Mitigating Risk from Common Driving Infractions

The high frequency of road accidents makes driver safety one of the biggest challenges fleet managers face each day. In the US alone, 6 million car accidents every year happen every year, with more than 40,000 motor vehicle accident-related deaths in 2017.

Several factors come into play when looking at the cause of traffic accidents. It could be the weather, changing road conditions, or the fault of other road users such as another driver or pedestrian.




Apart from the risks posed by accidents to drivers, companies face significant losses when such accidents and traffic violations occur. Road accident claims are among the most expensive, almost double that of other types of workplace injuries — especially if the accident includes fatalities.

Up to 68% of companies recorded recent accidents, according to popular reimbursement platform provider Motus in its 2018 Driver Safety Risk report. These road accidents often lead to millions of dollars in claims and other costs. In 2017 alone, road accidents cost companies over $56 billion.

The stats on individual crashes are just as shocking. A report by the Occupational Safety and Health Administration (OHSA) found that vehicle crashes can cost employers between $16,500 and $74,000 for each injured driver.

US collision insurance claims have also risen over the past five years. Although companies plan for accidents through insurance, there is still a significant loss of time, productivity, and money when they occur. Some direct effects of crashes include:

  • Severe injuries
  • Expensive property damage
  • Reduced productivity, slow operations and missed revenue opportunities due to decommissioned vehicles and injured drivers
  • Third-party liability claims that cost a lot of money to settle, depending on the severity of the accident.

In many accidents, one of the drivers is at fault; a significant number of traffic crashes happen because of driving infractions such as drunk and distracted driving. In many cases, these infractions are not easy to detect until they’ve caused accidents.

Common Driving Infractions

Several studies have been conducted to measure the frequency of road accidents caused by different infractions. A study by the National Highway Traffic Safety Administration has shown insights into the severity of some common driving infractions and their threat to driver safety.

Distracted Driving

Distracted driving refers to any behavior that takes a driver’s attention off the road. This could include texting, making a phone call, eating, talking to a passenger and looking off the road, or drowsiness.

Drivers are expected to be alert and fully focused on the road with a forward view. Any deviation from this position could lead to easily avoidable car accidents.

According to a report by the National Highway Traffic Safety Administration (NHTSA), distracted driving accounts for 16.7 percent of drivers who contributed to road accidents. Accidents caused by distracted driving claimed 3,166 lives in 2017.

As the following statistics by the National Safety Council show, texting is the most common distracted driving behaviors. Texting while driving accounts for up to 390,000 injuries and up to 25% of accidents annually. It is also 6 times more likely to lead to accidents than drunk driving, because it takes a driver’s attention off the road for up to 5 seconds.

Eating or drinking while driving is another distracted driving behavior that poses a serious risk to drivers. The NHTSA estimates that eating or drinking at the wheel accounts for up to 6 percent of near-miss crashes annually.

Drowsy Driving

Just like distracted driving, fatigued driving is also common and fleet managers must make it a priority to prevent it.

Drowsy driving due to fatigue, illness, or other conditions led to 72,000 crashes, 44,000 injuries, and 800 deaths in 2013. According to a report by the Center for Disease Control, drivers who are at risk of drowsy driving include those who work long shifts, do not get enough sleep, have untreated sleeping disorders, use medication that causes drowsiness, or are overworked.


Among the most dangerous driving infractions, driving while intoxicated on substances such as alcohol or marijuana accounts for a significant number of accidents each year.

According to research by Australia’s Transport Accident Commission (TAC), alcohol negatively affects a driver’s vision, perception, alertness, and reflexes. This makes it difficult to navigate roads or avoid accidents, presenting a plethora of safety issues that could easily be avoided by staying away from the driver’s seat.

The CDC puts the death toll of alcohol-related driving crashes at 10,497 in 2016 alone. In addition to the risks posed by drunk driving, narcotics were also found to be involved in about 16% of motor vehicle crashes.


Speeding is one of the most common causes of fatal road accidents. A study by the National Transportation Safety Board (NTSB) showed that speeding caused 112,580 deaths between 2005 and 2014.

