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Recently, I asked the president of a large Democratic womens organization in Washington, D.C., if they had been helping any of the more than 6,000 migrants who had been bussed to D.C. from Texas in recent weeks.Many D.C. charities and immigrant rights groups have tried to help, but their resources were overwhelmed. Most of the migrants usually single male adults ages 18 to 26 have ended up on the streets. Mayor Muriel Bowser has asked the NationalGuard for help. She was refused.

The non-profits president enthusiastically introduced me to a 26-year-old woman helping in their food bank. She had walked from Ecuador to Texas in 45 days in May and June, then took the offer of a free bus from Texas to D.C. in early July. The former Venezuelan naval helicopter pilot student was eager to tell me her story over a two-hour lunch speaking only Spanish.But she askedme not to use her name since she didnt have papers.I must tell you she is beautiful.Her lovely hair fell in swooping curls and she had long manicured fingernails.In her soft but passionate voice, she told me that she was still upset and traumatized by her trip. But she also acknowledged she had been very lucky to make itto the USAsafely and to find a hostess in D.C.

I recount her story here from my notes in Spanish and English. Her narrative brings up as many questions as it answers.

I am from a coastal town in Venezuela where my parents had a small construction supply business. Seven years ago, I joined the navy. A year or so after, I was happy to be accepted into the helicopter pilot training program. I wanted to do something different, that most girls dont do.During the governmental incursions of 2017-18,I had to do some security detail with my naval unit. That was OK.But then about four years ago, my parents were taken hostage in their home and all their belongings were taken away. We decided as a family to flee to Ecuador. I eventually became a nail technician, although its not what I wanted to do.I wanted to go to the United States where the jobs were better.

This May after preparing with five friends all male we started off from Ecuador to walk to the U.S. al norte.I knew we had to travel light so I only took a small backpack with some clothes.

How many shoes did you take? I asked. Only one, she replied.My sports shoes. How are they? I asked. Fine, she answered with a shrug.

What was the worst part of the trip? I inquired.Oh, theDarien jungle in Columbia before Panama,she answered without hesitation. If you cant afford a boat around it, you have to walk through it. It was very scary. No food or water. Took about a week. We saw almost no one.

More questions: Did you have guides? maps?Did people help you with food and transportation? Were you ever assaulted?

No, I was never assaulted, she maintained throughout the two-hour interview. My friends protectedme. Andseveral times during the trip we were able to pay for a bus, or a hotel or for some meals. Sometimes, people helped us.

But they did have to pay bribes. Usually to people in (fake?) uniforms.Mexico was the hardest border to get through, she said. I was kidnapped in central Mexico she used the verb kidnapped in English a couple of times, but we agreed that she meant more like taken hostage or detained until she paid a bribe. The biggest bribe she paid was in Mexico: $600.

How much money did you take with you?I asked.About 1,200 American dollars, she said.

So, what happened when you got to the Mexican-U.S. border? I asked.We took a bus and had a map to the Rio Grande river crossing. It was dark but there were about 600 people waiting there the most we had seen on the whole trip, she recounted.

No one guided or led them. At one point during the night, people started wading across the river. So, she and her friends decided to as well. The water was deep, up to here, she showed me pointing to her chest. It was hard because I had hurt my ankle. I was scared.

When she and her friends made it onto the Texas shore, however, uniformed U.S. border patrol came up to them. Are you OK? she recalled was the first thing they asked. Are you hurt? Hungry? Need water?

Then, they were taken by van to a registration center.They asked us for our names and nationality, the Venezuelan citizen said. She had no papers to show them (had been told not to bring any). No one asked anything about COVID.

Then, people from ISAP (Intensive Supervision of Appearance Program, a government migrant monitoring program)took charge.We have automatically registered you as claiming asylum, they told her and her friends. We will help you with the paperwork.

Did you see anyone turned away by the borderpatrol?I asked?No, she said firmly.

