Mediaboss Marketing

Britain will have enough gas to meet demand, and will even ship additional gas to Europe due to low storage levels there.

When underground storage facilities begin to fill up ahead of winter, National Grid expects gas exports to the EU to increasefrom April to September.

Interconnectors allow Britain to export gas to Europe, and National Grid forecasts UK average shipments to Europe to reach 5.4 billion cubic metres this summer, up from 0.7 billion cubic metres last summer.

This summer, liquefied natural gas (LNG) will be more crucial than ever, according to the grid operator. It anticipates LNG supply to average 6.4 billion cubic metres this summer, up from 5.1 billion cubic metres last year.

The United Kingdom imports only about 3% of its gas from Russia, while the European Union imports nearly all of itswhile the European Union relies on Russia for around 40% of its needs. Several European countries have said they are reducing reliance on Russian gas in the wake of the war in Ukraine.

Read this article:
United Kingdom plans to increase gas supply to the European Union - BOL News

President Biden recently announced the creation of a Task Force on Energy and Security with the European Union as a show of commitment to reducing Europes dependency on Russian energy. The plan is to increase liquified natural gas (LNG) exports to Europe by 15 billion cubic meters this year.

This sounds great in theory. But its hard not to wonder, how will the United States help Europe lessen their reliance on Russia for energy, when our president is pushing the same Green New Deal policies that got Europe there in the first place?

In the European Unions rush to go green, they prematurely abandoned fossil fuel production for less reliable forms of clean energy, such as wind and solar. Weather-dependent renewables were subsidized as these countries went all-in on eliminating fossil fuels from their energy portfolio.

Europe shut down coal and nuclear plants as part of their plan to reach carbon neutrality as quickly as possible. For example, Germany shut down three of its last six nuclear reactors even though nuclear energy is the only carbon neutral base-load power source and plans to close the final three by the end of 2022. France, Germany, and Bulgaria banned fracking, and France and Spain blocked LNG terminals which would allow them to import U.S. natural gas.

One pandemic and a windless summer later, Europes supply of always-on energy decreased while the demand for energy stayed the same. Wholesale gas prices rose by more than 400 percent since the start of 2021, over 20 energy suppliers in Britain went bankrupt, inventory stockpiles ran short, and many countries had to pay a 90 percent surge in carbon emission fines to revamp fossil fuel production. It was an energy crisis.

This rush to green made Europe reliant on corrupt foreign oil cartels to keep the lights on and fill the gap in their energy needs. Russia supplied 40 percent of Europes natural gas and 25 percent of their crude oil supply. This is more than double the amount of natural gas imports Europe was receiving from Norway. For Germany, they relied on Russia for 50 percent of their natural gas. Gazprom is still sending Russian oil to Europe currently.

This crisis should serve as a cautionary tale to President Biden and the Democratic Party that eliminating reliable energy here at home puts our country at the mercy of our adversaries and cripples our ability to help our allies.

President Biden began his assault on American energy when he cancelled the Keystone XL pipeline and rejoined the Paris Climate Agreement on his first day in office. He went on to diminish the federal leasing program into operating at a bare minimum. He slowed down the processing of applications for permits to drill on public lands and in U.S. waters and pledged to reach net zero emissions by 2050.

The Biden Environmental Protection Agency is pushing methane regulation on oil and gas and the Security and Exchange Commission is ruling that publicly traded companies must disclose their financial risk from climate change to support this effort.

Now, the Biden administration is propping up renewable energy to make it look more attractive than fossil fuels. He approved the nations first major offshore wind farm and is pushing for more development plans along the coastline. In the proposed Build Back Better legislation, the Democrats offered $300 billion in tax incentives to weatherize homes and create electric vehicle charging stations.

Sound familiar? President Biden cannot push the same policies as Europe and expect a different outcome. Thats the definition of insanity.

What has happened in Europe proved to the entire world that renewables cannot be the only source of energy. The war in Ukraine showed us they are also not safe.

