The post IndiaFirst Tech Startup Conclave & Awards’23 Success and Upcoming Events appeared first on AICRA.

Farmers’ interest in cutting-edge technology has generated a market for agricultural robots and drones worth $23.06 billion by 2028. In open fields and greenhouses, robots can already perform a range of tasks. From weeding and harvesting to sampling and spraying, advanced technology help farmers cultivate healthy crops in a financially and environmentally sustainable manner. While we are only at the beginning of this trend, there are already numerous examples of how agricultural robots might affect food production.

A Tertile robot that makes weeding easier than ever

In many homes, there are food and flower gardens. While this activity is fun and offers nutritious food, it is also a constant battle to eliminate weeds that deprive plants of water and nutrients. Weeding can be time-consuming and difficult, which is why Franklin Robotics, a technology company, has come up with a solution. Tertill, a 1.1-kg heavy robot from this American startup, trims itself as it walks about the garden. The four-wheeled device uses sensors to locate its target and a nylon thread on its underside to pull weeds out.

The robot’s solar panel can charge its battery when it is exposed to direct sunlight. The device can also be charged using a USB cord. To keep Tertill from fleeing and allowing it to patrol in a set area, the robot’s owners must enclose their garden with at least a five-centimeter-high wall. Although the machine is weather-resistant, rocks, mud, and uneven terrain might slow it down. Franklin Robotics has also developed a smartphone app that allows clients to monitor the robot’s activities as well as the weather in the garden.

Smart core to check soil samples

Crop production in large open fields faces unique challenges. Farmers must study soil samples to determine the number of nutrients required in certain field areas. Using the proper amount of nutrients can increase yields, cut costs, and minimize surface and groundwater pollution. Using too many or too few chemicals, on the other hand, may have a negative impact on agricultural productivity. Farmers often collect soil samples by hand, which is a time-consuming and error-prone procedure despite the necessity of the process.

Troy Fiechter and Drew Schumacher, both Purdue University graduates, have developed a superior soil analysis method. The duo developed SmartCore, an autonomous robot that navigates fields and collects samples from specific locations. Obstacle detection algorithms and GPS lead the equipment to take samples from the same region each year, allowing farmers to track the changes in their soil. After the sample is taken, SmartCore transports it to the edge of the field for shipment to a lab.

Swarm robots to plant seeds

The robots use satellite navigation to transmit their exact location, which helps operators optimize planting operations. Controlling and optimizing the machines is the responsibility of the OptiVisor algorithm. Fendt’s field robotic system is energy efficient due to its low weight and low-maintenance engine. The grid, farmers’ biogas facilities, wind energy, or fuel cells can all be used to charge the battery. As one of the pioneers in the development of farming tractors in the early twentieth century, Fendt believes that its latest technology solutions may help agriculture take the next big step forward.

The robots send their exact location via satellite navigation, which aids operators in optimizing planting operations. The OptiVisor algorithm is in charge of controlling and optimizing the machines. Because of its low weight and low-maintenance engine, Fendt’s field robotic system is energy efficient. The battery can be charged using the grid, farmers’ biogas facilities, wind energy, or fuel cells. Fendt believes that its latest technology solutions can assist agriculture to take the next significant stride ahead, as one of the pioneers in the creation of farming tractors in the early twentieth century.

Mamut robots to spot plant diseases

Experiments are also being conducted by Cambridge Consultants, a subsidiary of the French engineering firm Altran. The high-tech company is working on Mamut, an AI-powered autonomous robot that uses a stereo camera, an AI system, lidar, and a compass to map and navigate a natural area. Inspection sensors and six 360-degree cameras, one of which is a multispectral imaging camera, are included in the device. Mamut wanders around farm fields, gathering visual data and creating maps to aid farmers in spotting diseases, estimating crop yields, and selecting the best harvesting time. Unlike drones, the robot goes beneath the plants to get more detailed information.

Cambridge Consultants is still training the AI to monitor crops, which is a difficult task. The robot travels around 24 kilometers in eight hours, during which time the developers evaluate functions including collision avoidance, route planning, and route following. Although high-value crop growers expressed the most interest, the company thinks that the Robotics-as-a-Service commercial model will allow the product to reach a larger market. Farmers would only pay for a one-time service, and the technology supplier would own the robot. This allows consumers to adjust their robot usage based on their current needs and cost constraints.

