Tools & Platforms
Tech firms up ante on open-source AI models

Chinese companies are doubling down on open-source artificial intelligence-powered models as part of a broader push to bring fast-evolving AI technology to more businesses and developers worldwide, and bolster its application in a diverse range of fields.
Experts said the open-source approach will lower the threshold for the development and application of AI, greatly reduce computing power costs, and foster the sharing of AI tech around the world, as well as boost collaboration and innovation.
The recent progress in open-source large language models has showcased China”s growing technological prowess and open attitude in the AI domain, given that Chinese AI startup DeepSeek’s open-source models have taken the world by surprise, they added.
Tech heavyweight Alibaba Group has stepped up efforts to enable broad access to its AI technology and innovations by releasing large language models from its Qwen family as open-source, and boasting China’s largest AI open-source community platform, ModelScope.
The company has made more than 200 generative AI models open-source in recent years. The models have multimodal capacities and can process and generate various types of content, covering text, images, audio and video.
ModelScope, which was launched in November 2022, hosts over 70,000 open-source models, and the user base has expanded from 1 million in April 2023 to 16 million as of June 30, serving 16 million developers from 36 countries around the world.
It supports developers in experiencing, downloading, fine-tuning, training and deploying models. Various types of open-source AI models have been included in the community.
“We aim to simplify and reduce the cost of developing, customizing and deploying AI models for developers and corporations, thereby enabling the creation of revolutionary AI applications that have a positive impact on society,” said Zhou Jingren, chief technology officer at Alibaba Cloud Intelligence, emphasizing they are committed to making AI models more accessible and easier to use.
Baidu Inc has recently open-sourced its multimodal LLM Ernie 4.5 series, consisting of 10 distinct variants. The model family includes mixture-of-experts (MoE) models with 47 billion and 3 billion parameters, the largest model having 424 billion parameters, alongside a 0.3 billion dense model. The Ernie 4.5, launched in March, is Baidu’s multimodal foundational model.
The company said the MoE architecture has the advantages of enhanced multimodal understanding and improved performance on text-related tasks. All models are trained with optimal efficiency using the PaddlePaddle deep learning framework, which enables highper-formance inference and streamlined deployment.
Experimental results show that the models achieve state-of-the-art performance across multiple text and multimodal benchmarks, particularly in instruction following, knowledge memorization, visual understanding and multimodal reasoning.
Zhu Keli, founding director of the China Institute of New Economy, said the open-source approach adopted by a string of Chinese AI companies will lower the technical threshold, speed up the popularization of AI tech across various sectors including automobiles, manufacturing, finance and education, and allow more enterprises and developers to participate in AI research and development.
Zhu believes technological innovation is unstoppable, and international cooperation serves as an important way to promote the development of AI tech, adding that China’s open and inclusive attitude helps promote the advancement of the global AI industry.
“Open source will allow resource-constrained startups, small businesses and entrepreneurial developers to access cutting-edge AI tech and build their own models more cost-effectively,” said Pan Helin, a member of the Expert Committee for Information and Communication Economy, which is part of the Ministry of Industry and Information Technology.
Pan said it will accelerate AI technological advancements and breakthroughs by enabling global developers to create customized industry-specific models, and foster a more competitive and diverse AI ecosystem.
Chinese AI companies have the ability to take the lead in global AI innovation, as they have sought an alternative AI development approach that emphasizes efficiency and open-source collaboration — which is different from their US counterparts — while reshaping the global AI landscape, Pan added.
The market size of the nation’s AI sector will reach 1.73 trillion yuan ($241.2 billion) by 2035, accounting for 30.6 percent of the global total, said market research firm CCID Consulting.
Tools & Platforms
AI-Enabled Technology Automates and Optimizes Airport Parking Systems

Vehicle parking accounts for the top source (24%) of non-aeronautical revenue at airports, ahead of retail concessions, real estate investments, and rental car fees, according to the Airports Council International.
At the same time, aging parking access and revenue control equipment like ticket machines and pay stations fail regularly after years of operation. This results in operational cost increases, potential revenue leakage from downtime, and negative impacts on customer satisfaction due to traffic build up at entry and exit. For busy airport executives, these issues are easy to overlook and not prioritize because parking revenue is predictable and significant. However, those who do prioritize parking can quickly find a meaningful area of value creation in terms of revenue and customer experience improvement.
Artificial intelligence (AI) can help airport executives realize this untapped value by transforming airport parking into a hands-off operation that is always on, never breaks down, is more accurate and financially accountable, and provides a better parking experience for airport customers.
