Get ready for the great AI disappointment in 2024. The year 2023 was filled with hype and high expectations for generative AI, but the reality is likely to fall short. Evidence is emerging that these AI models can provide false information and even hallucinate, leading to concerns about their reliability. The hope for a quick fix through supervised learning is proving to be optimistic at best. As a result, the setbacks of generative AI will overshadow its potential for productivity improvements and advancements towards artificial general intelligence. Additionally, the dominance of tech giants like Google and Microsoft/OpenAI in the industry, as well as the increase in manipulation and misinformation online, will contribute to a sense of disappointment. Calls for antitrust measures and regulation will intensify, but meaningful action may not arrive in 2024. Despite the letdown, the importance of understanding the limitations and benefits of these AI models will become more apparent as we navigate the complexities they present in various aspects of society.
The False Information Problem
Generative AI and Large Language Models
Generative AI and large language models have gained significant attention and excitement in recent years. These models, such as ChatGPT, have been hailed as groundbreaking technology with the potential to revolutionize various industries. They offer the promise of improved productivity and automation, leading to a more efficient and advanced society. However, as the hype surrounding these models has grown, so too have concerns about the false information they can generate.
False Information and Hallucination
One of the key challenges with generative AI and large language models is their tendency to produce false information. These models are trained on vast amounts of data, which include both accurate and inaccurate information. As a result, when generating responses or predictions, they can sometimes provide incorrect or misleading information. Additionally, these models are also prone to hallucination, where they simply make things up and get it wrong. This can have serious consequences for users who rely on the outputs of these models for decision-making.
Optimism for Supervised Learning
Many experts and researchers initially believed that supervised learning could be the solution to the false information problem. Supervised learning involves training the models to avoid questionable sources or statements, thereby reducing the likelihood of false information generation. However, as expectations grew, it became clear that supervised learning alone was not enough to address this issue. The underlying architecture of these models, which is based on predicting the next word or sequence, makes it difficult to anchor their predictions to known truths.
Difficulty Anchoring Predictions to Known Truths
Anchoring the predictions of generative AI models to known truths has proven to be a significant challenge. The models excel at mimicking human-like responses and generating text that sounds plausible. However, determining the accuracy or truthfulness of these responses can be a complex task. Without a clear mechanism to verify the validity of the generated information, it becomes increasingly difficult to rely on these models for accurate and reliable outputs.
The Disappointing Productivity Improvements
Exponential Improvements in Productivity
There was much anticipation that generative AI and large language models would lead to exponential improvements in productivity. It was hoped that these models would automate various tasks, making them faster and more efficient. However, as we move further into 2024, it is becoming evident that these improvements have not materialized to the extent expected.
Blaming Faulty Implementation
A significant factor contributing to the lack of productivity improvements is the faulty implementation of generative AI by businesses. While the technology itself holds potential, its successful integration into existing workflows and systems is crucial. Many companies have struggled to effectively leverage generative AI, resulting in underwhelming performance and limited productivity gains. The blame cannot solely be placed on the technology itself, but rather on the organizations’ inability to properly implement and harness its capabilities.
Knowing Human Tasks to Augment
In order to achieve meaningful productivity improvements, it has become increasingly apparent that we need a deeper understanding of which human tasks can be augmented by generative AI models. Rather than replacing human workers entirely, these models should be viewed as tools to enhance and amplify human capabilities. Identifying the tasks that can be effectively augmented by AI and providing the necessary training for workers to collaborate with these models is crucial for achieving the desired productivity gains.
Additional Training for Workers
To fully realize the potential of generative AI, it is imperative to invest in additional training for workers. As these models continue to evolve and improve, workers must acquire the skills and knowledge required to effectively collaborate with AI systems. This may involve upskilling and reskilling programs to ensure that workers can adapt to the changing work landscape and make the most of AI technologies. By empowering workers with the necessary training, we can maximize the benefits of generative AI and drive productivity improvements.
The Reality of Generative AI
Adoption by Many Companies
Despite the challenges and disappointments, generative AI has been adopted by many companies across various industries. The allure of improved productivity and automation has convinced organizations to invest in this technology. However, the reality of its implementation has fallen short of the initial expectations. While some companies have successfully integrated generative AI into their workflows, many others have struggled to realize the promised benefits.
So-So Automation
One of the main reasons for the underwhelming performance of generative AI is its limited ability to deliver significant automation. While there are certainly tasks that can be automated to some extent, the true potential of fully autonomous systems is yet to be realized. Generative AI has proved to be more of a “so-so” automation tool, displacing workers in some cases but failing to deliver the transformative changes initially anticipated.
