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AI Autonomous Cars Gleaning Lessons From Accelarousal-Prone Human Drivers

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The driving systems of AI-based self-driving cars ought to be able to accommodate the driver or passengers who are prone to accelarousal, the need for speed. (Credit: Getty Images) 

By Lance Eliot, the AI Trends Insider   

I feel the need, the need for speed. We all know that famous line.   

I feel the need, the need for acceleration. That’s a line that doesn’t quite roll off the tongue. 

It is also a somewhat different way to frame or discuss the need or rapt desire for speed. If you are driving a car, there is the notion of a moment-in-time aspect entailing the speed of the vehicle. We are legally supposed to adhere to the stated speed limit. Our speed might be slow when in a school zone and might be relatively high or fast when driving on an open freeway.   

Some drivers relish driving fast and at times exceeding the speed limit. Perhaps they are late for work and are in a hurry to get to the office. Maybe the driver is someone that just relishes going fast. The posted speed limit might not necessarily be on their minds, other than as a general guide as to what their speed is supposed to be.   

Acceleration is the attainment of speed based on increasing velocity over time. We all understand this notion and experience it while driving or perhaps when enjoying a breathtaking ride on a roller coaster.   

Think about driving a car. Upon coming up to a red light, you bring your car to a stop and are no longer moving. Once the light turns green, you give your vehicle a bit of gas or push down on the accelerator pedal and the car rolls forward. Some drivers like to do those jackrabbit starts, pushing the pedal to the metal and skyrocketing down the street. Other drivers prefer a steady and gradual pace of acceleration. 

Are you the type of driver that likes to do a quick snap from a standing position and gun your engine as you zoom along, or are you the type that prefers a calmer incremental acceleration?   

In some ways, our driving laws tend to allow for a bit of latitude in your acceleration, while being tight when it comes to speeding. There usually isn’t a rule against rocketing accelerations. If you want to go from zero to 45 miles per hour on a street that has a speed limit of 45 miles per hour, you can choose generally how fast you want to get to that top speed. No rule normally exists that says you can only proceed at a pace of some X number of feet per second.   

That being said, there is no question that you can get busted if your acceleration causes traffic-related problems. A driver that zips forward might disturb other traffic. Pedestrians that are crossing the street might be endangered. In short, though acceleration itself is not specially regulated, your acceleration can create unsafe roadway conditions, and you can be cited accordingly.   

There are also real dangers to rapid acceleration. A driver that is screeching forward would seem more likely to get themselves into driving trouble. Your control of the car is lessened, and you have less reaction time.  

Someone that does accelerate as though being shot out of a canon will also usually get an adverse reaction from other drivers. Is that driver showing off? Indeed, this can induce other drivers to accelerate rapidly too. All of a sudden, a street race has been instigated. This worsens the situation since you now have a multitude of cars accelerating, whereas at least up until then it was just one vehicle doing so.  

The rule of thumb usually is steady as things go. Take your time. That doesn’t mean that you crawl at a snail’s pace. A really slow acceleration can be bad too. Not because you cannot control the vehicle, but because it might be confounding to other nearby drivers. 

Someone that does acceleration like a turtle is bound to get an adverse reaction from other drivers nearby. Why is that person not going faster and getting more quickly up-to-speed? Other cars will tend to try and go around the slowpoke, which can produce a cavalcade of potential near collisions as vehicles jockey back and forth. 

Confusion can also arise in that some other drivers might assume that there is a need to accelerate slowly, maybe due to a dog that has come onto the street or some other factor. Those other drivers will start looking around and needlessly be distracted by the belief that a phantom reason exists for this driving behavior. 

How does acceleration relate to stress for the driver? Do you think that while you are accelerating that you are under less stress or more stress than when driving along in general? 

I suppose it is hard to say. Some people get highly stressed by being in bumper-to-bumper traffic and for which there is minimal acceleration involved. You are going at a start-and-stop pace, crawling along. Some drivers get a bit of thrill by accelerating up an onramp onto a busy freeway since they know that once they get onto the freeway the traffic will be pretty much at a standstill and be agonizingly sluggish. They have a car, and cars can go fast, so they try to experience the fastness by zooming on the onramp. It is their only moment of glory at being behind the wheel of the vehicle while doing their daily commute. 

Okay, so we can readily agree or at least acknowledge that acceleration can be a stress-inducing driving act, though we might also willingly concede that it is not the only driving act that can produce stress. 

If you take a look at a newbie teenage driver, you can often see them sweating profusely while learning to drive a car. They grip the steering wheel like a vise. Facial expressions clearly show concentration, consternation, fear, etc. This can occur throughout an entire driving journey. As such, there isn’t any singular driving act or driving chore that they relax with, and the moment they start the engine until they turn off the car is filled with angst and dread.   

Envision that you wanted to perform an analysis of how much stress a driver might be having throughout their driving journey. You could ask the person about their perceived stress levels. You could try to hook the person to a device that could attempt to physiologically calculate their stress. You could study the face of the person to see what facial expressions they make. And so on, including that the sweatiness of the person could be a consideration too.   

Let’s focus on sweating. One means of trying to quantify the degree or magnitude of stress involves measuring the amount of perinasal perspiration (known as PP) that a person is showcasing. We typically think of having sweaty palms as an indicator of potential stress and will often wipe off our hands to try and get rid of the perspiration, especially if about to shake someone else’s hands. PP signals are typically commensurate with this palm-sweating, a form of electrodermal (EDA) activity.   

You could seek to detect the perinasal perspiration of a person by using a thermal imaging sensor that took readings via heat radiating from the face of a driver (akin to modern-day thermometers that let you point and detect if a person might have a fever or heightened temperature). Some parts of the face will be cooler and some parts of the face will be hotter. The abject temperature along with temperature differences can be readily measured and analyzed. An added advantage to this approach is that you don’t need to hook up the person to any particular device and nor do you have to ask them directly about their stress. 

