Connect with us


NTIA Receives Over 1,450 Comments On AI Accountability



NTIA Receives Over 1,450 Comments On AI Accountability

The National Telecommunications and Information Administration (NTIA), a United States Department of Commerce division, called for public commentary on strategies to encourage accountability in trustworthy artificial intelligence (AI) systems.

The objective was to solicit stakeholder feedback to formulate suggestions for a forthcoming report on AI guarantee and accountability frameworks. These suggestions might have guided future federal and non-governmental regulations.

Promoting trustworthy AI that upholds human rights and democratic principles was a principal federal focus per the NTIA request. Nonetheless, gaps remained in ensuring AI systems were responsible and adhered to trustworthy AI rules about fairness, safety, privacy, and transparency.

Accountability mechanisms such as audits, impact evaluations, and certifications could offer assurance that AI systems adhere to trustworthy criteria. But, NTIA observed that implementing effective accountability still presented challenges and complexities.

NTIA discussed a variety of considerations around the balance between trustworthy AI goals, obstacles to implementing responsibility, complex AI supply chains and value chains, and difficulties in standardizing measurements.

Over 1,450 Comments On AI Accountability

Comments were accepted through June 12 to aid in shaping NTIA’s future report and steer potential policy developments surrounding AI accountability.

The number of comments exceeded 1,450.

Comments, which can be searched using keywords, occasionally include links to articles, letters, documents, and lawsuits about the potential impact of AI.

Tech Companies Respond To NTIA

The comments included feedback from the following tech companies striving to develop AI products for the workplace.

OpenAI Letter To The NTIA

In the letter from OpenAI, it welcomed NTIA’s framing of the issue as an “ecosystem” of necessary AI accountability measures to guarantee trustworthy artificial intelligence.

OpenAI researchers believed a mature AI accountability ecosystem would consist of general accountability elements that apply broadly across domains and vertical elements customized to specific contexts and applications.

OpenAI has been concentrating on developing foundation models – broadly applicable AI models that learn from extensive datasets.

It views the need to take a safety-focused approach to these models, irrespective of the particular domains they might be employed in.

OpenAI detailed several current approaches to AI accountability. It publishes “system cards” to offer transparency about significant performance issues and risks of new models.

It conducts qualitative “red teaming” tests to probe capabilities and failure modes. It performs quantitative evaluations for various capabilities and risks. And it has clear usage policies prohibiting harmful uses along with enforcement mechanisms.

OpenAI acknowledged several significant unresolved challenges, including assessing potentially hazardous capabilities as model capabilities continue to evolve.

It discussed open questions around independent assessments of its models by third parties. And it suggested that registration and licensing requirements may be necessary for future foundation models with significant risks.

While OpenAI’s current practices focus on transparency, testing, and policies, the company appeared open to collaborating with policymakers to develop more robust accountability measures. It suggested that tailored regulatory frameworks may be necessary for competent AI models.

Overall, OpenAI’s response reflected its belief that a combination of self-regulatory efforts and government policies would play vital roles in developing an effective AI accountability ecosystem.

Microsoft Letter To The NTIA

In its response, Microsoft asserted that accountability should be a foundational element of frameworks to address the risks posed by AI while maximizing its benefits. Companies developing and using AI should be responsible for the impact of their systems, and oversight institutions need the authority, knowledge, and tools to exercise appropriate oversight.

Microsoft outlined lessons from its Responsible AI program, which aims to ensure that machines remain under human control. Accountability is baked into their governance structure and Responsible AI Standard and includes:

  • Conducting impact assessments to identify and address potential harms.
  • Additional oversight for high-risk systems.
  • Documentation to ensure systems are fit for purpose.
  • Data governance and management practices.
  • Advancing human direction and control.
  • Microsoft described how it conducts red teaming to uncover potential harms and failures and publishes transparency notes for its AI services. Microsoft’s new Bing search engine applies this Responsible AI approach.

