Copilot's powerful new 'Think Deeper' feature is free for all users - how it works - Related to deep, here's, pro, includes, this
Copilot's powerful new 'Think Deeper' feature is free for all users - how it works
On Wednesday, Microsoft AI CEO Mustafa Suleyman shared via an X post that the Think Deeper feature was made available for all Copilot individuals at no additional cost. The feature leverages OpenAI's O1 reasoning model to deliver higher-quality responses to complex prompts.
The o1 model was trained to "think before it speaks," and as a result, takes a bit longer to process your query -- around 30 seconds, . This is especially useful for STEM-related tasks such as coding, analysis, and advanced math problems. Other use cases include in-depth advice and planning.
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The only caveat is that it doesn't have access to the internet, but Suleyman expressed in a comment on the X post that Microsoft is "working on it."
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Don't want to pay for ChatGPT Deep Research? Try this free open-source alternative
Since DeepSeek challenged OpenAI two weeks ago, the open- vs. closed-source AI competition has shown no signs of stopping.
Also: Why Mark Zuckerberg wants to redefine open source so badly.
Just two days after OpenAI introduced Deep Research, a new AI agent within ChatGPT that can sift through online findings for you, its open-source counterpart has already emerged.
On Tuesday, Hugging Face released its equivalent to the new feature. Blatantly dubbed open Deep Research, the alternative uses OpenAI's o1 model and an agentic framework to navigate the web. The open alternative achieved 55% accuracy on the General AI Assistants benchmark (GAIA), a top assessment test for agents, compared to Deep Research's 67%, and ranks in first place for open submissions.
However, Hugging Face acknowledged the agent is not yet a full competitor to OpenAI's. "Deep Research is a massive achievement and its open reproduction will take time," the developer platform unveiled in a blog titled "Freeing Our Search Agents." "In particular, full parity will require improved browser use and interaction like OpenAI Operator is providing, [website] beyond the current text-only web interaction we explore in this first step."
Also: Dumping open source for proprietary rarely pays off: enhanced to stick a fork in it.
OpenAI's Deep Research is underpinned by a version of its latest and most advanced reasoning model, o3, of which there is currently no known open-source equivalent. 's blog, this model version also outperformed top models on Humanity's Last Exam , a new AI benchmark test released just last week, and is much more challenging than other popular tests, with a "new high" of nearly 27% accuracy.
That said, HLE's creators point out a potential "contamination": o3 was evaluated after HLE was released, meaning OpenAI had access to its prompts. Hugging Face did not mention whether it had tested open Deep Research on HLE. To better compete, the platform says it's building "agents that view your screen and can act directly with mouse & keyboard."
Considering its $200-per-month price tag via ChatGPT Pro, Deep Research may be inaccessible to most. If you want to try something similar for free, check out open Deep Research's live demo here, which Hugging Face refers to as a "simplified version" of the full agent.
Also: Are ChatGPT Plus or Pro worth it? Here's how they compare to the free version.
The pace at which Hugging Face was able to create something of a competitor -- under 24 hours -- marks the race that makers of proprietary models increasingly find themselves in. Researchers at UC Berkeley made a model comparable to o1-preview in just 19 hours earlier last month. DeepSeek's exact timeline on R1, its o1 rival model, is unknown, but it is understood to be lower-resource in terms of time and spend.
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Google's latest Gemini drop includes Pro access and Flash-Lite - here's what's new
Just last week, Google rolled out Gemini [website] Flash to all Gemini app people on mobile and desktop. Building on that momentum, Google is now expanding access and adding to its Gemini model offerings.
Gemini [website] Pro is here for Advanced clients.
On Wednesday, Google revealed it is finally releasing an experimental version of Gemini [website] Pro. The model, which has Google's largest context window in a model of 2 million tokens, is the firm's most robust and advanced model for coding and complex prompts.
Also: You could win $1 million by asking Perplexity a question during the Super Bowl.
