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AI can be leveraged to build efficient water plants and optimize water resources to reduce energy costs in the long run.

Artificial intelligence is disrupting industries with its wide range of capabilities including augmenting human intelligence and processing huge data chunks. There have been discussions and reports on sustainable AI which can work efficiently while conserving the environment. AI has also proved effective in renewable resources industries. Let us discuss the impact of AI in another sector the water sector. Water is an imperative need to live life and it has been going through pollution and scarcity for a long time. Climate change is a reality that can increase water stress in many places and increased water contamination will result in a huge water crisis which we are not yet ready to deal with. According to a report by UNICEF and WHO, 1 in 3 people globally does not have access to safe drinking water. This scenario is going to become grave in the coming years if we do not address the issue.

AI in water management might come off as a huge revelation but it can change the way we treat and manage water sources around us. Let us see how AI can impact the global water sector.

An India Today report states that it is estimated that around 40% of piped water in India is lost to leakage. According to a US EPA report, an average family can waste 180 gallons of water per week, or 9400 gallons of water annually, from household leaks, which is equivalent to the amount of water needed to wash more than 300 loads of laundry.

We waste a lot of water through leakages, burst pipes, etc. and AI and IoT can help reduce this wastage. Implementing AI to analyze real-time water loss and automating pipes to shut off whenever there is a leak can improve the amount of water wastage. AI can predict leaks in storage tanks and help in mending them before it is too late. Devices connected through IoT can communicate better and integrate various systems across a city or place.

AI can be used to reduce pollutants in the water which in turn decreases water contamination and scarcity of clean water. AI can be leveraged to detect the amount and composition of toxic contaminants since AI works on optics, which can increase the efficiency of waste management systems. Water quality can be continuously monitored and it is possible to get real-time data on the quality through machine learning and big data. Neural networks and IoT will reduce the energy costs which otherwise increases when using conventional methods.

AI can make the process of water management easier with data analytics, regression models, and algorithms. These cutting-edge technologies help in building efficient water systems and networks. AI can be used to build water plants and to get the status of water resources. Water managers and government bodies can use AI to build a smart water system that can build efficient infrastructure for water management and can adapt to changing conditions. These systems will be cost-effective and sustainable that can optimize all water management solutions and predict potential damages.

Agriculture is the biggest water-using sector and many lands use a good portion of groundwater for irrigation purposes. Smart Irrigation will leverage AI systems to minimize the use of water and also optimize the water resources without wastage. AI systems can detect the groundwater levels and also estimate the agricultural needs to balance the usage of water by guiding sprinkler systems.

More developed precision-based AI systems can predict the weather conditions, climate, and humidity to enable better management of agriculture. The smart farms will be able to reduce leakages and analyze the soil to determine the condition of plants and their water needs using AI sensors.

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Artificial intelligence (AI) and machine learning (ML) are positioned to disrupt the way we live and work, even the way we interact and think. Machine learning is a core sub-area of AI. It makes computers get into a self-learning mode without explicit programming.

At this point, most organizations are still approaching ML as a technology in the realm of research and exploration. In this first article of a series, we delve deeper into the world of machine learning and its applications. The following articles will focus on building an ML implementation plan. In doing so we not only understand the concepts behind the technology, but also why it can make the difference between keeping up with competition or falling further behind.

Gartner defines machine learning as:Advanced learning algorithms composed of many technologies (such as deep learning, neural networks and natural language processing), used in unsupervised and supervised learning, that operate guided by lessons from existing information.

Machine learning is the process of teaching computers to develop intuitive knowledge and understanding through the use of repetitive algorithms and patterns. Machine learning in lay-man's terms is the process of schooling a repetitive activity to a dumb system that needs to develop some innate intelligence. The goal is to feed the system large amounts of data so it learns from each pattern and its variations, so it can eventually be able to identify the pattern and its variants on its own. The advantage a machine has over the human mind here is its ability to ingest and process large amounts of data. The human brain, although limitless in its capacity to ingest data, may not be able to process it at the same time and can only recall a limited set at one time.

There are three key types of machine learning: supervised, unsupervised and reinforced.

Other aspects of machine learning include neural networks and deep learning.

