AI Research

UCR Researchers Strengthen AI Defenses Against Malicious Rewiring

Published

3 days ago

September 4, 2025

As generative artificial intelligence (AI) technologies evolve and establish their presence in devices as commonplace as smartphones and automobiles, a significant concern arises. These powerful models, born from intricate architectures running on robust cloud servers, often undergo significant reductions in their operational capacities when adapted for lower-powered devices. One of the most alarming consequences of these reductions is that critical safety mechanisms can be lost in this transition. Researchers from the University of California, Riverside (UCR) have identified this issue and have innovated a solution aimed at preserving AI safety even as its operational framework is simplified for practical use.

The reduction of generative AI models entails the removal of certain internal processing layers, which are vital for maintaining safety standards. While smaller models are favored for their enhanced speed and efficiency, this trimming can inadvertently strip away the underlying mechanisms that prevent the generation of harmful outputs such as hate speech or instructions on illicit activities. This represents a double-edged sword: the very modifications aimed at optimizing functional performance may render these models susceptible to misuse.

The challenge lies not only in the effectiveness of the AI systems but also in the very nature of open-source models, which are inherently different from proprietary systems. Open-source AI models can be easily accessed, modified, and deployed by anyone, significantly enhancing transparency and encouraging academic growth. However, this openness also invites a plethora of risks, as oversight becomes difficult when these models deviate from their original design. In situations devoid of continuous monitoring and moderation, the potential misuse of these technologies grows exponentially.

In the context of their research, the UCR team concentrated on the degradation of safety features that occurs when AI models are downsized. Amit Roy-Chowdhury, the senior author of the study and a professor at UCR, articulates the concern quite clearly: “Some of the skipped layers turn out to be essential for preventing unsafe outputs.” This statement highlights the potential dangers of a seemingly innocuous tweak aimed at optimizing computational ability. The crux of the issue is that removal of layers may lead a model to generate dangerous outputs—including inappropriate content or even detailed instructions for harmful activities like bomb-making—when it encounters complex prompts.

The researchers’ strategy involved a novel approach to retraining the internal structure of the AI model. Instead of relying on external filters or software patches, which are often quickly circumvented or ineffective, the research team sought to embed a foundational understanding of risk within the core architecture of the model itself. By reassessessing how the model identifies and interprets dangerous content, the researchers were able to instill a level of intrinsic safety, ensuring that even after layers were removed, the model retained its ability to refuse harmful queries.

The core of their testing utilized LLaVA 1.5, a sophisticated vision-language model that integrates both textual and visual data. The researchers discovered that certain combinations of innocuous images with malicious inquiries could effectively bypass initial safety measures. Their findings were alarming; in a particular instance, the modified model furnished dangerously specific instructions for illicit activities. This critical incident underscored the pressing need for an effective method to safeguard against such vulnerabilities in AI systems.

Nevertheless, after implementing their retraining methodology, the researchers noted a significant improvement in the model’s safety metrics. The retrained AI demonstrated a consistent and unwavering refusal to engage with perilous queries, even when its architecture was substantially diminished. This illustrates a momentous leap forward in AI safety, where the model’s internal conditioning ensures proactive, protective behavior from the onset.

Bachu, one of the graduate students and co-lead authors, describes this focus as a form of “benevolent hacking.” By proactively reinforcing the fortifications of AI models, the risk of vulnerability exploitation diminishes. The long-term ambition behind this research is to establish methodologies that guarantee safety across every internal layer of the AI architecture. This approach aims to craft a more resilient framework, capable of operating securely in varied real-world conditions.

The implications of this research span beyond the technical realm; they touch upon ethical considerations and societal impacts as AI continues to infiltrate daily life. As generative AI becomes ubiquitous in our gadgets and tools, ensuring that these technologies do not propagate harm is not only a technological challenge but a moral imperative. There exists a delicate balance between innovation and responsibility, and pioneering research such as that undertaken at UCR is pivotal in traversing this complex landscape.

