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Downtime is the enemy of profitability in manufacturing, which is why FANUC, a leading global automation solutions provider, has introduced a new Industrial Internet of Things (IIOT) software designed to prevent production problems before they happen. AI Servo Monitor uses artificial intelligence to predict possible failures of the drive systems for FANUC servomotors and spindle motors.

AI Servo Monitor, in conjunction with MT-LINKi through machine learning, analyzes the daily performance of machines equipped with FANUC CNCs. Daily data is displayed in intuitive graphs which allows users to easily monitor abnormalities on these machines. Artificial intelligence automatically creates a baseline model of the machine while running in a normal state. An anomaly score developed expresses a difference in the baseline model and the daily recorded values. On a web interface, users can easily see the anomaly scores in a graph. Plus, email notifications can be issued if this value exceeds the predefined thresholds.

The power of IIOT software is that it detects a failure before it happens, not after, says Jon Heddleson, General Manager of Factory Automation for FANUC America. Predictive maintenance is key in preventing unexpected downtime. FANUCs AI Servo Monitor helps ensure that production keeps running smoothly.

MT-Linki is FANUCs machine status monitoring and data collection software that connects shop floor equipment, including machine tools, robots and PLCs. MT-Linki monitors, collects, and presents data in color-coded graphical representations of the factory floor to provide more information about manufacturing processes as well as historical data. Non-FANUC CNCs, PLCs and various sensors can be connected using MTConnect or OPC-UA protocol.

Information presented via MT-Linki enables data-driven business decisions to optimize operations through enhanced maintenance capabilities such as scheduling memory backups, presenting alarm/operator history, and monitoring the status of memory backup batteries, cooling fans, motor temperatures, etc.

To learn more about AI Servo Monitor, visit https://www.fanucamerica.com/products/cnc/cnc-software/machine-tool-data-collection-software/ai-servo-monitor.

To learn more about MT-Linki, visit: https://www.fanucamerica.com/products/cnc/cnc-software/mtlink-i.

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New Machine Learning Tool for Predictive Maintenance - Automation World

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Artificial Intelligence (AI) is the science of making intelligent machines. This technology works to create programs which can not only calculate and solve complex problems but also learn from experience, research, reason and adapt to new situations and trends.

These learning features make AI a perfect tool to be used in stock trading. Whether youre looking to trade oil online or invest elsewhere in the stock market, AI can be used to automate and analyse huge amounts of data and present you with forecasts and prices to help make stock trading significantly easier. Bots can also be used to execute trades at the optimum times through their ability to carry out multiple trades every single second.

But how exactly is AI used in this process? The main ways are through pattern formation, predictive trading and an increased trading speed.

Two Types of AI

The two main types of AI are rules-based systems and machine learning. Rules-based systems are the simpler of the two, which consist of only sets of facts or sets of rules. Machine learning, however, is an improvement on the rules-based system in which the system is provided with information concerning the outcomes of each data point but not the decision-making process.

This allows the system to make more accurate decisions than a rules-based system could. There can be as many input variables or features as required, and machine learning operates on the basis of previous outcomes and predicts the most likely future outcomes.

Machine learning may fall short if it is not provided with all the relevant information to any one decision, but to get around this machine learning can make use of a decision tree method similar to that seen in a rules-based system. This can help AI get around any uncertainties or missing information to still predict an accurate response.

How do Artificial Intelligence and Machine Learning impact trading?

When it comes to trading, AI and Machine Learning have the capabilities to solve some of the biggest problems in trading such as forecasting, optimisation and analysis. Here are some of the main ways that AI and ML have impacted the trading industry:

AI and ML make use of neural networks combined with a variety of methods for learning, identifying and analysing the factors that influence stock prices. These factors can be used to predict future stock prices.

The automation of AI allows it to make fact-based decisions without external factors such as fear or greed. This allows trading to become more profitable and less risky.

Being able to predict the risks associated with trading to better forecast stock prices. This allows traders to maximise gains and simulate risk scenarios, allowing the trading industry to become even more profitable.

What are the implementations of AI and ML in stock trading?

The new technology in Artificial Intelligence and Machine Learning has played a vital role in the improvement of the trading industry, allowing trading to become faster, simpler and more profitable.

Machine Learning makes use of historical data to come to a decision. In order to predict stock prices (also known as target variables), Machine Learning utilises historical data (or predictor variables). This allows the Machine Learning algorithm to apply the predictor variables to forecast target variables.

Machine Learning is also able to be used in a way which can speed up the search for effective algorithmic trading strategies. Due to its automated approach, machine learning is much more efficient than using a manual process. The algorithmic trading strategies can help traders not only simulate risks but optimise their profits. Machine learning can make use of algorithmic optimisation, linear regressions, neural networks and deep learning (to name a few) to support users in any task through implementing automation.

Machine Learning can be particularly useful to traders by increasing the number of markets that are able to be monitored and responded to. The more markets that are available to a trader, the higher the chances of a profitable trade. This means that Machine Learning is a great way of increasing your chances of success in the trading field.

