By David Dittman, Wall Street Daily
The intersection of ride-sharing algorithms, self-driving cars, and artificial intelligence is a great metaphor for the food chain. There’s always a bigger fish.
One thing’s for sure: Art will suffer without New York City’s taxi fleet.
Imagine a world without Taxi Driver. “You talkin’ to me?”
And Taxi is at least one of the best 50 sitcoms in U.S. television history. Ever see the episode where Alex’s dog dies?
But, “progress,” and art is no enemy of that.
So now we know — or think we know — that “3,000 four-passenger cars could serve 98% of taxi demand in New York City, with an average wait time of only 2.7 minutes.”
That’s the major finding of a new algorithm developed by a team at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).
It’s enough to basically kill NYC’s more than 13,000-strong taxi fleet.
But it would also virtually eliminate road congestion in Gotham, and save billions of dollars by reducing fuel costs and cutting time spent in traffic.
So it’s also, basically, one big validation of/advertisement for ride-sharing services Uber and Lyft, which are working on ways to get strangers to use their respective services — together.
UberPool and Lyft Line offer users the opportunity to get where they need to go for a reduced fare.
It’s next-level “gig economy” stuff.
The next next level for Uber and Lyft would eliminate the “gig” part — unless you’re an AI.
So now we know — or think we know — that “3,000 four-passenger cars could serve 98% of taxi demand in New York City, with an average wait time of only 2.7 minutes.” Last week’s Computer Electronics Show in Las Vegas highlighted a lot of future tech, including 5G wireless connectivity, a bendable lithium-ion battery for internet of things (IoT) devices, and ultrathin “wallpaper” OLED TVs.
There was also some IoT stuff that’s probably a little too smart for its own good, including a hairbrush, an insole, and a toaster.
“Smart,” however, is the major buzzword. And these days, nothing’s “smarter” than the combination of artificial intelligence and autonomous vehicles.
Right now we’re working with Level 2 (“Partial Automation”) and Level 3 (“Conditional Automation”) self-driving tech, according to SAE International’s new standards.
These are the types of systems that result in first-timers experiencing “harrowing as hell” self-driving car rides, or that require not one but two humans — “one ready to grab the wheel and the other on the lookout for pedestrians” — to supervise them.
(As FT Alphaville’s Kadhim Shubber notes after citing Bloomberg, “On the one hand, going from one driver to two doesn’t seem like progress. On the other hand, at least they’re employees.”)
Uber’s system, in particular, includes “a flaw in the programming that advocates feared could have deadly consequences for cyclists,” says the MIT lab.
Fixes are on the way.
They better be: According to Uber co-founder and CEO Travis Kalanick, developing an autonomous vehicle “is basically existential for us.”
Among the 500 or so auto tech companies in attendance at CES 2017, perhaps none made more of an impression than Nvidia Corp. (NVDA), whose CEO, Jen-Hsun Huang, described a self-driving car that his company is developing in partnership with Audi AG (AUDVF).
According to Audi of America CEO Scott Keogh, “We’re talking highly automated cars, operating in numerous conditions, in 2020.”
As IEEE Spectrum’s Philip E. Ross reports, “A prototype based on Audi’s Q7 car was, as he spoke, driving itself around the lot beside the convention center, he added.”
According to Audi of America CEO Scott Keogh, “We’re talking highly automated cars, operating in numerous conditions, in 2020.”
“This implies,” writes Ross, that “the Audi-Nvidia car will have ‘Level 4‘ capability, needing no human being to supervise it or take the wheel on short notice, at least not under ‘numerous’ road conditions. So maybe it won’t do cross-country moose chases in snowy climes.”
Tesla Motors Inc. (TSLA), among others, is competing in the “truly autonomous” space too, and it has more than 1.3 billion miles of data from its Autopilot system, built into every one of the cars it’s produced since October 2014, to inform its tech.
What Nvidia and Audi have done, as Ross notes, is put a hard-and-fast deadline on their project.
By 2020, “the computational muscle” of Nvidia’s graphics processing units (GPUs), developed over decades for gaming applications, will flower in its Xavier system.
