.3.9 Computer Vision, Machine Learning, and Deep Learning Software Automate Drone Flight

9 Drone Software Market, by Architecture9.1 Introduction9.2 Open Source9.2.1 Constant Innovation due to High Number of Developers9.3 Closed Source9.3.1 Used by Commercial Drone Companies

10 Drone Software Market, by Deployment10.1 Introduction10.2 Onboard10.2.1 Ease of Use and Maneuverability Drives Segment Growth10.3 Ground-Based10.3.1 Ground Control Software Used to Manage Multiple Drones Simultaneously

11 Regional Analysis

12 Competitive Landscape12.1 Introduction12.2 Competitive Overview12.3 Ranking Analysis of Key Players in Drone Software Market, 202112.4 Market Share of Analysis, 202112.5 Competitive Evaluation Quadrant12.5.1 Stars12.5.2 Emerging Leaders12.5.3 Pervasive Companies12.5.4 Participants12.5.5 Start-Up/Sme Evaluation Quadrant12.5.5.1 Progressive Company12.5.5.2 Responsive Company12.5.5.3 Starting Block12.5.5.4 Dynamic Company12.6 Competitive Benchmarking12.7 Competitive Scenario

13 Company Profiles13.1 Introduction13.2 Key Players13.2.1 Pix4D Sa13.2.2 Dji13.2.3 Esri13.2.4 Precisionhawk, Inc.13.2.5 Dronedeploy, Inc.13.2.6 Skydio, Inc.13.2.7 Airmap, Inc.13.2.8 Skycatch, Inc.13.2.9 Dronebase, Inc.13.2.10 Sharper Shape13.2.11 Kespry13.2.12 Yuneec, Inc.13.2.13 Sky-Futures Ltd.13.2.14 Delair13.2.15 Skyward Io13.3 Other Players13.3.1 Measure13.3.2 Sensefly Ltd13.3.3 Emesent13.3.4 Altitude Angel13.3.5 Dreamhammer13.3.6 Unifly13.3.7 Anra Technologies13.3.8 Propeller Aerobotics Pty Ltd13.3.9 Cyberhawk13.3.10 Aloft Technologies, Inc.

14 Appendix

For more information about this report visit https://www.researchandmarkets.com/r/kmmx3t

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The Worldwide Drone Software Industry is Projected to Reach $11.2 Billion by 2027 - Yahoo Finance

Cloud Seed is a new open source project developed jointly by GitLab and Google Cloud. It allows developers to provision Google Cloud services right from within GitLabs UI.

We believe that it should be trivial to deploy web applications (and other workloads) from GitLab to major cloud providers. To support this effort, Cloud Seed makes it simple and intuitive to consume appropriate Google Cloud services within GitLab, GitLab wrote on the projects documentation page.

While it is initially available for Google Cloud, GitLab hopes to work with every major cloud provider to extend it to those platforms as well.

GitLab users can use the interface to set up automated deployments to Google Cloud Run, and all Git commits, branches, and tags will be deployed to the proper Cloud Run environment.

It can also be used to provision Google Cloud SQL databases, with support for all major versions of PostgreSQL, MySQL, and SQL Service.

To learn more, visit the Cloud Seed website.

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SD Times Open-Source Project of the Week: Cloud Seed - SDTimes.com

On Tesla AI Day 2022, Elon Musk was asked if he would be willing to open-source Optimus Robot to let the whole world explore humanoid research, rather than keeping it exclusively for Tesla. In his response, Musk expressed concerns about how Optimus can be potentially used in some ways that are bad. Though Tesla would allow users and researchers to provide instructions into the humanoid, those should be governed by some laws of robotics related to safety, like not being harmful to others.

Musks POV of the risks of robotic software getting into everyones hands probably comes from his perspective on the possible dangers of AI. In an interview in 2014, Musk had said that the developments in AI are akin to summoning the demon and also wrote on Twitter about getting inspired from The Terminator movie.

When it comes to the benefits of open-source, there are plenty and obvious ones. Developers do not have to start from scratch when experimenting and building their bots. This way, researchers and start-ups can focus on what they are trying to build rather than building the architecture from scratch. Though this can also be one of the reasons for big-league robotic firms like Tesla not open-sourcing everything they put into the field for grabs.

