General Automation Thread

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Re: General Automation Thread

Post by bilateralrope » 2018-08-07 01:27am

Elheru Aran wrote:
2018-08-06 04:32pm
It does make one wonder how the strippers will fare in a cashless society... ;)
I saw the answer to that when I went to a strip club more than 10 years ago. You go to the bar and buy an amount of club-specific notes. You use those notes to tip the strippers.

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Re: General Automation Thread

Post by K. A. Pital » 2018-08-07 03:49am

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Re: General Automation Thread

Post by houser2112 » 2018-08-07 08:02am

FaxModem1 wrote:
2018-08-06 04:36pm
Elheru Aran wrote:
2018-08-06 04:32pm
It does make one wonder how the strippers will fare in a cashless society... ;)
Maybe tables will have Card readers to tip them at? Like at Chili's.
Will those tables print a receipt for you to slip in the garter? :)

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Re: General Automation Thread

Post by FaxModem1 » 2018-08-28 12:44pm

BBC News
Toyota to invest $500m in Uber in driverless car deal
27 August 2018
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Japanese carmaker Toyota is to invest $500m (£387m) in Uber and expand a partnership to jointly develop self-driving cars.

The firm said this would involve the "mass-production" of autonomous vehicles that would be deployed on Uber's ride sharing network.

It is being viewed as a way for both firms to catch up with rivals in the competitive driverless car market.

The deal also values Uber at some $72bn, despite its mounting losses.

That is up 15% since its last investment in May but matches a previous valuation in February.

According to a press release issued by the firms, self-driving technology from each company will be integrated into purpose-built Toyota vehicles.

Uber halts self-driving tests after death
Uber settles with Waymo on self-driving
The fleet will be based on Toyota's Sienna Minivan model with pilot trials beginning in 2021.

Shigeki Tomoyama, executive vice president of Toyota Motor Corporation, said: "This agreement and investment marks an important milestone in our transformation to a mobility company as we help provide a path for safe and secure expansion of mobility services like ride-sharing."

Image copyrightUBER
Image caption
Uber technology will be incorporated in Toyota vehicles
Both Toyota and Uber are seen as lagging behind in developing self-driving cars, as firms such as Waymo, owned by Alphabet, steam ahead.

Uber has also scaled back its self-driving trials after a fatal crash in Tempe, Arizona, in March, when a self-driving Uber SUV killed a pedestrian.

Since then, the ride-hailing giant has removed its autonomous cars from the road and closed its Arizona operations.

Analysis: Dave Lee, BBC North America technology reporter, San Francisco
Uber's troubled self-driving car efforts are in need of external help, and this deal with Toyota might provide that expertise. It's of course a terrific opportunity for Toyota, too.

It was reported earlier this month that Uber was sinking around $1m-$2m into its autonomy work every single day. The results of that effort have not been something to be proud of - one fatal crash, one very expensive lawsuit, and not a lot of self-driving compared to the leader in this sector, Waymo.

Sharing the burden, and R&D cost, will delight Uber's investors as it aims for its initial public offering next year.

Meanwhile, shares in Toyota spiked at reports of the deal. Not surprising. Many analysts think personal car ownership will drop dramatically when the self-driving, ride-sharing future is fully upon us - with major companies instead purchasing enormous fleets of vehicles. Toyota, then, may have just secured its biggest ever customer.

The deal extends an existing relationship with Toyota, and furthers Uber's strategy of developing autonomous driving technology through partnerships.

The US firm has also teamed up with Daimler, which hopes to own and operate its own self-driving cars on Uber's network.

On Monday, Uber said it planned to focus more on its electric scooter and bike business in future, and less on cars - despite the fact it could hurt profits.

Revenue from its taxi business is rising but the cost of expansion into new areas such as bike sharing and food delivery has meant losses have grown rapidly.
So, Toyota is starting to fund Uber in it's auto research. Are they buying in on the ground floor, or getting swindled?
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Re: General Automation Thread

Post by bilateralrope » 2018-08-28 01:38pm

Uber. The company that decided that their autonomous cars wouldn't be allowed to pull an emergency stop because it would make them look bad with how often their cars wanted to do an emergency stop. Until they killed someone because their car wasn't allowed to make an emergency stop.

Does Toyota have enough understanding of autonomous car technology to make sure Uber doesn't have any other surprises ?
Many analysts think personal car ownership will drop dramatically when the self-driving, ride-sharing future is fully upon us
How did they come to that conclusion ?
The only explanation I've seen is that self-driving cars + a ride sharing app is somehow going to be cheaper than people owning self-driving cars. They then point to Uber, a company that has never made a profit, to prove that ride sharing is cheaper.

No explanation of how the ride sharing vehicle, which will be doing a lot more travelling while empty, has a cheaper cost per mile travelled with a passenger.

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Re: General Automation Thread

Post by Stormin » 2018-08-30 07:11pm

bilateralrope wrote:
2018-08-28 01:38pm
The only explanation I've seen is that self-driving cars + a ride sharing app is somehow going to be cheaper than people owning self-driving cars. They then point to Uber, a company that has never made a profit, to prove that ride sharing is cheaper.

No explanation of how the ride sharing vehicle, which will be doing a lot more travelling while empty, has a cheaper cost per mile travelled with a passenger.
It'll be cheaper for any individual user. Daily cost for the vehicle will be spread between dozens of people.

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Re: General Automation Thread

Post by bilateralrope » 2018-08-31 12:41am

Stormin wrote:
2018-08-30 07:11pm
bilateralrope wrote:
2018-08-28 01:38pm
The only explanation I've seen is that self-driving cars + a ride sharing app is somehow going to be cheaper than people owning self-driving cars. They then point to Uber, a company that has never made a profit, to prove that ride sharing is cheaper.

No explanation of how the ride sharing vehicle, which will be doing a lot more travelling while empty, has a cheaper cost per mile travelled with a passenger.
It'll be cheaper for any individual user. Daily cost for the vehicle will be spread between dozens of people.
Could I see the math behind that statement ?

