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Angels of the Zariman is Warframe’s foundation to the future

We spoke to Digital Extremes about the latest Warframe expansion

The New War was the culmination of years, almost a decade, of storytelling. In its aftermath, Digital Extremes have begun to lay the foundations for Warframe’s future with the Angels of the Zariman update.

It’s the beginning of something new and to get a sense of where Warframe is and where it’s going, we spoke to Rebecca Ford, Live Operations & Community Director at Digital Extremes.

“We said to ourselves, The New War is out, it’s finally out, let’s follow it up with the most to do that a player has ever had offered to them in, effectively, a quest follow-up,” Rebecca says of their plans for Angels of the Zariman. It brought with it a new tileset, a new hub, new mission types, new quests, new weapon systems, a focused rework and customisable apartments. “It was definitely the biggest update on the heels of another update in recent memory. Which came with its challenges for us to support everything perfectly, I think the blemishes that came with this update we have to work through constructively…And we have another update coming out pretty soon, that we’re treating as the follow up to most of the thorough feedback from the initial launch.”

Balancing Angels of Zariman

But how does the team balance their intent for Warframe with the feedback they receive from its players? “[After] nine years of doing this, the one reality we always have is we’re never gonna get it right the first time,” Rebecca explains, highlighting just how long Digital Extremes have been shaping their game. “But if we have intention, we can better calibrate ourselves to make changes.”

It helps that the team have more tools than just reading players’ responses to gather feedback on updates. “A really good example of the difference between a “data” difficult and a “feel” difficulty, is the quest we released with this update. We could see the wall of difficulty for it based on fall off points for players that couldn’t get through the mission and that right there is a must solve hard date situation. And then there was the people pushing themselves to go further in the endless missions, in the more “feel” category.”

New characters, stories and Warframes

What of the new characters? Digital Extremes knew that the player would need someone to interact with aboard the Zariman for the purposes of story but the question was, how could anyone have survived this long aboard the doomed vessel? The ingenious answer was that perhaps nobody did. “There were a lot of paths getting us to what they ended up being…but we always knew we needed some version of survivors on the Zariman that were more than what they seemed.” So the Holdfasts came to be, Warframe’s sci-fi answer to ghosts, vestiges of the crew recreated in the void, made more human by the player’s presence, who can fill in the blanks of their memories. Should they drift too far from their sense of self and be drawn to the mysterious song emanating form the void, they become Angels, monstrous foes that exist in both dimensions, posing a proper new threat for long term players. 

For those eager to know what exactly is on the horizon for Warframe, they won’t have too long to wait. “Make sure that you’re logged in and ready at Tennocon for a fun player activity…because it’s a digital event, so we’re doing some digital things and you won’t want to miss it.”

The Angels of the Zariman update is out for Warframe now and Tennocon takes place on the 16th of July.

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The Best Of Ces 2012: Popsci’s Products Of The Future

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It’s often tough to find a clean signal in all the noise of CES, but in putting this list together, we realized how excited we are about a lot of the new gear we saw this year. CES 2012 brought us the best TV we’ve ever seen, two killer new cameras, a fridge that can chill beer cans in five minutes, high-speed in-car mobile wireless, awesome new smartphones, and a lot more. Check out our picks for the Products of the Future in the gallery below.

Nokia Lumia 900

Modular Robotics Cubelets

The simplest way to wrap your head around what exactly the Cubelets are is to think of them as robotic Legos. Sold as a six-block starter kit, the Cubelets are pre-programmed 1.5-inch blocks, each with its own action—to move, sense nearby objects, display light, whathaveyou—and the way you stack them determines what your final robot will do. Snapping a battery block on top of a motion-sensing block and a roller block, for instance, will great a ‘bot that automatically moves when the lights go on (or off). Modular Robotics sells add-on blocks with other traits (sound sensitivity, loudspeakers, etc.) and will add a Bluetooth block this year, allowing users to re-program their bricks over a simple Web interface.

