Op-ed: Aaron Yeardley, Carbon Reduction Engineer, Tunley Engineering

The fourth industrial revolution (Industry 4.0) is already happening, and its transforming the way manufacturing operations are carried out. Industry 4.0 is a product of the digital era as automation and data exchange in manufacturing technologies shift the central industrial control system to a smart setup that bridges the physical and digital world, addressed via the Internet of Things (IoT).

Industry 4.0 is creating cyber-physical systems that can network a production process enabling value creation and real-time optimisation. The main factor driving the revolution is the advances in artificial intelligence (AI) and machine learning. The complex algorithms involved in AI use the data collected from cyber-physical systems, resulting in smart manufacturing.

The impact that Industry 4.0 will have on manufacturing will be astronomical as operations can be automatically optimised to produce increased profit margins. However, the use of AI and smart manufacturing can also benefit the environment. The technologies used to optimise profits can also be used to produce insights into a companys carbon footprint and accelerate its sustainability. Some of these methods are available to help companies reduce their GHG emissions now. Other methods have the potential to reduce global GHG emissions in the future.

Scope 3 emissions are the emissions from a companys supply chain, both upstream and downstream activities. This means scope 3 covers all of a companys GHG emission sources except those that are directly created by the company and those created from using electricity. It comes as no surprise that on average Scope 3 emissions are 5.5 times greater than the combined amount from Scope 1 and Scope 2. Therefore, companies should ensure all three scopes are quantitated in their GHG emissions baseline.

However, in comparison to Scope 1 and Scope 2 emissions, Scope 3 emissions are difficult to measure and calculate. This is because of a lack of transparency in supply chains, lack of connections with suppliers, and complex industrial standards that provide misleading information. The major issues concerning Scope 3 emissions are as follows:

AI-based tools can help establish baseline Scope 3 emissions for companies as they are used to model an entire supply chain. The tools can quickly and efficiently sort through large volumes of data collected from sensors. If a company deploys enough sensors across the whole area of operations, it can identify sources of emissions and even detect methane plumes.

A digital twin is an AI model that works as a digital representation of a physical piece of equipment or an entire system. A digital twin can help the industry optimise energy management by using the AI surrogate models to better monitor and distribute energy resources and provide forecasts to allow for better preparation. A digital twin will optimise many sources of data and bring them onto a dashboard so that users can visualise it in real-time. For example, a case study in the Nanyang Technological University used digital twins across 200 campus buildings over five years and managed to save 31% in energy and 9,600 tCO2e. The research used IESs ICL technology to plan, operate, and manage campus facilities to minimise energy consumption.

Digital twins can be used as virtual replicas of building systems, industrial processes, vehicles, and many other opportunities. The virtual environment enables more testing and iterations so that everything can be optimised to its best performance. This means digital twins can be used to optimise building management making smart strategies that are based on carbon reduction.

Predictive maintenance of machines and equipment used in industry is now becoming common practice because it saves companies costs in performing scheduled maintenance, or costs in fixing broken equipment. The AI-based tool uses machine learning to learn how historical sensor data maps to historical maintenance records. Once a machine learning algorithm is trained using the historical data, it can successfully predict when maintenance is required based on live sensor readings in a plant. Predictive maintenance accurately models the wear and tear of machinery that is currently in use.

The best part of predictive maintenance is that it does not require additional costs for extra monitoring. Algorithms have been created that provide accurate predictions based on operational telemetry data that is already available. Predictive maintenance combined with other AI-based methods such as maintenance time estimation and maintenance task scheduling can be used to create an optimal maintenance workflow for industrial processes. Conversely, improving current maintenance regimes which often contribute to unplanned downtime, quality defects and accidents is appealing for everybody.

An optimal maintenance schedule produced from predictive maintenance prevents work that often is not required. Carbon savings will be made via the controlled deployment of spare parts, less travel for people to come to the site, and less hot shooting of spare parts. Intervening with maintenance only when required and not a moment too late will save on the use of electricity, efficiency (by preventing declining performance) and human labour. Additionally, systems can employ predictive maintenance on pipes that are liable to spring leaks, to minimise the direct release of GHGs such as HFCs and natural gas. Thus, it has huge potential for carbon savings.

Research has shown that underpinning the scheduling of maintenance activities on predictive maintenance and maintenance time estimation can produce an optimal maintenance scheduling (Yeardley, Ejeh, Allen, Brown, & Cordiner, 2021). The work optimised the scheduling by minimising costs based on plant layout, downtime, and labour constraints. However, scheduling can also be planned by optimising the schedule concerning carbon emissions. In this situation, maintenance activities can be performed so that fewer journeys are made and GHG emissions are saved.