All told, the PP can serve as a surrogate for how much stress a person is experiencing. This is more formally cast as a stress-induced neurophysiological response that manifests as a transient form of perspiration taking place in the perinasal area.   

Let’s bring together all of these topics into a nice tidy package.   

An interesting new research study entitled “Arousal Responses To Regular Acceleration Events Divide Drivers Into High And Low Groups” was undertaken by researchers Tung Huynh (University of Houston), Mike Manser (Texas A&M), and Ioannis Pavlidis (University of Houston) to explore stress levels of drivers and made use of PP in doing so, closely examining acceleration events. Their paper was published in the ACM CHI Conference on Human Factors in Computing Systems Extended Abstracts, May 8–13, 2021. 

In their study, they asked a selected sample of drivers to go ahead and drive an everyday route in a city setting that consisted of relatively light traffic and fair-weather conditions. Each driver drove the same route under roughly the same or similar circumstances.   

This is worth noting, since the driving route and the manner of what kind of driving is required were able to be construed as a stable and consistent environment. If each driver had driven an entirely different route or in radically varying levels of traffic, it would make trying to contrast the stress-inducing moments and journey characteristics harder to compare. Likewise, if the driving had been snowy for some drivers and dry and sunny for others, this would have ostensibly significantly altered the driving practices and therefore variously prodded the stress exhibited during the driving task per each driver.   

The occasions for acceleration were of the ordinary type that would be encountered in such a setting and are customarily referred to as naturalistic driving. 

In contrast, if we put drivers onto a closed-off racetrack and asked them to drive in a spirited way, the odds are that you would witness quite a lot of rather strenuous acceleration efforts. This would be a somewhat artificially contrived setting that was unlike the natural or conventional driving chores that most of us face daily. For this study, the acceleration occurred in the ways to which we all are accustomed, such as when entering into a fast-moving stream of daily traffic, when proceeding from a red light that has turned green, etc. 

The main research questions being addressed in the study were stated this way: “How do arousal responses of normal drivers relate to acceleration and other driving variables in the context of a standard commute? Is there any underlying grouping in these responses?”   

Notice the use of the phrase “arousal responses” which simply connotes how reactive someone might be to a stimulus of some kind (this is the usual technical jargon common among such studies). The researchers had reviewed prior studies and identified the insight that there is apparently “a category of normal drivers who are hyperaroused during routine acceleration events—a phenomenon we call accelarousal, and which carries behavioral and design implications.” 

In brief, the research results indicated: “A key contribution of this study is the clustering result that reveals an underlying high and low arousal grouping of normal drivers with respect to acceleration. The finding bears implications for certain categories of the driving population.” The implication is that some drivers are more likely to be accelarousal-prone versus other drivers (referred to as non-acceleration-prone drivers). Those drivers that are accelarousal-prone could especially be incurring greater levels of stress, which over a lengthy spate of driving might have particularly adverse health effects. 

Imagine for example that we have two drivers, both serving as ridesharing or ride-hailing drivers. Assume that one of them is accelarousal-prone and the other is not. They serve as drivers for many months, perhaps years, and doing so in equivalent terms of the number of hours driven and in the same city areas. Of course, it might be hard to compare them because of other varying factors, but let’s stick with this scenario for the moment. 

It could be that the accelarousal-prone driver ends up with various stress-related ailments and suffers from commensurate health consequences. Meanwhile, the other driver seems just fine, and the stress has washed off his or her back like a duck in water. If we didn’t realize that one was more prone to this acceleration stress, there might be quite a mystery as to why one was more adversely impacted by the driving than the other. Again, as a caution, we must be careful to not overinflate this example and need to consider a slew of additional factors that might come into play.   

Which do you think that you are: the type of driver that is accelarousal-prone or the non-acceleration-prone driver? 

I’ll let you ponder that for a moment. Maybe ask a friend or family member whether you are of one type or the other. That will certainly garner quite a bit of heated discussion and likely engage all in a lively discussion on the topic, that’s for sure. 

Shifting gears, consider that the future of cars consists of self-driving cars. Self-driving cars are driven via an AI driving system. There isn’t a need for a human driver at the wheel, and nor is there a provision for a human to drive the vehicle.   

Here’s an intriguing question that has arisen: What impacts, if any, will the accelarousal-prone aspects have in an era of AI-based true self-driving cars?   

Before jumping into the details, I’d like to clarify what is meant when referring to true self-driving cars. 

For my framework about AI autonomous cars, see the link here: https://aitrends.com/ai-insider/framework-ai-self-driving-driverless-cars-big-picture/   

Why this is a moonshot effort, see my explanation here: https://aitrends.com/ai-insider/self-driving-car-mother-ai-projects-moonshot/   

For more about the levels as a type of Richter scale, see my discussion here: https://aitrends.com/ai-insider/richter-scale-levels-self-driving-cars/   

For the argument about bifurcating the levels, see my explanation here: https://aitrends.com/ai-insider/reframing-ai-levels-for-self-driving-cars-bifurcation-of-autonomy/ 

Understanding The Levels Of Self-Driving Cars   

As a clarification, true self-driving cars are ones where the AI drives the car entirely on its own and there isn’t any human assistance during the driving task.   

These driverless vehicles are considered Level 4 and Level 5, while a car that requires a human driver to co-share the driving effort is usually considered at Level 2 or Level 3. The cars that co-share the driving task are described as being semi-autonomous, and typically contain a variety of automated add-on’s that are referred to as ADAS (Advanced Driver-Assistance Systems).   

