DUBLIN--(BUSINESS WIRE)--The "Global Mining Ventilation Systems Market 2020-2024" report has been added to ResearchAndMarkets.com's offering.

The mining ventilation systems market is poised to grow by $ 81.73 mn during 2020-2024 progressing at a CAGR of 4% during the forecast period. This report on the mining ventilation systems market provides a holistic analysis, market size and forecast, trends, growth drivers, and challenges, as well as key vendor analysis.

The market is driven by the growing demand for safety in underground mining and demand for minerals. In addition, increasing demand for precious metals is anticipated to boost the growth of the market as well. This study identifies technological advances as one of the prime reasons driving the mining ventilation systems market growth during the next few years. Also, the growing adoption of AI and machine learning and increasing use of drones will lead to sizable demand in the market.

The mining ventilation systems market analysis includes product segment and geographic landscapes

The mining ventilation systems market covers the following areas:

This robust vendor analysis is designed to help clients improve their market position, and in line with this, this report provides a detailed analysis of several leading mining ventilation systems market vendors that include ABB Ltd., ABC Canada Technology Group Ltd., ABC Industries Inc., Epiroc AB, Howden Group Ltd., New York Blower Co., Sibenergomash-BKZ LLC, Stantec Inc., TLT-Turbo GmbH, and Zitron SA. Also, the mining ventilation systems market analysis report includes information on upcoming trends and challenges that will influence market growth. This is to help companies strategize and leverage on all forthcoming growth opportunities.

The study was conducted using an objective combination of primary and secondary information including inputs from key participants in the industry. The report contains a comprehensive market and vendor landscape in addition to an analysis of the key vendors.

This study presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources by an analysis of key parameters such as profit, pricing, competition, and promotions. It presents various market facets by identifying the key industry influencers. The data presented is comprehensive, reliable, and a result of extensive research - both primary and secondary.

The market research report provide a complete competitive landscape and an in-depth vendor selection methodology and analysis using qualitative and quantitative research to forecast an accurate market growth.

Key Topics Covered:

Executive Summary

Market Landscape

Market Sizing

Five Forces Analysis

Market Segmentation by Product

Customer landscape

Geographic Landscape

Vendor Landscape

Vendor Analysis

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/yl3xpg

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Growing Adoption of AI and Machine Learning and Increased Use of Drones is Driving Growth in the Global Mining Ventilation Systems Market -...

As part of its commitment to using data and analytics to solve the world's most pressing problems, SAS' recent work includes helping to save the world's No. 1 food crop pollinator the honey bee. With the number of bee colonies drastically declining around the world, SAS is using technology such as theInternet of Things (IoT), machine learning and visual analytics to help maintain and support healthy bee populations.

In honor of World Bee Day, SAS is highlighting three separate projects where technology is monitoring, tracking and improving pollinator populations around the globe. First, researchers at SAS have developed a noninvasive way to monitor real-time conditions of beehives through auditory data and machine learning algorithms. SAS is also working withAppalachian State Universityon the World Bee Count to visualize world bee population data and understand the best ways to save them. Lastly, recent SASViyaHackathon winners decoded bee communication through machine learning in order to maximize their food access and boost human food supplies.

"SAS has always looked for ways to use technology for a better world," said Oliver Schabenberger, COO and CTO of SAS. "By applying advanced analytics and artificial intelligence to beehive health, we have a better shot as a society to secure this critically important part of our ecosystem and, ultimately, our food supply."

Noninvasively Monitoring Beehive HealthResearchers from the SAS IoT Division are developing abioacoustic monitoring systemto noninvasively track real-time conditions of beehives using digital signal processing tools and machine learning algorithms available in SASEvent Stream Processingand SAS Viya software. This system helps beekeepers better understand and predict hive problems which could lead to colony failure, including the emergence of new queens something they would not ordinarily be able to detect.

Annual loss rates of U.S. beehives exceed 40%, and between 25% and 40% of these losses are due to queen failure. Acoustic analysis can alert beekeepers to queen disappearances immediately, which is vitally important to significantly reducing colony loss rates. With this system, beekeepers will have a deeper understanding of their hives without having to conduct time-consuming and disruptive manual inspections.

"As a beekeeper myself, I know the magnitude of bees' impact on our ecosystem, and I'm inspired to find innovative ways to raise healthier bees to benefit us all," saidAnya McGuirk, Distinguished Research Statistician Developer in the IoT division at SAS. "And as a SAS employee, I'm proud to have conducted this experiment with SAS software at our very own campus beehives, demonstrating both the power of our analytical capabilities and our commitment to innovation and sustainability."

