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giniPredict launches in ANZ for planning and forecasting in small businesses




giniPredict has recently launched as a new solution to give small businesses in Australia and New Zealand faster, more accurate and more powerful planning and forecasting capabilities.

An intuitive, no-code technology that runs on top of familiar software products, giniPredict unlocks the patterns hidden in business data. Making the power of enterprise data analytics accessible to SMBs, it helps them to assess the commercial impact of individual variables, identify the best outcomes, and prioritise investment and resources.

Fung Lim, giniPredict’s General Manager for Australia and New Zealand, said, “Most financial software focuses on measuring the past. But growing a profitable, sustainable business is based on understanding the future. giniPredict brings the speed, simplicity and convenience of consumer apps to business forecasting to make better, faster, more informed decisions.”

Previously, capitalising on the potential of artificial intelligence and machine learning required specialist in-house technical capabilities and the deployment of substantial resources. This effectively excluded most small and medium-sized businesses, which account for 98.5 percent of businesses in Australia, 97 percent in New Zealand and more than half of employment worldwide. giniPredict changes that by putting powerful data modelling technology within reach of every business.

giniPredict integrates with Xero, and with just a few clicks, small business leaders can model a variety of future commercial scenarios based on their historical data. As a special incentive for businesses to trial giniPredict, the first 200 licences in Australia and New Zealand will be provided free of charge for a period of six months. Otherwise, a subscription will cost $19.99 per licence per month.

Up until now, accessing the potential of machine learning to do predictive modelling required resources and technical capabilities most companies simply don’t have. That means the world’s small businesses have been unable to leverage this tech.

giniPredict was built so that snaller organisations can understand their business better through the use of analytics, and run accurate forecasts, model scenarios, and assess the impact of a range of variables to identify the most productive outcomes.

giniPredict plugs into familiar interfaces – integrating with familiar tools and workflows, such as Xero, Google Data Studio and Google sheets.

Fung Lim continues, ‘Growing businesses in Australia and New Zealand are at the head of the pack globally when it comes to adopting cloud business technology, and giniPredict effectively gives them access to a data scientist. It plugs straight into Xero and immediately enables non-technical and non-specialist users to model scenarios, explore options and select better commercial futures.”

gini chose to launch their product in the A/NZ region first, due to high levels of uptake for cloud solutions, the relatively advanced adoption of technology in general and maturity of the digital market.

Businesses interested in the service can find out more at

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Video: Tesla Cam Witnesses A Police Pursuit Takedown




YouTuber “Wham Baam Teslacam” has shared one Tesla owner’s incredible footage of something that’s usually seen in the movies — an insane police pursuit and takedown. The Model 3 owner, Ezekiel, was driving on the highway in Oregon when he noticed the police car speeding up behind him. Ezekiel moved over to let the police officer speed by and he noticed a car in the distance coming straight toward them. The police officer lined up with the vehicle to take it down by hitting the vehicle directly. The car, which was speeding down the highway in the wrong direction, was stopped.

Immediately after stopping the other vehicle, the police officer broke the window of the other car and arrested the driver in no time. The official story said that close to 2:00 PM, emergency dispatchers were notified about a wrong-way driver on the highway near Milepost 342.

The report cited a sedan that was driving westbound in the eastbound lanes. Oregan State Police officers responded to the area in hopes of spotting the driver. Less than five minutes later, one of the officers spotted the vehicle right before colliding with it. The wrong-way driver only had minor injuries and was taken to the hospital for treatment.

After being treated at the hospital, the driver was booked in the county jail on charges of second-degree attempted assault and reckless driving, and reckless endangerment.

“Imagine putting yourself in this kind of danger to stop an individual from hurting others. Wham bam, that trooper is a hero, man,” the narrator said. The video goes on to show other Tesla cam clips sent in by Tesla owners.

