Friday, 30 August 2019

Managing Jenkins with jcli

As a developer, I usually use Jenkins like this:

  • Find a job which is related with my current work

  • Trigger that job

  • Check the output of the build log

Sometimes, I might need to check the update center. Maybe a new plugin is needed, or I need to update an existing plugin. Or, I want to upload a plugin from my computer. For all these cases, I just don’t need a UI or even a browser. I like to use a CLI to complete most of my tasks. For example, I use kubectl to manage my Kubernetes cluster, to create or modify the kubernetes resources. So, I start to think, 'Why not use a CLI to manage my Jenkins?'.

Why create a new one?

First, I almost forgot about the existing Jenkins CLI, written in Java. Let me introduce how to use that one.

Visit Jenkins page from http://localhost:8080/jenkins/cli/. You’ll see a command like java -jar jenkins-cli.jar -s http://localhost:8080/jenkins/ help. So, a jar file needs to be download. We can use this command to complete this task wget http://localhost:8080/jenkins/jnlpJars/jenkins-cli.jar.

Now you can see that this is not a Linux-style CLI. Please consider some points below:

  • The users must have a JRE. This is not convenient for developers who don’t use Java.

  • The CLI is too wordy. We always need to type java -jar jenkins-cli.jar -s http://localhost:8080/jenkins/ as the initial command.

  • Cannot install it by some popular package manager, like brew or yum.

Of course, the Java CLI client is more native with Jenkins. But I’d like to use this more easily. So I decided to create a new CLI tool which would be written in Go and which would natively run on modern platforms.

That’s the story of creating jcli.

Features

  • Easy to maintain config file for jcli

  • Multiple Jenkins support

  • Plugins management (list, search, install, upload)

  • Job management (search, build, log)

  • Open your Jenkins with a browser

  • Restart your Jenkins

  • Connection with proxy support

How to get it?

You can clone jcli from the jenkins-cli repo. For now, we support these three most popular OS platforms: MacOS, Linux, and Windows.

MacOS

You can use brew to install jcli.

brew tap jenkins-zh/jcli
brew install jcli

Linux

It’s very simple to install jcli into your Linux OS. Just need to execute a command line at below:

curl -L https://github.com/jenkins-zh/jenkins-cli/releases/latest/download/jcli-linux-amd64.tar.gz|tar xzv
sudo mv jcli /usr/local/bin/

Windows

You can find the latest version by clicking here. Then download the tar file, cp the uncompressed jcli directory into your system path.

How to get started?

It’s very simple to use this. Once you get jcli on your computer, use this command to generate a sample configuration:

$ jcli config generate
current: yourServer
jenkins_servers:
- name: yourServer
  url: http://localhost:8080/jenkins
  username: admin
  token: 111e3a2f0231198855dceaff96f20540a9
  proxy: ""
  proxyAuth: ""
# Goto 'http://localhost:8080/jenkins/me/configure', then you can generate your token.

In most cases, you should modify three fields which are url, username and token. OK, I believe you’re ready. Please check whether you install the github plugin in your Jenkins:

jcli plugin list --filter name=github

That’s the end. It’s still in very early development stage. Any contribution is welcome.

A Distributed Future: Where Blockchain Technology Meets Organisation Design and Decision-making

Blockchain technology records and forever maintains data that cannot be changed. It also involves ‘smart contracts’ and consensus mechanisms that govern processes of automation, as well as the development, evaluation and execution of decisions. Blockchain technology has the potential to transform organisation design due to its decentralised and distributed characteristics. To understand how blockchain will change organisation design and decision-making, let’s first dive into the history of organisation design before investigating the impact of this fundamental technology on organising activity.

History of Organisation Design

The theory and practice of organisation design have evolved significantly over the past 100 years. At the beginning of the twentieth century, organisations were mostly viewed as closed bureaucracies. Involving a strict hierarchy of authority and power, these organisations were rational entities and assessed purely on economic performance criteria. It was called the ‘bureaucratic model’, as it captured standardised, authoritative, decision-making procedures, rational discipline and strict separation of planning and execution1. This meant that only managers had access to information and were solely responsible for strategic decision-making therein. Trust was based on controlling conformity with the organisational rules and technology, which was predominantly manufacturing technology, with very predictable effects on how organisations were designed to perform.2-4

Natural Systems Perspective

In ...


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Why We Need AI-Based Video Compression

Over the past couple of years, there’s been a significant increase in the popularity of videos. The word around the net is that videos are set to replace images. The problem is that video files are huge, and their current compression methods are clumsy. 

This article reviews the current state of video compression and explains how Artificial Intelligence can help solve the current challenges.

Video Compression—What It Is and How It Works

Video compression is the process of converting video files. The goal is to reduce the size of the video, so it would take up less space on devices and systems, and consume less bandwidth when loading. You can compress videos through the use of physical or video codecs, which encode and decode video files.

Compression techniques—lossy vs lossless

There are two compression types. Each utilises a different conversion method. The lossy compression technique eliminates redundant data from the file. By the end of the process, you can achieve a compression ratio of up to 300:1 per video. Lossy compression is irreversible, and every time you use it to convert the file, the quality degrades. 

Lossless compression algorithms remove redundant data without damaging quality. The algorithm creates statistical models, and then creates bit sequences. The ...


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Want To Contribute To An Upcoming Book?

I’ve just started working with O'Reilly on a new book, and we are looking for people to contribute their thoughts for inclusion in the book. The book is going to be called 97 Things About Ethics Everyone In Data Science Should Know. This book will be part of a larger series O’Reilly does called 97 Things, each of which contains -- wait for it -- 97 blog-length essays from a variety of authors on a given topic. 

Our goal with the collection is to represent a wide range of voices and ideas from people who have a clear point of view on some aspect of the ethical issues surrounding the field of data science. We're going to organize the book around broad themes, including the ethics surrounding:


What types of data science initiatives can be ethically undertaken
How to determine what data can be ethically utilized
Monitoring and maintenance needed to ensure a process's ongoing ethical state
How to ensure that the results of a data science initiative are used ethically
What policies and procedures are needed to support all of the above


We're looking for essays of 500 - 750 words by September 30, 2019. This can be something that you've already written (e.g., on your personal blog) or can be derived ...