Samsung Super OLED TV

OLED televisions are super thin, ultra-contrasty, and have colors that are saturated to the point of near surreality. But other than Sony’s dimunitive XEL-1, which has been on sale since 2008, they’re usually trotted out at CES only as dreamy concept showpieces. Samsung’s 55″ Super OLED, though, marks an important moment in TVs: large-panel OLEDs are now practical to manufacture and sell. No word on price (it will be exorbitant, surely), but Samsung stated plainly that the set would be available for purchase this year. And good thing: after you’ve stood in front of this OLED beauty, it’s hard to look at normal TVs the same way again.

Basis Band

LG’s French Door Refrigerator With Blast Chiller

This fridge wants us to never deal with warm beer ever again, and for that we are eternally thankful. Its special blast chiller compartment can take your beverage of choice from room temperature to a delicious 42º F in no time—5 minutes for a single can, or 8 minutes for two cans or a bottle of wine. A gentle rocking motion exposes the liquids to the cold evenly, without leaving you with a fizzy carbonation bomb upon opening. See a video of the chiller in action here.

MakerBot Replicator

The 3-D printer you build yourself is new and improved for 2012, now sporting a larger building area and twin extruders for printing with two colors (or entirely different materials) simultaneously. Your very own personal assembly line for under $2,000.

MicroVision PicoP Gen2 HD Laser Projector

If there’s one thing we love at PopSci, it’s a pico projector. And, well, we’ve seen a lot of them over the last three years. But MicroVision’s new PicoP Gen2 is sorta the one we’ve been looking for. Not only is it the first pico to display 720p HD video, it’s also the first one we’ve seen that’s small and efficient enough to be built into more things that just standalone projectors–we’re talking projector phones that aren’t bricks and even portable gaming systems that can be standalone mobile entertainment centers.

Fujifilm X-Pro1

Parrot ZIK Headphones

There are always tons of headphones at CES. So why’d we pick out these wireless ones from Parrot, a company known for fun flying drones rather than audio equipment? A few reasons: Designed by Philippe Starck, a well-known industrial designer, the headphones look awesome, all black leather and curved silver metal. The way you use them is really, really cool: they have controls built in, but not in any boring way like an inline remote or (and here we utter a bad-design shudder) some play/pause/forward/back buttons on the outside of the ear cups. Instead, it uses a proximity sensor to figure out when you’re wearing and when you’ve taken the headphones off, and it pauses automatically when you remove them. To change the volume, you gently stroke the ear cups up and down, and to go to the next or previous track, you stroke left and right. (You can see in this picture that our own John Mahoney got pretty into the stroking part of this.) The ZIK has a bunch of other features too: it’s got self-contained noise cancellation (Parrot says the batteries last about five hours with the battery-draining cancellation turned on), Bluetooth to connect, and even NFC, which to my knowledge has never been implanted into a pair of headphones before. And they’re super comfortable. Audio nerds: we only tested them in the midst of a raucous western-themed press event, so we can’t vouch for audio quality in any respectable way. They sounded pretty good but we can’t comfortably say much more than that. They’ll be available sometime this year for an undisclosed (but undoubtedly steep) price.

Sennheiser RS220

Thanks to a new wireless streaming standard, the RS220 home-theater headphones may well be the best-sounding wireless pair you can get. The pair transmit uncompressed audio over the 2.4GHz range—yes, the same as Kleer and other high-end transmission standards—but this new DSSS trick modulates the signal across several clustered frequencies, and the headphones recompose the signal at the other end. The result: better dynamic range and super low-latency, which might not matter as much when listening to music, but makes a world of difference when you need to sync with a TV screen.

Lenovo IdeaPad Yoga

It’s become clear this week that 2012 is the Year of the UltraBook, but in reality there’s not a lot to distinguish one super-trim laptop from another. Unless we’re talking about the IdeaPad Yoga, which in this case we happen to be. The Windows-running clamshell can morph into any of four form factors. From standard laptop clamshell, rotate the keyboard behind the screen (like the cover of a spiral notebook) to enter tablet-style mode with the keyboard deactivated. Or, use the keyboard as a stand in either a sandwich-board-style orientation or a right-angle hinge.

