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Machine Learning Trends To Impact Business In 2021-2022



Machine learning trends
Illustration: © IoT For All

Like many other revolutionary technologies of the modern day, machine learning was once science fiction. However, its applications in real world industries are only limited by our imagination. In 2021, recent innovations in machine learning have made a great deal of tasks more feasible, efficient, and precise than ever before.

Powered by data science, machine learning makes our lives easier. When properly trained, they can complete tasks more efficiently than a human.

Understanding the possibilities and recent innovations of ML technology is important for businesses so that they can plot a course for the most efficient ways of conducting their business. It is also important to stay up to date to maintain competitiveness in the industry.

Machine learning models have come a long way before being adopted into production.

Machine learning history, evolution, and future

In this article, we will discuss the latest innovations in machine learning technology in 2021 with various examples of how this technology can benefit you and your business.

Trend #1: No-Code Machine Learning

Although much of machine learning is handled and set up using computer code, this is no longer always the case. No-code machine learning is a way of programming ML applications without having to go through the long and arduous processes of pre-processing, modeling, designing algorithms, collecting new data, retraining, deployment, and more. Some of the main advantages are:

Quick implementation. Without any code needed to be written or the need for debugging, most of the time spent will be on getting results instead of development.

Lower costs. Since automation eliminates the need for longer development time, large data science teams are no longer necessary.

Simplicity: No-code ML is easier to use due to its simplistic drag and drop format.

No-code machine learning uses drag and drop inputs to simplify the process into the following:

  • Begin with user behavior data
  • Drag and drop training data
  • Use a question in plain English
  • Evaluate the results
  • Generate a prediction report

Since this greatly simplifies the machine learning process, taking the time to become an expert is not necessary. Although this makes machine learning applications more accessible to developers, it is not a substitute for more advanced and nuanced projects.

However, it may be suitable for simple data analysis predictive projects like retail profits, dynamic pricing, and employee retention rates.

No-code algorithms are the best choice for smaller companies that cannot afford to maintain a team of data scientists. Although its use cases are limited, no-code ML is a great choice for analyzing data and making predictions over time without a great deal of development or expertise.

Trend #2: TinyML

In a world increasingly driven by IoT solutions, TinyML makes its way into the mix. While large scale machine learning applications exist, their usability is fairly limited. Smaller scale applications are often necessary. It can take time for a web request to send data to a large server for it to be processed by a machine learning algorithm and then sent back. Instead, a more desirable approach might be to use ML programs on edge devices.

By running smaller scale ML programs on IoT edge devices, we can achieve lower latency, lower power consumption, lower required bandwidth, and ensure user privacy. Since the data doesn’t need to be sent out to a data processing center, latency, bandwidth, and power consumption are greatly reduced. Privacy is also maintained since the computations are made entirely locally.

This trending innovation has a great deal of applications in sectors like predictive maintenance for industrial centers, healthcare industries, agriculture, and more. These industries utilize IoT devices with TinyML algorithms to track and make predictions on collected data. For example, Solar Scare Mosquito is an IoT project which uses TinyML to measure the presence of mosquitos in real time. This can generate early warning systems for disease epidemics from mosquitos, for example.

Trend #3: AutoML

Similar in objective to no-code ML, AutoML aims to make building machine learning applications more accessible for developers. Since machine learning has become increasingly more useful in various industries, off-the-shelf solutions have been in high demand. Auto-ML aims to bridge the gap by providing an accessible and simple solution that does not rely on the ML-experts.

Data scientists working on machine learning projects have to focus on preprocessing the data, developing features, modeling, designing neural networks if deep learning is involved in the project, post processing, and result analysis. Since these tasks are very complex, AutoML provides simplification through use of templates.

An example of this is AutoGluon, an off-the-shelf solution for text, image, and tabular data. This allows developers to quickly prototype deep learning solutions and get predictions without the need of data science experts.