Reckless Driving Kills, & Solving Real World AI Will Help Save Lives

In 2018, the National Highway Traffic Safety Administration (NHSA) reported 51,490 fatal car crashes and noted that reckless driving caused the majority of those deaths. 17% of those fatalities were caused by driving too fast for the conditions. For over 20 years, speeding has been involved in at least one-third of all motor vehicle fatalities and is at the top of the list of related factors for drivers involved in fatal crashes. The Insurance Institute for Highway Safety (IIHS) found that rising state speed limits over the past 25 years have cost almost 37,000 lives.

With autonomous driving, these problems can be solved once Tesla and others master Level 5. Many believe Tesla is on the brink of solving real-world AI with Dojo and a massive fleet. The YouTuber “The Future Economy” explains in more detail. He referred back to Tesla’s Q1 2021 earnings call, where CEO Elon Musk said the following:

“Then with regard to Full Self-Driving, Full Self-Driving Beta continues to make great progress. It is definitely one of the — I think one of the hardest technical problems that exists, that’s maybe ever existed. And really, in order to solve it, we basically need to solve a pretty significant part of artificial intelligence, specifically real-world artificial intelligence. And that sort of AI, the neural net needs to be compressed into a fairly small computer, a very efficient computer that was designed, but nonetheless, a small computer that’s using on the order of 70 or 80 watts. So this is a much harder problem than if you were you, say, 10,000 computers in a server room or something like that.

“This has got to fit into a smaller brain. And this — I think with the elimination of radar, we’re finally getting rid of one of the last crutches. Radar was really — it was making up for some of the shortfalls of vision, but this is not good. You actually just need vision to work.

“And when your vision works, it works better than the best human because it’s like having eight cameras, it’s like having eyes in the back of your head, beside your head and has three eyes of different focal distances looking forward. This is — and processing it at a speed that is superhuman. There’s no question in my mind that with a pure vision solution, we can make a car that is dramatically safer than the average person. So — but it is a hard problem because we are actually solving something quite fundamental about artificial intelligence, where we basically have to solve real-world vision AI.”

Further breaking this down, the host of The Future Economy pointed out that real-world AI is the incredibly hard problem of making a robot that runs on a neural network that is similar to how our brains work.

“Our brains are basically neural networks, too, and we have two cameras that we use to drive. So Tesla’s mission is to basically have this come to life. A Model 3 with Full Self Driving isn’t just a car. It’s technically a robot in the shape of a car. The car has an artificial brain and it uses that to navigate around the world, read road signs, avoid obstacles, and everything that a human would do.”

Additionally, the computer cars don’t get emotional. The cars don’t get angry or take offense to someone cutting off in front of them. They just react in as safe a manner as possible. The car also doesn’t panic or get scared when having to slow down suddenly or move out of the way of an unexpected vehicle that is on a sudden collision course with it. This is how real-world AI can save lives — by utilizing defensive driving tactics while also not being a part of the problem. An AI doesn’t need to text its boyfriend while putting on lipstick while making a left turn.

The above video is further broken down into three sections.

1. The fundamental problems that Tesla needs to solve Full Self Driving. There are a total of three major problems that Tesla needs to solve to make FSD work.

  1. Collect millions of hours of training data.
  2. Processing all of that data. Tesla’s solution to this is the Dojo supercomputer.
  3. The finished neural network has to be able to run on the FSD chip that’s in all of the Tesla cars.

2. Scaling AI today. The way most neural networks are developed is by putting them in a simulation and training them for hundreds of thousands of hours. In Tesla’s case, since it’s controlling the simulation, it can make copies of the AI and train them all at the same time. “Tesla’s approach is probably the hardest to replicate but it’s also the best shot at solving full autonomy using only computer vision.”

3. The concept of how humans solve problems and why Tesla’s AI will be so powerful. In this section, he covers the Iceberg Theory — also known as the Iceberg Principle — which suggests that we can’t see or detect most of a situation’s data. It’s also known as the theory of omission.

Another way of looking at this theory is looking at the iceberg itself. What you see is how to drive a car or to ride a bike. What you don’t see are the harder parts such as maintaining balance on the bike or memorizing the rules of the road — this part is what your brain had to learn.