The study also showed that drunk driving and speeding are similar in their likelihood to cause fatal accidents for drivers and other road users. This is why the NTSB recommends more serious charges for speeding. Currently, it carries a lesser charge than driving under the influence of alcohol. Drivers would do well to keep a safe speed to avoid a hefty fine and keep themselves and others out of harm’s way.

Other driving infractions that lead to accidents include angry driving and ignoring stop signs. According to the Federal Highway Administration, 72 percent of crashes occur at stop signs.

Mitigating Driving Risks

Driving risks will always be present in a driver’s transport cycle; however, some of them can be mitigated with preventive measures in place. Since liability claims and damages are suffered by the company, not the driver, the onus lies with employers to ensure that accidents are avoided. Some ways to ensure safe driving include:

Training Drivers to Avoid Risky Driving Behaviors

Risky driving behaviors, such as distracted driving, seem harmless when they do not result in accidents. Since many driving infractions are within the driver’s control, proper training is necessary to prevent them from showing these behaviors. Drivers should be trained to avoid using cell phones, eating, fiddling with the stereo or doing anything else that takes their concentration off the road.

Companies should have policies that highlight the negative effects of these driving infractions, such as accidents which could lead to death, injury, and liability claims. Each driver should be mandated to sign this policy and adhere to it at all times. Rules prohibiting these risky behaviors should be displayed around the workplace to serve as a reminder.

Reducing the Drivers’ Workload

In addition to training, drivers should be given adequate rest periods between shifts to avoid fatigue or drowsy driving. They should also be monitored for signs of intoxication and encouraged to avoid driving when sick.

Many drivers who work long shifts show signs of fatigue with effects similar to those of drunk drivers. These effects include poor vision, perception, judgment, and reflexes. Drowsy drivers may fall asleep while driving and veer off the road or collide with other road users.

Introducing a Driver Rewards System

Another great way to mitigate these risks is to recognize and reward good drivers. A common model is the leaderboard/rating system in which drivers score points for good driving which add up over time.

As drivers amass points, they can rank higher on the leaderboard. You could raise the stakes by encouraging drivers to score a certain number of points to earn a reward. This reward could be a bonus, tuition reimbursement, extra paid time off, or other benefits.

Platforms like Driveri have a GreenZone system that updates a driver’s score in real-time. A system like that could be used to monitor a driver’s rating without any bias.

Correcting Infractions as They Occur

Another way to reduce the occurrence of driving infractions is to correct drivers with penalties. These penalties can range from losing driver points to taking serious disciplinary action. Platforms like Driveri have an app that sends real-time notifications when risky behavior happens so fleets can promptly correct the issue.

Implementing Reliable Driver Safety Technology

The world of driver safety has evolved, leading to the adoption of technological tools that aid drivers and fleets in mitigating risks. Several of these tools combine emerging technologies such as artificial intelligence, machine learning and big data to provide insights to drivers. Their functions span data collection and analysis, video recording, vehicle-to-vehicle communication, and accurate vehicle fault diagnosis.

Advanced automated fleet management systems such as Netradyne’s Driveri act as an all-purpose platform for fleets. In addition to serving as an onboard driving coach, they handle driver rating systems, offer access to road data which can be used to make informed decisions, and monitor drivers for distracted driving behaviors.

Data is essential to every process. The study of past data influences how events occur in the future. In the automotive vehicle industry where a large number of accidents owing to many different factors happen every day, it is important to collect data.

This task is tedious without the use of advanced technology especially due to the number of miles traveled daily and how often road and driving conditions change.

Data analysis is also necessary because it tells you how past data is relevant to future events. Legacy systems that collect data for humans to analyze are slowly giving way to smart systems that analyze data and provide insights in real-time.

Another application of technology in driver safety is driver monitoring. As much as it is possible to train drivers on which behaviors to avoid, there is still a chance that they manifest these behaviors when no authority is around.