Neural networks have been studies for a long time. These algorithms endeavor to recognize the underlying relationships in data, just the way the human brain operates.

Deep learning is a class of machine learning algorithms that involves multiple layers of neural networks where the output of one network becomes the input to another.

The key to understanding machine learning is to understand the power of data. These algorithms work by finding patterns in massive amounts of data. This data, encompasses a lot of thingsnumbers, words, images, videos, sound files etc. Any data or meta data that can be digitally stored, can be fed into a machine-learning algorithm.

Related Article: Machine Learning Fragmentation Is Slowing Us Down: There Is a Solution

Machine learning, in conjunction with deep learning, have a wide variety of applications in our home and businesses today. It is currently used in everyday services such as recommendation systems like those on Netflix and Amazon; voice assistants like Siri and Alexa; car technology in parking assist and preventing accidents. Deep learning is already heavily used in autonomous vehicles and facial recognition systems. As the technology matures and receives widespread acceptance, we expect to see its applicability grow in these areas:

And many more .

Related Article: Why Artificial Intelligence May Not Offer the Business Value You Think

The availability of widespread computing power though the use of cloud technologies along with an increasing volume of readily available data has driven a number of advancements in the field of AI and ML. Organizations need to first build an understanding of the technology itself, collaborate on building a vision for using the technology internally and then build an implementation plan collaboratively between business and IT. In part two of this ML series we will focus on building a vision and implementation plan.

Geetika Tandon is a senior director at Booz Allen Hamilton, a management and technology consulting firm. She was born in Delhi, India, holds a Bachelors in architecture from Delhi University, a Masters in architecture from the University of Southern California and a Masters in computer science from the University of California Santa Barbara.

The views and opinions expressed in these articles are those of the author and do not necessarily reflect the official policy or position of her employer.

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Getting Started With Machine Learning: Definition and Applications - CMSWire

It has been a landmark year for Artificial Intelligence. What was once the reserve of science fiction is now becoming an intrinsic part of our everyday lives. From voice-controlled digital assistants in our homes to customer service chat bots, AI is now entrenched in the mass market. Most significantly, it has also been a year in which AI in healthcare has put down roots for a more radical transformation.

AI and machine learning have been quietly revolutionising the health sector for years by delivering everything from robotic surgery and 3D image analysis to intelligence biosensors that allow diagnoses and treatments to be managed remotely. But while the COVID-19 pandemic has been devastating, it has also catalysed technological developments in and awareness of healthcare AI. In the first quarter of 2020 alone, almost $1bn was invested in AI-focused healthcare start-ups and a recent projection shows the global industry growing at a rate of 44% until 2026.

The potential uses of Artificial Intelligence in the healthcare sector are vast, and the technology is rapidly gaining momentum with investors as a result. With its applications ranging from disease prevention and diagnostics to acute care and long-term disease management, the industry is reaching a tipping point in 2020 and AI is finally becoming mainstream.

Yet it still seems we have only scratched the surface; and like any revolution witnessed in real time, the possibilities are seemingly limitless. For healthcare providers and associated organisations, it remains a real challenge to turn vision into reality. To move from testing to regular use, and to change the patient experience more fundamentally, organisations wanting to engage with AI must approach the issue strategically.

The technology behind Artificial Intelligence is evolving at breakneck speed, but the real test of an organisation is how it can harness and implement that technology for its own ends. The pressure of the pandemic has no doubt accelerated innovations, but before we look at how they can be put into practice, it is useful to consider what AI actually is and what it looks like in a healthcare setting.

At its core AI is machine learning, which is comprised of three cognitive nodes: computer vision, natural language processing and data inference. Computer vision is the eyes of AI, as it is capable of recognising visual patterns, objects, scenes and activities in digital imagery far quicker humans. Natural language processing refers to the technology that recognises and understands spoken language. Structured data inference is the technology that uses data, most often numerical, to solve problems. We have seen exciting developments for healthcare in all three in 2020.

Take natural language processing, which has come under the spotlight during the pandemic as healthcare providers have been forced to move operations online. The telehealth industry has grown exponentially because it has enabled providers to automate and streamline basic services in order to free up resources to deal with the crisis. In France, for instance, telemedicine appointments increased from 10,000 to a staggering 500,000 per week during the initial peak of the pandemic.