Mobile High-Definition Link

Mobile High-Definition Link, or MHL, wasn’t announced at CES 2012, but it was during CES that we at PopSci really started to get excited about it. It’s a new kind of technology that can be applied to just about any connector, like HDMI, USB, or any kind of proprietary port (including Apple’s iPod/iPhone/iPad port), and it essentially gives those dumb old connectors a whole bunch of new powers. Some of those, like HDMI, for example, can’t deliver power. But an MHL-supporting HDMI port sure can. That’s how we can get things like the Roku Streaming Stick, which is an entire Roku the size of a USB thumb drive that plugs right into your TV’s HDMI port. Other cool features include the ability to control anything plugged into an MHL-enabled HDMI port with your TV’s remote–no need to have several remotes scattered around anymore.

Able Planet Personal Sound Amp PS2500AMP

Unlike the majority of personal hearing aids, Able’s Planet’s Personal Sound Amp tucks almost entirely inside the ear canal—nearly invisibly so. Like a noise-canceling headphone pair, the Amp senses what noise it’s up against—wind, music, the din of a loud room—and automatically tunes itself to cancel out those noises. FI the wearer is still having trouble hearing (ie: if the earbud has yet to re-tune to the room), he can cup his hand over his ear; the change in pressure from that action tells the Amp to re-tune itself.

OnStar & Verizon Wireless Chevy Volt with LTE

The promise of Verizon’s 4G LTE network has long—well, since 2010—been the ability to stream audio and video consistently from anywhere, even if you’re moving. Until now there are have been demos involving telepresence robots and LTE-equipped broadcast cameras, but the new OnStar shows LTE the way a real person would use it: in a car. LTE connectivity allows the system to constantly connect to road-trip-friendly cloud services like Skype and Pandora. Netflix? Maybe not the best idea.

Canon PowerShot G1 X

Continuing the theme of exceptional image quality in ever-smaller packages, the PowerShot G1 X is an entirely new beast for Canon—a camera system within itself. Forgoing a buy-in to an interchangeable lens system to keep costs down, the G1 X offers a fixed 4x zoom feeding light into a brand new CMOS sensor that’s just a hair smaller than those found in most DSLRs.

Corning Gorilla Glass 2

If you have a modern smartphone, chances are you have some Gorilla Glass in your pocket. At CES, Corning announced a new formula for their chemically-strengthened glass that’s 20 percent tougher, which means tablet touchscreens and notebook LCDs can go 20 percent thinner without sacrificing strength and durability. The profusion of slim, MacBook-Air-like ultrabooks this year is no coincidence; Gorilla Glass is one of the enabling technologies pushing our gadgets ever-sleeker.

The Future Of Machine Learning: Automl

Do you ever wonder how companies develop and train machine learning models without experts? Well, the secret is in the field of Automated Machine Learning (AutoML). AutoML simplifies the process of building and tuning machine learning models for organizations to harness the power of these technologies. Figure 1 gives a visual AutoML. In this blog, we’ll explore a look at some of its key benefits and limitations. Get ready to be amazed by the power of AutoML.

Learning Objectives

Understand the basics of AutoML and its methods

Explore the key benefits of using AutoML

Understand the limitations of AutoML

Understand the practical impact of AutoML

This article was published as a part of the Data Science Blogathon.

Table of Contents

What is AutoML?

Methods of AutoML: A Comprehensive Overview

Effortless ML: The Merits of AutoML

AutoML: A Closer Look at the Drawbacks

AutoML in Practice: How Companies are Automating Machine Learning?


What is AutoML? The Future of Machine Learning

AutoML is a game-changer in the field of machine learning. It is a training of machine learning models to automate the process of selecting and tuning algorithms. This includes everything from data preprocessing to selecting the most suitable model for the given task. AutoML tools handle hyperparameter tuning and model selection tasks, which typically require time and expertise. With AutoML, users without experience in machine learning can train high-performing models with minimal effort. Whether you’re a small business owner, a researcher, or a data scientist, AutoML helps to achieve your goals with less time and effort. Examples of popular AutoML platforms include Google Cloud AutoML, chúng tôi and DataRobot.