Naio technologies robots

Naio Technologies has a number of robots that can not only operate as the ideal farm laborer, but also conserve and protect the environment.
The robots are capable of weeding, hoeing, and harvesting assistance. “We aim to provide access to the latest technologies to all partners in the agriculture industry, to help grow healthier, more abundant, and ecologically friendly crops,” the team says.

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Global Artificial Intelligence Summit & Awards

If you or your organization is building cutting-edge AI solutions to adapt to a world driven by technology, Please nominate yourself/or your company for GAISA AWARDS 2022.

Please follow the below-given link to nominate:https://gaisa.in/award

All further details regarding the event will be updated on the official website of Gaisa 2022. Please Visit: https://gaisa.in/

The post Top AI devices for agriculture lands appeared first on AICRA.

Artificial intelligence improves the lives of patients, physicians, and hospital managers by completing jobs usually performed by people, but in a fraction of the time and at a fraction of the expense.

The AI sector, one of the world’s fastest-growing sectors, was valued at over $600 million in 2014 and is expected to reach $150 billion by 2026.

• Early disease detection with AI

Delays can mean the difference between life and death in healthcare, so Viz.ai assists care teams in responding faster with AI-powered healthcare solutions. The company’s AI technologies can detect problems and rapidly alert care teams, allowing doctors to explore choices, make faster treatment decisions, and ultimately save lives.

• Accurate cancer diagnosis

PathAI is working on machine learning technologies to help pathologists make a more accurate diagnosis. The company’s current objectives include reducing cancer diagnostic errors and developing methods for personalized medical treatment. PathAI has collaborated with pharmaceutical companies such as Bristol-Myers Squibb and the Bill and Melinda Gates Foundation to expand its AI technology into other healthcare industries.

• AI Symptom checker

Buoy Health is a symptom and cure checker powered by AI that uses algorithms to identify and treat illness. It works like this: a chatbot listens to a patient’s symptoms and health problems, then directs the patient to the appropriate care based on its diagnosis. Harvard Medical School is just one of several hospitals and healthcare institutions that utilize Buoy’s AI to help identify and treat patients faster.

• Deep learning for actionable insights

Enlitic develops deep learning medical solutions that improve radiology diagnosis. The deep learning platform of the firm analyses unstructured medical data (radiology pictures, blood tests, EKGs, genomes, and patient medical history) to provide clinicians with superior insight into a patient’s real-time needs. Enlitic was awarded the world’s fifth smartest artificial intelligence startup by MIT, ahead of Facebook and Microsoft.

• AI IN BIOPHARMACEUTICAL DEVELOPMENT

BioXcel Therapeutics uses artificial intelligence to discover and develop novel drugs in the disciplines of immuno-oncology and neurology. Furthermore, AI is used in the company’s medication re-innovation initiative to uncover new applications for current treatments or to identify new patients. The work of BioXcel Therapeutics in AI-based medication discovery has been recognized as one of the “Most Innovative Healthcare AI Developments of 2019.”

• AI, CLOUD-BASED DIGITAL DRUG DISCOVERY

XtalPi’s ID4 platform predicts the chemical and pharmaceutical characteristics of small-molecule compounds for drug discovery and development by combining AI, the cloud, and quantum physics. Furthermore, the business claims that its crystal structure prediction technique (also known as polymorph prediction) predicts complicated chemical systems in days rather than weeks or months. Google, Tencent, and Sequoia Capital are among the company’s major investors.

• NEURAL NETWORK FOR CLINICAL TRIALS

Atomwise uses artificial intelligence to combat some of today’s most dangerous diseases, such as Ebola and multiple sclerosis. AtomNet, the company’s neural network, aids in the prediction of bioactivity and the identification of patient characteristics for clinical studies. Each day, Atomwise’s AI system evaluates between 10 and 20 million DNA molecules and claims to be 100 times quicker than traditional pharmaceutical businesses.