Overall, the impact of Artificial Intelligence and Machine Learning on stock trading is positive, with markets being expanded to greater identify and predict risks as well as increase the profitability of the industry. AI allows for significantly faster and more accurate predictions and trades and can automate and carry out processes much faster than their human equivalents.

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AI and Machine Learning: How Tech is Helping Trading Get More and More Instant - Mighty Gadget

Capitalizing on AI and open source will enable the IC to utilize other finite collection capabilities, like human spies and signals intelligence collection, more efficiently. Other collection disciplines can be used to obtain the secrets that are hidden from not just humans but AI, too. In this context, AI may supply better global coverage of unforeseen or non-priority collection targets that could quickly evolve into threats.

Meanwhile, at the National Geospatial-Intelligence Agency, AI and machine learning extract data from images that are taken daily from nearly every corner of the world by commercial and government satellites. And the Defense Intelligence Agency trains algorithms to recognize nuclear, radar, environmental, material, chemical, and biological measurements and to evaluate these signatures, increasing the productivity of its analysts.

In one example of the ICs successful use of AI, after exhausting all other avenuesfrom human spies to signals intelligencethe US was able to find an unidentified WMD research and development facility in a large Asian country by locating a bus that traveled between it and other known facilities. To do that, analysts employed algorithms to search and evaluate images of nearly every square inch of the country, according to a senior US intelligence official who spoke on background with the understanding of not being named.

While AI can calculate, retrieve, and employ programming that performs limited rational analyses, it lacks the calculus to properly dissect more emotional or unconscious components of human intelligence that are described by psychologists as system 1 thinking.

AI, for example, can draft intelligence reports that are akin to newspaper articles about baseball, which contain structured non-logical flow and repetitive content elements. However, when briefs require complexity of reasoning or logical arguments that justify or demonstrate conclusions, AI has been found lacking. When the intelligence community tested the capability, the intelligence official says, the product looked like an intelligence brief but was otherwise nonsensical.

Such algorithmic processes can be made to overlap, adding layers of complexity to computational reasoning, but even then those algorithms cant interpret context as well as humans, especially when it comes to language, like hate speech.

AIs comprehension might be more analogous to the comprehension of a human toddler, says Eric Curwin, chief technology officer at Pyrra Technologies, which identifies virtual threats to clients from violence to disinformation. For example, AI can understand the basics of human language, but foundational models dont have the latent or contextual knowledge to accomplish specific tasks, Curwin says.

From an analytic perspective, AI has a difficult time interpreting intent, Curwin adds. Computer science is a valuable and important field, but it is social computational scientists that are taking the big leaps in enabling machines to interpret, understand, and predict behavior.

In order to build models that can begin to replace human intuition or cognition, Curwin explains, researchers must first understand how to interpret behavior and translate that behavior into something AI can learn.

Although machine learning and big data analytics provide predictive analysis about what might or will likely happen, it cant explain to analysts how or why it arrived at those conclusions. The opaqueness in AI reasoning and the difficulty vetting sources, which consist of extremely large data sets, can impact the actual or perceived soundness and transparency of those conclusions.

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The Power and Pitfalls of AI for US Intelligence - WIRED

We are excited about the potential to help our customers know in advance when their vehicle could require maintenance. By alerting the driver of a potential future issue, the driver can pre-emptively seek maintenance or repairs, said Kazuhiro Doi, CVP at Nissan.

KITCHENER, Ontario (PRWEB) June 22, 2022

Acerta Analytics Solutions Inc. -- the Ontario-based company whose machine learning and artificial intelligence (ML/AI) solutions turn complex product data into actionable insights for customers in the automotive and transportation industries -- has partnered with Nissan, thanks to support from the Government of Ontario through the Ontario Vehicle Innovation Network (OVIN).

Acerta will develop an advanced analytics platform of customized machine learning models for Nissan Research Center. The new predictive maintenance technology will enable Nissan vehicle users to receive notifications of maintenance needs ahead of time, which translate into cost-savings and increased safety. The technology will help reduce the amount that Nissan vehicle owners spend on annual maintenance.

We are incredibly grateful for the OVIN program and our partnerships with both the Government of Ontario and Nissan, said Greta Cutulenco, CEO at Acerta. The funding will help us develop machine learning algorithms to detect signs of anomalies in powertrain components. Our models will also estimate the remaining distance that a vehicle can travel before maintenance is needed, which will improve the longevity of specific parts.

We are excited about the potential to help our customers know in advance when their vehicle could require maintenance. By alerting the driver of a potential future issue, the driver can pre-emptively seek maintenance or repairs, said Kazuhiro Doi, CVP at Nissan.

Through the OVIN R&D Partnership Funds C/AV & Smart Mobility program, led by the Ontario Centre of Innovation (OCI), the project received $344,000 and a further $1.016M in industry contributions, for a total project value of $1.36M CAD.

Ontario is home to innovators that are commercializing leading-edge technology for the automotive and mobility sector. Through the Government of Ontarios OVIN, we are ensuring that our homegrown companies form new customer-supplier relationships and grow as they export their products and services around the world. This project is another great example of how Made-in-Ontario technology will drive the transformation of this sector globally, said Raed Kadri, Head of OVIN.

About Acerta Forged from industrial experience and driven by data science,Acertaassists precision manufacturers to take their digital transformation beyond manually crunching sensor data. Our ML/AI-powered software services enable companies to make the right decisions fast, optimize production,and improve product quality. We translate complex product data into actionable insights. Founded in 2017, Acerta Analytics Solutions Inc. is based in Kitchener, Ontario, Canada.

About Nissan Nissan Research Center is responsible for developing and testing new technology. For more information on Nissan products, services and commitment to sustainable mobility, visit nissan-global.com. Nissan Canada Inc. (NCI) is the Canadian sales, marketing and distribution subsidiary of Nissan Motor Co., Ltd., situated in Mississauga, Ontario.

About OVIN The Ontario Vehicle Innovation Network (OVIN) is an initiative of the Government of Ontario, led by the Ontario Centre of Innovation (OCI), designed to reinforce Ontarios position as a North American leader in advanced automotive technology and smart mobility solutions such as connected vehicles, autonomous vehicles and electric and low-carbon vehicle technologies. Through resources such as research and development (R&D) support, talent and skills development, technology acceleration, business and technical supports, and demonstration grounds, OVIN provides a competitive advantage to Ontario-made automotive and mobility technology companies. Visit http://www.ovinhub.ca or @OVINhub for more information.

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Acerta Analytics to Develop Machine Learning Models to Predict Nissan Vehicle Maintenance Needs, with Support from the Government of Ontario Through...

Leesburg, Virginia quantum software company Quantum Computing Inc (QCI) has made plenty of advancements since its establishment in 2018. But to truly understand where the company can go, COO and CTO William McGann told Technical.ly that you need to bring it back to what he calls the quantum nature of things the idea that were all a little bit quantum.

Youre nothing more than a collection of electromagnetic fields that are interacting and creating protons and electrons, McGann said. So if you believe that, then there are many things I can determine about you, uniquely, with a quantum measurement.

Theres still plenty to cover to truly understand that aspect, McGann noted, but QCI is at work building quantum capabilities for the everyday. This month, the company released its QAmplify suite: an agnostic software platform that works to boost quantum hardware and enhance its capabilities.

Current quantum processing unit hardware has two main approaches: the gate model, used by players like College Park, Marylands IonQ and IBM;and annealing, used by D-Wave. Both, according to QCI, have limits in the number of variables and complexity of the problems they can solve with quantum. With the gate model, which McGann said uses neutral atoms, ions and superconductors for problem-solving, the QAmplify software uses machine learning for optimized problem-solving. Machine learning helps create a more accurate starting point for expressing the problem and produces a better answer quicker, McGann said.

Using this method in the gate model and its additional capability in annealing QCI says it can increase the size of the problems it processes. With the gate model, it says it can increase capabilities by 500%, along with up to 2,000% in annealing. In practice, this means that a computer using the gate model software could solve a problem that has 600 variables (it is currently limited to 127). An annealing computer could boost up to 4,000 variables.

People are very heads-down with their own technology, right now, in the industry, McGann said. And sometimes in the nascent industries, it takes a while before people pick their heads up. But Id like to think, in a small way, were helping the industry do that.

For QCI, the last few years have seen strong promise in the quantum market. IonQ reached an IPO in 2021. At home, QCI made its mark last year by moving from trading on the OTCQB to the Nasdaq Capital Market. And last week, the company completed its merger deal to acquire QPhoton.

Bill McGann. (Courtesy photo)

Now, its working with external partners like IonQ to validate the technology in a third-party setting. McGann hopes to create systems that can host thousands of qubits, the tiny particles that help make the calculation, in the coming months. Once thats finished, the technology can be used to help solve problems in the supply chain, logistics and even some finance applications.

We understand where the limitations of a system are, and we have a very comfortable road map that we can extend its capacity [with], McGann said.

Even with the new technologies, McGann noted that quantum, as a whole, is still in its first generation. In McGanns opinion, its still in the early stages of moving out of academia and into a more commercial market. QCI, he said, is staying where it was born in the quantum computing industry for the moment. But, if you include hardware as well, theres space to move into sensing and imaging and take full advantage of the quantum nature of things.

We want to be a part of shifting the industry from debating the physics though were happy to do that, but along the way, lets measure the machine in a meaningful way, McGann said. So, we think we can make a contribution and Im looking forward to doing so.

Knowing that humans, at the end of the day, are a collection of protons and electrons, McGann thinks there are near-endless possibilities to explore by using technology in the quantum nature of things. Whereas visual scans can be limited to measuring the visual parts of a person or object, quantum measurements can create a personalized stream of internal and external information. McGann described it as a movie made for me to take away important info about a subject.

Considering its potential to predict future issues, he noted tons of applications for quantum in healthcare, tech industries and beyond.

Quantum computing really is scratching the surface of the quantum nature of things, McGann said.

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With the help of machine learning, NoVa's QCI wants to change how we think about quantum - Technical.ly