Microsoft made six recommendations to advance accountability:

  • Build on NIST’s AI Risk Management Framework to accelerate the use of accountability mechanisms like impact assessments and red teaming, especially for high-risk AI systems.
  • Develop a legal and regulatory framework based on the AI tech stack, including licensing requirements for foundation models and infrastructure providers.
  • Advance transparency as an enabler of accountability, such as through a registry of high-risk AI systems.
  • Invest in capacity building for lawmakers and regulators to keep up with AI developments.
  • Invest in research to improve AI evaluation benchmarks, explainability, human-computer interaction, and safety.
  • Develop and align to international standards to underpin an assurance ecosystem, including ISO AI standards and content provenance standards.
  • Overall, Microsoft appeared ready to partner with stakeholders to develop and implement effective approaches to AI accountability.

Microsoft, overall, seemed to stand ready to partner with stakeholders to develop and implement effective approaches to AI accountability.

Google Letter To The NTIA

Google’s response welcomed NTIA’s request for comments on AI accountability policies. It recognized the need for both self-regulation and governance to achieve trustworthy AI.

Google highlighted its own work on AI safety and ethics, such as a set of AI principles focused on fairness, safety, privacy, and transparency. Google also implemented Responsible AI practices internally, including conducting risk assessments and fairness evaluations.

Google endorsed using existing regulatory frameworks where applicable and risk-based interventions for high-risk AI. It encouraged using a collaborative, consensus-based approach for developing technical standards.

Google agreed that accountability mechanisms like audits, assessments, and certifications could provide assurance of trustworthy AI systems. But it noted these mechanisms face challenges in implementation, including evaluating the multitude of aspects that impact an AI system’s risks.

Google recommended focusing accountability mechanisms on key risk factors and suggested using approaches targeting the most likely ways AI systems could significantly impact society.

Google recommended a “hub-and-spoke” model of AI regulation, with sectoral regulators overseeing AI implementation with guidance from a central agency like NIST. It supported clarifying how existing laws apply to AI and encouraging proportional risk-based accountability measures for high-risk AI.

Like others, Google believed it would require a mix of self-regulation, technical standards, and limited, risk-based government policies to advance AI accountability.

Anthropic Letter To The NTIA

Anthropic’s response described the belief that a robust AI accountability ecosystem requires mechanisms tailored for AI models. It identified several challenges, including the difficulty of rigorously evaluating AI systems and accessing sensitive information needed for audits without compromising security.

Anthropic supported funding for the following:

  • Model evaluations: Current evaluations are an incomplete patchwork and require specialized expertise. It recommended standardizing capability evaluations focused on risks like deception and autonomy.
  • Interpretability research: Grants and funding for interpretability research could enable more transparent and understandable models. However, regulations demanding interpretability are currently infeasible.
  • Pre-registration of large AI training runs: AI developers should report large training runs to regulators to inform them of novel risks under appropriate confidentiality protections.
  • External red teaming: Mandatory adversarial testing of AI systems before release, either through a centralized organization like NIST or via researcher access. However, red-teaming talent currently resides within private AI labs.
  • Auditors with technical expertise, security consciousness, and flexibility: Auditors need deep machine learning experience while preventing leaks or hacking, but must also operate within constraints that promote competitiveness.
  • Anthropic recommended scoping accountability measures based on a model’s capabilities and demonstrated risks, evaluated through targeted capabilities evaluations. It suggested clarifying IP ownership frameworks for AI to enable fair licensing and providing guidance on antitrust issues to allow safety collaborations.
  • Overall, Anthropic stressed the difficulties of rigorously evaluating and accessing information about advanced AI systems due to their sensitive nature. It argued that funding capabilities evaluations, interpretability research, and access to computational resources are critical to an effective AI accountability ecosystem that benefits society.

What To Expect Next

The responses to the NTIA request for comment shows that while AI companies recognize the importance of accountability, there are still open questions and challenges around implementing and scaling accountability mechanisms effectively.

They also indicate that both self-regulatory efforts by companies and government policies will play a role in developing a robust AI accountability ecosystem.

Going forward, the NTIA report is expected to make recommendations to advance the AI accountability ecosystem by leveraging and building upon existing self-regulatory efforts, technical standards, and government policies. The input from stakeholders through the comments process will likely help shape those recommendations.

However, implementing recommendations into concrete policy changes and industry practices that can transform how AI is developed, deployed, and overseen will require coordination among government agencies, tech companies, researchers, and other stakeholders.

The path to mature AI accountability promises to be long and difficult. But these initial steps show there is momentum toward achieving that goal.