Displacement of Workers
The adoption of generative AI has inevitably led to the displacement of certain workers. As tasks that were previously performed by humans are automated, the need for human labor decreases. This has implications for the workforce, as individuals find themselves being replaced by AI systems. The displacement of workers poses significant challenges, including potential job losses and the need for reskilling and reintegration into new roles.
Lack of Huge Productivity Improvements
Perhaps one of the most significant disappointments associated with generative AI is the lack of huge productivity improvements. While there have been pockets of success, the overall impact on productivity has been less impressive than initially anticipated. The fundamental limitations of the technology, combined with the challenges of implementation, have contributed to this lack of substantial gains. As a result, the expectations for generative AI’s ability to revolutionize productivity have been tempered.
The Impact on Social Media and Online Search
ChatGPT and Large Language Models in Social Media and Online Search
Generative AI models, such as ChatGPT, have found significant applications in social media and online search. These models have the ability to generate text and responses that mimic human-like interactions, making them ideal for chatbots and customer service applications. Additionally, in the realm of online search, large language models can assist in providing relevant and accurate information to users.
Monetization of Information
The proliferation of generative AI models in social media and online search has accelerated the monetization of information. Platforms that leverage these models can collect vast amounts of user data, which can then be used to personalize and target digital advertisements. The ability to generate tailored content has created new opportunities for companies to engage with users and monetize their platforms.
Competition for User Attention
As generative AI models become more commonplace in social media and online search, the competition for user attention intensifies. With an endless stream of content generated by these models, platforms are vying for users’ time and engagement. This has led to an increase in the volume and frequency of content, further contributing to information overload and potential user fatigue.
Growth of Manipulation and Misinformation
The rise of generative AI models in social media and online search has also resulted in the growth of manipulation and misinformation. These models, if not properly controlled and regulated, can be exploited to disseminate misleading or false information. The ability to generate highly engaging and persuasive content poses risks to the integrity of online information and the ability of users to discern truth from fiction.
Dominance of Google and Microsoft/OpenAI
Emergence of a Duopoly in the Industry
As generative AI continues to evolve and gain prominence, a duopoly is emerging in the industry, with Google and Microsoft/OpenAI dominating the field. These companies have developed gargantuan models that set the standard for generative AI capabilities. As a result, many other companies are compelled to rely on these foundation models to develop their own applications, further solidifying the dominance of these tech giants.
Reliance on Foundation Models
The reliance on foundation models developed by Google and Microsoft/OpenAI has created a dependency within the industry. While these models offer impressive capabilities, they are not without their limitations. The false information and hallucination issues observed in generative AI can still manifest in applications built on top of these foundation models. This reliance on a select few models limits the diversity and innovation in the industry, as companies find themselves constrained by the capabilities and constraints of these foundation models.
Disappointing Apps and Companies
Despite the dominance of Google and Microsoft/OpenAI, there has been a notable lack of groundbreaking applications and successful companies in the generative AI space. Many apps built on these foundation models have failed to deliver on their promises or meet user expectations. This can be attributed, in part, to the challenges associated with generative AI, including the false information problem and the difficulty in anchoring predictions to known truths. As a result, the industry as a whole has experienced a level of disappointment in terms of the practical applications and tangible impact of generative AI.
Calls for Antitrust and Regulation
Intensification of Calls for Antitrust
The dominance of tech giants like Google and Microsoft/OpenAI has sparked growing concerns about their market power and influence. As their control over generative AI technology becomes more evident, calls for antitrust actions have intensified. Critics argue that the concentration of power in a few companies stifles competition and hampers innovation. The push for antitrust measures aims to promote a more competitive and diverse landscape in the generative AI industry.
Lack of Action and Breakup of Tech Companies
Despite the intensifying calls for antitrust measures, the actual actions taken by policymakers and courts have been limited. Breaking up large tech companies is a complex and contentious endeavor, requiring strong political will and legal justifications. As a result, the likelihood of significant antitrust actions leading to the breakup of these companies remains uncertain. The challenges associated with dismantling the infrastructure and business operations of tech giants serve as a barrier to meaningful change in the industry.
Stirrings in the Regulation Space
While concrete actions in antitrust may be lacking, there are stirrings in the regulation space. As the detrimental effects of generative AI become more apparent, regulators are recognizing the need for oversight and control. Efforts to create regulations that address the responsible development, deployment, and use of generative AI models are gaining traction. However, the process of implementing and enforcing these regulations is a complex task that requires careful consideration of both technological advancements and societal implications.
Delayed Meaningful Regulation
Despite the growing recognition of the need for regulation, meaningful regulation is likely to be delayed. The rapid pace of technological advancements has outpaced the ability of governments and regulatory bodies to keep up. Additionally, generative AI presents unique challenges that require nuanced and well-informed regulations. Crafting effective regulations that strike a balance between promoting innovation and protecting societal interests takes time. Therefore, the arrival of meaningful regulation is expected to be delayed, prolonging the potential risks associated with generative AI.