There is not yet a true self-driving car at Level 5, which we don’t yet even know if this will be possible to achieve, and nor how long it will take to get there. 

Meanwhile, the Level 4 efforts are gradually trying to get some traction by undergoing very narrow and selective public roadway trials, though there is controversy over whether this testing should be allowed per se (we are all life-or-death guinea pigs in an experiment taking place on our highways and byways, some contend). 

Since semi-autonomous cars require a human driver, the adoption of those types of cars won’t be markedly different from driving conventional vehicles, so there’s not much new per se to cover about them on this topic (though, as you’ll see in a moment, the points next made are generally applicable). 

For semi-autonomous cars, it is important that the public needs to be forewarned about a disturbing aspect that’s been arising lately, namely that despite those human drivers that keep posting videos of themselves falling asleep at the wheel of a Level 2 or Level 3 car, we all need to avoid being misled into believing that the driver can take away their attention from the driving task while driving a semi-autonomous car.   

You are the responsible party for the driving actions of the vehicle, regardless of how much automation might be tossed into a Level 2 or Level 3.   

For why remote piloting or operating of self-driving cars is generally eschewed, see my explanation here: https://aitrends.com/ai-insider/remote-piloting-is-a-self-driving-car-crutch/ 

To be wary of fake news about self-driving cars, see my tips here: https://aitrends.com/ai-insider/ai-fake-news-about-self-driving-cars/ 

The ethical implications of AI driving systems are significant, see my indication here: https://aitrends.com/selfdrivingcars/ethically-ambiguous-self-driving-cars/   

Be aware of the pitfalls of normalization of deviance when it comes to self-driving cars, here’s my call to arms: https://aitrends.com/ai-insider/normalization-of-deviance-endangers-ai-self-driving-cars/   

Self-Driving Cars And Acceleration Stress   

For Level 4 and Level 5 true self-driving vehicles, there won’t be a human driver involved in the driving task. All occupants will be passengers; the AI is doing the driving. 

One aspect to discuss entails the fact that the AI involved in today’s AI driving systems is not sentient. In other words, the AI is altogether a collective of computer-based programming and algorithms, and most assuredly not able to reason in the same manner that humans can.   

Why this added emphasis about the AI not being sentient? Because I want to underscore that when discussing the role of the AI driving system, I am not ascribing human qualities to the AI. Please be aware that there is an ongoing and dangerous tendency these days to anthropomorphize AI. In essence, people are assigning human-like sentience to today’s AI, despite the undeniable and inarguable fact that no such AI exists as yet. 

With that clarification, you can envision that the AI driving system won’t natively somehow “know” about the facets of driving. Driving and all that it entails will need to be programmed as part of the hardware and software of the self-driving car.   

Let’s dive into the myriad of aspects that come to play on this topic.   

First, as mentioned, Level 4 and Level 5 won’t make use of a human driver and will instead be driven by an AI driving system. This means that whether a human driver is accelarousal-prone or not is no longer applicable since there isn’t a human driver at the wheel. That would seem to summarily close off any further discussion on the matter and we can go home now. 

Au contraire, mon ami (on the contrary, my friend)!   

There is the matter of putting some strident attention towards the passengers that are inside a self-driving car.   

Perhaps the passengers are accelarousal-prone. This makes indubitable sense. When you are driving a car, and if you have someone in the vehicle with you, the manner in which you accelerate is likely to impact any such passenger too. I’m sure you’ve had moments whereby you slammed your foot onto the accelerator pedal and your passenger looked at you with shock or concern, likely not aware of what the roadway situation is and why you have opted to rocketship forward. 

You can ostensibly argue that the passenger is potentially less impacted because they aren’t driving the car and therefore do not carry the burden of the driving task. The logic is that the driver has both the acceleration to be confronted with and the grave responsibility of controlling the vehicle, a double whammy of potential stress. The passenger is sometimes characterized as a compliant and otherwise relatively docile occupant that has no direct means of controlling the car, no more than a lump of clay. 

Well, two can play at that game.  You can make an additional argument that perhaps the stress on the passenger could actually be worse than for the driver. 

How so? 

The passenger is bereft of a direct means to control the vehicle and entirely at the whim of the driver. You could make the case that this is even more stressful than being the driver. The passenger is going along for the ride, and the use of acceleration can make that ride seem even more perilous and stressful. Meanwhile, the driver is calling the shots, as it were. When a person has no avenue of control over their destiny, there can be a tremendous amount of stress.   

Let’s though for now not get mired into an endless debate about which has more stress, the driver versus the passenger, and instead be willing to agree that the passenger can have stress as a result of acceleration events. You would be hard-pressed to argue to the contrary.   

To clarify, not all passengers would necessarily be subject to acceleration-induced stress. This brings us to a notable point. There is presumably the potential of having two types of passengers, those that are accelarousal-prone ones and those that are not accelarousal-prone.   

Aha, this provides some valuable insight into the ongoing and future design, development, and use of self-driving cars.   

AI driving systems of AI-based true self-driving cars ought to be devised to detect the different types of passengers and drive the vehicle differently depending upon the type present in the vehicle. For example, suppose there is a passenger alone in a self-driving car and the AI driving system has somehow ascertained that the person is accelarousal-prone. In that case, the AI driving system could purposely accelerate in rather steady ways and do so at a measured pace. No fast starts. No quick and startling moments of sudden acceleration.   

This driving action of being sensitive to the accelarousal-prone nature of the passenger would need to be tempered by the driving situation at hand. Imagine that a big truck is about to swerve into the path of the self-driving car and the only viable escape requires the self-driving car to rapidly accelerate out of the way. The overall safety of the passenger and the vehicle would outweigh the otherwise preferred mode of doing acceleration in a more subdued fashion.