Furthermore, if the software is open-source, researchers and developers can contribute to the project directly and benefit the robotics community at large.

The reasons to not open-source Optimus sound untrue when there are so many other robotic researchers and companies who have made their software publicly available. Probably the most notable, Boston Dynamics robot dog, Spot, was open-sourced back in January 2020. The very famous, promising, and 3D printable humanoid robot, Poppy, is also open-source and has a structure of a small version of human and biped locomotion, similar to Optimus. There are open-source humanoids including iCub, InMoov, and Thormang3, among others.

The much-awaited robot from Tesla received mixed reviews from across the robotics community. A large number of experts were excited as well as sceptical about Optimus as Musk did not hire academic experts of robotics.

Animesh Garg, assistant professor at the University of Toronto, said that though Tesla has done an astounding job in a year to develop Optimus, the company could have achieved more by being more open to the community and receiving feedback while working on their projects.

Gary Marcus from Robust.AI also showed disappointment about the vision of Optimus calling it a bit of a dud.

Not just robotics, several fields related to AI have witnessed improvements after their developers decided to open-source them. For example Stability.AI made Stable Diffusion open-source and that led to huge innovations in image generation. Unlike DALL-E or Midjourney, Stable Diffusion can run on consumer GPUs now. Emad Mostaque, the developer behind the software, posted on Twitter, Use this in an ethical, moral, and legal manner.

DeepMinds AlphaFold was also made available to the public. This protein-fold prediction software might probably also fall under the same degree of danger as robotics. But after the code was made available on GitHub, researchers have been making innovations like creating completely new folds in protein using AlphaFold, thus advancing the field even further.

On the Lex Fridman Podcast, Musk spoke extensively about how Tesla has the most-advanced real-world AI. I havent really thought about this, but there could be a time when there are millions of Tesla robots on the street, said Musk. And our goal starts with building a general-purpose help robot. We have been trying to build cars for a while now, which are essentially a robot with four wheels. Building a robot that is beneficial to the world comes with a great responsibility.

Before Tesla AI Day, Musk tweeted saying, The point of the AI Day is to show the immense depth & breadth of Tesla in AI, compute hardware & robotics. Engineers and researchers willing to dive deep into the field are moved by Musks freedom and passion to work for the betterment of humanity. But keeping their developments and innovations behind closed doors will not attract talented people who are motivated to move forward in the field.

Musk believes in empowering more engineers, and making innovations beneficial for humanity is what drives him. A probable reason for not open-sourcing Optimus could be that Tesla wants privacy and complete ownership of development of robots in the world. Being the experts in the race, Tesla is making advancements, though not quickly, but with great precision and competence.

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Should We Have An Open-source Humanoid? Elon Musk says 'No!' - Analytics India Magazine

Pro-Russian hacking group Killnet has claimed credit for a series of distributed denial-of-service (DDoS) attacks executed against US airport websites on October 10. Several websites for airports across the US were affected, including Los Angeles International Airport (LAX), Chicago OHare (ORD), and Atlanta Hartsfield-Jackson International. While the attacks did take down websites for some time, it appears that airport operations were not affected. But these DDoS attacks, and the motivation behind them, raise questions about growing cyber threats to critical infrastructure.

These DDoS attacks are not the first time Killnet has made headlines. Just weeks before, the hacktivist group claimed credit for cyberattacks against the Colorado, Kentucky, and Mississippi state government websites. The Cybersecurity & Infrastructure Security Agency (CISA) released an alert in April (updated in May) on Russian state-sponsored and criminal cyber threats facing the critical infrastructure sector. The alert featured a number of threat actors targeting critical infrastructure, including Killnet.

Airports were able to restore function to their websites relatively quickly following the DDoS attacks, but it is important to note the vulnerabilities attackers were able to exploit. FlyLAX.com, for example, operates utilizing the Nginx server, which is particularly vulnerable to attacks given its open-source nature. Open-source code is easy for hackers to exploit, and it is slow to be patched, Richard Gardner, CEO of technology company Modulus, explains. He recommends moving away from open-source servers and code to help prevent cyberattacks.