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Microsoft and IIT Roorkee partner for quantum computing

The course, has been structured by IIT Roorkee and Microsoft and the former will conduct lectures on quantum computing for a full semester.

Home-grown Fabindias, Urban Ladders to gain

About 112 brands have obtained government approval for single brand retail trade activities from 2006 till March 29, 2018. The single-brand retail sector has received total FDI equity of $1.6 billion so far.

Artificial Intelligence funding is at a record high

Intel Capital has invested in 51 AI ventures, while 500 Startups has put their money behind 45 investments. Other notable investment firms, like Y Combinator, have backed 32 AI centered startups.

India’s insurance industry using new-age tech to simplify processes

Insurers are adopting AI-powered platforms to help agents market the right policy, and setting up virtual branches and processing motor vehicle claims based on photographs.

Focus on Data to Succeed in AI, Say AI World Government Attendees

By AI Trends Staff

At the AI World Government conference last June, MeriTalk conducted a survey of 71 government and industry executives and IT decision-makers about how AI is being used in government. The findings were released this week.

The survey revealed that more than half of the survey respondents—61%are working on AI today and another 14% expect to be doing so within the year. Most called AI development a little (42%) or moderately (48%) mature, but 89% said AI will be ready for mission-critical tasks within five years.

We are close, but survey respondents still listed several changes needed for AI success across data, technologies, work force, and culture.

Focus on Data Governance, Consistency For Success in AI

Improved data governance, data-centric architecture, and increased consistency of data formats and tagging were the top three needs in terms of data. This is also the area where survey respondents expect the quickest returns; 77% expect better data analytics to be the top AI mission outcome.

For technology, respondents requested increased agility/scalability, the ability to seamlessly migrate to new generations of technology, and increased automation. Cloud technologies may be the answer. 66% of respondents said hybrid cloud environments were a primary driver or important enabler of AI adoption, with 35% of respondents expanding hybrid cloud adoption in direct support of AI.

Workforce needs are also significant, mainly centered on staff. We need increased training for our current workforces in data science and AI, the survey found, as well as increased hiring of AI-specific subject matter experts, the respondents said. And on the process side, survey respondents requested formal processes and methods to guide AI implementation.

Finally, the survey respondents identified needs in company culture. Senior management needs a more driven strategic vision around AI, they said, and culture must shift to value data across all functions. They’d like to see a commitment to data-driven decision making across government and enterprise.

Download the full results.

Drunk Drivers Versus AI Autonomous Self-Driving Cars

By Lance Eliot, the AI Trends Insider

On a recent Friday night, after an evening event, I got onto the road and had some trepidation due to the aspect that I would be using locally popular freeways and highways for which drunken drivers also often used late at night (especially on Fridays and Saturdays). I debated whether to instead use other less traveled roads, perhaps being able to avoid those potential drunken drivers, but it would have added considerable time to my journey home and there was no guarantee that I still wouldn’t encounter alcohol-impaired drivers.

You likely know that drunk drivers account for nearly one-third of all traffic-related deaths in the United States (per stats by the National Highway Traffic Safety Administration). The rule-of-thumb is that there’s an alcohol driving related death every hour, based on averaging the number of such deaths over the course of a year.

You might not realize that annually there are more than 1 million drivers arrested for driving under the influence (DUI), which is an equally scary statistic (one can only wonder how many of those might have gotten into a deathly incident were they not arrested!). Of course, the one million drivers only represent those that were actually arrested and so presumably there would be many more that didn’t get caught.

Some surveys indicate that there are perhaps more than 111 million instances of DUI “episodes” per year by U.S. adult drivers (this is based on self-reported indications by drivers). Though those episodes might not include actual driving related deaths, they likely include a significant number of driving fender benders, car or pedestrian sideswiping, frightening near-misses, and other dangerous mishaps.

Overall, you would be wise to be on the watch for drunk drivers.

You should presumably always be mindful of drunk drivers both at night and day, though I’d suggest that you should have an especially heightened awareness during the times of the day that drunk drivers are most apt to be on the road, along with considering the days of the week, and any other aspects involving seasonality or special occasions. For example, getting onto the roads just after the end of the Superbowl game would count as a time to be especially wary of drunk drivers (a few too many brewskies while watching the game).

Dangerous Encounters With Drunk Drivers

For my Friday night drive, it was nearing midnight and I knew it was the witching hour for many drivers that had been drowning their sorrows or partying it up at the bars after work and were giving up on going to the clubs (the next big-time risky spot would be 2:00 a.m. when the bars close-up). I usually try to arrange my schedule so that any late Friday night driving will be relatively close to home, but in this case, I’d attended an event that was about an hour from my house and so it would unfortunately provide plentiful chances for interacting with drunk drivers during the lengthy sojourn home.

I entered onto the freeway and for the first few minutes it was an uneventful drive. I had come onto the freeway into the slow lane and started to make my way toward the faster lanes, rather than sitting out the whole drive in the slow lane. As I tried to get into the lane just to the left of the slow lane, I noticed a car up ahead that I was approaching quite rapidly. My speed was a reasonable 55 miles per hour, the posted speed limit, while other traffic was tending to go a bit faster at that time of night. The car ahead of me was obviously moving at a much slower speed than the prevailing traffic.

As I got closer to the car, it was evident that the driver was moving along at about 35 miles per hour, which was nearly 20 miles per hour slower than the posted speed limit and probably 30+ miles per hour slower than the prevailing traffic. You might at first assume that maybe the driver was having troubles with their car. Perhaps it would explain the extremely slow speed on the freeway at that time. But, the driver was not in the slow lane and they didn’t have their emergency flashers on.

I also observed that the driver would tap on their brake lights, doing so periodically. I looked up ahead of the car to see if maybe they were following some other car. There wasn’t any car anywhere near the front of the turtle pacing car. The driver had the lane nearly to themselves. There they were, crawling along, late at night, doing so in the lane to the left of the slow lane, and occasionally pumping their brakes as though they thought they were already going too fast.