The large context window allows the model to analyze and reference a robust amount of information at once, improving overall performance and assistance by including additional context. It also enables the model to call on tools such as code execution, making it more suitable for a variety of tasks.
Furthermore, Gemini [website] Pro, the successor to [website] Pro unveiled a year ago, outperformed the rest of Google's Gemini models on a series of benchmarks, including the MMLU-Pro, which tests for general capabilities; GPQA (diamond), which tests for reasoning; LiveCodeBench (v5), which tests for code generation in Python; and MATH, which tests for challenging math problems in algebra, geometry, pre-calculus, and more.
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This model is available as an experimental offering for Gemini Advanced individuals in the drop-down toggle. To be Gemini Advanced user, you need to enroll in the Google One AI Premium plan that costs $20 per month. Of course, Google couldn't forget its developer user base, also offering it to developers via Google AI Studio and Vertex AI.
Beyond that, Gemini [website] Flash, Google's model for faster responses and stronger performance ideal for high-volume and high-frequency tasks at scale, is becoming available in more Google products, including the Gemini API in Google AI Studio and Vertex AI, in addition to Gemini app access, which was presented last week.
Also: Perplexity lets you try DeepSeek R1 - without the security risk.
Lastly, due to the positive feedback Google received on Gemini [website] Flash, it has now introduced a new model, [website] Flash-Lite. This model retains the 1 million token context window, multimodal input, speed, and cost of [website] Flash, while offering superior quality, . This model is available in Google AI Studio and Vertex AI in public preview.
Also: Gemini's Deep Research browses the web for you - try the Android app now for free.
Google addressed safety concerns, reassuring the public that the models were built using techniques designed to enable safe usage, such as new reinforcement learning techniques. The corporation also shared that the models went through automated red-teaming to assess security risks. This announcement follows Google's publication of its Responsible AI: Our 2024 analysis, .
SOPA Images / Contributor / Getty Images.
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Market Impact Analysis
Market Growth Trend
| 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 |
|---|---|---|---|---|---|---|
| 23.1% | 27.8% | 29.2% | 32.4% | 34.2% | 35.2% | 35.6% |
Quarterly Growth Rate
| Q1 2024 | Q2 2024 | Q3 2024 | Q4 2024 |
|---|---|---|---|
| 32.5% | 34.8% | 36.2% | 35.6% |
Market Segments and Growth Drivers
| Segment | Market Share | Growth Rate |
|---|---|---|
| Machine Learning | 29% | 38.4% |
| Computer Vision | 18% | 35.7% |
| Natural Language Processing | 24% | 41.5% |
| Robotics | 15% | 22.3% |
| Other AI Technologies | 14% | 31.8% |
Technology Maturity Curve
Different technologies within the ecosystem are at varying stages of maturity:
Competitive Landscape Analysis
| Company | Market Share |
|---|---|
| Google AI | 18.3% |
| Microsoft AI | 15.7% |
| IBM Watson | 11.2% |
| Amazon AI | 9.8% |
| OpenAI | 8.4% |
Future Outlook and Predictions
The Free Copilot Powerful 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:
Technology Maturity Curve
Different technologies within the ecosystem are at varying stages of maturity, influencing adoption timelines and investment priorities:
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
- Improved generative models
- specialized AI applications
- AI-human collaboration systems
- multimodal AI platforms
- 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:
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
| Factor | Optimistic | Base Case | Conservative |
|---|---|---|---|
| Implementation Timeline | Accelerated | Steady | Delayed |
| Market Adoption | Widespread | Selective | Limited |
| Technology Evolution | Rapid | Progressive | Incremental |
| Regulatory Environment | Supportive | Balanced | Restrictive |
| Business Impact | Transformative | Significant | Modest |
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.
platform intermediate
reinforcement learning intermediate
machine learning intermediate
large language model intermediate
generative AI intermediate
API beginner
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