Featured image: EQRoy/Shutterstock

Source link

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address


Google’s AI Overviews Go Viral, Draw Mainstream Media Scrutiny




Google's AI Overviews Go Viral, Draw Mainstream Media Scrutiny

Google’s rollout of AI-generated overviews in US search results is taking a disastrous turn, with mainstream media outlets like The New York Times, BBC, and CNBC reporting on numerous inaccuracies and bizarre responses.

On social media, users are sharing endless examples of the feature’s nonsensical and sometimes dangerous output.

From recommending non-toxic glue on pizza to suggesting that eating rocks provides nutritional benefits, the blunders would be amusing if they weren’t so alarming.

Mainstream Media Coverage

As reported by The New York Times, Google’s AI overviews struggle with basic facts, claiming that Barack Obama was the first Muslim president of the United States and stating that Andrew Jackson graduated from college in 2005.

These errors undermine trust in Google’s search engine, which more than two billion people rely on for authoritative information worldwide.

Manual Removal & System Refinements

As reported by The Verge, Google is now scrambling to remove the bizarre AI-generated responses and improve its systems manually.

A Google spokesperson confirmed that the company is taking “swift action” to remove problematic responses and using the examples to refine its AI overview feature.

Google’s Rush To AI Integration

The flawed rollout of AI overviews isn’t an isolated incident for Google.

As CNBC notes in its report, Google made several missteps in a rush to integrate AI into its products.

In February, Google was forced to pause its Gemini chatbot after it generated inaccurate images of historical figures and refused to depict white people in most instances.

Before that, the company’s Bard chatbot faced ridicule for sharing incorrect information about outer space, leading to a $100 billion drop in Google’s market value.

Despite these setbacks, industry experts cited by The New York Times suggest that Google has little choice but to continue advancing AI integration to remain competitive.

However, the challenges of taming large language models, which ingest false information and satirical posts, are now more apparent.

The Debate Over AI In Search

The controversy surrounding AI overviews adds fuel to the debate over the risks and limitations of AI.

While the technology holds potential, these missteps remind everyone that more testing is needed before unleashing it on the public.

The BBC notes that Google’s rivals face similar backlash over their attempts to cram more AI tools into their consumer-facing products.

The UK’s data watchdog is investigating Microsoft after it announced a feature that would take continuous screenshots of users’ online activity.

At the same time, actress Scarlett Johansson criticized OpenAI for using a voice likened to her own without permission.

What This Means For Websites & SEO Professionals

Mainstream media coverage of Google’s erroneous AI overviews brings the issue of declining search quality to public attention.

As the company works to address inaccuracies, the incident serves as a cautionary tale for the entire industry.

Important takeaway: Prioritize responsible use of AI technology to ensure the benefits outweigh its risks.

Source link

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address
Continue Reading


New Google Search Ads Resemble AI Assistant App




New Google Search Ads Resemble AI Assistant App

A keynote at Google’s Marketing Live event showed a new AI-powered visual search results that feature advertisements that engage users within the context of an AI-Assisted search, blurring the line between AI-generated search results and advertisements.

Google Lens is a truly helpful app but it becomes unconventional where it blurs the line between an assistant helping users and being led to a shopping cart. This new way of engaging potential customers with AI is so far out there that the presenter doesn’t even call it advertising, he doesn’t even use the word.

Visual Search Traffic Opportunity?

Google’s Group Product Manager Sylvanus Bent, begins the presentation with an overview of the next version of Google Lens visual search that will be useful for surfacing information and for help finding where to buy them.

Sylvanus explained how it will be an opportunity for websites to receive traffic from this new way to search.

“…whether you’re snapping a photo with lens or circling to search something on your social feed, visual search unlocks new ways to explore whatever catches your eye, and we recently announced a newly redesigned results page for Visual search.

Soon, instead of just visual matches, you’ll see a wide range of results, from images to video, web links, and facts about the knowledge graph. It gets people the helpful information they need and creates new opportunities for sites to be discovered.”

It’s hard to say whether or not this will bring search traffic to websites and what the quality of that traffic will be. Will they stick around to read an article? Will they engage with a product review?