• DEEP LEARNING FOR TARGETED TREATMENT

BenevolentAI’s major objective is to bring the appropriate therapy to the right patients at the right time by leveraging artificial intelligence to improve target selection and reveal previously unknown insights using deep learning. BenevolentAI is collaborating with large pharmaceutical companies to license medications, as well as charity to produce readily transportable treatments for uncommon diseases.

• PERSONALIZED HEALTHCARE PLANS WITH AI

The Cleveland Clinic collaborated with IBM to integrate artificial intelligence into its IT capabilities. To improve the patient experience, the world-renowned hospital is utilizing AI to collect information from billions of administrative and health record data points. This collaboration between AI and data is assisting the Cleveland Clinic in personalizing healthcare programs on an individual basis.

• FASTER HOSPITAL VISITS, COURTESY OF AI

The Johns Hopkins Hospital has announced a collaboration with GE to increase the efficiency of patient operational flow using predictive AI techniques. A task team, aided by artificial intelligence, swiftly prioritized hospital work to benefit all patients. Since adopting the approach, the institution has witnessed a 60% increase in inpatient admissions and a 21% rise in inpatient discharges before midday, resulting in a speedier and more favorable patient experience.

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Global Artificial Intelligence Summit & Awards

If you or your organization is building cutting-edge AI solutions to adapt to a world driven by technology, Please nominate yourself/or your company for GAISA AWARDS 2022.

Please follow the below-given link to nominate:https://gaisa.in/award

All further details regarding the event will be updated on the official website of Gaisa 2022. Please Visit: https://gaisa.in/

The post 10 examples of AI in healthcare that will make you feel better about the future appeared first on AICRA.

What is Artificial Intelligence in healthcare?

In the health care industry, AI-based medical devices could:

• Automate tasks, synthesize data from multiple sources, and pinpoint trends
• Process and analyze information from wearable sensors and identify disease or the onset of medical conditions
• Predict which patients are at an increased risk for disease, complications, or negative outcomes based on their medical records
• Support research by evaluating large amounts of data and monitoring treatment efficacy

Trends in Medical Devices with Artificial Intelligence

As technology advances, medical device companies are developing AI medical devices that serve three main functions:

Chronic disease management

Artificial intelligence-powered medical devices could monitor patients and deliver treatment or medication as needed. Diabetes patients, for example, could wear sensors to monitor their blood sugar levels and administer insulin to regulate them.

Medical imaging

Companies are developing artificial intelligence-powered medical devices to improve image quality and clarity in medical imaging. These devices would also reduce a patient’s radiation exposure.

Internet of Things (IoT) 

The Internet of Things (IoT) for medical devices is a network of wireless, interconnected, and linked digital devices used by medical professionals to manage data, keep patients informed, cut costs, monitor patients, and work more effectively and efficiently. Companies are collaborating with medical devices equipped with artificial intelligence to improve patient outcomes.

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Global Artificial Intelligence Summit & Awards

If you or your organization is building cutting-edge AI solutions to adapt to a world driven by technology, Please nominate yourself/or your company for GAISA AWARDS 2022.

Please follow the below-given link to nominate:https://gaisa.in/award

All further details regarding the event will be updated on the official website of Gaisa 2022. Please Visit: https://gaisa.in/

The post What Are The Uses Of AI In Medical Devices? appeared first on AICRA.

Anton Chekov practiced medicine in a time when diseases are just phenotypes. Today, the healthcare system has progressed significantly, and big data has the biggest impact on it. Data-driven thinking and methods have now opened the doors for personalized healthcare systems.

 Tailored experiences have become the standard in today’s environment. From the music we listen to the TV episodes we watch to the purchases we make, these are frequent suggestions based on data acquired about us, such as our purchasing and streaming histories.

The goal of a personalized healthcare system is to enable patients to have a personalized assessment of their health including a list of lifestyles change recommendations based on predicted health risks.