Germany has recently begun accelerating the development of two new LNG terminals to reduce dependence on Russia gas imports and is considering revamping their abandoned coal mines. Unfortunately, they cant reverse course fast enough. Years worth of infrastructure, development, and innovation have already been thrown away.

To best support our allies, America must be energy independent. We can only achieve that with an all-of-the-above energy portfolio, which includes both renewables and fossil fuels. In the time President Biden has spent crippling the industry and pointing fingers, our producers could have been providing the world with the energy they need. American energy independence is critical for global stabilization and through their rush to green, Democrats are putting that in jeopardy.

Markwayne Mullin represents Oklahomas 2nd District and is a member of the Energy and Commerce Committee.

See the article here:
Europes rush to green: A cautionary tale for America - The Hill

Students in the MIT course 6.036 (Introduction to Machine Learning) study the principles behind powerful models that help physicians diagnose disease or aid recruiters in screening job candidates.

Now, thanks to the Social and Ethical Responsibilities of Computing (SERC) framework, these students will also stop to ponder the implications of these artificial intelligence tools, which sometimes come with their share of unintended consequences.

Last winter, a team of SERC Scholars worked with instructor Leslie Kaelbling, the Panasonic Professor of Computer Science and Engineering, and the 6.036 teaching assistants to infuse weekly labs with material covering ethical computing, data and model bias, and fairness in machine learning. The process was initiated in the fall of 2019 by Jacob Andreas, the X Consortium Assistant Professor in the Department of Electrical Engineering and Computer Science. SERC Scholars collaborate in multidisciplinary teams to help postdocs and faculty develop new course material.

Because 6.036 is such a large course, more than 500 students who were enrolled in the 2021 spring term grappled with these ethical dimensions alongside their efforts to learn new computing techniques. For some, it may have been their first experience thinking critically in an academic setting about the potential negative impacts of machine learning.

The SERC Scholars evaluated each lab to develop concrete examples and ethics-related questions to fit that weeks material. Each brought a different toolset. Serena Booth is a graduate student in the Interactive Robotics Group of the Computer Science and Artificial Intelligence Laboratory (CSAIL). Marion Boulicault was a graduate student in the Department of Linguistics and Philosophy, and is now a postdoc in the MIT Schwarzman College of Computing, where SERC is based. And Rodrigo Ochigame was a graduate student in the Program in History, Anthropology, and Science, Technology, and Society (HASTS) and is now an assistant professor at Leiden University in the Netherlands. They collaborated closely with teaching assistant Dheekshita Kumar, MEng 21, who was instrumental in developing the course materials.

They brainstormed and iterated on each lab, while working closely with the teaching assistants to ensure the content fit and would advance the core learning objectives of the course. At the same time, they helped the teaching assistants determine the best way to present the material and lead conversations on topics with social implications, such as race, gender, and surveillance.

In a class like 6.036, we are dealing with 500 people who are not there to learn about ethics. They think they are there to learn the nuts and bolts of machine learning, like loss functions, activation functions, and things like that. We have this challenge of trying to get those students to really participate in these discussions in a very active and engaged way. We did that by tying the social questions very intimately with the technical content, Booth says.

For instance, in a lab on how to represent input features for a machine learning model, they introduced different definitions of fairness, asked students to consider the pros and cons of each definition, then challenged them to think about the features that should be input into a model to make it fair.

Four labs have now been published on MIT OpenCourseWare. A new team of SERC Scholars is revising the other eight, based on feedback from the instructors and students, with a focus on learning objectives, filling in gaps, and highlighting important concepts.

An intentional approach

The students efforts on 6.036 show how SERC aims to work with faculty in ways that work for them, says Julie Shah, associate dean of SERC and professor of aeronautics and astronautics. They adapted the SERC process due to the unique nature of this large course and tight time constraints.

SERC was established more than two years ago through the MIT Schwarzman College of Computing as an intentional approach to bring faculty from divergent disciplines together into a collaborative setting to co-create and launch new course material focused on social and responsible computing.