The three sections above are just the nutshell version of the second video and I encourage you to watch the full video for more details. Although this second half may not seem to have anything to do with the first half of this article, they are connected. Real-world AI will someday prevent scenarios such as those presented in the first video from happening as often as they currently do.

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Listen: OakNorth CIO shares automation trends in commercial lending




Commercial banks have been automating aspects of the lending and decisioning process, primarily at the lower end of the commercial lending spectrum, but hesitate to automate for loans more than $1 million. This means commercial banks have kept automations focused on loans of less than $1 million, explains Sean Hunter in this podcast discussion with […]

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Predictive Maintenance is a Killer AI App 




Predictive maintenance resulting from IoT and AI working together has been identified as a killer app, with a track record of ROI. (Credit: Getty Images) 

By John P. Desmond, AI Trends Editor 

Predictive maintenance (PdM) has emerged as a killer AI app. 

In the past five years, predictive maintenance has moved from a niche use case to a fast-growing, high return on investment (ROI) application that is delivering true value to users. These developments are an indication of the power of the Internet of Things (IoT) and AI together, a market considered in its infancy today. 

These observations are from research conducted by IoT Analytics, consultants who supply market intelligence, which recently estimated that the $6.9 billion predictive maintenance market will reach $28.2 billion by 2026.  

The company began its research coverage of the IoT-driven predictive maintenance market in 2016, at an industry maintenance conference in Dortmund, Germany. Not much was happening. “We were bitterly disappointed,” stated Knud Lasse Lueth, CEO at IoT Analytics, in an account in IoT Business News. “Not a single exhibitor was talking about predictive maintenance.”  

Things have changed. IoT Analytics analyst Fernando Alberto Brügge stated, Our research in 2021 shows that predictive maintenance has clearly evolved from the rather static condition-monitoring approach. It has become a viable IoT application that is delivering overwhelmingly positive ROI.” 

Technical developments that have contributed to the market expansion include: a simplified process for connecting IoT assets, major advances in cloud services, and improvements in the accessibility of machine learning/data science frameworks, the analysts state.  

Along with the technical developments, the predictive maintenance market has seen a steady increase in the number of software and service providers offering solutions. IoT Analytics identified about 100 companies in the space in 2016; today the company identifies 280 related solution providers worldwide. Many of them are startups who recently entered the field. Established providers including GE, PTC, Cisco, ABB, and Siemens, have entered the market in the past five years, many through acquisitions.  

The market still has room; the analysts predict 500 companies will be in the business in the next five years.  

In 2016, the ROI from predictive analytics was unclear. In 2021, a survey of about 100 senior IT executives from the industrial sector found that predictive maintenance projects have delivered a positive ROI in 83% of the cases. Some 45% of those reported amortizing their investments in less than a year. “This data demonstrated how attractive the investment has become in recent years,” the analysts stated.   

More IoT Sensors Means More Precision 

Implemented projects that the analysts studied in 2016 relied on a limited number of data sources, typically one sensor value, such as vibration or temperature. Projects described in the 2021 report described 11 classes of data sources, such as data from existing sensors or data from the controllers. As more sources are tapped, the precision of the predictions increase, the analysts state.  

Many projects today are using hybrid modeling approaches that rely on domain expertise, virtual sensors and augmented data. AspenTech and PARC are two suppliers identified in the report as embracing hybrid modeling approaches. AspenTech has worked with over 60 companies to develop and test hybrid models that combine physics with ML/data science knowledge, enhancing prediction accuracy. 

The move to edge computing is expected to further benefit predictive modeling projects, by enabling algorithms to run at the point where data is collected, reducing response latency. The supplier STMicroelectronics recently introduced some smart sensor nodes that can gather data and do some analytic processing. 