The internet of things (IoT) is the digital industrial control system, a network of physical objects that are connected over the internet by sensors, software and other technologies that exchange data with each thing. In time, the implementation of the IoT will be worldwide and every single production process and supply chain will be available as a virtual image.

Open access to a worldwide implementation of the IoT has the potential to provide a truly circular economy. Product designers can use the information available from the IoT and create value from other peoples waste. Theoretically, we could establish a work where manufacturing processes are all linked so that there is zero extracted raw materials, zero waste disposed and net-zero emissions.

Currently, the world has developed manufacturing processes one at a time, not interconnected value chains across industries. It may be a long time until the IoT creates the worldwide virtual image required, but once it has the technology is powerful enough to address losses from each process and exchange material between connected companies. Both materials and energy consumption can be shared to lower CO2 emissions drastically. It may take decades, but the IoT provides the technology to create a circular economy.

ConclusionAI has enormous potential to benefit the environment and drive the world to net-zero. The current portfolio of research being conducted at the Alan Turning Institute (UKs national centre for data science) includes projects that explore how machine learning can be part of the solution to climate change. For example, an electricity control room algorithm is being developed to provide decision support and ensure energy security for a decarbonised system. The national grids electricity planning is improved by forecasting the electricity demand and optimising the schedule. Further, Industry 4.0 can plan for the impact that global warming and decarbonisation strategies have on our lives.

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Artificial intelligence drives the way to net-zero emissions - Sustainability Magazine

SAN FRANCISCO There was a time when Google offered a wondrous vision of the future, with driverless cars, augmented-reality eyewear, unlimited storage of emails and photos, and predictive texts to complete sentences in progress.

A more modest Google was on display on Wednesday as the company kicked off its annual developers conference. The Google of 2022 is more pragmatic and sensible a bit more like its business-focused competitors at Microsoft than a fantasy play land for tech enthusiasts.

And that, by all appearances, is by design. The bold vision is still out there but its a ways away. The professional executives who now run Google are increasingly focused on wringing money out of those years of spending on research and development.

The companys biggest bet in artificial intelligence does not, at least for now, mean science fiction come to life. It means more subtle changes to existing products.

A.I. is improving our products, making them more helpful, more accessible, and delivering innovative new features for everyone, Sundar Pichai, Googles chief executive, said on Wednesday.

In a presentation short of wow moments, Google stressed that its products were helpful. In fact, Google executives used the words help, helping, or helpful more than 50 times during two hours of keynote speeches, including a marketing campaign for its new hardware products with the line: When it comes to helping, we cant help but help.

It introduced a cheaper version of its Pixel smartphone, a smartwatch with a round screen and a new tablet coming next year. (The most helpful tablet in the world.)

The biggest applause came from a new Google Docs feature in which the companys artificial-intelligence algorithms automatically summarize a long document into a single paragraph.

At the same time, it was not immediately clear how some of the other groundbreaking work, like language models that better understand natural conversation or that can break down a task into logical smaller steps, will ultimately lead to the next generation of computing that Google has touted.

Certainly some of the new ideas do appear helpful. In one demonstration about how Google continues to improve its search technology, the company showed a feature called multisearch, in which a user can snap a photo of a shelf full of chocolates and then find the best-reviewed dark chocolate bar without nuts from the picture.

In another example, Google showed how you can find a picture of a specific dish, like Korean stir-fried noodles, and then search for nearby restaurants serving that dish.

Much of those capabilities are powered by the deep technological work Google has done for years using so-called machine learning, image recognition and natural language understanding. Its a sign of an evolution rather than revolution for Google and other large tech giants.

Many companies can build digital services easier and faster than in the past because of shared technologies such as cloud computing and storage, but building the underlying infrastructure such as artificial intelligence language models is so costly and time-consuming that only the richest companies can invest in them.

As is often the case at Google events, the company didnt spend a little of time explaining how it makes money. Google brought up the topic of advertising which still accounts for 80 percent of the companys revenue after an hour of other announcements, highlighting a new feature called My Ad Center. It will allow users to request fewer ads from certain brands or to highlight topics they would like to see more ads about.

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Googles I/O Conference Offers Modest Vision of the Future - The New York Times

Researchers have created a machine-learning system that efficiently predicts the future trajectories of multiple road users, like drivers, cyclists, and pedestrians, which could enable an autonomous vehicle to more safely navigate city streets. If a robot is going to navigate a vehicle safely through downtown Boston, it must be able to predict what nearby drivers, cyclists, and pedestrians are going to do next. Credit: MIT

A new machine-learning system may someday help driverless cars predict the next moves of nearby drivers, pedestrians, and cyclists in real-time.

Humans may be one of the biggest roadblocks to fully autonomous vehicles operating on city streets.

If a robot is going to navigate a vehicle safely through downtown Boston, it must be able to predict what nearby drivers, pedestrians, and cyclists are going to do next.