AutoML brings improved data labeling tools to the table and enables the possibility of automatic tuning of neural network architectures. Traditionally, data labeling has been done manually by outsourced labor. This brings in a great deal of risk due to human error. Since AutoML aptly automates much of the labeling process, the risk of human error is much lower. This also reduces labor costs, allowing companies to focus much more strongly on data analysis. Since AutoML reduces these kinds of costs, data analysis, artificial intelligence, and other solutions will become cheaper and more accessible to companies in the market.

Another example of AutoML in action is OpenAI’s DALL-E and CLIP (contrastive language image pre-training) models. These two models combine text and images to create new visual designs from a text-based description. One of the early examples of this in action is how the models can be used to generate images based on the input description “armchair in the shape of an avocado.” This technology has many interesting applications, such as the creation of original images for article SEO, creating mockups of new products, and quickly generating product ideas.

Trend #4: Machine Learning Operationalization Management (MLOps)

Machine Learning Operationalization Management (MLOps) is a practice of developing machine learning software solutions with a focus on reliability and efficiency. This is a novel way of improving the way that machine learning solutions are developed to make them more useful for businesses.

Machine learning and AI can be developed with traditional development disciplines, but the unique traits of this technology mean that it may be better suited for a different strategy. MLOps provides a new formula that combines ML systems development and ML systems deployment into a single consistent method.

One of the reasons why MLOps is necessary is that we are dealing with more and more data on larger scales which requires greater degrees of automation. One of the major elements of MLOps is the systems life cycle, introduced by the DevOps discipline.

Understanding the ML systems lifecycle is essential for understanding the importance of MLOps.

  1. Design a model based on business goals
  2. Acquire, process and prepare data for the ML model
  3. Train and tune ML model
  4. Validate ML model
  5. Deploy the software solution with integrated model
  6. Monitor and restart process to improve ML model

One of the advantages of MLOps is that it can easily address systems of scale. It’s difficult to deal with these problems at larger scales because of small data science teams, gaps in internal communication between teams, changing objectives, and more.

When we utilize business objective-first design, we can better collect data and implement ML solutions throughout the entire process. These solutions need to pay close attention to data relevancy, feature creation, cleaning, finding appropriate cloud service hosts, and ease of model training after deployment to a production environment.

By reducing variability and ensuring consistency and reliability, MLOps can be a great solution for enterprises at scale.

Kubernetes is a DevOps tool that has proved to be efficient for allocating hardware resources for AI/ML workloads, namely, memory, CPU, GPU, and storage. Kubernetes implements auto-scaling and provides real-time computing resources optimization.

Trend #5: Full-stack Deep Learning

Wide spreading of deep learning frameworks and business needs to be able to include deep learning solutions into products led to the emergence of a large demand for “full-stack deep learning”.

What is full-stack deep learning? Let’s imagine you have highly qualified deep learning engineers that have already created some fancy deep learning model for you. But right after the creation of the deep learning model it is just a few files that are not connected to the outer world where your users live.

As the next step, engineers have to wrap the deep learning model into some infrastructure:

  • Backend on a cloud
  • Mobile application
  • Some edge devices (Raspberry Pi, NVIDIA Jetson Nano, etc.)

The demand of full-stack deep learning results in the creation of libraries and frameworks that help engineers to automate some shipment tasks (like the chitra project does) and education courses that help engineers to quickly adapt to new business needs (like open source fullstackdeeplearning projects).

Trend #6: General Adversarial Networks (GAN)

GAN technology is a way of producing stronger solutions for implementations such as differentiating between different kinds of images. Generative neural networks produce samples that must be checked by discriminative networks which toss out unwanted generated content. Similar to branches of government, General Adversarial Networks offer checks and balances to the process and increase accuracy and reliability.

It’s important to remember that a discriminative model cannot describe categories that it is given. It can only use conditional probability to differentiate samples between two or more categories. Generative models focus on what these categories are and distribute joint probability.