AutoML provides explainable AI to improve the interpretability of the model. This allows data scientists to understand how the model makes predictions, which is particularly helpful in healthcare, finance, and autonomous systems. This can be used to identify bias in data and prevent wrong predictions. For example, AutoML can be used in healthcare fo gnosis by analyzing medical images, in finance for fraud detection, in retail for product recommendations, and in transportation for self-driving cars. Figure 2 shows the AutoML process.

ethods: A Comprehensive Overview

AutoML automates the use of machine learning for real-world problems. This includes tasks such as algorithm selection, hyperparameter optimization, and f rent methods are being developed to tackle the various aspects of the problem. Some popular approaches are given below

Neural Architecture Search (NAS):

This method uses a search algorithm to automatically find the best neural network architecture for a given task and dataset.

Bayesian Optimization: This method uses a probabilistic model to guide the search for the best set of hyperparameters for a given model and dataset.

Evolutionary Algorithms: This method uses evolutionary algorithms such as genetic algorithms or particle swarm optimization to search for the best set of model hyperparameters.

Gradient-based methods: This method uses gradient-based optimization techniques like gradient descent, Adam, etc., to optimize the model hyperparameters.

Transfer Learning: This method uses a pre-trained model on a similar task or dataset as a starting point and then fine-tunes it on the target task and dataset.

Ensemble methods: This method combines multiple models to create a more robust and accurate final model.

Multi-modal methods: This method uses multiple data modalities such as image, text, and audio to train models and improve performance.

Meta-learning: This method uses a model to learn how to learn from data, which can improve the efficiency of the model selection process.

One-shot or few-shot learning: This method can learn to recognize new classes from only one or a few examples.

AutoML is broadly classified into a model selection and hyperparameter tuning, as shown in Fig 3. Many differen integrated into existing workflows.

Effortless Machine Learning: The Merits of AutoML in Machine Learning

AutoML simplifies the machine learning process and brings many benefits, some of which are given below:

Time-saving: Automating the process of model selection and hyperparameter tuning can save a significant amount of time for data scientists and machine learning engineers.

Accessibility: AutoML allows users with little or no experience with machine learning to train high-performing models.

Improved performance: AutoML methods can often find better model architectures and hyperparameter settings than manual methods, resulting in improved model performance.

Handling large amounts of data: AutoML can handle large amounts of data and find the best model even with more features.

Scalability: AutoML can scale to large datasets and complex models, making it well-suited to big data and high-performance computing environments.

Versatility: AutoML can be used in various industries and applications, including healthcare, finance, retail, and transportation.

Cost-effective: AutoML can save resources and money in the long run by reducing the need for manual labor and expertise.

Reduced risk of human error: Automating the model selection and hyperparameter tuning process can reduce the risk of human error and improve the reproducibility of results.

Increased Efficiency: AutoML can be integrated with other tools and processes to increase efficiency in the data pipeline.

Handling multiple data modalities: AutoML can handle multiple data modalities such as image, text, and audio to train models and improve performance.

AutoML offers several benefits for data scientists and engineers that save time and resources by automating tedious and time-consuming tasks. This also improves the interpretability of the model by providing explainable AI. These combined benefits make AutoML a valuable tool in many industries and applications.

AutoML: A the Drawbacks

AutoML has become a popular tool for data scientists and analysts. However, it has limitations. There are following limitations are given below

Limited control over the model selection and hyperparameter tuning process: AutoML methods operate based on predefined algorithms and settings, and users may have limited control over the final model.

Limited interpretability of the resulting model: AutoML methods can be opaque, making it difficult to understand how the model makes its predictions.

Higher costs than manually designing and training a model: AutoML tools and infrastructure can be costly to implement and maintain.

Difficulty in incorporating domain-specific knowledge into the model: AutoML relies on data and pre-defined algorithms, which can be less effective when incorporating domain-specific knowledge.

Potential for poor performance on edge cases or unusual data distributions: AutoML methods may not perform well on data that is significantly different from the training data.