Self-driving cars are anticipated to contain inward-facing sensors such as video cameras and onboard microphones. This provision is used for a multitude of purposes. Passengers can use Zoom-like interactive sessions while traveling inside a self-driving car and take remote education courses. Another use would be for the AI driving system to monitor the occupants and try to determine if they might be marking graffiti or marring the interior of the vehicle. That might seem a bit gloomy as a reason, so we can also include that the mechanisms could detect when someone is having a sudden heart attack or other emergency and needs assistance.   

Presumably, a thermal imaging sensor could be used too. This device could try to measure the PP and in a real-time attempt to ascertain the accelarousal-prone nature of the passenger. Based on that calculated analysis, the AI driving system would adjust the acceleration process accordingly.   

Some might say that seems overly convoluted as an approach to determining the accelarousal-prone nature and it might be simpler to ask the passenger what they want. AI driving systems are likely to include a Natural Language Processing (NLP) system that is used to interact with passengers. A passenger could be asked what levels of acceleration they prefer. This is an abundantly easy form of interaction and akin to the capabilities of a contemporary Siri or Alexa.   

For more details about ODDs, see my indication at this link here: https://www.aitrends.com/ai-insider/amalgamating-of-operational-design-domains-odds-for-ai-self-driving-cars/ 

On the topic of off-road self-driving cars, here’s my details elicitation: https://www.aitrends.com/ai-insider/off-roading-as-a-challenging-use-case-for-ai-autonomous-cars/ 

I’ve urged that there must be a Chief Safety Officer at self-driving car makers, here’s the scoop: https://www.aitrends.com/ai-insider/chief-safety-officers-needed-in-ai-the-case-of-ai-self-driving-cars/ 

Expect that lawsuits are going to gradually become a significant part of the self-driving car industry, see my explanatory details here: https://aitrends.com/selfdrivingcars/self-driving-car-lawsuits-bonanza-ahead/ 

Conclusion 

There are a variety of additional twists and turns involved.   

Briefly, for Level 2 and Level 3 conventional human-driven cars, there is an ongoing consideration about the handoff of the driving task, doing so in two ways. There is the driving system that requests the human driver to engage in the driving task, and there is the case of the human driver seeking to engage the driving system to undertake the driving task. To some degree, it might be useful for the handoff to incorporate whether the human driver is accelarousal-prone or not. Akin to the earlier discussion, this determination might be done via a thermal imaging sensor, via other sensors, or by using an NLP. 

There is a subtle but important undercurrent in mentioning the Level 2 and Level 3 conventional human-driven cars.   

The odds are that we are going to have a mixture of human-driven cars and AI-based true self-driving cars for quite a long time, likely decades or longer. There is not going to be an overnight magical switch from human-driven cars to all self-driving cars. Indeed, debates will become fierce about whether human drivers will have to entirely give up the driving task, for which some fervently vow they will not do so until you pry their cold dead hands from the wheel.   

Assume for the sake of discussion that there is going to be a mixture of human-driven cars and AI-based true self-driving cars on our streets, byways, highways, and freeways. One interesting question is whether acceleration as a standard practice will change due to this mixture.   

For example, pundits emphasize that self-driving cars will be programmed to always drive cautiously and fully legally abide strictly by the rules of the road. I’ve repeatedly pointed out that this already is having an “unintended adverse consequence” on human drivers that are nearby to self-driving cars (see my column for discussions on these matters).   

In short, human drivers get frustrated by the poking along with self-driving cars and are apt to try and accelerate around the seemingly slowpoke vehicles. You see, oddly enough, the advent of self-driving cars could indirectly prod human drivers into doing more acceleration, including drivers that prior had not particularly done so. Just imagine how accelarousal-prone human drivers might react.   

As they say, the best-laid plans of mice and humankind can oftentimes go astray.   

Copyright 2021 Dr. Lance Eliot  http://ai-selfdriving-cars.libsyn.com/website 

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Source: https://www.aitrends.com/ai-insider/ai-autonomous-cars-gleaning-lessons-from-accelarousal-prone-human-drivers/

Artificial Intelligence

Extra Crunch roundup: influencer marketing 101, spotting future unicorns, Apple AirTags teardown

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With the right message, even a small startup can connect with established and emerging stars on TikTok, Instagram and YouTube who will promote your products and services — as long as your marketing team understands the influencer marketplace.

Creators have a wide variety of brands and revenue channels to choose from, but marketers who understand how to court these influencers can make inroads no matter the size of their budget. Although brand partnerships are still the top source of revenue for creators, many are starting to diversify.

If you’re in charge of marketing at an early-stage startup, this post explains how to connect with an influencer who authentically resonates with your brand and covers the basics of setting up a revenue-share structure that works for everyone.


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Use discount code ECFriday to save 20% off a one- or two-year subscription


Our upcoming TC Early Stage event is devoted to marketing and fundraising, so expect to see more articles than usual about growth marketing in the near future.

We also ran a post this week with tips for making the first marketing hire, and Managing Editor Eric Eldon spoke to growth leader Susan Su to get her thoughts about building remote marketing teams.

We’re off today to celebrate the Juneteenth holiday in the United States. I hope you have a safe and relaxing weekend.

Walter Thompson
Senior Editor, TechCrunch
@yourprotagonist

As the economy reopens, startups are uniquely positioned to recruit talent

Little Fish in Form of Big Fish meeting a fish.

Image Credits: ballyscanlon (opens in a new window) / Getty Images

The pandemic forced a reckoning about the way we work — and whether we want to keep working in the same way, with the same people, for the same company — and many are looking for something different on the other side.

Art Zeile, the CEO of DHI Group, notes this means it’s a great time for startups to recruit talent.

