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DeFi and Web 3.0: Unleashing creative juices with decentralized finance

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Decentralized technologies are starting to revolutionize the world of finance, with cryptocurrencies applied in different ways to recreate traditional financial instruments. However, since cryptocurrencies aren’t backed by anything but people’s faith in them, they are extremely volatile. That means, when it comes to loaning value with crypto, neither party can be sure that they will get a fair deal.

There needs to be a way to secure the value of the assets loaned, which can be done by backing them up with a value in the real world. Here is where the tokenization of real assets comes in. This process is pretty straightforward when we consider tangible assets like a building or gold bars, but what about intangible assets like intellectual property?

Related: Understanding the systemic shift from digitization to tokenization of financial services

The rise of the creator economy has led to intangible assets accounting for over 90% of the S&P 500’s market value, a figure that is only set to grow. There needs to be a way to unlock more creativity to realize the potential of human capital.

Kickstarting creator financing

Finding a start with financing in the creator economy is a great challenge, especially for newcomers. As many entrepreneurs in this segment discover, sometimes it is much easier to give away a good idea than to create a business out of that idea.

Creativity, by definition, disrupts what came before; it’s about new ideas, new technologies, new products, new services and new ways of doing things. Driven in large part by the digital revolution, many creative industries are not just innovative in what they do but in how they do it.

Related: Bull or bear market, creators are diving headfirst into crypto

Raising funds may be difficult for several reasons. For one, banks and investors tend to be conservative. They like certainty and are unlikely to be impressed by an enthusiastic entrepreneur convinced that an entirely new and untried idea — whether it is a design, a software tool, a fashion concept or a video game — will be a commercial success. Furthermore, banks want collateral for their loans, but many creative businesses have no capital assets to offer.

Stumbling blocks in the state of play

Investors specializing in creative industries may indeed recognize an entrepreneur’s genius. But in return for their investment, they often want some ownership of the idea and, therefore, some control over its development and marketing. This may not seem acceptable to the creative entrepreneur who prefers debt-finance in the form of a loan rather than equity finance in the form of sharing ownership and control over the work with the investor.

Alex Shkor, the founder of DEIP — a company that is building a protocol for the creator economy — explained to me, “For creators to be able to tokenize their works and collateralize them for funding, there needs to be a set of smart contracts, which can register assets on-chain, issue NFTs, evaluate assets and manage both collateralization and liquidation in case of default.”

Loan framework for the creative economy

Just as loans can be issued in the real economy based on collateral, so can they be in the creator economy.

Imagine a game developer (let’s call them Jane) who begins working on a side project. After a while and some positive encouragement from friends and family, Jane decides to take the leap into converting their side project into a full-time job. But a few months down the line, and with slower progress than first anticipated, Jane’s funds start to dwindle; they begin to consider full-time roles again. This situation is a common one for budding creators out there.

However, with a decentralized platform for intellectual assets, Jane’s progress on their work could be assessed by a decentralized assessment system that pools the expertise of people in the domain to give the unfinished creation an appraisal guided by the intrinsic value of the idea. This inherent value is used as the input for the collateralization calculation, the loan value that it can be issued for. Jane can use the loan offered to them for whatever they like; in this case, to support themself while they finish the game’s development.

Moreover, with or without collateral, a small loan can be issued to newcomers. If Jane doesn’t have any project, ready-made or part-made creation, they still have the chance for initial financing as a newcomer to the platform. The loan amount will be smaller as it is unsecured, and the loan itself is backed by the segment decentralized autonomous organization (DAO) and budgets originating from its ecosystem fund. Sources of this fund come from transaction fees and bandwidth allocation payments of the underlying blockchain.

If loans are paid back on time, Jane’s personal credit rating will be upgraded. In this case, if Jane would like to apply for another loan, the collateralization factor will be less, enabling them to borrow more.

Should Jane default on their loan, any collateralized assets are assumed by the platform and can be sold off to recoup the funds via smart liquidation contracts. If Jane hasn’t collateralized anything, the default risk is realized by the platform and covered by the DAO.

As long as the creator’s credit history is solid and positively confirmed with each new loan, the next tranche can be issued with iteratively improved terms and conditions. Credit history becomes an integral and immutable part of the reputational profile of the creator. As Shkor noted:

“he whole purpose of Web 3.0 is to enable a decentralized creator economy nd all the tech for this already exists.”

He continued, “We just need to foster adoption of these technologies in real industries, in creative industries, for the assets produced by creators. It will not only increase liquidity of the creator economy assets, it will also open a flow of capital to creators.”

The views, thoughts and opinions expressed here are the author’s alone and do not necessarily reflect or represent the views and opinions of Cointelegraph.

Alexandra Luzan is a Ph.D. student researching the connection between new technologies and art at Ca’ Foscari University in Venice. For about a decade, Alexandra has been organizing tech conferences and other events in Europe dedicated to blockchain technology and artificial intelligence. She is equally interested in the relationship between blockchain tech and art.


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Source: https://cointelegraph.com/news/defi-and-web-3-0-unleashing-creative-juices-with-decentralized-finance

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Source: https://coingenius.news/defi-and-web-3-0-unleashing-creative-juices-with-decentralized-finance/?utm_source=rss&utm_medium=rss&utm_campaign=defi-and-web-3-0-unleashing-creative-juices-with-decentralized-finance

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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.

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Source: https://www.iotforall.com/ai-visual-inspection-for-defect-detection-in-manufacturing

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Look Beyond Big Tech Investments

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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.


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Source: https://earlyinvesting.com/look-beyond-big-tech-investments/

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