The agents then took them to a small tent city where they were given food, clean clothes, a chance to shower and clean beds. Her companions were already contacting relatives and friends they had in Texas, California and Chicago. But she had no one to call.A few days later, they were given a bus ride to a small Texas town and told they were free to go where they wanted.As she walked into town, people from another organization told her they were putting together a bus for migrants to travel free to Washington D.C.Would she like to go?She immediately said yes.

The bus arrived at Union Station 45 hours later.It was late at night and dark. A small number of people greeted them with food and water. Some migrants mostly women and a few with young children were offered to stay at volunteers homes for a night or two, but no more.They would be directed to agencies that could help them.

Then, she got reallylucky.One of the volunteers said she could stay with her.Now Im doing all I can to expedite my asylum status so I can get a job permit, she said.Thats what I want.A good job and security to stay.

I told her I was happy for her and wished her luck.Ididnt mention thatbecause she was coming from Ecuador where she and her family hadfound safe refuge, a home and jobs for over three years,thatmight taint her request for asylum status. But Venezuelans have been given special Temporary Protective Status on the basis of their nationality it is not safe for them to go back to their homeland right now (a United Nations Human Right). So, she might not have to prove she was fleeingimmediate mortal danger as asylum and refugee applicants usually are required to do.

Such are the realities of crossing the southern border. Meanwhile, nonprofits in D.C. that assist migrants arriving from Texas are running out of resources and are asking for help themselves.

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Exclusive Interview on the Migrant Crisis: Woman Walks From Ecuador to Texas - Georgetowner

The fire department chief of a small Texas border county sees no end in sight for his weary team of first responders who every day recover migrant children and adults from the Rio Grande.

Manuel Mello III leads the Eagle Pass Fire Department in south-central Texas, an area that has become a top location for illegal migration nationwide. As more migrants attempt to cross the river as their final step in a long journey to the United States, many do not make it.

Two years ago, we would probably make in a year's time about 20 to 25 drownings, Mello told the Washington Examiner in a phone call Friday. Right now, you're looking at maybe 30 body recoveries in a month.

Mellos team is not only responsible for responding to fire emergencies in the 75,000-resident Maverick County; they also handle all emergency medical services and have the only swift water rescue in the region. Not only is his staff working overtime at a rate they have never hit before and trying to manage with just four ambulances to respond to calls they are struggling with the reality of pulling babies and children from the water.

'FLAT-OUT LYING': ABBOTT TEAM DENIES MIGRANTS MUST SIGN NDAS TO BOARD BUSES

Dario Lopez-Mills/AP

The Border Patrol operates boats in the Rio Grande and will rescue migrants, but agents will not pull deceased migrants from the river, leaving it to the fire department to recover those who drowned. Sometimes, Mexican officials will call Mello to let him know about a body washed up in the overgrown brush that spans the U.S. side of the river. Because there is but one boat ramp into the river and bodies can be found miles up and down the river, it can be a time-consuming effort.

The fire department uses a simple system to track those it recovers but was unable to break down the numbers by immigration status, gender, or age. As a result, Mello has to go by what he has seen in recent months and could not provide data for the past year.

Child drownings are becoming very common, he said, noting the death of a 3-year-old boy this week.

We've been seeing a whole lot more children drowning not like years past. I've been here 30 years. Once in a while, you'd see a child drowning. It was mostly male migrants that were crossing the river, said Mello. Now, we see people of all ages. It's just overwhelming because you'll see pregnant females. ... We had a family crossing, and they lost their children.

The uptick in deaths comes as more people are being encountered attempting to enter the U.S. illegally than at any time in history. Given that more people are attempting to cross the river, the number who are unsuccessful is also rising.

Recently, the fire department pulled six bodies from the river in one day, and on that same day, Mexican first responders pulled six people from the south side of the river. In addition, the department recovers one to two bodies per week of migrants who died of dehydration and heat-related illnesses.

Maverick County does not have its own medical examiner, and the fire department must transport every body an hour's drive to Webb County. Medical Examiner Dr. CorrineStern has been in her role as a forensic pathologist for two decades and has never seen anything like what is unfolding.