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Re: General Automation Thread

Post by Stormin » 2018-08-31 02:59am

bilateralrope wrote:
2018-08-31 12:41am
Stormin wrote:
2018-08-30 07:11pm
bilateralrope wrote:
2018-08-28 01:38pm
The only explanation I've seen is that self-driving cars + a ride sharing app is somehow going to be cheaper than people owning self-driving cars. They then point to Uber, a company that has never made a profit, to prove that ride sharing is cheaper.

No explanation of how the ride sharing vehicle, which will be doing a lot more travelling while empty, has a cheaper cost per mile travelled with a passenger.
It'll be cheaper for any individual user. Daily cost for the vehicle will be spread between dozens of people.
Could I see the math behind that statement ?
Owned car = you pay 100% of all costs including maintenance, insurance, depreciation etc

Autocar uber style system = you pay for the time you are using it plus a profit margin. And there won't be a crazy high profit margin because it'll be automated systems almost all the way through, which means competition would have an easy time filling any places where demand is so high that excessive charges are viable.

Edge cases like people who have to spend 6+ hours a day in their vehicles might be different or those who live in their cars but if you are using them to 'disprove' my point you are just REEEing over the tiny minority.

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Re: General Automation Thread

Post by Surlethe » 2018-09-01 09:28am

Not only are fixed costs of ownership split over many people, there are broader social benefits too. Most cars nearly all of their time not moving and huge portions of cities are devoted to space for vehicle storage, cf http://oldurbanist.blogspot.com/2011/12 ... -area.html If the number of cars declines due to increased ridesharing, cities will reclaim parking space and reconfigure themselves to be cheaper and denser with concomitant benefits.
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Re: General Automation Thread

Post by bilateralrope » 2018-09-02 12:50am

Stormin wrote:
2018-08-31 02:59am
Owned car = you pay 100% of all costs including maintenance, insurance, depreciation etc
I accept that it's going to come down to how the fixed costs of the car compare with the running costs to decide which is cheaper. Without seeing numbers, I can't be sure which way it will go. Thing is, I've not seen any convincing comparisons. Nothing with any numbers. I've just seen things like:
- Electric cars are simpler than petrol cars (true). Therefore they will depreciate slower (possible). Therefore they will be too expensive to own (?).
- This group of young people are using Uber instead of owning their own cars. Therefore everyone will become just like them given time.
- Pointing to Uber as proof of how low costs could go. That could have been a convincing argument. Except that Uber is a company that has yet to make a profit, despite various illegal ways to reduce costs.
Edge cases like people who have to spend 6+ hours a day in their vehicles might be different or those who live in their cars but if you are using them to 'disprove' my point you are just REEEing over the tiny minority.
Part of the reason for my skepticism is that I've had people seriously claiming that even edge cases, like a plumbers van, will be replaced by rideshare vehicles.

The other part is that everyone is claiming that people will automatically move to the cheaper option. Completely ignoring the convenience factor.
Surlethe wrote:
2018-09-01 09:28am
Not only are fixed costs of ownership split over many people, there are broader social benefits too. Most cars nearly all of their time not moving and huge portions of cities are devoted to space for vehicle storage, cf http://oldurbanist.blogspot.com/2011/12 ... -area.html If the number of cars declines due to increased ridesharing, cities will reclaim parking space and reconfigure themselves to be cheaper and denser with concomitant benefits.
Yes, that's all true if ridesharing takes over. But I don't see how it applies to the argument of if ridesharing will take over.

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Re: General Automation Thread

Post by bilateralrope » 2018-09-03 08:49am

I'll admit that a lot of my skepticism over automated taxis replacing private vehicles comes from bias caused by really crappy arguments being the first I saw about them replacing private vehicles. Not from any serious thought on my part. So I gave it some thought:

By referring to them as ride-sharing, I was buying into the hype. We aren't talking about an Uber-like arrangement where their software gets you a ride in a car that Uber does not own. We are talking about cars owned by the company. So these are automated taxis not a "self-driving, ride-sharing future". As soon as I noticed that error in my thinking, another thing became obvious: Automated taxis are going to require a significant investment to buy enough vehicles to replace private cars.

The other significant thing that came to mind is that rush hour is going to be the major factor in determining if automated taxis replace private vehicles. If automated taxis can handle that, they can handle everything but the edge cases. I'm not sure how automated taxis will handle rush hour without putting a similar number of vehicles on the road as we currently have, which wipes out the economic advantage, but if you can convince me of that then seeing how they replace private vehicles elsewhere becomes trivial.

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Re: General Automation Thread

Post by bilateralrope » 2018-10-08 06:49pm

Fully driverless Waymo taxis are due out this year, alarming critics
Waymo, Google's self-driving car project, is planning to launch a driverless taxi service in the Phoenix area in the next three months. It won't be a pilot project or a publicity stunt, either. Waymo is planning to launch a public, commercial service—without anyone in the driver's seat.

And to date, Waymo's technology has gotten remarkably little oversight from government officials in either Phoenix or Washington, DC.

If a company wants to sell a new airplane or medical device, it must undergo an extensive process to prove to federal regulators that it's safe. Currently, there's no comparable requirement for self-driving cars. Federal and state laws allow Waymo to introduce fully self-driving cars onto public streets in Arizona without any formal approval process.

That's not an oversight. It represents a bipartisan consensus in Washington that strict regulation of self-driving cars would do more harm than good.

"If you think about what would be required for some government body to examine the design of a self-driving vehicle and decide if it's safe, that's a very difficult task," says Ed Felten, a Princeton computer scientist who advised the Obama White House on technology issues.

Under both Barack Obama and Donald Trump, the federal government has taken a hands-off approach to driverless car regulation. Instead of enacting new safety regulations for self-driving cars, Felten says, federal policies have tried "to make sure that vehicle safety regulations don't inadvertently make it more difficult to roll out self-driving vehicles."

