Monday, 6 January 2020

Yipitdata Example Highlights Benefits of Databricks integration with AWS Glue

At Databricks, we have partnered with the team at Amazon Web Services (AWS) to provide a seamless integration with the AWS Glue Metastore.   Databricks can easily use Glue as the metastore, even across multiple workspaces. YipitData, a longtime Databricks customer, has taken full advantage of this feature, storing all their metadata in AWS Glue.  Databricks’ integration with Glue enables YipitData to seamlessly interact with all data that is catalogued within their metastore.

YipitData is a data company that specializes in sourcing and analyzing alternative data to answer key questions for fundamental investors.  YipitData relies on the scale and processing ability of Databricks Unified Data Analytics for a competitive advantage.  They are able to able to incorporate a far greater variety of data enriched and analyzed in different ways than competitors in their space.  The ability to use the AWS Glue Metastore has been instrumental to their continued growth and success.

The key benefits for YipitData’s usage of AWS Glue with Databricks:

  • All their metadata resides in one data catalog, easily accessible across their data lake.  Synchronization of metastores was a difficult challenge, and using Glue removes this burden.
  • They are able to quickly and seamlessly integrate tools within their existing stack, with the same metastore.  For example, they often perform quick queries using Amazon Athena.  Data that has been ETL’d using Databricks is easily accessible to any tools within the AWS Stack, including Amazon Cloudwatch to enable monitoring.
  • AWS Glue’s API’s are ideal for mass sorting and filtering.   Understanding expiry across 10’s of thousands of tables is core to Yipidata’s business, and together with Databricks this used to take 8 hours to accomplish.  This now can be done in under 5 minutes.

Databricks also provides several advantages that help YipitData succeed.  The power of notebooks has enabled sharing of information rapidly, removing the siloes of tribal knowledge common in the past – now their analysts are able to easily share information.   Using AWS’s Single Sign On service has also been a huge benefit to the team as they haven’t needed to implement costly complex third-party solutions. Databricks’ ability to scale means, as Andrew Gross, Staff Engineer from Yipitdata puts it,  “Databricks allows us to effortlessly trade scale for speed, which was not possible before.”

Get Started with Databricks and AWS Glue

You can apply the power of Databricks and AWS Glue to help solve your toughest data problems.   Learn more at https://docs.databricks.com/data/metastores/aws-glue-metastore.html

Additional Resources

Using AWS Glue Data Catalog as the Metastore for Databricks 

AWS Data Lake Delta Transformation Using AWS Glue

 

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Try Databricks for free. Get started today.

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Your appliances are getting less energy-hungry by the day, thanks to tech

As people demand energy-efficient home appliances to keep power bills down, improvements in tech are helping manufacturers step up to the challenge

Saturday, 4 January 2020

The Birth of Big Data on Cloud Infrastructure and its use in Business Decisions

Big data - alongside sustainability, the “Internet of Things” and crypto - is arguably the buzzword of the decade.

It’s a term that describes the large volume of data that inundates a business on a day-to-day basis - and represents the potential means to an end for businesses with respect to some of the most complex and burdensome organisational procedures.

But having big data alone won’t help an organisation: it is what’s done with that data that truly matters. With the right tools, businesses can achieve anything, but only if they choose to fully embrace those tools.

Big data encompasses a huge range of analytics and data-gathering strategies, all of which enable a business or organisation to make more informed business decisions. If used correctly and effectively, big data can be analysed for insights that lead to better decisions and more informed strategic business moves. This is no new revelation: the more a business or organisation knows about their operations, the better decisions it can make and the more appropriate solutions it can find to problems.

Big data itself was born out of the need to more fully understand and comprehend trends and patterns in huge databases to transcend countries, cultures, and time. Take ...


Read More on Datafloq

The Birth of Big Data on Cloud Infrastructure and its use in Business Decisions

Big data - alongside sustainability, the “Internet of Things” and crypto - is arguably the buzzword of the decade. It’s a term that describes the large volume of data that inundates a business on a day-to-day basis - and represents the potential means to an end for businesses with respect to some of the most complex and burdensome organisational procedures. But having big data alone won’t help an organisation: it is what’s done with that data that truly matters. With the right tools, businesses can achieve anything, but only if they choose to fully embrace those tools. Big data encompasses a huge range of analytics and data-gathering strategies, all of which enable a business or organisation to make more informed business decisions. If used correctly and effectively, big data can be analysed for insights that lead to better decisions and more informed strategic business moves. This is no new revelation: the more a business or organisation knows about their operations, the better decisions it can make and the more appropriate solutions it can find to problems. Big data itself was born out of the need to more fully understand and comprehend trends and patterns in huge databases to transcend countries, ...


Read More on Datafloq

True automation, a bigger streaming revolution: Tech that will take over our lives this year

5G is expected to take center stage as one of the most important topics at CES 2020

Government approves 2,636 new charging stations in 62 cities: Prakash Javadekar

The Department of Heavy Industry had invited the expressions of interest from entities for availing incentives under the FAME India Scheme Phase II for deploying charging stations within cities.

Friday, 3 January 2020

Has Blockchain Failed Before It Even Really Began?

Investment into blockchain startup has already declined this year by 60 percent down to $1.6 billion, according to a CB Insights report. At the same time, money coming from corporations spent on blockchain technologies is on an even sharper decline. 

Just as everyone was getting really excited about its potential, it appears blockchain is dead. Or is it? Interestingly, the world’s biggest tech leaders - Samsung, Microsoft, IBM - who were also blockchain’s biggest adversaries in the beginning, seem to have taken to the newfangled technology of late. Understanding blockchain and its potential application took a while for them, admittedly, but it appears the big guys are now on board. They have either created their own blockchain, or joined partnerships to use the technology, and HSBC and JPMorgan have similarly developed their own blockchain technology. What is the story? Why are the original perpetrators no longer convinced, but latecomers to the game are?

Crypto enthusiasts have lost faith in blockchain, the record-keeping technology behind bitcoin, believing it to be a thing of the past. At the recent Consensus Invest conference in New York, where crypto lovers gathered to discuss the latest in digital assets market trends, it was acknowledged as such. So ...


Read More on Datafloq

The Rising Use of Artificial Intelligence and Smart Systems in Healthcare

Healthcare continues to be a field that comes with a lot of pressure attached, and specialists working in that area have to constantly battle the problems that arise from that. And there doesn’t seem to be any solution on the horizon – the job is simply highly demanding, and nothing is likely to change that anytime soon. However, we have seen the rise of various systems designed to assist the work of healthcare professionals, and simplify it in different ways. To that end, we’re already seeing some great progress – and it’s very likely that this is going to continue in the future as well. Artificial intelligence has made the lives of healthcare professionals easier in different ways, and we seem to be barely scratching the surface of what it can do in this regard.

Problems That Need Addressing

There are various problems in healthcare that remain open and largely unsolved. Fatigue is the main one, and it comes in all shapes and sizes. Professionals can get exhausted in a number of ways, and it’s not always easy to predict where it’s going to come from. Alert systems are one of the factors that seem to be playing into this the most. ...


Read More on Datafloq

2020 AI Predictions: Wholistic Adoption, AI-Enabled Business Analysts, Applied AI on Rise

By AI Trends Staff

We invited readers to submit their top predictions for the impact of AI on business in 2020. Here is a selection of responses:

Dawn Fitzgerald is Director of Digital Transformation, Data Center Operations for Schneider Electric. The French multinational corporation is focused on sustainability and efficiency with its software and services offerings to a range of customers, many in the oil and gas, utilities, and manufacturing industries.