A useful application of this technology is for identifying groups of images. With this in mind, large scale tasks such as image removal, similar image search, and more are possible. Another important application of GANs is image generation task.

Trend #7: Unsupervised ML

As automation improves, more and more data science solutions are needed without human intervention. Unsupervised ML is a trend that shows promise for various industries and use cases. We already know from previous techniques that machines cannot learn in a vacuum. They must be able to take new information and analyze that data for the solution that they provide. However, this typically requires human data scientists to feed that information into the system.

Unsupervised ML focuses on unlabeled data. Without guidance from a data scientist, unsupervised machine learning programs have to draw their own conclusions. This can be used to quickly study data structures to identify potentially useful patterns and use this information to improve and further automate decision-making.

One technique that can be used to investigate data is clustering. By grouping data points with shared features, machine learning programs can understand data sets and their patterns more efficiently.

Trend #8: Reinforcement Learning

In machine learning, there are three paradigms: supervised learning, unsupervised learning, and reinforcement learning. In reinforcement learning, the machine learning system learns from direct experiences with its environment. The environment can use a reward/punishment system to assign value to the observations that the ML system sees. Ultimately, the system will want to achieve the highest level of reward or value, similar to positive reinforcement training for animals.

This has a great deal of application in video game and board game AI. However, when safety is a critical feature of the application, reinforcement ML may not be the best idea. Since the algorithm comes to conclusions with random actions, it may deliberately make unsafe decisions in the process of learning. This can endanger users if left unchecked. There are safer reinforcement learning systems in development to help with this issue that take safety into account for their algorithms.

Once reinforcement learning can complete tasks in the real world without choosing dangerous or harmful actions, RL will be a much more helpful tool in a data scientist’s arsenal.

Trend #9: Few Shot, One Shot, & Zero Shot Learning

Data collection is essential for machine learning practices. However, it is also one of the most tedious tasks and can be subject to error if done incorrectly. The performance of the machine learning algorithm heavily depends on the quality and type of data that is provided. A model trained to recognize various breeds of domestic dogs would need new classifier training to recognize and categorize wild wolves.

Few shot learning focuses on limited data. While this has limitations, it does have various applications in fields like image classification, facial recognition, and text classification. Although not requiring a great deal of data to produce a usable model is helpful, it cannot be used for extremely complex solutions.

Likewise, one shot learning uses even less data. However, it has some useful applications for facial recognition. For example, one could compare a provided passport ID photo to the image of a person through a camera. This only requires data that is already present and does not need a large database of information.

Zero shot learning is an initially confusing prospect. How can machine learning algorithms function without initial data? Zero shot ML systems observe a subject and use information about that object to predict what classification they may fall into. This is possible for humans. For example, a human who had never seen a tiger before but had seen a housecat would probably be able to identify the tiger as some kind of feline animal.

Although the observed objects are not seen during training, the ML algorithm can still classify observed objects into categories. This is very useful for image classification, object detection, natural language processing and other tasks.

A remarkable example of a few-shot learning application is drug discovery. In this case, the model is being trained to research new molecules and detect useful ones that can be added in new drugs. New molecules that haven’t gone through clinical trials can be toxic or ineffective, so it’s crucial to train the model using a small number of samples.

Machine Learning: Powering Into the Future

With data science and machine learning, industries are becoming more and more advanced by the day. In some cases, this has made the technology necessary to remain competitive. However, utilizing this technology on its own can only get us so far. We need to innovate to achieve goals in novel and unique ways to truly stake a corner in the market and break into new futures that previously were thought to be science fiction.

Every objective requires different methods to achieve. Talking to experts about what’s best for your company can help you understand what technologies, such as machine learning, can improve the efficiency of your business and help you achieve your vision of supporting your clients.

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When to Contact an Attorney After a Car Accident



Car accident victims are frequently forced to deal with significant injuries and mounting medical expenditures following an accident. Insurance providers often provide accident victims with a lowball settlement that is only a fraction of what they require to recover. 