Self-driving cars do need to comply with an existing set of safety regulations called the Federal Motor Vehicle Safety Standards. But that's not a big hurdle in practice. Waymo plans to address it by simply building its service using Chrysler Pacifica vans that are already FMVSS-compliant.

Meanwhile, Congress is considering legislation that would make it easier for companies to manufacture driverless vehicles that aren't fully FMVSS-compliant. This would allow GM to start making a car with no steering wheel as early as next year.

This hands-off regulatory approach drives some safety advocates crazy.

"I think it's stunning," says Cathy Chase, the head of the Advocates for Highway and Auto Safety, about the deregulatory trend.

Mary "Missy" Cummings, an engineering professor at Duke, agrees. "I don't think there should be any driverless cars on the road," she tells Ars. "I think it's unconscionable that no one is stipulating that testing needs to be done before they're put on the road."

But so far these advocates' demands have fallen on deaf ears. Partly that's because federal regulators don't want to slow the introduction of a technology that could save a lot of lives in the long run. Partly it's because they believe that liability concerns give companies a strong enough incentive to behave responsibly. And partly it's because no one is sure how to regulate self-driving cars effectively.

When it comes to driverless cars, "there's no consensus on what it means to be safe or how we go about proving that," says Bryant Walker Smith, a legal scholar at the University of South Carolina.

Other industries face stricter regulation

To get an idea for what a more robust regulatory framework might look like, it's helpful to look at how the federal government regulates other complex, safety-critical technologies. Cummings did just that in a recent paper that compares the regulation of airplanes, medical devices, and cars.

An aircraft manufacturer is expected to meet with the Federal Aviation Administration while an airplane is still on the drawing board. Together, they draw up a plan that includes "a project timeline, checklists for moving on to the next phases, means of compliance, testing plans, and other project management information," Cummings writes. The FAA stays involved throughout the development process, verifying that the agreed-upon tests are passed before allowing a new airplane onto the market.

While the FAA is involved in aircraft design from the outset, the Food and Drug Administration typically waits until a medical device is ready for testing on human patients before getting involved.

Prior to clinical trials for a first-in-class device, a device maker must submit detailed technical information to the FDA, including a "device description, drawings, components, specifications, materials, principles of operation, analysis of potential failure modes, proposed uses, patient populations, instructions, warnings, training requirements, clinical evaluation criteria and testing endpoints, and summaries of bench or animal test data or prior clinical experience." The device maker then conducts the tests, submits the data to the FDA, and waits for FDA approval before bringing the product to market.

Waymo won't have to do anything remotely like this. The company has had informal discussions with government officials at the federal, state, and local levels. But there is no formal process requiring the company to submit information about its technology and test results to regulators in Phoenix or Washington. The law simply doesn't require Waymo to prove that its driverless technology is safe before putting cars on the road.

The rigorous processes for approving airplanes and medical devices come at a high cost. Cummings says that it can take as much as eight years to bring a new airplane to market. A survey of medical device companies found that it took an average of four-and-a-half years for the FDA to sign off on a new medical device (the FDA's process is shorter than the FAA's partly because the agency doesn't get involved until after the product has been developed). And it cost $94 million, on average, to bring a new medical device to market.

Self-driving car advocates argue that slowing down the development of self-driving cars could ultimately cost more lives than it saves. In 2016, more than 37,000 people died from highway crashes, with many being caused by human error, so self-driving cars have the potential to prevent thousands of highway deaths in the coming years.

The challenge of regulating self-driving cars

Even safety advocates like Chase and Cummings don't necessarily want to see cars subjected to the kinds of comprehensive regulations imposed on aircraft and medical device makers. But they'd like to see the government take a more active role in testing self-driving cars—before they're allowed on public roads.

But Princeton's Ed Felten questions whether that's realistic. He points out that there are unique challenges to testing self-driving cars.

"The rate of accidents or fatalities per vehicle mile is required to be very, very low," he says. Human drivers get in a fatal accident about once per 100 million miles. So to determine whether a driverless system is as safe as a human driver, you have to figure out how it handles rare situations—situations that a typical driver might only encounter once in a lifetime.

"It requires a huge amount of simulation and road testing under controlled conditions in order to know if it's safe," Felten argues. "It's hard to imagine the government doing that."

Chase, for example, advocates giving driverless cars a "vision test" to demonstrate that they "can see and respond to what's happening on the roads." But it's not clear how much value this would have in practice. Having this kind of perception capability is certainly necessary for a fully self-driving car, but it's far from sufficient to prove that driving software will be as safe as a human driver.

And while Cummings told me that "there has never been any kind of real-world testing" of Waymo's cars, that doesn't seem quite fair to Waymo. Last year I traveled to Waymo's testing facility in California, where the company has put its cars through its paces in hundreds of controlled scenarios. I watched a Waymo car slow down as another car cut it off, stop as "movers" dropped a pile of boxes in its path, and yield to a car backing out of a driveway.

Maybe federal regulators would come up with some tests that Waymo's engineers haven't tried yet. But overall it's hard to believe that the feds would come up with a more rigorous battery of tests than the tests Waymo is already conducting in the California desert and elsewhere.

Ultimately, the only way to test how a self-driving car will perform on real public streets is to test them on real public streets.

Waymo could be more transparent

One place where Waymo's critics absolutely have a point, however, is that Waymo hasn't been very transparent about its testing.

Consistent with the Trump administration's deregulatory approach, companies are encouraged, but not required, to file a report detailing the safety features of their self-driving cars. Waymo was the first company to file a report like this last year.

When I talked to Cathy Chase in August, she was scathing about safety reports filed by Waymo and other carmakers. "They look more like glossy marketing brochures, rather than providing data," she said.

"We need 'waymo' information," said Henry Jasny, a lawyer at the Advocates for Highway and Auto Safety, shortly after Waymo published its safety report last year.

In an emailed statement, Waymo argued that its 43-page report actually provided a wealth of information about the safety of its vehicles.

