These three paragraphs, about IBM's failure to deliver on Watson Health's soaring ambitions, hold several important lessons relevant to AI and AVs. Though very different, health care and driving are both promising but deeply challenging areas for AI.…
First: lots of data doesn't solve every problem. Particularly when the costs of failure are high, as they are in health care and driving, achieving the necessary level of consistent accuracy is very difficult. As the level of accuracy/reliability rises, the challenge deepens.
Second: The "customization problem" that Ng refers to here is one reason that most serious AV developers are pursuing SAE Level 4 (geofenced) autonomy. Limiting the domain and tailoring systems to it is key to achieving and validating safety-critical levels of AI performance.
Third: the successful uses of AI in these kinds of applications solve "narrow, discrete problems." This is why the dream of a "general solution for autonomous driving" is relegated to long-term research and scammers. Serious developers use AI in focused, narrow applications.
This is why AVs are now widely considered "a systems engineering problem" and not "an AI problem": AI is a critical piece, but it must be thoughtfully applied to the problems where it's necessary, and its chances of success must be maximized by the rest of the system.
AI is complex stuff, but at the end of the day we're talking about putting human lives in the hands of probabilistic systems that consistently follow a 90-90 rule and which can fail in opaque ways. Limiting its use to the most necessary tasks enjoys a simple logic.

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More from @Tweetermeyer

21 Feb
In 2015, I stumbled onto my first real Tesla story when I found they'd rather hook up Superchargers to diesel generators than make their battery swap station available. I asked their comms department about their emissions claims and the answer shocked me.…
Tesla keeps a running count of its "carbon impact" at How, I asked, did they calculate this number? The answer: they simply assumed that every vehicle had zero direct or indirect emissions. But did they buy zero-emission power for all Superchargers? No.
Go back and look at the claims Tesla has made over the years and you'll find that it has always said/implied that Superchargers were zero-emission. They have even claimed, repeatedly, that they would be 100% solar, off-grid and "zombie apocalypse-proof."…
Read 7 tweets
4 Feb
Here's a fun mental exercise: if you did want to run a self-driving car scam starting in 2016, how would you have made it different from Tesla's "Full Self-Driving"?

It's genuinely difficult to think of a better way to pull that off than exactly what Tesla has done.
Start with the most important consideration: the marks. Do you target VCs? Sure, they are hype-susceptible and fallible but they also have access to experts and lawyers. No, you would target the public, who know nothing.

Incidentally, only one AV developer takes consumer cash.
What's the pitch? Well, everyone else is pitching Level 4 robotaxis because the tech is pricy, so not that. Sell the dream: SAE Level 5 autonomy, but in a car that you can afford to own yourself. Better yet, it can be a robotaxi that works for you and pays for itself!
Read 12 tweets
3 Feb
This starts in five minutes, so you just have time to register and tune in. Really excited to learn more about the semifinalists in @USDOT's Inclusive Design Challenge!
Kathy Klinich of @UMTRI is explaining their work on a universal docking interface geometry for wheelchairs and an automated seatbelt system, aimed at allowing wheelchair users to use an autonomous vehicle without any human assistance. Such a cool and important project! 🦾🦾🦾
AV developer @May_Mobility's @tara_a_lanigan says accessibility is a core value for the company, which is why they teamed up with @UMTRI to pilot implementation of their wheelchair anchoring system in their autonomous shuttles as part of the USDOT Inclusive Design Challenge.
Read 8 tweets
2 Feb
I needed some comedy today, and boy did this do the trick
Musk doesn't materially address any of the meaningful manufacturing questions/issues and Munro quickly stops even hinting at them and lobs softballs, but apparently this comes across as a deep, substantive and focused talk about manufacturing to lots of folks🤷‍♂️
"The real obstacle to Full Self-Driving is state-to-state variations in road lane markings" sure is a take.
Read 7 tweets
2 Feb
Great job of spelling things out here by @Tim_Stevens. In my entirely personal opinion, showing this yoke publicly as a future vehicle option reeks of the sense of regulatory impunity Tesla has enjoyed in the US for years now.
Even where regulatory bodies are not particularly zealous or capable, most industries cultivate ~the appearance~ of deep respect for their regulator. That's certainly the norm in the auto industry. Breaking the rules is one thing, flouting the rules is very much another.
Musk likes to "pre-announce" things well before they're ready. New Tesla models. Self-Driving options. Funding rounds. LBOs. In 2009, he claimed DOE would soon disburse loans Tesla hadn't fully applied for. Here's a more recent and famous example 👇
Read 6 tweets
1 Feb
Wow, this is huge. AI and the cloud have more short-term transformative potential for automotive product development, manufacturing and supply chain than AVs. Interesting consumer-facing moves here too, with unique Android-based "digital experiences" and an app ecosystem.
In LUDICROUS I talk about the fact that an app ecosystem is more fundamental to the smartphone revolution than the hardware form factor or UI.

Here's Elon Musk telling Steve Jurvetson in 2009 that "we've got people like writing apps for the car". 🤷‍♂️

The first "smartphone on wheels" will not be the first car with a giant screen or a smartphone-inspired UI, but a car that has a robust and meaningful ecosystem of useful and popular third party apps. That opportunity is still out there!
Read 4 tweets

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