Strata SF day 1 Highlights: from Edge to AI, scoring AI projects, cyberconflict, cryptography

Probably not, it’s a hype rocket.

We can’t afford to be wobbly, touchy, feely in our definitions.

We need a rubric to define a project’s “AI Score” to decide if we should do it or not.

An AI score should do two things:He shared the following AI score matrix (developed by Florian Douetteau, CEO, Dataiku):Jed shared scoring of some examples:The moral of the talk was companies should build their foundations and then decide whether and how they would benefit from the types of technologies to register high AI score before climbing into the hype rocket.

David Sanger, Data Journalist, The New York Times gave a compelling keynote onCyberconflict: A new era of war, sabotage, and fear.

He explained how the rise of cyberweapons has transformed geopolitics since the invention of the atomic bomb.

He shared an iconic image, which shows Watergate filing cabinet next to the recently hacked DNC server.

A filing cabinet broken into in 1972 as part of the Watergate burglary sits beside a computer server that Russian hackers breached during the 2016 presidential campaign at the Democratic National Committee’s headquarters in WashingtonTalking about state of cyber attacks and lack of an effective approach to combat them he emphasized that, “We know the delivery vehicle, but we don’t know the warhead”.

In this new world whoever owns the network owns society.

Today, cyber attacks have become pervasive – the primary way that states exercise power without triggering war.

For many years, we have investigated technological solutions for it.

Now, we recognize that, like terrorism and climate change, this is a problem we’ll have to manage for decades.

There are 4 ways nations use cyber:He concluded the talk stating that the problem is about to get worse as IoT devices are increasing multi-fold.

Shafi Goldwasser, Professor, UC Berkeley, and Co-founder & Chief Scientist, Duality Technologies gave a thought-provoking keynote on AI and Cryptography: challenges and opportunities.

She emphasized that Cryptography can play a key role in enabling “safe” machine learning.

There is a shift of power from human decision making to machine-led decision making through AI/ML.

In the absence of proper safety and security measures this shift makes us more vulnerable and unpredictable.

In order to prevent “power abuse” via Machine Learning, we need to ensure that all data models work in accordance with privacy policies and cannot be maliciously tampered for profit or control.

Cryptography has recently been focused on privacy and correctness of computation (rather than the legacy focus on communication).

In the context of ML, a few years back, the prevalent approach was to integrate cryptographic building blocks (Homomorphic Encryption, Garbled Circuits) with ML-based classifiers to ensure privacy and security.

In recent years, as the focus has moved to deep neural networks, it has become more challenging to preserve privacy as its harder to work on encrypted data without losing performance (mostly because of non-linear activation functions).

It’s also hard to track the usage of data, so that the institutions sharing data can keep control over how that data is being used.

Particularly, how can they know if someone used their training data without a proper “privacy preserving” mechanism.

One way to trace the unauthorized use of a data model is through Watermark DNN models by training the network to accept watermarks (planted adversarial examples).

She concluded with the assertion that there is a real opportunity for developing new cryptography motivated by machine learning, and new machine learning motivated by cryptography.

Kurt Brown, Director, Data Platform, Netflix gave an interesting session onThe journey toward a self-service data platform at Netflix.

He explained how Netflix is working toward an easier, “self-service” data platform without sacrificing any enabling capabilities.

He started by sharing key characteristics of a “self-service” platform:During last 10 years, the Netflix data platform has also evolved to handle 100 thousand times more events (~10s millions -> ~1+ trillion) with a number of technology/products being used now.

Meanwhile, the underlying data has increased from 34 TB to 100+ PB.

He shared following principles/approaches for different components used to create “self-service” platform:He also shared quote by Phil Karlton – “There are only two hard things in Computer Science: cache invalidation & naming things.

” He mentioned folks should keep in mind that clear is better than clever when trying to name projects.

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