Roy-Chowdhury encapsulates the team’s vision by asserting, “There’s still more work to do. But this is a concrete step toward developing AI in a way that’s both open and responsible.” His words resonate deeply within the ongoing discourse surrounding generative AI, as the conversation evolves from mere implementation to a collaborative effort aimed at securing the future of AI development. The landscape of AI technologies is ever-shifting, and through continued research and exploration, academic institutions such as UCR signal the emergence of a new era where safety and openness coalesce. Their commitment to fostering a responsible and transparent AI ecosystem offers a bright prospect for future developments in the field.

The research was conducted within a collaborative environment, drawing insights not only from professors but also a dedicated team of graduate students. This collective approach underscores the significance of interdisciplinary efforts in tackling complex challenges posed by emerging technologies. The team, consisting of Amit Roy-Chowdhury, Saketh Bachu, Erfan Shayegani, and additional doctoral students, collaborated to create a robust framework aimed at revolutionizing how we view AI safety in dynamic environments.

Through their contributions, the University of California, Riverside stands at the forefront of AI research, championing methodologies that underline the importance of safety amid innovation. Their work serves as a blueprint for future endeavors that prioritize responsible AI development, inspiring other researchers and institutions to pursue similar paths. As generative AI continues to evolve, the principles established by this research will likely have a lasting impact, shaping the fundamental understanding of safety in AI technologies for generations to come.

Ultimately, as society navigates this unfolding narrative in artificial intelligence, the collaboration between academia and industry will be vital. The insights gained from UCR’s research can guide policies and frameworks that ensure the safe and ethical deployment of AI across various sectors. By embedding safety within the core design of AI models, we can work towards a future where these powerful tools enhance our lives without compromising our values or security.

While the journey towards achieving comprehensive safety in generative AI is far from complete, advancements like those achieved by the UCR team illuminate the pathway forward. As they continue to refine their methodologies and explore new horizons, the research serves as a clarion call for vigilance and innovation in equal measure. As we embrace a future that increasingly intertwines with artificial intelligence, let us collectively advocate for an ecosystem that nurtures creativity and safeguards humanity.

Subject of Research: Preserving AI Safeguards in Reduced Models
Article Title: UCR’s Groundbreaking Approach to Enhancing AI Safety
News Publication Date: October 2023
Web References: arXiv paper
References: International Conference on Machine Learning (ICML)
Image Credits: Stan Lim/UCR

Keywords

Tags: AI safety mechanismsgenerative AI technology concernsinnovations in AI safety standardsinternal processing layers in AImalicious rewiring in AI modelsopen-source AI model vulnerabilitiesoperational capacity reduction in AIoptimizing functional performance in AIpreserving safety in low-powered devicesrisks of smaller AI modelssafeguarding against harmful AI outputsUCR research on AI defenses

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AI Research

Meet the Artificial Intelligence (AI) Stock With $368 Billion in Revenue Coming Down the Pipeline

Published

1 hour ago

September 7, 2025

The Motley Fool

Key Points

A handful of big tech companies are set to spend over $300 billion building AI infrastructure this year.

Demand for compute is growing just as fast as companies can stand up new servers.

This giant has more commitments and is growing faster than almost everyone in the market.

10 stocks we like better than Microsoft ›

The artificial intelligence boom is only getting bigger, with just a handful of big tech companies on track to spend over $300 billion on AI infrastructure this year alone. Alphabet (NASDAQ: GOOG)(NASDAQ: GOOGL) recently bumped its capital expenditure outlook for the full year from $75 billion to $85 billion. Amazon (NASDAQ: AMZN) is on track to spend over $100 billion in capital expenditures, mostly going toward new data centers and servers to fill them. And Microsoft (NASDAQ: MSFT) is planning a whopping $10 billion of spending per month for the current quarter.

By far the biggest beneficiary from all that spending has been Nvidia (NASDAQ: NVDA). The chipmaker has seen its data center chip sales soar over the last few years, including a 56% jump in its most recent quarter. And demand doesn’t seem to be slowing down anytime soon, with analysts expecting revenue to grow nearly as much next year as this year (on an absolute basis).

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But another AI giant just disclosed it has $368 billion of contracted revenue, and it’s doing everything it can to keep up with the massive demand it’s seeing.

Image source: Getty Images.

Huge long-term commitments give this stock a massive runway

All three major cloud computing platforms disclose their backlog, or remaining performance obligations, and all three are seeing healthy growth.