These insurance firms do not have your best interests at heart; they care about their shareholders and decrease their obligations. It would be best if you considered hiring a lawyer as soon as possible. Columbia injury lawyers will help to gather the necessary shreds of evidence that will be crucial to your case.

Below, we will discuss when you need to contact an attorney if you are involved in a car accident.

When Should One Contact an Accident Attorney?

1. When It’s Not Clear Who Is at Fault

In the event of an accident, no one wants to take blame and fault for the accident. Everyone will be throwing blame on each party involved just to avoid being liable and dealing with the consequences.

An accident attorney will conduct their investigations and collect the necessary evidence to show which party is liable. The earlier you contact a lawyer, the better your chances for a successful case.

2. More Than One Party Is Involved

If there were many parties involved, it’s important to contact an attorney to help you with the litigation process. An example is when a truck accident occurs; in such a scenario, you will be dealing with the driver’s liability and the manufacturers whose goods were being transported.

3. When There Is Significant Injury and Damage

The aftermath of car accidents can be fatal, with others suffering severe injuries and sometimes even death. When facing a severe injury, it can be challenging to deal with your injuries and case at the same time. Hiring the assistance of a personal injury lawyer will enable you to take care of your injuries as they work on your case.

4. Dealing With Rogue Insurance Companies

In most cases, insurance companies will try to give you a lower settlement than you deserve. This is usually because you may not know how to air your complaints. Therefore, your lawyer will negotiate with the insurance companies on your behalf to ensure you get your compensation in full and on a timely basis.

5. The Police Report Doesn’t Match What Happened

When you detect inconsistencies with the police report on what took place, a lawyer will be best brought in to guide you on the way forward. They will examine the evidence at hand and advise you on the appropriate action to take.

As we have seen above, lawyers play a vital role in the success of a case; after an accident, let’s take a look at some of the benefits of hiring a lawyer.

Benefits of Hiring a Lawyer

1. Great at Negotiations

Since they are aware of all the tricks insurance companies and lawyers use to avoid giving total compensation, they will provide an excellent defense to ensure you get compensated fully and in a timely manner.

2. Familiar with the Court Systems

Going to court alone may be stressful, as many legal terms are used that you likely won’t be familiar with. A lawyer will explain these words and make sure everything proceeds on schedule.

3. Conduct Thorough Investigations

Lawyers have a lot of expertise in examining vehicle accidents. They frequently employ accident reconstruction teams, forensic specialists, and experts to identify all guilty parties. They will bring even those not included in the police report, such as the automobile manufacturer, the municipality accountable for road maintenance, or the bar that supplied the drunk driver.

4. Higher Compensation

Those who hire a lawyer get more compensation than those who do not. Lawyers understand how to develop a case to demonstrate to the insurance company how much money you need to recover. They are not afraid to go toe-to-toe with huge insurance companies, and they will battle to ensure that all of your medical expenses are taken into account – both now and in the future.

Do Not Settle for Just Any Lawyer

Ensure you do your research diligently before appointing a lawyer. Ask around and look for lawyers with good reputations.

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5 Ways to Attract Clients with Law Firm SEO



As a business owner, your primary goal is to succeed and make an impact on society. The dream is universal, and law firms are no different. You want a scenario where your listing appears first whenever anyone searches for what you offer. So, what can you do to ensure your listing ranks first on Google search engine results pages (SERPs)? How can you outrank your competition to win more clients? Law firm local search engine optimization is the answer. 

How to Improve Your Law Firm’s SEO

Gone are the days where advertising your business would need word-of-mouth referrals or print media. I mean, these methods, though outdated, are still helpful. However, they sure cannot beat the effectiveness of technology and digital media. This is why local SEO is important. It achieves fast results and has the capacity to reach a bigger audience. Moreover, through Google Analytics, you can track your progress and manage your marketing campaigns.