Behavior prediction is a tough problem, however, and current artificial intelligence solutions are either too simplistic (they may assume pedestrians always walk in a straight line), too conservative (to avoid pedestrians, the robot just leaves the car in park), or can only forecast the next moves of one agent (roads typically carry many users at once.)

MIT researchers have devised a deceptively simple solution to this complicated challenge. They break a multiagent behavior prediction problem into smaller pieces and tackle each one individually, so a computer can solve this complex task in real-time.

These simulations show how the system the researchers developed can predict the future trajectories (shown using red lines) of the blue vehicles in complex traffic situations involving other cars, bicyclists, and pedestrians. Credit: MIT

Their behavior-prediction framework first guesses the relationships between two road users which car, cyclist, or pedestrian has the right of way, and which agent will yield and uses those relationships to predict future trajectories for multiple agents.

These estimated trajectories were more accurate than those from other machine-learning models, compared to real traffic flow in an enormous dataset compiled by autonomous driving company Waymo. The MIT technique even outperformed Waymos recently published model. And because the researchers broke the problem into simpler pieces, their technique used less memory.

This is a very intuitive idea, but no one has fully explored it before, and it works quite well. The simplicity is definitely a plus. We are comparing our model with other state-of-the-art models in the field, including the one from Waymo, the leading company in this area, and our model achieves top performance on this challenging benchmark. This has a lot of potential for the future, says co-lead author Xin Cyrus Huang, a graduate student in the Department of Aeronautics and Astronautics and a research assistant in the lab of Brian Williams, professor of aeronautics and astronautics and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL).

Joining Huang and Williams on the paper are three researchers from Tsinghua University in China: co-lead author Qiao Sun, a research assistant; Junru Gu, a graduate student; and senior author Hang Zhao PhD 19, an assistant professor. The research will be presented at the Conference on Computer Vision and Pattern Recognition.

The researchers machine-learning method, called M2I, takes two inputs: past trajectories of the cars, cyclists, and pedestrians interacting in a traffic setting such as a four-way intersection, and a map with street locations, lane configurations, etc.

Using this information, a relation predictor infers which of two agents has the right of way first, classifying one as a passer and one as a yielder. Then a prediction model, known as a marginal predictor, guesses the trajectory for the passing agent, since this agent behaves independently.

A second prediction model, known as a conditional predictor, then guesses what the yielding agent will do based on the actions of the passing agent. The system predicts a number of different trajectories for the yielder and passer, computes the probability of each one individually, and then selects the six joint results with the highest likelihood of occurring.

M2I outputs a prediction of how these agents will move through traffic for the next eight seconds. In one example, their method caused a vehicle to slow down so a pedestrian could cross the street, then speed up when they cleared the intersection. In another example, the vehicle waited until several cars had passed before turning from a side street onto a busy, main road.

While this initial research focuses on interactions between two agents, M2I could infer relationships among many agents and then guess their trajectories by linking multiple marginal and conditional predictors.

The researchers trained the models using the Waymo Open Motion Dataset, which contains millions of real traffic scenes involving vehicles, pedestrians, and cyclists recorded by lidar (light detection and ranging) sensors and cameras mounted on the companys autonomous vehicles. They focused specifically on cases with multiple agents.

To determine accuracy, they compared each methods six prediction samples, weighted by their confidence levels, to the actual trajectories followed by the cars, cyclists, and pedestrians in a scene. Their method was the most accurate. It also outperformed the baseline models on a metric known as overlap rate; if two trajectories overlap, that indicates a collision. M2I had the lowest overlap rate.

Rather than just building a more complex model to solve this problem, we took an approach that is more like how a human thinks when they reason about interactions with others. A human does not reason about all hundreds of combinations of future behaviors. We make decisions quite fast, Huang says.

Another advantage of M2I is that, because it breaks the problem down into smaller pieces, it is easier for a user to understand the models decision-making. In the long run, that could help users put more trust in autonomous vehicles, says Huang.

But the framework cant account for cases where two agents are mutually influencing each other, like when two vehicles each nudge forward at a four-way stop because the drivers arent sure who should be yielding.

They plan to address this limitation in future work. They also want to use their method to simulate realistic interactions between road users, which could be used to verify planning algorithms for self-driving cars or create huge amounts of synthetic driving data to improve model performance.

Predicting future trajectories of multiple, interacting agents is under-explored and extremely challenging for enabling full autonomy in complex scenes. M2I provides a highly promising prediction method with the relation predictor to discriminate agents predicted marginally or conditionally which significantly simplifies the problem, wrote Masayoshi Tomizuka, the Cheryl and John Neerhout, Jr. Distinguished Professor of Mechanical Engineering at University of California at Berkeley and Wei Zhan, an assistant professional researcher, in an email. The prediction model can capture the inherent relation and interactions of the agents to achieve the state-of-the-art performance. The two colleagues were not involved in the research.

