AI Research

Introducing Mobility AI: Advancing urban transportation

Published

3 months ago

April 23, 2025

1. Measurement: Understanding mobility patterns

Accurately evaluating the current state of the transportation network and mobility patterns is the first step to improving mobility. This involves gathering and analyzing real-time and historical data from various sources to understand both current and historical conditions and trends. We need to track the effects of changes as we implement them in the network. ML powers estimations and metric computations, while statistical approaches measure impact. Key areas include:

Congestion functions

Similar to well-known fundamental diagrams of traffic flow, congestion functions mathematically describe how rising vehicle volume increases congestion and reduces travel speeds, providing crucial insights into traffic behavior. Unlike fundamental diagrams, congestion functions are built based on a portion of vehicles (e.g., floating car data) rather than all traveling vehicles. We have advanced the understanding of congestion formation and propagation using an ML approach that created city-wide models, which enable robust inference on roads with limited data and, through analytical formulation, reveal how traffic signal adjustments influence flow distribution and congestion patterns in urban areas.

Foundational geospatial understanding

We develop novel frameworks, leveraging techniques like self-supervised learning on geospatial data and movement patterns, to learn embeddings that capture both local characteristics and broader spatial relationships. These representations improve the understanding of mobility patterns and can aid downstream tasks, especially where data might be sparse or when complementing other data modalities. Collaboration with related Google Research efforts in Geospatial Reasoning using generative AI and foundation models is crucial for advancing these capabilities.

Parking insights

Understanding urban intricacies includes parking. Building on our work using ML to predict parking difficulty, Mobility AI aims to provide better insights for managing parking availability, crucial for various people, including commuters, ride-sharing drivers, commercial delivery vehicles, and the emerging needs of self-driving vehicles.

Origin–destination travel demand estimation

Origin–destination (OD) travel demand, which describes where trips — like daily commutes, goods deliveries, or shopping journeys — start and end, is fundamental to understanding and optimizing mobility. Knowing these patterns is crucial because it reveals exactly where the transportation network is stressed and where services or infrastructure improvements are most needed. We calibrate OD matrices — tables quantifying these trips between locations — to accurately replicate observed traffic patterns, providing a spatially complete understanding essential for planning and optimization of transportation networks.

Performance metrics: Safety, emissions and congestion impact

We use aggregated and anonymized Google Maps traffic trends to assess impact of transportation interventions on congestion, and we build models to assess safety and emissions impact. To build safety metrics scalably, we go beyond reactive crash data by utilizing hard braking events (HBEs). HBEs are shown to be strongly correlated with crashes and can be used for road safety services to pinpoint high-risk locations and predict future collision risks.

To measure environmental impact, we’ve developed AI models in partnership with the National Renewable Energy Laboratory (NREL) that predict vehicle energy consumption (whether gas, diesel, hybrid, or electric). This powers fuel-efficient routing in Google Maps, estimated to have helped avoid 2.9M metric tons of GHG emissions in the US alone, which is equivalent to taking ~650,000 cars off the road for a year. This capability is fundamental for monitoring climate and health impacts related to transportation choices.

Impact evaluation

Randomized trials are often infeasible for evaluating transportation policy changes. To assess the impact of a change, we need to estimate outcomes in its absence. This can be done by finding cities or regions with similar mobility patterns to serve as a “control group”. Our analysis of NYC’s congestion pricing demonstrates this method through use of sophisticated statistical techniques like synthetic controls to rigorously estimate the policy’s impact and by providing valuable insights for agencies evaluating interventions.

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AI Research

2 Artificial Intelligence (AI) Stocks Even Risk-Averse Investors Can Buy Without Hesitation

Published

38 minutes ago

July 8, 2025

Keith Speights

Betting big on the next hot thing can sometimes burn investors. That can be true even when the next hot thing is as exciting and promising as artificial intelligence (AI).

Concerns about being burned might cause some investors to be leery of buying AI stocks. However, this fear could result in them missing out on huge long-term returns. Are there alternatives for investing in AI that aren’t super risky? Absolutely. Here are two AI stocks that even risk-averse investors can buy without hesitation.

Image source: Getty Images.

Two AI titans

If bigger is better, you won’t find many better AI stocks than Amazon (AMZN -0.07%) and Microsoft (MSFT -0.24%). Amazon ranks as the fourth-largest publicly traded company based on market cap, while Microsoft holds the No. 2 spot. And their AI credentials are impeccable.

Amazon Web Services (AWS) is the global leader in cloud services, with a market share of 29%. Microsoft Azure is in second place with a market share of 22%. Both cloud platforms continue to enjoy strong growth, thanks in large part to organizations rushing to build and deploy AI models in the cloud.

Amazon and Microsoft boast partnerships with other top AI companies as well. Both companies have teamed up with Nvidia. Microsoft’s investments in ChatGPT creator OpenAI are paying off handsomely, and Amazon has invested $8 billion in Anthropic, the developer of the powerful Claude large language model (LLM).