In my view, this driver was performing the driving task in a manner that suggested they might be a drunk driver. If this had been during daylight, I might have been less inclined to sway towards the drunk driving aspect, but it was the proper day of the week and the proper time of the night to believe that this could be a drunk driver. The driver seemed to be unaware of the true speed of prevailing traffic. They weren’t trying to stay out of the way of traffic, which if they had been in the slow lane and had their emergency flashers on, we could all sympathize and assume perhaps there’s some kind of car mechanical problems.

I realize you might at first be thinking that this driver wasn’t harming anyone, so maybe it is harsh to say they are drunk driving. Well, I’d like to differ with the suggestion the driver wasn’t harming anyone. Car after car was having to avoid the snail-paced car. Some cars would come right up to the bumper of the sluggish car and then dart into another lane. Some cars that wanted to move across lanes of traffic were at times having to jockey around to find an opening either just in front of the plodding car or just behind it. Imagine if you had water flowing in a stream and placed a rock in the middle of the stream. The slowpoke car was causing the other cars to contort themselves into dealing with getting safely around this “rock” in the middle of the freeway.

I figured that it wouldn’t be too long before some other car misjudged the situation and ended-up getting into a car wreck. This might involve hitting the plodding car. It might involve other cars hitting some other cars as they were trying to get around the plodding car, maybe inadvertently colliding with each other while trying to dance out of the way of the slow car. Perhaps the plodding car might even rear-end another car, which I suppose could happen if some innocent driver got in front of the slow car and maybe the slow car driver was so out-of-it they might ram into the other car.

There is also the chance that one drunken driver can sometimes meet-up with another drunken driver. As I watched the slowpoke driver, I realized that luckily the surrounding drivers seemed to be cognizant of the plodding car and were giving it relatively wide berth. Suppose though that some other drunken driver came along and was impaired in their driving capabilities. They might not be as attentive to the plodding car and be so careful to avoid it.

I opted to make my way gingerly around the slowpoke car and continue with my journey. I was trying to decide whether to call 911 and report the car, but in this circumstance,  there wasn’t anything demonstrably wrong per se. It was more of a hunch about the driver and the nature of the driving situation.

About ten minutes later, I encountered what seemed like another potential drunk driver, though the situation was quite different than the slowpoke instance. I was moving along at the speed of prevailing traffic, doing so in the fast lane. To my left was the HOV lane, which I couldn’t use since I did not meet the necessary requirements for its use (such as needing to have 2 or more occupants in the car).

I saw a car up ahead that was entering into the freeway. The car came onto the freeway at a lightening like speed. I’d guess the driver might have been going 90 miles per hour or more. The driver then cut across all lanes of traffic and just narrowly missed me, having cut me off as the driver decided to go directly over into the HOV lane. It was illegal to enter into the HOV lane at the point that the driver did so (there are designed areas that you need to use to enter into and exit from the HOV lane).

I suppose you could say this was just a rude driver and one that apparently was willing to flaunt the law. They were driving at excessive speeds. They drove recklessly and had cut across multiple lanes of traffic, causing other cars to tap on their brakes to avoid hitting the interloper. Keeping in mind that it was a Friday night, late at night, I opted to consider this driver to also be a potential drunk driver.

As the driver rapidly gained distance from me, I could see that the driver was weaving in and out of the HOV lane. It was as though the driver could not steer the car in a straight-ahead manner. This led me to further deduce that it was likely a drunk driver. Little regard for other cars, could not drive straight, speeding and driving recklessly, and so on. It all added up.

Once again, you might criticize that I’ve pointed out what seemed to be another potential drunk driver. You might be thinking that the driver did nothing to harm anyone. If they want to speed, let them do so, and presumably the cops will eventually get them and give them a ticket.

I’d like to emphasize that the other cars on the freeway were quite taken aback by this driver. As mentioned earlier, some drivers had to tap their brakes or maneuver their cars to avoid hitting the rocketing car. I admittedly didn’t see any crash take place, but I’d say it was a pretty strong bet that this driver was heading towards something untoward. Plus, given the high speed involved, whatever might occur was likely to involve really bad outcomes (at least injuries, more likely deaths).

Driving Actions Of Drunk Drivers

Let’s consider the types of driving actions that could be an indicator of a drunken driver: 

  • Driving too slowly for the roadway situation 
  • Driving too fast for the roadway situation 
  • Nearly hitting another car 
  • Cutting off another car 
  • Swerving across lanes needlessly 
  • Straddling a lane without apparent cause 
  • Taking wide turns rather than proper tight turns 
  • Driving onto the wrong side of the road 
  • Driving onto the shoulder of the road 
  • Driving in an emergency lane 
  • Nearly hitting a pedestrian, bicyclist, or motorcyclist 
  • Being too close to the car ahead of it 
  • Stopping when it seems unnecessary 
  • Rolling past stop signs 
  • Running a red light 
  • Other

I realize that those driving actions could be accounted for by some other reason beyond just drunk driving. As such, I am not saying that it is automatically an indicator of drunk driving if you happen to see a car do any of those specific actions.

You need to look at the context of the driving situation. Is the adverse act a seeming pattern of driving or was it a one-moment act and then didn’t reoccur? Are there extenuating circumstances that could justify performing the act? Were more than one of the acts used in combination? What was the time of day and the road conditions? What was the weather like? Etc.

Overall, those types of driving acts are telltale clues that can be used to try and guess whether there might be a drunk driver involved. It is useful to try and assess whether a driver is a drunk driver, since it tends to suggest that they will be dangerous in their driving efforts and you would be wise to then take extra precautions when near them.

As a seasoned driver, I try to be on the watch for drunk drivers and then take precautionary or “defensive” driving postures to reduce my risk and hopefully also reduce the risks of others. Other innocents that come along might not be as alert or not yet have seen enough to detect a potential drunken driver.

AI Autonomous Cars Must Contend With Drunk Drivers

What does this have to do with AI autonomous self-driving driverless cars?

At the Cybernetic AI Self-Driving Car Institute, we are developing AI software for self-driving cars.

Allow me to elaborate.

I’d like to clarify and introduce the notion that there are varying levels of AI self-driving cars. The topmost level is considered Level 5. A Level 5 self-driving car is one that is being driven by the AI and there is no human driver involved. For the design of Level 5 self-driving cars, the automakers are even removing the gas pedal, the brake pedal, and steering wheel, since those are contraptions used by human drivers. The Level 5 self-driving car is not being driven by a human and nor is there an expectation that a human driver will be present in the self-driving car. It’s all on the shoulders of the AI to drive the car.