So, how do you go about optimizing your website with local SEO? This can be difficult, especially considering how demanding your job already is. Now imagine not being tech-savvy? Quite the uphill climb, isn’t it? Do not despair, read on to learn a few tips to help you get started. 

1. Collect Customer Testimonials

The best and probably most straightforward way to begin optimization would be to collect reviews from your existing clients. How great the reviews are can propel your law firm into the stratosphere. You can imagine how many prospective clients will respond positively after seeing these glowing reviews. It will likely increase your trustworthiness. Eventually, google analytics will pick up on it and get you to rank higher on search engine results.

2. Choose the Right Keywords

You need to predetermine what your prospective clients will be searching for when in need of your services. Pay attention to what keywords your competition is using to attract clients to their website. Combine these two to come up with keyword-rich, high-quality content to incorporate into your site. Keyword research will help you determine relevant legal keywords to strengthen your search results.

3. Avoid Excessive Legal Jargon

Attorneys are well known for their impeccable use of words. Their career largely depends on such. On the contrary, law firm websites need to be as simple as possible. Most clients are regular people who may not understand legal jargon. 

For you to increase your law firm SEO effort, you will need to appeal to people, most of whom don’t have a law degree. This, in combination with the keywords, will definitely boost your listing on google search results.

4. Utilize Proper Meta Descriptions

Meta descriptions are a synopsis of what your law firm is all about. It tells anyone interested in what your specialties are and what they should expect when they click on your website. While these descriptions are auto-generated, you could still write a killer description to boost your rankings and click-through rates.

5. Prioritize Locality

Search engine data shows local searches are the most common. People often look for products and services that are close to them. Proximity matters a lot to many people, and your target audience is most likely in this demographic as well. Targeting your law firm SEO to the locality you are based on will most likely attract more local clients.

Equip Your Law Firm with SEO Strategies to Stand Out

All these great tips can help you gain more website traffic, which will likely increase your client base. Join the bandwagon and optimize your site and propel past your competitors.

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Artificial Intelligence

AI Visual Inspection For Defect Detection in Manufacturing



defect detection artificial intelligence
Illustration: © IoT For All

Artificial intelligence in manufacturing is a trendy term. When describing AI-based defect detection solutions, it’s often about visual inspection technology based on deep learning and computer vision.

What Is Deep Learning in a Visual Inspection?

Deep learning is an aspect of machine learning technology powered by artificial neural networks. The operating principle of deep learning technology is teaching machines to learn by example. By providing a neural network with labeled examples of specific data types, it’s possible to extract common patterns between those examples and then transform them into a math equation. This helps to classify future pieces of information.

With visual inspection technology, integrating deep learning algorithms allows differentiating parts, anomalies, and characters, which imitate a human visual inspection while running a computerized system. 

So, what does it mean exactly? Let’s use an example:

If you were to create a visual inspection software for automotive manufacturing, you should develop a deep learning-based algorithm and train it with examples of defects it must detect. With enough data, the neural network will eventually detect defects without any additional instructions.

Deep learning-based visual inspection systems are good at detecting defects that are complex in nature. They address complex surfaces and cosmetic flaws and generalize and conceptualize the parts’ surfaces.

How to Integrate AI Visual Inspection System

1. State the Problem

Visual inspection development often starts with a business and technical analysis. The goal here is to determine what kind of defects the system should detect.

Other important questions to ask include:

  • What is the visual inspection system environment?
  • Should the inspection be real-time or deferred? 
  • How thoroughly should the visual inspection system detect defects, and should it distinguish them by type?
  • Is there any existing software that integrates the visual inspection feature, or does it require a development from scratch?
  • How should the system notify the user(s) about detected defects?
  • Should the visual inspection system record defects detection statistics?
  • And the key question: Does data for deep learning model development exist, including images of “good” and “bad” products and the different types of defects?

Data science engineers choose the optimal technical solution and flow to proceed based on the answers they receive.