We haven’t completely reached the realm of personalized medicine yet, but it’s a few years ahead.AI powered Big data analytic tools will be the driving force for achieving this. IDC research predicts the global data sphere will grow to 175 zettabytes by 2025. Medical practices leverage the power of machine learning techniques to identify trends, patterns, and anomalies in the data to help experts make better diagnoses and treatment decisions. Complete personalized healthcare will only be possible when clinicians can understand which gene mutations or differences may cause specific illnesses (Since each one of us has a unique variation of the human genome)

To accomplish the comprehensive perspective of each patient, we must collect data from health records and lifestyle habits from many sources regularly and combine it into a unified perspective. This data is critical for understanding and analyzing each patient’s demands, which may be used to guide both medication development and the sort of treatment that a patient receives.

AI and machine learning help pharmaceutical companies gather, store, and analyze massive data sets easier than traditional procedures. It enables quick research based on genetic variation data from different patients and develops personalized medicines.

In a NutShell

In an ideal world, we would like to eliminate diseases. We may introduce preventative measures and therapies far earlier, sometimes even before a patient begins to exhibit symptoms, if we have a greater understanding of why, how, and in which people’s illnesses develop. Personalized medicine has the potential to enhance and possibly save many people’s lives, and artificial intelligence and machine learning will be the driving forces behind future discoveries.

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Global Artificial Intelligence Summit & Awards

If you or your organization is building cutting-edge AI solutions to adapt to a world driven by technology, Please nominate yourself/or your company for GAISA AWARDS 2022.

Please follow the below-given link to nominate:https://gaisa.in/award

All further details regarding the event will be updated on the official website of Gaisa 2022. Please Visit: https://gaisa.in/

The post How AI Helps in Personalized Healthcare? appeared first on AICRA.

How is the Mobility Landscape Changing?

Mobility technology is a driving force in making transportation more user-friendly and accessible. Smart mobility is propelling city economic growth, improving logistics supply chains, increasing labor market accessibility, and opening up new markets for businesses.

Smart mobility technologies such as shared bikes and e-scooters are changing the urban mobility ecosystem, and cities are responding by building dedicated lanes and improved transportation systems.

Why is AI Important to Mobility?

Smart cities all over the world strive to provide more efficient and environmentally friendly transportation options. Transportation options such as Connected Autonomous Vehicles (CAV) and drone deliveries will soon dominate the mobility landscape. Because of the constant increase in traffic, traffic-induced noise and pollution, and the limited availability of space in urban areas, the way people and goods move will have to change dramatically.

Machine learning, artificial intelligence, and data analytics are used by organizations to identify, predict, and solve mobility challenges. Companies and cities can use artificial intelligence technology to transition to autonomous mobility — highly personalized and environmentally friendly systems.

Eight Ways AI is Impacting Mobility

Here are the top eight ways in which AI is changing mobility.

1. Smart Grid Management

Electric cars need to charge their batteries now and then. But many people don’t know that they can also resell the power stored in electric cars’ batteries back to the electrical grid. AI can predict the best times to charge your electric vehicle and when to use it for Vehicle-to-Grid (V2G) “un-charging.” Smart grid management enables drivers to reduce costs and increases the efficiency and stability of the entire grid.

2. Transportation Systems

Mobility-as-a-Service (MaaS) enables users to quickly plan trips using different means of transportation. With MaaS, commuters can book, manage, and pay for rides using personal devices like smartphones or other connected devices.

When powered by AI, MaaS systems gain significant benefits, like fully autonomous driving and smart tracking. Additionally, MaaS with AI-based controllers can optimize, monitor, and coordinate autonomous car fleets while offering great options to individual users.

AI-based MaaS can also be applied to ride-sharing, enabling users to share autonomous cars across an optimized route in a much cheaper and safe way. Ride-sharing users can also get a greater social experience when riding with people of similar interests. This can change traditional transportation networks and transform the way people commute.

3. Driver Tracking

A driving monitoring system uses cameras to monitor the alertness of the driver. The system detects the driver and assesses his or her level of concentration. When a driver is distracted, the system alerts them. Drivers can use AI-based systems to customize vehicle settings such as seat position, temperature, and mirrors.

This technology is based on AI algorithms that monitor head position, eye openness, and other alertness indicators. If necessary, the system alerts the driver to refocus or take a break. Posture management allows for the most effective deployment of airbags in the event of an accident.

4.Self-Driving Vehicles

Despite public skepticism, driverless vehicles are slowly but steadily making their way into the transportation sector. While the majority of self-driving vehicle companies are still running pilot projects to ensure passenger safety, some have already deployed vehicles on public roads. As this technology advances, self-driving vehicles may gain widespread acceptance and become the norm for consumers.

Computer vision and deep learning systems are the brains of a self-driving car. These systems are responsible for processing and giving context to all the information that comes from the sensors. A self-driving car’s data is coming from multiple different sources like cameras, radar, and Light Detection and Ranging (LiDAR). Computer vision systems need to process data from all these different sources. As a result, environment information gathering becomes a very comprehensive task.

5.Traffic Management

Every day, people face traffic congestion issues. AI has matured to the point that it can now solve this problem. Cameras and sensors are embedded throughout roads to collect traffic data. These details are sent to cloud-based big data analytics and AI-based systems. These big data tools perform an analysis to identify traffic patterns and predict congestion.

Drivers can also use AI to improve road safety and reduce wait times. Important traffic information such as accidents, road closures, and the shortest routes to a destination can assist drivers in traveling without encountering traffic congestion.

6.Manufacturing

AI is also changing the way cars are built. For example, Kia recently introduced the Hyundai Vest Exoskeleton (H-VEX) wearable robot. The robot is used to improve car assembly lines as well as to protect factory workers.

Another example is Automated Guided Vehicles (AVGs), which are designed to transport materials without the use of humans. Welding and painting robots powered by AI can detect manufacturing flaws and adjust their processes accordingly. These AI-powered robots make automobile manufacturing safer, more efficient, and less expensive.

7.Insurtech

Technology applications in insurance, also known as insurtech apps enable drivers to file their damage assessments after an accident and driver risk assessments based on risk factors filtered out of big data.

8.Smart Cities

AI-based systems have a huge impact on the way cities expand and grow. For instance, autonomous vehicles can initiate a de-urbanization trend. Autonomous vehicles offer a cheaper, faster, and safer way to commute. People can live outside of cities and use self-driving cars to get to work quickly. And since they aren’t driving, they can be fully productive during the ride, using commute time to continue working while the AI drives the vehicle

Conclusion

The transportation industry is ever-changing. Artificial intelligence and machine learning are already reshaping the automotive industry. AI-based systems aid in the reduction of traffic accidents and fatalities, the reduction of manufacturing costs, and the improvement of service levels. On the other hand, when businesses fail to adapt and follow the new trend, AI-based automation and systems can result in technological unemployment.

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Global Artificial Intelligence Summit & Awards

If you or your organization is building cutting-edge AI solutions to adapt to a world driven by technology, Please nominate yourself/or your company for GAISA AWARDS 2022.

Please follow the below-given link to nominate:https://gaisa.in/award

All further details regarding the event will be updated on the official website of Gaisa 2022. Please Visit: https://gaisa.in/

The post How AI is Changing the Mobility Landscape appeared first on AICRA.

We’ve collected a list of top-tier universities and online forums that provide Best Artificial Intelligence courses (Best AI Courses) to help you gain this knowledge and achieve your career goals.

Artificial Intelligence Certification Program by Stanford University

This is the best artificial intelligence course, and it’s ideal for students studying computer programming and language programming, as well as software engineers who will eventually work with AI. Stanford University offers an AI certification programme taught by famous Professor Andrew Ng.

The course covers topics such as:

• Machine learning, knowledge representation.
•  Logic and probabilistic models.
• Robotics, natural language processing, and visual learning.

Artificial Intelligence A-Z

This course provides a thorough knowledge of Artificial Intelligence, such as Machine Learning, Data Science, and Deep Learning, to create AI designs for real-world applications.

The course covers topic such as:

• Artificial Intelligence, it’s working, and its uses.
• Artificial Intelligence Designs.
• Intuition Q-learning, Deep Q-learning, and Deep Convolutional Q-learning.
• Learning to work with A3C.
• Control advanced AI models.
• Build Virtual Self-driving cars.
• AI programming to test games and defeat them.
•  Actively solving real problems in the world by using various AI designs.

AI course for Everyone by Coursera

This course is offered by deeplearning.ai, which was founded by Andre Ng of Stanford University, the founder of Google Brain and a prominent Adjunct professor.

The course covers topics such as:

• Machine learning and what its functions are.
• The technical know-how of machine learning and non-technical explanations of deep learning.
• Short quizzes to assist in grasping new and difficult concepts

Introduction to Artificial Intelligence with Python by EdX

Harvard University brings forth this incredible opportunity to obtain a Verified Certificate in using AI with Python to understand and implement AI technology using complex mathematical concepts.

The course covers topics such as:

• Probability theories and logical inference to understand the process of creating AI powered devices.
• Knowledge of machine learning, reinforcement learning, neural networks, and various other techniques to process data for building AI.
• To work with Bayesian networks and Markov models.
• Working with Graph Search Algorithms.
• Creating constraint satisfaction designs.

IBM Applied AI certification course by Coursera

This is a great opportunity for people to learn Python, develop virtual assistants and chatbots for their companies, and gain knowledge of neural networks, machine learning, and deep learning.

The course covers topics such as:

• Usage of minimal coding to create AI-powered programs with APIs, Python, and IBM’s Watson AI service.
• Virtual assistants and chatbots to assist customers and clients in the absence of physical customer support for your business.
• Application of vision techniques to upload various design and classification models.

AI application with Watson by edX

This course provides a thorough understanding and application of IBM’s Watson to create smarter AI programs for more realistic and humane responses and functions.

The course covers topics such as:

•  Programming AI to transcribe and evaluate big data sets and apply specific functions to improve efficiency.
• Chatbots to grasp grievances by improving its IQ through Watson’s discovery automatically.
• Designing Watson to analyze tone and customer preferences.

Google AI Powered by Google

Google has designed an education program to help people identify the use of Artificial Intelligence to further the social agenda in our various communities.

The course covers topics such as:

• A basic understanding of machine learning, AI explorable, recommendation systems, and data clustering.
• How to test and debug Machine learning and prepare data.
• Using AI responsibly for the benefit of society.

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Global Artificial Intelligence Summit & Awards

If you or your organization is building cutting-edge AI solutions to adapt to a world driven by technology, Please nominate yourself/or your company for GAISA AWARDS 2022.

Please follow the below-given link to nominate:https://gaisa.in/award

All further details regarding the event will be updated on the official website of Gaisa 2022. Please Visit: https://gaisa.in/

The post Which are the best AI courses available? appeared first on AICRA.

Using data sensors, AI can assist in creating well-thought-out roadmaps for a variety of industries, including waste management, mobility, traffic systems, policing, and healthcare. The promise of such endeavours, however, can only be realised when AI is no longer considered in isolation while developing the city framework. Having a separate conversation about AI systems contradicts the objective.

For smart cities to genuinely transform into “intelligent” settings, AI modelling should be applied in three important areas: monitoring and responsiveness; connectivity and anchoring; and executing predictive simulations to navigate novel circumstances.

• Smart public spaces and parks

AI technology will track visitor behaviour and regulate related systems, such as park upkeep, pavement illumination, and other operating conditions, potentially saving money and enhancing accessibility and safety.

• The Smart Home

The use of AI to domestic tasks like smart roofs, water-saving software, and smarter water use would assist reduce human effort required for daily tasks.

• AI-powered delivery

Applications for AI include the supply of predictive services based on citizen data, the rationalisation of administrative staff based on anticipated service demand, the analysis of migration trends, and AI-based chatbots for grievance redress.

• savvy safety measures

Smart command centres with cutting-edge surveillance systems that could keep an eye on prospective criminal occurrences and the general security of the population might be used AI technology to give safety.

By gathering information from social networking sites and forecasting prospective activities that pose a threat to the public, social media intelligence systems can help to ensure public safety. After the installation of AI-driven security systems, the crime rate in Surat has decreased by 27%.

• Crowd control

The Singaporean government worked with a private company to create a method for anticipating crowd behaviour and possible reactions to incidents. In terms of high crowd activity, crowd size estimation, and object detection, the solution had an accuracy of 85%.