Dawn Fitzgerald, Director of Digital Transformation, Data Center Operations for Schneider Electric

Wholistic Adoption on the Horizon

2020 is the year for Wholistic Adoption. Wholistic Adoption is the realization that it is the people, process, and tools combination that create the massive benefit of the AI Shangri-La.  Adoption comes with experiencing Real Value and those that experience Value when using AI in their work will continue to use it… and this value will spread in the organization. But to realize the value we frequently need to redesign our business processes to be digital and AI ready. And to engage the digital ecosystem the user must be trained, encouraged, and their engagement measured. Simply put, a new AI algorithm or application with new AI-enabled features is not enough.

By way of example, the technician of the Data Center world may have only used a PC to check their email, sum numbers in excel, or log their hours.  If we now ask them do their daily rounds on a tablet and respond to predictive analytics then we must understand that new training is required.  Their progress of adoption must be tracked with continuous improvement on training and use. We must encourage a Digital Cultural which is an evolution not an instantaneous response. We must look for the need to redesign non-digital process; reordering or eliminating entire steps due to the digital efficiencies.

This is Wholistic Adoption and it will be a key focus and success factor in 2020.  The AI community will embrace the fact that, when it comes to adoption success, the whole is greater than the sum of its parts.

Per Nyberg is COO of Stradigi AI, an AI services company based in Montreal. Stradigi offers applied AI from a team of experienced and well-qualified practitioners. The firm offers the Kepler AI platform. Nyberg joined the company in July 2019, after working for many years at Cray, the supercomputer company, where he helped to grow Cray’s global AI business.

Per Nyberg, COO, Stradigi AI

Rise of the AI-Enabled Business Analyst; AI is No Longer for the Precious Few ML Experts and Data Scientists

“Businesses have been working to break through the logjam of AI projects that have been placed on the back-burner in the face of machine learning skills shortages. However, we’re seeing the real world reach of AI expand as companies look for ways to foster collaboration, gain economies of scale, and accelerate their AI paths from concept to production with maturing tools. AI is no longer for the small minority of machine learning experts and data scientists. With data at their core, business analysts are also eager for a slice of the pie. With AI and ML tools at their disposal, the skills of business analysts are expanding toward data science to explore insights from more diverse and richer data sets through the use of machine learning.

Technology and automated machine learning techniques will begin shifting the use of data and AI to a greater proportion of a company’s business analysts. The demand for these skills are also starting to shape higher-ed curriculums to contend with this new wave of expectations.”

A big concern of customers and prospects is that the shortage of machine learning skills is holding up AI projects. Also, many ML implementations continue to be focused on developing the pipelines and proving the applicability of ML with projects and models from scratch. This approach simply doesn’t scale in many ways—from efficient reuse of learnings to accelerating the ideation cycle. This is one area where an AI platform really makes sense. The key is for businesses to start scaling their specialists’ skills and focus them on the most important tasks. Also, day-to-day AI adoption needs to extend beyond specialized data scientists. Companies can support data scientists, business analysts and other critical roles with intuitive AI platforms that can help take projects from the ideation phase and into production throughout the organization.”

John Desmond is Editor of AI Trends. He has worked as a journalist for seven entrepreneurs in the publishing business, including Eliot Weinman, founder of the AI World Conference & Expo and the AI Trends newsletter.

Applied AI and AI DevOps to Rise

As AI gets more real in business, the practicality of rolling out an application that relies on the processing demands and data requirements of an AI application becomes much more real. These are unprecedented platform requirements. They will require investments in hardware and networking. And companies will always need to assess the business benefit, and quantify whether the investment in AI is worth it, or whether the investment is being made the right way. Recent accounts of massive AI models maxing out hardware are a cause for concern. Will the new AI applications be too brittle to be of lasting practical business value? Will the target of the data science be the right one or a misfire?  Or will there be enough hits to make it worth it?

Re-skilling the workforce will also be a major theme. Young and mid-career workers in all fields without question need to become conversant in AI. If they are not taking courses from the likes of Coursera or a university, they need to bootstrap their way to AI competence somehow.  Otherwise, the threat of job loss is real. If you are asleep at the switch, the AI might come and get you. Workers need to take advantage of the opportunity AI provides to further their careers, and not get victimized by it. In whatever role they play in their companies, workers should be trying to assess how AI could be helpful and how they could be involved in putting AI to work. Those that succeed in adding AI competence to their resumes will benefit greatly. The value of experience in business combined with competence in AI is high.

Savvy Marketers Using AI Need to Know about New CCPA in Effect on Jan. 1, 2020

By John P. Desmond, AI Trends Editor

Just as marketers are increasingly pursuing the use of AI tools to help personalize marketing to website visitors, the new California privacy law goes into effect. Now savvy marketers have to try to protect their companies from any exposure to violations.

That the march of AI is traversing online marketing is not in doubt. A survey of 3,000 consumers in North America conducted by the Harris Poll and RedPoint Global found that 63% expected personalized services and seek promotions and offers tailored to their needs, according to an account in Martech Advisor.

The authors cited these trends:

Embedding AI-powered chatbots to collect data for personalization. The chatbot can engage in a conversation with the consumer site visitor, and thereby feed data into forms on the CRM platform, enabling later personalization.

Use AI to personalize micro-elements of content. Tweak the greeting, the subject line to emails and follow ups to personalize the conversation. The Einstein AI engine from Salesforce is an example tool that can help improve micro-elements of content, such as by recommending subject lines to enhance targeting.

Target customers in real-time as they click through your site. As the visitor browses products, clicks on links, explores different catalog pages, the screen is adding and removing visual elements and retargeting ads accordingly. Match2One offers a tool to help predict which visitors would be interested in your product based on their website behavior.

Results are coming in for early experience with AI marketing personalization tools. For example, Gap built their own customer data platform (CDP) and implemented AI techniques to help with customer resolution, segmentation, and clustering. Gap partnered with AI startup Amperity to strengthen its technology. Efficiency of digital marketing is said to have increased by some 50% from 2018 as a result.

The benefits of AI for targeted marketing include the ability to deliver product recommendations, according to a recent account in Forbes. On many online retailer websites, a view of a product will result in suggestions of additional related items at the time of purchase. Retailers are also exploring how to improve the user experience (UX), how the site functions, in order to improve sales. The use of chatbots to enhance customer support is also becoming more prevalent, with the bot available to answer questions about product features, shipping time or where to make a purchase instantly.

CCPA Took Effect on Jan. 1, 2020

Now comes the California Consumer Privacy Act, approved in 2018 but going into effect on Jan. 1, 2020. The law gives consumers more protection over how their information can be used by for-profit companies that do business in California. The businesses in the purview of CCPA need to have more than $25 million in revenue, receive information from over 50,000 consumers or derive 50% or more of revenue from selling personal consumer information.

If the CCPA applies to your business, whenever you collect personal information on your site visitor, you need to disclose what information you are collecting and how you will be using it, according to an account in the blog of Hubspot, a supplier of marketing software. Also, you now need to give consumers the right to opt-out of having their information sold to third-parties, and you need to give consumers view access and the ability to delete the information you have collected about them.

CCPA penalties are up to $2,500 per violation or $7,500 per intentional violation, plus an additional $100 to $750 per incident to the affected persons. The CCPA applies only to data collected directly from and about California consumers. The European GDPR in contrast, applies to all data collected about EU citizens.

Awareness of CCPA is low. A recent ESET survey cited by Hubspot found that 44% of respondents had never heard of CCPA, 12% did not know if the law applied to them, and 34% were not sure if they needed to change how they capture and process data to comply with the law.

