Industrial AI

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01
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Part
01

AI in Aviation

The most recent AI trends and innovations in the aviation sector related include revenue management, air safety and airplane maintenance, feedback analysis, messaging, contact and workflow automation, mrew Management, fuel efficiency optimization, in-flight sales and food supply, operations improvement and optimization, use of virtual assistants, passenger identification, baggage screening and generative product design.

Methodology

We started the search by looking for recent trends and innovations when it comes to AI technology in the aviation industry. We also looked into different companies that use these innovations and trends. We found the related information in reputable sites such as Datafloq, Future Travel Experience, Social Hospitality, Techopedia, and Forbes. Additionally, we focused more on the industrial applications of these trends and innovations. We concentrated on service processes, operations, maintenance, and product designs of airlines. We also found that the trends and innovations we found were becoming quite popular in the industry because of the recent surge of the AI industry. All information was synthesized and summarized in the research brief below.

Introduction to the AI Market

Artificial intelligence is one of the leading markets in the aviation industry, with an average annual growth of 45.16%. In 2014, the market was valued at $34 million. The value grew to $104 million in 2017 and is predicted to reach $714 million in 2022. Additionally, AI is part of the top ten leading trends in airlines and airports in 2019. Various airlines and airports are now using AI-powered products such as chatbots and virtual assistants. While some airports use Virtual Reality (VR) to create immersive experiences in both the terminal and in-flight. One example of the use of VR in airports is from Iberia, in where the airport uses a “motion sickness-free” VR in-flight entertainment system (IFE) in both its premium lounge in the Madrid Airport and some selected onboard flights. Other airlines using and testing the product are Etihad and Emirates and Alaska Airlines. Etihad and Emirates tested the VR headsets in their lounges, while Alaska Airlines partnered with Skylights to test VR IFE.

AI Trends and Innovations in the Aviation Industry

Service Manufacturing Processes and Operations

AI has ten trending uses in aviation service manufacturing processes and operations. These are revenue management, feedback analysis, messaging, crew management, operations improvement, passenger identification, baggage screening, fuel efficiency optimization, in-flight sales and food supply, and virtual assistants.

AI is used in managing the revenue of individual airlines. AI does this by analyzing flight destinations and adjusting ticket prices through the results of the analyzation. The technology also looks for the best distribution channels and manage customers’ seats. This keeps airlines competitive and customer-friendly. An example of this would be Easyjet, who uses AI to improve their pricing strategy and to manage their inventory. Overall, its impact can be seen in improved pricing and inventory.

Another use of AI is through feedback analysis. Much like revenue, AI can also analyze feedback from customers. This provides the airlines with a list of services to improve on. One of the innovations in this platform is found in the PureStrategy platform in where they use an Automated Neural Intelligence Engine (ANIE) to provide data review, categorization, visualization, and sentiment analysis to airlines. The platform also makes sure that customers have an easy time finding, booking, and paying for their flights. The overall impact of this in the industry is the improvement of on-brand services.

AI is also improving both messaging and workflow in the aviation industry. AI is enhancing communication between passengers and the airline by providing a smooth channel to communicate in certain situations such as flight delay and baggage loss. It overall improves the speed of customer queries. An example of this would be the Coseer program made by Arbot Solutions. The program speeds and simplifies both customer service and employee workflow through the use of algorithms that processes natural language or unstructured texts. Additionally, Southwest uses leverages on this trend by optimizing the interaction between contact center agents and travelers. It also helps streamline existing employee workflow.

AI manages airline crews by managing their flight route, licensing and qualifying individual crew members, choosing the aircraft type and fuel usage, monitoring airplane maintenance schedules, suggesting training requirements, advising work regulations, and assigning and tracking vacations and days off schedule for pilots and flight crew. An example of this would be from Jeppesen, who uses the Crew Rostering Solution to provide predicted and real-time fatigue analytics of their crew members. Overall, it improves workflow, scheduling, and staff management.

AI also optimizes and improves airline operations. An example of this would be from Delta Airlines. The airlines use AI to improve overall customer service and to predict aircraft maintenance. Specifically, for customer service, the airlines use AI technology such as bag tracking technology and self-service bag checking machines. When it comes to analytics, the airline uses the SmartSignal predictive analytics software, BitStew, and Asset Performance Management (APM). These technological innovations are meant to analyze and provide insights to airlines, notably to support maintenance engineers so they can work proactively. The overall impact of this trend in the airline industry is optimizing and improving current operations and maintaining costs of providing innovative customer service.