Each semester, the SERC team invites about a dozen faculty members to join an Action Group dedicated to developing new curricular materials (there are several SERC Action Groups, each with a different mission). They are purposeful in whom they invite, and seek to include faculty members who will likely form fruitful partnerships in smaller subgroups, says David Kaiser, associate dean of SERC, the Germeshausen Professor of the History of Science, and professor of physics.

These subgroups of two or three faculty members hone their shared interest over the course of the term to develop new ethics-related material. But rather than one discipline serving another, the process is a two-way street; every faculty member brings new material back to their course, Shah explains. Faculty are drawn to the Action Groups from all of MITs five schools.

Part of this involves going outside your normal disciplinary boundaries and building a language, and then trusting and collaborating with someone new outside of your normal circles. Thats why I think our intentional approach has been so successful. It is good to pilot materials and bring new things back to your course, but building relationships is the core. That makes this something valuable for everybody, she says.

Making an impact

Over the past two years, Shah and Kaiser have been impressed by the energy and enthusiasm surrounding these efforts.

They have worked with about 80 faculty members since the program started, and more than 2,100 students took courses that included new SERC content in the last year alone. Those students arent all necessarily engineers about 500 were exposed to SERC content through courses offered in the School of Humanities, Arts, and Social Sciences, the Sloan School of Management, and the School of Architecture and Planning.

Central to SERC is the principle that ethics and social responsibility in computing should be integrated into all areas of teaching at MIT, so it becomes just as relevant as the technical parts of the curriculum, Shah says. Technology, and AI in particular, now touches nearly every industry, so students in all disciplines should have training that helps them understand these tools, and think deeply about their power and pitfalls.

It is not someone elses job to figure out the why or what happens when things go wrong. It is all of our responsibility and we can all be equipped to do it. Lets get used to that. Lets build up that muscle of being able to pause and ask those tough questions, even if we cant identify a single answer at the end of a problem set, Kaiser says.

For the three SERC Scholars, it was uniquely challenging to carefully craft ethical questions when there was no answer key to refer to. But thinking deeply about such thorny problems also helped Booth, Boulicault, and Ochigame learn, grow, and see the world through the lens of other disciplines.

They are hopeful the undergraduates and teaching assistants in 6.036 take these important lessons to heart, and into their future careers.

I was inspired and energized by this process, and I learned so much, not just the technical material, but also what you can achieve when you collaborate across disciplines. Just the scale of this effort felt exciting. If we have this cohort of 500 students who go out into the world with a better understanding of how to think about these sorts of problems, I feel like we could really make a difference, Boulicault says.

Read more:
Learning to think critically about machine learning | MIT News | Massachusetts Institute of Technology - MIT News

Manufacturers, to keep up with the latest changes in technology, need to explore one of the most critical elements driving factories forward into the future: machine learning. Lets talk about the most important applications and innovations that ML technology is providing in 2022.

Machine learning is a subfield of artificial intelligence, but not all AI technologies count as machine learning. There are various other types of AI that play a role in many industries, such as robotics, natural language processing, and computer vision. If youre curious about how these technologies affect the manufacturing industry, check out our review below.

Basically, machine learning algorithms utilize training data to power an algorithm that allows the software to solve a problem. This data may come from real-time IoT sensors on a factory floor, or it may come from other methods. Machine learning has a variety of methods such as neural networks and deep learning. Neural networks imitate biological neurons to discover patterns in a dataset to solve problems. Deep learning utilizes various layers of neural networks, where the first layer utilizes raw data input and passes processed information from one layer to the next.

Lets start by imagining a box with assembly robots, IoT sensors, and other automated machinery. At one end you supply the materials necessary to complete the product; at the other end, the product rolls off the assembly line. The only intervention needed for this device is routine maintenance of the equipment inside. This is the ideal future of manufacturing, and machine learning can help us understand the full picture of how to achieve this.

Aside from the advanced robotics necessary for automated assembly to work, machine learning can help ensure: quality assurance, NDT analysis, and localizing the causes of defects, among other things.