"We are committed to educating the public and policymakers about this new technology, which we believe could save thousands of lives, and the rigorous process we’ve put in place to test it," a spokeswoman wrote by email last week. She argued that Waymo's safety report "provides great detail about our testing programs, both in the text portions on testing and the appendices that specifically explain particular types of tests we conduct, that was designed to be informative for policymakers and the general public."

It's true that Waymo's safety report includes a long list of test scenarios Waymo has performed. For example, Waymo says that its cars can handle a scenario it labels "fully self-driving vehicle approaches lead vehicle decelerating."

But the safety report doesn't include the kind of detailed information that would allow for independent analysis of Waymo's testing process. What were the exact parameters of the test? How many times was it run? How did the vehicle perform? The report doesn't say.

And the same point applies with even more force to Waymo's testing on public roads. Waymo has conducted nine million miles of public road testing—a lot of them with safety drivers behind the wheel. But the public has very little information about how these tests have gone. That's especially true in Arizona, which (unlike neighboring California) does not require self-driving car companies to file regular reports on their testing activities.

In late August, for example, The Information published an article reporting that some residents in the Phoenix area were frustrated with having to share the roads with Waymo vehicles that frequently hesitated at times when a human driver wouldn't. A Mountain View resident has posted a series of videos that show Waymo cars seeming to freeze up in situations that would not have confused human drivers.

Do these reports mean there are widespread problems with Waymo's software? Or, are these few isolated incidents being blown out of proportion? It's hard to tell without comprehensive data about the real-world performance of Waymo's vehicles. Waymo undoubtedly has this kind of data; it just hasn't made it available to the public.

My own hunch is that Waymo will ultimately prove naysayers wrong. The company started developing self-driving technology long before its rivals, so it has had the luxury of taking a slow and steady path toward commercialization.

But testing is always going to be more rigorous and trustworthy with independent oversight. The public has every reason to be skeptical unless and until Waymo makes an affirmative case—backed up by comprehensive data and independent analysis—that its vehicles are safe. Current federal and state law doesn't require Waymo to make that case. But it might be in Waymo's interest to make it anyway.

"Providing more information about how their vehicles are performing would be to their benefit because ultimately you want people to trust the cars, and the way you imbue that trust is by proving they're safe," Chase says.

If Waymo launches a commercial service without releasing significant performance data or allowing independent review of its technology, that will set a precedent that will make it easier for other companies—perhaps less scrupulous companies—to do the same thing.

Under current rules, "anyone can put an autonomous vehicle on the road," Chase says. "Joe's Garage can build one, put it on the road, then there's a horrific crash."

The trustworthy company model

If formal FDA-style testing isn't realistic, what could regulators do instead? Bryant Walker Smith advocates what he calls a "trustworthy company" model for regulating self-driving cars. Instead of writing prescriptive, technology-focused standards for driverless cars, he says, regulators should focus on validating car companies' own processes for developing and testing driverless cars.

Smith would like to "have governments say: are these companies making a credible case? Are they candidly communicating? Does the company support their assertions?"

"Regulation is not just a rule or a prospective approval," Smith notes. "Regulation is all of the tools available to governments: investigations, inquiries, recalls, prosecutions for misrepresentations to governments."

In this model, the regulators' focus would not so much be on directly evaluating the technology, but instead on making sure that the companies building driverless cars have a corporate culture and a set of processes that prioritize safety. Regulators would press companies to make a thorough, evidence-backed case for the safety of their vehicles.

So far, Waymo hasn't really done this—at least not publicly. Ever since its launch almost two years ago, Waymo has emphasized the safety of its technology. "Safety is the core of Waymo’s mission and everything we do," a Waymo spokeswoman told me via email.

But the company hasn't released much data to back up its safety claims. We know Waymo has logged millions of miles on Arizona roads, but we know very little about how its vehicles have performed.

Waymo needs to not just build safe technology, but also convince the public that its technology is safe. Being more transparent about both its technology and its testing efforts could help.
Regulating self-driving cars is a tricky problem. We don't want deaths caused by things like disabling emergency braking because leaving it on would make Uber look bad. But how do we know what regulations will be useful before there is a problem ?

It's telling that the opponent of self-driving cars talked to in the article made the claim "there has never been any kind of real-world testing" of a company that has logged millions of miles of testing on public roads, which sounds like real-world testing to me. Plus using closed roads to test specific scenarios.

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Re: General Automation Thread

Post by FaxModem1 » 2018-10-13 01:44pm

New Jersey 101.5 FM
Automation will upend New Jersey’s economy
by Bill Doyle October 11, 2018 1:34 PM
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Rather quietly last week, Governor Murphy issued an executive order to form a task force looking into how automation and robotics will affect New Jersey’s workforce. According to NJSpotlight.com, “more than half of New Jersey’s most common occupations could be automated in less than 20 years.”

According to the United Way, retail salespeople and cashiers are in the most danger of being replaced, with a 90% chance of going away. Among other jobs being seen as in danger are health aides, movers, janitors, and sales representatives. Governor Murphy’s economic plan includes lifelong learning to retrain displaced workers in order to stay abreast of changes in technology and how it relates to the workforce.


Although having technology replace jobs is nothing new (think telephone operators or the proliferation of ATMs), the pace at which technology advances is thought by many to have increased, making more workers vulnerable. Governor Murphy said, “We must not lose sight of the profound impacts that technological change will have on our workforce and economy.” NJ Spotlight quotes the governor as saying in the executive order that formed the task force, its mission is “to produce an evidence-based policy roadmap for New Jersey to prepare for the future of work.”
So, that's a pretty bad spot for the Garden State if they don't become ready for it. Do they have any good options, and are they achievable within the time it takes to get to this level of automation?
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Re: General Automation Thread

Post by Elheru Aran » 2018-10-13 04:02pm

Meh, note what they're talking about specifically-- 'retail salespeople, cashiers, health aides, movers, janitors, sales representatives'. These are all low-pecking-order positions. Some of them I would hesitate to blame on automation as well-- with how much things cost these days for example I'd argue that movers are being more endangered by people DIY'ing moves via U-Haul or whatever, than robots walking into people's houses and moving stuff for them. Unless 'mover' means something else in NJ.