More predictive maintenance apps are being integrated with enterprise software systems, such as enterprise resource planning (ERP) or computerized maintenance  management systems (CMMS). Litmus Automation offers an integration service to link to any industrial asset, such as a programmable logic controller, a distributed control system, or a supervisory control and data acquisition system.   

Reduced Downtime Results in Savings 

Gains come from preventing downtime. Predictive maintenance is the result of monitoring operational equipment and taking action to prevent potential downtime or an unexpected or negative outcome,” stated Mike Leone, an analyst at IT strategy firm Enterprise Strategy Group, in an account from TechTarget.  

Felipe Parages, Senior Data Scientist, Valkyrie

Advances that have made predictive maintenance more practical today include sensor technology becoming more widespread, and the ability to monitor industrial machines in real time, stated Felipe Parages, senior data scientist at Valkyrie, data sense consultants. With more sensors, the volume of data has grown exponentially, and data analytics via cloud services has become available. 

It used to be that an expert had to perform an analysis to determine if a machine was not operating in an optimal way. “Nowadays, with the amount of data you can leverage and the new techniques based on machine learning and AI, it is possible to find patterns in all that data, things that are very subtle and would have escaped notice by a human being,” stated Parages. 

As a result, one person can now monitor hundreds of machines, and companies are accumulating historical data, which enables deeper trend analysis. “Predictive maintenance “is a very powerful weapon,” he stated.  

In an example project, Italy’s primary rail operator, Trenitalia, adopted predictive maintenance for its high-speed trains. The system is expected to save eight to 10% of an annual maintenance budget of 1.3 billion Euros, stated Paul Miller, an analyst with research firm Forrester, which recently issued a report on the project.  

They can eliminate unplanned failures which often provide direct savings in maintenance but just as importantly, by taking a train out of service before it breaks—that means better customer service and happier customers,” Miller stated. He recommended organizations start out with predictive maintenance by fielding a pilot project. 

In an example of the types of cooperation predictive maintenance projects are expected to engender, the CEOs of several European auto and electronics firms recently announced plans to join forces to form the “Software Republique,” a new ecosystem for innovation in intelligent mobility. Atos, Dassault Systèmes, Groupe Renault, and STMicroelectronics and Thales announced their decision to pool their expertise to accelerate the market.   

Luca de Meo, Chief Executive Officer, Groupe Renault

Luca de Meo, Chief Executive Officer of Groupe Renault, stated in a press release from STMicroelectronics, In the new mobility value chain, on-board intelligence systems are the new driving force, where all research and investment are now concentrated. Faced with this technological challenge, we are choosing to play collectively and openly. There will be no center of gravity, the value of each will be multiplied by others. The combined expertise in cybersecurity, microelectronics, energy and data management will enable us to develop unique, cutting-edge solutions for low-carbon, shared, and responsible mobility, made in Europe.”    

The Software République will be based in Guyancourt, a commune in north-central France at the Renault Technocentre in a building called Odyssée, a 12,000 square meter space which is eco-responsible. For example, its interior and exterior structure is 100 percent wood, and the building is covered with photovoltaic panels. 

Read the source articles in IoT Business News TechTarget, and in a press release from STMicroelectronics.

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Post Office Looks to Gain an Edge With Edge Computing 




By AI Trends Editor John P. Desmond  

NVIDIA on May 6 detailed a partnership with the US Postal Service underway for over a year to speed up mail service using AI, with a goal of reducing current processing time tasks that take days to hours.   

The project fields edge servers at 195 Post Services sites across the nation, which review 20 terabytes of images a day from 1,000 mail processing machines, according to a post on the NVIDIA blog.  

Anthony Robbins, Vice President of Federal, Nvidia

“The federal government has been for the last several years talking about the importance of artificial intelligence as a strategic imperative to our nation, and as an important funding priority. It’s been talked about in the White House, on Capitol Hill, in the Pentagon. It’s been funded by billions of dollars, and it’s full of proof of concepts and pilots,” stated Anthony Robbins, Vice President of Federal for NVIDIA, in an interview with Nextgov “And this is one of the few enterprisewide examples of an artificial intelligence deployment that I think can serve to inspire the whole of the federal government.”  