Limited support for certain models or tasks: AutoML methods may not be well-suited to all models or tasks.

Dependence on large amounts of labeled data: AutoML methods typically require large amounts of labeled data to train models effectively.

Limited ability to handle data with missing values or errors: AutoML methods may not perform well on data with missing values or errors.

Limited ability to explain the model’s predictions and decisions: AutoML methods can be opaque, making it difficult to understand how the model makes its predictions, which can be an issue for certain applications and industries.

Overfitting: AutoML methods may lead to overfitting on the training data if not properly monitored, which can result in poor performance on new unseen data.

AutoML is a powerful tool for automating the machine-learning process, but it is with its limitations. It is important to consider these limitations in the presence of expert supervision to validate the results.

AutoML in Practice: How Companies are Automating Machine Learning?

A few practical examples of AutoML are given below:

Google’s AutoML Vision allows users to train custom machine-learning models for image recognition using th mage datasets’s AutoML enables data scientists and analysts to automatically train and optimize machine learning models without having to write code

DataRobot provides an AutoML platform that can automatically build, evaluate and deploy machine learning models for a wide range of use cases, including fraud detection, customer churn prediction, and predictive maintenance

Amazon SageMaker is a fully managed service that enables data scientists and developers to quickly and , train, and deploy machine learning models at scale

IBM Watson AutoAI is a platform that automates the process of building, training, and deploying machine learning models and provides interpretability and explainability features that help users understand the models’ decision-making processes

Microsoft Azure ML is a cloud-based platform that provides a wide range of tools and services for building, deploying, and managing machine learning models, including AutoML capabilities.

These are a few examples of how companies leverage AutoML in different industries to automate model building and hyperparameter tuning, allowing data scientists to focus on model selection and evaluation.


AutoML automates the process of building and tuning machine-learning models. This method uses algorithms to search the best model and hyperparameters rather than relying on human expertise. AutoML includes increased efficiency and the ability to handle large amounts of data. It can be useful in the shortage of experienced machine learning practitioners. However, there are also limitations to AutoML. It can be computationally expensive and difficult to interpret the results of the automated search process. Additionally, the practical use of AutoML is limited by the data’s quality and computational resources’ availability. In practice, AutoML is mainly used in an indus prove productivity and model performance in scenarios like image, speech, text, and other forms of data.

Key Takeaways:

Simplify the process of building and training models.

AutoML suffers limitations such as a lack of control over the model selection process, huge data requirements, computationally expensive, and overfitting issues.

Expert supervision is important to validate the results of AutoML to counter available limitations.

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What Is Copywriting And Can Ai Be The Future Of Copywriting?

Although copywriting is an essential part of any business, online or offline it can also be a significant investment. You can either hire many freelancers or have your own copywriters. It is a large investment and takes time. Potential customers will see your brand through emails, product descriptions, and articles.

Low-quality content can lead to negative results. Employees have the option of using AI software to write copy. These software programs are not only cheaper but also more efficient than traditional copywriting software. The question is: Is AI software the future of content marketing and copywriting?

What’s copywriting?

Before we get into AI (” AI”) or copywriting, let’s first explain what copywriting is. To create written content for companies, copywriting is essential. This style of writing is more focused on selling products than entertaining.

Copywriting is also a part of SEO. SEO uses specific keywords to create articles that can be found easily on search engines such as Google.

A copywriter’s job is to create high-quality content to sell the product or service, while also using SEO skills to rank the article highly on search engines. This sounds like a job you would love to do. It is! It is!

Can AI be the future of Copywriting?

Both yes and no. Yes and no! While AI offers many potential uses in copywriting, it is lacking variety at the moment. AI software programs might be helpful if your goal is to create as much generic content as possible.

However, if you are trying to reach a large audience and get them to continue reading, AI is not the right tool for you. AI has been proven to be successful in some areas, including AI trading robots that automate trades.

No emotions

AI can only implement specific keywords and information. It cannot apply emotions. If your goal is to sell products you must have articles that elicit an emotional response from the reader. This is something that only a skilled copywriter can achieve.