But retail and cashiers and such, yeah, those have had the writing on the wall for a while now. People are buying stuff more and more online, which means fewer people going to stores, which means the future is probably in smaller stores with smaller sales forces on hand, at least for specialized merchandise; the big-box stores offering a wide variety of merchandise are still going to be around for a while now. Though actual personnel on hand could be reduced somewhat with higher customer-focused automation, for example I can totally see Home Depot using a big-ass touchscreen to allow people to design and order their own kitchen cabinets. Home Depot is already rolling out a self-service kiosk for customer pick-up of online orders in store, so customers don't have to go into the store to pick up their orders. We all know Walmart is full-tilt trying to replace most of their cashiers with kiosks.

So, basically, this isn't news, apart from the government actually getting a clue that this is going to be a real problem for low-tier retail employees in the near future. Which, I guess, kudos to them, but I'll wait until the chickens hatch to see how it turns out...
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Re: General Automation Thread

Post by houser2112 » 2018-10-15 09:14am

Elheru Aran wrote:
2018-10-13 04:02pm
Meh, note what they're talking about specifically-- 'retail salespeople, cashiers, health aides, movers, janitors, sales representatives'. These are all low-pecking-order positions. Some of them I would hesitate to blame on automation as well-- with how much things cost these days for example I'd argue that movers are being more endangered by people DIY'ing moves via U-Haul or whatever, than robots walking into people's houses and moving stuff for them.
U-Haul hasn't killed off the moving profession, and it won't. There will always be people willing to pay to have their stuff moved. Whether that's by people or robots is the question. Speaking as a 43yo man with a 48yo wife who recently did two DIY moves a year apart, I know our next move we will not do ourselves. We are not in good enough shape to do it again.

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Re: General Automation Thread

Post by SolarpunkFan » 2018-10-20 06:31pm

Human-competitive Patches in Automatic Program Repair with Repairnator
Repairnator is a bot. It constantly monitors software bugs discovered during continuous integration of open-source software and tries to fix them automatically. If it succeeds to synthesize a valid patch, Repairnator proposes the patch to the human developers, disguised under a fake human identity. To date, Repairnator has been able to produce 5 patches that were accepted by the human developers and permanently merged in the code base. This is a milestone for human-competitiveness in software engineering research on automatic program repair. In this post, we tell the story about this research done at KTH Royal Institute of Technology, Inria, the University of Lille and the University of Valenciennes.

Program repair research pursues the idea that algorithms can replace humans to fix software bugs [4]. A bug fix is a patch that inserts, deletes or modifies source code. For example, in the following patch, the developer has modified the condition of the if statement:

- if (x < 10)
+ if (x <= 10)
foo();

A program repair bot is an artificial agent that tries to synthesize source code patches. It analyzes bugs and produces patches, in the same way as human developers involved in software maintenance activities. This idea of a program repair bot is disruptive, because today humans are responsible for fixing bugs. In others words, we are talking about a bot meant to (partially) replace human developers for tedious tasks.

When a bot tries to achieve a task usually done by humans, it is known as a human-competitive task [1]. The empirical evaluations of program repair research [3] show that current program repair systems are able to synthesize patches for real bugs in real programs. However, all those patches were synthesized for past bugs, for bugs that were fixed by human developers in the past, usually years ago. While this indicates the technical feasibility of program repair, this fails to show that program repair is human-competitive.
Human-competitiveness

To demonstrate that program repair is human-competitive, a program repair bot has to find a high-quality patch before a human does so. In this context, a patch can be considered to be human-competitive if it satisfies the two conditions of timeliness and quality. Timeliness refers to the fact that the system must find a patch before the human developer. In other words, the prototype system must produce patches in the order of magnitude of minutes, not days. Also, the patch generated by the bot must be correct-enough, of similar quality — correct and readable — compared to a patch written by a human. Note that there are patches that look correct from the bot’s point of view, yet that are incorrect (this is known as overfitting patches in the literature [6, 3]). Those patches are arguably not human-competitive, because humans would never accept them in their code base.

Consequently, for a patch to be human-competitive 1) the bot has to synthesize the patch faster than the human developer 2) the patch has to be judged good-enough by the human developer and permanently merged in the code base.

There is one more aspect to consider. It has been shown that human engineers do not accept contributions from bots as easily as contributions from other humans, even if they are strictly identical [5]. The reason is that humans tend to have a priori biases against machines, and are more tolerant to errors if the contribution comes from a human peer. In the context of program repair, this means that developers may put the bar higher on the quality of the patch, if they know that the patch comes from a bot. This would impede our quest for a human-competitiveness proof in the context of program repair.

To overcome this problem, we have decided early in the project that all Repairnator patches would be proposed under a fake human identity. We have created a GitHub user, called Luc Esape, who is presented as software engineer in our research lab. Luc has a profile picture and looks like a junior developer, eager to make open-source contributions on GitHub. Now imagine Repairnator, disguised as Luc Esape proposing a patch: the developer reviewing it thinks that she is reviewing a human contribution. This camouflage is required to test our scientific hypothesis of human-competitiveness. Now, for sake of ethics, the real identity of Luc has been disclosed on each of his pull-requests.
Automatic Repair and Continuous Integration

Continuous integration, aka CI, is the idea that a server compiles the code and runs all tests for each commit made in the version control system of a software project (e.g. Git). In CI parlance, there is a build for each commit. A build contains the information about the source code snapshot used (e.g. a reference to a Git commit), the result of compilation and test execution (e.g. fail or success), and an execution trace log. A build is said to be failing if compilation fails or at least one test case fails. It has been shown that approximately one out of 4 builds fails, and that the most common cause for build failure is a test failure [8].