For self-driving cars less than a Level 5, there must be a human driver present in the car. The human driver is currently considered the responsible party for the acts of the car. The AI and the human driver are co-sharing the driving task. In spite of this co-sharing, the human is supposed to remain fully immersed into the driving task and be ready at all times to perform the driving task. I’ve repeatedly warned about the dangers of this co-sharing arrangement and predicted it will produce many untoward results.

For my overall framework about AI self-driving cars, see my article: https://aitrends.com/selfdrivingcars/framework-ai-self-driving-driverless-cars-big-picture/

For the levels of self-driving cars, see my article: https://aitrends.com/selfdrivingcars/richter-scale-levels-self-driving-cars/

For why AI Level 5 self-driving cars are like a moonshot, see my article: https://aitrends.com/selfdrivingcars/self-driving-car-mother-ai-projects-moonshot/

For the dangers of co-sharing the driving task, see my article: https://aitrends.com/selfdrivingcars/human-back-up-drivers-for-ai-self-driving-cars/

Let’s focus herein on the true Level 5 self-driving car. Much of the comments apply to the less than Level 5 self-driving cars too, but the fully autonomous AI self-driving car will receive the most attention in this discussion.

Here’s the usual steps involved in the AI driving task: 

  • Sensor data collection and interpretation 
  • Sensor fusion 
  • Virtual world model updating 
  • AI action planning 
  • Car controls command issuance

Another key aspect of AI self-driving cars is that they will be driving on our roadways in the midst of human driven cars too. There are some pundits of AI self-driving cars that continually refer to a utopian world in which there are only AI self-driving cars on public roads. Currently there are about 250+ million conventional cars in the United States alone, and those cars are not going to magically disappear or become true Level 5 AI self-driving cars overnight.

Indeed, the use of human driven cars will last for many years, likely many decades, and the advent of AI self-driving cars will occur while there are still human driven cars on the roads. This is a crucial point since this means that the AI of self-driving cars needs to be able to contend with not just other AI self-driving cars, but also contend with human driven cars. It is easy to envision a simplistic and rather unrealistic world in which all AI self-driving cars are politely interacting with each other and being civil about roadway interactions. That’s not what is going to be happening for the foreseeable future. AI self-driving cars and human driven cars will need to be able to cope with each other.

For my article about the grand convergence that has led us to this moment in time, see: https://aitrends.com/selfdrivingcars/grand-convergence-explains-rise-self-driving-cars/

See my article about the ethical dilemmas facing AI self-driving cars: https://aitrends.com/selfdrivingcars/ethically-ambiguous-self-driving-cars/

For potential regulations about AI self-driving cars, see my article: https://aitrends.com/selfdrivingcars/assessing-federal-regulations-self-driving-cars-house-bill-passed/

For my predictions about AI self-driving cars for the 2020s, 2030s, and 2040s, see my article: https://aitrends.com/selfdrivingcars/gen-z-and-the-fate-of-ai-self-driving-cars/

Returning to the topic of human drunk drivers, some AI self-driving car pundits have said that there is no need to contend with human drunk drivers because there shouldn’t be any human drivers allowed on the roadways.

By exclusively having only AI self-driving cars on public roads, you’d be able to eliminate the aspects of having to do deal with human drivers at all, regardless of whether those humans might be sober or drunk, since they would not be allowed to drive cars.

As mentioned, this is a rather crazy and at best naive viewpoint about the real-world.

In the real-world, we are going to have human drivers and we are going to have AI self-driving cars. The AI self-driving cars need to be able to mix with human driven cars. I suppose maybe fifty or one hundred years from now we might somehow have agreed to get rid of human driving, but in the meantime there’s going to be a lot of human and AI driving going on.

Another perspective by some pundits is that we should restrict human driving to particular lanes or roads and have AI self-driving cars also be restricted to particular lanes or roads.

The theory is that if you separate the two, meaning that you have AI self-driving cars driving on their designated roads and you have humans driving on their designated roads, you’ll avoid any kind of contention between the AI driving and the human driving.

Again, this is not a particularly practical approach.

There would be a substantive cost to set aside the lanes or roads for their appropriate designated kind of driver, whether the AI or the human, and the infrastructure costs would be relatively high to achieve this. It would also tend to imply that there are likely going to be some paths that will be a disadvantage to one or the other approach, suggesting that perhaps the AI might get roads that are going to be circuitous to get to where a passenger wants to go versus via human driven car there is a faster path (or, vice versa).

Some counter-argue that they are simply suggesting that there might be specialized lanes like HOV lanes, wherein the lane markings are established to indicate whether the lane is to be used by an AI self-driving car versus a human driven car. Though this might seem like a less costly and easier way to deal with separating the two, I point out that the nature of the separation is rather slim in this case of merely marking lanes.

If all you do is mark lanes by painting on the asphalt or using botts dots, there is still a significant chance of having the human driven cars mixing into the AI self-driving cars. In other words, just as human drivers regularly and readily violate HOV lanes when they aren’t supposed to be using the HOV, it would be tempting to human drivers to go ahead and jump into the AI self-driving car lanes, if those lanes were moving faster than the human driven lanes or for any other reason that the human might decide to do so.

Furthermore, since we are focused herein on drunk drivers, it seems quite unlikely that a drunk driver is going to respect the AI self-driving car lanes and stay out of them.

The drunk driver, in their inebriated state, will perhaps not even realize that the AI self-driving car lanes are only for AI self-driving cars. Or, the drunk driver might think it “fun” to proceed into the AI self-driving cars lanes, doing so due to their intoxicated state of mind.

You also have the rather practical matter of how to allow the AI self-driving cars to get into their lanes and how to let the human driven cars get into their lanes. For example, let’s pretend that you opted to change the HOV lanes to become designated as AI self-driving car lanes. This seems easy to do, since the HOV lanes already are in existence in some areas. You just tell humans to stay out of those now AI self-driving car lanes, and you program the AI self-driving cars to stay out of the other lanes of traffic that are for human driven cars.