AI is also used to identify passengers and help with the check-in operations in the airport. For example, Delta Airlines uses advanced AI ticketing kiosks and online check-in with the use of the Fly Delta mobile app. The overall impact of this trend in the industry is efficiently identifying passengers and improving check-in services.

Baggage screening is also another process that AI controls within the industry. For example, American Airlines created the Team Avatar app so passengers may quickly screen their baggage. The app allows passengers to determine the size of their baggage and to know if their baggage could carry extra costs. This trend helps the industry by facilitating and speeding baggage screening.

AI systems also help optimize aircraft fuel efficiency. AI does this by analyzing flight data, which includes identifying route distance and altitude, aircraft type and weight, and the in-flight weather. Southwest is airlines capitalizing on this trend by partnering with GE Aviation and using their flight analytics system to improve the fuel consumption of their fleet of 700 Boeing 737s. The overall impact of this trend is the optimization and monitoring of fuel supply based on distance.

AI also monitors in-flight sales and food supply by defining how many snacks and drinks they must onboard. EasyJet is capitalizing on this trend by using AI to determine if in-flight meals would be enough for all passengers onboard. It does this by analyzing attributes such as weather, types of passengers expected to be on board, and the time of the year. The trend overall improves food supply services in the aviation industry.

Lastly, AI-based virtual assistants help the aviation industry by improving overall productivity and efficiency of their pilots. It does this by reducing repetitive tasks, such as changing radio channels, reading wind forecasts, and providing position information on request. Garmin (US) capitalizes on this trend by using AI-enabled audio panels which help their pilots.

Safety and Maintenance

AI predictive analytics reduce unplanned maintenance and delayed flights in the aviation industry. It does this by managing and analyzing data from aircraft health monitoring sensors. For example, AirTran uses the AI platform known as SynapseMX to manage and utilize both historical and real-time data to better improve and speed up technical decisions within the airline.

Product Design

The airline industry uses AI to efficiently requisition materials and identifies manufacturing methods for their products. On top of this, AI also helps in calculating cost constraints found within these products. From this analysis, AI can then explore all possible alterations for the product, and then it could design various alternatives from it. AI also uses machine learning to see which design works and which does not. It improves overall product design in the industry.
Part
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Part
02

AI in Energy

The recent trends and innovations in the energy sector related to AI include using of robots for exploration of oil and gas, using of drones to process electricity grid data, and using of Al in designing smart manufacturing plants and enhancement of coal-fired power plants. Below are our detailed methodology and deep findings.

METHODOLOGY

To find recent trends and innovations in energy sector related to Al focusing on the manufacturing process, product design or services or maintenance of products within this industry, we began our search by going through credible news articles written or published in the past two years highlighting new innovations and trends in the energy sector. From this strategy, we found the Emerj Artificial Intelligence Research website, the leading industry source for authoritative market research on artificial intelligence, which has information on how Al is being used in the deployment of robots for the ocean exploration of oil and gas. The source also reveals that Al is being used by the Chinese telecom company, Huawei, and China Petroleum and Chemical Corp (Sinopec) to design the Smart Manufacturing Plants. Further, we expanded our search by looking into sources like Industry Week, CB Insight, UK publication ITPRO, among others and we found that the National Grid of the UK is using artificial intelligence to process electricity grid data using drones.

Next, using the information we found in the above strategy, we searched for common or recurrent trends pertaining to innovation in the energy sector related to Al among different companies like ExxonMobil, Total, among others. From this strategy, we found various trends that answers the research criteria. Further, we scoured through various credible publications to find the specific technological aspects that have been implemented in the use of the intelligent robots and drones. We found that the drones are equipped with high-res cameras with infrared capabilities, while some robots have a unified network based on eLTE broadband among other AI capabilities.

Lastly, we searched for the impact or potential impact of the use of Al on the industry on sources stated above. We found that the use of AI has greatly improved the productivity and cost-effectiveness of production/manufacturing.

THE RECENT TRENDS AND INNOVATIONS IN THE ENERGY SECTOR RELATED TO AI

1. The use of AI robots for ocean exploration in the oil and gas industry

Technology being used: The Al robots have been designed with AI capabilities for hydro-carbon exploration and production.