You can think of this factory in a box example as a way of simplifying a larger factory, but in some cases its quite literal.Nokiais utilizing portable manufacturing sites in the form of retrofitted shipping containers with advanced automated assembly equipment. You can use these portable containers in any location necessary, allowing manufacturers to assemble products on site instead of needing to transport the products longer distances.

Using neural networks, high optical resolution cameras, and powerfulGPUs, real-time video processing combined with machine learning and computer vision can complete visual inspection tasks better than humans can. This technology ensures that the factory in a box is working correctly and that unusable products are eliminated from the system.

In the past, machine learnings use in video analysis has been criticized for the quality of video used. This is because images can be blurry from frame to frame, and the inspection algorithm may be subject to more errors. With high-quality cameras and greater graphical processing power, however, neural networks can more efficiently search for defects in real-time without human intervention.

Using various IoT sensors, machine learning can help test the created products without damaging them. An algorithm can search for patterns in the real-time data that correlate with a defective version of the unit, enabling the system to flag potentially unwanted products.

Another way that we can detect defects in materials is through non-destructive testing. This involves measuring a materials stability and integrity without causing damage. For example, you can use an ultrasound machine to detect anomalies like cracks in a material. The machine can measure data that humans can analyze to look for these outliers by hand.

However, outlier detection algorithms, object detection algorithms, and segmentation algorithms can automate this process by analyzing the data for recognizable patterns that humans may not be able to see with much greater efficiency. Machine learning is also not subject to the same number of errors that humans are prone to make.

One of the core tenants of machine learnings role in manufacturing is predictive maintenance. PwCreportedthat predictive maintenance will be one of the largest growing machine learning technologies in manufacturing, having an increase of 38 percent in market value from 2020 to 2025.

With unscheduled maintenance having the potential to deeply cut into a businesss bottom line, predictive maintenance can enable factories to make appropriate adjustments and corrections before machinery can experience more costly failures. We want to make sure that our factory in a box will have as much uptime with the fewest delays possible, and predictive maintenance can make that happen.

Extensive IoT sensors that record vital information about the operating conditions and status of a machine make predictive maintenance possible. This may include humidity, temperature, and more.

A machine learning algorithm can analyze patterns in data collected over time and reasonably predict when the machine may need maintenance. There are several approaches to achieve this goal:

Thanks to the IoT sensors powering predictive maintenance, machine learning can analyze the patterns in the data to see what parts of the machine need to be maintained to prevent a failure. If certain patterns lead to a trend of defects, its possible that hardware or software behaviors can be identified as causes of those defects. From here, engineers can come up with solutions to correct the system to avoid those defects in the future. This enables us to reduce the margin of error of our factory in a box scenario.

Digital twins are a virtual recreation of the production process based on data from IoT sensors and real-time data. They can be created as an original hypothetical representation of a system that doesnt yet exist, or they could be a recreation of an existing system.

The digital twin is a sandbox for experimentation in which machine learning can be used to analyze patterns in a simulation to optimize the environment. This helps support quality assurance and predictive maintenance efforts as well. We can also use machine learning alongside digital twins for layout optimization. This works when planning the layout of a factory or for optimizing the existing layout.

If we want to optimize every part of the factory, we also need to pay attention to the energy that it requires. The most common way to do this is to use sequential data measurements, which can be analyzed by data scientists with machine learning algorithms powered by autoregressive models and deep neural networks.

Weve used machine learning to optimize the factorys production processes, but what about the product itself? BMWintroducedthe BMW iX Flow at CES 2022 with a special e-ink wrap that can allow it to change the color (or more accurately, the shade) of the car between black and white. BMW explained that Generative design processes are implemented to ensure the segments reflect the characteristic contours of the vehicle and the resulting variations in light and shadow.

Generative design is where machine learning is used to optimize the design of a product, whether it be an automobile, electronic device, toy, or other items. With data and a desired goal, machine learning can cycle through all possible arrangements to find the best design.

ML algorithms can be trained to optimize a design for weight, shape, durability, cost, strength, and even aesthetic parameters.