By connecting sensors to SAS' four Bee Downtown hives at its headquarters inCary, NC, the team startedstreaming hive datadirectly to the cloud to continuously measure data points in and around the hive, including weight, temperature, humidity, flight activity and acoustics. In-stream machine learning models were used to "listen" to the hive sounds, which can indicate health, stress levels, swarming activities and the status of the queen bee. To ensure only the hum of the hive was being used to determine bees' health and happiness, researchers used robust principal component analysis (RPCA), a machine learning technique, to separate extraneous or irrelevant noises from the inventory of sounds collected by hive microphones.

The researchers found that with RPCA capabilities, they could detect worker bees "piping" at the same frequency range at which a virgin queen pipes after a swarm, likely to assess whether a queen was present. The researchers then designed an automated pipeline to detect either queen piping following a swarm or worker piping that occurs when the colony is queenless. This is greatly beneficial to beekeepers, warning them that a new queen may be emerging and giving them the opportunity to intervene before significant loss occurs.

The researchers plan to implement the acoustic streaming system very soon and are continuing to look for ways to broaden the usage of technology to help honey bees and ultimately humankind.

Visualizing the World's Pollinator PopulationsOn World Bee Day, SAS is launching a data visualization that maps out bees "counted" around the globe for theWorld Bee Count, an initiative co-founded by theCenter for Analytics Research and Education(CARE) atAppalachian State University. The goal of a World Bee Count is to engage citizens across the world to take pictures of bees as a first step toward understanding the reasons for their alarming decline.

"The World Bee Count allows us to crowdsource bee data to both visualize our planet's bee population and create one of the largest, most informative data sets about bees to date," saidJoseph Cazier, Professor and Executive Director atAppalachian State University'sCARE. "SAS' data visualization will show the crowdsourced location of bees and other pollinators. In a later phase of the project, researchers can overlay key data points like crop yield, precipitation and other contributing factors of bee health, gathering a more comprehensive understanding of our world's pollinators." Bayer has agreed to help sponsor CARE to allow its students and faculty to perform research on the World Bee Count data and other digital pollinator data sources.

In early May, the World Bee Count app was launched for users both beekeepers and the general public, aka "citizen data scientists" to add data points to the Global Pollinator Map. Within the app, beekeepers can enter the number of hives they have, and any user can submit pictures of pollinators from their camera roll or through the in-app camera. Through SAS Visual Analytics, SAS has created avisualization mapto display the images users submit via the app. In addition to showing the results of the project, the visualizations can potentially provide insights about the conditions that lead to the healthiest bee populations.

In future stages of this project, the robust data set created from the app could help groups like universities and research institutes better strategize ways to save these vital creatures.

Using Machine Learning to Maximize Bees' Access to FoodRepresenting the Nordic region, a team from Amesto NextBridgewon the 2020 SAS EMEA Hackathon, which challenged participants to improve sustainability using SAS Viya. Their winning project used machine learning to maximize bees' access to food, which would in turn benefit mankind's food supply. In partnership withBeefutures, the team successfully accomplished this by developing a system capable of automatically detecting, decoding and mapping bee "waggle" dances using Beefutures' observation hives and SAS Viya.

Bees are responsible for pollinating nearly 75% of all plant species directly used for human food, but the number of bee colonies are declining, which will lead to a devastating loss for human food supply. A main reason for the decline of bee populations is a lack of access to food due to an increase in monoculture farming. When bees do find a good food source, they come back to the hive to communicate its exact location through a "waggle dance." By observing these dances, beekeepers can better understand where their bees are getting food and then consider establishing new hives in these locations to help maintain strong colonies.

"Observing all of these dances manually is virtually impossible, but by using video footage from inside the hives and training machine learning algorithms to decode the dance, we will be able to better understand where bees are finding food," said Kjetil Kalager, lead of the Amesto NextBridge and Beefutures team. "We implemented this information, along with hive coordinates, sun angle, time of day and agriculture around the hives into an interactive map in SAS Viya and then beekeepers can easily decode this hive information and relocate to better suited environments if necessary."

This systematic real-time monitoring of waggle dances allows bees to act as sensors for their ecosystems. Further research using this technology may uncover other information bees communicate through dance that could help us save and protect their population, which ultimately benefits us all.

See thiswaggle dance project in actionand learn about howSAS is committed to corporate social responsibility.

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Advanced Analytics and Machine Learning Boost Bee Populations - Transmission & Distribution World

Every week, Analytics India Magazine reaches out to developers, practitioners and experts from the machine learning community to gain insights into their journey in data science, and the tools and skills essential for their day-to-day operations.

For this weeks column, Analytics India Magazine got in touch with Dipanjan Sarkar, a very well known face in the machine learning community. In this story, we take you through the journey of Dipanjan and how he became an ML expert.

Dipanjan currently works as a Data Science Lead at Applied Materials where he leads a team of data scientists to solve various problems in the manufacturing and semiconductor domain by leveraging machine learning, deep learning, computer vision and natural language processing. He provides the much needed technical expertise, AI strategy, solutioning, and architecture, and works with stakeholders globally.

He has a bachelors degree in computer science & engineering and a masters in data science from IIIT Bangalore. Currently, he is pursuing a PG Diploma in ML and AI from Columbia University and an executive education certification course in AI Strategy from Northwestern University Kellogg School of Management.

Apart from academia, Dipanjan is a big fan of MOOCs. He also beta-test new courses for Coursera before they are made public.

Dipanjan is also a Google Developer Expert in Machine Learning and has worked with several Fortune 500 companies. For an expert in ML, mathematics is a prerequisite, but we were surprised when we learnt that Dipanjan actually hated mathematics at school and this continued until ninth grade where he picked up statistics, linear algebra and calculus, the three pillars of machine learning.

I always loved the way you could program a computer to do specific tasks and make a machine actually learn with data!

Dipanjans renewed interest in mathematics was followed by his fascination for computer programming. With his growing fascination from mathematics to statistics and traditional computer programming, his career choice became almost obvious.

Reminiscing about his initial days, when the word data science wasnt worshipped yet, Dipanjan spoke about how the field was more conceptual and theoretical. Back then, there werent any active ecosystems of tools, languages and frameworks dedicated for data science. Hence, it took more time to learn theoretical concepts since it took more efforts to actually implement them or see them in practice.

With the advent of Python, R and a whole suite of tools and libraries, he believes that it has become easier to tame the learning curve of data science. However, he also warns that this can be a double-edged sword if one focuses on hands-on without deep-diving into the math and concepts behind algorithms and techniques to understand how it works or why it is used.

I have always been a strong advocate of self-learning, and I believe that is where you get maximum value

Due to the lack of mentors or proper guides, which are plenty nowadays on LinkedIn and other forums, Dipanjan had no other option than to self-learn with the help of the web and books.

For aspirants, he recommends the following books:

To dive deep into the concepts and to get hands-on, he recommends Deep Learning with Keras, Python Machine Learning and Hands-On Machine Learning as practical books with examples. Dipanjan has also written a handful of books on practical machine learning.

When it comes to practice and deploying ML models, Dipanjan extensively uses the CRISP-DM (cross industry process for data mining) framework, which he considers to be one of the best frameworks to tackle any data science problem.

Also, before diving into models or data, he insists on the importance of identifying and articulating the business problem in the right manner. For conceptualising an AI use-case, Dipanjan recommends something called AI Canvas, which he has learnt from the Kellogg School of Management:

Use the right tools for the job without waging wars of Python vs R or PyTorch vs TensorFlow

When asked about his favourite tools, Dipanjan explained the importance of not paying attention towards Python vs R or PyTorch vs TensorFlow and using the right tools that get the job done.

For instance, he and his team use the ecosystem of tools and libraries centered around Python very frequently. This includes the regular run-of-the-mill pandas, matplotlib, seaborn, plotly for data wrangling and exploratory data analysis. For statistical modelling he prefers libraries like scikit-learn, statsmodels and pyod.

Dipanjans toolkit looks as follows:

Along with picking the right tools, he recommends practitioners to always go with the simplest solution unless complexity is adding substantial value and last but not the least, he urges people not to ignore documentation.

To those looking to break into the world of data science, Dipanjan suggests one to follow a hybrid approach, i.e. learn concepts, code and apply them on real-world datasets.

First, learn all the math and concepts and then try to actually apply the methods you have learnt

In the long, tedious process of learning, Dipanjan warns that people might lose focus and get sidetracked into thinking why are they even learning a certain method. To remedy this, he insists on learning and applying if one aims of becoming a good data scientist without deviating from the goal.

Addressing the overwhelming hype around AI and ML, Dipanjan says that he is already witnessing the dust settling down and how companies are now actually starting to realise both the limitations and value of AI. Deep learning and deep transfer learning are actually starting to provide value for companies working on complex problems involving unstructured data like images, audio, video and text and things are only going to get bigger and better with advanced tools and hardware in future. However, he admits that there is definitely still a fair bit of hype out there.

Traditional machine learning models like linear and logistic regression will never go out of fashion

No matter how advanced the field gets, he believes that traditional machine learning models like linear and logistic regression will never go out of fashion since they are the bread and butter of various organisations and use-cases out there. And, models that are easy to explain, including linear models and decision trees will continue to be used extensively.

Going forward, he is optimistic about the use-cases and applications to optimise manufacturing, predicting demand and sales, inventory planning, logistics and routing, infrastructure management optimisation and enhancing customer support and experience, will continue to be the key drivers for almost all major organisations for the next decade.

When it comes to breakthroughs, Dipanjan expects something big to happen in newer domains like self-learning, continuous-learning, meta-learning and reinforcement learning.

Always remember to challenge others opinions with a healthy mindset because a good data scientist doesnt just follow instructions blindly.

Talking about his tireless efforts to guide youngsters, he recollects how not having a mentor had been a major hindrance and how he had to unlearn and relearn overtime to correct his misconceptions. To help aspirants avoid the same mistakes, he mentors them whenever possible.

On a concluding note, Dipanjan said that he is mightily impressed by the relentless efforts of the data science community to share ideas through blogs, vlogs and online forums. Confessing his love for Analytics India Magazine, Dipanjan spoke about how AIM has been fostering a rich analytics ecosystem in India by reaching out to the global community.

Dipanjan will be speaking at Analytics India Magazines inaugural virtual conference, Plugin on 28th of May 2020. For more information, check our portal here.

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The ML Expert Who Hated Mathematics: Interview With Dipanjan Sarkar - Analytics India Magazine

Gartner believes Artificial Intelligence (AI) security will be a top strategic technology trend in 2020, and that enterprises must gain awareness of AIs impact on the security space. However, many enterprise IT leaders still lack a comprehensive understanding of the technology and what the technology can realistically achieve today. It is important for leaders to question exasperated Marketing claims and over-hyped promises associated with AI so that there is no confusion as to the technologys defining capabilities.

IT leaders should take a step back and consider if their company and team is at a high enough level of security maturity to adopt advanced technology such as AI successfully. The organizations business goals and current focuses should align with the capabilities that AI can provide.

A study conducted by Widmeyer revealed that IT executives in the U.S. believe that AI will significantly change security over the next several years, enabling IT teams to evolve their capabilities as quickly as their adversaries.

Of course, AI can enhance cybersecurity and increase effectiveness, but it cannot solve every threat and cannot replace live security analysts yet. Today, security teams use modern Machine Learning (ML) in conjunction with automation, to minimize false positives and increase productivity.

As adoption of AI in security continues to increase, it is critical that enterprise IT leaders face the current realities and misconceptions of AI, such as:

AI is not a solution; it is an enhancement. Many IT decision leaders mistakenly consider AI a silver bullet that can solve all their current IT security challenges without fully understanding how to use the technology and what its limitations are. We have seen AI reduce the complexity of the security analysts job by enabling automation, triggering the delivery of cyber incident context, and prioritizing fixes. Yet, security vendors continue to tout further, exasperated AI-enabled capabilities of their solution without being able to point to AIs specific outcomes.

If Artificial Intelligence is identified as the key, standalone method for protecting an organization from cyberthreats, the overpromise of AI coupled with the inability to clearly identify its accomplishments, can have a very negative impact on the strength of an organizations security program and on the reputation of the security leader. In this situation, Chief Information Security Officers (CISO) will, unfortunately, realize that AI has limitations and the technology alone is unable to deliver aspired results.

This is especially concerning given that 48% of enterprises say their budgets for AI in cybersecurity will increase by 29 percent this year, according to Capgemini.

Read more:Improve Your Bottom Line With Contract Automation and AI

We have seen progress surrounding AI in the security industry, such as the enhanced use of ML technology to recognize behaviors and find security anomalies. In most cases, security technology can now correlate the irregular behavior with threat intelligence and contextual data from other systems. It can also use automated investigative actions to provide an analyst with a strong picture of something being bad or not with minimal human intervention.

A security leader should consider the types of ML models in use, the biases of those models, the capabilities possible through automation, and if their solution is intelligent enough to build integrations or collect necessary data from non-AI assets.

AI can handle a bulk of the work of a Security Analyst but not all of it. As a society, we still do not have enough trust in AI to take it to the next level which would be fully trusting AI to take corrective actions towards those anomalies it identified. Those actions still require human intervention and judgment.

Read more:The Nucleus of Statistical AI: Feature Engineering Practicalities for Machine Learning

It is important to consider that AI can make bad or wrong decisions. Given that humans themselves create and train the models that achieve AI, it can make biased decisions based on the information it receives.

Models can produce a desired outcome for an attacker, and security teams should prepare for malicious insiders to try to exploit AI biases. Such destructive intent to influence AIs bias can prove to be extremely damaging, especially in the legal sector.

By feeding AI false information, bad actors can trick AI to implicate someone of a crime more directly. As an example, just last year, a judge ordered Amazon to turn over Echo recordings in a double murder case. In instances such as these, a hacker has the potential to wrongfully influence ML models and manipulate AI to put an innocent person in prison. In making AI more human, the likelihood of mistakes will increase.

Whats more, IT decision-makers must take into consideration that attackers are utilizing AI and ML as an offensive capability. AI has become an important tool for attackers, and according to Forresters Using AI for Evil report, mainstream AI-powered hacking is just a matter of time.

AI can be leveraged for good and for evil, and it is important to understand the technologys shortcomings and adversarial potential.

Though it is critical to acknowledge AIs realistic capabilities and its current limitations, it is also important to consider how far AI can take us. Applying AI throughout the threat lifecycle will eventually automate and enhance entire categories of Security Operations Center (SOC) activity. AI has the potential to provide clear visibility into user-based threats and enable increasingly effective detection of real threats.

There are many challenges IT decision-makers face when over-estimating what Artificial Intelligence alone can realistically achieve and how it impacts their security strategies right now. Security leaders must acknowledge these challenges and truths if organizations wish to reap the benefits of AI today and for years to come.

Read more:AI in Cybersecurity: Applications in Various Fields

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Reality Check: The Benefits of Artificial Intelligence - AiThority

AI artificial intelligence concept Central Computer Processors CPU concept, 3d rendering, Circuit ... [+] board, Technology background, Motherboard digital chip, Tech science background, machine learning

Machine learning is definitely a confusing term.Is it AI or something different?

Well, its actually a subset of AI (which, by the way, is a massive category). Machine learning is a method of analyzing data using an analytical model that is built automatically, or learned, from training data, said Rick Negrin, who is the VP of Product Development at MemSQL. The idea is that the model gets better as you feed it more data points.

There are two key steps with machine learning.First, you need to collect and train the data, which can be a long and tough process.Then, you will operationalize the machine learning, such as by using it to help provide insights or as part of a product.There are a myriad of tools to help with the process, such as open source platforms like TensorFlow and commercial systems, such as DataRobot.

Successful machine learning is only as good as the data available, which is why it needs new, updated data to provide the most accurate outputs or predictions for any given need, said Panagiotis Angelopoulos, who is the Chief Data Officer at Persado.And unlike what any one person can analyze, machine learning can take vast amounts of data over time and make predictions to improve the customer experience and provide real value to the end-user.

Sometimes the models are so intricate that they are nearly impossible to understand. The lack of transparency can make it so that certain industries, like healthcare and banking, may not be able to use machine learning models. Because of this, more research is being focused on the explainability of models.

Another challenge with machine learning is the need to form an experienced team. To build this team in-house, you will have to hire more than just data scientists, said Ji Li, who is the director of data science at CLARA analytics.Full deployment of a new solution requires product managers, software engineers, data engineers, operational experts to develop process and operational workflows, staff to integrate data models into operations, people to manage onboarding and training of the employees who will ultimately use the solution, and staff who can quantify value generation.

In other words, for many organizations, the best option with machine learning may be to buy an off-the-shelf solution.The good news is that there are many on the marketand they are generally affordable.

But regardless of what path you take, there needs to be a clear-cut business case for machine learning.It should not be used just because it is trendy.There also needs to be sufficient change management within the organization. One of the greatest challenges in implementing machine learning and other data science initiatives is navigating institutional changegetting a buy-in, dealing with new processes, the changing job duties, and more, said Ingo Mierswa, who is the founder and president of RapidMiner.

Then what are the use cases for machine learning?According to Alyssa Simpson Rochwerger, who is the VP of AI and the Data Evangelist at Appen:Machine learning can solve lots of different types of problems.But it's particularly well suited to decisions that require very simple and repetitive tasks at large scale. For example, the US Postal Service has been successfully using machine learning systems to sort the mail for decades. The task was simple:read the address on the mail (sense) and then understand the zip code (perceive) and then sort into different buckets (decide). The US Postal Service processes almost two hundred million pieces of mail per dayso sorting this by hand wouldn't work.

In fact, the examples are seemingly endless for machine learning.Here are just a few:

Machine learning is a tool and like most tools, it works best when used properly, said Matei Zaharia, who is the chief technologist and co-founder of Databricks.Machine learning can take something as simple as some images and some annotations or just drawings on those images and create a solution that can be automated efficiently and at scale. However, we are not in a technological state where a machine learning model can just work on anything that is thrown at itthat is, not without some kind of external guidance. A machine learns, a human teaches.

Tom (@ttaulli) is an advisor to startups and the author of Artificial Intelligence Basics: A Non-Technical Introduction and The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems. He also has developed various online courses, such as for the Python programming language.

Link:
Machine Learning: What Is It Really Good For? - Forbes

With the energy landscape set to change considerably soon, now is the perfect time to combine machine learning and electricity networks.

Bill Gates stated in 2017 that If I were starting out today and looking for the same kind of opportunity to make a big impact in the world, I would consider three fields. One is artificial intelligence [] The second is energy [] The third is the biosciences.

Without doubt, the future of energy lies in sustainable, reliable, and smart generation and distribution systems, and in a network that is proactive instead of reactive. Power companies have vast and ever-growing volumes of data associated with network failures, network models, operational information from generators and asset databases.

This data has huge potential for predicting network failures and assisting with maintenance. In the future, through machine learning, adding records of network failures will be part of the solution, not the problem; by adding more records additional data for the analysis are provided to the model and which makes more accurate and precise predictions. For example, a machine learning algorithm could have access to a database with types, locations, ages or age profiles and conditions of assets, circuit and load data, as well as existing failure data, and return the probability and the cost of the failure as well as the likely time to happen (in hours, days, weeks or months).

Machine learning has the potential to be utilised as an economic modelling tool, evaluating strategic development and decisions relating to the use of electricity network reinforcement solutions using a cost-benefit analysis. In future, we will not only react to failures but also anticipate and avoid them using models which predict failures by analysing the techno-economic data. So, through machine learning, the industry is a step forward in developing a proactive rather than a reactive system.

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In a post-Coronavirus era where the most imminent challenge is climate change, and alongside the UKs commitment to transition to a net-zero economy by 2050, electricity networks will evolve to an ever more renewable basis. We can already see the prominence of renewables growing as power from clean energy provided 40% of the electricity used in Britain during the first three months of this 2020 the first time renewable energy has overtaken fossil fuels.

Analysts argue that the renewable and sustainable energy industry should play a greater role and power a green economic recovery as it did during the last economic recession. Whilst not without its challenges, this is possible and machine learning could be a solution to some problems.

Fluctuations from renewable generation, such as wind and solar, are hard to predict accurately, even with the most sophisticated weather forecasts. Moreover, the small distributed generation and storage from in-house installed devices such as PV and batteries (numbering 50 million worldwide) add additional uncertainty in the system. Machine learning and artificial intelligence might solve these problems as these algorithms could be used to predict demand more accurately, as well as the outputs from renewable generation, with the predictions being used in both the short and long term.

Installed energy storage, including batteries, are beginning to be used to minimise the uncertainty of renewable generation and help to achieve a higher percentage in demand from renewable sources. This solution can, however, have reliability issues and limitations such as battery degradation and unexpected failures that require constant monitoring as well as maintenance. Using machine learning as a tool to monitor and anticipate potential failures in energy storage systems could lead to a more reliable and efficient system, and by using AI and machine learning algorithms, electricity demand and renewable generation could be more predictable and energy storage more reliable and efficient.

The promising future of smart energy and machine learning in electricity networks is already being examined by the scientific community. Much has been said about the prediction of energy demand, solar energy generation forecasting and even precise predictions of the energy that can be harvested from food waste in urban environments. Taking into consideration the in-depth knowledge and wide usage of AI and machine learning across other sectors, the possibilities for the electricity network sector as we transition into a net-zero economy and society are exciting.

This article with contributed byThalis Avramidis, Power Systems Engineer at WSP

For more information on energy digitaltopics - please take a look at the latest edition ofEnergy Digital Magazine.

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Machine learning set to change the UKs electricity networks - Energy Digital - Energy News, Magazine and Website

Market Expertz have recently published a new report on the global Machine Learning in Finance market. The study provides profound insights into updated market events and market trends. This, in turn, helps one in better comprehending the market factors, and strongly they influence the market. Also, the sections related to regions, players, dynamics, and strategies are segmented and sub-segmented to simplify the actual conditions of the industry.

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The report also emphasizes the initiatives undertaken by the companies operating in the market including product innovation, product launches, and technological development to help their organization offer more effective products in the market. It also studies notable business events, including corporate deals, mergers and acquisitions, joint ventures, partnerships, product launches, and brand promotions.

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Ignite LtdYodleeTrill A.I.MindTitanAccentureZestFinance

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In market segmentation by types of Machine Learning in Finance, the report covers-

Supervised LearningUnsupervised LearningSemi Supervised LearningReinforced Leaning

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BanksSecurities CompanyOthers

This Machine Learning in Finance report umbrellas vital elements such as market trends, share, size, and aspects that facilitate the growth of the companies operating in the market to help readers implement profitable strategies to boost the growth of their business. This report also analyses the expansion, market size, key segments, market share, application, key drivers, and restraints.

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The data in this report presented is thorough, reliable, and the result of extensive research, both primary and secondary. Moreover, the globalMachine Learning in Financemarket report presents the production, and import and export forecast by type, application, and region from 2020 to 2027.

Customization of the Report:

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Machine Learning in Finance Market Provides in-depth analysis of the Machine Learning in Finance Industry, with current trends and future estimations...

Artificial intelligence (AI) and machine learning are opening opportunities for brands to personalise sound like never before.

Earlier this month, a team of Australians used AI and machine learning to win the inaugural Eurovision AI contest held by Dutch public broadcaster VPRO.

The contest was initially planned to run alongside the original Eurovision competition in the Netherlands this year but ended up running in lieu of it due to pandemic restrictions.

The team, music tech collective Uncanny Valley, used an AI trained on Eurovision songs to create the melody and lyrics as well as blending audio samples of Australian animals including koalas, kookaburras and Tasmanian devils with a real producer and vocalists.

Uncanny Valley producer and strategist Caroline Pegram says the win highlights the co-creative opportunities that AI and machine learning can bring to a variety of industries including advertising.

The competition brought together the top minds in this field to explore the idea of what can be achieved when musicians rage with the machines, Pegram says.

The fact that we were able to use technology to blend in the message of the devastation of the bushfires in Australia early this year, clearly struck a chord.

Uncanny Valley is made up of a diverse team with a variety of academic backgrounds including maths, computer science, social anthropology, evolutionary and adaptive systems, music and computer science and interactive design.

The team was inspired after collaborating on a recent project with Googles Creative Lab in Sydney, which uses machine learning to advance music innovation.

Uncanny Valley head of innovation Charlton Hill says the team are now starting to work with brands to use AI as a tool for experiential storytelling through sound.

The surprising results that we can deliver takes creativity and art to a new place, but then it also provides a fantastic story behind the creation of the campaign and the art, Hill says.

I think that that's what's inspiring from a commercial perspective, [to] the marketeers whose interests we've peaked with what we're up to.

He says the Eurovision win has been a great showcase piece for where AI can take personalisation in advertising and the arts.

Hill, alongside co-founder Justin Shave, have backgrounds in writing and producing songs for both advertising and renowned artists including Sia and Darren Hayes.

Over the last decade, they have been working towards finding a way to automate some of the music making process while maintaining some human emotion.

Shave says one way they have done this is with a music generation system called Memu.

If you design a system that can input any kind of variables - whether that be personalized locations, musical preferences, anything like that - these musical systems can be taught to compose music based on those preferences in real-time, Shave says.

A lot of our research has been focused on finding particular emotions that are tweaked by certain musical ideas and sounds.

I guess our experience in writing music for advertising has led us in this direction a little bit, but we're just taking it that one step further and working on systems to automate that stuff.

An infinite live stream of music that never repeats, Memu uses a unique algorithm to intuitively select and combine elements of music, producing and mixing a real-time musical experience.

As the team continues to build out further solutions for brands using AI and machine learning, Hill says it is important to remember that successful end results are still reliant on human input.

The heart of what we do is around human emotional response to music, and the need to quantify that simply because if you're working in AI systems, it's a mathematical universe, it needs to understand the language, Hill says.

We're going to continue to use the power of AI, but if we or advertisers miss the point that it's about human emotional response, it's just a piece of dumb code.

The Uncanny Valley team is made up of Justin Shave, Charlton Hill and Caroline Pegram alongside data scientist Brendan Wright, senior lecturer and co-director of the Interactive Media Lab at the faculty of Art and Design at the University of New South Wales Oliver Bown, expert in algorithmic choral compositions and sonification Alexandra Uitdenbogerd and CEO of Cicada, Australias National Centre for Innovation Sally-Ann Williams.