Tony Anscombe, global security evangelist, ambassador for ESET

“It’s clear that businesses are confused about this upcoming regulation, they do not know whether they are subject to the law and what they need to do to become compliant,” stated Tony Anscombe, global security evangelist and industry ambassador for ESET, an IT security and services company, in a press release.. “This is a serious situation, as the penalties will be severe, and the financial harm could be grave to these firms. Businesses should particularly focus on the ‘reasonable security’ aspect of the law by ensuring they have stringent processes and practices in place, including strong endpoint protection and encryption, throughout their organization.”

Look for the savvy AI marketing tool suppliers to be offering assistance in staying in compliance with evolving consumer data privacy laws.

Read the source posts in Martech Advisor, Forbes. on the Hubspot blog and on BusinessWire.

Cars Careening Out-of-Control In Crash Mode: The Case Of AI Autonomous Cars

By Lance Eliot, the AI Trends Insider

Bam!

While innocently sitting at a red light, a car rammed into the rear of my car. I was not expecting it.

Things began to happen so quickly that I barely remember what actually did happen once the crash began.

Within just a few brisk seconds, my car was pushed into a car ahead of me, ripping the back and left-side of my car. The gas tank ruptured and gasoline leaked onto the ground, my airbag deployed, most of the windows fractured and bits of glass flew everywhere. Basically all heck broke loose.

This actually happened some years ago when I was a university professor. I had been driving past an elder care facility on my way to the campus. A car driven by someone quite elderly had come up behind me at the red light and he inadvertently punched on the accelerator rather than the brake. His car rammed into my car, and my car rammed into the car ahead of me.

Fortunately, none of us were badly injured, but if you saw a picture of my car after the incident, you’d believe that no one in my car should or could have survived the crash.

My car was totaled.

I think back to that crash and can readily talk about it today, but at the time it was quite a shocker.

Speaking of shock, I am pretty sure that I must have temporarily gone into shock when the crash first started.

I say this because I really do not remember exactly how things went in those few life-threatening seconds. All I can remember is that I kind of “woke-up” in that I consciously realized the airbag had deployed, and that my windshield was busted, otherwise I was utterly confused about what was going on. It was as though a magic wand had transformed my setting into some other bizarre world.

As I sat there in the driver’s seat looking stunned, and as I slowly looked around to survey the scene, trying to make sense of what had just occurred, some people from other cars nearby had gotten out of their cars right away and ran to my car. With my driver’s side window nearly entirely smashed and gone, they yelled into the car and asked me if I was okay. I looked at them and wasn’t sure that I understood what they were asking me and nor why they were even talking to me.

It was at that point that I smelled the strong odor of gasoline.

In that same instant, the people standing outside my driver’s side window were yelling for me to get out of the car because of the gasoline that had poured onto the street. I realized later on that these good Samaritans were very brave and generous to have endangered themselves in order to warn me about the dangers that I faced.

Luckily the car door still worked, so I opened it, undid my seatbelt, pushed away the remains of the air bag, shifted my body and my legs to position outside the door, and stepped out of the car.

I nearly collapsed.

Turns out my legs had gone weak as the aftermath of the shock and fright involved. Several people helped walk and semi-drag me to the curb and get away from the car itself. I sat there on the curb, watching as everyone was running around trying to help, and for a moment I thought it had occurred without me being involved at all. I was just a bystander sitting at the curb after a car accident had happened.

When the police and an ambulance showed-up, I had regained my composure. I was standing up sturdily now and calmly examining the cars. At first, the police officers and the medical crew doubted that I had been inside the car and certainly doubted that I had been the driver. I had nary a scratch on me. I seemed coherent and able to talk about what had happened.

In fact, and you’ll maybe laugh at this, I was mainly worried that I would be late to teach my class at the college.

I had never been late to any of my lectures.

What would the students do, what would they think?

Of course, I realized later on, after several years of being a professor, the students probably welcomed being able to skip a lecture and would fruitfully use their time for other “academic” purposes.

The main aspect about the incident was that my mind was blurry about those key seconds between having gotten hit from behind and the realization that I was sitting in my driver’s seat and glass was around me and my airbag was in front of me.

I cannot to this day tell you exactly what happened in those precious few seconds.

I am pretty sure that my body was likely a rag doll and merely flopped around as the impact to the car occurred.

Which way was my head facing?

Well, I had been looking straight ahead at the intersection while waiting for a green light, so presumably my head was still pointed in that direction when the initial impact occurred. Where were my arms and hands? I had been lightly holding the steering wheel and so that’s where my arms and hands were, at least up until the impact. My legs and feet were under the dash and positioned at the pedals, including that my right foot was on the brake, doing so because I was at a red light and stopped, again that was just before the impact.

I wondered whether there was anything I could have done once the impact began.

Suppose I had been forewarned and told or knew that a car was going to violently ram into the back of my car. Let’s further assume that I didn’t have sufficient time to get out of the way or make any kind of evasive maneuver.

It’s an interesting problem to postulate.

Acting As A Car Crash Begins To Emerge

We usually think about the ways to avoid a car accident.

What about trying to cope with an accident that is emerging, supposing you have a brief chance to take some form of action, aiding in perhaps reducing the impact but not being able to fully avoid the incident overall.

In this case, if I had some premonition or clue that the accident was going to happen, maybe I could have tried to turn the wheels of the car so that it might move away from the car ahead of me once my car got rammed.

Or, maybe I might have put on the parking brake in hopes it would further keep my car from being pushed by the ramming action.

The medics at the scene told me that I was probably lucky that I did not realize that the ramming was going to occur, since most people tense up.

They said that tensing up is often worse for you when you get into a car accident. According to their medical training and experience, there is a greater chance that when being jarred harshly, jostled and tossed around, the tightened or tensed muscles of my body would try to fight against the movement, and likely lose, thus it would lead to greater physical injury to my body. Instead, by being loose and unknowing, my body was more fluid and accommodated the rapid pushing, shoving, and fierce shaking.

I’d like to put aside the idea that I might have been forewarned, and instead consider a slightly different angle to the incident.

Suppose that my mind had remained clearly alert and available during those few seconds in which the accident evolved. I mentioned to you earlier that I have no particular recall and those moments are blurry in my mind, let’s pretend differently.

Pretend that my mind was completely untouched and could act as though it was separate from the severe contortions happening to my physical body.

What then?

Reenactment Of Car Crash Timing

We’ll start the clock at the moment of impact.

The car behind me has just collided into the rear of my car.

This is time zero.

Over the next few seconds, the impact will work its way throughout my car.

You might want to consider this akin to those popular online videos in which things are filmed in slow motion. You know, the videos that show what it looks like in the split seconds of a bullet going through a piece of wood or a watermelon being smashed. Imagine a slow-motion version of my car incident.

We’re now assuming that my mind can undertake whatever kind of thinking might be pertinent to the matter at-hand. Of course, my mind might be thinking about that lecture I was going to give that day, or maybe what I was going to eat for dinner that night. Put those thoughts aside. In this slow-motion version, devote my mind to focusing on the car accident that is happening.

I’d also suggest that we assume that my senses are all in perfect working order too. You might argue that my senses are going to get muddled by the forceful jerking efforts of the car being rammed, which I agree seems likely.

In a moment, I’ll revisit the pretend with that mushing effect to my senses as another variation.

Okay, my mind is fully active, focused on the car incident as the clock starts to tick, and I’ve got control over my sensory faculties, and we’ll include that I have control over my body. This means that I can take whatever kind of driving action that I want to undertake.

Is there anything that I can do to drive the car in those few seconds that might in some manner lessen the impact of the car accident?

Maybe I had taken my foot off the brakes when the real accident occurred, reflexively, and in the case of this pretend we could assert that I am going to keep my foot on the brakes. Perhaps my arms and hands flew off the steering wheel in the real incident.

Let’s pretend that I keep them on the steering wheel.

It’s not evident how much my added ability to control the car in this particular incident is going to be aided by my clear mind and the use of my senses and my body.

One limiting factor is the car and the circumstances of where the car was positioned.

The car was being pushed fiercely from behind. In this case, the brakes weren’t doing much in those split seconds anyway. The fact that there was a car ahead of me pretty much stopped my car from going much further ahead, due to my ramming into it, and I was pinned between two cars now. One car pushing from behind, the other car at a standstill and preventing me from readily driving forward.

The car itself is a limiting factor too in that the brakes might have gotten cut anyway upon the impact to the car.

In that case, pushing on the brake pedal might not have had any material effect. Likewise, the steering wheel might not be useful during those few seconds, if the linkages and internal steering controls were damaged or unable to relay my positioning of the steering wheel.

In my case, I’m going to toss in the towel and say that it is unlikely that if my mind had remained clear and available, and if my senses were continually available and working, and if my body was functioning so that I could use it to actively and purposely drive the car, there’s not much that could have gone differently to improve what happened during those seconds of impact and reaction.

If you look at different circumstances, the results might come out differently.

Remove the car that was ahead of me.

Pretend I have a straight-ahead path.

Assume too that I can see the intersection and there are no cars in it, meaning that I can use the intersection if I want to do so.

Does this change things?

In theory, depending upon the pace at which my car can accelerate, and depending upon the pace at which the car from behind me is ramming into me, there is some chance that I could have punched down on the accelerator and tried to leap ahead. It would have become a kind of race, starting when the impact began, the zero-clock point that I mentioned earlier. This could potentially have allowed me to lessen the blow from the rear of my car. I might even have accelerated fast enough to escape much of the impact, ending up on the other side of the intersection without much damage to the rear of my car.

I’d bet there are many car accidents wherein if the driver involved could magically have a clear and present mind, and be able to control their car, there is a chance that whatever dire results occurred could have been lessened.

On the news, I saw an instance recently of a driver that veered their car to avoid hitting something in the street and the car driver lost control of the car, which resulted in the car ramming into a parked car and a light post and a fire hydrant. It sheared off the fire hydrant and sent water shooting into the sky.

How did the driver lose control of the car?

Was it because of the mechanics of the car, or was it because the driver themselves lost their presence of mind and no longer were of their right mental faculties? It could be that the shock of veering caused the person to mentally go into a blur. This blurred mental state meant that the human was no longer actively driving the car. The car was out-of-control.

There was no driver actively driving the car.

Out-Of-Control Cars

I’m sure you’ve seen lots of news clips and videos of cars that became a kind of mindless projectile.

There was an incident captured on YouTube of a car that swerved to avoid hitting an animal in the street and the car smashed through a wood fence, continued onto a farm adjacent to the road, plowed a bunch of planted vegetables, and finally the car came to a stop.

Out-of-control car.

Another incident showed a car that didn’t make a left turn very well, veering beyond the confines of the left turn. The car continued to make too large a turn and rammed into a mailbox. This car then rammed into a hot dog vendor stand and ultimately came to a stop once it hit a storefront.

There are plenty of videos of cars that missed a turn and went through a fence into someone’s swimming pool. Having a car fly off a bridge is another example of an out-of-control car.

There are situations whereby an out-of-control car might be due to the car having mechanical problems and there is seemingly nothing that the driver can potentially do. For example, the accelerator pedal getting stuck and refusing to budge, forcing the car into going faster and faster.

This might happen because something is lodged into the accelerator pedal like a floor mat.

It has also happened as a result of an intrinsic defect in the car design.

Assume that the driver did not cause the accelerator pedal to be jammed downward. In that instance, is the driver now merely a passenger in that there is nothing the driver can do? I’d dare say we would all agree that the driver can still do something. They need to try and steer the car to avoid hitting other cars and other objects. They could try to see if they could dislodge the pedal to curtail the rapid acceleration. They could start honking their horns to try and warn other drivers and pedestrians that the car is a runaway.

And so on.

Not everyone would have the presence of mind to do those things.

If you’ve never had your accelerator pedal get stuck, the odds are that when it does get stuck, you’ll be shocked and unsure of what to do. You might lose your mental presence and become panicked. Even though there are actions you could take, those actions might not come to your mind. If they do come to your mind, you’d have to remain calm enough to enact those actions by forcing your body to undertake the desired actions.

Have you ever been to a demolition derby or seen one on TV?

At a demolition derby, the cars all try to smash into each other. It’s the purpose for the derby. Usually, the last running car gets the grand prize. I bring up the topic of demolition derbies to point out that those drivers are well-prepared to deal with their cars when the car is out-of-control.

A driver in one car might get hit from the left side by another car, meanwhile be getting hit from the right side by another car, and at the same time trying to hit a car ahead of them. The cars are all being pushed and shoved. Driver’s in those cars are generally able to keep their mind and wits about them. They are trained for the situation and know what to do, though of course it is somewhat easier when the matter is expected versus when unexpected (in the derby, it is expected that your car is going to be hit and go out-of-control).

One aspect of a car being out-of-control is when the car is sliding or otherwise in a motion that you as a driver did not intentionally seek to have the car do. Have you ever had your car slide on ice or snow?

That’s an example of the car being out-of-control.

Again, how you react as the driver can make a big difference. If you aren’t aware of the sliding action and aren’t prepared to react, or if your mind is muddled, you might not try the usual techniques that are recommended for dealing with a sliding car. You can potentially regain control by typically turning the wheels in the direction of the slide and avoid jamming on the brakes.

Dealing With Out-Of-Control Cars

In essence, there are actions that you can take to bring the car back into control, or you can take no actions and hope for the best, or you can take misguided actions that cause the car to go into a further out-of-control result.

You need to not only determine what is the proper course of action, you need to try to prevent the situation from getting worse, you need to take into account what your car can and cannot do, you need to consider any damage the car is undertaking and how it will limit what you can do, and consider a slew of other factors.

Demolition derby drivers are able to do this.

I don’t want to make them into seeming to be super drivers per se. Their cars are usually jiggered in a manner to make things simpler for them. Usually, the cars are stripped of items that can fly around. Cables are reinforced. There aren’t any passengers on-board. Gas tanks get special protections. Plus, the derby typically takes place in a confined area that has no pedestrians, no other obstacles, and it is like a playground in which all you can do is ram into other cars.

That’s a far cry from dealing with a real-world crash-mode and having to figure out what to do, and cope with bystanders, and cope with a myriad of other factors. Nonetheless, the derby drivers get a chance to practice dealing with the stresses of being in a car crash and able to train themselves to keep a mental awareness, enabling them to continue driving a car and maintain control, as much as feasible.

AI Autonomous Cars And Cars Out-Of-Control

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

At the Cybernetic AI Self-Driving Car Institute, we are developing AI software for self-driving cars. One aspect that few automakers and tech firms are considering at this time is the special characteristics of driving a car while it is in crash-mode and how the AI should be skilled to do so.

Allow me to elaborate.

I’d like to first 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 4 and 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 out-of-control cars and dealing with them as a driver when the car is seemingly out-of-control, let’s consider how the AI of an AI self-driving car should be coping with such situations.

We’ll start by debunking a popular false myth, namely that true Level 4 and Level 5 AI self-driving cars will never get into car accidents, therefore the claim is that the AI does not need to be able to cope with car crashes.

Wrong!

Well of course AI self-driving cars are going to get into car crashes.

It is nonsense and foolhardy to think otherwise.

First, as mentioned earlier, the roadways will have a mixture of human driven cars and AI driven cars.