Writes Ross:
“[Xavier] has eight high-end CPU cores, 512 of our next-gen GPUs,” Huang said. “It has the performance of a high-end PC shrunk onto a tiny chip, [with] teraflop operation, at just 30 watts.” By teraflop, he meant 30 of them: 30 trillion operations per second, 15 times as much as the 2015 machine could handle.
That power is used in deep learning, the software technique that has transformed pattern recognition and other applications in the past three years. Deep learning uses a hierarchy of processing layers that make sense of a mass of data by organizing it into progressively more meaningful chunks.
For instance, it might begin in the lowest layer of processing by tracing a line of pixels to infer an edge. It might proceed up to the next layer up by combining edges to construct features, like a nose or an eyebrow. In the next higher layer, it might notice a face, and in a still higher one, it might compare that face to a database of faces to identify a person. Presto, and you have facial recognition, a long-standing bugbear of AI.
And if you can recognize faces, why not do the same for cars, sign posts, roadsides and pedestrians?
So self-driving cars could kill Uber and Lyft.
Ride-sharing apps like UberPool and Lyft Line could theoretically kill New York City’s taxi service (No more Travis Bickle, no more “Wizard.” No more Alex Reiger, no more Jim Ignatowski — which is not, in fact, “Starchild” spelled backward).
But eliminating drivers destroys one half of Uber’s and Lyft’s “two-sided market,” as described by Ben Thompson of Stratechery.
So self-driving cars could kill Uber and Lyft.
But “progress.”
Upticks, Downticks
The Russell 2000 posted a total return of 19.41% for 2016. That’s the best year since 2013 (37.0%) and the seventh-best annual performance since 1997. The 20-year average total return is 9.91%. We’re off to a positive start through the first four trading sessions of 2017, with the main small-cap benchmark up 0.7%.
Congress is back in session.
Congress is going to make a run at passing “Audit the Fed” legislation, according to The Hill, helping us shed the myth that the Federal Reserve is in any way “independent” of political influence. There’s a term for what ails the Fed: “Regulatory capture,” which describes the central bank’s political relationship with the banks for which it acts as the bank and is supposed to supervise.
According to the U.S. Energy Information Administration, crude oil inventories declined by 7.1 million barrels during the week ended December 30, 2016, exceeding a consensus forecast of a 2.2 million barrel decrease. As Reuters reports, “Some of the drawdown, analysts said, is attributable to year-end tax considerations, as refiners use up crude that is on hand to avoid facing higher taxes for larger inventories.”
Final data sets for 2016 suggest the U.S. economy, as reflected by employment trends, remains on decent footing. The Department of Labor reported that initial claims for unemployment insurance declined by 28,000, to a seasonally adjusted 235,000 for the week ended December 31, 2016. That’s actually an all-time low for weekly initial claims. Meanwhile, the Bureau of Labor Statistics reported that the economy added 156,000 jobs in December. Hourly wages grew by 2.9% in 2016, the fastest pace in seven years. Unemployment ticked up to 4.7% from 4.6%, as more people re-entered the labor force.
President-elect Trump owes a lot — literally — to Wall Street. According to analysis conducted by The Wall Street Journal, “more than 150 institutions” hold debt of businesses connected to Donald Trump totaling “more than $1 billion.”
As Eddy Elfenbein of Crossing Wall Street reports, the S&P 500 posted its 27th consecutive quarter of dividend growth during the final three months of 2016. The group increased its collective payout by 5.95%, the fastest growth rate of the year.
Yes, within the prevailing parlance, this is a “hate crime.”
“Remittances to Mexico spike in anticipation of Donald Trump’s wall,” notes The Economist’s Graphic Detail team.
On one hand, David Byrne seems like a d*ck for not getting back together with Jerry Harrison, Chris Frantz and Tina Weymouth. On the other hand, it’s refreshing that he doesn’t want to simply cash in on some nostalgia tour, like so many other rock ‘n’ roll icons. But ultimately, it sounds like there’ll still be no new Talking Heads music or tour in the near future.
Courtesy of David Dittman. Editorial Director, Wall Street Daily (More from Wall Street Daily Here)
The views and opinions expressed herein are the author's own, and do not necessarily reflect those of EconMatters.