Visual Search Results

Sylvanus shares a hypothetical example of someone at an airport baggage claim who falls in like with someone else’s bag. He explains that all the person needs to do is snap a photo of the luggage bag and Google Lens will take them directly to shopping options.

He explains:

“No words, no problem. Just open Lens, take a quick picture and immediately you’ll see options to purchase.

And for the first time, shopping ads will appear at the very top of the results on linked searches, where a business can offer what a consumer is looking for.

This will help them easily purchase something that catches their eye.”

These are image-heavy shopping ads at the top of the search results and as annoying as that may be it’s nowhere near the “next level” advertising that is coming to Google’s search ads where Google presents a paid promotion within the context of an AI Assistant.

Interactive Search Shopping

Sylvanus next describes an AI-powered form advertising that happens directly within search. But he doesn’t call it advertising. He doesn’t even use the word advertising. He suggests this new form of AI search experience is more than offer, saying that, “it’s an experience.”

He’s right to not use the word advertisement because what he describes goes far beyond advertising and blurs the boundaries between search and advertising within the context of AI-powered suggestions, paid suggestions.

Sylvanus explains how this new form of shopping experience works:

“And next, imagine a world where every search ad is more than an offer. It’s an experience. It’s a new way for you to engage more directly with your customers. And we’re exploring search ads with AI powered recommendations across different verticals. So I want to show you an example that’s going live soon and you’ll see even more when we get to shopping.”

He uses the example of someone who needs to store their furniture for a few months and who turns to Google to find short term storage. What he describes is a query for local short term storage that turns into a “dynamic ad experience” that leads the searcher into throwing packing supplies into their shopping cart.

He narrated how it works:

“You search for short term storage and you see an ad for extra space storage. Now you can click into a new dynamic ad experience.

You can select and upload photos of the different rooms in your house, showing how much furniture you have, and then extra space storage with help from Google, AI generates a description of all your belongings for you to verify. You get a recommendation for the right size and type of storage unit and even how much packing supplies you need to get the job done. Then you just go to the website to complete the transaction.

And this is taking the definition of a helpful ad to the next level. It does everything but physically pick up your stuff and move it, and that is cool.”

Step 1: Search For Short Term Storage

1716722762 15 New Google Search Ads Resemble AI Assistant App

The above screenshot shows an advertisement that when clicked takes the user to what looks like an AI-assisted search but is really an interactive advertisement.

Step 2: Upload Photos For “AI Assistance”

1716722762 242 New Google Search Ads Resemble AI Assistant App

The above image is a screenshot of an advertisement that is presented in the context of AI-assisted search.  Masking an advertisement within a different context is the same principal behind an advertorial where an advertisement is hidden in the form of an article. The phrases “Let AI do the heavy lifting” and “AI-powered recommendations” create the context of AI-search that masks the true context of an advertisement.

Step 3: Images Chosen For Uploading

1716722762 187 New Google Search Ads Resemble AI Assistant App

The above screenshot shows how a user uploads an image to the AI-powered advertisement within the context of an AI-powered search app.

The Word “App” Masks That This Is An Ad

Screenshot of interactive advertisement for that identifies itself as an app with the words

Above is a screenshot of how a user uploads a photo to the AI-powered interactive advertisement within the context of a visual search engine, using the word “app” to further the illusion that the user is interacting with an app and not an advertisement.

Upload Process Masks The Advertising Context

Screenshot of interactive advertisement that uses the context of an AI Assistant to mask that this is an advertisement

The phrase “Generative AI is experimental” contributes to the illusion that this is an AI-assisted search.

Step 4: Upload Confirmation

1716722762 395 New Google Search Ads Resemble AI Assistant App

In step 4 the “app” advertisement is for confirming that the AI correctly identified the furniture that needs to be put into storage.

Step 5: AI “Recommendations”

1716722762 588 New Google Search Ads Resemble AI Assistant App

The above screenshot shows “AI recommendations” that look like search results.

The Recommendations Are Ad Units

1716722762 751 New Google Search Ads Resemble AI Assistant App

Those recommendations are actually ad units that when clicked takes the user to the “Extra Space Storage” shopping website.