“While all startups are certainly not focused on being disruptive, they often rely on cutting-edge technology and processes to give their customers something truly new,” Zeile writes. “Many are trying to change the pattern in their particular industry. So, by definition, they generally have a really interesting mission or purpose that may be more appealing to tech professionals.”

Here are four considerations for high-growth company founders building their post-pandemic team.

Refraction AI’s Matthew Johnson-Roberson on finding the middle path to robotic delivery

Matthew Johnson-roberson

Image Credits: Bryce Durbin

“Refraction AI calls itself the Goldilocks of robotic delivery,” Rebecca Bellan writes. “The Ann Arbor-based company … was founded by two University of Michigan professors who think delivery via full-size autonomous vehicles (AV) is not nearly as close as many promise, and sidewalk delivery comes with too many hassles and not enough payoff.

“Their ‘just right’ solution? Find a middle path, or rather, a bike path.”

Rebecca sat down with the company’s CEO to discuss his motivation to make “something that is useful to the general public.”

How to identify unicorn founders when they’re still early-stage

Image Credits: RichVintage (opens in a new window)/ Getty Images

What are investors looking for?

Founders often tie themselves in knots as they try to project qualities they hope investors are seeking. In reality, few entrepreneurs have the acting skills required to convince someone that they’re patient, dedicated or hard working.

Johan Brenner, general partner at Creandum, was an early backer of Klarna, Spotify and several other European startups. Over the last two decades, he’s identified five key traits shared by people who create billion-dollar companies.

“A true unicorn founder doesn’t need to have all of those capabilities on day one,” Brenner, writes “but they should already be thinking big while executing small and demonstrating that they understand how to scale a company.”

Founders Ben Schippers and Evette Ellis are riding the EV sales wave

disrupt mobility roundup

Image Credits: TechCrunch

EV sales are driving demand for services and startups that fulfill the new needs of drivers, charging station operators and others.
Evette Ellis and Ben Schippers took to the main stage at TC Sessions: Mobility 2021 to share how their companies capitalized on the new opportunities presented by the electric transportation revolution.

Scale AI CEO Alex Wang weighs in on software bugs and what will make AV tech good enough

Image Credits: Alexandr Wang

Scale co-founder and CEO Alex Wang joined us at TechCrunch Sessions: Mobility 2021 to discuss his company’s role in the autonomous driving industry and how it’s changed in the five years since its founding.

Scale helps large and small AV players establish reliable “ground truth” through data annotation and management, and along the way, the standards for what that means have shifted as the industry matures.

Even if two algorithms in autonomous driving might be created more or less equal, their real-world performance could vary dramatically based on what they’re consuming in terms of input data. That’s where Scale’s value prop to the industry starts, and Wang explains why.

Edtech investors are flocking to SaaS guidance counselors

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The prevailing post-pandemic edtech narrative, which predicted higher ed would be DOA as soon as everyone got their vaccine and took off for a gap year, might not be quite true.

Natasha Mascarenhas explores a new crop of edtech SaaS startups that function like guidance counselors, helping students with everything from study-abroad opportunities to swiping right on a captivating college (really!).

“Startups that help students navigate institutional bureaucracy so they can get more value out of their educational experience may become a growing focus for investors as consumer demand for virtual personalized learning increases,” she writes.

Dear Sophie: Is it possible to expand our startup in the US?

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Image Credits: Bryce Durbin/TechCrunch

Dear Sophie,

My co-founders and I launched a software startup in Iran a few years ago, and I’m happy to say it’s now thriving. We’d like to expand our company in California.

Now that President Joe Biden has eliminated the Muslim ban, is it possible to do that? Is the pandemic still standing in the way? Do you have any suggestions?

— Talented in Tehran

Companies should utilize real-time compensation data to ensure equal pay

Two women observing data to represent collecting data to ensure pay equity.

Image Credits: Rudzhan Nagiev (opens in a new window) / Getty Images

Chris Jackson, the vice president of client development at CompTrak, writes in a guest column that having a conversation about diversity, equity and inclusion initiatives and “agreeing on the need for equality doesn’t mean it will be achieved on an organizational scale.”

He lays out a data-driven proposal that brings in everyone from directors to HR to the talent acquisition team to get companies closer to actual equity — not just talking about it.

Investors Clara Brenner, Quin Garcia and Rachel Holt on SPACs, micromobility and how COVID-19 shaped VC

tc sessions mobility speaker_investorpanel-1

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Few people are more closely tapped into the innovations in the transportation space than investors.

They’re paying close attention to what startups and tech companies are doing to develop and commercialize autonomous vehicle technology, electrification, micromobility, robotics and so much more.

For TC Sessions: Mobility 2021, we talked to three VCs about everything from the pandemic to the most overlooked opportunities within the transportation space.

Experts from Ford, Toyota and Hyundai outline why automakers are pouring money into robotics

disrupt mobility roundup

Image Credits: TechCrunch

Automakers’ interest in robotics is not a new phenomenon, of course: Robots and automation have long played a role in manufacturing and are both clearly central to their push into AVs.

But recently, many companies are going even deeper into the field, with plans to be involved in the wide spectrum of categories that robotics touch.

At TC Sessions: Mobility 2021, we spoke to a trio of experts at three major automakers about their companies’ unique approaches to robotics.

Apple AirTags UX teardown: The trade-off between privacy and user experience

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Apple’s location devices — called AirTags — have been out for more than a month now. The initial impressions were good, but as we concluded back in April: “It will be interesting to see these play out once AirTags are out getting lost in the wild.”

That’s exactly what our resident UX analyst, Peter Ramsey, has been doing for the last month — intentionally losing AirTags to test their user experience at the limits.

This Extra Crunch exclusive helps bridge the gap between Apple’s mistakes and how you can make meaningful changes to your product’s UX.