This is why driver monitoring is necessary. Constantly monitoring drivers via video cameras may be perceived as invasive and antagonistic. Instead, Driveri monitors signs of distracted driving behaviors such as yawning, head turns and drowsy eye movement to report in real-time. Its artificial intelligence system includes:

  • Internal lens for the detection of distracted or drowsy driving behaviors such as yawning. After detecting such behaviors, the application adjusts a driver’s greenzone score (rating) in real-time. This ensures that managers are aware and can take immediate action.
  • A comprehensive database and data analytics system. The platform has also analyzed over 1 million unique miles of US roads to date and makes this information accessible.
  • A real-time video capture system consisting of forward, side, and interior HD cameras that capture high-quality videos of internal and external road events. Also, fleet managers can access up to 100 hours of video playback for records. This can be used as evidence during legal proceedings in the case of accidents.
  • Access to 4G LTE / WiFi / BT connection within fleets for data transmission, analytics, and communication
  • Location mapping using OBDII and GPS technology
  • Vehicle Speed and Orientation mapping using a 3-Axis Accelerometer and Gyro Sensor
  • A single module installation system.

Final Thoughts

Driving infractions are responsible for a significant number of motor vehicle accidents annually which cost employers millions of dollars in damages. Infractions such as distracted driving, intoxicated driving, speeding, and drowsy driving, account for the most crashes.

Fortunately, these behaviors can be prevented through driver training, the introduction of policies, rewards systems, and the use of technology.

Driver safety technology is necessary for data collection and analytics which helps fleets mitigate the risks associated with accidents. It also serves as a navigation and monitoring system while coaching the driver.

Netradyne’s Driveri uses artificial intelligence and other features like cameras, sensors, and machine learning to achieve these functions. It offers an advanced monitoring system for risky driving behaviors and notifies managers when any such behavior has been detected. Driveri also coordinates all its functions through a simple platform that drivers and managers can use and understand easily.

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The world’s largest AI research conference is underway in Vancouver, Canada. Researchers are presenting more than 1,400 papers at the Neural Information Processing Systems (NeurIPS) conference, ranging from work that organizers believe has had the greatest impact over the past decade to Yoshua Bengio’s continued march toward consciousness for deep learning.

But even as the conference showed theoretical research and neuroscience-related papers on the rise alongside categories like algorithms and deep learning, the mushrooming of the event itself — and the associated growing pains — was a constant theme, and it speaks to the growth of the AI field in general.

Organizers said that at the start of the conference Sunday, they expected about 400 people to show up for registration. Instead, 4,200 queued up in the conference center registration line.  All told, NeurIPS 2019 welcomed 13,000 attendees, up 40% from the prior year. In all, the conference has quadrupled in size in the past five years.

Organizers started to use a lottery system this year, after 9,000 tickets to 2018 NeurIPS sold out in 12 minutes.

Researchers submitted a record 6,600 papers for consideration.

With industry, governments, and academia investing billions, machine learning is red hot right now. The AI Index 2019 report released Wednesday found that private investment, research, and the number of PhD candidates in AI are all growing at healthy rates.

But that growth can come at a cost. As intellectually driven as NeurIPS can be, crowds can get in the way of the intellectual exchanges and scientific critique the event is intended to spawn.

Organizers invited all conference attendees to share feedback in a town hall Thursday, and one of the scientists’ main concerns was how to deal with the event’s rapid growth.

Poster sessions are the most valuable part of the conference, according to many who spoke at the town hall, since they allow you to speak directly with research authors and gain exposure to a broad range of work. At one point, the poster sessions was filled to capacity, making those dialogues difficult.

This year’s organizers are considering a lot of potential ways forward: NeurIPS regional meetups were held in more 35 countries this year, and organizers considered splitting the global conference into regional events.

“The idea is instead of having one big meeting, we could have a bunch of satellite meetings,” Terrence Sejnowski, president of the NeurIPS Foundation, said. “I think the idea is we’ll have a bigger audience and greater participation if we had more than one location.”

Should the conference break into regional events, Sejnowski said organizers could randomly dispatch speakers to regional locations and livestream their talks for consumption around the world.