2. Gather and Prepare Data

Data science engineers must gather and prepare data required to train a future model before deep learning model development starts. For manufacturing processes, it’s important to implement IoT data analytics. When discussing visual inspection models, the data is often video records, where images processed by a visual inspection model include video frames. There are several options for data gathering, but the most common are:

  1. Taking an existing video record provided by a client
  2. Taking open-source video records applicable for defined purposes
  3. Gathering data from scratch according to deep learning model requirements

The most important parameters here are the video record’s quality. Higher quality data will lead to more accurate results. 

Once we gather the data, we prepare it for modeling, clean it, check for anomalies, and ensure its relevance.

3. Develop Deep Learning Model

The selection of a deep learning model development approach depends on the complexity of a task, required delivery time, and budget limitations. There are several approaches:

Using a deep learning model development service (e.g: Google Cloud ML Engine, Amazon ML, etc.)

This type of approach makes sense when requirements for defect detection features are in line with templates provided by a given service. These services can save both time and budget as there is no need to develop models from scratch. You have to upload data and set model options according to the relevant tasks. 

What’s the catch? These types of models are not customizable. Models’ capabilities are limited to options provided by a given service.

Using Pre-trained Models

A pre-trained model is an already created deep learning model that accomplishes tasks similar to what we want to perform. We do not have to build a model from scratch as it uses a trained model based on our data.

A pre-trained model may not 100% comply with all of our tasks, but it offers significant time and cost savings. Using models previously trained on large datasets lets us customize these solutions according to our problem. 

Deep Learning Model Development from Scratch

This method is ideal for complex and secure visual inspection systems. The approach may be time and effort-intensive, but the results are worth it. 

When developing custom visual inspection models, data scientists use one or several computer vision algorithms. These include image classification, object detection, and instance segmentation.

Many factors influence the choice of a deep learning algorithm(s). These include:

  • Business goals
  • Size of objects/defects 
  • Lighting conditions
  • Number of products to inspect
  • Types of defects
  • Resolution of images

An example of defect categories:

Let’s say that we’re developing a visual inspection model for quality assessment in buildings. The main focus is to detect defects on the walls. An extensive dataset is necessary to obtain accurate visual inspection results, as the defect categories might be incredibly diverse, from peeling paint and mold to wall cracks. The optimal approach here would be to develop an instance segmentation-based model from scratch. A pre-trained model approach is also viable in some cases.

Another example is a visual inspection for pharmaceutical manufacturing, where you want to differentiate air bubbles from particles in products like highly viscous parental solutions. The presence of bubbles is the only defect category here, so the required dataset will not be as extensive as in the example above. The optimal deep learning model development approach might be to use a model development service over developing one from scratch.

4. Train and Evaluate

The next step after developing the visual inspection model is to train it. In this stage, data scientists validate and evaluate the performance and result accuracy of the model. A test dataset is useful here. A visual inspection system may be a set of video records that are either outdated or similar to ones we want to process after deployment.

5. Deploy and Improve

When deploying a visual inspection model, it’s important to consider how software and hardware system architectures correspond to a model capacity.


The structure of visual inspection-powered software bases itself on the combination of web solutions for data transmission and a Python framework for neural network processing. 

The key parameter here is data storage. There are three common ways to store data: on a local server, a cloud streaming service, or serverless architecture. 

A visual inspection system involves the storage of video records. The choice of a data storage solution often depends on a deep learning model functionality. For example, if a visual inspection system uses a large dataset, the optimal selection may be a cloud streaming service.