© EconMatters All Rights Reserved | Facebook | Twitter | YouTube | Email Digest | Kindle
The intersection of ride-sharing algorithms, self-driving cars, and artificial intelligence is a great metaphor for the food chain. There’s always a bigger fish.
One thing’s for sure: Art will suffer without New York City’s taxi fleet.
Imagine a world without Taxi Driver. “You talkin’ to me?”
And Taxi is at least one of the best 50 sitcoms in U.S. television history. Ever see the episode where Alex’s dog dies?
But, “progress,” and art is no enemy of that.
So now we know — or think we know — that “3,000 four-passenger cars could serve 98% of taxi demand in New York City, with an average wait time of only 2.7 minutes.”
That’s the major finding of a new algorithm developed by a team at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).
It’s enough to basically kill NYC’s more than 13,000-strong taxi fleet.
But it would also virtually eliminate road congestion in Gotham, and save billions of dollars by reducing fuel costs and cutting time spent in traffic.
So it’s also, basically, one big validation of/advertisement for ride-sharing services Uber and Lyft, which are working on ways to get strangers to use their respective services — together.
UberPool and Lyft Line offer users the opportunity to get where they need to go for a reduced fare.
It’s next-level “gig economy” stuff.
The next next level for Uber and Lyft would eliminate the “gig” part — unless you’re an AI.
So now we know — or think we know — that “3,000 four-passenger cars could serve 98% of taxi demand in New York City, with an average wait time of only 2.7 minutes.” Last week’s Computer Electronics Show in Las Vegas highlighted a lot of future tech, including 5G wireless connectivity, a bendable lithium-ion battery for internet of things (IoT) devices, and ultrathin “wallpaper” OLED TVs.
There was also some IoT stuff that’s probably a little too smart for its own good, including a hairbrush, an insole, and a toaster.
“Smart,” however, is the major buzzword. And these days, nothing’s “smarter” than the combination of artificial intelligence and autonomous vehicles.
Right now we’re working with Level 2 (“Partial Automation”) and Level 3 (“Conditional Automation”) self-driving tech, according to SAE International’s new standards.
These are the types of systems that result in first-timers experiencing “harrowing as hell” self-driving car rides, or that require not one but two humans — “one ready to grab the wheel and the other on the lookout for pedestrians” — to supervise them.
(As FT Alphaville’s Kadhim Shubber notes after citing Bloomberg, “On the one hand, going from one driver to two doesn’t seem like progress. On the other hand, at least they’re employees.”)
Uber’s system, in particular, includes “a flaw in the programming that advocates feared could have deadly consequences for cyclists,” says the MIT lab.
Fixes are on the way.
They better be: According to Uber co-founder and CEO Travis Kalanick, developing an autonomous vehicle “is basically existential for us.”
Among the 500 or so auto tech companies in attendance at CES 2017, perhaps none made more of an impression than Nvidia Corp. (NVDA), whose CEO, Jen-Hsun Huang, described a self-driving car that his company is developing in partnership with Audi AG (AUDVF).
According to Audi of America CEO Scott Keogh, “We’re talking highly automated cars, operating in numerous conditions, in 2020.”
As IEEE Spectrum’s Philip E. Ross reports, “A prototype based on Audi’s Q7 car was, as he spoke, driving itself around the lot beside the convention center, he added.”
According to Audi of America CEO Scott Keogh, “We’re talking highly automated cars, operating in numerous conditions, in 2020.”
“This implies,” writes Ross, that “the Audi-Nvidia car will have ‘Level 4‘ capability, needing no human being to supervise it or take the wheel on short notice, at least not under ‘numerous’ road conditions. So maybe it won’t do cross-country moose chases in snowy climes.”
Tesla Motors Inc. (TSLA), among others, is competing in the “truly autonomous” space too, and it has more than 1.3 billion miles of data from its Autopilot system, built into every one of the cars it’s produced since October 2014, to inform its tech.
What Nvidia and Audi have done, as Ross notes, is put a hard-and-fast deadline on their project.
By 2020, “the computational muscle” of Nvidia’s graphics processing units (GPUs), developed over decades for gaming applications, will flower in its Xavier system.