Generative design process can be based on these algorithms:

Lets step away from the factory in a box example for a bit and look at a broader picture of needs in manufacturing. Production is only one element. The supply chain roles from a manufacturing center are also being improved with machine learning technologies, such as logistics route optimization and warehouse inventory control. These make up a cognitive supply chain that continues to evolve in the manufacturing industry.

AI-powered logistics solutions use object detection models instead of barcode detection, thus replacing manual scanning. Computer vision systems can detect shortages and overstock. By identifying these patterns, managers can be made aware of actionable situations. Computers can even be left to take action automatically to optimize inventory storage.

At MobiDev, we have researched a use case of creating a system capable of detecting objects for logistics. Read more aboutobject detection using small datasetsfor automated items counting in logistics.

How much should a factory produce and ship out? This is a question that can be difficult to answer. However, with access to appropriate data, machine learning algorithms can help factories understand how much they should be making without overproducing. The future of machine learning in manufacturing depends on innovative decisions.

Visit link:
Machine Learning Application in the Manufacturing Industry - IoT For All

Suggesting that some radiologists may not be aware of the intended use of computer-aided triage and notification (CADt) devices, the Food and Drug Administration (FDA) has issued an advisory on the use of the imaging software for patients with suspected large vessel occlusion (LVO) in the brain.

Emphasizing proper use of CADt software, the FDA notes these devices are not intended to substitute for diagnostic assessment by radiologists. While CADt devices can help flag and prioritize brain imaging with findings that are suspicious for LVO, the advisory points out that an LVO, a common cause of acute ischemic strokes, may still be present even if it is not flagged by the CADt imaging software.

If there is any potential over-reliance on CADt software, Vivek Bansal, MD said it may stem from a team of health-care providers striving to do the right thing for the patient under tight time constraints. While interventionalists, neurosurgeons and neurologists all have strong knowledge of brain vessels, there may be different levels of experience, according to Dr. Bansal, the national subspecialty lead for neuroradiology at Radiology Partners. He added that while these specialists look closely at images they take in the operating suite, they may not look at the actual CT images to the same level.

In regard to the imaging, Dr. Bansal said one may be looking at tiny branching vessels that are diving up and down into different slices of the images, and you have to scroll up and down to really trace them out vessel by vessel. This can be challenging and particularly hard to do on a smartphone in a brightly lit room, pointed out Dr. Bansal.

The clock is ticking, and time is brain. We are trying to race against the clock because every minute we take to arrive at a diagnosis, more brain cells may be dying (if the patient has a clot). The quicker we can get them to a diagnosis and the patient gets to a cath lab, the better the outcomes for the patient. I think that is the biggest challenge: trying to do something that is very meticulous in a very small amount of time, explained Dr. Bansal.

The FDA advisory also maintained that it is important to have awareness of the design capabilities of different CADt devices, many of which have artificial intelligence (AI) or machine learning technology, For example, the FDA cautioned that LVO CADt devices may not assess all intracranial vessels. Dr. Bansal said this is an important distinction with AI tools.

While some AI tools are very good at looking at an M1 occlusion, which is the proximal part of the middle cerebral artery, the newer AI tools are capable of looking at M2 occlusions with proximal anterior cerebral artery (ACA) and posterior cerebral artery (PCA) occlusions. All of these things are important in terms of patient care, maintained Dr. Bansal, who is affiliated with the East Houston Pathology Group in Texas.

Dr. Bansal said the key is understanding the role of AI-enabled devices and their value in triaging cases.

At any given moment, I might have 40 stat exams on my list. Im cranking through them as fast as I can but if AI tools are saying 'Hey, look at this one next, whether it is a potential large vessel occlusion or brain bleed, that is very helpful, suggested Dr. Bansal. Where we are at right now, I think that the only way we can look at AI is to look at it as a triaging tool.

Continue reading here:
FDA Issues Advisory on Use of AI and Machine Learning for Large Vessel Occlusion in the Brain - Diagnostic Imaging