Just reproduction

AI software programs cannot reproduce content because they only take information and phrases from existing articles. Although they may have put these phrases together in a unique way, it is still reproduction at the end.

This must be double-checked

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AI copywriting can be used for product descriptions

Does that mean AI copywriting doesn’t work? AI copywriting programs can be used to create product descriptions. This is especially true if there are many products. These descriptions don’t require emotion or exceptional writing skills. Instead, you should simply give basic information about your product.

The Future Of Nfts? Legal Experts Respond To The Hermès Lawsuit

In 2023, new rules and regulations are entering the NFT space at an unprecedented pace. The collapse of FTX kicked Web3 regulation into overdrive, and prominent NFT projects are being investigated for fraud. Yet, perhaps the best evidence of this turning of the tide is the recent resolution of Hermès v. Rothschild trademark lawsuit.

Last year, Hermès International sued artist Mason Rothschild for trademark infringement following the release of MetaBirkins — a collection of 100 NFT Birkin bags covered in faux fur in a range of colors and designs. On February 8, 2023, Hermès won the lawsuit. A jury found that Rothschild’s collection of NFT purses bore such a striking resemblance to Hermès Birkin bags that it was “likely to cause consumer confusion and mistake in the minds of the public.” Hermès ultimately won the lawsuit after only six days of proceedings in a Manhattan courtroom.

While many expected a ruling stating that the sale of the NFTs violated Hermès’ rights to the “Birkin” trademark, the finding that Rothschild’s NFTs aren’t protected speech under the First Amendment understandably stirred up a bit of conversation throughout Web3. The situation — and what it means for the future of Web3 — is best distilled through the reactions of the lawyers and attorneys with an understanding of the case.

What lawyers and attorneys have to say

In a statement sent to nft now, Jonathan Harris, a lawyer for Rothschild, implied that the lawsuit would be a blow to independent artists everywhere and a boon for big brands. Specifically, he stated that the decision marked a “good day for luxury brands” and a “bad day for artists.” Another of Rothschild’s attorneys, Rhett Millsaps, issued a similar statement to nft now. “Great day for big brands. Terrible day for artists and the First Amendment,” he said.

Speaking to the Financial Times, Gaëtan Cordier, partner at Eversheds Sutherland in Paris, said it was an “important decision” and a reminder that a lack of regulation does not mean people are free to do as they please with no ramifications. Ultimately, she argued that it sends a “message to NFT developers, reminding them that in the absence of specific regulations, intellectual property standards that apply in the physical world as well as on the internet remain applicable to NFTs.”

Meanwhile, Megan Noh, an art lawyer unaffiliated with the case, went on the record arguing that the closing of the case will likely open the floodgates and lead to a host of new brands entering Web3. “Some brand owners have likely been waiting for better guideposts before jumping into Web3 and enforcing their marks in that space,” she said to the New York Times. Noh went on to add that this verdict would finally provide brands with some needed guidance, “specifically in the context of digital artworks and collectibles, about the line between works of artistic expression and commercial goods.”

In a previous article by nft now, Andrew Rossow, an attorney who focuses on fintech and intellectual property law, noted that the case will ultimately determine how future Web3 cases are decided. “Hermès’ lawsuit against Rothschild will undoubtedly set the stage for how intellectual property is applied to the world of digital assets and NFTs. As more luxury brands enter into the metaverse and launch their respective NFT projects, courts will be required to weigh in on the confines and parameters of what it means to introduce originality while balancing artistic expression and the right to create,” he wrote.

However, statements made by David Leichtman, Managing Partner at Leichtman Law, indicate that the case may not have as wide of an impact as many believe. Speaking on CoinDesk TV, Leichtman noted that the case wasn’t really about what qualifies as art or even Rothschild’s use of the Birkin brand in his work. Rather, he noted that the case was specifically about whether Rothschild intended to mislead consumers into thinking that MetaBirkin NFTs were associated with Hermès. “The question is, were [consumers] really going to be confused by the MetaBirkins, whether or not the relevant consuming audience for Hermès products would be confused by the defendant’s works,” he said.