Recent developments in AI show that telehealth can be more than a platform for consultation. One startup, Vocalis Health, is exploring the use of voice data as a biomarker for disease progression. Using AI, the technology can detect signs of pulmonary hypertension in specific segments of speech, which can be recorded into a smartphone. Similar efforts are being focused on voice-based COVID-19 screening apps and also on using data to track neurological conditions like Parkinsons disease. The potential for this is significant and it promises to elevate telehealth to whole new level.

Huge strides in healthcare AI have been made by larger operations too, such as Alphabets AI subsidiary DeepMind. In November, DeepMinds AlphaFold project revealed it had in large part resolved a half-century-old challenge for scientists by understanding how a protein folds into a unique three-dimensional shape. This paves the way for a much greater understanding of diseases and the creation of designer medicines. On a wider scale, it even can help break down plastic pollution. Once more, the implications are enormous and not only for research scientists but for the role of Artificial Intelligence in the healthcare sector as a whole.

AIs ability to solve incredibly complex problems using huge sets of data far surpasses our own; and for the decades ahead, the sky really is the limit for the businesses pioneering change so how can a healthcare provider think about effectively building-in such developments into strategy?

Artificial Intelligence is a vast field with many potential applications. There is no single, fool proof blueprint for its implementation, so healthcare organisations looking to harness its potential must make choices that fit their financial and technical capabilities.

The first key question that providers should ask themselves before embarking on their AI journey is: do we have the capacity to build out these capabilities in-house? Having the internal resources, proprietary data and capital to develop AI solutions in-house comes with obvious benefits in terms of control, but businesses will need to decide for themselves whether its realistic given their goals and timeline.

Next, should we consider partnerships or acquisitions? Even with the best resources and in-house capabilities, partnerships can rapidly increase the development and deployment of AI systems and tools. Investments in AI start-ups or acquisitions of smaller companies can also give an organisation fast access to development phases and provide greater expertise and capabilities.

Finally, businesses will need to think about which key enablers will accelerate their AI strategy. This means thinking about everything from building or acquiring new technologies, to leadership alignment and team allocation.

We know that AI can transform many aspects of healthcare; and as we have seen this year, it is evolving rapidly on a global scale. However, healthcare providers engaging with AI face specific challenges, especially when implementing it.

Data is AIs raison dtre: without a continuous supply of data, AI technology simply could not have achieved what it has to date. However, it can also be a nuisance for organisations which are grappling with the challenge of dirty data, which is not yet standardised and remains disparate. Privacy protocols and security requirements present additional barriers to progress, but as they concern protections for patient rights, these are hills that must be climbed. Consent for the use of patients data and the need to address perceived bias in algorithms are additional ethical issues of which all organisations must be wary.

Necessity is the mother of invention, which explains in part why so much ground has been made this year. However, the healthcare business model could do more to incentivise innovation. While there is a broad range of industry players in this sector, larger technology companies are known to lure talent away from start-ups, who also face difficulties scaling up their products without partnerships.

These challenges are certainly real, but they are by no means insurmountable. While the success of engaging with AI relies on careful preparation, it is an innovation that is not just worth pursuing, but one that will be integral to healthcares story in the years to come. As such, organisations need to prioritise AI initiatives and plan for implementation. On a basic level, this means ensuring leadership is on board and the right talent is being supported.

Many organisations throughout the healthcare chain are already deep into their digital transformation journey. While some of these will have well-developed AI strategies in play, others will not. It is worth bearing in mind that the road to AI-enabled healthcare is long, which makes having a strategy to turn vision into reality key to a successful journey.

Overall, approaches may vary and will be dependent on specialism and sub-sector. But what sets healthcare ahead of other industries is the universal recognition of the power of AI and machine learning, and the sheer scale from start-ups to multinational companies involved.

The medical landscape of tomorrow is likely to look very different, but it is down to healthcare organisations across the board to steer their own path in a future defined by Artificial Intelligence.

This article is from issue 16 ofHealth Europa.Clickhere to get your free subscription today

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From vision to reality: the rise of Artificial Intelligence in the healthcare sector - Health Europa

Pune, India, Feb. 17, 2021 (GLOBE NEWSWIRE) -- The global process mining software market size is estimated to showcase robust growth owing to the increasing implementation of artificial intelligence (AI) in the software by leading industry players, observes Fortune Business Insights in its report, titled, Process Mining Software Market Size, Share & COVID-19 Impact Analysis, By Type (Cloud-base, and On-premises), By Enterprise Size (Large Enterprises and Small & Medium Enterprises), By End User (BFSI, Healthcare, Retail, Manufacturing, IT and Telecommunication, Logistics and Transportation, and Others), and Regional Forecast, 2021-2028. As per our findings, the market value stood at USD 421.9 million in 2020 and is anticipated to reach USD 10,383.0 million by 2028, exhibiting a CAGR of 49.3% during the forecast period.

Information Technology to Boom amid COVID-19 due to Increasing Demand

The COVID-19 pandemic has compelled various industries to establish their presence online owing to prolonged nationwide lockdowns across countries. This has positively impacted the information technology sector. In addition, the sudden increase in demand for digital infrastructure after the rapid adoption of work-from-home settings due to social distancing norms has strengthened the growth of the global IT sector. However, the demand-supply gap amid the crisis has brought fresh challenges to prominent players. At Fortune Business Insights, we are focusing on finding innovative solutions to the current challenges.

To get to know more about the short-term and long-term impact of COVID-19 on this market,

Please Visit: https://www.fortunebusinessinsights.com/process-mining-software-market-104792

Highlights of the Report:

While making the report, we segmented the market on the basis of product, type, consumption, distribution channel, and region. Based on the segmentation, we made a list of companies and conducted a detailed analysis of their financial positions, product portfolios, and growth strategies. Our next step included the study of core competencies of key players and their market share to anticipate the degree of competition. The bottom-up procedure was conducted to arrive at the overall size of the market.

Drivers & Restraints-

Integration of Software with Latest Technologies to Drive Growth

Advantages, such as regular insights from real-time analysis and efficient operational business tasks, are the key factors driving developers and leading corporations to integrate artificial intelligence with process mining software. An increasing demand for such software is augmenting the growth. For instance, in April 2020, Automation Hero introduced Hero_Sonar, an AI-enabled intelligent process mining software. The software offers valuable insights from low-quality data, which helps in developing AI decision models. However, the high risk associated with customers privacy is predicted to hinder process mining software market growth.

Segment-

BFSI Segment to Lead the Market Owing to High Adoption

Based on the end-user, the BFSI segment dominated the market with a leading share of 26.8% in 2020. The segment growth is attributed to the seamless management offered by the software to banks for their operations. Based on type, the cloud-based segment is estimated to hold the leading share. The increasing growth of this segment is attributed to the cloud-based process mining softwares capability of providing valuable insights on a real-time basis.

Get Sample PDF Brochure: https://www.fortunebusinessinsights.com/enquiry/request-sample-pdf/process-mining-software-market-104792

Regional Insights-

Presence of Key Players to Help Europe Dominate

Europe is anticipated to dominate the global market with a value of USD 165.3 million in 2020. Increasing demand for process mining software in the energy space amid the surging digital transformation across industries is projected to drive its growth in the region. In addition, the presence of key players in major countries of the region is estimated to further propel growth.

North America is predicted to register a considerable process mining software market share during the projected timeline. One of the major factors set to propel the demand for this software in the region is the increasing adoption of automation in the U.S.

Competitive Landscape-

Key Players Focus on Offering Innovative Products to Expand their Product Lines

Prominent enterprises operating in the global market are focusing on innovating cutting-edge products in order to help their customers run smooth business operations. This will also help them expand their product portfolios. For instance, in April 2020, Celonis GmbH launched the next-generation platform of its AI-assisted process mining software. This new platform will help businesses and clients develop purpose-built operational applications.

Industry Developments-

A List of Key Manufacturers Operating in the Global Process Mining Software Market:

Quick Buy Process Mining Software Market Research Report: https://www.fortunebusinessinsights.com/checkout-page/104792

Detailed Table of Content:

TOC Continued..

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Our reports contain a unique mix of tangible insights and qualitative analysis to help companies achieve sustainable growth. Our team of experienced analysts and consultants use industry-leading research tools and techniques to compile comprehensive market studies, interspersed with relevant data.