Reference: M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction by Qiao Sun, Xin Huang, Junru Gu, Brian C. Williams and Hang Zhao. 28 March 2022, Computer Science > Robotics.arXiv:2202.11884

This research is supported, in part, by the Qualcomm Innovation Fellowship. Toyota Research Institute also provided funds to support this work.

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Predicting Others Behavior on the Road With Artificial Intelligence - SciTechDaily

Paris: With his hand pushed firmly into his cheek and his eyes fixed on the table, Garry Kasparov shot a final dark glance at the chessboard before storming out of the room: the king of chess had just been beaten by a computer.

May 11, 1997 was a watershed for the relationship between man and machine, when the artificial intelligence (AI) supercomputer Deep Blue finally achieved what developers had been promising for decades.

It was an "incredible" moment, AI expert Philippe Rolet told AFP, even if the enduring technological impact was not so huge.

Developers at IBM, the US firm that made Deep Blue, were ecstatic with the victory but quickly refocused on the wider significance.

"This is not about man versus machine. This is really about how we, humans, use technology to solve difficult problems," said Deep Blue team chief Chung-Jen Tan after the match, listing possible benefits from financial analysis to weather forecasting.

Even Chung would have struggled to comprehend how central AI has now become -- finding applications in almost every field of human existence.

"AI has exploded over the last 10 years or so," UCLA computer science professor Richard Korf told AFP.

"We're now doing things that used to be impossible."

'One man cracked'

After his defeat, Kasparov, who is still widely regarded as the greatest chess player of all time, was furious.

He hinted there had been unfair practices, denied he had really lost and concluded that nothing at all had been proved about the power of computers.

He explained that the match could be seen as "one man, the best player in the world, (who) has cracked under pressure".

The computer was beatable, he argued, because it had too many weak points.

AI-powered machines have mastered every game going and now have much bigger worlds to conquer.

Korf cites notable advances in facial recognition that have helped make self-driving cars a reality.

Yann LeCun, head of AI research at Meta/Facebook, told AFP there had been "absolutely incredible progress" in recent years.

LeCun, one of the founding fathers of modern AI, lists among the achievements of today's computers an ability "to translate any language into any language in a set of 200 languages" or "to have a single neural network that understands 100 languages".

It is a far cry from 1997, when Facebook didn't even exist.

Machines 'not the danger'

Experts agree that the Kasparov match was important as a symbol but left little in the way of a technical legacy.

"There was nothing revolutionary in the design of Deep Blue," said Korf, describing it as an evolution of methods that had been around since the 1950s.

"It was also a piece of dedicated hardware designed just to play chess."

Facebook, Google and other tech firms have pushed AI in all sorts of other directions.

They have fuelled increasingly powerful AI machines with unimaginable amounts of data from their users, serving up remorselessly targeted content and advertising and forging trillion-dollar companies in the process.

AI technology now helps to decide anything from the temperature of a room to the price of vehicle insurance.

Devices from vacuum cleaners to doorbells come with arrays of sensors to furnish AI systems with data to better target consumers.

While critics bemoan a loss of privacy, enthusiasts believe AI products just make everyone's lives easier.

Despite his painful history with machines, Kasparov is largely unfazed by AI's increasingly dominant position.

"There is simply no evidence that machines are threatening us," he told AFP last year.

"The real danger comes not from killer robots but from people -- because people still have a monopoly on evil."

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The 1997 chess game that thrust AI into the spotlight - ETTelecom

Technology for MSME: The shift towards more efficient technology solutions from the good old websites and emails, in the name of digital adoption, is apparent among MSMEs that have shied away from evolving technologies for a long time. The shift has largely been visible because of better affordability due to growing on-demand, or pay as you go or what is called the software-as-a-service (SaaS) ecosystem in India liberating small businesses from the cost conundrum to some extent.

As India observes the National Technology Day on Wednesday to commemorate its entry into the elite club of countries having nuclear weapons with the Pokhran nuclear tests in 1998, it is also the day to remember the countrys achievement in science and innovation. While a large number of MSMEs are yet to fully benefit from the technology revolution, some of them have certainly been warming up to the new age solutions such as artificial intelligence (AI) and using it also for better growth.

The implementation of AI is across multiple use cases. For instance, Delhi-based long-haul logistics services provider JCCI Logistics has deployed AI and internet of things (IoT) solutions to manage its fleet of around 150 trucks. The company, launched in 2004, uses on-demand fleet management software for GPS tracking of vehicles, fuel management, driver analytics, and route planning.