AI-enabled parking management systems can improve conditions for operational and landside managers by eliminating the need to:
● Schedule and pay staff to operate payment booths.
● Schedule and pay maintenance and repair staff for traditional fee-collection ticket machines and kiosks.
● Reroute parking traffic when lanes unexpectedly need to close or back up from slow operation.
For airport executives and managers, AI can provide better predictability and control of parking revenue through more accurate vehicle and fee identification. It can also replace electromechanical fee-collection machines and kiosks, which disrupt automatic collections and require significant labor expense when they break down.
In addition, AI has the power to attract more airport parkers by automating every aspect of airport parking — from reserving a spot to paying — consistently and reliably.
How does an AI-enabled parking system work?
An AI-enabled parking system works by creating a digital fingerprint of each vehicle that includes necessary data about the vehicle. Combined hardware and software identify the vehicle instantly via its digital fingerprint as it approaches a parking facility, prompting actions that enable the driver to enter, exit, and pay without stopping. Vehicle identification, fee calculation, and the payment transaction all happen in the background, without intervention from drivers or airport staff.
Dayton International Airport, San Antonio International Airport, Northwest Arkansas National Airport, and Aspen/Pitkin County Airport all recently adopted the Metropolis for Airports AI-enabled modern parking system.
It’s an upgrade that is paying off at Dayton International Airport (DAY): Growing popularity and consistently reliable collections have lifted in-plane revenue per passenger from $10 to $13.14.
“It makes [parking] seamless for our customers,” Gil Turner, Director of DAY, says of Metropolis. “They just drive up. They don’t have to roll the window down. They pull up, the system recognizes the vehicle, takes the license plate information, and they’re able to go right in and park. And when they leave, there’s no interaction with the booth person. So it’s all great. I know that flying can be a frustrating process, so when you have a system, you come home, you can just leave and don’t have to have any stress or worries. I mean, it just helps the customer.”
The Metropolis system runs in the cloud and uses computer vision, license plate recognition (LPR), camera-based hardware, machine learning, and AI to automate parking events, as well as parking data collection. The system requires no upfront capital investment from airports and integrates with existing gate equipment.
A driver registers once and is ready to go. Here’s how it works:
● When a car enters the lot, the computer vision-enhanced camera captures the license plate. Intelligent software then recognizes the car as a Metropolis user and prompts the entry gate.
● When the car leaves, the system again recognizes the license plate, calculates the fee, charges the driver’s selected payment method, and prompts the exit gate to open.
● The system automatically sends a receipt to the driver via email or text.
● Data on the parking event is stored and made available.
In addition to automation benefits, AI-enabled parking systems provide real-time data that is highly valuable for more informed staffing, lane management, and lot maintenance planning. This data can also help airport managers and directors respond to customer input and demand in real time, such as instituting dynamic pricing.
And as an intelligent system, an AI-enabled parking management solution becomes faster and more accurate with each data input as it continues to learn, which provides a consistently superior and more predictable experience for parking users. Combined with reservations and dynamic pricing, AI can increase parking revenue because when parking is easier and priced competitively, drivers are more likely to choose an airport lot or garage over an alternative, such as an off-airport facility or ride-hailing service.
Elevate airport parking with AI
Metropolis operates a network with over 4,500 parking locations in more than 50 countries and 350 cities, including over 80 airports. More than 18 million people are Metropolis members.
Relates DAY’s Manager of Business Development, “Metropolis was part of a competitive bid process, and one of the things that stood out between them and the others who were in the process was the technology but also how they implemented it and their focus on the positive impacts to the customer. Positive customer service leads to additional revenue, happier passengers, and everything that we’re looking for here at Dayton.”
He adds, “On the very first day Metropolis rolled out, I said I’m going to go test this technology. I tested each of the parking lots. Each one of them worked perfectly, and so the fact is that our passengers can now get that same experience without having to roll down their window and wait in line. They just access our parking. Our slogan is easy to and through, and [the Metropolis solution] is really the definition of that slogan.”
When an airport joins the Metropolis network, the company installs its computer vision cameras and maintains them at no cost. Metropolis also helps with systems integration with existing gates. Then, the payment model shifts to a set-up fee and subscription, as is common for cloud-hosted software services. And like other software-based, consumer-facing brands, Metropolis is constantly improving its product with updates that are automatically deployed across the network.
The bottom line is that airport parking facilities that use Metropolis are faster, easier, more convenient, and less congested than those using traditional systems.
Are you ready to elevate your airport’s parking? Talk to Metropolis today.