DDoS attacks like this do not cause damage to underlying systems, but that doesnt mean they can be easily dismissed. Attacks like these erode the confidence in our cybersecurity protection for critical infrastructure services we rely on, Matt Hayden, vice president of cyber client engagement at IT company General Dynamics Information Technology (GDIT) and former assistant secretary for cyber, infrastructure, risk, and resilience policy at the US Department of Homeland Security, points out.

In light of Russias ongoing war in Ukraine, pro-Russian threat actors are likely to continue targeting countries that support Ukraine. CISA warned that Russias invasion of Ukraine could expose organizations both within and beyond the region to increased malicious cyber activity in its April alert.

Killnet rallied supporters by posting its intended targets on messaging service Telegram. These DDoS attacks were successful in causing disruption and garnering significant amounts of media attention, and other threat actors could be interested in achieving that same success.

Even if Killnet remains focused on DDoS attacks to shake American confidence in its institutions, because this was an ideological attack, it is likely that there will be others who are inspired to pick up the mantle and escalate, Gardner says.

DDoS attacks are on the rise in 2022. Web performance and security company Cloudflare reported that it has seen some of the largest ever DDoS attacks in the second quarter of this year. In Q2, application-layer DDoS attacks were up 72% year-over-year, and network-layer DDoS attacks were up 109% year-over-year.

Victims of DDoS attacks may escape more serious damage, such as leaked data, but their vulnerability to cyber threats is now public knowledge. After being hit with a DDoS, it is important to identify the type of attack that occurred and the source(s) of the attack. This should be used to evaluate architecture or application security changes that can be used to mitigate or stop future attacks, says Sally Vincent, senior threat research engineer at IT security company LogRhythm. Organizations hit by a KillNet DDoS attack should evaluate their entire attack surface in case KillNet switches tactics or uses DDoS to cover up other attacks.

Using an onslaught of requests to overwhelm and crash websites, DDoS attacks are a relatively rudimentary tool for threat actors. Critical infrastructure is also an appealing target for attacks that do more lasting damage than DDoS campaigns. My grave concern is that these DDoS attacks serve as a smokescreen for [a] long-term intrusion campaign, Tom Kellermann, CISM, senior vice president of cyber strategy at security technology company Contrast Security, cautions.

Critical infrastructure is certainly susceptible to cyberattacks. With distributed assets and a mix of legacy and modern equipment, real-world operations have been incredibly difficult to secure, making them prime targets for ransomware and nation state attacks, says Roman Arutyunov, co-founder and vice president of products for zero-trust security company Xage.

Killnets latest attacks are an opportunity to examine critical infrastructure cybersecurity and prepare for potentially more damaging attacks that could lead to widespread service disruptions affecting critical services like power, fuel, supply chain, and healthcare.

Adopting cybersecurity best practices, like zero trust and vulnerability scanning, can help potential targets protect themselves from DDoS attacks. Vincent also recommends threat intelligence monitoring. Targets may be announced ahead of attacks; Killnet named the airport website targets on Telegram and called for support.

Given their [Killnets] motivations, Id suspect that they will likely continue to target critical infrastructure in NATO countries, and well need to be ready for it, Arutyunov concludes.

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DDoS Attacks on US Airport Websites and Escalating Cyberattacks - InformationWeek

IT pros who want to move their applications to the cloud will benefit from the combination of Asperitas modernization framework and CASTs software intelligence technology

CHICAGO, Oct. 13, 2022 (GLOBE NEWSWIRE) -- Asperitas, a cloud services company, and CAST, the leading provider of software intelligence technology, announce a partnership to help enterprise IT professionals more quickly and successfully modernize and move their applications to the cloud.

Deploying an application to the cloud requires more than moving it from on-premises infrastructure. Getting it right means analyzing software source code to understand an applications technical readiness, including its security posture, dependencies, overall composition and more, and having the skills and experience to manage the modernization and migration. The partnership between Asperitas and CAST aims to make this process faster and safer for IT professionals in large and midsize enterprises.

Asperitas Application Modernization Framework leverages key tenets of modern cloud architecture such as containerization, serverless, continuous integration, continuous delivery and infrastructure as code, as a lens to examine clients desired business outcomes, application architecture and organizational business processes. Asperitas then works with its clients to identify concrete, actionable recommendations that will enable clients to capitalize on their cloud investment in a cost-effective, secure manner. The framework has been successful for large and upper mid-market enterprises across industries, including dozens of Fortune 500 companies.

