A report by the Data Provenance Initiative warns that generative AI models may suffer as websites increasingly restrict crawler bots, blocking access to high-quality data. This trend, driven by fears of data misuse, could shift AI training reliance from well-maintained sources to lower-quality data. Companies may turn to synthetic data or direct licensing to maintain AI model efficacy amid growing data scarcity.

Goldman Sachs released a critical 31-page report titled "Gen AI: Too Much Spend, Too Little Benefit?", arguing that generative AI's productivity benefits and returns are significantly limited and that its power demands will drastically increase utility spending. The report highlights doubts about AI's ability to transform industries, pointing out high costs, power grid challenges, and lack of clear productivity gains or significant revenue generation. It suggests a potentially bleak future for the technology without major breakthroughs.

AI-generated content is becoming a big problem in Google Search results. About 10% of Google results now consist of AI content, posing challenges for Google's algorithms. There are concerns that this may lead to a collapse in model quality as AIs feed on each other's output.

That Generative AI may turn out to be a disappointment. There are concerns about the technology's lack of profitability, security issues, and the inherent problem of hallucinations in language models. Unless a groundbreaking model like GPT-5 is released by the end of 2024, addressing key issues and offering a killer application, the hype surrounding Generative AI may start to dissipate.

AI hallucinations, when AI models generate plausible but incorrect outputs, pose a significant challenge and cannot be fully solved with current technologies. These issues stem from the fundamental design of generative AI, which relies on recognizing patterns in data but lacks an understanding of truth, leading to random occurrences of misleading information.

This article addresses the copyright challenges posed by AI models trained on copyrighted material without permission. It suggests AI developers respect copyright signals, implement guardrails to prevent generating infringing content, and develop business models that ensure fair compensation for content creators, including techniques like retrieval-augmented generation (RAG) and creating cooperative AI content ecosystems.

Recent research has highlighted a concerning trend in the performance of larger artificial intelligence (AI) chatbots, revealing that as these models grow in size and complexity, they are increasingly prone to generating incorrect answers. This phenomenon is particularly troubling because users often fail to recognize when the information provided by these chatbots is inaccurate. The study, conducted by José Hernández-Orallo and his team at the Valencian Research Institute for Artificial Intelligence, examined three prominent AI models: OpenAI's GPT, Meta's LLaMA, and the open-source BLOOM model. The researchers analyzed how the accuracy of these models changed as they were refined and expanded, utilizing more training data and advanced computational resources. They discovered that while larger models generally produced more accurate responses, they also exhibited a greater tendency to answer questions incorrectly rather than admitting a lack of knowledge. This shift means that users are likely to encounter more incorrect answers, as the models are less inclined to say "I don't know" or to avoid answering altogether. The study's findings indicate that the fraction of incorrect responses has risen significantly among the refined models, with some models providing wrong answers over 60% of the time when they should have either declined to answer or provided a correct response. This trend raises concerns about the reliability of AI chatbots, as they often present themselves as knowledgeable even when they are not, leading to a phenomenon described as "bullshitting" by philosopher Mike Hicks. This behavior can mislead users into overestimating the capabilities of these AI systems, which poses risks in various contexts, especially when users rely on them for accurate information. To assess the models' performance, the researchers tested them on a wide range of prompts, including arithmetic, geography, and science questions, while also considering the perceived difficulty of each question. They found that while the accuracy of responses improved with larger models, the tendency to provide incorrect answers did not decrease proportionately, particularly for more challenging questions. This inconsistency suggests that there is no guaranteed "safe zone" where users can trust the answers provided by these chatbots. Moreover, the study revealed that human users struggle to accurately identify incorrect answers, often misclassifying them as correct. This misjudgment occurred between 10% and 40% of the time, regardless of the question's difficulty. Hernández-Orallo emphasized the need for developers to enhance AI performance on easier questions and encourage models to refrain from answering difficult ones, thereby helping users better understand when they can rely on AI for accurate information. While some AI models are designed to acknowledge their limitations and decline to answer when uncertain, this feature is not universally implemented, particularly in all-purpose chatbots. As companies strive to create more capable and versatile AI systems, the challenge remains to balance performance with reliability, ensuring that users can navigate the complexities of AI-generated information without falling prey to misinformation.