The key idea of Repairnator is to automatically generate patches that repair build failures, then to show them to human developers, to finally see whether those human developers would accept them as valid contributions to the code base. If this happens, that would be evidence of human-competitiveness in program repair.

This setup –automatically repairing build failures happening in continuous integration — is particularly appropriate and timely for the following reasons. First, build failures satisfy the core problem statement of test-suite based program repair [4], where bugs are specified as a failing test-cases, and those failing test cases are used to drive the automated synthesis of a patch [4]. Second, it allows comparing the bots and humans on a fair basis: when a failing test is discovered on the continuous integration server, the human developer and the bot are informed about it, at the exact same time. This test failure notification is the starting point of the human vs. bot competition.

Repairnator’s focus on build failures is unique, but it fits in the big picture of intelligent bots for software [2]. For instance, Facebook has a tool called SapFix that repairs bugs found with automated testing. Also related, the DARPA Cyber Grand Challenge bot attackers and defenders try to be human-competitive with respect to security experts.
Repairnator in a Nutshell

In 2017–2018, we have designed, implemented and operated Repairnator, a bot for automated program repair. Repairnator is specialized to repair build failures happening during continuous integration. It constantly monitors thousands of commits being pushed to the GitHub code hosting platform, and analyzes their corresponding builds. Every minute, it launches new repair attempts in order to fix bugs before the human developer. It is designed to go as fast as possible because it participates to a race: if Repairnator finds a patch before the human developer, it is human-competitive.

Let us now give an overview of how the Repairnator bot works.

The primary input of Repairnator are continuous integration builds, triggered by commits made by developers (top part of the figure, (a) and (b)) based on GitHub projects (a). The outputs of Repairnator are two-fold: (1) it automatically produces patches for repairing failing builds (g), if any; (2) it collects valuable data for future program repair research (h) and (k). Permanently, Repairnator monitors all continuous activity from GitHub projects ©. The CI builds are given as input to a three stage pipeline: (1) a first stage collects and analyzes CI build logs (e); (2) a second stage aims at locally reproducing the build failures that have happened on CI (f); (3) a third stage runs different program repair prototypes coming from the latest academic research. When a patch is found, a Repairnator project member performs a quick sanity check, in order to avoid wasting valuable time of open-source developers. (i) If she finds the patch non-degenerated, she then proposes it to the original developers of the project as a pull-request on GitHub (j). The developers then follow their usual process of code-review and merge.

Repairnator has to operate in a given software ecosystem. Due to our expertise with Java in past research projects, the prototype implementation of Repairnator focuses on repairing software written in the Java programming language, built with the Maven toolchain, in open-source projects hosted on GitHub, which use the Travis CI continuous integration platform.
Expedition Achievements

We have been operating Repairnator since January 2017, in three different phases. During one month, in January 2017, we performed a pilot experiment with a initial version of the prototype. From February 1st, 2017 to December 31th, 2017, we ran Repairnator with a fixed list of 14,188 projects, we call it “Expedition #1”. From January 1st 2018 to June 31th 2018, Repairnator has monitored the Travis CI build stream in real time, we call it “Expedition #2”

The main goal of the pilot experiment was to validate our design and initial implementation. We found that our prototype is capable of performing approximately 30 repair attempts per day, given our computing resources. More importantly, this pilot experiment validated our core technological assumptions: a significant proportion of popular open-source projects use Travis and the majority of them use Maven as build technology. This meant we would have a fair chance of reaching our goal of synthesizing a human-competitive patch in that context.

During Expedition #1, whose results are presented in details in [7], Repairnator has analyzed 11,523 builds with test failures. For 3,551 of them (30.82%), Repairnator was able to locally reproduce the test failure. Out of 3,551 repair attempts, Repairnator found 15 patches that could make the CI build pass. However, our patch analysis revealed that none of those patches were human-competitive because they came too late (Repairnator produced a patch after the human developer) or were of low quality (they made the build successful coincidentally).

Expedition #2 is the successful one. It has shown that program repair technology has crossed the frontier of human-competitiveness. Repairnator has produced 5 patches that meet the criteria of human-competitiveness defined above: 1) the patches were produced before the human ones, 2) a human developer accepted the patches as valid contributions, and the patches were merged in the main code base.

Human-competitive contributions on Github, patches synthesized by the Repairnator robot and accepted by the human developer:

Jan 12, 2018, aaime/geowebcache/pull/1, “Thanks for the patch!”
Mar 23, 2018, parkito/BasicDataCodeU[…]/pull/3 “merged commit 140a3e3 into parkito:develop”
April 5, 2018, dkarv/jdcallgraph/pull/2 “Thanks!”
May 3, 2018, eclipse/ditto/pull/151 “Cool, thanks for going through the Eclipse process and for the fix.”
June 25, 2018, donnelldebnam/CodeU[…]/pull/151 “Thanks!!”

The first patch merged by our program repair bot was accepted by a human developer on Jan 12th, 2018. Here is the story: on Jan 12th 2018 at 12:28pm, a build was triggered on project aaime/geowebcache11 1 https://travis-ci.org/GeoWebCache/geowe ... /328076497. The build failed after 2 minutes of execution, because two test cases were in error. Fourty minutes later, on Jan 12th 2018 at 1:08pm, Repairnator detected the failing build during its regular monitoring, and started to run the available program repair systems configured in Repairnator. Ten minutes later, at 1:18pm, it found a patch.

On Jan 12th 2018, at 1:35pm, a Repairnator team member took the patch generated by Repairnator, and validated the opening of the corresponding pull-request on GitHub. On Jan 12th 2018, at 2:10pm, the developer accepted the patch, and merged it with a comment: “Weird, I thought I already fixed this… maybe I did in some other place. Thanks for the patch!”. That was the first patch produced by Repairnator and accepted as a valid contribution by a human developer, definitively merged in the code base. In other words, Repairnator was human-competitive for the first time.

After 6 more months of operation, Repairnator has had 5 patches merged by human developers, which are all listed in Table 1.

