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Pros and Cons of Using AI in Your Hiring Process

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As with everything else in the world, there are pros and cons of using Artificial Intelligence (AI) tools to supplementing human resources management (HRM). AI seems to be stepping into every industry imaginable today, from autonomous cars to genomic diagnostics.

Data drives everything, yet as you can imagine, problems tend to arise when using AI tools in a genuinely neutral environment. They are natively blind to issues that seem overly influenced by society today, resulting in a potential clash with regulations or ethics.

Today we’ll examine some of the advantages and concerns towards more significant AI deployment in this multi-billion-dollar industry.

Benefits of AI in Hiring

While percentages vary, the importance of digital HR is well-recognized worldwide (source: Deloitte)

Before we begin, it should be clear that this discussion leans towards in-house engagement with full-time employees. For temporary talent engagement, using an existing freelance platform will be more cost-effective and efficient.

Less Potential for Human Bias

Regardless of guidelines, policy, or other factors, ensuring an utterly bias-free hiring process can be incredibly difficult. It’s a human trait to be biased towards certain aspects – in fact, a defining characteristic.

AI, on the other hand, is purely data-driven. As long as the intent is not present in the AI tool, it won’t introduce unnecessary characteristics into the hiring process. While data-driven assessment may sound a bit cold, it is impartial.

Speeds Up the Hiring Process

Recruitment times vary widely depending on role, industry, and other factors. Yet, it’s undeniable that you can speed up at least part of the process using AI tools. For example, you can use them to create or improve job descriptions, match applications with requirements, carry out candidate screening, and more.

Taking too long to connect with potential talent is something that can cost you dearly. Remember, while there are ample candidates, almost all of them will be applying to multiple companies. If your hiring process drags on, talented individuals may quickly get snapped up by the competition.

Reduce HR Expenditure

From start to finish, hiring new talent is expensive. An increasingly large number of HR tasks can quickly lead to ballooning staff costs simply for talent acquisition. By introducing AI tools, not only can you speed up the process (as discussed above), but repetitive tasks can be kept away from expensive HR personnel and reduce cost.

Remember that the cost of AI tools is often much less in the context of large companies. As your organization grows, it makes much more financial sense to replace rote tasks with automation. You may be surprised at the overall impact on ROI.

Lowers Chances of Talent Leaks

When HR is recruiting, the focus is often on specific roles that need filling. While some companies do keep applications on file, cross-matching doesn’t always occur for various reasons. This shortcoming can easily lead to talent leaks where a candidate suited for an alternative role is lost.

Cross-matching often gets neglected due to the amount of time consumed trying to match multiple candidates and roles. Rather than onboarding more HR to fill this gap, you can leverage AI for much faster results.

Matching a potentially leaked talent with alternative roles also saves on the future need to hire specifically for that purpose. Keep these identified talents on file, or simply hire them early in anticipation of filling a need.

Improve the Sourcing Process

The traditional hiring process makes extensive use of job agencies or boards. While this helps save time and money, AI tools can give you many of the capabilities these channels offer. For example, an AI scraper can collate data from many sources and assess them for suitability.

With a single tool, you gain access to a massive potential talent pool that may not directly apply for a vacancy in the company. In this aspect, AI tools are even more important given how well individuals today reduce their digital footprint.

Disadvantages of AI in Hiring

Increasing Regulation

Like many other IT-related elements, AI remains but a tool in the hiring process. Unless you take great care selecting these tools, some form of bias may remain. The reality is that regulation isn’t seeking to eliminate bias but to direct it towards the desired outcome.

Because of this, many countries often have some form of discrimination built into regulatory systems – for instance, mandates for specific proportions of gender, domestic versus expatriate labor, or other mandated ratios.

One example of this is New York’s proposed legislation to regulate AI algorithms allowed for use in the hiring process. Similar proposals also exist in the European Union, with initial legal frameworks already in draft.

There are also varying general guidelines on occasions, such as privacy laws concerning video interviews, data collection activities, and such. Since 2019 the US state of Illinois has regulated the use of AI in video interviews mandating disclosure and specific prohibitions.

Specific Areas of Challenge Exist

Professionals in many countries believe company culture is a strong influencer in their choice of employment. (source Deloitte)

AI and data often work well together and can introduce elements of analysis effectively as well. However, it isn’t perfect, and when assessing individuals, there may be some areas challenging to factor in and match.

Intangible factors are especially prominent in this area and can include company culture, values, and mission cohesion. If too much weight is placed on tangible areas of analysis, mismatches in this area can still result in poor hires.

The risk of this happening is exceptionally high if the AI algorithms deployed are less intelligent than optimal. For example, some AI algorithms do nothing more than field matching and are extremely poor in a human relationship context.

Lack of Transparency in the AI Industry

Most companies will rely on external sources for AI algorithms used in the hiring process. Unfortunately, like many commercial products, exactly how they work is often considered proprietary. The result is a high risk as they may introduce areas contrary to the company culture or legislation.

May Lower Company Image

People often have different attitudes towards the use of tech tools. These varying attitudes can mean alienating a proportion of potential candidates who prefer more direct human interaction with a prospective employer.

What makes things worse is that AI elements are often used in the first line of the hiring process. Only when data has been sorted are results provided to human recruiters to make the final judgments.

This prospective alienation may lead to a poor impression of the brand among prospective employees, which may spread in the community and be hard to counter should processes change in the future.

AI Recruiting Tools Currently Available

If you’d like to try some of the available AI recruitment tools, the good news is the abundance of choice. There’s a lot of noise in the industry, so picking the right one can be a long process for each company.

Some of the available are;

XOR – You can design this AI chatbot to fit perfectly with your brand and serve as the first line of interaction with prospective hires. It can be highly customized to reflect branding, possible queries, and more. Many big brands are already using XOR, including Ikea, McDonald’s, and Mars.

Arya – For something more comprehensive, Arya serves as a complete recruitment platform that can work relatively independently. At the same time, it offers recruiters the necessary features to reach out to candidates directly via the platform. Arya takes care of employee screening and can help drastically reduce the cost of hiring.

Seekout – If your company needs to reach out to extend the reach of HR, then Seekout is a solid choice. It’s a talent-sourcing platform capable of scouring a massive database to find candidates based on job descriptions. The scope and scale of Seekout make it more suited to enterprise-scale users.

PymetricsProfessionals today often make use of gamification in multiple professional settings. Pymetrics does that for hiring and adds behavioral science into the mix. The result is a very modern tool that most younger professionals can relate with easily as they take Pymetrics tests.

HireVue – Originally a video software, HireVue entered the AI recruitment space relatively late, in 2020. It offers a HR chatbot suite capable of end-to-end assistance in the recruitment process. The platform helps source, screen, and naturally act as a video interview system.

Final Thoughts: Will AI Replace Human Recruiters

As with most industries new to the adoption of technology, HR is currently in a state of flux. This state is partially due to transient technology coupled with developing regulations. Overall, AI at the moment won’t replace human recruiters.

Instead, they should be seen as valuable assets capable of lowering overall recruitment costs and process enhancement.

Image Credit: Photo by Alex Knight from Pexels

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Source: https://datafloq.com/read/pros-cons-using-ai-your-hiring-process/18105

AI

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.

Recommended Products

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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.


PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
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Source: https://earlyinvesting.com/look-beyond-big-tech-investments/

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