"This is my busiest year in my career ever," Stern told CNN's Rosa Flores and Rosalina Nieves this week.

She had tracked 196 migrant deaths this time last year across 12 counties. This year, she is responsible for autopsies in 11 counties and has already surpassed 218 deaths.

The human toll is a challenge logistically. Her five coolers have 260 bodies inside. Short on room, Stern asked local funeral home directors to hold bodies until she could make more room, CNN reported. Stern did not return a request for an interview.

Maverick County Sheriff Tom Schmerber said one morgue recently refused to take in any more bodies because it was out of space. One morgue director suggested the city move bodies outside the jail, an idea that Schmerber rejected, the New York Post reported. A funeral home director who spoke with the outlet on condition of anonymity said he had to "stack" bodies at his facility due to a space shortage.

The bodies of migrants who cannot be identified are being buried at the back of a county cemetery with crosses made from PVC pipes, according to CNN. They are fingerprinted before burial in hopes of being able to identify them later.

The crisis is also taking a toll on first responders. Counseling and mental health services are available to the fire department and EMS employees, but Mello worried that pulling babies and children from the river is hitting his staff especially hard given that around 70% of fire employees are in their 20s and 30s and have children that age.

Its very heartbreaking. It's stressful. You go through a lot of emotions, said Mello.

Mello lamented that local officials had not heard from the Biden administration at any point since January 2021. The state has reached out, and Mello requested emergency funding and equipment.

CLICK HERE TO READ MORE FROM THE WASHINGTON EXAMINER

Several years ago, I told a reporter, This thing's not going to stop, Mello said, referring to when illegal migration through Eagle Pass began to rise in 2019. I said, You guys are going to continue coming down here and covering drownings. And look at us right now, recovering double the drownings that we used to have back then.

Eagle Pass Mayor Rolando Salinas could not be reached for comment.

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Fire department in Texas border county recovering bodies of migrants every day - Washington Examiner

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Todays demand for real-time data analytics at the edge marks the dawn of a new era in machine learning (ML): edge intelligence. That need for time-sensitive data is, in turn, fueling a massive AI chip market, as companies look to provide ML models at the edge that have less latency and more power efficiency.

Conventional edge ML platforms consume a lot of power, limiting the operational efficiency of smart devices, which live on the edge. Thosedevices are also hardware-centric, limiting their computational capability and making them incapable of handling varying AI workloads. They leverage power-inefficient GPU- or CPU-based architectures and are also not optimized for embedded edge applications that have latency requirements.

Even though industry behemoths like Nvidia and Qualcomm offer a wide range of solutions, they mostly use a combination of GPU- or data center-based architectures and scale them to the embedded edge as opposed to creating a purpose-built solution from scratch. Also, most of these solutions are set up for larger customers, making them extremely expensive for smaller companies.

In essence, the $1 trillion global embedded-edge market is reliant on legacy technology that limits the pace of innovation.

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MetaBeat will bring together thought leaders to give guidance on how metaverse technology will transform the way all industries communicate and do business on October 4 in San Francisco, CA.

ML company Sima AI seeks to address these shortcomings with its machine learning-system-on-chip (MLSoC) platform that enables ML deployment and scaling at the edge. The California-based company, founded in 2018, announced today that it has begun shipping the MLSoC platform for customers, with an initial focus of helping solve computer vision challenges in smart vision, robotics, Industry 4.0, drones, autonomous vehicles, healthcare and the government sector.

The platform uses a software-hardware codesign approach that emphasizes software capabilities to create edge-ML solutions that consume minimal power and can handle varying ML workloads.

Built on 16nm technology, the MLSoCs processing system consists of computer vision processors for image pre- and post-processing, coupled with dedicated ML acceleration and high-performance application processors. Surrounding the real-time intelligent video processing are memory interfaces, communication interfaces, and system management all connected via a network-on-chip (NoC). The MLSoC features low operating power and high ML processing capacity, making it ideal as a standalone edge-based system controller, or to add an ML-offload accelerator for processors, ASICs and other devices.