Have something to say on this? Share your views in the comments section below. Or if you have a news story or tip-off, drop us a line at adnews@yaffa.com.au

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Trusted Business Insights answers what are the scenarios for growth and recovery and whether there will be any lasting structural impact from the unfolding crisis for the Machine Learning market.

Trusted Business Insights presents an updated and Latest Study on Machine Learning Market 2019-2026. The report contains market predictions related to market size, revenue, production, CAGR, Consumption, gross margin, price, and other substantial factors. While emphasizing the key driving and restraining forces for this market, the report also offers a complete study of the future trends and developments of the market.The report further elaborates on the micro and macroeconomic aspects including the socio-political landscape that is anticipated to shape the demand of the Machine Learning market during the forecast period (2019-2029).It also examines the role of the leading market players involved in the industry including their corporate overview, financial summary, and SWOT analysis.

Get Sample Copy of this Report @ Global Machine Learning Market 2020 (Includes Business Impact of COVID-19)

Global Machine Learning Market Insights, Ongoing Trends, End-use Applications, Market Size, Growth, and Forecast to 2029 is a research report on the target market, and is in process of completion at Trusted Business Insights. The report contains information and data, and inputs that have been verified and validated by experts in the target industry. The report presents a thorough study of annual revenues, historical data and information, key developments and strategies by major players that offer applications in the market. Besides critical data and information, the report includes key and ongoing trends, factors that driving market growth, factors that are potential restraints to market growth, as well as opportunities that can be leveraged for potential revenue generation in untapped regions and countries, as well as threats or challenges. The global treadmill ergometer market is segmented on the basis of application, end user, and region. Regions are further branched into key countries, and revenue shares and growth rates for each of the segment, and region as well as key countries have been provided in the final report.

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Machine Learning: Overview

Machine Learning (ML) is a sub-segment of Artificial Intelligence (AI) platform. This scientific concept studies computational learning, statistics and algorithms models of computers used to perform specific tasks without input of instructions, and recognition of patterns in AI. Basically, it explores and analysis construction of statistical data and algorithms and estimates forecasts on analyzed data. Various applications of ML include Optical Character Recognition (OCR), e-mail filtering, detection of network intruders, learning to rank, and computer vision.

Machine learning has paved its way across several applications. In advertising sector, ML is implemented to analyze customers behavior, which can help in improving advertising strategies. AI-driven marketing and advertising is based on usage of various models in order to automate and optimize, and to use data into appropriate actions. In case of banking, financial services, and insurance (BFSI), machine learning is used to manage process such as assets management and loan approval, among others. Security, and management and publishing of documents are among other applications of machine learning.

In the recent past, the scope of applications of machine learning technology has widened into certain new aspects. For instance, the US Defense department plans to implement machine learning in combat vehicles for predictive maintenance, to determine when and where the repair and maintenance is required. In stock market, this technology is being used to make estimations and projections about the market with approximately 60% accuracy level.

Dynamics: Global Machine Learning Market

The machine learning market in North America is expected to record dominant share and is projected to continue with its dominance over the 10-year forecast period. This can be attributable to increasing investments and higher adoption of machine learning technology by to numerous organizations in BFSI sector in the region. In 2019 for instance, New York-based financial company, JPMorgan Chase & Co., invested in a startup Limeglass Ltd., which is a service provider of artificial intelligence, machine learning, and Natural Language Processing (NLP) to analyze organizational research. Limeglass Ltd. assists companies in developing technologically advanced products required for banking and finance.

The Asia Pacific machine learning market is projected to register highest growth rate over the 10-year forecast period. This is attributable to increasing adoption of advanced technologies including machine learning, along with a huge talent-base in countries such as China and India. In addition, emerging markets are projected to offer revenue opportunities by allowing entrance into these untapped markets and reach large consumer base that is willing to opt for AI-enabled products and services, which is further projected to drive Asia Pacific market growth. In 2018 for instance, NITI Aayog a policy think-tank of the Government of India, in collaboration with a multinational technology company, Google LLC will train and incubate AI-based firms and start-ups in India.

Global Machine Learning Market Segmentation:

Segmentation by Component:

HardwareSoftwareServices

Segmentation by Enterprise Size:

Small and Medium Enterprises (SMEs)Large Enterprises

Segmentation by End-use Industry:

HealthcareBFSILawRetailAdvertising & MediaAutomotive & TransportationAgricultureManufacturingOthers

Quick Read Table of Contents of this Report @ Global Machine Learning Market 2020 (Includes Business Impact of COVID-19)

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