Vehicles need to run as much as possible and thats what matters. Before deploying this solution in 2020, our monthly cumulative running was around 8,000 to 10,000 kilometres. It has increased by around 20 per cent now. The jump, I think, is primarily because of the on-board diagnostics (OBD) device that you can fit in a vehicle to get data related to fuel consumption, drivers driving behaviour, whether there is unnecessary hard acceleration or not, etc., Sachin Jain, Founder, JCCI Logistics told Financial Express Online.

OBD is essentially a machine learning (ML) and internet of things (IoT) based device that gets signals from different sensors in a vehicle and conveys them to the users dashboard with the help of the software.

JCCI Logistics have been among post-Covid adopters of deep technology solutions as the pandemic perhaps necessitated the use of software and digital for sustenance.

Covid might have caused a faster switch to some AI/ML applications since the labor force was locked up. AI/ML provides a significant opportunity for reduction in input costs, particularly those of human capital. The advent of edge AI/ML will further hasten adoption, particularly as it gets married to IoT on small devices and sensors that are available at scale and used routinely by businesses of all sizes, Utkarsh Sinha, Managing Director at advisory firm Bexley Advisors told Financial Express Online.

Among the top sectors where the use of AI accelerated during the pandemic was restaurant as the pandemic precipitated the eateries into looking at ways that could help them optimize their processes right from sales to inventory management and more.

Kabir Suri who runs Azure Hospitality, which owns restaurant chains like Mamagoto, Dhaba, Speedy Chow, etc., has been using AI in the companys operations for the past five years while Covid only reinforced his commitment to AI for efficiency and growth. We have had a direct saving of 30 per cent in past five years along with getting customer insights due to AI that has led to an uptake in revenue as well. Five years back we had around 10 outlets and now have 60 across India, Suri told Financial Express Online.

Subscribe to Financial Express SME newsletter now: Your weekly dose of news, views, and updates from the world of micro, small, and medium enterprises

The company has an in-house AI solution that shows live sales, total transactions, menus, items sold, total consumption per restaurant, etc. The solution captures data from every restaurant throughout the day on a real time basis and consolidates it to show up for analysis on its dashboard. This becomes important for restaurants with chains to understand the consumer-behaviour pattern, the impact of different occasions on business like festivals such as Navratras in North particularly, Christmas in Goa, and some other festivals in South, said Suri.

Moreover, the AI solution at Azure Hospitality helps Suri control the HR module as well. You can look at your salary component, leaves, attendance, holidays, payslips, etc., through a single system every day whenever you want. Basically, AI helps you make better decisions as you grow bigger by minimizing the impact of any uncertainty, Suri added.

Another sector that depends heavily on technology and AI particularly is tourism for purposes ranging from travel booking via chatbots, flight forecasting in terms of the current best price and future prices, recommendations for hotel and cab booking based on travel-related searches, etc.

There is AI at every stage in tourism and aviation, Subhas Goyal, Founder and Chairman at B2B travel company STIC Travel told Financial Express Online. The company is the exclusive General Sales Agent (GSA) a sales representative of a company in a specific region or country for 11 international airlines in India including United Airlines, Air China, Croatia Airlines etc.

STIC has been using for the past five years AI-based Microsoft Dynamics CRM to manage customer relationships, track sales leads, marketing, etc., and streamline administrative processes in sales and marketing. The company is now also implementing a chatbot assistant to answer customer queries on its platform. Goyal noted the standard queries around bookings, holiday searches can be answered by the AI bot while for further details and feedback, there would be manual intervention.

Post-Covid, more MSMEs had started to use primary technology tools at least such as social media, online service aggregators, company websites etc. According to a Crisil survey of around 540 micro and small units released in April this year, over 65 per cent respondents adopted or upgraded their use of online aggregators, social media platforms, and company websites. Among sectors, manufacturing reported higher adoption with 71 per cent respondents adopting or upgrading their use of digital platforms in comparison to 66 per cent respondents in the services sector.

Good technology is invisible. AI/ML will soon form a fundamental layer in all operations and interactions for small businesses. As technology offerings scale, it will soon be easier to get good AI to do certain tasks than to get a human to do it. The impact of this on labor force utilization will be significant, added Sinha.

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National Technology Day: How artificial intelligence is helping MSMEs to optimize processes, accelerate growth - The Financial Express

Machine learning and other artificial intelligence (AI) methods have had immense success with scientific and technical tasks such as predicting how protein molecules fold and recognising faces in a crowd. However, the application of these methods to the humanities are yet to be fully explored.

What can AI tell us about philosophy and religion, for example? As a starting point for such an exploration, we used deep learning AI methods to analyse English translations of the Bhagavad Gita, an ancient Hindu text written originally in Sanskrit.