The software-first approach includes carefully-defined intermediate representations (including the TVM Relay IR), along with novel compiler-optimization techniques. This software architecture enables Sima AI to support a wide range of frameworks (e.g., TensorFlow, PyTorch, ONNX, etc.) and compile over 120+ networks.

Many ML startups are focused on building only pure ML accelerators and not an SoC that has a computer-vision processor, applications processors, CODECs, and external memory interfaces that enable the MLSoC to be used as a stand-alone solution not needing to connect to a host processor. Other solutions usually lack network flexibility, performance per watt, and push-button efficiency all of which are required to make ML effortless for the embedded edge.

Sima AIs MLSoC platform differs from other existing solutions as it solves all these areas at the same time with its software-first approach.

The MLSoC platform is flexible enough to address any computer vision application, using any framework, model, network, and sensor with any resolution. Our ML compiler leverages the open-source Tensor Virtual Machine (TVM) framework as the front-end, and thus supports the industrys widest range of ML models and ML frameworks for computer vision, Krishna Rangasayee, CEO and founder of Sima AI, told VentureBeat in an email interview.

From a performance point of view, Sima AIs MLSoC platform claims to deliver 10x better performance in key figures of merit such as FPS/W and latency than alternatives.

The companys hardware architecture optimizes data movement and maximizes hardware performance by precisely scheduling all computation and data movement ahead of time, including internal and external memory to minimize wait times.

Sima AI offers APIs to generate highly optimized MLSoC code blocks that are automatically scheduled on the heterogeneous compute subsystems. The company has created a suite of specialized and generalized optimization and scheduling algorithms for the back-end compiler that automatically convert the ML network into highly optimized assembly codes that run on the machine learning-accelerator (MLA) block.

For Rangasayee, the next phase of Sima AIs growth is focused on revenue and scaling their engineering and business teams globally. As things stand, Sima AI has raised $150 million in funding from top-tier VCs such as Fidelity and Dell Technologies Capital. With the goal of transforming the embedded-edge market, the company has also announced partnerships with key industry players like TSMC, Synopsys, Arm, Allegro, GUC and Arteris.

VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Discover our Briefings.

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Machine learning at the edge: The AI chip company challenging Nvidia and Qualcomm - VentureBeat

A New Zealand startup producing its own servers is expanding into the realm of artificial intelligence, creating machine learning solutions that carry out common tasks while relieving people of repetitive, unsatisfying work. Having spotted an opportunity for the development of low-cost, high-efficiency and environmentally sustainable hardware, Kauricone has more recently pivoted in a fascinating direction: creating software that thinks about mundane problems, so we dont have to. These tasks include identifying trash for improved recycling, looking at items on roads for automated safety, pest identification and in the ultimate alleviation of a notoriously sleep-inducing task counting sheep.

Managing director, founder and tech industry veteran Mike Milne says Kauricone products include application servers, cluster servers and internet of things servers. It was in this latter category that the notion emerged of applying machine learning at the networks edge.

Having already developed low-cost-low power edge hardware, we realised there was a big opportunity for the application of smart computing in some decidedly not-so-enjoyable everyday tasks, relates Milne. After all, we had all the basic building blocks already: the hardware, the programming capability, and with good mobile network coverage, the connectivity.

Situation

Work is just another name for tasks people would rather not do themselves, or that we cannot do for ourselves. And despite living in a fabulously advanced age, there is a persistent reality of all manner of tasks which must be done every day, but which dont require a particularly high level of engagement or even intelligence.

It is these tasks for which machine learning (ML) is quite often a highly promising solution. ML collects and analyses data by applying statistical analysis, and pattern matching, to learn from past experiences. Using the trained data, it provides reliable results, and people can stop doing the boring work, says Milne.