This mixture is going to have car crashes, involving a human driven car that crashes into an AI self-driving car, and likely too occasions whereby an AI self-driving car crashes into a human driven car. In some instances, the AI self-driving car might be the instigator of the car crash, while in other cases it is carried into the car crash as a cascading action of the car crash.

There will also be instances of AI self-driving cars crashing into other AI self-driving cars, as I’ll describe in a moment.

Recall that in my story about how I had gotten hit from behind while sitting at a red light, I was in a conventional car, but for pretend sake let’s assume that the car was actually a Level 4 or Level 5 AI self-driving car.

Would it have been able to avoid getting hit?

No.

There was no place to escape to and the car that rammed me from behind did so with almost no warning.

The AI might have detected the aspect that the car behind it was suddenly speeding up, and therefore the AI would likely have had a few seconds heads-up that the crash was going to occur.

But, given that there was a car immediately ahead of my car, and there were other cars also sitting at the intersection and all around my car, the AI would have been boxed-in or essentially surrounded and would have had no opportunity to escape. The crash would have happened.

This is an instance of a human driven car hitting an AI self-driving car.

An AI self-driving car could get hit by another car such as a human driven car and could even get hit by an AI self-driving car.

Suppose the car ahead of me at the intersection was an AI self-driving car and continue assuming that my car was an AI self-driving car.

Once the car behind me has hit my car, it would have forced my AI self-driving car to ram into the car ahead, which we’re pretending was another AI self-driving car.

This is an instance of an AI self-driving car hitting another AI self-driving car.

Sometimes when I mention this possibility, there are those that will say that it is “cheating” to say that one AI self-driving car hit another one in the sense that they were both pinned into a situation whereby the impact was unavoidable.

I then say indeed they were pinned, that’s the case here, but it still doesn’t negate the fact that one AI self-driving car hit another AI self-driving car.

My point is that it can happen.

Use Of V2V For Crash Tip-off

Some say that an AI self-driving car won’t get hit because it will have V2V (vehicle-to-vehicle) electronic communications.

This means that one AI self-driving car can electronically communicate with other AI self-driving cars, perhaps doing so to forewarn that the road ahead has debris on it or maybe that the traffic is snarled.

Okay, let’s assume in my pretend scenario that my AI self-driving car has V2V and the AI self-driving car sitting ahead of me at the intersection has V2V too.

In those few seconds wherein my AI self-driving realizes it is going to get hit, it sends quickly a V2V broadcast, which the AI self-driving car ahead of me receives and decodes. Based on the electronic message, the AI of this car ahead of me has to decide whether it believes what it is being told, which in of itself is a potential question and problem associated with V2V aspects, and if the AI does believe that my car is about to hit it, the next aspect involves figuring out what to do.

The AI self-driving car ahead of me, now forewarned and with presumably a second or two maybe to react, could try to proceed into the intersection to avoid my car hitting it from behind. The AI has to ascertain whether it is worse or not to remain in-place and get hit from behind, or potentially try to gun the engine and rush into the intersection. If the intersection has other traffic in it, the idea of aiming to rush ahead is not so attractive, though likewise staying in place is not attractive either.

This highlights the kind of ethical choices that an AI system is going to need to make when driving an AI self-driving car.

It has to decide in this instance the risks of injury, death, damages from waiting to get hit from behind by my AI self-driving car versus the instance of risks of injury, death, damages if it attempts to rush into the intersection. There is also the matter of whether the amount of time involved would actually allow the AI self-driving to rush into the intersection, depending upon the acceleration capability of the AI self-driving car.

More Examples Of AI Driverless Car Crashes

I’ll give you another example of how an AI self-driving car might hit another AI self-driving car.

Suppose there are two AI self-driving cars are going down a street (hey, this sounds like an AI self-driving car joke of some kind, like two people going into a bar!).

They are following each other at the proper distance, based on their speeds and car lengths, and is supposed to be how humans are to drive a car, though I’d wager few humans allow sufficient distances between their cars when driving.

A dog darts from seemingly nowhere and into the street. In this case, there was no possibility of detecting the dog prior to its entering into the street.

The AI self-driving car that’s ahead of the other AI self-driving car has insufficient distance to come to a stop and avoid hitting the dog. The choices for the AI are to either try to stop and yet know it will ram into the dog, or try to swerve to avoid the dog, but let’s assume there are parked cars and other cars coming down the street too.

This means that the AI will need to decide whether to hit and likely kill the dog or take a chance and swerve into the oncoming lane of traffic and possibly get hit head-on or ram itself into a parked car to try to avoid the dog.

For more about AI self-driving cars and accidents, see: https://www.aitrends.com/selfdrivingcars/accidents-happen-self-driving-cars/

What should the AI do?

The AI is between the proverbial rock and a hard place.

There aren’t any “good” choices to be made here.

Which is the least of the worst options is more akin to this problem. Suppose the AI opts to ram into a parked car, figuring that the parked car has no humans in it and thus no humans will be put at risk, and it is only property damage that will result. This saves the dog, prevents potentially hitting an oncoming car, and perhaps seems to be the least-of-the-worst choices.

The AI quickly sends out a V2V to forewarn that it is going to ram into a parked car.

The AI self-driving car coming up behind is given a somewhat sudden heads-up that this action is going to occur.

Can the AI self-driving car stop in time and avoid hitting the AI self-driving car that is going to ram into the parked car?

Maybe yes, maybe not.

We also don’t know if ramming into the parked car will cause the AI self-driving car to perhaps bounce back into the street and maybe make the situation from the perspective of the upcoming AI self-driving car even worse.

The point of these scenarios is that there will absolutely be car crashes involving AI self-driving cars.

I want to make sure that we all agree with that possibility.

Some might argue that we’ll have less car crashes due to the advent of AI self-driving cars, and for that I’d be willing to say it is hopefully the case that we’ll have less, but in no manner at all will we have zero instances of car crashes involving AI self-driving cars.

For the false notion of zero fatalities, see my article: https://www.aitrends.com/selfdrivingcars/self-driving-cars-zero-fatalities-zero-chance/

For more on ethics and AI, see my article: https://www.aitrends.com/selfdrivingcars/ethics-review-boards-and-ai-self-driving-cars/

For the need of AI to be a defensive driver, see my article: https://www.aitrends.com/selfdrivingcars/art-defensive-driving-key-self-driving-car-success/

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

For my article about safety issues of AI self-driving cars, see: https://www.aitrends.com/selfdrivingcars/safety-and-ai-self-driving-cars-world-safety-summit-on-autonomous-tech/

Missing The Boat On Crash Avoidance

In terms of AI self-driving cars and getting involved in car crashes, most of the automakers and tech firms are focused on avoiding car crashes and not particularly considering what the AI should do once a car crash is imminent or underway.

This is troubling.

If the AI is not intentionally established to have special processes or procedures for dealing with a car accident once underway, it means that the AI self-driving car is essentially going to become out-of-control.

The AI developers are assuming that the AI will be able to handle the self-driving car as if the AI self-driving car is just nonchalantly driving along, but once the car accident starts, all bets are off. The AI self-driving car is going to be likely pushed, shoved, and otherwise taken out of the “comfort zone” in which one assumes the self-driving car is operating most of the time. Assumptions about being able to brake, accelerate, and steer are no longer going to be valid due to the extenuating circumstances involving what is about to happen to the self-driving car.

Some automakers and tech firms aren’t working on this at all, or they are working on it but put it on the back-burner as a so-called edge problem.

Their logic to put this on the back-burner is that they assume the AI self-driving car is highly unlikely to get into a car accident, thus, why worry about it now.

If it is going to only happen once in a blue moon, deal with it later on.