Impact or potential impact on the industry: This innovation has improved productivity and cost-effectiveness, while reducing worker risk.

Examples of companies using this innovation: ExxonMobil

2. The use of AI in the Electricity grid to process drone data

Technology being used: The drones have been equipped with high-res cameras with infrared capabilities. They are then positioned to assess the pylons, check the steelworks, wear and corrosion, and any damage to conductors.

Impact or potential impact on the industry: Drones are a better option when conducting inspections in areas where the use of a helicopter might prove cumbersome or in built-up areas where it is difficult to park them at low altitudes.

Examples of companies using this innovation: British firm BMJ

3. The use of Al in designing Smart Manufacturing Factories

Technology being used: A unified network based on eLTE broadband.

Impact or potential impact on the industry: This innovation has improved the efficiency in the manufacturing sector leading to more business, increased sales, and higher profits.

Examples of companies using this innovation: Huawei collaborating with China Petroleum and Chemical Corp. (Sinopec).

4. Al is being used to enhance coal-fired power plants

Technology being used: A combination of analytics, sensors, and operational data to predict when the critical infrastructure could fail.
Impact or potential impact on the industry: Artificial intelligence checks the equipment and detects failures before they happen, hence, saving money, time, and lives.

Examples of companies using this innovation: GEPower

5. Al is used to map and identify oil and natural gas deposits that are beneath the earth’s surface

Technology being used: Use of the robots that can collect data in real-time and deliver reports on inspection points and analysis around the effectiveness of the locations of interest.

Impact or potential impact on the industry: Environmental conditions are increasingly challenging for workers carrying out hydrocarbon exploration beneath the earth surface. This technology can execute this task while retaining optimal functionality, which is very highly desirable.

Examples of companies using this innovation: Total
Part
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Part
03

AI in Healthcare

Some recent AI innovations in the healthcare sector include medical imaging, AI-assisted robotic surgery, radiology AI, AI machine vision, drug design, and Machine Learning on unlocking bioactive proteins. These AI innovations are trending in the healthcare industry, and they bring a positive impact on the sector by aiding in different healthcare areas like drug design, diagnostic of various diseases, and saving money and time used on research projects in the healthcare sector. Below is our methodology, as well as a deep dive into our findings.

METHODOLOGY

Since the primary focus of this research was to obtain the most recent trends and innovations in the global healthcare sector, we first searched through the Emerj website. This website is a reputable source since it specializes in the top AI trends and innovations and their applications across multiple industries. From this search, we found several articles containing most of the trends and innovations discussed in this brief. The first article located from the Emerj website described the AI medical imaging technology used by GE Healthcare. Other articles found on this website included the application of AI by Aidence, Kheiron Medical, AtomNet, and Nuritas. We then visited the official sites of the healthcare providers and the websites of the AI technologies identified from the Emerj articles to corroborate the information given and dig even deeper. All these healthcare providers leveraged on the recent trends and innovations in AI to streamline their daily operations.

Next, we searched through major news sites, and in the Forbes website, we found an article that describes how AI helps surgeons to perform heart surgeries. To corroborate the information, we visited the project's official website. The AI innovative technologies obtained from this research are rapidly becoming trends as several companies in the healthcare sector are using and research on these AI technologies is increasing as seen in the "companies using AI" or "partners" section of the Emerj articles. This affirmation is corroborated by AI tech experts like CB Insights and TechBullion.

RECENT AI INNOVATIONS in healthcare

Medical Imaging by ge healthcare

One pacesetter in this AI innovation is GE Healthcare. In 2017, GE Healthcare got into a 10-year partnership with NVIDIA, which was to help them integrate their 500,000 imaging devices with the NVIDIA artificial intelligence platform. The technology used in this medical imaging AI innovation is the NVIDIA AI in GE Healthcare’s imaging devices. This innovative AI technology not only increases the speed and accuracy of CT scans, but also improves their clarity and reduces the patient's exposure to radiation.

This AI technology gives the healthcare sector the potential to diagnose diseases faster with a reduced degree of errors. NVIDIA's AI technology has algorithms that are specifically tailored to "reorganize small patterns of organ damage" that are typically missed during a normal scan. This helps significantly in the treatment of liver and kidney lesions. According to GE Healthcare, their new CT system is two times faster than their previous one.