There is in fact more to it than meets the eye (so to speak) when it comes to computer image recognition. Thats why Capcha challenges are often little more than Identify all the images containing traffic lights: because distinguishing objects is hard for bots. ML overcomes the challenge through the training mentioned by Milne: the computer is shown thousands of images and learns which are hits, and which are misses.

Potentially, there are as many use cases as you have dull but necessary tasks in the world, Milne notes. So far, weve tackled a few. Rocks on roads are dangerous, but monitoring thousands of kilometers of tarmac comes at a cost. Construction waste is extensive, bad for the environment and should be managed better. Sheep are plentiful and not always in the right paddock. And pests put New Zealands biodiversity at risk.

Solution

Tackling each of these problems, Kauricone started with its own-developed RISC IoT server hardware as the base. Running Ubuntu and programmed with Python or other open-source languages, the servers typically feature 4GB memory and 128GB solid state storage, the solar-powered edge devices consume as little as 3 watts and run indefinitely on a single solar panel. This makes for a reliable, low-cost field-ready device, says Milne.

The Rocks on Roads project made clear the challenges of simple image identification, with Kauricone eventually running a training model around the clock for 8 days, gathering 35,000 iterations of rock images, which expanded to 3,000,000 identifiable traits (bear in mind, a human identifies a rock almost instantly, perhaps faster if hurled). With this training, the machine became very good at detecting rocks on the roads.

For a new project involving construction waste, the Kauricone IoT server will maintain a vigilant watch on the types and amounts of waste going into building-site skips. Trained to identify types of waste, the resulting data will be the basis for improving waste management and recycling or redirecting certain items for more responsible disposal.

Counting sheep isnt only a method for accelerating sleep time, its also an essential task for farmers across New Zealand. Thats not all as an ML exercise, it anticipates the potential for smarter stock management, as does the related pest identification test case pursued by Kauricone. The ever-watchful camera and supporting hardware manage several tasks: identifying individual animals, numbering them, and also monitoring grass levels, essential for ovine nourishment. Tested so far on a small flock, this application is ready for scale.

Results

Milne says the small test cases pursued by Kauricone to date are just the beginning and anticipates considerable potential for ML applications across all walks of life. There is literally no end to the number of daily tasks where computer vision and ML can alleviate our workload and contribute to improved efficiency and, ultimately, a better and more sustainable planet, he notes.

The Rocks on Roads project promises improved safety with a lower human overhead, reducing or eliminating the possibility of human error. Waste management is a multifaceted problem, where the employment of personnel is rendered difficult owing to simple economics (and potentially stultifying work); New Zealands primary sector is ripe for technologically powered performance improvements which could boost already impressive productivity through automation and improved control; and pest management can help the Department of Conservation and allied parties achieve better results using fewer resources.

Its early days yet, says Milne, But the results from these exploratory projects are promising. With the connectivity of ever-expanding cellular and low-power networks like SIGFOX and LoraWan, the enabling infrastructure is increasingly available even in remote places. And purpose-built low power hardware brings computing right to the edge. Now, its just a matter of identifying opportunities and creating the applications.

For more information visit Kauricone's website.

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Kauricone: Machine learning tackles the mundane, making our lives easier - IT Brief New Zealand

They help to improve efficiency and profitability for utilities.

The utilities space is rapidly transforming today. Its shifting from the conventional and a highly-regulated environment to a tech-driven market at a fast clip. Collating data and optimizing manpower is a constant struggle. The smarter optimization of infrastructure has increased monumentally with the outbreak of the pandemic, and also the dependency on technology. There is an urgent need to balance the supply and demand for which Artificial Intelligence (AI) and Machine Learning (ML) can come into play. Data Science, aided by AI and ML, has been leading to several positive developments in the utilities space. Digitalization can increase the profitability of utilities by significant percentages by utilizing smart meters for grids, digital productivity tools and automating back-office processes. According to a study firms can increase their profitability from 20 percent to 30 percent.

Digital measures rewire organizations to do better through a fundamental reboot of how work gets done.