This article discusses the transformative potential and current limitations of generative AI like ChatGPT, noting that while it excels in tasks like coding and generating drafts, it struggles with complex tasks that require specific programming. It highlights the need for a vision that matches AI solutions with practical applications, emphasizing that identifying and integrating these into daily workflows remains a significant challenge.

The Generative AI bubble might be unsustainable. Despite significant advancements in the space, there are still core issues like hallucinations and security risks, and revenue generation remains disproportionately low. If no groundbreaking solution emerges to address these problems and justify the high costs by the end of 2024, the bubble may begin to burst.

Building useful scalable AI applications requires developers to have good data preparation (data cleansing and management) and use retrieval-augmented generation. Models used should be pre-trained or fine-tuned. Custom models can be developed in-house, but usually will require a large amount of capital. Developers should be mindful of latency, memory, compute, caching, and other factors to make sure the user experience is good.

The development of AI, particularly large language models like GPT-3, is heavily reliant on vast amounts of data, with companies like Meta and Google racing to gather more as high-quality online data may run out by 2026. Tech giants are employing controversial methods, including using YouTube data and considering the purchase of publishers, to fuel their AI advancements. The use of 'synthetic' data is a potential solution, though it carries the risk of amplifying AI errors.

The development of AI, particularly large language models like GPT-3, is heavily reliant on vast amounts of data, with companies like Meta and Google racing to gather more as high-quality online data may run out by 2026. Tech giants are employing controversial methods, including using YouTube data and considering the purchase of publishers, to fuel their AI advancements. The use of 'synthetic' data is a potential solution, though it carries the risk of amplifying AI errors.

This article discusses the evolution and growing complexity of generative pre-trained transformer models. It touches upon how AI development and use are influenced by the regulatory landscape, with examples stretching from cryptographic software to AI-specific executive orders. The piece highlights several steps in AI model creation, from data collection to inference. It also highlights the potential of utilizing crypto and decentralized technology to make AI more user-aligned, verifiable, and privacy-conscious. Despite the progress, AI democratization remains a challenge.

Generative AI tools like ChatGPT are increasingly producing fraudulent research papers, infiltrating databases like Google Scholar alongside legitimate studies. These papers, often on controversial topics like health and the environment, pose significant risks to scientific integrity and public trust. Enhanced vigilance and more robust filtering in academic search engines are essential to curb this growing issue.

In a recent analysis, Edward Zitron delves into the troubling dynamics of the Software as a Service (SaaS) industry and its relationship with the burgeoning field of generative AI. He highlights a concerning incident where Microsoft considered reallocating resources to prioritize AI capabilities, reflecting a broader trend of Big Tech's aggressive push into AI. Zitron expresses skepticism about the effectiveness of generative AI products from major tech companies, noting that many offerings are underwhelming and often serve as mere enhancements to existing services rather than groundbreaking innovations. Zitron explains that the SaaS model, which charges businesses on a subscription basis for software they do not own, has become a dominant force in the tech industry. While this model can provide cost savings and flexibility for companies, it also creates a dependency that can lead to inefficiencies and frustration. As organizations grow, managing multiple SaaS applications becomes increasingly complex, often resulting in a situation where businesses are locked into ecosystems that are difficult to escape. The author argues that the SaaS market is experiencing a decline in growth, with many companies struggling to maintain their revenue streams. This stagnation is compounded by rising customer acquisition costs and a decrease in customer retention rates. Zitron points out that many SaaS companies are now resorting to price increases and aggressive upselling tactics to sustain their business models, which may not be sustainable in the long run. Zitron connects these trends to the current AI boom, suggesting that the desperation for growth in the SaaS sector is driving companies to adopt AI technologies, even when the practical benefits remain unclear. He critiques the way AI is being marketed, often as a superficial enhancement rather than a genuine solution to business challenges. The author warns that the high costs associated with generative AI could further strain the profitability of SaaS companies, leading to a potential crisis in the industry. Ultimately, Zitron paints a bleak picture of the future for SaaS and AI, suggesting that many companies may be overextending themselves in a bid for growth, risking their financial stability in the process. He calls attention to the need for a reevaluation of business strategies in light of these challenges, emphasizing that the current trajectory may not be sustainable for the tech industry as a whole.