Depending on the industry and automation processes, devices required to integrate visual inspection system may include:

  • Camera: The key camera option is real-time video streaming. Some examples include IP and CCTV.
  • Gateway: Both dedicated hardware appliances and software programs work well for a visual inspection system.
  • CPU / GPU: If real-time results are necessary, a GPU would be the better choice than a CPU, as the former boasts a faster processing speed when it comes to image-based deep learning models. It is possible to optimize a CPU for operating the visual inspection model, but not for training. An example of an optimal GPU might be the Jetson Nano
  • Photometer (optional): Depending on the lighting conditions of the visual inspection system environment, photometers may be required.
  • Colorimeter (optional): When detecting color and luminance in light sources, imaging colorimeters have consistently high spatial resolutions, allowing for detailed visual inspections. 
  • Thermographic camera (optional): In case of automated inspection of steam/water pipelines and facilities it is a good idea to have thermographic camera data. Thermographic camera data provides valuable information for heat/steam/water leakage detection. Thermal camera data is also useful for heat insulation inspection.
  • Drones (optional): Nowadays it is hard to imagine automated inspection of hard-to-reach areas without drones: building internals, gas pipelines, tanker visual inspection, rocket/shuttle inspection. Drones may be equipped with high resolution cameras that can do real-time defect detection.

Deep learning models are open to improvement after deployment. A deep learning approach can increase the accuracy of the neural network through the iterative gathering of new data and model re-training. The result is a “smarter” visual inspection model that learns by increasing data during operation.

Visual Inspection Use Cases


In the fight against COVID-19, most airports and border crossings can now check passengers for signs of the disease.

Baidu, the large Chinese tech company, developed a large-scale visual inspection system based on AI. The system consists of computer vision-based cameras and infrared sensors that predict the temperatures of passengers. The technology, operational in Beijing’s Qinghe Railway Station, can screen up to 200 people per minute. The AI algorithm detects anyone who has a temperature above 37.3 degrees.

Another real-life case is the deep learning-based system developed by the Alibaba company. The system can detect the coronavirus in chest CT scans with 96% accuracy. With access to data from 5,000 COVID-19 cases, the system performs the test in 20 seconds. Moreover, it can differentiate between ordinary viral pneumonia and coronavirus.


According to Boeing, 70% of the $2.6 trillion aerospace services market is dedicated to quality and maintenance. In 2018, Airbus introduced a new automated, drone-based aircraft inspection system that accelerates and facilitates visual inspections. This development reduces aircraft downtime while simultaneously increasing the quality of inspection reports.


Toyota recently agreed to a $1.3 billion settlement due to a defect that caused cars to accelerate even when drivers attempted to slow down, resulting in 6 deaths in the U.S. Using the cognitive capabilities of visual inspection systems like Cognex ViDi, automotive manufacturers can analyze and identify quality issues much more accurately and resolve them before they occur.

Computer Equipment Manufacturing

The demand for smaller circuit board designs is growing. Fujitsu Laboratories has been spearheading the development of AI-enabled recognition systems for the electronics industry. They report significant progress in quality, cost, and delivery.


The implementation of automated visual inspection and a deep learning approach can now detect texture, weaving, stitching, and color matching issues.

For example, Datacolor’s AI system can consider historical data of past visual inspections to create custom tolerances that match more closely to the samples.

We will conclude with a quotation from the general manager we mentioned earlier: “It makes no difference to me whether the suggested technology is the best, but I do care how well it’s going to solve my problems.”

Solar Panels

Solar panels are known to suffer from dust and microcracks. Automated inspection of solar panels during manufacturing and before and after installation is a good idea to prevent shipment of malfunctioning solar panels and quick detection of damaged panels on your solar farm. For example, DJI Enterprise uses drones for solar panels inspection.

Pipeline Inspection

Gas and oil pipelines are known to have a huge length. The latest data from 2014 gives a total of slightly less than 2,175,000 miles (3,500,000 km) of pipeline in 120 countries of the world. Gas and oil leakages may lead to massive harm to nature by chemical pollution, explosions, and conflagrations.

Satellite and drone inspection with the help of computer vision techniques is a good tool for early detection and localization of a gas/oil leakage. Recently, DroneDeploy reported that they mapped about 180 miles of pipelines.