The difficulty with implementing this will be the transit aspects of how the AI self-driving cars can get into and out of the designated AI self-driving car lanes. Right now, human driven cars enter onto a freeway and make their way across numerous lanes of traffic to then reach the HOV lane. If you kept the same overall roadway infrastructure and merely designated those HOV lanes as AI self-driving car lanes, the only existing means for the AI self-driving cars to use those designated lanes involves traversing across the other lanes of traffic, which means that you once again have human driven cars and AI self-driving cars mixing together.

In short, I assert that we need to assume that AI self-driving cars will be mixing with human driven cars. And, those human driven cars might at times do some nutty driving, especially when a drunk driver is at the wheel of the car.

For my article about how an AI self-driving car might seemingly drive like it is drunk, see: https://aitrends.com/selfdrivingcars/dui-drunk-driving-self-driving-cars-prevention-cure/

For my article about the importance of AI self-driving cars doing defensive driving, see:  https://aitrends.com/selfdrivingcars/art-defensive-driving-key-self-driving-car-success/

For the aspect that AI self-driving cars will be likely driving non-stop on our roads, see: https://aitrends.com/selfdrivingcars/non-stop-ai-self-driving-cars-truths-and-consequences/

Autonomous Cars Can’t Have A Head-In-The-Sand Approach

For those pundits that are willing to concede that there will be a mixture of human drivers and AI self-driving cars on our roadways, they sometimes will say that there is no need for the AI self-driving car to do anything special about the fact that there are human drivers. In essence, they suggest that if the AI self-driving car just follows the law and properly drives, it has no need to be concerned with drunk drivers.

I call this the head-in-the-sand approach to AI self-driving car driving.

Imagine if you had a novice teenage driver that you were training how to drive a car. You tell the teenager to generally ignore the rest of the traffic and just drive the roads in a legal manner. The novice dutifully follows your instructions and stops fully at stop signs, obeys intersection traffic signals, remains under the posted speed limits, etc.

Is the teenage novice now really ready to drive on our roads?

Would you feel comfortable that the novice driver will be able to contend with the practical day-to-day dog-eat-dog world of driving a car?

I would dare say that the novice is not yet ready. That teenage novice driver has to learn how to deal with the other human drivers that will do things beyond the norm of so-called proper and legal driving.

Most of the automakers and tech firms are currently knee-deep in trying to get AI self-driving cars to simply drive along our roads in a manner somewhat akin to the novice teenager capability. Beyond that kind of driving, the auto makers and tech firms tend to consider anything else to be an edge problem. An edge problem is treated as a narrower use case and you presumably ignore it or postpone it until at some later time you have the resources and span of attention to take a closer look at it.

For the kinds of edge problems in AI self-driving cars, see my article: https://aitrends.com/selfdrivingcars/edge-problems-core-true-self-driving-cars-achieving-last-mile/

I’d assert that these simpleton AI self-driving cars are going to be unsatisfactory and until the edge problems also get solved there is either going to be limited use of those “mindless” kinds of AI self-driving cars, or worse still those AI self-driving cars will get themselves involved in various car-related incidents with other drivers and pedestrians, some of which will involve human injuries or deaths. Besides the danger to humans, it also could spell a large public backlash against the advent of AI self-driving cars, slowing down or possibly even halting progress on AI self-driving cars.

When I observed a suspected drunk driver on my Friday night journey, I did more than simply detect that the car might be driven by a drunk driver.

I also took defensive driving measures in anticipation of what the drunk driver might do.

That’s something that a novice teenage driver is unlikely to be aware of, namely, they are not versed in how to detect a potential drunk driver and are equally unsure of what to do if they spot one.

Thus, for AI self-driving cars, they should be AI equipped to detect whether human driven cars might be driven by a drunk driver. This involves observing how the nearby cars are behaving. I had noticed that a car was driving in the lane to the left of the slow lane, moving quite slower than prevailing traffic, and that was periodically tapping on its brakes. These are all telltale clues that the driver might be a drunk driver.

Drunk Drivers And Other Out-Of-Sorts Drivers

When I refer to a drunk driver, I don’t want you to necessarily think that the AI self-driving car will be able to know or determine that a human driver is actually in a drunken state per se.

I’m not suggesting that the AI will be able to any certainty determine the physical and mental state of the human driver that is driving a car nearby to the AI self-driving car. The AI is merely observing the driving behavior of the human driven car. It is then a logical inference that the human driver is somehow amiss, due to the driving behavior. The amiss nature of the human could be due to intoxication, or it could be that they are just emotionally distraught, or maybe are suffering from some other physical or mental ailment.

What the AI self-driving car mainly needs to ascertain is whether the human driver is driving in a “normal” human driving fashion or whether the human is driving in a reckless manner. If the recklessness suggests drunken driving, there is an overall generic pattern of how drunk drivers tend to drive. By deducing that someone is a drunken driver, the AI can then potentially aptly predict what the human driver will do. As a result of those predictions, the AI self-driving car can and should take proper defensive and evasive actions to avoid a potential collision or other adverse consequence of that human driver.

For my article about contending with human road rage, see: https://aitrends.com/selfdrivingcars/road-rage-and-ai-self-driving-cars/

For my article about human drivers that drive recklessly, see:  https://aitrends.com/selfdrivingcars/rocket-man-human-drivers-ai-self-driving-cars-outrunning/

For my article about the top ten driving foibles, see: https://aitrends.com/selfdrivingcars/ten-human-driving-foibles-self-driving-car-deep-learning-counter-tactics/

For product liability aspects of AI self-driving cars, see: https://aitrends.com/selfdrivingcars/product-liability-self-driving-cars-looming-cloud-ahead/

Suppose you got into a ridesharing car that was an Uber or Lyft provided service, being driven by a human driver. It’s Friday night, and as per my earlier example, imagine if the ridesharing driver gets onto the freeway and up ahead you see a car that is going slower than traffic and tapping on its brakes. I’d bet that you would look anxiously at your ridesharing driver and assume or hope that they notice this car up ahead and how it is driving. If you felt that the ridesharing driver was oblivious to the situation, you’d likely even say something to your ridesharing driver, cautioning them to be on the watch for that other car.

Let’s now substitute AI for the ridesharing human driver.

