I tried Sanctum's local AI app, and it's exactly what I needed to keep my data private - Related to journey, creating, pollution, plastic-eating, exactly
5 Essential Tips Learned from My Data Science Journey
5 Essential Tips Learned from My Data Science Journey.
Ten years ago, I embarked on my journey in the field of data science. I clearly remember the beginning of this adventure — my thoughts, my emotions, and the excitement that came with stepping into a new territory.
It all started with a consultancy position at a large insurance corporation.
I was part of the Research and Development team, where our goals were — quite frankly — undefined. We craved data within the business to experiment, try something different, and innovate.
Neither I nor the corporation was truly ready for this. But looking back, I see how essential that step was. The corporation has since grown into a tech leader, and for me, those years were a playground of learning — exploring, studying, and mastering the tools and technologies of the time.
When I remember myself in that context, I see a young professional Brimming with energy and a desire to innovate. I look back on all my daily failures with great affection and a hint of nostalgia because they shaped the professional I am today.
If I could speak to that younger version of myself, I would offer some advice to ease his journey. Interestingly, they would be the same advice I now give to my….
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Researchers are on a quest to develop enzymes that can break down plastics so they can be 100% recycled.
The world produces about 400 million tonnes of plastic waste each year. Much of it ends up in landfills, and a significant portion is polluting the world’s oceans. Yet even when plastic is recycled, the process degrades the material, limiting its future recyclability.
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I tried Sanctum's local AI app, and it's exactly what I needed to keep my data private
Locally installed AI is the way to go, especially if privacy is significant to you. Instead of sending your queries to a third party, you can keep them private, so no one else has access to your questions or the generated answers. When you run a query with the locally installed Sanctum, your data is encrypted, secure, and never leaves the app.
Also: How I made Perplexity AI the default search engine in my browser (and why you should too).
I've been using locally installed AI for a while now (mostly Ollama with the addition of the Msty front-end) and have found it to be quite useful.
Thousands of GGUF models on Hugging Face.
You can choose your LLM (from Gemma, Llama, Mistral, and more).
You can choose if any information is shared.
Real-time information on system resources in use.
Sanctum could easily become instrumental for research on any given subject, especially when you don't want your queries to go beyond your local machine. No matter what you're researching, Sanctum can help.
Also: How to install Perplexity AI's app on Linux (I found an easier way).
Let me walk you through the process of getting Sanctum up and running. It's quite easy.
What you'll need: The only things you'll need are either a MacOS or Windows computer (Linux version coming soon) and a network connection. I'll demonstrate the installation on MacOS. If you're using a Windows computer, the installation is as simple as installing any other application.
At this point, you can start using Sanctum as a locally installed AI tool to assist you with all (or some) of your research.
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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 From Data Essential 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.
computer vision intermediate
platform intermediate
generative AI intermediate
API beginner
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