How to launch a successful RPA initiative

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Robotic process automation (RPA) is no longer in the early-adopter phase.

Though it requires buy-in from across the organization, contributor Kevin Buckley writes, it’s time to gather everyone around and get to work.

“Automating just basic workflow processes has resulted in such tremendous efficiency improvements and cost savings that businesses are adapting automation at scale and across the enterprise,” he writes.

Long story short: “Adapting business automation for the enterprise should be approached as a business solution that happens to require some technical support.”

Mobility startups can be equitable, accessible and profitable

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Mobility should be a right, but too often it’s a privilege. Can startups provide the technology and the systems necessary to help correct this injustice?

At  our TC Sessions: Mobility 2021 event, we sat down with Revel CEO and co-founder Frank Reig, Remix CEO and co-founder Tiffany Chu, and community organizer, transportation consultant and lawyer Tamika L. Butler to discuss how mobility companies should think about equity, why incorporating it from the get-go will save money in the long run, and how they can partner with cities to expand accessible and sustainable mobility.

CEO Shishir Mehrotra and investor S. Somasegar reveal what sings in Coda’s pitch doc

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Coda CEO Shishir Mehrotra and Madrona partner S. Somasegar joined Extra Crunch Live to go through Coda’s pitch doc (not deck. Doc) and stuck around for the ECL Pitch-off, where founders in the audience come “onstage” to pitch their products to our guests.

Extra Crunch Live takes place every Wednesday at 3 p.m. EDT/noon PDT. Anyone can hang out during the episode (which includes networking with other attendees), but access to past episodes is reserved exclusively for Extra Crunch members. Join here.

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AI-driven hedge fund rules out Bitcoin for lack of ‘fundamentals’

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A Swedish hedge fund that returned roughly four times the industry average last year using artificial intelligence won’t touch Bitcoin, based on an assessment that the cryptocurrency doesn’t lend itself to sensible analysis.

Photo by Bloomberg Mercury

Patrik Safvenblad, the chief investment officer of Volt Capital Management AB, says the problem with Bitcoin and other crypto assets is that they “do not have accessible fundamentals that we could build a model on.”

“When there is a crisis, markets generally move toward fundamentals. Not the old fundamentals but new, different fundamentals,” he said in an interview. So if an asset doesn’t provide that basic parameter, “we stay away from that,” he said.

The role of Bitcoin in investment portfolios continues to split managers, as the world’s most popular cryptocurrency remains one of its most volatile asset classes. One coin traded at less than $40,000 on Friday, compared with an April peak of $63,410. This time last year, a single Bitcoin cost around $10,000.

Among Volt’s best-known investors is Bjorn Wahlroos, the former Nordea Bank Abp chairman. His son and former professional poker player, Thomas Wahlroos, is Volt’s board chairman. The fund currently manages assets worth just $73 million, on which it returned 41% in 2020, four times the industry average.

Bitcoin enthusiasts recently received a boost when hedge fund manager Paul Tudor Jones told CNBC he likes it “as a portfolio diversifier.” He went on to say that the “only thing” he’s “certain” about is that he wants “5% in gold, 5% in Bitcoin, 5% in cash, 5% in commodities.”

Meanwhile, Bank of America Corp. research shows that Bitcoin is about four times as volatile as the Brazilian real and Turkish lira. And the International Monetary Fund has warned that El Salvador’s decision to adopt Bitcoin as legal tender “raises a number of macroeconomic, financial and legal issues that require very careful analysis.”

Safvenblad says it’s more than just a matter of Bitcoin’s lack of fundamentals. He says he’s not ready to hold an asset that’s ultimately designed to dodge public scrutiny.

Volt would “much prefer to be in a regulated market with regulated trading,” he said. “And Bitcoin is not yet fully regulated.”

The hedge-fund manager has chosen 250 models it thinks will make money, and its AI program then allocates daily weightings. Volt’s investment horizon is relatively short, averaging about 10-12 trading days. It holds roughly 60 positions at any given time, and its current analysis points toward what Safvenblad calls a “nervous long.”

“In the past few weeks the program has turned more bearish,” he said. We have some positions that anticipate a slowdown, for example long fixed-income, and the models have now trimmed our long positions in commodities. Today, the portfolio reflects a more balanced outlook.”

Safvenblad says the advantage to Volt’s AI model is that it’s unlikely to miss any signals. “We don’t say that we know where the world is heading. But we have a system that monitors everything that could mean something.”

— Jonas Cho Walsgard, Bloomberg Mercury

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Source: https://bankautomationnews.com/allposts/wealth/ai-driven-hedge-fund-rules-out-bitcoin-for-lack-of-fundamentals/

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Digital twins help transform the construction industry

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Elevate your enterprise data technology and strategy at Transform 2021.


Digital twins promise to be a key enabler as the construction industry races to catch up with demand for new facilities and new layouts in the wake of COVID-19. Use of such technology, which creates a digital representation of real-world systems and components, is important for an industry seen as slow to adopt digital technology relative to others.

Construction is a complex undertaking, with legacy processes that span regulators, architects, contractors, and building owners. Digital transformation requires finding ways to bridge these divides — not just within elements of each participant’s domain, but also between them.

Still, practical benefits will come from harmonizing the way different groups create and manage data, according to John Turner, vice president of innovative solutions at Gafcon, a digital twin systems integrator.

Growing demand, increased complexity, and more sophisticated design authoring tools will drive the change, according to Rich Humphrey, vice president of construction at infrastructure software maker Bentley Systems. He estimates that the construction software market is currently upwards of $10 billion and could grow significantly thanks to the adoption of digital twins. “The industry is already seeing value in managing risk, reducing rework, and driving efficiencies in the way they deliver projects using digital twins,” Humphrey told VentureBeat.