Alphabet said its Google Cloud backlog climbed to $108 billion last quarter, a 37% increase from a year ago.
Amazon said its Amazon Web Services backlog totaled $195 billion, a 25% increase from a year ago.
Microsoft revealed a $368 billion backlog last quarter, a 37% increase from last year.

Indeed, Microsoft is the AI giant with $368 billion coming down the pipeline.

There’s an important caveat about Microsoft’s remaining performance obligations. It includes contracts for commercial software and services like Microsoft 365 and its Azure cloud computing platform. As such, it’s not a perfect apples-to-apples comparison with Amazon or Alphabet. Still, the growth is impressive, and the metric Microsoft shares suggests Azure may be growing its commitments significantly faster than its two biggest rivals.

Importantly, Microsoft’s backlog growth is stemming from a growing number of long-term commitments. Microsoft said just 35% of those commitments will be recognized as revenue in the next 12 months, with the total increasing 21% year over year. The amount recognized beyond 12 months grew 49%. By comparison, Alphabet saw the percentage of commitments set to be recognized as revenue within 24 months drop from 55% to 50% last quarter. Amazon saw the average length of its long-term commitments get a slight bump from 3.9 years to 4 years.

That shift can skew just how much faster Azure is growing compared to its rivals. If Microsoft is extending the length of its contracts, it’ll naturally have a bigger backlog. Still, the long-term commitments put Microsoft in a position to generate strong growth for Azure for years to come. Management shared that Azure is now a $75 billion business, after exhibiting 39% year-over-year revenue growth last quarter. It expects 37% growth next quarter. That makes it roughly 50% larger than Google Cloud, but growing faster. And its massive backlog means it can continue outpacing the competition in the future.

Demand continues to outpace supply

Microsoft management has been telling investors for well over a year that demand for its cloud computing services, particularly its Azure AI services, is higher than its supply. That remained the case in the fourth quarter. To be sure, that’s not a situation unique to Microsoft. Both Amazon and Alphabet have made similar comments on their earnings calls.

But Microsoft is spending more than anyone building out its data centers. As mentioned, it committed to spending $30 billion on capex this quarter, and management refused to provide guidance on how much it might spend through the rest of fiscal 2026. But given the massive and rapidly growing backlog of demand for its AI services, investors should be happy to see Microsoft build as quickly as possible. It’s important to remember that Microsoft also holds a leading position as enterprises migrate more of their systems from on-premise to the cloud, specializing in hybrid cloud environments using Windows. As such, overbuilding shouldn’t be a huge concern.

Azure is the biggest growth driver for Microsoft right now, but it’s not the only one. As mentioned, that $368 billion backlog also includes commitments for Microsoft 365, Dynamics 365, and Microsoft’s other enterprise software and services. Those are getting a boost from AI as well, as Microsoft integrates its Copilot AI assistant into its software. That helps workers get more out of its products and increases productivity. As a result, Microsoft is able to charge more and gain bigger commitments from commercial customers.

Investors will have to pay a premium price to buy Microsoft stock. With a forward P/E ratio of 32, it trades for a much higher price than Alphabet, which sports a 23 multiple. It’s even approaching Amazon’s 34 times earnings multiple, despite the cloud computing leader historically trading for a much higher earnings multiple. But with a massive pipeline of long-term growth ahead for the company, it’s well worth paying up for.

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Adam Levy has positions in Alphabet, Amazon, and Microsoft. The Motley Fool has positions in and recommends Alphabet, 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.

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.

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AI Research

Artificial Intelligence For Video Surveillance Market

Published

2 hours ago

September 7, 2025

Market Research Intellect

New Jersey, US State: “The global Artificial Intelligence For Video Surveillance market in the Information Technology and Telecom category is projected to reach USD 16.8 billion by 2031, growing at a CAGR of 11% from 2025 to 2031. With rising industrial adoption and continuous innovation in Information Technology and Telecom applications, the market is estimated to hit USD 6.5 billion in 2024, highlighting strong growth potential throughout the forecast period.”