A true Level 5 self-driving car should be able to on its own detect those kinds of driving circumstances and automatically adjust to it. If you got into an AI self-driving car and it did not detect that suspected drunk driver, you’d be pretty worried that the AI was not up-to-par for fully handling the driving task.

I’ve had some pundits that say the human occupant could just tell or warn the AI, but this is nonsensical.

The definition of a true AI self-driving car is that it can handle the driving task, and now you are wanting to carve out that the human occupants are supposed to be advising the AI about the driving task? This is letting the AI off-the-hook, so to speak. Plus, it is generally impractical. Suppose there isn’t any human occupant in the AI self-driving car and the AI self-driving is simply driving to get to some destination – there’s no human inside to help out the AI. Suppose too that only a child is in the AI self-driving car, are you expecting the child to be watching the traffic so as to make-up for the deficiency of the AI? I think not.

I’m not suggesting that human occupants or passengers won’t be interacting with the AI of a self-driving car. Indeed, I am fully expecting that human occupants will be able to interact with the AI. But, this does not imply that the human occupants therefore have some kind of duty or responsibility to then watch the road for the AI. When we get into a taxi or cab today, we assume that the driver will be watching the road for us. We assume that the driver is savvy about the driving task. We don’t expect that we as a passenger will need to aid the driver in the driving task.

For human conversations with AI self-driving cars, see my article: https://aitrends.com/features/socio-behavioral-computing-for-ai-self-driving-cars/

For dealing with the identification of bugs in self-driving cars, see my article: https://aitrends.com/ai-insider/debugging-of-ai-self-driving-cars/

For having AI self-driving cars learn from human interactions about the driving task, see my article: https://aitrends.com/ai-insider/human-aided-training-deep-reinforcement-learning-ai-self-driving-cars/

For my article about omnipresence and AI self-driving cars, see: https://aitrends.com/selfdrivingcars/omnipresence-ai-self-driving-cars/

As an aside, I realize that we all have at one time or another been in a ridesharing car and found ourselves forced into advising the human driver, perhaps out of concern that the ridesharing driver was not paying attention to the road or was otherwise unaware of something significant that could impact the safety of the driving journey.

Yes, this might well happen in the case of AI self-driving cars. But, it should not be the expected nature of the AI driving. In other words, if a human occupant is desirous of bringing something about the driving task to the attention of the AI, that’s fine, and the AI should indeed consider such input, but it should not be that the AI can readily only successfully perform the driving task by a reliance upon a human occupant. That’s not a true Level 5 self-driving car.

Use Of V2V To Warn Other Nearby Self-Driving Cars

An AI self-driving car can be driving and not only observe potential drunken driving, but also share with other nearby AI self-driving cars what it is detecting on the roadway.

In my example of having seen the seemingly drunk driver that was driving slower than traffic and tapping their brake lights, the odds are that other nearby drivers were seeing the same thing. Indeed, I noticed that other drivers were giving a rather wide berth to the suspected drunken driver. I was not able to readily communicate with those other nearby cars, but they likely also saw me maneuvering out of the way too. We all were potentially of a like awareness that this was a suspected drunk driver, and we were each taking precautionary measures accordingly.

In the case of AI self-driving cars, via V2V (vehicle-to-vehicle) electronic communications, the AI of one car could communicate with the AI of other nearby cars. Imagine if on that Friday evening there were several AI self-driving cars that were near to the suspected drunk human driver. Those AI self-driving cars could have notified each other to be wary of the car. This could be quite handy such that if another AI self-driving car begins to enter onto the freeway, it could be forewarned even before being able to directly observe the drunk driving car that there is a potential drunk driver up ahead.

In addition to communication via V2V, there is also the possibility of V2I (vehicle to infrastructure) being used for this same kind of situation. Increasingly, roadways are becoming “smart” by adding various computer capabilities and the use of V2I could allow for a roadway to communicate with the AI of self-driving cars. The roadway might have detected that there was a driver moving at a much slower speed than prevailing traffic, and it could then send a broadcast to nearby AI self-driving cars to be on the watch for this other car.

If AI self-driving cars are not versed in detecting, predicting, and acting to avoid potential drunk driven cars, the desire to see a dramatic reduction in drunk driving car accidents might not budge much. It has been hoped and stated that the advent of AI self-driving cars will get us toward zero fatalities, including making drunk driving accidents a thing of the past. This though assumes a world of all and only AI self-driving cars.

For my debunking of the zero fatalities myths, see: https://aitrends.com/selfdrivingcars/self-driving-cars-zero-fatalities-zero-chance/

For how rear-end collisions will still occur, see my article: https://aitrends.com/selfdrivingcars/rear-end-collisions-and-ai-self-driving-cars-plus-apple-lexus-incident/

Conclusion

In a world of mixed human drivers and AI self-driving cars, we could sadly end-up with even more drunk driving injuries and deaths due to AI systems that are not adept at contending with human drunk drivers.

As mentioned in my example about driving on a Friday night, the other human drivers purposely got out of the way of the drunk driver.

Suppose these other cars were AI self-driving cars and they were not yet “edge” wise to the nature of human drunk drivers.

As such, those AI self-driving cars might get entangled into a drunk driver in a manner and frequency even higher than if there were only other human drivers on the roads.

Drunk driving is bad.

We all want to find ways to reduce or even eliminate the dangers of drunk driving. AI self-driving cars that are not able to contend with drunk drivers are going to potentially increase the risks of drunk driving incidents rather than reduce them. There will be a trade-off between potential reductions in drunk driving fatalities because people are riding in AI self-driving cars which are not being driven by drunk drivers, versus the instances of AI self-driving cars that get hit or come in contact with human drunk drivers and get into a fatality.

The AI needs to be savvy about how to detect, predict, and out maneuver those human drunk drivers.

This is more than just an edge or corner case.

Copyright 2019 Dr. Lance Eliot

This content is originally posted on AI Trends.

Tesla’s 500,000 Vehicles on the Road Give it a Lead in Generating Data for its Self-Learning AI Self-Driving Cars

By AI Trends Staff

Tesla is making its own way in the application of AI to self-driving cars, making several key decisions that contrast with designs of other AI self-driving car software developers, and having the advantage of a large installed base of vehicles on the road.