These two AI titans are also benefiting from AI in their internal operations. Amazon is using AI to recommend products to customers on its e-commerce platform, for example, while Microsoft has rolled out OpenAI’s GPT-4 throughout its product lineup.

Why risk-averse investors should like Amazon and Microsoft

Risk-averse investors know what they’re getting with Amazon and Microsoft. Both companies are AI leaders, but they’re also much more.

Amazon and Microsoft offer tremendous financial stability. Amazon generated revenue of nearly $638 billion last year, with profits totaling over $59 billion. Microsoft’s revenue topped $245 billion, with earnings of more than $88 billion.

Each of the companies has a boatload of cash — $94.6 billion for Amazon and $79.6 billion for Microsoft.

We’ve already seen that Amazon and Microsoft dominate the cloud services market. These two companies are also leaders in other areas. Amazon reigns as the 800-pound gorilla of e-commerce with a market share of 37.6%. Microsoft’s Windows commands a 70% market share among desktop operating systems. The company’s Office 365 suite ranks No. 2 in the productivity software market.

Both companies continue to deliver solid growth. Amazon’s revenue increased 9% year over year in its latest quarter, with earnings soaring 64%. Microsoft’s revenue jumped 13% year over year, with profits up 18%.

More importantly, both Amazon and Microsoft have strong growth prospects. Each company is poised to benefit from the ongoing AI tailwind and the shift from on-premises IT to the cloud. Amazon’s e-commerce platform and Microsoft’s software products also have solid growth potential.

Not risk-free

I don’t want to leave the impression that Amazon and Microsoft don’t have any risks, though. There’s no such thing as a risk-free stock.

Both Amazon and Microsoft face significant competition despite their current market dominance, and growth could be derailed by regulators in the U.S. and in Europe. Both stocks also trade at high valuations: Amazon’s forward price-to-earnings ratio is 34.6, while Microsoft’s forward earnings multiple is 33.2. These valuations make them more exposed if they experience a significant business disruption.

However, longtime investors know that the best stocks often command premium valuations. Amazon and Microsoft are two of the best stocks, with lifetime gains of around 227,800% and 123,200%, respectively.

Although Amazon and Microsoft face some risks, I think the pros of both stocks far outweigh the cons. If you’re a risk-averse investor who wants to profit from the AI boom, I can’t think of two better picks.

John Mackey, former CEO of Whole Foods Market, an Amazon subsidiary, is a member of The Motley Fool’s board of directors. Keith Speights has positions in Amazon and Microsoft. The Motley Fool has positions in and recommends Amazon, Microsoft, and Nvidia. The Motley Fool recommends the following options: long January 2026 $395 calls on Microsoft and short January 2026 $405 calls on Microsoft. The Motley Fool has a disclosure policy.

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AI Research

Cognigy Leads in Opus Research’s 2025 Conversational AI Intelliview

Published

50 minutes ago

July 8, 2025

BusinessWire

Distinguished for Innovation, Enterprise Readiness, and Visionary Approach to Agentic AI

Cognigy, a global leader in AI-powered customer service solutions, has been recognized as the leader in the newly released 2025 Conversational AI Intelliview from Opus Research. The report, titled “Decision-Maker’s Guide to Self-Service & Enterprise Intelligent Assistants,” shows Cognigy as the leading platform across critical evaluation areas including product capability, enterprise fit, GenAI maturity, and deployment performance.

This recognition underscores Cognigy’s commitment to empowering enterprises with production-ready, scalable AI solutions that go far beyond chatbot basics. The report cites Cognigy’s strengths in visual AI agent orchestration, tool and function calling, AI Ops and observability, and a deep commitment to enterprise-grade control—all delivered through a platform built to scale real-time customer interactions across voice and digital channels.

“Cognigy exemplifies the next stage of conversational AI maturity,” said Ian Jacobs, VP & Lead Analyst at Opus Research. “Their agentic approach—combining real-time reasoning, orchestration, and observability—demonstrates how GenAI can move beyond experimentation into meaningful, measurable transformation in the contact center.”

Cognigy was one of the few vendors identified in the report as a “True Believer” in the evolution of GenAI-driven self-service, with tools designed to simplify deployment while giving enterprises full control. The platform’s AI Agent Manager enables businesses to create, configure, and continuously improve intelligent agents—defining persona, memory scope, and access to tools and knowledge—all through a flexible, low-code interface. Cognigy uniquely blends deterministic logic with generative capabilities, ensuring both speed and reliability in automation.

“This recognition from Opus Research is more than a milestone—it’s validation that our strategy is working,” said Alan Ranger, Vice President at Cognigy. “We’re delivering real-world, enterprise-grade automation that’s transforming contact centers. From financial services to healthcare to global retail, our customers are scaling faster, resolving issues in real time, and delivering truly modern service experiences.”

With global Fortune 500 customers and partnerships across the CCaaS and AI ecosystem, Cognigy continues to lead the way in delivering enterprise-ready AI that combines usability, speed, and impact. This latest industry acknowledgment further solidifies its position as the go-to platform for intelligent self-service.

To download a copy of the report, visit https://www.cognigy.com/opus-research-2025-conversational-ai-intelliview.

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