AI-assisted robotic surgery by Hearthlander Surgical

Using locomotion AI technology, robots can be incorporated into surgical procedures to help surgeons in analyzing pre-operation medical records to guide the surgeon during the actual surgery. The robots can also gather data from previous operations to inform new surgery techniques. A study on 379 orthopedic patients who went through AI -assisted robotic surgery revealed that the robot-assisted procedure had five times fewer complications as compared to the procedure when surgeons were working alone.

The latest AI-assisted robotic device by HeartLander also helps surgeons perform minimally invasive surgical procedures on a beating heart's surface. HeartLander's AI-assisted robotic devices for heart surgery have a market potential of $450 million, with almost 550,000 ablations and lead placement surgical procedure done every year globally. This will improve the precision and accuracy of surgical procedures, make protocols more efficient, and reduce patients' exposure to fluoroscopic radiation.

Radiology by AI Aidence

Aidence has an AI software called Veye Chest that uses machine vision and helps radiologists "detect, track, and report on pulmonary nodules." According to Aidence, their software can significantly impact the healthcare industry as it can be integrated with a hospital’s existing reading and reporting software to help radiologists determine the extent of the pulmonary nodules. The Veye Chest software has received the CE marking, allowing it to be applied in hospitals across the European Union. Companies using this AI tech are Ergooi Hospital, Albert Schweitzer Hospital, and British SBRI Healthcare organization.

AI Machine Vision by Kheiron Medical

UK-based Kheiron Medical uses Machine vision (AI Machine learning) to help radiologists conduct mammograms and cut down the "number of false negatives and false positives" from the mammograms. Kheiron Medical received a CE marking in 2018, allowing its use in hospitals across the European Union. The software will help the cancer sector to conduct a more accurate diagnosis.

Drug design by AtomNet

This AI-assisted technology uses convolutional neural networks and a statistical approach to extracts the insights from "millions of experimental affinity measurements and thousands of protein structures." This helps the software in predicting how small molecules bind to form proteins. This AI technology will directly impact the healthcare industry by aiding the drug design of new virus and bacterial pathogens. For example, in 2015, Atomwise collaborated with the University of Toronto and IBM to develop a treatment for the Ebola virus infections that had caused about 11,310 deaths Africa, Europe, and the United States. Companies using AI this way include Merck, DNDI, and CAMH.

Machine learning on unlocking proteins by Nuritas

Using a combination of machine learning AI and genomics, Nuritas can discover and unlock natural Bioactive Peptides which have extraordinary health benefits. As the world's population continues to grow, the management of chronic metabolic diseases is becoming a major problem. Nuritas' Bioactive Peptides have the potential to create new and innovative treatments for the many common illnesses. Companies using Nuritas' innovative AI technology include Enterprise Ireland, Cultivian Sandbox Ventures, European Union, Marc Benioff, VisVires New Protein, Ali Partovi, Enterprise Ireland, and NDRC.

Top trends in use of AI tech in the healthcare sector

Other top AI trends disrupting the healthcare sector include:

  • Image recognition: AI-assisted image recognition revolutionizing diagnostics. Recently, Google DeepMind’s neural networks which use image recognition software were as accurate as of the diagnosis done by medical experts on 50 sight-threatening eye diseases.
  • Deep Learning is being used by Pharma companies in the design of new drugs. For example, Merck partnered with Atomwise and GlaxoSmithKline is partnering with Insilico Medicine.
  • By using AI pattern recognition and machine learning technologies, artificial intelligence is seeking to bridge the gap between a doctor's diagnosis and AI-based diagnosis.
Sources
Sources

From Part 02
Quotes
  • "Artificial intelligence is helping compress and analyze the massive amounts of data that the energy industry produces. "
  • "The energy industry produces massive amounts of data. To turn this data into insights that can improve productivity and cut costs, major energy players — from oil and gas giants, to renewables companies — are turning to artificial intelligence."
Quotes
  • "Today, AI is helping the oil and gas industry chart its future course. Since no previous sources have provided an in-depth look at the impact of AI among the leading oil and gas companies, we set out in this week’s research to help answer questions that oil and gas leaders are asking: What types of AI applications are currently in use by leading oil and gas companies such as ExxonMobil and Shell? What (if any) results have been reported on AI applications implemented by leading companies in the oil and gas industry?"