Writes Ross:
“[Xavier] has eight high-end CPU cores, 512 of our next-gen GPUs,” Huang said. “It has the performance of a high-end PC shrunk onto a tiny chip, [with] teraflop operation, at just 30 watts.” By teraflop, he meant 30 of them: 30 trillion operations per second, 15 times as much as the 2015 machine could handle.
That power is used in deep learning, the software technique that has transformed pattern recognition and other applications in the past three years. Deep learning uses a hierarchy of processing layers that make sense of a mass of data by organizing it into progressively more meaningful chunks.
For instance, it might begin in the lowest layer of processing by tracing a line of pixels to infer an edge. It might proceed up to the next layer up by combining edges to construct features, like a nose or an eyebrow. In the next higher layer, it might notice a face, and in a still higher one, it might compare that face to a database of faces to identify a person. Presto, and you have facial recognition, a long-standing bugbear of AI.
And if you can recognize faces, why not do the same for cars, sign posts, roadsides and pedestrians?
So self-driving cars could kill Uber and Lyft.
Ride-sharing apps like UberPool and Lyft Line could theoretically kill New York City’s taxi service (No more Travis Bickle, no more “Wizard.” No more Alex Reiger, no more Jim Ignatowski — which is not, in fact, “Starchild” spelled backward).
But eliminating drivers destroys one half of Uber’s and Lyft’s “two-sided market,” as described by Ben Thompson of Stratechery.
So self-driving cars could kill Uber and Lyft.
But “progress.”
Upticks, Downticks
The Russell 2000 posted a total return of 19.41% for 2016. That’s the best year since 2013 (37.0%) and the seventh-best annual performance since 1997. The 20-year average total return is 9.91%. We’re off to a positive start through the first four trading sessions of 2017, with the main small-cap benchmark up 0.7%.
Congress is back in session.
Congress is going to make a run at passing “Audit the Fed” legislation, according to The Hill, helping us shed the myth that the Federal Reserve is in any way “independent” of political influence. There’s a term for what ails the Fed: “Regulatory capture,” which describes the central bank’s political relationship with the banks for which it acts as the bank and is supposed to supervise.
According to the U.S. Energy Information Administration, crude oil inventories declined by 7.1 million barrels during the week ended December 30, 2016, exceeding a consensus forecast of a 2.2 million barrel decrease. As Reuters reports, “Some of the drawdown, analysts said, is attributable to year-end tax considerations, as refiners use up crude that is on hand to avoid facing higher taxes for larger inventories.”
Final data sets for 2016 suggest the U.S. economy, as reflected by employment trends, remains on decent footing. The Department of Labor reported that initial claims for unemployment insurance declined by 28,000, to a seasonally adjusted 235,000 for the week ended December 31, 2016. That’s actually an all-time low for weekly initial claims. Meanwhile, the Bureau of Labor Statistics reported that the economy added 156,000 jobs in December. Hourly wages grew by 2.9% in 2016, the fastest pace in seven years. Unemployment ticked up to 4.7% from 4.6%, as more people re-entered the labor force.
President-elect Trump owes a lot — literally — to Wall Street. According to analysis conducted by The Wall Street Journal, “more than 150 institutions” hold debt of businesses connected to Donald Trump totaling “more than $1 billion.”
As Eddy Elfenbein of Crossing Wall Street reports, the S&P 500 posted its 27th consecutive quarter of dividend growth during the final three months of 2016. The group increased its collective payout by 5.95%, the fastest growth rate of the year.
Yes, within the prevailing parlance, this is a “hate crime.”
“Remittances to Mexico spike in anticipation of Donald Trump’s wall,” notes The Economist’s Graphic Detail team.
On one hand, David Byrne seems like a d*ck for not getting back together with Jerry Harrison, Chris Frantz and Tina Weymouth. On the other hand, it’s refreshing that he doesn’t want to simply cash in on some nostalgia tour, like so many other rock ‘n’ roll icons. But ultimately, it sounds like there’ll still be no new Talking Heads music or tour in the near future.
Courtesy of David Dittman. Editorial Director, Wall Street Daily (More from Wall Street Daily Here)
The views and opinions expressed herein are the author's own, and do not necessarily reflect those of EconMatters.
© EconMatters All Rights Reserved | Facebook | Twitter | YouTube | Email Digest | Kindle