GPT-4's dominance in AI benchmarks has been challenged by four new models from different vendors, each showing the potential to surpass GPT-4's capabilities. However, concerns arise as, amidst growing legal and ethical considerations, none of these models are open-source or transparent about their training data. The push for models trained on public domain or licensed content continues, highlighting the complexity of creating competitive AI without proprietary data.

While generative AI can help in producing code fast, it's not a substitute for the experience and mentorship required to develop junior engineers into seniors and beyond. The industry will face a bottleneck in the future if it believes that AI can simply replace junior engineers (which it can't).

The discussion surrounding the impact of Generative AI (GenAI) on computer programming has been marked by significant hype, with claims that it could enhance programmer productivity by a factor of ten. However, recent data and studies suggest that these expectations may be overly optimistic. Gary Marcus highlights that after 18 months of anticipation regarding GenAI's potential to revolutionize coding, the evidence does not support the notion of a tenfold increase in productivity. Two recent studies illustrate this point: one involving 800 programmers found minimal improvement and an increase in bugs, while another study indicated a moderate 26% improvement for junior developers but only marginal gains for senior developers. Additionally, earlier research pointed to a decline in code quality and security, raising concerns about the long-term implications of relying on GenAI tools. Marcus argues that the modest improvements observed, coupled with potential drawbacks such as increased technical debt and security vulnerabilities, indicate that the reality of GenAI's impact is far from the promised tenfold enhancement. He suggests that a good Integrated Development Environment (IDE) might offer more substantial and reliable benefits for programmers than GenAI tools. The underlying reason for the lack of significant gains, according to AI researcher Francois Chollet, is that achieving a tenfold increase in productivity requires a deep conceptual understanding of programming, which GenAI lacks. While these tools can assist in speeding up the coding process, they cannot replace the critical thinking necessary for effective algorithm and data structure design. Marcus reflects on his own experience as a programmer, noting that clarity in understanding tasks and concepts has historically been a greater advantage than any tool could provide. In the comments section, other programmers echo Marcus's sentiments, sharing their experiences with GenAI coding assistants like Copilot and ChatGPT. Many report that while these tools generate more code, they often introduce bugs and require additional time for debugging, ultimately detracting from productivity rather than enhancing it. Overall, the initial excitement surrounding GenAI's potential to transform programming practices is tempered by the reality of its limitations, emphasizing the importance of foundational knowledge and critical thinking in software development.

The hype surrounding AI has led to flawed research practices in various scientific fields, resulting in a reproducibility crisis that is likely to worsen due to the growing adoption of LLMs.

Researchers have created a generative AI worm called Morris II that can attack AI systems like ChatGPT, spreading autonomously while potentially stealing data. The worm uses “adversarial self-replicating prompts” to perpetuate and compromise AI email assistants, highlighting new cyberattack risks within the AI ecosystem. Security experts urge AI developers to take potential AI-driven threats seriously as AI applications become more autonomous and interconnected.

