LinkedIn gets its own suite of video tools as it grows video presence on platform - Related to got, grows, chatgpt, an, actually
AlphaQubit tackles one of quantum computing’s biggest challenges
Quantum computers have the potential to revolutionize drug discovery, material design and fundamental physics — that is, if we can get them to work reliably.
Certain problems, which would take a conventional computer billions of years to solve, would take a quantum computer just hours. However, these new processors are more prone to noise than conventional ones. If we want to make quantum computers more reliable, especially at scale, we need to accurately identify and correct these errors.
In a paper , we introduce AlphaQubit, an AI-based decoder that identifies quantum computing errors with state-of-the-art accuracy. This collaborative work brought together Google DeepMind’s machine learning knowledge and Google Quantum AI’s error correction expertise to accelerate progress on building a reliable quantum computer.
Accurately identifying errors is a critical step towards making quantum computers capable of performing long computations at scale, opening the doors to scientific breakthroughs and many new areas of discovery.
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ChatGPT in WhatsApp just got an update that'll make you actually want to text it
ChatGPT was originally available only on browsers, but since then, OpenAI has expanded access to mobile and desktop apps. In December, OpenAI took it a step further by adding a toll-free 1-800-CHATGPT number, where people can access the chatbot with a quick dial and even text it on WhatsApp. The experience has just received an upgrade.
On Monday, OpenAI unveiled that people could now upload images in the WhatsApp chat, just like they would when using the chatbot on the browser or app. This feature is helpful for multimodal assistance, where referencing a photo adds useful context that superior informs ChatGPT's response.
Also: OpenAI's new Deep Research agent can do in 5 minutes what might take you hours.
For example, if you want to know the name of a plant or vegetable, you can simply upload the image and ask ChatGPT directly in WhatsApp. More elaborate tasks of that nature include taking a photo of a sign to ask for a translation or showing it fridge ingredients and asking for a recipe.
To enhance ChatGPT's multimodal assistance in WhatsApp even further, clients can now send it audio messages in the chat and receive text responses, just like the regular Voice Mode experience available in ChatGPT.
The ChatGPT in WhatsApp experience is free, and to get started, all you have to do is download the app and message ChatGPT as you would any other contact. You can also scan the QR code below, which will walk you through the sign-up process.
Typically, when accessing ChatGPT through WhatsApp, people were subject to the messaging limits of non-logged-in, free accounts, which included a limit of 15-minute ChatGPT calls per month. However, starting today, OpenAI is rolling out the ability for people to link their ChatGPT Plus, Free, or Pro accounts on WhatsApp, resulting in expanded access for all people within the app.
Also: OpenAI launches new o3-mini model - here's how free ChatGPT customers can try it.
Some advantages of using WhatsApp to access ChatGPT instead of other methods include not having to download an additional app or switch contexts from the platform you use to text your family and friends to message ChatGPT.
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LinkedIn gets its own suite of video tools as it grows video presence on platform
The skyrocketing popularity of short-form video has transformed social media. LinkedIn says video on LinkedIn is bring watched 36% more year over year, with video creation growing at twice the rate of other post formats. As a result, the professional networking platform is leaning into video content.
On Tuesday, LinkedIn revealed a suite of video tools, including new creator analytics, video feed updates, enhanced video search, and more.
Also: LinkedIn's new AI tool could be your dream job matchmaker.
These tools will help creators to move beyond text and post more videos on the platform, which should, in turn, help them expand their reach and networks. Simultaneously, all customers will enjoy more video content, so the tools are a win-win for everyone.
To further the reach of video, LinkedIn will also surface more relevant content in search results, presented in a swipeable carousel format.
Also: How to clear the cache on your TV (and why you shouldn't wait to do it).
If you are a content creator on the platform, several updates will help your audience connect with you. The first is a new profile preview feature, which allows consumers to see a snapshot of a creator's profile within the full-screen video player while watching a video.
The preview also displays recent video content from the creator. A more prominent 'Follow' button within the video player makes it easier to form long-term connections if a user wants to follow the creator.
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Lastly, creators can now see their videos' average watch time, an insight that can be used to determine what content resonates best with audiences and how to make people feel engaged longer.
LinkedIn also offers nano-learning courses with expert tips and insights for creators who may not be as familiar with video creation but are ready to get started.
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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 Video Alphaqubit Tackles 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
generative AI intermediate
machine learning intermediate
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