Step 6: Searcher Visits Advertiser Website

1716722762 929 New Google Search Ads Resemble AI Assistant App

Blurring The Boundaries

What the Google keynote speaker describes is the integration of paid product suggestions into an AI assisted search. This kind of advertising is so far out there that the Googler doesn’t even call it advertising and rightfully so because what this does is blur the line between AI assisted search and advertising. At what point does a helpful AI search become just a platform for using AI to offer paid suggestions?

Watch The Keynote At The 32 Minute Mark

Featured Image by Shutterstock/Ljupco Smokovski

Source link

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address
Continue Reading


How Do I Get A Job With A PPC Agency




Conversion Tracking In PPC Campaigns

This month’s “Ask A PPC” question is particularly significant because the job market has been quite volatile.

“How do I get a job with a PPC agency when I have only worked in-house. What experience would they want?” – Karl Toronto

It’s understandable that people want to know which skills employers seek when hiring for a PPC team. There can be a disparity between what people think they need and what the market actually demands.

We’ll delve into some data and commentary to explain why various traits are valued.

It’s crucial to understand that the ideal candidates will be versatile and have an aptitude for all aspects of digital marketing.

However, no one can excel at everything, so leveraging your strengths or preferences is beneficial.

Ensure that you’re securing the best role for yourself while the company hiring you finds the best fit for them.

Here Are The Essential Skills

  • Analytics.
  • Creativity.
  • Ad network knowledge.
  • Willingness to test/learn.
  • Culture fit.

Discrepancy Between Market Demands And Perceived Needs

I conducted a poll on my LinkedIn to gauge the skills desired by current employers and practitioners.

Screenshot from author, LinkedIn, April 2024

Analytical skills emerged as the most sought-after trait. Employers seek individuals who can interpret numbers and discern the story behind them.

However, relying solely on analytical prowess may overlook the importance of creativity.

Creative skills are vital in today’s ad networks, especially emphasizing visual content like videos and campaign types that force visual content (Performace Max/Demand Gen). Neglecting creativity can hinder a company’s branding efforts.

Unexpectedly, ad network skills and cultural fit were deemed far less critical than analytical skills. Brands should prioritize team cohesion for long-term success, yet this aspect is often undervalued.

The disparity between job descriptions and actual skill requirements contributes to the difficulty in the job market.

Agencies that hire for how PPC used to work will be left wanting. Practitioners who only focus on popular skills instead of needed ones will be made obsolete by the privacy-first era obscuring data and AI owning creative.

Analytical Skills

Analytical abilities involve knowing where to find relevant data sources and understanding how they contribute to success.

While PPC historically relied on measurable outcomes, the landscape is evolving, necessitating adaptability in data analysis. Technical proficiency and strategic acumen are crucial for navigating different data sources.

These include:

  • Customer relationship management (CRM) systems.
  • Google Analytics 4 (GA4).
  • Ecommerce platforms.
  • Content management platforms (CMS).

Empathy for various ad channels improves your candidacy, and knowing how to work with post-click data will give you an edge over those who can only work with ad platform data.

While being highly technical isn’t required, having empathy for coding and scripts will give you a better chance to stay current with evolving data mechanics (especially as APIs become even more important for accessing data blocked by privacy-first regulations).

Here are some takes from PPC experts on why analytics is the most important:

A screenshot of a LinkedIn comment by Georgi Zayakov, who describes himself as analytical Screenshot from author, LinkedIn, April 2024
A LinkedIn post by Kathryn B., a paid media specialist at a PPC agencyScreenshot from LinkedIn, April 2024
Screenshot of a LinkedIn post by Nikolaos B., discussing how marketers must become data-savvyScreenshot from author, LinkedIn, April 2024


Creativity is essential for crafting compelling ad content, yet many PPC agencies struggle in this area.

Clients are often tasked with providing creative materials due to cost or complexity constraints.

You’ll get a competitive edge if you have these skills:

  • Video Editing: With the rise of PMax, as well as many ad networks leaning heavily into connected TV, having video editing chops will be a huge asset for any team. If you’re not comfortable using conventional editing tools, AI tools like Descript are a great way to take on those tasks.
  • Graphic Design: No matter the ad network your potential employer is hiring for, you will need some ability to design static images. Whether you use stock photos or AI-generated images or come up with the creative yourself, the days of purely text ads are over. Tools like Canva can help bridge the gap for less technical designers, but don’t discount ad network AI.
  • Content Creation: While the first two categories leaned toward visual content, written content is still important (i.e., most ad formats include some text). Having the ability to understand how diverse audiences prefer to be addressed while respecting the specific requirements of each format is a great skill to hone.