Tools & Platforms
Most Employees Don’t Know How To Adopt AI—Survey

The majority of employees say they don’t know how to adopt artificial intelligence, according to a new survey by The Harris Poll on behalf of MasterClass.
In the report, 49 percent of respondents said they feel direct pressure to adopt AI, yet 55 percent said they don’t know where to start.
Why It Matters
Artificial intelligence has changed the larger workforce and business landscape in America, with most companies looking to employ it as a way to boost productivity.
But unclear rules and policies around the technology have led to some confusion among workers. A previous study from Howdy.com found that 16 percent of professionals sometimes pretend to use AI.
Cheng Xin/Getty Images
What To Know
In the MasterClass survey of nearly 1,700 U.S. workers, 66 percent said they had to teach themselves AI on the job.
That’s in addition to 54 percent who say their employers aren’t providing adequate AI training.
The percentage was roughly the same among men and women, at 57 and 50 percent, respectively. Meanwhile, 57 percent of Gen Z, 53 percent of millennials and 53 percent of Gen X professionals said they were going without proper AI workplace training.
“In many ways, the rise of AI in recent years is similar to the same integration environment involving social media nearly 20 years ago,” Alex Beene, a financial literacy instructor for the University of Tennessee at Martin, told Newsweek. “Whereas some employers with more tech-savvy employees were able to easily adapt to utilizing a new medium for communication and marketing, others took years to grasp how to use it effectively.”
Reza Hashemi, the CEO and founder of Binj and ZEROin AI, said there’s a perception that AI adoption is synonymous with career security, but many organizations often overestimate how seamlessly the new technology fits into daily workflows.
“Long term, if businesses don’t bridge the gap between hype and practical application, they risk creating a culture of fear and superficial adoption instead of true innovation,” Hashemi previously told Newsweek.
What People Are Saying
HR consultant Bryan Driscoll told Newsweek: “Most employees aren’t struggling with AI itself – they’re struggling with employers who won’t equip them. Workers are told to figure it out without training, support, or guardrails, while leadership races to show they’re innovative. Forcing workers to self-teach under pressure is a short-sighted gamble that reveals a hollow commitment to development.”
Beene told Newsweek: “With AI, we’re seeing businesses accustomed to a decades-long workflow now struggling to see what AI would truly change and benefit in their operations. Much like with social media, these employers will eventually find which methods best suit them, but don’t expect it to be rapid for all.”
What Happens Next
In the next few years, companies that don’t properly address AI could reduce the trust of their employees, Driscoll said.
“Long term, this isn’t just poor management. It’s a recipe for widening inequality and eroding trust in the workplace,” Driscoll said.
Tools & Platforms
AI-enhanced teaching techniques earns Shenango professor a Faculty Engagement Award

SHARON, Pa. — Tammy Divens, teaching professor and program coordinator of Penn State Shenango’s occupational therapy assistant program, recently received a 2025 Faculty Engagement Award from Penn State Teaching and Learning with Technology (TLT).
The mission of TLT is to explore new and emerging technologies and find collaborative ways to innovate and transform teaching pedagogy and learning strategies for faculty and students at Penn State. The theme of the 2025 Faculty Engagement Awards is “Generative Artificial Intelligence (AI) for Teaching including Microsoft Copilot 365.” According to a release, faculty award recipients will collaborate with TLT to experiment with AI-driven tools to support course planning, content design and instructional delivery.
“I’ve been able to explore innovative educational technologies, collaborate with TLT, and engage with colleagues about using AI tools to enhance teaching and creativity,” said Divens, whose primary focus has been integrating generative AI into the classes that she teaches.
“I’ve used ChatGPT and Microsoft Copilot to support course planning, improve presentations, develop new assignments, and align course content with accreditation requirements,” Divens said. “These tools have streamlined my preparation and allowed me to design more engaging and effective learning materials.”
According to Inside Higher Ed’s 2025-26 Student Voice survey, 85% of students who participated claim to have used generative AI for coursework in the last year. As part of the same survey, 43% of students felt somewhat positive or very positive about faculty use of generative AI, as long as it was done thoughtfully and made the instruction more relevant and efficient.
Divens plans to do just that.
“I have leveraged AI image generation through Adobe Express and ChatGPT to create course introduction videos and produce humorous or creative content to increase student engagement,” Divens said. “These efforts have enriched both the instructional design and the overall classroom experience.”
Faculty Engagement Award recipients work with an instructional designer and technology support staff from TLT to help identify the most effective in-class uses of a particular technology. Additionally, a TLT researcher may assess the impact of this technology and share relevant findings of the program with the greater Penn State community.
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