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Celsius CEO Refutes Allegations of Uncomplying With US State Laws

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As regulatory pressure mounts in the U.S., policymakers are putting stablecoins at the top of their agendas.

Citing “people familiar with the matter,” Bloomberg has reported that officials are crafting a policy framework set to be released in the coming weeks. Their primary concern is ensuring that investors can reliably move money in and out of tokens, it added.

The anonymous insiders are worried that a “fire-sale run on crypto assets could threaten financial stability and that certain stablecoins could scale up dangerously fast.”

Strengthening Regulatory Efforts

The Financial Stability Oversight Council is also preparing a formal review into whether stablecoins pose an economic threat.

The officials are focusing on how stablecoin transactions are processed and settled and whether market conditions have an impact, it added. Tomicah Tillemann, global head of policy at a crypto fund run by venture capital giant Andreessen Horowitz, commented:


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“It is significant and very consequential that we are witnessing early steps to create a regulatory framework around digital assets. That’s a big deal.”

The report, when released, will go to the President’s Working Group on Financial Markets. The body includes key agency heads such as Treasury Secretary Janet Yellen, Federal Reserve Chair Jerome Powell, and Securities and Exchange Commissioner Chair Gary Gensler.

In late July, Yellen called for urgency in regulating stablecoins after stating that they are not adequately supervised. Gary Gensler echoed the sentiment in early August, stating that regulators must act to protect investors from fraud.

Also, in late July, Acting Comptroller of the Currency, Michael Hsu, said regulators are looking into Tether’s commercial papers to see whether each USDT token was really backed by the equivalent of one U.S. dollar.

Tether has repeatedly issued assurances that its reserves are fully backed but has yet to produce a full independent audit.

Stablecoin Ecosystem Update

Tether remains the market leader with a current supply of 69.4 billion, according to the Tether Transparency report. This is close to the all-time high for USDT, which tapped 70 billion earlier this week.

Of that total, 36 billion or 51.8% is based on the Tron network, with 33.8 billion or 48.7% running on Ethereum. USDT supply has grown by 232% since the beginning of the year.

Rival stablecoin, USDC, from Circle currently has 29.3 billion in circulation after gaining 651% in terms of supply growth so far in 2021.

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Source: https://coingenius.news/celsius-ceo-refutes-allegations-of-uncomplying-with-us-state-laws-21/?utm_source=rss&utm_medium=rss&utm_campaign=celsius-ceo-refutes-allegations-of-uncomplying-with-us-state-laws-21

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When to Contact an Attorney After a Car Accident

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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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Source: https://1reddrop.com/2021/10/16/when-to-contact-an-attorney-after-a-car-accident/?utm_source=rss&utm_medium=rss&utm_campaign=when-to-contact-an-attorney-after-a-car-accident

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5 Ways to Attract Clients with Law Firm SEO

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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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Source: https://1reddrop.com/2021/10/16/5-ways-to-attract-clients-with-law-firm-seo/?utm_source=rss&utm_medium=rss&utm_campaign=5-ways-to-attract-clients-with-law-firm-seo

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Artificial Intelligence

AI Visual Inspection For Defect Detection in Manufacturing

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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.

Software 

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.

Hardware

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

Healthcare

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.

Airlines

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.

Automotive

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.

Textile

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.

PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
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Source: https://www.iotforall.com/ai-visual-inspection-for-defect-detection-in-manufacturing

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Can Artificial Intelligence be Used to Win Online Casino Games?

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It is fair to say that the online gambling industry has been a big beneficiary of advancements in technology. In fact, without these advancements, the industry would not even exist. The internet and mobile technology have allowed many casino games to be enjoyed in an online format. 

Not only that, but they advanced from basic games that used simple graphics to what they are today. Video slots at top rated online casinos for US players, for example, now feature amazing graphics, while we can play live casino games streamed to our devices via webcam, and there is also talk of virtual reality becoming a big part of the industry in the future. However, could the advancement of technology actually threaten the existence of this industry?

If artificial intelligence continues to advance at the rate it has done, could it be used to beat online casino games? If so, the industry could be at risk of those using it to profit. 

How Has Artificial Intelligence Faired in Other Games?

We all know how AI is increasingly used in marketing, transportation, construction, hospitality, and many other industries, but this is to their benefit. However, can AI better a human when playing games? There has certainly been evidence that it can. For instance, AI has beaten the best chess players, poker players, and when playing professional players of video games, such as DOTA and League of Legends.  

Artificial intelligence can learn to make the most optimal decisions at the perfect time. It also does not make the mistakes that humans are capable of. Therefore, it will always beat experienced WSOP poker players at their own game. 

The fundamental similarity of each of those games, however, is that they all rely on skill. Artificial intelligence is called artificial intelligence for a reason. It is intelligence that software can learn and improve upon, so it makes sense that it could learn how to out skill somebody in games such as those mentioned. Players also compete against each other on a level playing field.

So How About Casino Games?

Casino games are not skill-based at all. Instead, they are games of chance. No matter the action a player takes, it is luck that determines the result. Plus, the casino always has a competitive edge, as without one, the gambling industry would not exist. They designed the games in such a way that while players can win, the casino will always win in the long run.  

As it stands right now, it would seem fairly unlikely that artificial intelligence could learn to beat a casino game that is luck-based and has an edge. Results of these games are determined by random number generators (RNGs) which is software that ensures that results are fair and random. 

However, the worrying thing about that, is that humans designed this software. If artificial intelligence can already beat humans at many things, could it one day figure out and beat the algorithms used in RNG software? While we would all like to say no, we would not like to bet against AI one day having the beating of online casino games.

What Can the Casino Industry Do?

Well, as of now, artificial intelligence is still in its infancy. We do not think there is a risk of anything being developed that could harm the casino industry just yet. A few years down the line though, and with the rate of advancement the technology is enjoying, and online casinos might have a problem.

The biggest threat is the development of AI bots that could be used to consistently win at casino games. So if that were to happen, the industry would have to think of ways to detect this behavior and prevent it. In fact, we could even implement AI in the battle against this kind of behavior. 

Whatever the solution may or may not be, it is certainly time for the industry to put some thought into it. 

The Prize Takeaway

There is reason to be concerned about the increase in which artificial intelligence is being used in every industry. Why it is currently used to enhance customer support, the healthcare industry, and marketing, people are concerned that computers and software will eventually take replace humans in jobs and professions. However, the casino industry has deeper concerns. Not only could it replace humans, but it could also actually learn how to beat the casino games that have been designed to win for the casino.  

 

Source: Plato Data Intelligence

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