Customer Service and AI

According to a Gartner report, most AI investments by utilities most often go into customer service solutions. Some 86% of the utilities studied used AI in their digital marketing, towards call center support and customer application. This is testimony to the investments in AI and ML that can deliver a high ROI by improving speed and efficiency, thus enhancing customer experience. The AI thats customer-facing is a low-risk investment as customer enquiries are often repetitive such as billing enquiries, payments, new connections etc. AI can deliver tangible results for business on the customer service front.

Automatic Meters for Energy conservation

As manual entry and billing systems are not only time-consuming, but also susceptible to errors and are expensive too. The Automatic Meter Reading (AMR) System has made a breakthrough. The AMR enables large infrastructure set ups to collect data easily and also analyze the cost centers and the opportunities for improving the efficiencies of natural gas, electric, water sectors and more. It offers real-time billing information for budgeting. It has the advantage of being precise compared to manual entry. Additionally, it is able to store data at distribution points within the networks of the utility. This can be easily accessed over a network using devices like the mobile and handhelds. Energy consumption can be tracked to aid conservation and end energy theft.

Predictive Analytics Enable Smart grid options

By leveraging new-age technologies, utilities can benefit immensely. These technologies in the energy sector help in building smart power grids. The energy sector heavily relies on a complex infrastructure that can face multiple issues as a result of maintenance issues, weather conditions, failure of the system or equipment, demand surges and misallocation of resources. Overloading and congestion leads to a lot of energy being wasted. The grids produce a humongous data which help with risk mitigation when properly utilized. With the large volume of data that continuously pass over the grid, it can be challenging to collect and aggregate it. The operators could miss these insights which could lead to malfunction or outages. With the help of the ML algorithms, the insights can be obtained for smooth functioning of the grids. Automated data management can help maintain the data accurately. With the help of predictive analytics, the operators can predict grid failures before the customers are affected and also create greater customer satisfaction and mitigate any financial loss.

Efficient and Sustainable energy consumption

These allow for better allocation of energy for consumption as it would be based on demand and can save resources and help in load management and forecasting. AI can also deal with issues pertaining to vegetation by analyzing operational data or statistics. This can help to proactively deal with wildfires. Thus, it can become a sustainable and efficient system. To overcome issues pertaining to weather-related maintenance, automation helps receive signals and prioritize the areas that need attention to save money and cut down the downtime. To achieve this, the sector adopts ML capabilities as they need to be able to access automation fast and easily.

The construction sector is also a major beneficiary of the solutions. Building codes and architecture are often a humongous challenges that take a long time to meet. But, some solutions help the builders and developers test these applications seamlessly without any system interruptions. By integrating AI and ML in the data management platforms, the developers enable the data-science teams to spend enough time innovating and much less time on maintenance. With the rise in the computational power and accessibility to the Cloud, the deep learning algorithms are able to train faster while their cost is optimized. AI and ML are able to impact different aspects of business. AI can enhance the quality of human jobs by facilitating remote working. They can help in data collection and analysis and also provide actionable inputs. Data analytics platforms can throw light on the areas of inefficiency and help the providers keep costs down.

Though digital transformation might appear intimidating, its opportunities are much more than the cost and risk associated. Gradually, all utilities will undergo digital transformation as it has begun to take roots in the industrial sectors. This AI-led transformation will improve productivity, revenue gains, make networks more reliable and safe, accelerate customer acquisition, and facilitate entry into new areas of business. Globally, the digital utility market is growing at a CAGR of 11.7% for the period of 2019 to 2027. In 2018, the revenue generated globally for the digital utility market was 141.41 Bn and is expected to reach US$ 381.38 Bn by 2027 according to a study by ResearchAndMarkets.com. As the sector evolves, the advantages of AI and ML will come into play and lead to smarter grids, efficient operations and higher customer satisfaction. The companies that are in a position to take advantage of this opportunity will be ready for the future challenges that could emerge in the market.

Views expressed above are the author's own.

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Artificial intelligence and machine learning now integral to smart power solutions - Times of India