For more about edge problems, see my article: https://www.aitrends.com/selfdrivingcars/edge-problems-core-true-self-driving-cars-achieving-last-mile/

Part of the grave concern with this kind of thinking is that it means that when an AI self-driving car does get into a car accident, it will likely do little to try to minimize the impacts and be unable or ill-equipped to find ways to either escape or  at least try to “improve” upon a bad situation.

I’ve predicted many times over that when AI self-driving cars get into car crashes, society is going to become hyper-focused on why and how it happened, and the entire future of AI self-driving cars is going to be based on these instances. It becomes the classic “bad apple” that spoils the entire barrel.

I know many AI developers are frustrated that this can occur and feel that it is unfair of society and the media to react in such a manner, but, hey, that’s the way the cookie crumbles.

Generally, the public and the media are not especially forgiving about AI self-driving cars getting involved in car accidents.

Tesla’s Elon Musk has bitterly complained that society over-hypes these instances and should try to balance those instances against the thousands of car accidents with conventional cars, but he’s barking up a rough tree to think that society will be willing to view AI self-driving cars in that kind of context.

Auto makers and tech firms need to be doing as much as they can to cope with not only avoiding car crashes but also being able to have the AI enter into a kind of “crash mode” when a car accident is either imminent or underway.

I would likely anticipate that if the auto makers and tech firms don’t have such a provision in their AI, besides the aspect that it means the AI will be somewhat acting in a willy-nilly manner during a car accident, I would predict that the auto makers and tech firms are going to be faced with some hefty legal bills and potential product liability issues.

Lawyers for those humans that are immersed in a car accident are going to ask tough questions about what the AI did, why it did so, etc.

For my article about product liability and AI self-driving cars, see: https://www.aitrends.com/selfdrivingcars/product-liability-self-driving-cars-looming-cloud-ahead/

For my article about the lawsuits over AI self-driving cars, see: https://www.aitrends.com/selfdrivingcars/first-salvo-class-action-lawsuits-defective-self-driving-cars/

For the crossing of the Rubicon, see: https://www.aitrends.com/selfdrivingcars/crossing-the-rubicon-and-ai-self-driving-cars/

For the public perception of AI self-driving cars, see my article: https://www.aitrends.com/selfdrivingcars/roller-coaster-public-perception-ai-self-driving-cars/

Tick-Tock Goes The Clock

As suggested earlier, let’s put a stopwatch into the car accident aspects and assume that at the initial point of impact we start the clocking ticking.

This is almost as though we are able to slow down time and do a slow-motion analysis of a car accident.

In a manner of speaking, we might look at this from the perspective of “speeding up” rather than slowing down. A human might not be able to give much mental concentration to a car accident once the accident begins to unfold. The time allotted is very short, perhaps fractions of a second or just a few split seconds.

On the other hand, the AI might be running on some very fast computer processors and so it could potentially do a lot of computational processing in that rather short amount of time.

There are those that would argue too that the AI won’t go into shock and so it will keep its head about it, while a human is likely to not keep their presence of mind. In my car accident, I still don’t know exactly what happened from the moment of impact until I was suddenly aware that I was seated in my car and something untoward had just occurred. Whether I was in shock or maybe blacked out momentarily, we presumably don’t need to worry about the AI suffering that same fate.

I would though wish to put a caveat on this idea that the AI won’t suffer from the shock aspects.

Those that make such a claim are leaving out the important element that the AI is running on computer processors that are on-board the self-driving car. When the self-driving car is getting rammed, there is a high chance that those processors are going to suffer too. The physics of the situation can mess with the electronics. The physical crushing actions and blows to the car are likely to mess with the electronics of the computer processors and computer memory on-board the self-driving car.

In a manner of speaking, you could assert that there is a chance that the AI will go into “shock” or maybe we call it “artificial shock,” involving damage being done to the AI systems and its on-board computers.

This could alter what the AI is able to do during the crash itself. What kind of fail-safe capabilities does the AI have?

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

For cognitive timing of the AI, see my article: https://www.aitrends.com/selfdrivingcars/cognitive-timing-for-ai-self-driving-cars/

For my article about self-awareness being crucial to AI, see: https://www.aitrends.com/selfdrivingcars/self-awareness-self-driving-cars-know-thyself/

Maybe the AI is not able to do anything once the crash gets underway and has become completely inoperative.

Maybe the AI is messed up and does not realize that it has become messed up, and yet still tries to drive the car, doing so in a manner that actually makes the situation worse!

Overall, the special “crash mode” of the AI needs to be able to discern what it can and cannot do, what it’s own status is in terms of working properly, and have a number of contingencies ready to go.

Special AI Capability For Crash Mode

There is no doubting that the “crash mode” becomes a highly complex problem.

The self-driving car is likely becoming less drivable as the car crash clock ticks, starting at point in time t=0. At some time, we’ll say is t+1, perhaps the brakes are no longer functioning. At some time, t+2, it could be that the car is now in a slide as a result of the ramming and the wheels are unable to gain traction to redirect the direction of the self-driving car. And so on.

I had earlier mentioned that I wasn’t sure in my car accident as to the capability of my limbs, such as whether I still had any ability to keep my arms and hands on the steering wheel or have my foot on the brake pedal.

The AI is going to have similar “driving controls” issues to cope with. Though the AI doesn’t have arms or legs, it does have electronic systems and various means to undertake the driving controls of the self-driving car.

Are those driving controls still available to the AI or might they have been damaged or cut as a result of the underway car crash as it evolves in those split seconds?

In essence, you have these aspects:

  • AI system as “mindset” for driving the car
  • Sensors of the self-driving car that the AI needs to sense what’s happening
  • Car controls for the AI to use to drive or control the self-driving car

The AI system itself might be degraded or faulty during the car crash and must have a provision to ascertain its own status and reliability. This then would be used to try to decide what actions the AI ought to be taking, and also avoid taking actions that the AI ought to not be taking.

The sensors such as the cameras, the LIDAR, radar, and the ultrasonics can become degraded or faulty during the car crash. This means that whatever the sensor fusion is reporting might be false or incomplete. This means that the updating of the virtual world model might be false or faulty. The AI action planner needs to try to ascertain what about the sensors and virtual world model seem to make sense and what aspects might now be suspect.

The car controls might no longer be accessible by the AI, due to the car crash aspects as they unfold.

Or, maybe the AI can issue car controls commands, but the controls themselves are non-responsive, or the car controls attempt to carry out the order, but the physics of the car and the evolving situation preclude the car from physically being able to carry out the instructions.

Impacts To Human Passengers Inside The Autonomous Car

One aspect that I’ve not brought up herein involves the AI having to decide what to do about any human passengers that are in the AI self-driving car.

This is quite important and must be taken into consideration.

Here’s what I mean.

For the AI to consider what action to take during the car crash, there is the matter of how the humans within the AI self-driving car are going to be impacted too.

Which is better or worse for the passengers, having the AI attempt to accelerate out of the full impact or instead maybe letting the impact happen but steer the car so that the impact happens on one side of the car versus the side that the humans are sitting in?

The crux is that the number of human passengers, where they are seated, possibly their size and age (adult versus child), could all play into how to “best” respond to the car crash as it is underway. This takes us again into an ethics laden situation. If the AI can find a means to more likely save let’s say an adult in the self-driving car versus the child, should it take such action, or should it attempt to save the child more so than the adult?

I know that you might be saying that the AI should seek to save all humans inside of the AI self-driving car. Sorry, that’s too easy an answer.

There is a myriad of options that the AI might be able to consider.

Each of those options will involve uncertainties.

We also need to consider the humans outside the AI self-driving car, such as there might be pedestrians standing nearby that are at risk, and humans in the other nearby cars that are cascading into the car crash.