NeurIPS livestreamed major presentations this year and gave researchers ways to critique and question posters online, but a regional approach naturally limits in-person interactions and could also dilute the kind of valuable interactions the global event is intended to produce.

NeurIPS 2020 is back in Vancouver and 2021 will move to Sydney, in a larger space than the Vancouver Convention Center. Organizers may aim for larger venues in the future, like convention centers in places like New Orleans and San Diego, which have capacity of 30,000. Doing so could allow doing away with the lottery system.

Among other changes under consideration for next year: a policy to exclude certain vendors from the industry expo. No rules currently exist to exclude specific vendors. The issue came up following a question from the audience about why the National Security Agency (NSA), a U.S. intelligence organization, had a booth at a conference held in Canada. Next year, researchers may also be asked to share the carbon footprint of training their AI model.

Toward a more open and welcoming NeurIPS

Like last year, attendees from parts of Asia and Africa encountered issues with Canadian immigration officials that kept them from reaching Vancouver. Diversity and inclusion chair Katherine Heller said that as of Monday, 158 initially denied visas were approved, 14 were in progress, and five were denied. Despite the change, some poster sessions displayed signs that said the presenter received a visa too late to attend.

Since the conference will take place in Vancouver again next year, organizers hope to improve communication with the Canadian government in 2020.

But organizers seem to be trying hard to make NeurIPS a more accepting and welcoming place. A child care program was full to capacity for the kids of about 75 researchers. That program is expected to expand next year.

UC Berkeley professor Celeste Kidd gave the opening keynote address, and touched on how tech can drive false facts and misunderstandings of the #metoo movement.

{Dis}ability in AI had its first meeting, and workshops highlighted the work of diverse machine learning practitioners as well as AI that can positively impact the lives of people of African or Latin descent, women, and followers of Islam and Judaism.

At the Queer in AI workshop, people got to choose whether they wanted to be photographed or not, and you could hear presentations about research that seeks to remove gender identity from AI systems.

Some workshops even opened with acknowledgment that the convention took place on the land of the native Coast Salish people.

Machine learning at scale

What you hear about at NeurIPS are the advances in stochastic gradient descent, covariants, and all manner of technical discoveries or steps forward, but there are also workshops on disaster response, privacy, machine learning for the developing world, climate change, and using computer vision to battle cancer.

It’s an insane rush of theoretical, complex approaches to improve the performance of machine intelligence, but also a drive to solve real-world problems and contribute to society.

The other challenge in front of the community, it seems, is growth management. Perhaps regional events help, but interest in a unified international event does not appear to be subsiding.

AI is the top focus of computer science school PhD programs today, and other conferences like ICLR or CVPR are also experiencing growth.

In other words, growth in the AI field will likely continue to drive the growth of NeurIPS. The organizers of the biggest machine learning research conference of the year have to deal with the challenges that growth brings, but that’s a nice problem to have. While they’re making progress toward solving some of those challenges, it seems clear that the importance of physical access to the conference will not subside anytime soon and will likely continue to grow in importance.

For AI coverage, send news tips to This email address is being protected from spambots. You need JavaScript enabled to view it. and This email address is being protected from spambots. You need JavaScript enabled to view it. and This email address is being protected from spambots. You need JavaScript enabled to view it. — and be sure to subscribe to the AI Weekly newsletter and bookmark our AI Channel.

Thanks for reading,

Khari Johnson

Senior AI Staff Writer

Source: venturebeat

"Community groups, workers, journalists, and researchers—not corporate AI ethics statements and policies—have been primarily responsible for pressuring tech companies and governments to set guardrails on the use of AI.” AI Now 2019 report

AI Now’s 2019 report is out, and it’s exactly as dismaying as we thought it would be. The good news is that the threat of biased AI and Orwellian surveillance systems no longer hangs over our collective heads like an artificial Sword of Damocles. The bad news: the threat’s gone because it’s become our reality. Welcome to 1984.