Recognizing the Limits of AI
Complex Human Cognition as a Pipe Dream
As discussions around generative AI and its capabilities continue, it is becoming increasingly clear that complex human cognition is a difficult feat to achieve. The ability to truly understand and replicate human intelligence is still far from our reach. While generative AI models can mimic human-like responses and generate impressive outputs, they are ultimately limited by their underlying architecture and training methods.
Promises of Intelligence Just Around the Corner
Despite the challenges and limitations, there are still those who believe that true artificial general intelligence (AGI) is just around the corner. The concept of AGI refers to AI systems that possess the ability to understand, learn, and perform tasks at a human-like level across a wide range of domains. However, the promises of AGI need to be tempered with a realistic understanding of the current limitations and the complexity of achieving such a feat.
Neglecting Mundane Risks of AI
While the focus often lies on the potential dangers and risks associated with AGI, it is important not to neglect the more mundane risks of AI. The uncontrolled rollout and deployment of generative AI models can have significant consequences for society. From job displacement to increased inequality and threats to democracy, the impacts of AI on these aspects of society should not be overlooked. Understanding and addressing these risks is crucial for a responsible and ethical approach to AI.
Costs and Consequences in 2024
Increased Time Spent Using Screens
One of the notable consequences of generative AI adoption in social media and online search is the increased time people spend using screens. The constant availability of AI-generated content and the allure of personalized recommendations contribute to prolonged screen time. This has implications for mental health, as excessive screen time has been linked to issues such as digital addiction, anxiety, and decreased well-being.
Mental Health Problems Associated with Screen Time
The impact of increased screen time on mental health cannot be ignored. As individuals spend more time engaging with AI-generated content, they may become more susceptible to issues such as information overload, online harassment, and the negative effects of social media comparison. The potential consequences on mental health and well-being highlight the need for balanced and mindful screen usage in the age of generative AI.
Disappointment with Generative AI Adoption
Despite the initial hype and excitement surrounding generative AI, the reality of its adoption has led to disappointment for many. The failure to deliver on promised productivity improvements, the prevalence of false information generation, and the limited impact on industries have left individuals and organizations underwhelmed. The high expectations set for generative AI have not been met, leading to a sense of disillusionment and a need to recalibrate future expectations.
Impact on Jobs, Inequality, and Democracy
Another significant cost and consequence of generative AI adoption is its impact on jobs, inequality, and democracy. The displacement of workers due to automation can result in job losses and economic disparities. The concentration of power in tech giants further exacerbates inequality and poses risks to democratic principles. These socio-economic consequences necessitate thoughtful consideration and proactive measures to mitigate the potential negative effects of generative AI.
Intensifying Discussions and Bipartisan Dialogue
Government’s Lack of Technological Pace
One of the key challenges in regulating generative AI and addressing its potential risks is the government’s lack of technological pace. The rapid advancement of AI technology has outpaced the ability of governments to keep up with its complexities and implications. Bridging this technological knowledge gap and fostering a deeper understanding of generative AI within governmental bodies is crucial for meaningful regulation and oversight.
Discussions on New Laws and Regulations
Despite the challenges, discussions on new laws and regulations surrounding generative AI are gaining momentum. Policymakers, experts, and stakeholders are actively engaging in dialogues to explore potential regulatory frameworks. These discussions aim to strike a balance between encouraging innovation and ensuring the responsible development and deployment of generative AI models.
Bipartisanship in the Regulation Space
The need for regulation and oversight of generative AI has transcended political boundaries, leading to a surprising bipartisanship in the regulation space. Both sides of the political spectrum recognize the potential risks and consequences associated with uncontrolled AI proliferation and are engaging in discussions to address these concerns. The emergence of bipartisan dialogue creates opportunities for collaboration and the development of meaningful regulatory frameworks.
Conclusion: The Great AI Disappointment
In conclusion, the grand promises and expectations surrounding generative AI have given way to a sense of disappointment. The false information problem, limited productivity improvements, and challenges in anchoring predictions to known truths have highlighted the shortcomings of current generative AI models. The dominance of a few tech giants, the growing concerns about manipulation and misinformation, and the delay in meaningful regulation have further compounded the disappointment.
It is essential to recognize and understand the limitations of generative AI, including its inability to replicate complex human cognition and the potential risks and consequences it poses to society. While the future of AI may still hold great promise, it is imperative to approach its development and adoption with caution, responsibility, and a realistic understanding of its capabilities and limitations. Only through thoughtful regulation, collaboration, and ongoing dialogue can we navigate the complexities of generative AI and ensure its potential is harnessed for the benefit of all.