Using a deep learning-based language model called BERT, we studied sentiment (emotions) and semantics (meanings) in the translations. Despite huge variations in vocabulary and sentence structure, we found that the patterns of emotion and meaning were broadly similar in all three.

This research opens a path to the use of AI-based technologies for comparing translations and reviewing sentiments in a wide range of texts.

The Bhagavad Gita is one of the central Hindu sacred and philosophical texts. Written more than 2,000 years ago, it has been translated into more than 100 languages and has been of interest to western philosophers since the 18th century.

The 700-verse poem is a part of the larger Mahabharata epic, which recounts the events of an ancient war believed to have occurred at Kurukshetra near modern-day Delhi in India.

Read more: Indian philosophy helps us see clearly, act wisely in an interconnected world

The text of the Bhagavad Gita relates a conversation between the Hindu deity Lord Krishna and a prince called Arjuna. They discuss whether a soldier should go to war for ethics and duty (or dharma) if they have close friends or family on the opposing side.

The text has been instrumental in laying the foundations of Hinduism. Among many other things, it is where the philosophy of karma (a spiritual principle of cause and effect) originates.

Scholars have also regarded the Bhagavad Gita as a book of psychology, management, leadership and conflict resolution.

There have been countless English translations of the Bhagavad Gita, but there is not much work that validates their quality. Translations of songs and poems not only break rhythm and rhyming patterns, but can also result in the loss of semantic information.

In our research, we used deep learning language models to analyse three selected translations of the Bhagavad Gita (from Sanskrit to English) with semantic and sentiment analyses which help in the evaluation of translation quality.

We used a pre-trained language model known as BERT, developed by Google. We further tuned the model using a human-labelled training dataset based on Twitter posts, which captures 10 different sentiments.

These sentiments (optimistic, thankful, empathetic, pessimistic, anxious, sad, annoyed, denial, surprise, and joking) were adopted from our previous research into social media sentiment during the onset of the COVID-19 pandemic.

The three translations we studied used very different vocabulary and syntax, but the language model recognised similar sentiments in the different chapters of the respective translations. According to our model, optimistic, annoyed and surprised sentiments are the most expressed.

Moreover, the model showed how the overall sentiment polarity changes (from negative to positive) over the course of the conversation between Arjuna and Lord Krishna.

Arjuna is pessimistic towards the beginning and becomes optimistic as Lord Krisha imparts knowledge of Hindu philosophy to him. The sentiments expressed by Krishna show that with philosophical knowledge of dharma and mentorship, a troubled mind can get clarity for making the right decisions in times of conflict.

One limitation of our model is that it was trained on data from Twitter, so it recognises joking as a common sentiment. It applies this label inappropriately to some parts of the Bhagavad Gita. Humour is complicated and strongly culturally constrained, and understanding it is too much to ask of our model at this stage.

Due to the nature of the Sanskrit language, the fact that the Bhagavad Gita is a song with rhythm and rhyme, and the varied dates of the translations, different translators used different vocabulary to describe the same concepts.

The table below shows some of the most semantically similar verses from the three translations.

Our research points the way to the use of AI-based technologies for comparing translations and reviewing sentiments in a wide range of texts.

This technology can also be extended to review sentiments expressed in entertainment media. Another potential application is analysing movies and songs to provide insights to parents and authorities about the suitability of content for children.

The author would like to acknowledge the invaluable contribution of Venkatesh Kulkarni to this research.

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AI, philosophy and religion: what machine learning can tell us about the Bhagavad Gita - The Conversation

Artificial Intelligence in Healthcare market analysis report highlights the idea of high level analysis of major market segments and recognition of opportunities. Market analysis and market segmentation has been reviewed here in terms of markets, geographic scope, years considered for the study, currency and pricing, research methodology, primary interviews with key opinion leaders, DBMR market position grid, DBMR market challenge matrix, secondary sources, and assumptions. This market report carries out comprehensive analysis of company profiles of key market players that offers a competitive landscape. Artificial Intelligence in Healthcare market document exhibits important product developments and tracks recent acquisitions, mergers and research in the healthcare industry by the top market players.

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Leading Key Players Operating in the Artificial Intelligence in Healthcare Market Includes:NVIDIA Corporation (US), Intel Corporation (US), IBM (US), Google LLC (US), Microsoft (US), General Vision Inc. (US), Johnson & Johnson Services, Inc. (US), Siemens Healthcare GmbH (Germany), Medtronic (Ireland), CloudMedx Inc. (US)

Market Analysis and Insights: Global Artificial Intelligence in Healthcare Market:

This Artificial Intelligence in Healthcare market report provides details of new recent developments, trade regulations, import export analysis, production analysis, value chain optimization, market share, impact of domestic and localised market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growths, application niches and dominance, product approvals, product launches, geographic expansions, technological innovations in the market. To gain more info on Data Bridge Market Research Artificial Intelligence in Healthcare market contact us for an Analyst Brief, our team will help you take an informed market decision to achieve market growth.