The annual report from AI Now is a deep-dive into the industry conducted by the AI Now Institute at New York University. Its focused on the social impact that AI use has on humans, communities, and the population at large. It sources information and analysis from experts in myriad disciplines around the world and works closely with partners throughout the IT, legal, and civil rights communities.

This year’s report begins with twelve recommendations based on the institute’s conclusions:

  • Regulators should ban the use of affect recognition in important decisions that impact people’s lives and access to opportunities.
  • Government and business should halt all use of facial recognition in sensitive social and political contexts until the risks are fully studied and adequate regulations are in place.
  • The AI industry needs to make significant structural changes to address systemic racism, misogyny, and lack of diversity.
  • AI bias research should move beyond technical fixes to address the broader politics and consequences of AI’s use.
  • Governments should mandate public disclosure of the AI industry’s climate impact.
  • Workers should have the right to contest exploitative and invasive AI—and unions can help.
  • Tech workers should have the right to know what they are building and to contest unethical or harmful uses of their work.
  • States should craft expanded biometric privacy laws that regulate both public and private actors.
  • Lawmakers need to regulate the integration of public and private surveillance infrastructures.
  • Algorithmic Impact Assessments must account for AI’s impact on climate, health, and geographical displacement.
  • Machine learning researchers should account for potential risks and harms and better document the origins of their models and data.
  • Lawmakers should require informed consent for use of any personal data in health-related AI.

The permeating theme here seems to be that corporations and governments need to stop passing the buck when it comes to social and ethical accountability. A lack of regulation and ethical oversight has lead to a near total surveillance state in the US. And the use of black box systems throughout the judiciary and financial systems has proliferated even though such AI has been proven to be inherently biased.

AI Now notes that these entities saw a significant amount of push-back from activist groups and pundits, but also points out that this has done relatively little to stem the flow of harmful AI:

Despite growing public concern and regulatory action, the roll-out of facial recognition and other risky AI technologies has barely slowed down. So-called “smart city” projects around the world are consolidating power over civic life in the hands of for-profit technology companies, putting them in charge of managing critical resources and information.

For example, Google’s Sidewalk Labs project even promoted the creation of a Google-managed citizen credit score as part of its plan for public-private partnerships like Sidewalk Toronto. And Amazon heavily marketed its Ring, an AI-enabled home-surveillance video camera. The company partnered with over 700 police departments, using police as salespeople to convince residents to buy the system. In exchange, law enforcement was granted easier access to Ring surveillance footage.

Meanwhile, companies like Amazon, Microsoft, and Google are fighting to be first in line for massive government contracts to grow the use of AI for tracking and surveillance of refugees and residents, along with the proliferation of biometric identity systems, contributing to the overall surveillance infrastructure run by private tech companies and made available to governments.

The report also gets into “affect recognition” AI, a subset of facial recognition that’s made its way into schools and businesses around the world. Companies use it during job interviews to, supposedly, tell if an applicant is being truthful and on production floors to determine who is being productive and attentive. It’s a bunch of crap though, as a recent comprehensive review of research from multiple teams concluded.

Per the AI Now 2019 report:

Critics also noted the similarities between the logic of affect recognition, in which personal worth and character are supposedly discernible from physical characteristics, and discredited race science and physiognomy, which was used to claim that biological differences justified social inequality. Yet in spite of this, AI-enabled affect recognition continues to be deployed at scale across environments from classrooms to job interviews, informing sensitive determinations about who is “productive” or who is a “good worker,” often without people’s knowledge.

At this point, it seems any company that develops or deploys AI technology that can be used to discriminate – especially black box technology that claims to understand what a person is thinking or feeling – is willfully investing in discrimination. We’re long past the time that corporations and governments can feign ignorance on the matter.