Baldur Bjarnason, a web developer from Hveragerði, Iceland, recently shared insights on the evolving discourse surrounding fair use in the context of generative AI models. He referenced a paper by Jacqueline Charlesworth, a former general counsel of the U.S. Copyright Office, which critically examines the claims of fair use made by proponents of generative AI. The paper highlights a significant shift in legal scholarship regarding the applicability of fair use to the training of generative models, particularly as a clearer understanding of the technology has emerged. Charlesworth argues that the four factors outlined in Section 107 of the Copyright Act generally weigh against the fair use claims of AI, especially in light of a rapidly changing market for licensed training materials. A key point made in the analysis is that the argument for fair use often relies on a misunderstanding of how AI systems operate. Contrary to the belief that works used for training are discarded post-training, these works are actually integrated into the model and continue to influence its outputs. The process of converting works into tokens and incorporating them into a model does not align with the principles of fair use, as it represents a form of exploitation rather than a transformative use. Charlesworth draws a distinction between the copying of expressive works for functional purposes—such as searching or indexing—and the mass appropriation of creative content for commercial gain. The latter, she argues, lacks precedent in fair use cases and cannot be justified by existing legal frameworks. The paper emphasizes that the act of encoding copyrighted works into a more usable format does not exempt it from being considered infringement. Furthermore, the notion that generative AI's copying should be deemed transformative because it enables generative capabilities is critiqued as a broad and unfounded assertion. This argument essentially posits that the rights of copyright owners should be overridden by the perceived societal benefits of generative AI, which does not hold up as a legal defense in copyright disputes. The narrative pushed by AI companies—that licensing content for training is unfeasible—faces scrutiny, as these companies have shown they can engage in licensing when it serves their interests. This undermines their claims that copyright owners are not losing revenue from the works being appropriated. Overall, Bjarnason encourages readers to explore Charlesworth's paper, noting its accessible language and the importance of understanding the legal implications of generative AI in relation to copyright law.

The effectiveness of large language models is primarily influenced by the quality of their training data. Projections suggest that high-quality data will be scarce by 2027. Synthetic data generation emerges as a promising solution to this challenge, potentially reshaping internet business models and highlighting the importance of equitable data access and antitrust considerations.

Together AI and Morph Labs have put together a great blog post on tuning models for retrieval augmented generation. They showcase some uses of synthetic data as well.

This blog post outlines common themes in building generative AI systems. It covers many of the building blocks a company should consider when deploying its models to production.

This article clarifies key AI terms amidst growing confusion due to marketing jargon, highlighting concepts such as Artificial General Intelligence (AGI), Generative AI, and machine learning. It addresses AI challenges like bias and hallucinations and elaborates on how AI models are trained, referencing various models, algorithms, and architecture, including transformers and retrieval-augmented generation (RAG). The piece also mentions leading AI companies and their products, such as OpenAI's ChatGPT, and hardware used for AI, like NVIDIA's H100 chip.

Neural networks' limited ability to generalize beyond their training data restricts their reasoning and reliability, necessitating alternative approaches to achieve artificial general intelligence.

Google’s latest core update targets sites that are mass-producing low-quality content. Marketers can still utilize AI responsibly for tasks such as drafting content and FAQS. It’s unclear if Google can actually detect AI-generated content. However, it can identify content that summarizes existing content and websites creating content at an unreasonable scale. The core update also gives paid search ads a boost.

Contrary to the claim that AI content is flooding the web, only about 3% of pages are purely AI-generated content. Crypto, Commerce, Finance, and Local pages have the most, with roughly 20% of their URLs featuring AI-generated content. A page's average rank decreases as the amount of AI-generated content increases — suggesting that human-written content performs better in search.

Google's AI Overviews, powered by the Gemini language model, faced heavy criticism for inaccuracies and dangerous suggestions after its U.S. launch. Despite the backlash, Google expanded the feature to six more countries, raising concerns among publishers about reduced traffic and misrepresented content. AI strategists and SEO experts emphasize the need for transparency and better citation practices to maintain trust and traffic.

The tool Google uses to sift through web content to come up with AI answers is the same one that keeps track of web pages for search results. Sites that block Google's AI bot may not show up in search. Publishers either have to choose to offer up their content for use by AI models, which could make their sites obsolete, or disappear from Google search, a top source of traffic. Google has signaled to publishers it is not interested in negotiating data-sharing deals and media companies have little leverage in the situation.