Similar to this, “Kumbh Mela Experiment” was established to forecast crowd behaviour and the potential for a stampede using AI. Over 1,000 CCTV cameras were utilised to monitor movement from the Kumbh Mela’s 32 square kilometre venue, which is the largest religious gathering in the world.

The management of responses and advanced prediction could be aided by other such big data and AI technologies.

• Cyber-attacks

Artificial intelligence (AI) tools can identify flaws and take corrective action to reduce exposure to secure internet platforms that house highly sensitive data.

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Global Artificial Intelligence Summit & Awards

If you or your organization is building cutting-edge AI solutions to adapt to a world driven by technology, Please nominate yourself/or your company for GAISA AWARDS 2022.

Please follow the below-given link to nominate:https://gaisa.in/award

All further details regarding the event will be updated on the official website of Gaisa 2022. Please Visit: https://gaisa.in/

The post How AI Helps In Smart City Developments? appeared first on AICRA.

STEM problem formulation is now a highly laborious process that is difficult to scale. The reason for implementing AI in education is more crucial than ever and may be an attempt to address developmental issues in STEM education as artificial intelligence (AI) is increasingly being used in professional settings.

A major challenge in STEM is the limited number of problems, exercises, and tasks it can take for students to master a skill. Because of the manual effort required, education providers are forced to outsource experts. This typically comes at a high cost and requires large amounts of time to produce a sufficient amount of STEM problems that cover the varying levels, diverse teaching styles, and learning objectives.

As this research is done individually, another element educators need to be increasingly aware of is ensuring all problems are similar in quality, which can be difficult to manage due to human factors and error. AI eliminates human error and reduces the time required for repetitive tasks, giving teachers more time to focus on more creative and targeted content to better suit individual student needs.

TWO PRIMARY ELEMENTS OF AI-BASED LEARNING

Addressing the challenges of STEM, two primary elements of AI-based learning can enhance students’ and teachers’ curricula learning capabilities.

STEM PROBLEM DEVELOPMENT AUTOMATION

Deep neural networks and other machine learning techniques for natural language processing are used to automate the development of STEM problems (NLP). By using the original STEM problem as a template, NLP models can teach an AI-based system to find, analyze, and automatically create a similar problem.

In comparison to a human expert-based approach, this enables the creation of new, high-quality educational content (such as STEM problem sets for exams) more quickly, at scale, and at a lower cost.

When textbook tagging is automated, the process can be used to identify existing problems in textbooks or other source material and tie them to a learning objective associated with Bloom’s Taxonomy, which characterizes higher learning in six stages:

• knowledge

• comprehension

• application

• analysis

• synthesis

• evaluation

Adaptive learning

Adaptive learning often referred to as adaptive teaching, is a teaching strategy in which a machine learning algorithm adapts the learning material to each student’s goals, pace of learning, and aptitude.

The adaptive learning engine needs a selection of STEM challenges to choose from by matching those with students’ needs to tailor STEM content to a learner. As was already established, automated development can be used to solve a wide enough range of STEM challenges.

Educational providers can use customized and scalable content to meet the individual needs of students by utilizing adaptive learning and development automation. AI assists in reducing the manual time, money, and effort that traditionally rested on educators by including automated content. With self-paced, adaptive learning pathways accessible at a cheaper cost and higher quality, this move benefits both the educator and the student.

CHALLENGES TO STEM PROBLEM DEVELOPMENT AUTOMATION

However, technology and challenges go hand in hand. These are some challenges to STEM problem development automation we face today.

• Ingesting Content for Machine Training at Scale – Much STEM content exists, but it has been produced in human-readable rather than machine-sensible formats. Therefore, large-scale content ingestion may be time-consuming and resource-intensive.

• Obtaining High-Quality Content – Content publishers seek to protect their content from use, and public-domain content may be poorly curated or moderated, and therefore may be difficult to trust. The adage of “Garbage In, Garbage Out” holds, that training machines with poor content will lead to poor outcomes.

• Machine Training Time is Long and Costly – Training machines take substantial expert time and significant elapsed time. As time is money, this challenge is one of intellectual and financial resource availability.

