Baidu to release next-generation AI model this year
The new model, Ernie 5, will feature multimodal capabilities enabling it to process and convert between different formats including text, video, images and audio, CNBC reported earlier.
The planned release comes amid intensifying competition in China's AI sector, notably from startup DeepSeek, which gained prominence after launching a reasoning model that matches OpenAI's GPT performance at lower costs.
Despite being among China's first movers in AI following ChatGPT's 2022 debut, Baidu has struggled to gain widespread adoption for its Ernie large language model, even as the company claims its latest version, Ernie 4, matches OpenAI's GPT-4 capabilities.
The tech giant's AI offerings have lagged domestic competitors, including ByteDance's Doubao chatbot and newcomer DeepSeek, in terms of user adoption.
Baidu CEO Robin Li told attendees in a conference held in Dubai on Tuesday that DeepSeek's sudden emergence demonstrated the unpredictable nature of innovation.
"You just don't know when and where innovations come from," he said.
During the event, Li also said that investment in data centres and cloud infrastructure is still needed despite DeepSeek challenging the cost efficiency of large AI models.
Follow Emirates 24|7 on Google News.
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Channel Post MEA
15 hours ago
- Channel Post MEA
Rapid7 Launches Active Patching
Rapid7 has announced the release of Active Patching, a fully automated patching and remediation solution integrated into Rapid7's Exposure Command solution. Powered by Automox, Active Patching empowers security and IT teams to proactively mitigate risk across vulnerable assets. The pace at which attackers exploit zero-day vulnerabilities and misconfigurations continues to accelerate. At the same time, traditional patching methods often leave critical gaps due to delays, complexity, and limited coverage. Active Patching within Exposure Command addresses this challenge by automating risk remediation and providing continuous, real-time visibility into which systems require patches and which have no available fixes. Powered by Automox's Autonomous Endpoint Management platform, this new solution provides security and IT teams with another powerful way to prioritize effectively and accelerate response times with Exposure Command. The result is a proactive and compliant security posture that addresses vulnerabilities head-on. 'The visibility and context Exposure Command delivers is unmatched. It's not just about seeing where you're vulnerable, it's about knowing exactly what to do next,' said Craig Adams, Chief Product Officer at Rapid7. 'We've built a platform that doesn't just highlight risk, it contextualizes it. Active Patching is another way that Rapid7's Command Platform turns insights into action, enabling teams to automatically remediate vulnerabilities or apply compensating controls in real time, even when a patch doesn't exist. That's the difference between reactively managing vulnerabilities and proactively eliminating exposures.' Active Patching augments Exposure Command's complete attack surface visibility, native and third-party vulnerability management, and enriched threat intelligence with automated patching and remediation capabilities from Automox, providing organizations the following: Impact-driven, scalable mitigation: Efficiently reduce risk and eliminate manual process by automating remediation actions across multiple assets at once. Threat intelligence embedded into every finding: Remediate risks automatically and with confidence by knowing which vulnerabilities impact mission-critical assets by combining contextual insights, dynamic risk scores, and actionable threat intelligence from Rapid7 Labs. Actionable risk acceptance: Protect assets without known fixes via an expansive array of pre-built virtual patching templates that can help automatically configure endpoints and prevent attacks targeting unpatched systems. Automated remediation workflows: Leverage hundreds of out-of-box actionsto automate risk remediation, drive compliance, and respond to vulnerabilities faster. Closed-loop vulnerability management: Continuously view the status of all deployed patches to establish trust that vulnerabilities have been properly mitigated. Unmatched patching and configuration coverage: Automate fixes across almost any device, including Linux, MacOS, and Windows operating systems and their third-party software. 'Modern security demands more than just knowing where you're exposed—it requires the ability to take action, fast. Our partnership with Rapid7 brings that capability to life,' said Jason Kikta, CISO and Senior vice president of product at Automox. 'By embedding our patch and configuration automation technology into Exposure Command, we're enabling customers to go from identification to remediation in a matter of minutes, dramatically reducing risk while eliminating manual overhead.'
