4 posts tagged with "AI"

Goal​

In this analysis, we explore how Large Language Models (LLMs) and AI technologies have fundamentally transformed the internet landscape since ChatGPT's debut. By examining market dynamics and how major players are adapting, we present four key predictions for the AI industry through 2025. Our forecast draws from current trends, market data, and emerging patterns in how businesses and users interact with AI technologies:

1. New Frontiers in Product Design
2. The Search Will Shift
3. LLMs as a Market Disruptor
4. Future of LLM Development

New Frontiers in Product Design​

Shifting Metrics at Airbnb​

A conversation with a senior design manager at Airbnb revealed a paradigm shift: instead of focusing on Page Performance Scores, the company now measures “Resolution Time”—how quickly an AI agent can meet user needs, whether it’s booking a stay or finding relevant info.

• Implication: Traditional UI metrics, such as load times and animations, may soon take a back seat to AI responsiveness and contextual understanding.

UI & UX Evolution​

Historically, thoughtful design and playful animations helped mask slow load times or complicated workflows. In an AI-empowered future, design may prioritize minimizing friction:

• Fewer Visible Elements: As AI handles more behind-the-scenes tasks, users may interact through chat-style or voice-based UIs rather than multi-step pages.
• Analogies in Tech: Cars evolved from bulky dashboards with physical buttons to sleek touchscreens—and now, self-driving vehicles with minimal driver interaction at all.

Evolution of Car Interface	Description
	Traditional dashboard with physical buttons (2017 Porsche Macan)
	Minimalist touchscreen interface (2024 Porsche Macan)
	Future autonomous vehicle with minimal driver controls

Designing for “Agentic” Experiences​

With AI agents poised to handle complex tasks autonomously, product designers must consider how to balance transparency, control, and convenience. Users may want to see how an AI is making decisions, but they also want effortless, immediate results. Striking this balance will define UX best practices in the years ahead.

The AI agent has two main methods of coming up with a good move. When processing time and RAM space is sufficient, it utilizes a Alpha Beta Pruning Search Tree algorithm to search the seed amount that it will have after few rounds of moves for all current legal moves; while running time and RAM space is limited or board states are numerous to be processed, it will filter and compare the pre-defined heuristics, evaluate the board state at a level and sum up all heuristic factors to find the best move. A search tree algorithm is more accurate than Heuristic method because it inspects all cases in the following “n” rounds instead of narrowing itself down to one certain state. Since the given constrains give enough space and running time for the AI agent to go for at least a depth of 6 rounds for alpha pruning search, and this Has game has a maximum of 32 pits for one side to be counted for each turn, it will use Alpha Beta Pruning mainly.

Equations: EV(expected value)=P(outcome 1)+(1-P)(outcome 2) EU(expected utility)=P(√(outcome 1))+(1-P)(√(outcome 2))

Metanautix now has Personal Quest online where individual users can download and do analytics on desktop. I have been working on a new systematic way to learn music theory and do musical analysis with mathematical matrix and vectors, and Quest is a critical tool in query on large dataset such as a pool of thousands of song scores. In this article, I will talk about my methodology in detail.

An overview for my system will be: