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OpenAI Cofounder John Schulman Quits Anthropic, Likely to Join Mira Murati’s AI Startup - Related to microsoft, murati’s, off, startup, schulman

Microsoft Build kicks off May 19 - here's what we know so far

Microsoft Build kicks off May 19 - here's what we know so far

Every year, the spring season brings a series of launch events from the major tech companies, including Microsoft. The software giant just revealed the dates of its annual developer conference, Microsoft Build. Here's everything you need to know.

Microsoft Build will take place from May 19 to 22 in Seattle. Although a schedule is yet to be published, the event typically includes a two-hour keynote on the first day, during which the enterprise makes its major announcements, including software and hardware reveals.

Because the event is still months away, Microsoft has not shared details about attending or viewing the conference. However, even though Microsoft Build takes place in Seattle, the enterprise typically includes options for the public to livestream the keynotes and breakout sessions on the enterprise's website.

If you already know that date won't work for you, don't worry: ZDNET will be covering the announcements and developments in real time on the site.

Although Microsoft does not have all of the attention OpenAI has in the AI space, it's been a major player, offering competitive consumer, developer, and enterprise AI solutions, such as its Copilot chatbot.

As a result, we expect Microsoft will unveil the latest of its AI offerings, which will likely span its Office 365 applications, AI chatbot, Copilot+ PCs, and developer offerings such as GitHub, Fabric, Azure AI, and Azure DevOps.

Also: The best laptops of 2025: Expert tested and reviewed.

Most likely, there'll be hardware launches as well. Last year, the day before Microsoft Build, Microsoft unveiled its first consumer AI PCs -- the Surface Laptop and Surface Pro. It also unveiled new PCs from its partners, including Acer, Asus, Dell, HP, Lenovo, and Samsung.

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OpenAI Cofounder John Schulman Quits Anthropic, Likely to Join Mira Murati’s AI Startup

OpenAI Cofounder John Schulman Quits Anthropic, Likely to Join Mira Murati’s AI Startup

OpenAI cofounder John Schulman, who joined Anthropic last year, departed from the corporation in just five months. He is reportedly joining former OpenAI CTO Mira Murati’s AI startup, .

Schulman confirmed his departure on X, while his role at Murati’s startup remains unclear.

“Confirming that I left Anthropic last week. Leaving wasn’t easy because I enjoyed the stimulating research environment and the kind and talented people I was working with,” he expressed. “But I decided to go with another opportunity that I found extremely compelling.”.

Schulman, who left OpenAI in August 2024, cited a desire to focus on AI alignment research and hands-on technical work. His move signals Murati’s growing influence in the AI space.

Last year, Murati left her role as OpenAI’s Chief Technology Officer in September 2024.

The Information lately reported that Murati’s enterprise, named Thinking Machines Lab, could be inspired by the intelligent “thinking machines” from Frank Herbert’s Dune or the defunct supercomputer firm Thinking Machines Corporation.

Incorporated in Delaware as a benefit corporation, ‘Thinking Machines Lab’ has a structure that allows it to consider societal impact alongside profit, offering some protection against activist investors.

Murati’s corporation is reportedly developing Agentic AI and has already secured over $100 million in funding.

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UK startup launches ‘world’s first’ AI deepfake-detecting browser

UK startup launches ‘world’s first’ AI deepfake-detecting browser

UK startup Surf Security has launched a beta version of what it asserts is the world’s first browser with a built-in feature designed to spot AI-generated deepfakes.

The tool, available through Surf’s browser or as an extension, can detect with up to 98% accuracy whether the person you’re interacting with online is a real human or an AI imitation, the firm showcased.

The London-based cybersecurity upstart uses “military-grade” neural network technology to detect deepfakes. The system uses State Space Models, which detect AI-generated clones across languages and accents by analysing audio frames for inconsistencies.

“To maximise its effectiveness, we focused on accuracy and speed,” stated Ziv Yankowitz, Surf Security’s CTO. The tool’s neural network is trained using deepfakes created by the top AI voice cloning platforms, he stated.

The system has an integrated background noise reduction feature to clear up audio before processing. “It can spot a deepfake audio in less than 2 seconds,” stated Yankowitz.

The new feature is available for audio files, including online videos or communication software such as WhatsApp, Slack, Zoom, or Google Meet. You just need to press a button and the system verifies if the audio – recorded, or live – is genuine or AI-generated. Surf stated it will also add AI image detection to the browser’s toolkit in the future.