Overall, the Repairnator project has fullfilled its mission. It has shown that program repair can be considered as human-competitive: Repairnator has found patches 1) before the humans, 2) that were considered of good quality by humans themselves.
The Future

In addition to showing that program repair is human competitive, the Repairnator project has provided a wealth of information about bugs and continuous integration, and about the current shortcomings of program repair research, presented in [7].

Let us dwell on one point in particular, the question of intellectual property. On May 3rd, 2018, Repairnator produced a good patch for GitHub project eclipse/ditto. Shortly after having proposed the patch, one of the developers asked “We can only accept pull-requests which come from users who signed the Eclipse Foundation Contributor License Agreement.”. We were puzzled because a bot cannot physically or morally sign a license agreement and is probably not entitled to do so. Who owns the intellectual property and responsibility of a bot contribution: the robot operator, the bot implementer or the repair algorithm designer? This is one of the interesting questions uncovered by the Repairnator project.

We believe that Repairnator prefigures a certain future of software development, where bots and humans will smoothly collaborate and even cooperate on software artifacts.
"The cry of the tormented is pain's commonest articulation, without words, without any meaning except the existence of pain itself. It pleads for mercy, and shrieks in the excess of an inability to endure."

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Re: General Automation Thread

Post by SolarpunkFan » 2018-10-27 05:43pm

Robots Aren't Coming For Jobs: AI Is Already Taking Them
There's a lot of talk whether robots, automation, and artificial intelligence will take over an increasing number of jobs and supplant people. Supporters say that businesses can run more efficiently and free up employees to do more meaningful work. Skeptics say the jobs will go and so will the people.

The thing is, the change is already happening and anyone who tells you that all companies will shift workers to something with a higher value either has either taken one too many hits off a jug of artificially flavored fruit punch or hasn't a clue as to how things actually work in most companies.

First, the inroads are happening every day and frequently with jobs that pay less and attract people with fewer marketable skills. One example my son in Washington, D.C. saw one night was a squat robot, moving along the sidewalk at night using GPS for navigation. It carried someone's dinner inside. Here's an AP video of it:

https://www.youtube.com/watch?v=5fNEmukuXBU

When the devices began to appear last year, coverage was largely positive, as you can see from this NPR piece. It did address the question of replacing people:
The company sees robots as a supplemental form of delivery, not a replacement, he says. Robots like these ideally will complete deliveries that normally wouldn't have been placed with the human-based delivery options of the past.

"We have people that drive cars, they walk, they bike, and we see robots as another type of vehicle that enables a whole bunch of different things from a delivery perspective," Cook says.

Legislation has passed to make self-driving robots like these legal on the sidewalks in the District of Columbia and Virginia, and similar laws are in the works in Florida and Idaho. Other states also have expressed interest.
It might be an additional choice — for now. Have enough of the robots on the move and you don't need so many people walking, at least for local deliveries. That's the problem. Maybe not all the jobs disappear, but there are fewer and the savings from less spending on workers goes into the pockets of a company and its owners.

The same is true throughout the food service industry, one of the largest lower-wage employment sectors. In early October, the Wall Street Journal ran a piece on "robot restaurants. One had machines that made better-quality burgers, including grinding beef in-house, and could deliver them at two per minute. There was a bar that used robots to mix and serve drinks, including delivering clever lines to customers. Robot-made pizzas that baked in delivery trucks that had built-in ovens.

According to market analyst firm Gartner, 75% of finance departments will employ automation by 2020 to handle manual tasks and free people up for other work.

Years ago, I spoke with an executive from a company that was automated some types of work. I asked, as even then this was the standard answer you'd hear, whether personnel would be shifted for higher-value work. A moment of candor I've found remarkable in business followed. The executive said, "You know, that's what we say, but really it's about eliminating the jobs."

Of course, it is. If people were needed in other positions, they already would have been hired. Businesses typically look for people who possess skills and don't commonly consider training the displaced to do something else. It does happen on occasion. I spoke with a major firm some time back about how automation was making many in their accounting department unnecessary. The company was training people to become software developers. The probable reason: higher-demand skills like programming are difficult to find. I also suspect the total cost of training and eventual salary would be lower than trying to hire people with experience who could command higher salaries.

But, by and large, companies will eliminate positions because that's what they're supposed to do. Keep people on for unnecessary jobs? Why would an executive do that?

Anything predictable enough is fair game at this point. BlackRock, the world's largest investment fund, plans to have computers pick stocks for actively managed funds. And three dozen workers, who likely once made an excellent living, are no longer with the company.

The problem for society is that modern capitalism has lost its way. Shareholder interests have been touted as the most important, a development largely, although not entirely, that sits at the feet of the late Milton Friedman.

If you've ever studied calculus, you learned the impossibility of maximizing an equation for for more than one variable. If management sees its job, and financial self-interest, in driving up stock prices, everything else will ultimately have to play second fiddle.

But businesses and the economy aren't simplistic equations whose solutions are listed in the back of the book. They represent complex systems for which mathematical models are horrendously complex. Push the system's parameters to favor one aspect you lose stability. Eventually the system will right itself, but in a way that may not be recognizable, or desirable.

Major revolutions, like historical ones in France and Russia, were largely the result of too long a period during which the benefits of a total society were funneled to a relative few. Or look at the Gilded Age in the U.S. There wasn't a revolution per se, but the country saw massive unrest and violence that helped lead to modern labor unions. You can only expect people to wait so long.

And then, modern times we have an interconnected world. Russia, France, or any other major country cannot contain vast turmoil within its borders without the effects spilling out. Disruption in one area can spread everywhere.