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

For pedestrians as roadkill, see my article: https://www.aitrends.com/selfdrivingcars/avoiding-pedestrian-roadkill-self-driving-cars/

For my Top 10 predictions about AI self-driving cars, see: https://www.aitrends.com/ai-insider/top-10-ai-trends-insider-predictions-about-ai-and-ai-self-driving-cars-for-2019/

For the aspects about rear-end collisions, see my article: https://www.aitrends.com/selfdrivingcars/rear-end-collisions-and-ai-self-driving-cars-plus-apple-lexus-incident/

Twists And Turns Involved

The AI has quite an arduous problem to solve during a car crash.

That’s partially why it is being avoided by some AI developers, it is a really tough nut to crack.

I don’t think that this means that we should just shrug it off and wave our hands in the air.

Without any kind of crash mode capability, the AI is going to be potentially useless and merely add fuel to the fire of the self-driving car becoming a kind of unguided missile.

When I say this, there are some that try to retort that the AI might have a crash mode and try to deal with a car crash as it evolves, and yet ultimately be unable to do anything of substance anyway. It might be that the car controls are unavailable or non-functioning. It might be that the choices of what to do are so rotten that doing nothing is a better choice. Etc.

Yes, it is true that the AI might end-up not being able to aid the lessening of the car crash repercussions. Does this mean that the AI should not even try to do so? Are you willing to toss away the chance that the AI might be able to assist? I don’t believe that’s prudent and nor what we might hope a true AI self-driving car will do.

Each situation will have its own particulars that dictate what becomes feasible during the crash.

Was there any in-advance indication of what was about to occur?

Was any preparation possible prior to the actual crash?

Once the crash began, what possibilities existed of still being able to exert control over the self-driving car?

Throughout the car crash, what could be done and what was done?

There’s also the post-crash aspects too.

If the AI self-driving is still in some functional capability, the AI should be trying to ascertain what to do. Is the car still drivable by the AI such that the AI can pull the self-driving car off to the side of the road, and avoid possibly getting hit therefore by other traffic that might be soon coming upon the accident scene?

The AI must be continually monitoring the car controls status to try to discern what is usable and what is not:

  • No steering, limited steering, steering is stuck
  • No accelerator, limited accelerator, accelerator stuck
  • No brakes, limited brakes, brakes stuck

Use Of Machine Learning

Here’s a bit of a twist that might catch your interest.

Some say that we should be using Machine Learning (ML) or Deep Learning (DL) to cope with and aid the crafting of the special “crash mode” of the AI for a self-driving car.

The notion is that the use of deep or large-scale artificial neural networks might allow the AI to identify patterns in what to do during car crashes. By examining perhaps hundreds or thousands of car crashes, in the same way that the ML or DL studies pictures of street signs to identify what street signs consist of, maybe the AI could become versed in handling car crashes.

This seems sensible.

One question for you, where are we going to get all of this car crash data that will be needed to do the ML or DL training? Right now, the AI self-driving cars and auto makers and tech firms are doing everything they can to avoid car crashes.

There isn’t a vast trove of car crash data available to do this kind of pattern matching and training with.

Sure, there are tons of car crashes daily that are occurring with conventional cars.

This though does not encompass the kind of car crash data that we need to have collected. For today’s car crashes, at best there is info about what happened before a crash and what the end result was.

Whatever happened in-between is not particularly data captured and nor analyzed.

Unfortunately, there is not a handy treasure trove of car crash data that includes what took place during the crash itself.

The closest that we can come would be to use simulations.

The simulations though need to be based on the reality of what happens during car crashes. This might seem obvious, but I point this out because it is “easy” to make a simulation based on aspects that have little to do with what really happens in the real-world. Training via ML or DL via simulations that aren’t realistic is not likely to be overly helpful, though it at least provides a potential step forward.

For more about Deep Learning, see my article: https://www.aitrends.com/selfdrivingcars/plasticity-in-deep-learning-dynamic-adaptations-for-ai-self-driving-cars/

For ensemble Machine Learning, see my article: https://www.aitrends.com/selfdrivingcars/ensemble-machine-learning-for-ai-self-driving-cars/

For my article about street signs and DL/ML, see: https://www.aitrends.com/selfdrivingcars/making-ai-sense-of-road-signs/

For my article about the importance of simulations in AI self-driving car development, see: https://www.aitrends.com/selfdrivingcars/simulations-self-driving-cars-machine-learning-without-fear/

Conclusion

We need to have the AI of a self-driving car be able to deal with car crashes.

This includes not just the pre-crash aspects and the post-crash aspects, which is usually where the attention by the AI developers is aimed.

There must be a “crash mode” that is able to cope with the unwinding or evolving elements that happen during a car crash.

The crash mode could be a kind of last-resort core portion that does what it can to try to keep aware of the moment-to-moment situation and exert any car control that it can, doing so in hopes of minimizing injury, death, or damages. Similar to humans, in a manner of speaking, the AI can suffer from a type of “artificial shock” that means it will become degraded in being able to figure out what is taking place and what can be done about the emerging situation.

The complexities during a crash are enormous.

What on the self-driving car is still working and usable?

What is the status of the humans on-board? What is the situation outside the self-driving car?

How can all of these variables be coalesced into a sensible plan of action and carried out by the AI?

The odds are that whatever the AI derives, the plan itself will need to be instantly re-planned, based on the aspect that the situation is rapidly changing.

Other than demolition derby drivers, I’d suggest that most drivers are unable to remain steady and have the presence of mind during a car accident to do much to mitigate the consequences. The AI has a chance to be that demolition derby driver, though let’s subtract the part about wanting to purposely hit other cars as is the goal of a derby.

The AI potentially has fast-enough processing speed to try to find ways to cope with the car crash while it is occurring and take rudimentary actions related to the self-driving car. For the sake of AI self-driving cars, and for the sake of human lives, let’s put some keen focus on having “crash mode” savvy AI.

Copyright 2020 Dr. Lance Eliot

This content is originally posted on AI Trends.

[Ed. Note: For reader’s interested in Dr. Eliot’s ongoing business analyses about the advent of self-driving cars, see his online Forbes column: https://forbes.com/sites/lanceeliot/]

Delivery Robots With AI On the March

By AI Trends Staff

Delivery robots incorporating AI are on the march, being deployed more widely on the ground, sometimes crowding sidewalks. Here is an update.

Delivery robot providers include Starship Technologies, a startup created by Janus Friis and Ahti Heinla, founders of Skype. The company offers a general-purpose home delivery robot that today is an array of cameras and GPS sensors, but in the future will include microphones, speaker, and the ability via AI-driven natural language processing, to talk to customers. Since 2016, Starship has carried out 50,000 delivers in over 100 cities across 20 countries, according to an account in SingularityHub written by Dr. Peter H. Diamandis, the founder of Singularity University and the founder and executive chairman of the XPrize Foundation.

Dr. Peter Diamandis, Founder of Singularity University and founder and executive chairman of the XPrize Foundation

Another startup delivery provider is Nuro, co-founded by Jiajun Zhu, an engineer who helped develop Google’s self-driving car. The Nuro, looking like a toaster on wheels, is designed to carry about 12 bags of groceries in version 1. The company has been working in select Kroger stories since 2018, and partnered with Domino’s in 2019.

Retailers will be under pressure to save money on labor by using robots, Diamandis asserts. The US minimum wage is projected to be $15/hour by 2024; on Jan. 1, 2020, federal minimum wage will increase to $12/hour. Most states have historically set higher minimum wages than the federal standard, which was at $7.25/hour for many years.

The numbers make it tough for retailers to avoid employing more robots. “Robots work 24-7. They never take a day off, never need a bathroom break, health insurance, or parental leave,” Diamandis stated.

