Data science expert Sadie St. Lawrence joins Jon to analyze 2024 trends and predict AI hardware accelerators, large language models, tool consolidation, and workforce upheaval. They delve into slow-thinking models, AI bubble bursting, Edge AI breakthroughs, and productivity planning.
Anticipated hardware evolution with AI accelerators shaping tech innovation.
Transition to LLMOS platforms offering interactive AI assistant capabilities.
Development of slow-thinking models for advanced logic and mathematical tasks.
Tool consolidation in tech stacks driving efficiency and productivity.
Deep dives
Hardware Demand on the Rise
There is an increasing demand for hardware in the tech industry as organizations look towards innovation. This trend is fueled by advancements like AI hardware accelerators and models like Q-STAR solving complex problems. The consolidation of enterprise systems is also contributing to the rise in hardware demand.
LLM Operating System Emergence
A shift towards LLM operating systems is anticipated, marking a new era in tech platforms. These systems, functioning as AI assistants, have the ability to see, hear, and provide comprehensive support. The move towards conversational interfaces could revolutionize how users interact with technology.
Introduction of Slow Thinking Models
Expectations for the development of slow thinking models, focusing on logic, reasoning, and enhanced mathematical capabilities, are on the horizon. These models aim to address gaps in current AI performance, particularly in mathematical tasks. The emergence of such models could lead to significant advancements in scientific fields and robotics.
Tool Consolidation in Tech Stacks
Organizations are likely to streamline their tech stacks by consolidating tools to enhance efficiency and cost-effectiveness. Solutions like GPT-4 function calling API and Microsoft's fabric are enabling seamless integration across different systems. The shift towards consolidated tech stacks could streamline operations and drive productivity.
Workforce Changes and Data Science Roles
Anticipated workforce upheaval in data science roles reflects evolving job requirements and responsibilities. The traditional full stack data scientist role has expanded, leading to a diverse range of specialized positions including data engineers, ML engineers, and MLOps engineers. The data science job family is expected to undergo significant transformations to adapt to changing industry demands.
Impact of AI Assistants on Work Roles and Categorization
As AI assistants become more integrated into the workforce, traditional roles like data analysts are beginning to shift. The idea of 'work upheaval' is introduced, indicating a change in how individuals work alongside AI assistants. This transformation suggests that job roles may no longer be as siloed as before, with the expectation of individuals taking on more versatile 'multimodal' roles that encompass a variety of tasks and responsibilities. The emergence of AI assistants that can handle multiple functions challenges traditional job distinctions, leading to a period of adjustment and reevaluation of what different roles entail.
Future Trends in AI and Technology Development
Looking ahead to 2024, predictions highlight significant shifts in the AI and technology landscape. Anticipated trends include the burst of an AI bubble, leading to a focus on better practices and revenue-generating businesses in the industry. Advances in edge AI are envisioned, allowing for more processing power on devices like smartphones and Raspberry Pis, potentially revolutionizing industries such as agriculture, mining, healthcare, and retail. The rise of LLMs as a new operating system is foreseen, enabling enhanced interaction and integration of applications through advanced language models, paving the way for innovative business applications and tool consolidation.
2024 data science trends take the spotlight in this special episode, where Jon joins Sadie St. Lawrence to analyze last year's predictions and delve into the emerging technologies reshaping the field. From AI hardware accelerators to the transformative role of large language models, this episode is a treasure trove of insights for anyone interested in the future of data science.
This episode is brought to you by CloudWolf, the Cloud Skills platform. Interested in sponsoring a SuperDataScience Podcast episode? Visit passionfroot.me/superdatascience for sponsorship information.
In this episode you will learn: • Reviewing predictions for 2023 [05:56] • Sadie's trend predictions for 2024 [20:49] • 1: Hardware evolution [21:17] • 2: LLMOS [35:30] • 3: Slow-thinking model [48:18] • 4: Tool consolidation [54:46] • 5: Workforce Upheaval [58:06] • Jon's predictions [1:06:26] • 1: AI bubble bursting [1:08:11] • 2: Breakthroughs in Edge AI [1:12:22] • Sadie on her productivity planner [1:17:50]