Rebecca Tushnet, a Harvard Law School professor who helped prepare Rothschild’s defense, seemingly reinforced Leichtman’s understanding of the case being more about intent than freedom of speech and the First Amendment. In a statement, she noted that “you can’t hold someone liable for infringement unless their work is artistically irrelevant or explicitly misleading.”

The takeaways

Who is right? It’s difficult to say at this point. But one thing, at least, is certain. This case will set the tone for future proceedings on how intellectual property law is applied in Web3. And in light of the derivative and copycat NFT collections that are frequently launched in response to notable brands (like Porsche) entering the space, Web3 creators should think carefully before launching — or buying — new NFTs.

The Future Of Artificial Intelligence In Manufacturing

Industrial Internet of Things (IIoT) systems and applications are improving at a rapid pace. According to Business Insider Intelligence, the IoT market is expected to grow to over $2.4 trillion annually by 2027, with more than 41 billion IoT devices projected.

Providers are working to meet the growing needs of companies and consumers. New technologies, such as Artificial Intelligence (AI), and machine learning make it possible to realize massive gains in process efficiency. 

With the growing use of AI and its integration into IoT solutions, business owners are getting the tools to improve and enhance their manufacturing. The AI systems are being used to: 

Detect defects

Predict failures

Optimize processes

Make devices smarter

Using the correct data, companies will become more creative with their solutions. This sets them apart from the competition and improves their work processes.

Detect Defects

AI integration into manufacturing improves the quality of the products, reducing the probability of errors and defects.

Defect detection factors into the improvement of overall product quality. For instance, the BMW group is employing AI to inspect part images in their production lines, which enables them to detect deviations from the standard in real time. This massively improves their production quality.

Nokia started using an AI-driven video application to inform the operator at the assembly plant about inconsistencies in the production process. This means issues can be corrected in real time. 

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Predict Failures

Predicting when a production line will need maintenance is also simple with machine learning. This is useful in the sense that, instead of fixing failures when they happen, you get to predict them before they occur.

Using time-series data, machine learning models enhance the maintenance prediction system to analyze patterns likely to cause failure. Predictive maintenance is accurate using regression, classification, and anomaly detection models. It optimizes performance before failure can happen in manufacturing systems.

General Motors uses AI predictive maintenance systems across its production sites globally. Analyzing images from cameras mounted on assembly robots, these systems are identifying the problems before they can result in unplanned outages.

High speed rail lines by Thales are being maintained by machine learning that predicts when the rail system needs maintenance checks.

Optimize Processes

The growth of IIoT allows for automation of most production processes by optimizing energy consumption and predictions for the production line. The supply chain is also improving with deep learning models, ensuring that companies can deal with greater volumes of data. It makes the supply chain management system cognitive, and helps in defining optimal solutions. 

Make Devices Smarter

By employing machine learning algorithms to process the data generated by hardware devices at the local level, there is no longer a need to connect to the internet to process data or make real-time decisions. Edge AI does away with the limitation of networks.

The information doesn’t have to be uploaded to the cloud for the machine learning models to work on it. Instead, the data is processed locally and used within the system. It also works for the improvement of the algorithms and systems used to process information.

Also read: The 15 Best E-Commerce Marketing Tools

What’s Next?

The manufacturing market is seeing a huge boost thanks to the IIoT and AI progress. Machine learning models are being used to optimize work processes. 

The quality of products is getting improved by reducing the number of defects that are likely to occur. This is expected to improve over time, and it also will heavily improve the production process to reduce errors and defects in products.

There is still a huge potential of AI that has yet to be utilized. Generative Adversarial Networks (GAN) can be used for product design, choosing the best combination of parameters for a future product and putting it into production.

The workflow becomes cheaper and more manageable. Companies realize this benefit in the form of a faster time to market. New product cycles also ensure that the company stays relevant in terms of production.

Networks are set to upgrade to 5G, which will witness greater capacities and provide an avenue for artificial intelligence to utilize this resource better. It will also be a connection for the industrial internet of things and see a boost in production processes. Connected self-aware systems will also be useful for the manufacturing systems of the future.

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