Zetta Bytes: Privacy Preserving Machine Learning
FEATURED ANNOUNCEMENT — Aptology: The Science of Fit
We’re elated to welcome Aptology to the Zetta family. Aptology is a talent management platform that uses behavioral traits based in organizational psychology to help sales organizations hire, develop and retain for success. Read More
FEATURED PODCAST — Capital Allocators: Investing in AI
Ash recently spoke with Capital Allocators about investing in AI. Episode highlights include details on our research process, how to calculate an investment fund size, the state of AI tech today, how Zetta utilizes data driven sourcing and how we help companies with go to market strategies. Listen
We’re diving into ten key areas for innovation as a part of a monthly series on AI-enabled applications and the technological breakthroughs needed to support them. Thus far, we’ve written about six areas of innovation and today, we’re offering a seventh: privacy preserving machine learning.
In the age of nearly constant data breaches, data privacy has become a defining issue for tech regulators, leading to sweeping laws like GDPR in Europe and San Francisco’s recent decision to ban facial recognition software. This poses a particular challenge for machine learning where user data is collected and pooled in the cloud in order to train intelligent systems. However, a new class of privacy-preserving machine learning architectures such as ‘Federated Learning’ could hold the key to maintaining user privacy while allowing models to train on real world data.
In federated learning, a model is downloaded to an edge device — like a mobile phone — where it runs locally and sends a periodic summary of its learnings as an encrypted message to the cloud. There, thousands of individual summaries are averaged together to update the model without user data ever leaving the device. Originally developed at Google in 2017 to power keyboard recommendations, federated learning since inspired a range of new techniques and applications.
In particular, privacy-preserving ML is catching on in healthcare where it’s being used to read EEGs, develop drugs and predict cardiac arrest through smart speakers at home. Other groups, concerned with device-level vulnerabilities, are combining federated learning with encrypted hardware and are working with major healthcare systems like Stanford.
It’s still early days for the technology — with plenty of performance limitations and security vulnerabilities to work out — but we expect to see many more applications of privacy-preserving machine learning in the coming decade.
Warmest regards, Mark, Jocelyn, Ash, Dylan, James and Todd
READING ABOUT AI — AI Must Make Money
Alex Dalyac, Co-founder and CEO of Tractable, argues the importance of applying AI to real, commercial ends to ensure its sustainability. He believes chasing investments in the most recent academic successes, often without foreseeable monetization plans, could lead to an ‘AI Winter’. How to stop the looming “AI winter”
Nathan Benaich explores how creating a full-stack ML company will set you ahead of those building single ML tool companies. Though more operationally complex he points to five key areas where being a full-stack company creates an advantage: high-risk experimentation, overcoming adoption inertia, market maturity, defensibility and the value attribution problem. Machine learning: go full stack or go home. See also our article from 2016 on vertically integrated machine learning companies.
Verusen Founder and CEO, Paul Noble, breaks down how AI-enabled platforms can be used to cleanse data in real-time, removing the burden and risks of making business decisions with dirty data in The Death of the Data Cleanse.
HIGHLIGHTS — Focal Systems Expands Partnership
Wakefern Food Corp. is expanding their partnership with Focal Systems, integrating Focal in 50 stores. “Focal Systems’ out-of-stock detection through computer vision and artificial intelligence enabled us to automatically identify shelf gaps,” said Cheryl Williams, CIO of Wakefern.
Alex MacCaw, Co-founder and CEO of Clearbit, sits down with Harry Stebbings for his podcast Twenty Minute VC. They discuss a variety of management topics including delivering and absorbing feedback, and how to lead with vulnerability.
OPPORTUNITIES — Looking to Work on AI?
Head of Revenue Operations - Clearbit (San Francisco, CA)
Head of Business Development, UK - Tractable (London, England)
Director, Partnerships and Alliances - Marketing Evolution (New York, NY)
VP of Coaching & Community - Myia Health (San Francisco, CA)
Product Marketing Manager - Opsani (Redwood City, CA)
Senior Software Engineer, Backend - Lilt (San Francisco, CA)
- AI-First Companies: Flipped
- The AI-first startup playbook
- AI adoption is limited by incurred risk, not potential benefit
- AI-First Companies
- Data rights are the new IP rights
- The Intelligent Enterprise Stack
- Beating Behemoths
- Don't sell your data
- Framework to grasp industrial analytics opportunities
- Beyond systems of record
- Positioning a machine learning company
- The intelligence era and the virtuous loop
- Vertical beats horizontal in machine learning
- Zetta Bytes AMA: Questions to ask about pricing
- Zetta Bytes AMA: Hiring a CTO
- AI Entrepreneurs: 3 things to check before you pursue a customer
- There are more data scientists than you think
- Stages of funding in the intelligence era
- Could data costs kill your AI startup?
- Measuring AI startups by the right yardstick
- Finding the Goldilocks zone for applied AI
- Data is not the new oil
- Machine Learning in the Deployment Age
- Innovations of the Next Decade
- Zetta Bytes: Privacy Preserving Machine Learning
- GDPR panic may spur data and AI innovation
- Computing like a human
- New opportunities for hardware acceleration in data analytics
- Hardware acceleration in data analytics
- Skan: Visualizing the Future (of Work)
- Announcing Zetta Fund III, a $180M fund for AI-first companies
- Aptology: The Science of Fit
- Verusen (The intelligent supply chain)
- Opsani: What's Next? Continuous Optimization
- Promethium: Starting a Fire
- Lilt: Translation for all