The Role of AI-Generated Content in Search Engine Performance

With the release of Gemini, a multimodal language model, Google made significant progress as a response to Microsoft’s ChatGPT. It was immediately clear that Gemini would be integrated into Bard and SGE results, inevitably impacting search engine outcomes and potentially displacing human-created content.

This has important implications for website operators, as they anticipate a rise in “no-click”searches, leading to a loss of traffic. This is because SGE will generate sufficiently detailed answers to users’ queries, eliminating the need to click on links to individual websites. For example, in the U.S., where SGE has been active the longest, it has been observed that websites have experienced traffic losses ranging from 20% to 64%, depending on the domain and industry.

A greater threat has been identified for smaller brands with limited budgets, as Google aims to promote larger brands that can allocate substantial budgets. It is anticipated that this will directly affect those who create informational and editorial content since high-ranking results were only granted to a few major websites, including Reddit.

It has inevitably been discussed that human-created content will find it difficult to compete with AI-generated results, which are presented at the top of the query results. To achieve higher rankings, human-created content will need to meet even higher quality, uniqueness, and thoroughness standards. SEO strategies will need to be even more finely optimized to stand a chance of appearing in AI-generated summaries.

Some, in response to the looming threats, have already started developing strategies to find new opportunities for expanding search. There has been talk about optimizing newer search channels, allowing users to discover content on other platforms such as YouTube, social media, or e-commerce platforms.

Did Expectations Surpass Reality?

Despite various concerns about the potential for drastic changes in search systems, reality turned out to be somewhat different. AI-generated content has not yet surpassed or completely filled search systems.

Human-generated content remains a crucial aspect, offering more uniqueness, emotion, and authenticity. Users still tend to focus more on articles created by humans rather than AI-generated content.

It’s true that these changes have indeed impacted search, but so far, they have not been as significant as expected. According to Semrush, Google remains dominant, and AI search has not had the effect that many anticipated. Retail search trends remain stable, although the rise of AI is being observed.

Based on a study byNeil Patel, it can be highlighted that traffic metrics for an article vary depending on whether it was created by AI or by a human. A specific case showed that traffic for AI-generated content fluctuated, while traffic for human-generated content steadily increased over five months.

Another experiment focused on how users react to and how much time they spend reading materials that were labelled as AI-generated, AI-assisted but modified, or left as human-created product. The results revealed that people spent the most time and showed the highest levels of trust and engagement with human-generated materials.

Final Word

While machine learning systems have not yet replaced human resources, their impact remains significant. No one doubts that AI will continue to advance, bringing even more opportunities for change in the future. Sources: NEILPATEL, Proficio, The Drum, X

AI Voice Features

In September, OpenAI announced that advanced voice mode (AVM) functionality would be available at the ChatGPT Plus and Teams levels, allowing AI-generated voices to sound more natural. This change comes with the introduction of the enhanced voice mode, and ChatGPT will offer a total of nine voices.

AI voice technology is focused on creating human-like speech using cutting-edge methods. Advanced voice assistant tools are not only capable of understanding and deciphering spoken words but also of analysing context, recognizing the speaker’s tone and emotions, and providing an appropriate response based on these factors.

When analysing AI-generated voices, several technical aspects can be discussed. The creation of these voices involves three main methods:

First, machine learning algorithms. These are significant because they enable systems to continuously learn from data, improving AI outcomes over time. The datasets incorporate a large amount of detailed linguistic models, including phonetic structures and speech dynamics. This allows AI-generated voices to refine their sound, making them as similar as possible to human speech, without standing out due to differences in phonetics or intonation.

Second, natural language processing (NLP). This is perhaps the most crucial technological aspect of AI voice, as it directly impacts the understanding and interpretation of human speech. NLP enables AI to transform specific data, which may be expressed in numbers or facts, into narratives that sound highly natural. This opens up the possibility for synthetic voices to speak in complex sentences and incorporate advanced language features, regardless of word similarities or ambiguities.

Third, the speech synthesis method. These methods are at the core of AI and a prerequisite for machines to process text. First, syntax is involved. NLP algorithms break down sentences into structural data and segments that the system can process. Then comes sentiment analysis, which essentially reads between the lines to understand the overall tone.

Different synthesis methods can be used. For example, concatenative synthesis, where recorded speech is combined, parametric synthesis, which creates mathematical models, and one of the most advanced methods, neural TTS, which uses deep learning models–neural networks.

One crucial aspect of this technology’s advancement remains its continuous interaction with humans. By communicating in everyday tasks, machine systems learn and, as they improve, can better understand queries and provide accurate responses.

AI Voice Technologies from Different Companies

For instance, Google has introduced a tool called WaveNet, which is also capable of generating speech that sounds natural and is considered one of the leaders in this field, although it has weaker capabilities in analysing contextual information.

Microsoft has also developed Azure Cognitive Services. While its voice naturalness is slightly behind the two companies mentioned, it remains an excellent AI voice synthesis option, especially well-suited and easily integrated into various Microsoft products.

Since OpenAI uses advanced methods and incorporates the most innovative neural networks in its AI voice technology, the generated voices sound extremely realistic, capturing natural speech rhythm and tone after conducting high-quality context and emotion analysis. This sets OpenAI’s tools apart from others.