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Market Analysis and Insights: Global Artificial Intelligence in Healthcare Market:

This Artificial Intelligence in Healthcare market report provides details of new recent developments, trade regulations, import export analysis, production analysis, value chain optimization, market share, impact of domestic and localised market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growths, application niches and dominance, product approvals, product launches, geographic expansions, technological innovations in the market. To gain more info on Data Bridge Market Research Artificial Intelligence in Healthcare market contact us for an Analyst Brief, our team will help you take an informed market decision to achieve market growth.

Artificial Intelligence in Healthcare Market, By Region:

Global Artificial Intelligence in Healthcare market is analyzed and market size insights and trends are provided by country, product as referenced above.

The countries covered in the Artificial Intelligence in Healthcare market report are the U.S., Canada and Mexico in North America, Germany, France, U.K., Netherlands, Switzerland, Belgium, Russia, Italy, Spain, Turkey, Rest of Europe in Europe, China, Japan, India, South Korea, Singapore, Malaysia, Australia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific (APAC) in the Asia-Pacific (APAC), Saudi Arabia, U.A.E, South Africa, Egypt, Israel, Rest of Middle East and Africa (MEA) as a part of Middle East and Africa (MEA), Brazil, Argentina and Rest of South America as part of South America.

North America dominates the Artificial Intelligence in Healthcare market because of the rise in the cases of arrhythmic diseases, favorable reimbursement policies for patients, high demand for advanced treatment methods and developed healthcare infrastructure in the region. Asia-Pacific is estimated to grow in the forecast period due to the high prevalence of cardiovascular diseases, increase in adoption of advanced digital devices, large population and launch of new innovative products.

Table of Contents

Global Artificial Intelligence in Healthcare Market Size, status and Forecast to 2029

1 Market summary2 Manufacturers Profile3 Global Artificial Intelligence in Healthcare Sales, Overall Revenue, Market Share and Competition by Manufacturer4 Global Artificial Intelligence in Healthcare market analysis by numerous Regions5 North America Artificial Intelligence in Healthcare by Countries6 Europe Artificial Intelligence in Healthcare by Countries7 Asia-Pacific Artificial Intelligence in Healthcare by Countries8 South America Artificial Intelligence in Healthcare by Countries9 Middle east and Africas Artificial Intelligence in Healthcare by Countries10 Global Artificial Intelligence in Healthcare Market phase by varieties11 Global Artificial Intelligence in Healthcare Market phase by Applications12 Artificial Intelligence in Healthcare Market Forecast13 Sales Channel, Distributors, Traders and Dealers14 Analysis Findings and Conclusion15 Appendix

Check Complete Table of Contents with List of Table and Figures @ https://www.databridgemarketresearch.com/toc/?dbmr=global-artificial-intelligence-in-healthcare-market&AZ

What are the market opportunities, market risks, and market overviews of the Artificial Intelligence in Healthcare Market?

Who are the distributors, traders, and merchants in the Artificial Intelligence in Healthcare Market?

What is the analysis of sales, income, and prices of the leading manufacturers in the Artificial Intelligence in Healthcare Market?

What are the Artificial Intelligence in Healthcare market opportunities and threats faced by the global Artificial Intelligence in Healthcare Market vendors?

What are the main factors driving the worldwide Artificial Intelligence in Healthcare Industry?

What are the Top Players in Artificial Intelligence in Healthcare industry?

What is the analysis of sales, income, and prices by type, application of the Artificial Intelligence in Healthcare market?

What is regional sales, income, and price analysis for Artificial Intelligence in Healthcare Market?

Research Methodology: GlobalLiquid Chromatography Devices Market

Data collection and base year analysis is done using data collection modules with large sample sizes. The market data is analyzed and estimated using market statistical and coherent models. Also market share analysis and key trend analysis are the major success factors in the market report. To know more please request an analyst call or can drop down your enquiry.

The key research methodology used by DBMR research team is data triangulation which involves data mining, analysis of the impact of data variables on the market, and primary (industry expert) validation. Apart from this, data models include Vendor Positioning Grid, Market Time Line Analysis, Market Overview and Guide, Company Positioning Grid, Company Market Share Analysis, Standards of Measurement, Global versus Regional and Vendor Share Analysis.

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Artificial Intelligence in Healthcare Market Global Analysis, Opportunities and Forecast to 2029 Queen Anne and Mangolia News - Queen Anne and...