One early decision was to make over the air (OTA) software updates available to Tesla drivers. In October 2015, each of Tesla’s 60,000 owners received an OTA update, based on data gathered for a year from all Tesla drivers. In 2014, Tesla sent a software fix for overheating to its 30,000 owners at the time, according to an account in TEchiexpert.

Tesla has compiled data from over 100 million miles driven with its autopilot software. The is being used to generate roadmaps for self-driving cars, which Tesla claims are 100 times more accurate than alternative navigation systems. The company is considering whether to offer the data for sale to other automakers, or possibly offer it to the government to help make roads safer.

McKinsey and Co. has estimated the market value for vehicle-gathered data will be some $750 billion a year by the end of 2030.

Today Tesla is estimated to have 500,000 vehicles on the road, driving 15 million miles a day, which is 5.4 billion miles per year. That is more than 200 times the expected experience from Waymo a year from now, according to an account in Towards Data Science. And Tesla is adding 5,000 cars per week to its fleet.

In the areas of computer vision, prediction and path planning, this data makes a difference. Object detection will become better over time, especially for rare instances such as a horse in the road. People are still being paid to manually label or tag images, a bottleneck. But Tesla is in a position to take snapshots, contributing images from driving experience. Tesla’s director of AI, Andrej Karpathy, has established a process for sourcing images to train for object detection.

Anthony Levandowski, a former top engineer at Waymo, has said full autonomy for self-driving cars will be difficult to achieve, in part because “today’s software is not good enough to predict the future,” and a primary category of failure is to predict the behavior of nearly cars and pedestrians.

Tesla has a self-learning advantage from its fleet of 500,000 vehicles on the road. Instead of having humans label images, Tesla’s prediction neural network can learn correlations between the past and future from sequences of events. They can see behavior that preceded behavior in any recording.

Path planning involves staying in the lane, changing lanes, changing speeds, going around parked cars, stopping for jaywalkers, and so on. This is difficult to code. One approach is to get a neural network to copy the actions of human drivers, in what is known as imitation learning, or apprenticeship learning. The neural network draws correlations between what it sees via computer vision, and the actions taken by human drivers. Tesla will be extending imitation learning to more tasks over time, such as changing lanes. Tesla can target “replays” of situations it wants to study, such as left turns.

Tesla appears to have the lead in generating training data that feeds its self-learning systems, a lead it will be difficult to close for other self-driving car automakers.

Read the source articles in TEchiexpert and Towards Data Science.

EmPOWER Uses AI to Better Serve Electricity-Dependent Populations

By Deborah Borfitz

Every community in the country includes people for whom electricity is literally a life-or-death matter—including individuals who receive dialysis because their kidneys stopped working and those who rely on an oxygen concentrator or ventilator to help them breath. A power outage can be a life-threatening event in a matter of hours for some medically fragile people, especially if backup batteries fail.

The increasing frequency of natural disasters—most notably Hurricane Sandy in 2012—heightened awareness of electricity dependency as a new social determinant of health, says Kristen Finne, director of the emPOWER program of the U.S. Department of Health and Human Services (HHS) and senior program analyst in the Assistant Secretary for Preparedness and Response’s (ASPR) Office of Emergency Management and Medical Operations.

The federal response was to painstakingly map the location of at-risk populations, down to the ZIP Code level, so public health officials could find them and factor them into their emergency preparedness plans. A publicly available HHS emPOWER Map is also now in continuous use by over 54,000 individuals, says Finne.

The data have been used in hundreds of disasters nationwideincluding tornadoes and hurricanes. During the 2016 Great Smoky Mountains wildfires, the emPOWER map helped Tennessee public health authorities and the National Guard identify 70 oxygen-dependent seniors in plume area at heightened risk of breathing difficulties and death.

The focus of the emPOWER program are individuals who can reside independently in the community provided their life-maintaining or -saving medical equipment has a power source, as well as those requiring healthcare services such as oxygen tank delivery, home health nursing or dialysis in a facility—needs that would “put them at risk very quickly in a disaster,” Finne says.

Rapid technological advancements and changes in healthcare service delivery over the past dozen years has enabled their transition from an inpatient residential facility to independent living supported by a community-based model of care, she continuesThe at-risk population has been growing in lockstep with the fast-growing baby boomer age groupEven with advance notification of an impending disaster, supporting and evacuating those power-dependent seniors takes time, Finne says.

Proof of Concept

As early as 2003, it was being reported that individuals were showing up in emergency rooms (ERs) in the aftermath of a natural disaster simply to gain access to power for their ventilator or oxygen concentrator or to obtain a tank of oxygen, says Finne. By early 2012, health officials were routinely communicating to HHS that hospitals were being overwhelmed by people they couldn’t accommodate, hampering efforts to take care of disaster-related illnesses and injuries. Many emergency managers were also struggling to address the power demands of those with electricity-dependent medical and assistive equipment in emergency shelters.

They had no way of knowing these individuals were living in their community, let alone their street address, because they didn’t have any data, Finne says. “They were really struggling to prepare and understand the needs [of at-risk populations].”

Finne was part of a study testing the idea of using Medicare claims data to identify vulnerable individuals and understand their access and utilization patterns during a power outage by looking at what happened as Hurricane Sandy was making its U.S. landfall. A key discovery, published in the American Journal of Kidney Diseaseswas that pre-landfall treatment provided dialysis patients with a protective health buffer, allowing them to avoid trips to the ER that they would otherwise be making within three days post-outage if their dialysis facility was unable to reopenWith prolonged power outages, electricity-dependent individuals were also starting to die because emergency managers were unaware of their whereabouts and need.

ASPR subsequently formed a strategic partnership with the chief medical officer at the Centers for Medicare & Medicaid Services (CMS) to figure out how to use Medicare claims data to help protect health and save lives while staying within bounds of the Health Insurance Portability and Accountability Act (HIPAA), says Finne. Meanwhile, pressure mounted to come up with answers in the aftermath of Hurricane Isaac storm surges in New Orleans that involved mass evacuations.

HHS policy was ultimately amended to allow Medicare claims data to be used by public health officials, and to give them access to the claims before they were run through the lengthy adjudication process, Finne says. The information needed to be timely enough for first responders to save lives, not needlessly put them in harm’s way.