This is especially true when it comes to surveillance. In the US, like China, we’re now under constant public and private surveillance. Cameras record our every move in public at work, in our schools, and in our own neighborhoods. And, worst of all, not only did the government use our tax dollars to pay for all of it, millions of us unwittingly purchased, mounted, and maintained the surveillance gear ourselves. AI Now wrote:

Amazon exemplified this new wave of commercial surveillance tech with Ring, a smart-security-device company acquired by Amazon in 2018. The central product is its video doorbell, which allows Ring users to see, talk to, and record those who come to their doorsteps. This is paired with a neighborhood watch app called “Neighbors,” which allows users to post instances of crime or safety issues in their community and comment with additional information, including photos and videos

A series of reports reveals that Amazon had negotiated Ring video-sharing partnerships with more than 700 police departments across the US. Partnerships give police a direct portal through which to request videos from Ring users in the event of a nearby crime investigation.

Not only is Amazon encouraging police departments to use and market Ring products by providing discounts, but it also coaches police on how to successfully request surveillance footage from Neighbors through their special portal. As Chris Gilliard, a professor who studies digital redlining and discriminatory practices, comments: “Amazon is essentially coaching police on . . . how to do their jobs, and . . . how to promote Ring products.

The big concern here is that the entrenchment of these surveillance systems could become so deep that the law enforcement community would treat their extrication the same as if we were trying to disarm them.

Here’s why: In the US, cops are supposed to get a warrant to invade our privacy if they suspect criminal activity. But they don’t need one to use Amazon’s Neighbors app or Palantir’s horrifying LEO app.  With these, Police can essentially perform digital stop-and-frisks on any person they come into contact with using AI-powered tools.

AI Now warns that these problems — biased AI, discriminatory facial recognition systems, and AI-powered surveillance — cannot be solved by patching systems or tweaking algorithms. We can’t “version 2.0” our way out of this mess.

In the US, we’ll continue our descent into this Orwellian nightmare as long as we continue to vote for politicians that support the surveillance state, discriminatory black-box AI systems, and the Wild West atmosphere that big tech exists in today.

Amazon and Palantir shouldn’t have the ultimate decision over how much privacy we’re entitled to.

If you’d like to read the full 60-page report, it’s available online here.

Source: TheNextWeb

Whether we’re learning to cook an omelet or drive a car, the path to mastering new skills often begins by watching others. But can artificial intelligence learn the same way? A new challenge teaching AI agents to play Minecraft suggests it’s much trickier for computers.

Announced earlier this year, the MineRL competition asked teams of researchers to create AI bots that could successfully mine a diamond in Minecraft. This isn’t an impossible task, but it does require a mastery of the game’s basics. Players need to know how to cut down trees, craft pickaxes, and explore underground caves while dodging monsters and lava. These are the sorts of skills that most adults could pick up after a few hours of experimentation or learn much faster by watching tutorials on YouTube.

But of the 660 entries in the MineRL competition, none were able to complete the challenge, according to results that will be announced at the AI conference NeurIPS and that were first reported by BBC NewsAlthough bots were able to learn intermediary steps, like constructing a furnace to make durable pickaxes, none successfully found a diamond.

“The task we posed is very hard,” Katja Hofmann, a principal researcher at Microsoft Research, which helped organize the challenge, told BBC News. “While no submitted agent has fully solved the task, they have made a lot of progress and learned to make many of the tools needed along the way.”

This may be a surprise, especially when you think that AI has managed to best humans at games like chess, Go, and Dota 2. But it reflects important limitations of the technology as well as restrictions put in place by MineRL’s judges to really challenge the teams.

The bots in MineRL had to learn using a combination of methods known as imitation learning and reinforcement learning. In imitation learning, agents are shown data of the task ahead of them, and they try to imitate it. In reinforcement learning, they’re simply dumped into a virtual world and left to work things out for themselves using trial and error.

Often, AI is only able to take on big challenges by combining these two methods. The famous AlphaGo system, for example, first learned to play Go by being fed data of old games. It then honed its skills — and surpassed all humans — by playing itself over and over.

The MineRL bots took a similar approach, but the resources available to them were comparatively limited. While AI agents like AlphaGo are created with huge datasets, powerful computer hardware, and the equivalent of decades of training time, the MineRL bots had to make do with just 1,000 hours of recorded gameplay to learn from, a single Nvidia graphics processor to train with, and just four days to get up to speed.