AI Visual Inspection: Key Takeaways

  1. Concept: Al visual inspection bases itself on traditional computer vision methods and human vision.
  2. Choice: Deep learning model development approach depends on the task, delivery time, and budget limits.
  3. Algorithm: Deep learning algorithms detect defects by imitating a human analysis while running a computerized system.
  4. Architecture: Software & hardware should correspond to deep learning model capacity.
  5. Main question: When initiating a visual inspection, the main question is “What defects should the system detect?”
  6. Improvements: After deployment, deep learning model becomes “smarter” through data accumulation.

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Look Beyond Big Tech Investments



Last Monday, Facebook experienced an outage that lasted for about six hours. Users couldn’t access Facebook, Messenger, Instagram, Whatsapp or OculusVR. 

The effects were felt across the world. Suddenly, everyone who relied on Facebook or WhatsApp for communication was left in a lurch. Even Facebook’s own employees had to rely on Outlook emails and Twitter (ouch!) to communicate. 

The outage came a day before whistleblower Frances Haugen testified before Congress about her experiences at Facebook.

When she took the stand, Haugen called on Congress to change the business incentives that encourage Facebook to highlight harmful content for its users. And to push the company to be more transparent. 

“It is unaccountable until the incentives change,” Haugen said. “Facebook will not change.”

Haugen also urged lawmakers to reform Section 230 of the Communications Decency Act, which shields internet companies from legal liability for its users’ content. She said the rules need to be changed to make Facebook responsible for its algorithms, which are used to rank content. 

Senator Ed Markey (D-MA) answered Haugen’s call: 

Here’s my message for Mark Zuckerberg: Your time of invading our privacy, promoting toxic content, and preying on children and teens is over. Congress will be taking action. You can work with us or not work with us, but we will not allow your company to harm our children and our families and our democracies any longer. Thank you, Ms. Haugen. We will act.

Looking Forward

Haugen’s testimony is only adding more fuel to Congress’s fire. Regulators have been coming after big tech for months. Over the summer, lawmakers have proposed six different bills aimed at reining in big tech companies, including Facebook, Google, Amazon, Apple and Microsoft. 

Whether the bills will pass in the Senate this year is questionable. But eventually, regulators will clamp down on big tech.

So what does this mean for investors?

Big tech companies won’t stop being profitable anytime soon. But as with any tech investment, investors should still look toward the future, however distant it may seem. And they should consider not just the technology itself, but the technology that surrounds it.

Cybersecurity is a major example. Remote work — which also isn’t going away anytime soon — often requires employees to access sensitive information. Crypto exchanges handle billions of dollars in transactions on a daily basis. Social media companies handle billions of users’ data at any given moment. All of this requires top-notch cybersecurity and encryption technology to keep information safe. 

Artificial intelligence is another space worth watching. As scientists and engineers discover more ways to leverage AI capabilities, its potential applications continue to expand. The healthcare industry is a particularly exciting use case. As telehealth services continue to grow rapidly thanks to COVID-19, companies are using AI to collect patient data, analyze it and even provide health recommendations to patients. Some companies are leveraging AI to treat conditions directly.

And finally, investors should also keep an eye on social media. While Facebook is a dominant force today, a growing number of people are seeking alternative social networks. Many of them — like many of my own friends — have grown disillusioned with Facebook’s data mining and lack of privacy. And as regulators continue to crack down on the company, Facebook’s power may diminish. Discord, for example, grew from 56 million monthly users to 100 million monthly users in 2020 alone. It now has 150 million active monthly users. And growing privacy concerns are only creating more opportunities for new social media startups to reinvent the world that Facebook pioneered.

These are just a few examples of tech sectors that have enormous potential. Agile startups run by smart founders have the ability to disrupt and revolutionize dozens of industries. 

And startups have at least one advantage over big tech. While big tech companies may have the revenue to dominate their markets, public opinion is turning on them. And it’s only a matter of time before the government tightens the leash.

When it does, startups will be there. And they’ll change everything.

PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
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