ARCHITECHT Daily: Can black-box AI outrun the reality of lawyers, regulators and real-life?

The thing with computers is that they're not actually intelligent. I think most everyone can agree th
ARCHITECHT
ARCHITECHT Daily: Can black-box AI outrun the reality of lawyers, regulators and real-life?
By ARCHITECHT • Issue #61
The thing with computers is that they’re not actually intelligent. I think most everyone can agree there’s not a robot in existence that, even equipped with the most advanced AI models around, could navigate a day in the real world—at least not anytime in the foreseeable future. The walking, the driving, the judgments, the interactions, the reading of people, the unwritten rules, the actual rules, the times when it’s probably OK to bend the rules … our brains and our bodies do a lot.
I say all of this to introduce two thought-provoking items from last week:
On the latter, I disagree with the notion that our computers understand the world. But I agree that they’re able to identify patterns and connections beyond the scale of what humans can presently do, at least in an even remotely comparable timeframe. And I think it’s fair to suggest that the next big push in computer science is to try cracking the black boxes of these models so that we can get a better of sense of why they do what they do. 
Better understanding AI models could actually prove very useful to scientists, but let’s focus on the economy. As the Techonomy post argues, while AI might replace some jobs outright, there are many places where it will just serve as a very powerful tool. The better that people understand machines, the better we’ll be able to work alongside them and use them to augment our judgment. The better we’ll be able to effectively apply AI to new areas and even integrate AI into the social fabric.
I would also argue that AI left as a black box runs the risk of running into a combination political-legal-regulatory wall at some point in the not-too-distant future—a wall that could fundamentally effect its continued adoption and advancement. The buck has to stop somewhere, and it’s not going to be at the algorithm. Whether we’re talking about cars, credit scores, medical diagnosis, business decisions or even just Amazon Alexa, people and organizations are going to be held responsible when something goes wrong. 
Mitigating that risk involves understanding what’s going on inside your AI system so you know when and how to use it safely and effectively. And so you know how to answer when someone wielding a congressional summons or a civil complaint comes calling. Relying on machines’ “intelligence” might be sufficient for voice search and chatbots, but I can’t imagine it will suffice when lives and, frankly, money are on the line.

Sponsor: Cloudera
Artificial intelligence
That’s its plan, at least, and it sounds pretty confident. That would be a very big deal, even if a viable production system is still years away.
Amazon’s James Hamilton is on a roll. He suggests that Google (which he only mentions by name once) probably has moved on to second-gen TPUs, and that AWS does a lot of custom hardware, too.
There have been lots of these stories written by now, but I always love LeCun’s combination of realism and optimism. And the understanding that product design is a whole other beast from research.
There’s a sound argument to be made that, awesomeness aside, Facebook needs AI because text is on the way out. You can’t sell ads against photos or videos unless you know what’s happening in them.
The moral of the story here is we all need to be smart enough to realize that Musk et al are selling an idea behind a business, and then hope they can accomplish overcome lots of obstacles to achieve part of that vision.
Getting more people developing voice and text applications using Lex is obviously good for Amazon, but it might be even better for Slack, who’s a partner on this. Meaningful “ChatOps” applications will make Slack that much more sticky.
VentureBeat has a 3-part series profiling startups participating in Nvidia’s Inception competition. If you’re interested in finding out what 14 of them are up to, check out these posts:
Cloud and infrastructure
The part that sticks out is that it appears OpenStack has a smaller percentage of promotors and a larger percentage of detractors and passive users. That’s not an ideal trend.
This battle over water rights highlights my point above about AI. Tech can move fast for a while, but political, legal and civil realities eventually catch up. 
I love this type of post, where startups talk about their tech stacks and how they’re maximizing the components. Others should pay attention, because there’s still a lot to learn about cloud architectures.
This is where the world is headed. The challenge for startups, of course, is convincing customers to go with them rather than what cloud providers will ultimately offer.
A good rundown of some challenges and opportunities for multi-cloud architectures. I think many CIOs just like the idea of knowing they could move if they had to.
redmonk.com  •  Share
The company is reportedly tripling the size of its technology hub in NYC. There are lots of angle from which to view this type of expansion, one of which is that it’s part of the reason on-prem software still exists.
Sponsor: Marshal.io
All things data
A nice analysis of what these terms mean, especially to the people who do them for a living. This is one of those discussions that got hot, then people moved onto the next thing, but was never really resolved and still matters if we really think that data matters.
medium.com  •  Share
This is a toy project, but it’s not crazy to think that a new scheduler for big data workloads could actually work. 
Out at the NSA! Feuding with the CIA! Drama aside, what’s remarkable about Palantir is how it exists at the nexus of new and old (both in terms of tech and business), and of humans judgment and data. 
sfist.com  •  Share
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