Deepfakes, which use AI to create convincing fake audio or video, are a rising threat.

Just this week, researchers at the BBC unearthed deepfake audio clips of David Attenborough that sound indistinguishable from the famous presenter’s own voice. Various websites and YouTube channels are using the deepfake to get him to say things – about Russia, about the US election – that he never stated.

of an ugly iceberg. Deepfakes have been used to enable large-scale fraud, incite political unrest through fake news, and destroy reputations by creating false or harmful content.

Surf stated it launched the new deepfake detector to help protect enterprises, media organisations, police, and militaries around the world from the growing risk of AI cloning.

However, battling deepfakes is a continuous battle between humans using machines for good, against other humans using machines for nefarious means.

“AI voice cloning software becomes more capable by the day,” admitted Yankowitz. “So like all of cybersecurity, we are committing to winning an ever-evolving arms race.”.

Surf expects to release the full version of its deepfake detector early next year.

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Market Impact Analysis

Market Growth Trend

2018201920202021202220232024
23.1%27.8%29.2%32.4%34.2%35.2%35.6%
23.1%27.8%29.2%32.4%34.2%35.2%35.6% 2018201920202021202220232024

Quarterly Growth Rate

Q1 2024 Q2 2024 Q3 2024 Q4 2024
32.5% 34.8% 36.2% 35.6%
32.5% Q1 34.8% Q2 36.2% Q3 35.6% Q4

Market Segments and Growth Drivers

Segment Market Share Growth Rate
Machine Learning29%38.4%
Computer Vision18%35.7%
Natural Language Processing24%41.5%
Robotics15%22.3%
Other AI Technologies14%31.8%
Machine Learning29.0%Computer Vision18.0%Natural Language Processing24.0%Robotics15.0%Other AI Technologies14.0%

Technology Maturity Curve

Different technologies within the ecosystem are at varying stages of maturity:

Innovation Trigger Peak of Inflated Expectations Trough of Disillusionment Slope of Enlightenment Plateau of Productivity AI/ML Blockchain VR/AR Cloud Mobile

Competitive Landscape Analysis

Company Market Share
Google AI18.3%
Microsoft AI15.7%
IBM Watson11.2%
Amazon AI9.8%
OpenAI8.4%

Future Outlook and Predictions

The Startup Microsoft Build landscape is evolving rapidly, driven by technological advancements, changing threat vectors, and shifting business requirements. Based on current trends and expert analyses, we can anticipate several significant developments across different time horizons:

Year-by-Year Technology Evolution

Based on current trajectory and expert analyses, we can project the following development timeline:

2024Early adopters begin implementing specialized solutions with measurable results
2025Industry standards emerging to facilitate broader adoption and integration
2026Mainstream adoption begins as technical barriers are addressed
2027Integration with adjacent technologies creates new capabilities
2028Business models transform as capabilities mature
2029Technology becomes embedded in core infrastructure and processes
2030New paradigms emerge as the technology reaches full maturity

Technology Maturity Curve

Different technologies within the ecosystem are at varying stages of maturity, influencing adoption timelines and investment priorities:

Time / Development Stage Adoption / Maturity Innovation Early Adoption Growth Maturity Decline/Legacy Emerging Tech Current Focus Established Tech Mature Solutions (Interactive diagram available in full report)

Innovation Trigger

  • Generative AI for specialized domains
  • Blockchain for supply chain verification

Peak of Inflated Expectations

  • Digital twins for business processes
  • Quantum-resistant cryptography

Trough of Disillusionment

  • Consumer AR/VR applications
  • General-purpose blockchain

Slope of Enlightenment

  • AI-driven analytics
  • Edge computing

Plateau of Productivity

  • Cloud infrastructure
  • Mobile applications

Technology Evolution Timeline

1-2 Years
  • Improved generative models
  • specialized AI applications
3-5 Years
  • AI-human collaboration systems
  • multimodal AI platforms
5+ Years
  • General AI capabilities
  • AI-driven scientific breakthroughs

Expert Perspectives

Leading experts in the ai tech sector provide diverse perspectives on how the landscape will evolve over the coming years:

"The next frontier is AI systems that can reason across modalities and domains with minimal human guidance."

— AI Researcher

"Organizations that develop effective AI governance frameworks will gain competitive advantage."

— Industry Analyst

"The AI talent gap remains a critical barrier to implementation for most enterprises."