Zawya
a day ago
- Zawya
AI and gravity-defying US GDP: Mike Dolan
(The opinions expressed here are those of the author, a columnist for Reuters) LONDON - For the second time in three years - and straddling two separate presidencies - heightened U.S. fears of recession have proved wide of the mark. The artificial intelligence boom is once again the prime suspect. The AI explosion and arms race since the launch of OpenAI's ChatGPT in November 2022 had its own mini crisis of confidence earlier this year with the emergence of China's cheaper DeepSeek rival. But neither those doubts nor the market angst about President Donald Trump's tariff war seems to have slowed the staggering capital spend and data center building supporting the AI push. As the brave new world of generative AI enters its third year, the financial impact has expanded well beyond the share price of chipmakers, as data center construction and capex spending more broadly are flattering U.S. GDP to a remarkable degree. Carlyle's Head Investment Strategist Jason Thomas has been arguing all year that this capex spending is an effective re-industrialization of corporate America, reshaping its focus away from software and intangible assets and toward investing in plants and machinery and energy like never before. The GDP impact from that is huge. RE-INDUSTRIALIZE AND BORROW Thomas reckons the AI-related capex boom – in data centers, graphics processing units, server infrastructure, power and related hardware and applications – already accounts for more than one-third of this year's second-quarter U.S. GDP growth. And he points out that order books in the relevant industries continue to grow at annual rates of more than 40%. With Q2 GDP data due out next week expected to show annualized growth of 2.5%, that's a sizeable AI tailwind. The scale of AI-related spending that has already occurred suggests that these are not just finger-in-the-wind estimates. Spending on physical data center infrastructure alone is up fourfold from 2020 levels, offsetting weakness in other construction categories. Among AI chip giant Nvidia's top U.S. customers, Thomas writes, capex has grown 1.5 times as fast as revenue. Property, plant and equipment now account for 70% of typical book value for these firms, up from just 20% before the GenAI scramble began. The big question remains whether AI's contribution to economic growth is now peaking - even if that's difficult to deduce given the hype and optimism around the sector and evidence of robust chip demand growth worldwide. FINANCING GAP But Morgan Stanley this week analyzed this question by considering the borrowing needed to finance the ongoing AI infrastructure and data center buildout. Analysts Vishwanath Tirupattur and Vishwas Patkar forecast roughly $2.9 trillion of additional global data center spending through 2028, comprising $1.6 trillion on chips and servers and $1.3 trillion on real estate, building costs and maintenance. For context, that suggests an annual global outlay over the next three years that's close to the $950 billion spent by all S&P 500 firms combined last year. While some of this can be funded with cash, Morgan Stanley estimates that a $1.5 trillion financing gap remains, which will have to be raised via debt, likely a mix of loans, bonds, asset-backed securities and private funding. On top of this, they reckon that investment spending related to data center construction and power generation will add up to 0.4 percentage points to U.S. GDP through this year and next. Once again, AI is helping frustrate all other macro bets. And megacap quarterly earnings due over the coming weeks will again be examined forensically for signs of a sting in the tail. The opinions expressed here are those of the author, a columnist for Reuters. -- Enjoying this column? Check out Reuters Open Interest (ROI), your essential new source for global financial commentary. Follow ROI on LinkedIn. Plus, sign up for my weekday newsletter Morning Bid U.S. (By Mike Dolan; Editing by Lisa Shumaker)
TECHx
2 days ago
- TECHx
Alibaba AI Model Targets Coding Efficiency with Qwen3
Home » Latest news » Alibaba AI Model Targets Coding Efficiency with Qwen3 Alibaba AI Model Qwen3-Coder enables developers to automate coding tasks, generate code, and manage workflows with open-source access. Alibaba Group announced the launch of Qwen3-Coder, an open-source artificial intelligence model designed to support software development. The company described it as its most advanced coding tool so far. The new Alibaba AI model is built to handle a range of programming tasks. These include code generation and managing complex workflows. According to the company, the model is particularly effective in agentic AI coding. This refers to automated tasks where the AI system can independently solve programming problems. The move reflects ongoing competition among Chinese tech firms in global AI development. Companies in China and the United States continue to introduce increasingly advanced models. In performance tests, Alibaba reported that Qwen3-Coder outperformed local models such as DeepSeek and Moonshot AI's K2 in key coding tasks. The company also revealed that its model performed at levels comparable to U.S.-based tools like Anthropic's Claude and OpenAI's GPT-4 in certain areas. Key points: Qwen3-Coder focuses on coding tasks and automation Alibaba says it surpasses some Chinese peers in performance Reported to match certain U.S. models in specific capabilities The Qwen3-Coder model is available as open-source, making it accessible for developers and organizations looking to integrate AI into software development processes. Source: Reuters
