Google’s AI For Hyper-Personalized Maps
Plus: The Rise and Potential of LLM-Based Agents, AI to personalize 3D printing.
Hello Engineering Leaders and AI Enthusiasts!
Welcome to the 107th edition of The AI Edge newsletter. This edition brings you Google’s AI for hyper-personalized route suggestions in Maps.
And a huge shoutout to our incredible readers. We appreciate you! 😊
In today’s edition:
🌍 Google’s AI for hyper-personalized Maps
🧠The Rise and Potential of LLM-Based Agents: A survey
🤖 AI makes it easy to personalize 3D-printable models
📚 Knowledge Nugget: Multimodal Learning by
Let’s go!
Google’s AI for hyper-personalized Maps
Google and DeepMind have built an AI algorithm to make route suggestions in Google Maps more personalized. It includes 360 million parameters and uses real driving data from Maps users to analyze what factors they consider when making route decisions. The AI calculations include information such as travel time, tolls, road conditions, and personal preferences.
The approach uses Inverse Reinforcement Learning (IRL), which learns from user behavior, and Receding Horizon Inverse Planning (RHIP), which uses different AI techniques for short- and long-distance travel. Tests show that RHIP improves the accuracy of suggested routes for two-wheelers by 16 to 24 percent and should get better at predicting which route they prefer over time.
Why does this matter?
In the past, Google’s attempts to use AI systems at scale for route planning have often failed due to the sheer complexity of real-world road networks. RHIP can now overcome this hurdle with a sophisticated approach, confirming that better performance is related to scale both in terms of data set and model complexity.
The Rise and Potential of LLM-Based Agents: A survey
Probably the most comprehensive overview of LLM-based agents, this survey-cum-research covers everything from how to construct AI agents to how to harness them for good. It starts by tracing the concept of agents from its philosophical origins to its development in AI and explains why LLMs are suitable foundations for AI agents. It also:
Presents a conceptual framework for LLM-based agents that can be tailored to suit different applications
Explores the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation
Delve into agent societies, exploring the behavior and personality of LLM-based agents, the social phenomena that emerge when they form societies, and the insights they offer for human society
Discuss a range of key topics and open problems within the field
Here’s a scenario of an envisioned society composed of AI agents in which humans can also participate.
Why does this matter?
It is a practical resource for developers to build AI agents. It also serves as a guide for researchers, practitioners, and policymakers to further advancement in the field, potentially leading to breakthroughs in AI and LLM development in a responsible way.
AI makes it easy to personalize 3D-printable models
MIT researchers have developed a generative AI-driven tool that enables the user to add custom design elements to 3D models without compromising the functionality of the fabricated objects. A designer could use this tool, called Style2Fab, to personalize 3D models of objects using only natural language prompts to describe their desired design. The user could then fabricate the objects with a 3D printer.
Why does this matter?
The AI tool empowers novice designers and makes 3D printing more accessible. It could also be used in the emerging area of DIY assistive technology and devices, such as for clinicians and medical patients.
📚Knowledge Nugget: Multimodal Learning
Humans have five senses. How many does AI have?
In this article,
talks about the next step in AI that's still in the works: multimodal learning. With interesting analogies, the article discusses how multimodal models work, their use cases, and Meta’s efforts in leading open-source research on multimodal models.
Why does this matter?
It gives insights into the technical aspects of multimodal learning, encouraging new applications and research directions and, thus, leading to advancements in multimodal AI and its practical applications. and its practical applications.
What Else Is Happening❗
🌐Meta is prepping world’s first AI-powered holiday season with AI bid multipliers and budget scheduling (Link)
💰SoftBank considers investment or partnership with ChatGPT creator OpenAI (Link)
🤝Anthropic and BCG form a new alliance to deliver enterprise AI to clients (Link)
🧠Generative AI is just a phase. What’s next is interactive AI, says DeepMind’s cofounder (Link)
🚀Defense AI startup Helsing breaks the record for European AI, raises $223M (Link)
🛠️ Trending Tools
Rails Guard: One-line install for passwordless Google SSO with MFA, session recording, and AI data masking.
Insta Learn: Personalize tech courses, choose learning style, and get AI-powered code reviews.
Dreambience: AI app for personalized meditation experiences. Input three keywords and relax.
GPT Mind Maps Maker: AI tool to generate/edit mind maps from text prompts, pdf, video, web page.
AgentStore: AI-driven manga buddies from top series. Customize via Commander Mode. Chat like never before.
ReleaseFlow: AI to generate and publish release notes. Increase productivity and focus on what matters.
AIQRhub: Online tool that generates artistic QR codes using AI. More attractive and easier to spread.
Briefy: AI tool turning lengthy texts, audios, and videos into easy-to-digest summaries.
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🧐Monday Musings: Do LLMs diminish diversity of thought?
Do large language models too often give “the correct answer” when a more diverse sequence of answers might be more useful and representative?
For example, if you ask (non-deterministic) GPT 100 times in a row if you should prefer $50 or a kiss from a movie star, 100 times it will say prefer the kiss. Of course, some of us will think– which movie star!? Others might wonder about Fed policy for the next quarter.
Either way, the answer should not be so clear.
An interesting research paper, Diminished Diversity-of-Thought in a Standard Large Language Model, delves into this. Check it out!
(Source)
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