Artificial Intelligence For Video Surveillance Market Size & Forecast 2033

The artificial intelligence for video surveillance market is expected to grow substantially by 2033, supported by rising demand for advanced security and monitoring solutions. AI-powered systems enhance surveillance by enabling real-time analytics, facial recognition, anomaly detection, and automated alerts. Increasing urbanization, smart city projects, and security concerns in public and private sectors are key drivers of adoption worldwide.

Technological innovations integrating AI with cloud storage, IoT devices, and edge computing are improving accuracy and scalability of surveillance systems. Vendors are focusing on developing customizable platforms for industries such as retail, transportation, and critical infrastructure. The emphasis on proactive threat detection and regulatory compliance is further boosting demand. By 2033, the AI for video surveillance market is forecast to reach robust global valuation, driven by continuous innovation and expanding applications across diverse sectors.

Key Players in the Artificial Intelligence For Video Surveillance Market

Hikvision

Dahua

Huawei

Cisco Meraki

Hanwha

ZTE

Honeywell Security

Simshine Intelligent Technology Co. Ltd.

For Further Detail, Download the Sample PDF with Complete TOC, Tables, Figures, Charts, And More @ https://www.marketresearchintellect.com/download-sample/?rid=1031128&utm_source=OpenprJune&utm_medium=023

Factors Supporting Growth of Artificial Intelligence For Video Surveillance Market in the Future:

1.Technological Advancements and Innovation:

The continuous evolution of technology is playing a vital role in driving the Artificial Intelligence For Video Surveillance market forward. Cutting-edge innovations are improving product functionality, enhancing performance, and reducing costs, making these solutions more accessible to a broader range of industries. Emerging technologies such as AI, IoT, advanced analytics, and automation are also enabling smarter and more efficient use cases, further expanding the scope of the market. These advancements are not only upgrading existing systems but are also creating entirely new application opportunities that will support long-term market expansion.

2. Expanding Applications Across End-Use Sectors:

The increasing integration of Artificial Intelligence For Video Surveillance solutions across diverse industries such as automotive, healthcare, consumer electronics, telecom, and industrial manufacturing is significantly boosting market demand. Each sector brings unique requirements, pushing companies to diversify their offerings and customize solutions. This cross-industry relevance ensures consistent demand growth, while rising digitalization and adoption of smart technologies amplify the market potential across both developed and developing regions.

3. Favorable Government Policies and Infrastructure Push:

Supportive initiatives by governments around the world, including funding programs, tax incentives, and policy frameworks, are providing a strong foundation for market development. Efforts to strengthen digital infrastructure, promote energy efficiency, and drive sustainable development are fueling demand for advanced Artificial Intelligence For Video Surveillance technologies. Moreover, public-private partnerships and national transformation agendas such as smart cities and Industry 4.0 are creating favorable conditions for rapid market expansion, especially in emerging economies

4. Increased Investment and Focus on Research & Development:

The Artificial Intelligence For Video Surveillance market is experiencing a surge in investment from both private and public entities, driven by the urgency to innovate and stay competitive. Companies are dedicating substantial resources to research and development to create next-generation products with higher efficiency, scalability, and environmental sustainability. Venture capital funding, mergers, acquisitions, and collaborations are also contributing to a dynamic ecosystem that fosters experimentation and accelerates commercialization of novel solutions, ensuring sustained market growth in the future.

To avail a discount on the purchase of this report visit the link @ https://www.marketresearchintellect.com/ask-for-discount/?rid=1031128&utm_source=OpenprJune&utm_medium=023

Key Segments Covered in Our Report: Artificial Intelligence For Video Surveillance Industry

Artificial Intelligence For Video Surveillance Market by Type

Software

Hardware

Artificial Intelligence For Video Surveillance Market by Application

Public & Government Infrastructure

Commercial

Residential

The Application segment showcases the industries and sectors that use Artificial Intelligence For Video Surveillance products for example Artificial Intelligence For Video Surveillance targeting healthcare and automotive industries etc. It also provides a perspective of the market rate of acceptance, usage of the products, and new applications that are paving the way for the future of the market.

Global Artificial Intelligence For Video Surveillance Market Regional Analysis

The Global Artificial Intelligence For Video Surveillance Market is examined in dimensions of regions, wherein each region has its own market growth, trends as well as dynamics. This section highlights on the detailed market performance, major shifts, and trends and underlying factors explaining growth in different places around the world.

