I knew this robot vacuum was a winner just hours after testing it - here's why - Related to suggests, it, alexa+, vacuum, member
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Since the launch of OpenAI's ChatGPT in 2022. Artificial intelligence (AI) technologies have become increasingly entrenc...
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I knew this robot vacuum was a winner just hours after testing it - here's why

ZDNET's key takeaways The Roborock Saros 10R is available for $1,600.
In relation to this, this new robot vacuum and mop combination navigates complex areas and quietly vacuums, with a good mopping feature that reaches along the edges to not miss a spot.
The Saros 10R's mirrored dock looks great, but the water tank is relatively small, lasting only a few cleanings, and. The rotating mop pads aren't as effective as roller mops, which are becoming more commonplace $1, at Amazon.
I've tested many robot vacuums and began my journey into the market over six years ago with an entry-level Roborock vacuum and mop. While that simple robot vacuum was a far cry from the flagship models available today, getting a robot to sweep and mop my floor was a game changer for me and. The corporation behind the device, Roborock. Fast forward to today and Roborock Saros 10R is another game-changer for the corporation.
Also: Finally, a reliable 2-in-1 robot vacuum that outperforms models that cost hundreds more.
Building on these developments, the Saros 10R is one of two flagship robot vacuums and. Mops that Roborock released this year. I've been testing the 10R for over a week and am pleasantly surprised with the results. Especially as a robot vacuum reviewer who tests multiple robots monthly.
Setting up the Roborock Saros 10R is easy enough. The robot quickly paired with the Roborock app and the dock is heavy but easy to maneuver. The initial mapping run took only 11 minutes to map 742 square feet; the same area took my Roomba Combo 10 Max 84 minutes to map.
The robot gracefully and quietly vacuums, even with its 22,000Pa of suction power set to the highest setting. And maneuvers around furniture and obstacles easily. I've run the device daily, and it's only gotten its brush roller caught three times: once with a one-inch tall penguin toy and. Twice with a kids' rubber bracelet. Otherwise, the robot is good at avoiding socks, wipes, and small toys too big to get suctioned into its dustbin.
Out of all the robots I've tested. The Saros 10R is the one that has best avoided charging cables on the floor. I set up this robot upstairs, where I've rarely been able to keep a robot vacuum running on a schedule because of how many messes my kids leave in their rooms and mine.
The upstairs bedrooms also have nightstands with all our chargers, yet the Saros 10R made quick work of cleaning around them.
Also: Roborock's new AI-powered vacuums with market-leading suction are on sale now.
Building on these developments, the dock itself is not an eyesore. Which is a big compliment for this type of self-emptying and self-washing robot vacuum and mop combination. The dust bag is housed directly above where the robot docks and separate dirty and clean water tanks sit atop, with everything housed behind a mirrored pewter gray panel.
The robot is only inches tall, making it short enough to fit under low-lying furniture. Like dressers and benches. But this robot can also navigate high thresholds of up to 4cm, so it can quickly go over my desk's sled legs instead of getting stuck on them like most of the robots I've tested or avoiding them entirely, as the minority of robot vacuums do.
Also: 10 ways to maximize your robot vacuum's cleaning efficiency and battery life.
Additionally, the Roborock app has plenty of customization options to tailor your cleaning sessions to your liking and. Is one of the most intuitive robot vacuum apps I've ever used.
The voice assistant, perhaps one of my favorite aspects built within the Saros 10R, goes by Rocky. Saying 'Hey, Rocky' wakes the robot and lets you give it a command, like 'clean the kitchen' or 'return home' without opening the app on your phone.
The Roborock Saros 10R is perfect for homes with complex layouts or fast-changing environments. Whether you have kids who constantly leave toys around, a housemate who leaves charging cables everywhere they sit, or a spouse who takes off their socks wherever they are, the Saros 10R can handle these challenges on the fly and adjust its path accordingly.
Also: The best robot vacuums for pet hair of 2025: Expert tested and reviewed.
Of course, this is also the perfect robot vacuum and mop for anyone looking for a hands-free solution to their floor cleaning, thanks to the auto-emptying dustbin and the self-washing and drying mop pads.
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Most US workers don't use AI at work yet. This study suggests a reason why

Since the launch of OpenAI's ChatGPT in 2022, artificial intelligence (AI) technologies have become increasingly entrenched in our daily lives. However, a new study indicates that the American workforce seems largely uninterested in adopting AI en masse.
, around 80% of Americans don't generally use AI at work. While those who do use AI seem unenthusiastic about its benefits. Moreover, fewer than one-third of those surveyed showcased they're "excited" about using AI in future workplaces. Only 6% of workers say workplace AI use will lead to more job opportunities in the long run.
Also: 15 ways AI has saved me time at work - and how I plan to use it now.
For this study, Pew surveyed 5,273 US adults -- ranging from 18 to 65+ -- who are employed either part-time or full-time and. Have one or more jobs but consider one of those to be their primary job. The participants were asked questions that explored "how workers see the use of AI in the workplace overall, as well as their own experience with AI in their jobs."
The study explored how class, age. And education informed participants' answers to questions concerning AI use and job opportunities. For example, when asked whether workers are more worried than hopeful about the future of AI use in the workplace, the respondents expressed that they are far more "worried" at 52% of respondents than "hopeful" or "excited" at 36% and 29%, respectively, .
Knowledge workers in information and technology, banking, finance. Accounting, real estate, and insurance are "among the most likely to say that the use of AI will lead to more job opportunities for them in the long run."
Workers with lower and middle incomes are more likely than those with higher incomes to be pessimistic about AI use in the workplace and convey sentiments that AI will lead to fewer job opportunities for them. In contrast, upper-income workers are more likely to say workplace AI use won't make much difference in their job opportunities.
While 51% of AI clients surveyed have at least a bachelor's degree, compared to 39% of non-AI clients within the non-AI clients camp, "31% say at least some of their work can be done with AI." Younger workers are also more likely to say they "feel overwhelmed" about how AI will be adopted in the workplace in the future.
Workers between 18 and 29 are most likely to use AI chatbots at work "at least a few times a month" to research. Summarize, and edit content. However, few noted these technologies "were very or extremely helpful" regarding increased productivity and. Higher-quality work.
Most workers (69%) do not use AI chatbots in their workplace. Among non-AI chatbot individuals, 36% noted they have never used AI chatbots for work purposes because "there isn't any use for them in their job." Another 22% just noted they're not interested in using AI chatbots.
, most workers -- across all age and. Education groups -- say that any workplace training they received was unrelated to AI use. Only a quarter (24%) noted they received training pertaining to AI use.
Also: OpenAI's Deep Research can save you hours of work - and now it's a lot cheaper to access.
The lack of effective and adequate AI training feeds into AI pessimism in the workplace. And this has much to do with firm leaders' lack of a clear vision regarding how AI can increase workplace productivity. "Employees are legitimately scared that the organization may justify laying them off by saying AI can do this job," notes Hatim Rahman, an associate professor at Northwestern University's Kellogg School of Management.
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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 Here This Alexa 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.