May
14
2019
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Beyond costs, what else can we do to make housing affordable?

This week on Extra Crunch, I am exploring innovations in inclusive housing, looking at how 200+ companies are creating more access and affordability. Yesterday, I focused on startups trying to lower the costs of housing, from property acquisition to management and operations.

Today, I want to focus on innovations that improve housing inclusion more generally, such as efforts to pair housing with transit, small business creation, and mental rehabilitation. These include social impact-focused interventions, interventions that increase income and mobility, and ecosystem-builders in housing innovation.

Nonprofits and social enterprises lead many of these innovations. Yet because these areas are perceived to be not as lucrative, fewer technologists and other professionals have entered them. New business models and technologies have the opportunity to scale many of these alternative institutions — and create tremendous social value. Social impact is increasingly important to millennials, with brands like Patagonia having created loyal fan bases through purpose-driven leadership.

While each of these sections could be their own market map, this overall market map serves as an initial guide to each of these spaces.

Social impact innovations

These innovations address:

Apr
10
2019
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The right way to do AI in security

Artificial intelligence applied to information security can engender images of a benevolent Skynet, sagely analyzing more data than imaginable and making decisions at lightspeed, saving organizations from devastating attacks. In such a world, humans are barely needed to run security programs, their jobs largely automated out of existence, relegating them to a role as the button-pusher on particularly critical changes proposed by the otherwise omnipotent AI.

Such a vision is still in the realm of science fiction. AI in information security is more like an eager, callow puppy attempting to learn new tricks – minus the disappointment written on their faces when they consistently fail. No one’s job is in danger of being replaced by security AI; if anything, a larger staff is required to ensure security AI stays firmly leashed.

Arguably, AI’s highest use case currently is to add futuristic sheen to traditional security tools, rebranding timeworn approaches as trailblazing sorcery that will revolutionize enterprise cybersecurity as we know it. The current hype cycle for AI appears to be the roaring, ferocious crest at the end of a decade that began with bubbly excitement around the promise of “big data” in information security.

But what lies beneath the marketing gloss and quixotic lust for an AI revolution in security? How did AL ascend to supplant the lustrous zest around machine learning (“ML”) that dominated headlines in recent years? Where is there true potential to enrich information security strategy for the better – and where is it simply an entrancing distraction from more useful goals? And, naturally, how will attackers plot to circumvent security AI to continue their nefarious schemes?

How did AI grow out of this stony rubbish?

The year AI debuted as the “It Girl” in information security was 2017. The year prior, MIT completed their study showing “human-in-the-loop” AI out-performed AI and humans individually in attack detection. Likewise, DARPA conducted the Cyber Grand Challenge, a battle testing AI systems’ offensive and defensive capabilities. Until this point, security AI was imprisoned in the contrived halls of academia and government. Yet, the history of two vendors exhibits how enthusiasm surrounding security AI was driven more by growth marketing than user needs.

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