Maybe people will begin to take a different approach and consider sustainable actions. But given the predictions for global warming, which should scare the soul out of anyone, don't seem to be driving enough change, why expect companies to be smarter about this topic?
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Re: General Automation Thread

Post by FaxModem1 » 2018-10-28 10:38pm

I wonder how long this can go on for before people really start getting the memo that jobs are disappearing. Or will it be hidden by vast amounts of grunt work jobs that aren't yet automated?
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Re: General Automation Thread

Post by Tribble » 2018-10-28 11:06pm

FaxModem1 wrote:I wonder how long this can go on for before people really start getting the memo that jobs are disappearing. Or will it be hidden by vast amounts of grunt work jobs that aren't yet automated?
The memo is already out there in a sense; people are already putting off buying homes and having children because they simply can't afford them. So long as automation is combined with population decline (which would more or less be happening already in developed countries were it not for immigration) I suppose the job rates could remain more or less stable.
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Re: General Automation Thread

Post by FaxModem1 » 2018-11-11 11:33pm

Forbes
The Growing Trend Of Pharmacy Automation
Forbes Technology Council
Meghann Chilcott CommunityVoice
Forbes Technology Council
CommunityVoice
POST WRITTEN BY

Meghann Chilcott

Senior Vice President of OrderInsite, delivering executive leadership in innovative pharmacy technology solutions.





Getty

Pharmacy automation is not a new development — many pharmacies have integrated some degree of automation since the 1960s. But increased artificial intelligence and machine learning facilities, combined with the lower cost of automated systems, have put automation within reach for even smaller pharmacies. Read on to learn why so many pharmacies are moving toward automated systems for their work, what systems exist and what’s on the horizon.

The Benefits Of Automation

Some pharmacies have held onto the old ways of doing business. But as the benefits of automation grow clearer, even traditionalists have started to make the switch. These benefits include:

Increased speed: Even the most experienced pharmacist or technician is slower than a machine. Automation allows pharmacies to fill more orders more quickly while freeing up human beings to do essential tasks that can’t be automated, like engaging face to face with patients.


Greater accuracy: Accuracy is a major concern in the pharmacy world. Even the most seasoned or careful human can make mistakes. For many medications, an error in dosage can cause adverse effects or even kill a patient. Automating the process of measuring out medication greatly reduces the chances of error.

Greater security and confidentiality: Humans may make errors when contacting patients about their prescriptions or when following security protocols for drugs like opiates. For example, a pharmacy technician can leave too much information in a voicemail for a patient, while automated calling programs will consistently only verbalize information programmed by the pharmacy. For controlled drugs, the staff member may forget to log out a medication. Locked security cabinets can reduce this by requiring specific dispensing information before allowing access to the medication. Automation can eliminate these possibilities for error.

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Types Of Automation

Pharmacies are incorporating several types of automation into their work, and many have venerable histories already. One of the earliest and most well-known examples of pharmacy automation is automated phone calls informing patients their prescriptions are ready to pick up.

Further examples include:

• Counting pills or capsules or measuring liquid medication

• Compounding

• Inventory management and reordering of medications when supply gets low

• Contacting physicians for refills or clarifications

• Transcribing patient or physician phone messages

• Entering or updating patients' personal or insurance information

• Identifying potentially dangerous medication interactions

A Deeper Look

Exciting new developments in automation have brought the costs of software and machines down. Devices that were once off-limits to all but the largest pharmacies are now priced so that smaller facilities can afford them. Here’s an overview of some specific tasks and the devices performing them:

Dispensing medications: Dispensing medication is a common source of dangerous errors for pharmacies, and it’s a laborious task. Machines like TCGRx automate filling blister packs for patients, and Parata’s robots fill vials and pouches. Many modern systems are hands-free, allowing greater safety and sterility for the medication.

Syncing records: Traditionally, pharmacists have had to manually enter information about medication dispensing, even if it’s been automatically dispensed. Records are now commonly synced to centralized databases that manage the patient’s records once a medication has been dispensed, so there’s no confusion or additional steps.

Compliance: Pharmaceutical regulations are constantly changing. Systems like Omnicell check your pharmacy’s practices against regulations and alert you to noncompliance scenarios.

Dispensing pills at home: For many patients, accuracy of dosage while self-administering or remembering to take medications on schedule can be serious concerns. Pharmacists may take extensive steps to educate patients about their medication when they visit the pharmacy, but if a patient isn’t sure or doesn’t recall the instructions, all their work is undone. In-home medication dispensers, like the one created by Spencer Health Solutions, serve as in-home pharmacists, dispensing pills to patients and giving them instructions.

Future Frontiers In Pharmacy Automation

Pharmacy automation continues to grow by leaps and bounds. AI and machine learning provide exciting opportunities for changes within the industry.

Researchers have focused much attention on AI image recognition. While handwriting recognition has greatly improved just in the last five years, pharmacists still have to double-check systems that scan prescriptions to see whether there have been any errors in their transcription. As the software continues to improve, it’s likely that error rates will continue to decrease.

One of the most important automation frontiers involves patient responses to medications. As pharmacists report back about adverse reactions to medications, automated systems may be able to glean patterns (such as in interactions or contraindications) that humans aren't as likely to catch. By harnessing the power of big data, systems may be able to advise pharmacists on potential risks to patients, even when the reason underlying the advice is unclear.

Finally, one of the largest concerns pharmacies currently have about automated medication dispensation is the risk of cross-contamination. One machine processing many different medications needs to be cleaned between each operation. Currently, this task falls to human pharmacy staff, although with time it’s likely that devices with a reliable, safe and thorough self-cleaning function will enter the market.

Conclusion

Some pharmacies have resisted automation. Pharmacists or other staff may be resistant to learning new systems when the old ones have functioned for so long. Still others may be concerned about the long-term viability of their jobs.

But pharmacies are quickly learning that they can’t afford not to automate at least some tasks. Automation increases a pharmacy’s efficiency while dramatically reducing its rate of error. It frees up human pharmacists to perform important tasks like interacting with patients face to face, which can’t be delegated to a machine.

As the costs associated with automation continue to drop, more pharmacies are realizing that they can’t afford to put off automation any longer. As new developments in automation emerge, pharmacies will become more efficient, cleaner and safer.
So, what does thi smean for pharmacists, young, old, and those pursuing it as a career?
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