It’s the difference between the resources available to an MLB team — coaches, nutritionists, the finest equipment money can buy — and what a Little League squad has to make do with.

It may seem unfair to hamstring the MineRL bots in this way, but these constraints reflect the challenges of integrating AI into the real world. While bots like AlphaGo certainly push the boundary of what AI can achieve, very few companies and research labs can match the resources of Google-owned DeepMind.

The competition’s lead organizer, Carnegie Mellon University PhD student William Guss, told BBC News that the challenge was meant to show that not every AI problem should be solved by throwing computing power at it. This mindset, said Guss, “works directly against democratizing access to these reinforcement learning systems, and leaves the ability to train agents in complex environments to corporations with swathes of compute.”

So while AI may be struggling in Minecraft now, when it cracks this challenge, it’ll hopefully deliver benefits to a wider audience. Just don’t think about those poor Minecraft YouTubers who might be out of a job.

Souce: The Verge

Business Intelligence has had an impactful history as traditional BI originally appeared in the 1960s as a system of sharing information across organizations. In the 1980s, it further developed alongside computer models for decision-making and turning data into insights before becoming a specific offering from BI teams with IT-reliant service solutions. In today’s vast data producing environment, modern BI solutions prioritize flexible self-service analysis, governed data on trusted platforms, empowered business users, and speed to insight.

Business intelligence software are rapidly developing as them becomes compulsive for many organizations. Currently, a number of leading organizations are leveraging GPU parallel processing technology to infuse AI into their BI applications, and this strategy will quickly define the next generation of business analytics. Adding AI into BI is the most impactful way to speed up data insight. Establishing an integrated AI+BI database, an insight engine means an organization can shift from an analytics position that looks back to the one that looks forward.

There are several use cases where businesses combine AI and BI for next-gen analytical insight engines that utilize both in-memory storage and GPU processing. For instance, retailers are transforming supply chain management as they can now feed and assess streaming data from suppliers and shippers against real-time inventory data from retail operations.


AI is at the Core of Next-Gen Analytics

Augmented intelligence, machine learning and natural language processing (NLP) have become key parts of business intelligence platforms. However, as analytics platforms have developed, AI for BI still hasn’t progressed to the point where analytics tools can truly free up humans from the tedious tasks associated with data analysis, as well as where data analysis is part of everyday applications instead of a stand-alone application unto itself.

Today, enterprises are entering into a new era governed by data. AI, specifically, is increasingly evolving as a key driver that shapes business processes and BI decision making on a daily basis. From small to large enterprises, all are leveraging AI to enhance the efficiency of business processes and deliver smarter, more specialized customer experiences.


Why There is a Need for AI-Powered BI Systems?

The explosion of new big data sources like smartphones, tablets and the Internet of Things (IoT) devices compel businesses to no longer be oppressed by massive amounts of static reports created by BI software systems. They need more actionable insights. This leads AI-driven BI systems that can transform business data into simple, precise, real-time narratives and reports.

Avoiding data overload – Data nowadays is growing at an unprecedented rate and can easily choke the companies’ business operations. When a company has data blasting its BI platform from different sources, this is where AI-powered BI tools come in. It helps to analyze all the data and deliver tailor-made insights. Thus, investing in AI-powered business intelligence software can assist companies to break down data into manageable insights.

Delivering Insights in Real-Time – The growth of big data in the market makes it hard to make strategic decisions on time. But AI leaps in recent years power BI tools to offer dashboards that provide alerts and business insights to managers for key decision making.

Easing the Talent Shortage – There is a huge shortage of professionals with data analytical skills worldwide and in the United States also, there is an acute shortage of nearly 1.5 million analysts. So, employing data experts in every department within an organization has become vital. However, practicing AI-powered BI software can bring tremendous changes in businesses, keeping them competitive in the tech-driven business environment.

In the years to come, AI-infused BI tools will go beyond surfacing insights. They will propose ways to address or fix issues, run simulations to optimize processes, make new performance targets based on predictions, and take action automatically.


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