— Chief AI Officer

Areas of Expert Consensus

  • Acceleration of Innovation: The pace of technological evolution will continue to increase
  • Practical Integration: Focus will shift from proof-of-concept to operational deployment
  • Human-Technology Partnership: Most effective implementations will optimize human-machine collaboration
  • Regulatory Influence: Regulatory frameworks will increasingly shape technology development

Short-Term Outlook (1-2 Years)

In the immediate future, organizations will focus on implementing and optimizing currently available technologies to address pressing ai tech challenges:

  • Improved generative models
  • specialized AI applications
  • enhanced AI ethics frameworks

These developments will be characterized by incremental improvements to existing frameworks rather than revolutionary changes, with emphasis on practical deployment and measurable outcomes.

Mid-Term Outlook (3-5 Years)

As technologies mature and organizations adapt, more substantial transformations will emerge in how security is approached and implemented:

  • AI-human collaboration systems
  • multimodal AI platforms
  • democratized AI development

This period will see significant changes in security architecture and operational models, with increasing automation and integration between previously siloed security functions. Organizations will shift from reactive to proactive security postures.

Long-Term Outlook (5+ Years)

Looking further ahead, more fundamental shifts will reshape how cybersecurity is conceptualized and implemented across digital ecosystems:

  • General AI capabilities
  • AI-driven scientific breakthroughs
  • new computing paradigms

These long-term developments will likely require significant technical breakthroughs, new regulatory frameworks, and evolution in how organizations approach security as a fundamental business function rather than a technical discipline.

Key Risk Factors and Uncertainties

Several critical factors could significantly impact the trajectory of ai tech evolution:

Ethical concerns about AI decision-making
Data privacy regulations
Algorithm bias

Organizations should monitor these factors closely and develop contingency strategies to mitigate potential negative impacts on technology implementation timelines.

Alternative Future Scenarios

The evolution of technology can follow different paths depending on various factors including regulatory developments, investment trends, technological breakthroughs, and market adoption. We analyze three potential scenarios:

Optimistic Scenario

Responsible AI driving innovation while minimizing societal disruption

Key Drivers: Supportive regulatory environment, significant research breakthroughs, strong market incentives, and rapid user adoption.

Probability: 25-30%

Base Case Scenario

Incremental adoption with mixed societal impacts and ongoing ethical challenges

Key Drivers: Balanced regulatory approach, steady technological progress, and selective implementation based on clear ROI.

Probability: 50-60%

Conservative Scenario

Technical and ethical barriers creating significant implementation challenges

Key Drivers: Restrictive regulations, technical limitations, implementation challenges, and risk-averse organizational cultures.

Probability: 15-20%

Scenario Comparison Matrix

FactorOptimisticBase CaseConservative
Implementation TimelineAcceleratedSteadyDelayed
Market AdoptionWidespreadSelectiveLimited
Technology EvolutionRapidProgressiveIncremental
Regulatory EnvironmentSupportiveBalancedRestrictive
Business ImpactTransformativeSignificantModest

Transformational Impact

Redefinition of knowledge work, automation of creative processes. This evolution will necessitate significant changes in organizational structures, talent development, and strategic planning processes.

The convergence of multiple technological trends—including artificial intelligence, quantum computing, and ubiquitous connectivity—will create both unprecedented security challenges and innovative defensive capabilities.

Implementation Challenges

Ethical concerns, computing resource limitations, talent shortages. Organizations will need to develop comprehensive change management strategies to successfully navigate these transitions.

Regulatory uncertainty, particularly around emerging technologies like AI in security applications, will require flexible security architectures that can adapt to evolving compliance requirements.

Key Innovations to Watch

Multimodal learning, resource-efficient AI, transparent decision systems. Organizations should monitor these developments closely to maintain competitive advantages and effective security postures.

Strategic investments in research partnerships, technology pilots, and talent development will position forward-thinking organizations to leverage these innovations early in their development cycle.

Technical Glossary

Key technical terms and definitions to help understand the technologies discussed in this article.

Understanding the following technical concepts is essential for grasping the full implications of the security threats and defensive measures discussed in this article. These definitions provide context for both technical and non-technical readers.

Filter by difficulty:

platform intermediate

algorithm Platforms provide standardized environments that reduce development complexity and enable ecosystem growth through shared functionality and integration capabilities.

generative AI intermediate

interface

neural network intermediate

platform

DevOps intermediate

encryption

machine learning intermediate

API