North America: North America accounts for a large share of the Artificial Intelligence For Video Surveillance market which is a result of the developed technology, intense consumer market, and huge investments in the Artificial Intelligence For Video Surveillance industry. To add, the U.S. market also plays a crucial role as this economy is more concerned with innovation and was also one of the first to implement Artificial Intelligence For Video Surveillance products in its Artificial Intelligence For Video Surveillance sectors. The region is expected to see a gradual rise till 2031 and this is because of its reinforced infrastructure and existing regulation mechanisms.

Europe: Global has the fastest growing Artificial Intelligence For Video Surveillance market and is oriented around environmental protection, renewed efforts and environmental awareness. The market is dominated by countries like Germany, the UK, and France that have improved their technologies and have a strong industrial structure. Increased request for green solutions along with regulatory efforts are increasing demand in the market’s key areas such as Artificial Intelligence For Video Surveillance sectors.

Asia-Pacific: The growth potential in the Artificial Intelligence For Video Surveillance market is expected to be maximum for Asia-Pacific region. Increased maturation, urban migration as well as expanding middle class in China, India, and Japan and other developing economies are great constituents of market growth. Further, there is an increasing contribution to investments in the Artificial Intelligence For Video Surveillance sector which is increasing the demand for Artificial Intelligence For Video Surveillance regions-supplying throughout the area.

Rest of the World: Countries and areas like Latin America, Middle East & Africa have also been showing moderate Artificial Intelligence For Video Surveillance market growth. Although still developing, these markets are fueled by a fast increasing infrastructure, expending industrial activities and growing consumer demand for Artificial Intelligence For Video Surveillance goods. These regions pose great opportunities for the market players to tap into other sources of growth.

Frequently Asked Questions (FAQ) – Artificial Intelligence For Video Surveillance Market

Q1: What is the anticipated growth rate of the Global Artificial Intelligence For Video Surveillance Market?

A1: With a growth rate of CAGR of 11%, the Global Artificial Intelligence For Video Surveillance Market is anticipated to reach USD 16.8 billion by 2031. Industrial demand and innovation will lead it to reach USD 6.5 billion by 2024.

Q2: Which regions provide the highest growth opportunities for the Artificial Intelligence For Video Surveillance Market?

A2: Asia-Pacific is likely to provide the highest growth prospects based on speedy industrialization and infrastructure growth, followed by robust markets in Europe and North America.

Q3: Which are the primary drivers of market growth?

A3: The primary drivers are technology innovation, growing industrial applications, heightened government initiatives, and expanding use of Artificial Intelligence For Video Surveillance solutions in different industries.

Q4: What are the challenges faced by the Artificial Intelligence For Video Surveillance Market?

A4: The challenges are tight regulatory systems, high upfront capital expenditures, fragmentation of the market in the emerging markets, and geopolitical risks in some regions.

Q5: Which are the major players in the Global Artificial Intelligence For Video Surveillance Market?

A5: The market has a number of leading players with a focus on innovation, strategic alliances, and global expansion.

Q6: How does innovation influence the Artificial Intelligence For Video Surveillance Market?

A6: Market growth is driven by innovation, which enhances product efficiency, lowers costs, and facilitates new applications, making the overall market potential broader.

Q7: Which industries utilize Artificial Intelligence For Video Surveillance products mostly?

A7: Major industries include manufacturing, automotive, energy, electronics, and infrastructure, among others, where Artificial Intelligence For Video Surveillance solutions deliver operational efficiency and sustainability.

Q8: How is the market anticipated to change after 2031?

A8: Although projections beyond 2031 are uncertain, continued technological advancement and increasing industrial demand are expected to continue supporting long-run growth patterns.

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AI Research

Better Artificial Intelligence (AI) Stock: Palantir vs. BigBear.ai

Published

2 hours ago

September 7, 2025

Harsh Chauhan

Both stocks have been flying high in the past year, but one of them looks like a much better buy right now.

The spending on artificial intelligence (AI) software and tools has been picking up momentum at a solid pace of late, and that’s not surprising as this technology is expected to deliver terrific productivity gains. According to McKinsey, AI has the potential to deliver $4.4 trillion worth of productivity gains in the long run.