Artificial Intelligence in Medical Imaging Market gives a detailed study of the industry, replete with a spec sheet, market segmentation, and other information, in Data Bridge Market Research. The study analyses the potential and present market scenario for the projected period of 2022-2028, offering data and updates on the worldwide Artificial Intelligence in Medical Imaging markets major segments. An influential Artificial Intelligence in Medical Imaging market report is a window to the industry which explains what market definition, classifications, applications, engagements and market trends are. By keeping end users at the centre point, a team of researchers, forecasters, analysts and industry experts work exhaustively to formulate this market research report. The market is supposed to grow during the forecast period due to growing demand at the end user level. According to this market report, new highs will take place in the Artificial Intelligence in Medical Imaging market. Artificial Intelligence in Medical Imaging market survey report offers the best professional in-depth study on the current state for the industry.

Artificial intelligence in medical imaging market is expected to gain market growth in the forecast period of 2021 to 2028. Data Bridge Market Research analyses the market to reach at an estimated value of USD 1,579.33 million and grow at a CAGR of 4.11% in the above-mentioned forecast period. Increased numbers of diagnostic procedures drives the artificial intelligence in medical imaging market.

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Top Key Players Included in This Report:

BenevolentAI, OrCam, Babylon, Freenome Inc., Clarify Health Solutions, BioXcel Therapeutics, Ada Health GmbH, GNS Healthcare, Zebra Medical Vision Inc., Qventus Inc, IDx Technologies Inc., K Health, Prognos, Medopad Ltd., Viz.ai Inc., Voxel Technology, Renalytix AI plc, Beijing Pushing Technology Co. Ltd., PAIGE, mPulse Mobile, Suki AI Inc., BERG LLC, Zealth Inc., OWKIN INC., and Your.MD Ltd. UK

The Study Is Segmented By Following:

By Technology (Deep Learning, Computer Vision, NLP, Others), Offering (Hardware, Software, Services), Deployment Type (On-Premise, Cloud), Application (X-Ray, CT, MRI, Ultrasound, Molecular Imaging), Clinical Applications (Breast, Lung, Neurology, Cardiovascular, Liver, Prostate, Colon, Musculoskeletal, Others), End-User (Hospitals, Clinics, Research Laboratories, Others)

To receive extensive list of important regions, ask for TOC here @https://www.databridgemarketresearch.com/toc/?dbmr=global-artificial-intelligence-in-medical-imaging-market&AS

Geographic Segment Covered in the Report:

The Artificial Intelligence in Medical Imaging report provides information on the market area, which is divided into sub-regions and countries / regions. In addition to the market share in each country and sub-region, this chapter in this report also contains information on profit opportunities. This chapter of the report mentions the market share and growth rate for each region, country and sub-region during the estimated period.

The study has covered and anatomized the eventuality of Worldwide Artificial Intelligence in Medical Imaging Industry and provides statistics and information on market dynamics, growth factors, crucial challenges, major motorists & conditions, openings and Forecast 2028. Businesses can confidently rely on the information mentioned in the reliable Artificial Intelligence in Medical Imaging Market analysis report as it is derived only from the valuable and genuine resources. This market research report delivers comprehensive analysis of the market structure along with the estimations of the various segments and sub-segments of the market. The transformation in market landscape is analyzed in the finest Artificial Intelligence in Medical Imaging marketing report which is mainly observed due to the moves of key players or brands which include developments, product launches, joint ventures, mergers and acquisitions that in turn change the view of the global face of the industry.

The Report Answers Questions Similar as:

What are the modes and strategic moves considered suitable for entering the Artificial Intelligence in Medical Imaging Industry?

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Which are the products/ parts/ operations/ areas to invest in over the cast period in the Artificial Intelligence in Medical Imaging Industry?

What are the inhibiting factors and impact of COVID-19 shaping the Artificial Intelligence in Medical Imaging Market during the cast period?

What are the technology trends and nonsupervisory fabrics in the Artificial Intelligence in Medical Imaging Market?

Whats the request size and Forecast of the Artificial Intelligence in Medical Imaging Market?

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The report has been prepared using the latest methods and tools for primary and secondary research. Our analysts rely on government documents, white papers, press releases, reliable investor information, financial and quarterly reports, and public and private interviews to gather data and information about the market in which they operate.

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Data Bridge Market Research set forth itself as an unconventional and neoteric Market research and consulting firm with unparalleled level of resilience and integrated approaches. We are determined to unearth the best market opportunities and foster efficient information for your business to thrive in the market. Data Bridge endeavours to provide appropriate solutions to the complex business challenges and initiates an effortless decision-making process. Data bridge is an aftermath of sheer wisdom and experience which was formulated and framed in the year 2015 in Pune.

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Artificial Intelligence in Medical Imaging Market In Depth Analysis Along with Statistics and Forecast 2022-2029 Queen Anne and Mangolia News - Queen...