Change could be far-reaching in an industry that represents one of the largest asset classes in the world.

“There are more than 4 billion buildings in the world today, which is twice as many as websites are online,” said RJ Pittman, CEO of Matterport, a reality capture service for buildings. The rush is on, not only to build more efficiently, but also to increase the value of existing buildings, which today represent a $230 trillion asset class.

Warp speed ahead

COVID is accelerating the demand for digital twin technology. CRB, a construction provider for the biotech industry, recently turned to Matterport to help design and build new vaccine plants as part of Operation Warp Speed. They used Matterport to capture the layout of existing plants, as well as to improve the design and layout of new ones. A digital twin also allowed them to model the workflow and safety properties of the new facilities to identify and rectify any bottlenecks before the new facilities were started.

“Tools like Matterport enable seamless collaboration in the same space because it’s browser-based,” said Chris Link, virtual design and construction manager at CRB. Data is not lost from multiple handoffs between a designer, builder, and owner.

Digital twins also dramatically reduced the need for engineers to travel to existing or new plants. On one project, CRB reduced the number of onsite engineers from 10 to 1, reduced travel costs by 33%, and expedited design by three weeks. One key benefit is that Matterport can capture and harmonize data across different participants and enable people to collaborate within a single platform instead of what was previously a handoff scenario between design and engineering.

Digital twins can reduce the operational expenditures associated with a facility occurring after facility handoff, accounting for 80% or more of the total facility lifetime cost.

“A digital twin is a goldmine to a facility owner because there is currently a significant data loss in engineering and construction,” Link said. Building managers can use digital twins to understand why things were engineered and designed in the manner they were, and this understanding translates to simplified maintenance. For example, maintenance technicians called in to repair a broken pump can utilize the digital twin to understand the design and intent of the pump. They can see the bigger picture, not just the broken pump in front of them.

Reshape, rewire, rethink

Construction-related spending accounts for about 14% of the world GDP and is expected to grow from $10 trillion in 2017 to $14 trillion in 2025, according to McKinsey. The consulting firm also says that about $1.6 trillion in additional value could be created through higher productivity. McKinsey identified seven best practices that could use digital twins to boost productivity by 50 to 60%:

  1. Reshape regulation — Accelerate approvals with testable plans and enable the adoption of performance-based requirements.
  2. Rewire contracts — Improved information sharing enables new contractual models.
  3. Rethink design — New designs could be tested and iterated more efficiently.
  4. Improve onsite execution — Easier detection of scheduling clashes.
  5. Infuse technology and innovation — Improve orchestration with IoT, drones, and AI planning.
  6. Reskill workers — Facilitate new training programs for innovative technologies using VR.
  7. Improve procurement and supply chain — Better harmonization between current progress and deliveries.

McKinsey predicts that firms could see further productivity gains by adopting a manufacturing system of mass production, with much more standardization appearing across global factory sites. These efforts require greater harmonization between design, manufacturing, and construction, as well as much tighter tolerances. Some early successes include: Barcelona Housing Systems estimates it can reduce labor 5 to 10 times for multi-story homes;
Finnish industrial company Outotec has created a process for small mines that reduces labor by 30%, capital by 20%, and time by 30%; and Broad Sustainable Buildings of China erected a 30-story hotel in 15 days.

Digital twins mind the gaps

“Digital twins are about connecting to real-life objects or information,” said Connor Christian, senior product manager at Procore, a construction software provider. That is a key issue in an area that combines so many different engineering facets.

In fact, the construction industry has evolved a piecemeal approach to managing different data sources, including GIS for location data, building information modeling (BIM) for 3D data, and virtual design and construction (VDC) for project management. This challenges digital twin implementation.

While any job site with sensors or cameras has the potential to create digital twins that allow for access, control, and reporting from those devices, the fact is that not all data is good data, so there must be standards, processes, and verifications in place to help filter out unnecessary data, Christian said.

Different processing stages are involved in turning raw data into the higher-level abstractions required to improve construction processes, said David McKee, CEO, CTO, and founder at Slingshot Simulations and co-chair at the Digital Twin Consortium. For example, Slingshot recently deployed a workflow that combined European Space Agency Sentinel-1 InSAR data from SatSense that looks at ground movement, merged this with infrastructure data, correlated this with traffic data, and presented that back to stakeholders to understand the risks to transport infrastructure.

McKee has found it helpful to adopt IBM Design Thinking approach and Agile software engineering practices for building and deploying digital twins.

“This approach means that even in some of the biggest infrastructure projects, you can start engaging stakeholders within a couple of weeks,” McKee said. For example, his team has recently kicked off a project to improve the transport network in one of the busiest shipping hubs in the UK in the wake of Brexit.

Digital twins can also help fill in the semantic gaps in traditional BIM and GIS tools, said Remi Dornier, vice president of construction, cities, and territories at Dassault Systemes. Digital twins also provide a way to include all the necessary details to perform purchasing and construction assembly. And they can also improve ergonomics. For example, Dassault has been working on a simulation for nursing homes to help eliminate heavy lifting associated with caring for patients.

DevOps for construction

Gafcon’s Turner said the next era of digital twins involves using digital twins to bring a DevOps-like approach to construction. That can transform the entire construction lifecycle.

But teams need to rethink the entire construction and management process to see the highest efficiencies. For example, mass timber construction is a new approach to building that uses standardized manufactured wood products with different properties than traditional wood. It involves gluing small pieces of wood together in the proper orientation.

If teams treat the material like traditional timber, they might see marginal improvements in costs, productivity, and speed. But more dramatic improvement may be possible. The kinship to IT DevOps should be apparent. Digital transformation for construction will mean including test and ops teams earlier in the process. This collaboration can sort out issues like defining assembly steps and how components must be delivered to create, hopefully, far better results.