• Creating a pedagogical taxonomy – Supervised training uses samples of various pedagogical content knowledge taxonomies. Over time, we will create a universal taxonomy that would cover the learning objectives of the major textbook publishers.

• Developing Appropriate Datasets – For each topic in the taxonomy, we need an associated dataset. This ensures that STEM problems in the dataset will match the parameters in the taxonomy topic.

• Achieving a High Accuracy Rate – A high accuracy rate in STEM problem generation saves both expert training time and the time that experts would need to create problems manually. High error rates consume valuable resources and waste time.

The demand for the implementation of digital tools in education has increased due to the pandemic. 

Instead of concentrating on the monotonous processes of producing problem sets, educators may now offer creative and more targeted learning in response to the increased demand for higher skill sets. Better STEM learning outcomes for more kids are the end consequence.

All-inclusive

Open-source AI software that incorporates commercially available tools, and works with libraries and machine learning algorithms to increase server-side performance is one way that AI is rapidly becoming a community-driven initiative.

With these developments, educators and businesses dedicated to STEM education may employ automation to create high-quality content more rapidly, in larger quantities, and for less money—all of which are advantageous to the student.

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Global Artificial Intelligence Summit & Awards

If you or your organization is building cutting-edge AI solutions to adapt to a world driven by technology, Please nominate yourself/or your company for GAISA AWARDS 2022.

Please follow the below-given link to nominate:https://gaisa.in/award

All further details regarding the event will be updated on the official website of Gaisa 2022. Please Visit: https://gaisa.in/

The post AI In Education Centered On STEM appeared first on AICRA.

The fear of robots replacing humans in job roles is unnecessary. Automation has very less effect on job roles that involve managing people, applying expertise, and social interactions, where machines are unable to match human performance at least for now. There are also some jobs (Mostly jobs in unpredictable environments) that are technically difficult to automate.

People see how automation is replacing humans from different job roles but one important thing that they don’t notice is the new jobs created by automation and new technologies. According to experts, we might see a spur of job creation by 2030.

Globally, we estimate that 250 million to 280 million new jobs could be created from the impact of rising incomes on consumer goods alone, with up to an additional 50 million to 85 million jobs generated from higher health and education spending.

Article: Jobs lost, jobs gained: What the future of work will mean for jobs, skills, and wages from www.mckinsey.com

The investments made in technologies like artificial intelligence are expected to gain by 50 % by 2030, this will create 20 to 30 million jobs globally.

Automation will transform the world and workplace but it is not going to cause any harm to the workforce until and unless employees are ready to adapt to the new scenario and update themselves with new skills required to go hand in hand with automation and technology.

Will there be enough work in the future?

Sufficient economic growth, technological innovations, and investments are enough to create new jobs in the future.

Benefits of automation in workplaces

• Improved safety and security: With automation robots are taking over hazardous jobs such as jobs involving blades, welding, chemical exposure, and more. It’s obvious why human error can have huge consequences in this setting, especially after workers have been on the line for long hours. Automation also reduces human errors in office settings by accurately monitoring unusual cyber activities.
• Product/Service quality: One of the most significant advantages of automation is the elimination of weariness. Furthermore, robots do not require restroom breaks or sick days. They can produce the same thing in the same way for as long as you want, with almost little fluctuation in quality. In an office setting, automation also provides higher product quality. Automation personal assistance can plan for your office meetings and also remind you about your hospital appointment.
• Higher profits: Automation has the potential to create higher profits by accelerating business growth. For example, while the development of ATMs may have reduced bank teller jobs, the number of branches expanded since ATMs made it cheaper to operate each branch. As a result of the capacity to service more clients over long hours at a lower cost, bank teller employment grew overall.
• More value to employees: By automating aspects of your business, you may provide your employees more value and make them more productive. Furthermore, it will most likely lead to more interesting work.
• Freedom from boredom: Automation allows human employees to focus on more critical jobs band free them from busy and repetitive works.

Conclusive thoughts

Automation in the workplace has huge benefits for everyone. It expedites time-consuming activities, enhancing the candidate and employee experience. This boosts revenue, productivity, and the employer brand. While some people are concerned that AI, automation, and robots will take their jobs away, the facts.

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