While some roles may prioritize analytics or ad network knowledge, emphasizing creative abilities can distinguish you during the hiring process.

Here are some experts who value creativity:

A screenshot of a LinkedIn post by Erik PetersonScreenshot from author, LinkedIn, April 2024
A screenshot of a Linkedin post by Amy HebdonScreenshot from author, LinkedIn, April 2024

Ad Network Knowledge

Ad network expertise is valuable, but adaptability is paramount as platforms evolve rapidly.

Some agencies will have specialists, while others hire folks they expect to be passable at every network they service. It’s important to understand what workflow will enable you to succeed.

If you’re happy working with all platforms, then don’t shy away from it. However, if you do better in focusing on one aspect of PPC, that’s totally valid. Just know it might limit your ability to get hired into smaller “familyesque” agencies.

Understanding auction dynamics and bidding strategies is crucial.

Many of us who entered the industry when manual bidding was more popular have an unfair advantage over those who came in during the Smart Bidding era (i.e., anything from 2020).

This is because manual bidding requires you to think about the mechanics of each ad platform’s auction and how you could use those mechanics to your advantage in building account structure.

Knowing what to track and allocating appropriate budgets are key considerations.

Understanding that some networks require more conversions than others to run (e.g., Meta Ads’ 50 in a 7-day period vs. Google Ads’ 15 in a 30-day period) should influence what you choose to track, as well as how you report the data.

Additionally, if you are under or over budget, you’ll set yourself up to fail. Knowing which channels require a big investment upfront and what the breaking point for each network is (either on underspending or spending too much) is critical.

Awareness of potential pitfalls, such as false positives or negatives, enhances campaign effectiveness. For example, it’s important to know how to check if automatically applying recommendations is on and what tasks it’s on for.

It’s worth noting that none of the experts who chimed in on the poll made a clear case for ad network knowledge specifically.

Willingness To Test

Success in PPC requires openness to experimentation and a willingness to adapt. While this wasn’t one of the criteria in the poll, it was one of the most popular traits experts look for in hiring.

Perfectionism can hinder progress in a fast-changing environment. Testing new ideas and embracing failure as an opportunity for growth are essential.

While analytical skills aid in test design, empathy and creativity are equally vital for devising effective experiments.

Here is an expert who favors a willingness to test:

Screenshot of a social media post by Mike RhodesScreenshot from author, LinkedIn, April 2024

Cultural Fit

Cultural alignment with an agency fosters productivity and job satisfaction. However, you can only achieve that by being honest with yourself about what you want and the mechanics of how you work.

Agencies demand intense effort and collaboration, making compatibility with colleagues crucial.

Anyone looking to make the shift from in-house to agency needs to be prepared for a much faster pace of work and a lot more agency.

Open communication with leadership regarding preferred management and learning styles will ensure a positive working relationship.

Respect for peers and a supportive atmosphere contribute to a fulfilling work environment.

Here are a few thoughts on cultural fit from polled experts:

The image shows a LinkedIn post by David Zebrout containing text discussing the importance of integrating PPC network knowledge with intertimed optimizations in generating profitable growth.Screenshot from author, LinkedIn, April 2024
LinkedIn post by Lisa Erschbamer discussing the importance of cultural fit and individual personality in team dynamics for effective performance at a PPC Agency.Screenshot from author, LinkedIn, April 2024
A screenshot of a LinkedIn post by Aaron Davies discussing the importance of cultural fit, individual skills, and team communication in marketing for a PPC agency. The post has reactions and a question comment by NavahScreenshot from author, LinkedIn, April 2024

Final Thoughts

Navigating the current job market can be challenging, but understanding industry needs and honing relevant skills increases your chances of success.

Balancing technical proficiency with creativity and cultural fit is essential for thriving in a PPC role. By aligning with market demands and showcasing your strengths, you can secure rewarding opportunities in the field.

Have a question you’d like us to address? Fill out the form!

More resources:

Featured Image: Paulo Bobita/Search Engine Journal

Source link

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address
Continue Reading