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Press Release: https://www.fortunebusinessinsights.com/press-release/global-process-mining-software-market-10526

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Process Mining Software Market to Reach USD 10,383.0 Million by 2028; Increasing Utilization of Artificial Intelligence to Aid Growth, states Fortune...

This is an automated machine translation of an article published by Business Insider in a different language. Machine translations can generate errors or inaccuracies; we will continue the work to improve these translations. You can find the original version here.

The asset management industry is moving at the same pace as the planet as a whole.

Increased digitization and the use of digital tools is taking hold. Artificial intelligence is making its way into the financial industry and one of the debates is whether it can end up doing away with the figure of the manager and whether, in addition, it is the key factor on which indexed management - an investment strategy based on replicating indexes - is focused.

Business Insider Spain has exclusively interviewed Unai Ansejo, CEO of Indexa Capital, a fintech focused on indexed management and with a growing volume of clients, to discuss this series of questions about the future of the investment scheme, as well as delving into the expansion of its range of products with the launch of occupational pension plans.

Focusing on the advantages of artificial intelligence when it comes to managing the assets in which to invest Ansejo expounds that from his professional experience he realizes that long-term savings is not about using an algorithm that beats others, but rather about greatly reducing costs, diversifying and being invested in different areas.

"I'm incredulous of these things," he relates about nonparametrics. "I have analyzed many quantitative investment funds for more than 20 years and they always seemed very good, but then there came a time when something happened or there was any problem," he adds.

Therefore, as he explains, in the end, artificial intelligence is a very broad concept, but they would still be algorithms in which you create a series of entry points to then find an exit.

"What happens is that the process by which inputs become outputs is a black box: you don't know," he says.

At Indexa Capital, they don't use artificial intelligence to build investment models but instead focus on criteria they think are reasonable for how portfolios should be constructed over the long term: diversify a lot, reduce costs, incorporate the effect of direct taxes into portfolio construction. "In my view, AI as such is not the best way to obtain long-term performance," he notes.

Artificial intelligence with a Spanish stamp to revolutionize the financial sector: Ultramarine, the investment technology that stops trading if it detects uncertainty in the market.

Ansejo assures, however, that in the fintech they use technology a lot: "Our goal is that half of our team are technical profiles such as engineers, analysts or developers and we use technology for what needs to be done: automating processes where a person does not contribute any value".

For example, something that automates, as he relates, is that, once the client's portfolio is configured, based on their risk profile, they apply an algorithm that is public to guide how the allocation of their investors should be. "When you already have a model portfolio the daily management of your portfolio, or the request for a withdrawal to find the best fund in which there is a lower tax impact can be automated," he explains.

The Indexa Capital CEO asserts that you can't automate portfolio construction."You can't ask a computer or a machine what to invest in because there are many parameters to take into account," he says.

In this way, Ansejo reveals that to build their portfolios they carry out a quarterly review in which they try to see, among other things, if there is a new asset class in which they can invest cheaply and efficiently.

On the other hand, Indexa Capital has expanded its range of indexed products by incorporating occupational pension plans. "We do it with indexing because we think it's the best way to maximize your options to monetize a portfolio over the long term," he says. "What we have is 32,000 clients for whom this proposition works," he adds.

Along these lines, Ansejo says that they have had pension plans for 4 years and with a very clear vocation: that they should be indexed because they are cheaper. However, they saw that, apart from individual plans, in employment plans (where it is the company that creates a payment plan and contributes for the worker) the solutions available were once again very analogical. "Everything with a lot of paper and regulatory information," he describes.

On the other hand, they were usually active management, oriented towards SMEs and high costs. " So we decided to launch it to make it easier for an SME to have a plan quickly and online, and we did so by incorporating another feature, which is the life cycle," he says.

Ansejo confirms that they incorporated a large dose of innovation: that it could be done digitally, low costs and life cycle. "So, the response we are having is very good, although the amount we have is small, it is normal because in the end, when you create an employment plan you are contributing little by little to your employees," he says.

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Artificial intelligence helps automation, but can't tell you where to put your money, Indexa CEO says - Business Insider