The project started with USPS AI architect at the time Ryan Simpson, who had the idea to try to expand an image analysis system a postal team was developing, into something much bigger, according to the blog post. (Simpson worked for USPS for over 12 years, and moved to NVIDIA as a senior data scientist eight months ago.) He believed that a system could analyze billions of images each center generated, and gain insights expressed in a few data points that could be shared quickly over the network.  

In a three-week sprint, Simpson worked with half a dozen architects at NVIDIA and others to design the needed deep-learning models. The work was done within the Edge Computing Infrastructure Program (ECIP), a distributed edge AI system up and running on Nvidia’s EGX platform at USPS. The EGX platform enables existing and modern, data-intensive applications to be accelerated and secure on the same infrastructure, from data center to edge. 

“It used to take eight or 10 people several days to track down items, now it takes one or two people a couple of hours,” stated Todd Schimmel, Manager, Letter Mail Technology, USPS. He oversees USPS systems including ECIP, which uses NVIDIA-Certified edge servers from Hewlett-Packard Enterprise.  

In another analysis, a computer vision task that would have required two weeks on a network of servers with 800 CPUs can now get done in 20 minutes on the four NVIDIA V100 Tensor Core GPUs in one of the HPE Apollo 6500 servers.  

Contract Awarded in 2019 for System Using OCR  

USPS had put out a request for proposals for a system using optical character recognition (OCR) to streamline its imaging workflow. “In the past, we would have bought new hardware, software—a whole infrastructure for OCR; or if we used a public cloud service, we’d have to get images to the cloud, which takes a lot of bandwidth and has significant costs when you’re talking about approximately a billion images,” stated Schimmel. 

AI algorithms were developed on these NVIDIA DGX servers at a US Postal Service Engineering facility. (Credit: Nvidia)

Today, the new OCR application will rely on a deep learning model in a container on ECIP managed by Kubernetes, the open source container orchestration system, and served by NVIDIA Triton, the company’s open-source inference-serving software. Triton allows teams to deploy trained AI models from any framework, such as TensorFlow or PyTorch. 

The deployment was very streamlined,” Schimmel stated. “We awarded the contract in September 2019, started deploying systems in February 2020 and finished most of the hardware by August—the USPS was very happy with that,” he added 

Multiple models need to communicate to the USPS OCR application to work. The app that checks for mail items alone requires coordinating the work of more than a half dozen deep-learning models, each checking for specific features. And operators expect to enhance the app with more models enabling more features in the future. 

“The models we have deployed so far help manage the mail and the Postal Service—they help us maintain our mission,” Schimmel stated.  

One model, for example, automatically checks to see if a package carries the right postage for its size, weight, and destination. Another one that will automatically decipher a damaged barcode could be online this summer.  

“We’re at the very beginning of our journey with edge AI. Every day, people in our organization are thinking of new ways to apply machine learning to new facets of robotics, data processing and image handling,” he stated. 

Accenture Federal Services, Dell Technologies, and Hewlett-Packard Enterprise contributed to the USPS OCR system incorporating AI, Robbins of NVIDIA stated. Specialized computing cabinets—or nodes—that contain hardware and software specifically tuned for creating and training ML models, were installed at two data centers.   

The AI work that has to happen across the federal government is a giant team sport,” Robbins stated to Nextgov. “And the Postal Service’s deployment of AI across their enterprise exhibited just that.” 

The new solutions could help the Postal Service improve delivery standards, which have fallen over the past year. In mid-December, during the last holiday season, the agency delivered as little as 62% of first-class mail on time—the lowest level in years, according to an account in VentureBeat . The rate rebounded to 84% by the week of March 6 but remained below the agency’s target of about 96%. 

The Postal Service has blamed the pandemic and record peak periods for much of the poor service performance. 

Read the source articles and information on the Nvidia blog, in Nextgov and in VentureBeat.

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