This is altering our relationship with commerce.

Starship Technologies Has Raised $85 Million So Far

Starship Technologies in August 2019 raised $40 million in private funding that they plan to use to deploy thousands of its autonomous six-wheeled delivery robots on college campuses around the country over the next two years, according to an account in The Verge. The San Francisco startup’s robots have been tested in over 100 cities in 20 countries, traveling 350,000 miles, crossing 4 million streets and recently completing delivery number 100,000. College campuses have many walking paths, well-defined boundaries and students with smartphones.

Starship works closely with college administrations, including at George Mason University, Northern Arizona University, the University of Pittsburgh, and Purdue University. The company plans to deploy 25 to 50 robots at each campus, which implies more than 5,000 robots running around the schools by 2021.

The trunk of the electric robot can fit about 20 pounds of cargo and has a suite of cameras around the outside that guide the robot. The delivery radius is three to four miles; the maximum speed is 4 mph, slower than delivery by a human on a bike or in a car. Starship CEO Lex Bayer stated students might even prefer the robot over a fellow student making deliveries. “There’s no guilt or shame,” he stated.

Starship has now raised a total of $85 million. The company charges $1.99 per delivery. Competition is from human delivery firms including DoorDash and Postmates.

Postmates is not standing still however. The company is identified in an account in TechRepublic as among the top five of delivery companies, along with Starship. The Postmates Serve robot has humanoid eyes that change to help people on the sidewalk understand where it wants to go next. The robot was being tested in Los Angeles in 2019; the expectation is that it could be used to replace human delivery workers.

Another top five entry is startup Nuro, which offers an autonomous small delivery van that drives on regular roads. It has heated and chilled compartments for delivering groceries. Testing started at a grocery store in Scottsdale in 2019 and later expanded to two grocery stores in Houston.

The rest of the top five is populated by the majors: Amazon with Scout and FedEx with the SameDay Bot, designed by Dean Kamen, inventor of the Segway. Testing was happening at the FedEx headquarters in Memphis in 2019. Amazon Scout conducted testing in 2019 in Washington state.

Robots Need to Be Well-Behaved on Crowded Sidewalks

While delivery robots vie for space on public sidewalks, they may free up the roadways a bit, suggests an account in Scientific American.  A study by Mobility Lab, a transportation policy research center in Arlington, Va. and George Mason University found 73% of freight and delivery vehicles in Arlington were parked outside authorized areas, often blocking bike lanes, fire hydrants, and crosswalks. By moving the last leg of deliveries from the road to the sidewalk, cities could reduce congestion and eliminate the parking problem entirely, suggested Paul Mackie, director of research at Mobility.

Renia Ehrenfeucht, chair of the Community and Regional Planning Department, University of New Mexico

That assumes the sidewalk will have room for the delivery robots. To gain trust, the robots need to demonstrate they can safely share pedestrian spaces, suggests Renia Ehrenfeucht, chair of the Community and Regional Planning Department at The University of New Mexico in Albuquerque, and co-author of the book Sidewalks: Conflict and Negotiation in Public Space.

“It’s actually really hard to navigate crowded sidewalks and not bump into people, and do it smoothly,” Ehrenfeucht says. “Until delivery robots are that skilled, if they could be, they will be disruptive.”

Read the source articles in SingularityHub, The Verge, TechRepublic and  Scientific American.

Specialized AI Chip Market Seen Expanding Rapidly

By AI Trends Staff

The fragmenting and increasingly specialized AI chip market will cause developers of AI applications to have to make platform choices for upcoming projects, choices with potentially long-term implications.

AI chip specialization arguably began with graphics processing units, originally developed for gaming then deployed for applications such as deep learning. When NVIDIA released its CUDA toolkit for making GPUs programmable in 2007, it opened the market up to a wider range of developers, noted a recent account in IEEE Spectrum written by Evan Sparks, CEO of Determined AI.

GPU processing power has advanced rapidly. Chips originally designed to render images are now the workhorses powering AI R&D. Many of the linear algebra routines necessary to make Fortnite run at 120 frames per second, are now powering the neural networks at the heart of advanced applications in computer vision, automated speech recognition and natural language processing, Evans notes.

Market projections for specialized AI chips are aggressive. Gartner projects specialized AI chip sales to project to $8 billion in 2019 and grow to $34 billion by 2023. NVIDIA’s internal projections reported by Evans have AI chip sales projected to reach $50 billion by 2023; most of those anticipated for data center GPUs used to power deep learning. Custom silicon research is ongoing at Amazon, ARM, Apple, IBM, Intel, Google, Microsoft, NVIDIA and Qualcomm. Many startups are also in the competition, including Cerebras, Graphcore, Groq, Mythic AI, SambaNova Systems and Wave Computing, who together have raised over $1 billion.

Allied Market Research projects the global AI chip market to reach $91 billion by 2025, with growth rates of 45% a year until then. Market drivers include a surge in demand for smart homes and smart cities, more investment in AI startups, the emergence of quantum computing and the rise of smart robots, according to a release from Allied on the Global Newswire. Market growth, however is being slowed by too few skilled workers.

The market splits into chip type, application, industry vertical, technology processing type and region, according to Allied. The chip types are divided into the GPU, the application-specific integrated circuit (ASIC), the field-programmable gate array (FPGA), the central processing unit (CPU) and others. The ASIC segment is expected to register the fastest growth at 52% per year until 2025.

At the recent International Electron Devices Meeting (IEDM) conference in San Francisco, IBM discussed innovations into making hardware systems that advance with the pace of demands of AI software and data workloads, according to an account in Digital Journal.

Among the highlights: nanosheet technology aims to meet the requirements of AI and 5G. Researchers discussed how to stack nanosheet transistors and multiple-Vt solutions (multi-threshold voltage devices).

Phase-change memory (PCM) has emerged as an alternative to conventional von Neumann systems to train deep neural networks (DNNs) where a synaptic weight is represented by the device conductance. However, a temporal evolution of the conductance values, referred to as conductance drift, poses challenges for the reliability of the synaptic weights. IBM presented an approach to reduce the impact of PCM conductance drift. IBM also demonstrated an ultra-low power prototype chip, with the potential to execute AI tasks in edge computing devices in real time.

An example of a specific application driving an AI chip design is happening at the Argonne National Laboratory, a science and engineering research institution in Illinois. Finding a drug that cancer patients can best respond to, tests the limits of modern science. With the emergence of AI, scientists are able to combine machine learning and genomics to sequence data and help clinicians better understand how to tailor treatment plans to individual patients, according to an account in AIMed (AI in Medicine).

Argonne National Lab Employing CS-1 for Cancer Research

Argonne recently announced the first deployment of a new AI processor, the CS-1, developed by Cerebras, a computer systems startup. The chip enables a faster rate of training for deep learning algorithms. CS-1 is said to house the fastest and largest AI chip ever built.

Rick Stevens, Argonne Associate Lab Director for Computing, Environment and Life Sciences, stated in a press release, “By deploying the CS-1, we have dramatically shrunk training time across neural networks, allowing our researchers to be vastly more productive.”

CS-1 also has the ability to handle scientific data reliably and in an easy to use manner, including higher-dimensional data sets with data coming from diverse data sources. The deep learning algorithms developed to work these models are extremely complex, compared to computer vision or language applications, Stevens stated.

The main job of the CS-1 is to increase the speed of developing and deploying new cancer drug models. The hope is that the Argonne Lab will arrive at a deep learning model that can predict how a tumor may respond to a drug or combination of two or more drugs.

Read the source articles in IEEE Spectrum,  on the Global Newswire, in Digital Journal and in AIMed (AI in Medicine).

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