Nikolai Yakovenko, a former professional poker player and research scientist at Google, now CEO of DeepNewz, discusses the state of AI as of 2024. He highlights the seamless integration of AI into daily work, particularly in software development, and how this transformation counters fears of mass unemployment. Yakovenko reviews the evolution of large language models, touches on the potential of AI-driven news apps, and examines the competitive landscape in AI innovation, all while sharing insights on the public's shifting perceptions of technology.
Nikolai Yakovenko discusses the unexpected resilience of employment amid the AI revolution, contrary to widespread predictions of mass unemployment.
The podcast highlights the pivotal role of large language models and neural networks in advancing AI technologies and applications.
DeepNews exemplifies innovative AI applications by enhancing news summarization and providing users with unbiased, timely, and diverse information.
Deep dives
Understanding Artificial Intelligence and Large Language Models
Artificial intelligence (AI) seeks to enable machines to make human-like decisions and perform tasks typically associated with human intelligence. In recent years, large language models (LLMs) have gained significant attention due to their advanced capabilities, largely stemming from their training on vast amounts of text data. The success of LLMs lies in their ability to perform tasks such as predicting the next word in a sequence, thus facilitating coherent and contextually relevant responses. Aspects like neural networks and the utilization of big data, even if noisy, have been pivotal in the advancement of these models, setting them apart from earlier AI approaches.
The Unpredictability of AI's Evolution and Industry Dynamics
The trajectory of AI development has often been less predictable than industry experts anticipated, with companies like NVIDIA and Intel experiencing differing levels of success. NVIDIA's heavy investment in GPUs positioned them as a leader in AI technology, while Intel's reluctance to pivot towards this sector highlights the unpredictable market dynamics. Predictions about the future of AI technologies have frequently been proven wrong, revealing the challenges inherent in forecasting technological progress. This unpredictable element has resulted in significant opportunity alongside notable pitfalls, with many companies pivoting based on emerging trends.
Creating Deep News: A New Approach to News Consumption
Deep News offers an innovative application that summarizes news articles more efficiently by utilizing AI technology to scan multiple sources and present concise, factual content. The application addresses the shortcomings of traditional news media, providing users with unbiased reporting while aggregating information from various perspectives. Its ability to produce a higher volume of news articles than conventional outlets signifies a shift towards more automated and accessible news delivery. Users can receive timely updates and summaries aligned with their interests without the biases that often characterize conventional news platforms.
AI's Role in Job Dynamics and Economic Impact
While AI technology is expected to displace certain jobs, its overall impact on employment and the economy remains nuanced and multifaceted. Current observations suggest that AI has not yet led to widespread unemployment, even as it reshapes specific industries. The dynamism in the American labor market allows for job transitions, with significant opportunities arising in the tech and AI sectors. There is an ongoing transformation in workflows, with many professionals adapting to utilize AI tools rather than seeing them purely as replacements.
The Future of AI and Its Societal Implications
As AI continues to evolve, discussions around its implications for society and the economy are becoming more critical. This technology is expected to reform various sectors dramatically, yet it also raises concerns about governance, national security, and ethical ramifications. The balance between encouraging innovation and implementing regulatory frameworks remains a focal point for policymakers. While the hype surrounding AI may fluctuate, its integration into everyday life is likely to deepen, leading to a complex blend of opportunities and challenges for future generations.
On this episode of Unsupervised Learning, Razib catches up with Nikolai Yakovenko about the state of AI at the end of 2024. Yakovenko is a former professional poker player,and research scientist at Google, Twitter and Nvidia. With a decade in computer science, Yakovenko has been at the forefront of the large-language-model revolution that has given rise to multi-billion dollar companies like OpenAI, Anthropic and Perplexity and hundreds of smaller startups. Currently, he is the CEO of DeepNewz, an AI-driven news startup that leverages OpenAI’s latest model. Full disclosure: Razib actively uses and recommends the service and is an advisor to the company.
Razib and Yakovenko first review what makes the last few years special, the rise of large-language-models on top of neural network architecture of transformers. Yakovenkoi discusses how far they’ve come since OpenAI released ChatGPT to the public in the fall of 2022, and how people have been using the underlying technology to develop applications atop it. Despite predictions of mass unemployment, Razib points out that two years later America is at full employment, and only niche fields like translation have been impacted. In contrast, Yakovenko points out that most software developers use artificial intelligence in some form to aid in their daily engineering work, noting the possibility that the AI revolution is integrating itself seamlessly as a utility for preexistent jobs. They also discuss the fact that though AI is a booming field, only one brand-name company has so far emerged in the industry, OpenAI. Though they agree that the current hype cycle is now abating, it is clear that the major investments in the field like data centers will continue from major players as AI-driven applications like self-driving cars become more and more mainstream.
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