Through the partnership, Asperitas specialists will use CAST Highlight, which automates analysis of an applications source code to determine its cloud readiness, open-source risk, resiliency and agility. Using CAST Highlight, Asperitas can quickly identify application code that may not be fully prepared to take advantage of the clouds dynamic scaling capabilities. Asperitas can also use CAST Highlight to help clients prioritize the order in which applications are moved to the cloud.

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CAST automates much of the application architecture discovery process, which allows our team to focus on providing clients a full view of their application modernization opportunity and a roadmap for success, said Derek Ashmore, Application Transformation Principal at Asperitas. When paired with our framework, CASTs software can help clients identify an approach to modernizing their application architecture in a cloud-friendly way.

Asperitas application modernization framework is uniquely suited to leverage the intelligence CAST products provide, said Rado Nikolov, EVP Software Intelligence Platforms, CAST. Partnering with Asperitas further expands our ecosystem, making it easier for organizations to reap the benefits of using software intelligence as they modernize their critical applications, the brains of the business.

About Asperitas

Founded in 2016, Asperitas Consulting helps organizations capitalize on the value of the cloud through its unique, holistic approach to cloud adoption. For mid-market and large enterprises, the approach maximizes cloud benefits, while Asperitas multi-disciplined expertise streamlines the adoption process. The Asperitas team has worked on complex solutions for many of the worlds largest, industry-leading enterprises. Our team of highly sought-after industry specialists cover the full spectrum of technologies, which is critical in successfully implementing a cloud-enabled enterprise. Asperitas is a Microsoft Azure and AWS partner.

For more information visit http://www.asperitas.consulting.

About CAST

CAST is the software intelligence category leader. CAST technology can see inside custom applications with MRI-like precision, automatically generating intelligence about their inner workings - composition, architecture, transaction flows, cloud readiness, structural flaws, legal and security risks. Its becoming essential for faster modernization for cloud, raising the speed and efficiency of Software Engineering, better open source risk control, and accurate technical due diligence. CAST operates globally with offices in North America, Europe, India, China. Visit http://www.castsoftware.com.

Contact:

Kevin Wolf

kevin@tgprllc.com

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Asperitas and CAST partner to accelerate clients application modernization initiatives - Yahoo Finance

Apple is reportedly working on a new version of the Messages app that could be released alongside its mixed-reality headset next year.

Twitter leaker known as "Majin Bu" today claimed that Apple is working on a completely new version of iMessage, featuring a new home view, chat rooms, video clips, and more. The app purportedly offers "new chat features in AR" and, as such, it "should" be released next year alongside Apple's headset.

iOS 16 introduced a range of new features for the Messages app, including the ability to edit or delete a recently sent message, mark conversations as unread, and collaboration invitations and activity updates, so further iteration next year seems believable especially if it is linked to the launch of Apple's headset.

Bloomberg's Mark Gurman, who often reveals accurate insights into Apple's plans, has said that Apple's mixed-reality headset will focus on gaming, media consumption, and communication. He believes that Memojis and SharePlay could be central to the experience. iOS 16 brought SharePlay to Messages, making it possible for multiple users to enjoy synced content like movies or songs with shared playback controls while chatting. This would necessitate a convenient experience for initiating SharePlay sessions in the Messages app on Apple's headset.

The headset itself is rumored to run an entirely new operating system called "rOS" or "realityOS," internally codenamed "Oak." Apple's work on realityOS has been rumored since 2017, but the existence of the operating system was finally confirmed earlier this year when references to it were found in App Store upload logs and Apple open source code. realityOS will presumably feature versions of many of Apple's existing apps, so a wider revitalization of the Messages app seems plausible, particularly if the app will be important to collaborative experiences on the device.

Majin Bu has revealed some accurate information about Apple's plans, particularly with regards to cases and color options, and shared the rumor of a 14.1-inch iPad before display analyst and heavyweight Apple leaker Ross Young did. That being said, their reputation with regards to software is much more variable. In March, Majin Bu shared information about a new smart multitasking system for iPadOS that would be exclusive to M1 iPads. It is unclear whether this information was legitimate since it could have referred to an early version of what is now known to be Stage Manager.

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Apple Could Be Planning to Redesign Messages App Next Year - MacRumors