In June 2013, during a disaster simulation exercise in New Orleans, teams of HIPAA-trained personnel at the local and federal level began knocking on doors to confirm Medicare data could accurately identify individuals on an oxygen concentrator or ventilator. As reported in the American Journal of Public Health, the information was correct 93% of the time—a figure later replicated in a larger population concentration in upstate New York.

The local New Orleans electric company’s registry intended to identify the power-dependent wasn’t working as well, says Finne, noting that only eight of the 611 identified individuals were registered. Similarly, only 15 were on a special needs registry required after Hurricane Katrina. The simulation exercise revealed that over half of the 611 would need assistance in an emergency.

Despite public communication efforts to encourage registration, a significant number of those visited indicated they were unaware of the opportunities, continues Finne. Gaps in the registries has a lot to do with health literacy issues—e.g., people calling their oxygen concentrator a “breathing machine” and inadvertently being disqualified.

Deidentified datasets were created from privacy-protected Medicare information and provided to state and public health authorities, who in turn shared the information with their designated partners to do targeted planning for electricity-dependent populations, she says. A new, updated dataset is now provided monthly to all 50 states, five territories, and four metropolitan areas that participate in the ASPR Hospital Preparedness Program.

All told, the emPOWER program is protective of 4.1 million Americans, 2.5 million of whom depend on 14 types of life-maintaining or -saving medical equipment and cardiac devices, says Finne. The remaining 1.6 million Americans rely on facilitybased dialysis services or home oxygen, healthcare or hospice services, rendering them similarly at risk during an emergency.

Use Cases

Emerging partnerships between state and local public health officials, community organizations and concerned citizens to ensure the safety of electricity-dependent Medicare beneficiaries was the catalyst for creation of a publicly available emPOWER Map providing a total count of their number—at the state, territory, county and ZIP Code levels. The goal was to provide communities with the right data in the right tool to the right person at the right time, Finne says, building awareness nationally so more community members are aware and help by offering assistance during an emergency.”

Users don’t have to be tech-savvy or even have a geographic information system (GIS) to benefit from the data, Finne says. The emPOWER Map can be used by all, although those who have their own GIS can connect and consume the map’s data layer using a GIS Representational State Transfer service that is automatically updated every month “into perpetuity. Communities can better plan and ensure at-risk individuals have safe places to go—including emergency shelters, local businesses, places of worship, public schools or libraries—to access power during an outage.

“We did this all by leveraging dedicated AI [artificial intelligence] algorithms that can be applied to the CMS data,” she continues. Granular level details, including limited individual-level beneficiary and healthcare provider information, can be securely disclosed only to public health officials in the event of an imminent or current disaster to support life-saving outreach activities.

For emergency planning, the Arizona Department of Health and Nevada Department of Health and Human Services have leveraged the datasets to anticipate how much and the types of durable medical equipment to have on hand at their shelters, Finne says. In anticipation of Hurricane Matthew, the Florida Department of Health leveraged individual-level CMS data and an existing public alerting system to identify 45,000 individuals in eight counties with a potential medical need and perform a reverse lookup of phone numbers. A life safety call elicited responses by 17,000 residents, 169 of whom requested assistance.

During Hurricane Irma, ASPR collaborated with the Federal Emergency Management Agency and the Department of Defense (DoD) to use the individual dataset to rapidly identify, locate and safely evacuate dialysis-dependent beneficiaries on St. Thomas, U.S. Virgin Islands, says Finne. Only weeks later, they did likewise after Hurricane Maria hit St. Croix. Unfortunately, she adds, the two hurricanes catastrophically damaged the entire healthcare system on the islands, so individuals who were relying on outpatient dialysis were put in immediate life-threatening situations. We were able to use this data to rapidly identify almost all of the [235] dialysis-dependent individuals and evacuate them to safety.”

More AI

Public health officials are, of course, also interested in assisting disabled pediatric and adult populations who are insured by their own state-operated Medicaid program and Childrens Health Insurance Program (CHIP) and would similarly be at risk in the event of an emergency. Having access to data for children is particularly critical as they may have unique equipment and supply needs that require special planning before an emergency, says Finne. To that end, through another innovative partnership with CMS, state and territory programs can now volunteer to participate in a pilot that provides them with the knowledge, tools and technical assistance for replicating and applying algorithms used for the HHS emPOWER program in their own state-operated databases.

Several pilots are underway around the country, the first in resource-strapped Nevada where the HHS emPOWER program has already assisted the states and several counties to better prepare for severe flooding events. Florida is looking to grow its auto-call capabilities to include Medicaid and CHIP beneficiariesSome states, as a next step, have also expressed an interest in further extending the algorithms to all-payer datasets to capture yet more electricity-dependent individuals, Finne adds.

The instruction manual gives state officials “a comprehensive overview of the analytical framework, algorithms, and code libraries,” she says, but remains a work in progress. ASPR will work closely with individual states to identify differences between systems and have those reflected in FAQs and other types of tools and resources to minimize the hurdles and guesswork.

That includes greater use of AI and, especially, virtual assistants. One of the lessons learned during the 2017 hurricanes was how much rescue efforts were being hampered by spotty internet connectivity, says Finne. So many first responders wanted to use the emPOWER Map but getting down to the ZIP Code level was a multi-step approach that took too much time on tablets and smartphones in the field.

Google Virtual Assistant had recently made its debut and Amazon Alexa was at the top of everyone’s wish list. The time was also right for ASPR to make the leap, she says. Later this fall, ASPR will be launching emPOWER AI—a modernized version of the Google and Alexa virtual assistants that will be a first application for HHS and emergency responders serving on the front lines of disasters. Using voice commands and cloud computing, virtual assistants can deliver answers in fractions of a second as opposed to manual use of the current applications on a computer, she notes.

With emPOWER AI, emergency responders in the field will be able to use their smartphone to get immediate answers to questions about electricity-dependent people in a disaster area and places with minimal connectivity, Finne says. “We’re really excited about that. To our knowledge, we’re the first to leverage this technology in our field and only second to the DoD that launched the NORAD [North American Aerospace Defense Command] Tracks Santa [an Alexa Skill]” last year.