It is not entirely clear how the construction industry will evolve from a patchwork of different tools to the well-orchestrated CI/CD-like pipelines transforming software development.

But vendors are in the hunt. Leading vendors include a patchwork of companies expanding beyond their core strengths in fields such as GIS (Trimble, ESRI), BIM (Autodesk, Bentley, Dassault), construction management (Procore, and Oracle Construction), reality capture (Matterport and SiteAware), and supply chain management (SiteSense). Digital twins integrators such as Swinerton, Gafcon, and Lendlease Podium help to meld these tools into well-orchestrated workflows that span the design, construction, and operations lifecycle.

Construction ahead

This industry’s attempts at transformation are complicated, and a lot of subsidiary elements need to successfully evolve in order for digital twins to gain traction. The recent Katerra bankruptcy underscores the challenges that even high-profile operations face in trying to transform the construction industry.

For one thing, the industry needs better data quality and context, Oracle senior director of new products, BIM, and innovation Frank Weiss told VentureBeat. The technology to gather and integrate data to create an ecosystem of digital twins is available today.

But it comes from many different sources in different formats, which can be challenging for analysis. “It’s going to take vendors, governments, and other stakeholders to work together,” Weiss said.

In addition, the industry will also have to find consensus on what defines digital twins and how they plug into existing processes. “There is still a general lack of understanding of what a digital twin is,” said Procore’s Christian. Right now, any virtual object associated with data is being called a digital twin, he suggested.

And more challenges are in the offing, including the lack of a common data interchange environment that would allow data to easily flow from software to software.

“Even with all the great APIs, cloud-based data, and platform solutions, there still remains a massive amount of data stuck in silos that are not able to be fully accessed,” Christian said.

Assembly required

Today, experts believe enterprises are barely scratching the surface of what digital twins can accomplish, Steve Holzer, principal at Holzer, an architectural and planning consultancy and member of the infrastructure working group at the Digital Twin Consortium, told VentureBeat.

While much attention focuses on the bright shiny side of digital twins, pragmatic considerations are coming into greater play, and guides from other industries are being studied. In the long run, the industry will need to adopt a new mindset to replace most legacy construction methods and processes with the product-driven mindset used in other industries.

“Once we have project thinking replaced with product thinking, construction will be replaced with assembly,” Holzer said.

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Source: https://venturebeat.com/2021/06/18/digital-twins-help-transform-the-construction-industry/

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8 Open-source NLP Tools You Should Try

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Gaurav Hacker Noon profile picture

@gauravsharmaGaurav

A technical writer with Cogito, who writes about AI. National basketball player. Photographer.

Innovative technologies like voice assistants, predictive text, autocorrect, chatbots, and others have rapidly evolved in recent years, and the force behind it is Natural Language Processing (NLP).

NLP is a sub-field of Artificial Intelligence, which aims to emulate human intelligence and focuses on the interactions between computers and human language.

It typically allows computers to process and carefully analyze massive
amounts of natural language data.

Through effective implementation of NLP, one can naturally access relevant information in just seconds. Several businesses have implemented this technology by building customized chatbots, voice assistants and using their optical character & text simplification techniques to reap maximum benefits.

To help the businesses, there are several open-source NLP tools available which businesses can utilize according to their specific
requirements.

These open-source tools will not only help businesses to systemize the unstructured text but will also combat several other problems.

Below are the open-source NLP toolkit platforms anyone can use :

1. Natural Language Toolkit (NLTK)

It is an open-source platform used for python programming. It gives over 50 corpora and lexical resources like WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging,
parsing, and semantic reasoning, wrappers for industrial-strength NLP
libraries.

NLTK is appropriate for linguists, engineers, students, educators, researchers, etc., and is available for Windows, Mac OS X, and Linux.

2. SpaCy

SpaCy is another open-source library and typically comprises pre-trained statistical models and word vectors that support over 60 languages. Licensed under MIT, anyone can use it commercially. SpaCy supports custom models in PyTorch, TensorFlow, and other frameworks.

The main USP of SpaCy is Named Entity Recognition, part-of-speech tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, entity linking, and others.

3. OpenNLP

OpenNLP supports the tasks such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, language detection, and coreference resolution. Apart from this, it additionally includes maximum entropy and perceptron-based machine learning.

3. CoreNLP

It is another open-source platform which is developed by the Stanford NLP group as a possible solution for NLP in Java. It is currently supporting six languages (Arabic, Chinese, English, French, German, Spanish).

The USP of CoreNLP is sentence boundaries, parts-of-speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, quote attributions, and relations.

5. AllenNLP

Allen is an open-source platform based on PyTorch. It is a deep learning library for NLP used for the tasks such as responding to questions, semantic role labeling, textual entailment, text to SQL.

6. Flair

Like AllenNLP, Flair is also built on PyTorch. This open-source platform allows using the platform’s state-of-art NLP models of text, such as Named Entity Recognition (NER), part-of-speech tagging, sense disambiguation and
classification.

It includes simpler interfaces where one can combine various words and document embeddings.

7. SparkNLP

SparkNLP is an open-source platform that gives over 200 pre-trained pipelines and models supporting more than 40 languages. SparkNLP supports transformers like BERT, XLNet, ELMO and carries out accurate and clear annotations for NLP.

8. Gensim

Gensim is a free and open-source python library uniquely designed to process raw texts using quality machine learning algorithms. It is used for topic modeling, document indexing.

The USP of the platform is tokenization, part-of-speech tagging, named entity recognition, spell checking, multi-class text classification, multi-class sentiment analysis.

Natural Language Processing is a crucial and revolutionary technology. I expect this technology to flourish in the possible future with the successful adoption of more personal assistants, dependencies on smartphones, and the evolution of Big Data.

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