Final Word

The development of artificial intelligence opens up new solutions and brings us closer to the substitution of human resources, as it can now easily mimic human voices. This solution brings countless new discoveries and innovations to both businesses and everyday life, while also presenting certain challenges that lie ahead. Sources: AutoGPT, Google, Microsoft, OpenAI, Podcastle, TechCrunch

Key Takeaways from AWS Summit New York 2024

Firstly, AWS updated its AWS App Studio—a service that allows users to develop applications using generative AI. This major update significantly reduces development time, enabling the creation of applications in just a few minutes. AWS App Studio generates a functional prototype based on a description of the application and even incorporates mock data to provide insight into how the newly created app will function.

Secondly, with Amazon Q Business, a feature within Amazon Q Apps, even non-technical users can now create software, derive insights from data, or generate content using Gen AI. This means users can automate tasks such as developing applications that summarize actions discussed during meetings, making it easier to streamline workflows.

Thirdly, Amazon SageMaker Studio now saves time for data scientists, as the new Amazon Q Developer offers code recommendations for building machine learning models, further optimizing development efforts.

Fourthly, Amazon Bedrock, a fully managed service that provides access to large language models (LLMs) and other foundational models from leading AI companies, now offers an array of customization options. Customers can fine-tune these models with their own data to ensure security and privacy when deploying generative AI applications.

Amazon Bedrock is also the only fully managed service that allows precise tuning of Claude models—the most compact, accessible, and fast models in their category. This allows developers to incorporate their organization’s unique data, use cases, and branding while ensuring the protection of proprietary training data.

Additionally, the Retrieval Augmented Generation method helps identify and mitigate AI-generated inaccuracies, commonly referred to as “hallucinations.” The summit also introduced enhanced capabilities in Agents for Amazon Bedrock, which can now recall recent interactions and integrate them into subsequent recommendations, improving the continuity of AI assistance.

Finally, the summit highlighted Amazon’s rapid progress in surpassing its own expectations. The company had initially set a goal of providing free cloud computing training to 29 million people globally by 2025. However, as of today, Amazon has already trained more than 31 million people across 200 countries.

Final Word

Amazon’s continuous development and integration of Generative AI technologies not only enhance its services but also drive users towards an inevitable adoption of this technology. By making generative AI tools more accessible and practical, Amazon is shaping the future of how businesses and individuals engage with AI, encouraging an ecosystem where the technology becomes a seamless part of everyday life.

Source: Amazon

Foundation AI Models

Foundation AI models can be understood as models pre-trained on large datasets, accumulating a broad knowledge base. This allows these models to perform various AI tasks and adapt them to specific domains. These models serve as a foundational tool because they come equipped with general tasks built in, and later, based on the need and the domain in which AI will be applied, they can be further adapted and fine-tuned with specific data for specialized tasks.

Some of these technologies include machine learning models, which are used to predict continuous data, deep learning models, or generative models, which create new content.

There are several key features that illustrate how these models work:

• Pre-training on large datasets: These models are trained on vast amounts of data and cover a broad range of content such as books, articles, images, websites, etc. This enables them to tackle and perform various tasks.
• Versatility: These models can be applied to different applications, such as text generation on various levels—summarization, translation, image generation from text, and speech recognition.
• Scalability: As mentioned, these models are flexible and can be trained with increasingly larger datasets, making the technology more advanced and efficient over time.

Foundation models are fundamental tools used in a wide range of applications. Here are a few examples of programs that use foundation models with fine-tuning to create high-quality applications:

• ChatGPT by OpenAI – Provides answers and tips, summarizes notes, and generates written content.
• DALL·E by OpenAI – Creates realistic images and artwork from natural language descriptions.

Meta’s Sapiens

Facebook introduced a new family of pre-trained computer vision models called Sapiens, following the well-known principle in the field that bigger models and more data equal better systems. These models improve results in areas such as 2D pose estimation, body-part segmentation, depth estimation, and surface normal prediction.

Key features include:

• 300 million photos: The Sapiens models were pre-trained on Humans-300M, a dataset compiled by Facebook that contains 300 million diverse, unlabeled images of humans. These images were used to train a family of vision transformers with parameter sizes ranging from 300 million to 2 billion.
• Compute resources: The largest model, Sapiens-2B, was pre-trained using 1024 A100 GPUs for 18 days via PyTorch, which equates to approximately 442,368 GPU hours. For comparison, Facebook notes that this is significantly less compute than required for their LLaMa language models (e.g., 1.46 million hours for the 8B model and 30.84 million hours for the 403B model).

The success of the Sapiens models highlights the importance of scale in AI development. Facebook attributes the superior performance of these models to three main factors:

1. Large-scale pre-training on a curated dataset focused on understanding humans.
2. The use of high-capacity vision transformers with high-resolution capabilities.
3. High-quality annotations derived from both augmented studio and synthetic data.

Together, these factors emphasize the critical role of scale in driving advancements in computer vision.

Final Word

These models prove that scale, data, annotations are necessary for improving artificial intelligence. By investing in Foundation models, Meta and other technology giants push the boundaries of technology even further and provide new opportunities.

Sources: Ada Lovelace Institute, Meta, IBM