Palantir Technologies (PLTR -2.04%) and BigBear.ai (BBAI 0.71%) are two companies that can help investors benefit from the massive generative AI software market that’s expected to clock a compound annual growth rate (CAGR) of 36% through 2030. But if you have to choose from one of these two AI stocks for your portfolio right now, which one should it be?

Let’s find out.

Image source: Getty Images.

The case for Palantir Technologies

Palantir is considered to be the leading player in the AI software platforms market by third-party research firms such as Forrester and IDC. That explains why the company has been landing new customers for its AI software solutions at a terrific clip.

Its overall customer count was up by 43% year over year in the second quarter of 2025. But more importantly, the productivity gains delivered by Palantir’s AI solutions are helping it expand its business with existing customers. As a result, the company’s deal size is improving, allowing it to close 157 deals worth at least $1 million last quarter. That was a jump of 64% from the year-ago period, exceeding the growth in its customer base.

It is easy to see why customers spend more money on Palantir’s AI software if we take a look at management’s comments on the recent earnings conference call. As pointed out by Chief Revenue Officer Ryan Taylor:

The impact our software is delivering for our customers as they cross the chasm is ever widening their advantage over the AI have-nots. Citibank shared that the customer onboarding process and relevant KYC and security checks that once took them nine days now take seconds. Fannie Mae recently announced they’re working with Palantir, decreasing the time to uncover mortgage fraud from two months down to seconds.

These are just two of the many examples highlighted by management about how its Artificial Intelligence Platform (AIP) is helping it win more customers and strengthen its relationship with existing ones. As such, it won’t be surprising to see Palantir sustain its healthy growth rates following the 45% spike in revenue forecast for 2025.

Another important thing worth noting is that Palantir’s ability to gain more business from existing customers drives stronger bottom-line growth. Its earnings are expected to jump 57% this year to $0.64 per share, followed by impressive growth over the next couple of years as well.

Data by YCharts.

So, Palantir is likely to remain a top AI stock for a long time to come, thanks to the secular growth opportunity in the AI software market.

The case for BigBear.ai

Just like Palantir, BigBear.ai also provides AI software solutions that help its customers make faster and better decisions. The stock has more than tripled in value in the past year, as investors buy it in anticipation that it could become a big winner of the lucrative opportunity in the AI software market. However, investors can buy this stock at a much cheaper valuation despite its red-hot rally.

BigBear.ai stock trades at 9 times sales as compared to Palantir’s way more expensive price-to-sales ratio of 115. Another thing working in BigBear’s favor is its fast-growing revenue backlog that could lead to an acceleration in the company’s growth. It ended the second quarter with a backlog of $380 million, up by 43% from the year-ago period.

However, a closer look at BigBear.ai will tell us that the company’s growth is nowhere near that of Palantir’s. Its revenue slid 18% year over year in Q2 to $32.5 million, as it was unable to convert some of its Army contracts into revenue. This brings us to the reason why BigBear.ai has been in hot water of late.

The company relies on government contracts for a majority of its revenue. So, its business is dependent on government budgets and policies, which is why it was forced to lower its 2025 guidance when it released its Q2 results. Investors pressed the panic button, and BigBear.ai stock went into free-fall mode since the earnings’ release on Aug. 11.

The company’s updated revenue guidance of $132.5 million for 2025 would be lower than the $158 million in revenue it generated last year. Moreover, BigBear.ai’s backlog doesn’t necessarily guarantee that its growth will pick up due to certain caveats associated with that metric. As such, just because BigBear.ai is cheaper than Palantir doesn’t make it a better buy than the latter.

The verdict

Palantir, though extremely expensive right now, has the ability to justify its rich valuation thanks to its solid position in the fast-growing AI software space. The company is quickly building up a solid customer base and is also winning a bigger share of their wallets. That’s the reason why its forward sales multiples are significantly lower than the trailing multiple.

Data by YCharts.

So, investors looking to choose from one of these two AI stocks for their portfolio right now would be better off buying Palantir, given the company’s fast pace of growth and sunny prospects considering its leading position in the AI software market.

Citigroup is an advertising partner of Motley Fool Money. Harsh Chauhan has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Palantir Technologies. The Motley Fool has a disclosure policy.

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