Sunday, October 4, 2020

Brainware University, India Webinar's International Conference: The Impact Of Artificial Intelligence In Modern Society Cannot Be Under-estimated- H.E, UNESCO Laureate, Professor Sir Bashiru Aremu, The Vice Chancellor Crown University International Chartered Inc.




By Victor Bieni



UNESCO Laureate, and a World Acclaimed Distinguished Professor of Information and Communication and Technology, Professor Sir Bashiru Aremu has said that Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals.

Professor Sir Bashiru Aremu who doubled as the Vice Chancellor Crown University International made this disclosure during an International Webinar Conference meeting of Crown University International Chartered with her partner, Brainware University, India recently while he stated that,
Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.
According to him the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving".

The ICT Professor added that machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect".

He said: "A quip in Tesler's Theorem says "AI is whatever hasn't been done yet.
For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology".

"Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go), autonomously operating cars, intelligent routing in content delivery networks, and military simulations".

Speaking further, Professor Aremu noted that, Artificial intelligence was founded as an academic discipline in 1955, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success and renewed funding, also that following the history, AI research has been divided into sub-fields that often fail to communicate with each other.

In his words: "These sub-fields are based on technical considerations, such as particular goals (e.g. "robotics" or "machine learning"), the use of particular tools ("logic" or artificial neural networks), or deep philosophical differences.
Sub-fields have also been based on social factors (particular institutions or the work of particular researchers)".

"Artificial intelligence in healthcare is the use of complex algorithms and software in other words artificial intelligence (AI) to emulate human cognition in the analysis, interpretation, and comprehension of complicated medical and healthcare data. Specifically, AI is the ability of computer algorithms to approximate conclusions without direct human input".

"Radiology:
The ability to interpret imaging results with radiology may aid clinicians in detecting a minute change in an image that a clinician might accidentally miss. A study at Stanford created an algorithm that could detect pneumonia at that specific site, in those patients involved, with a better average F1 metric (a statistical metric based on accuracy and recall), than the radiologists involved in that trial.
Several companies (icometrix, QUIBIM, Robovision, ...) have popped up that offer AI platforms for uploading images to. There are also vendor-neutral systems like UMC Utrecht's IMAGR AI.
These platforms are trainable through deep learning to detect a wide range of specific diseases and disorders. The radiology conference Radiological Society of North America has implemented presentations on AI in imaging during its annual meeting. The emergence of AI technology in radiology is perceived as a threat by some specialists, as the technology can achieve improvements in certain statistical metrics in isolated cases, as opposed to specialists".

"Screening:
Recent advances have suggested the use of AI to describe and evaluate the outcome of maxillo-facial surgery or the assessment of cleft palate therapy in regard to facial attractiveness or age appearance.
In 2018, a paper published in the journal Annals of Oncology mentioned that skin cancer could be detected more accurately by an artificial intelligence system (which used a deep learning convolutional neural network) than by dermatologists. On average, the human dermatologists accurately detected 86.6% of skin cancers from the images, compared to 95% for the CNN machine.
In January 2020 researchers demonstrate an AI system, based on a Google DeepMind algorithm, that is capable of surpassing human experts in breast cancer detection.
In July 2020 it was reported that an AI algorithm by the University of Pittsburgh achieves the highest accuracy to date in identifying prostate cancer, with 98% sensitivity and 97% specificity".

"Psychiatry:
In psychiatry, AI applications are still in a phase of proof-of-concept.
Areas where the evidence is widening quickly include chatbots, conversational agents that imitate human behaviour and which have been studied for anxiety and depression
Challenges include the fact that many applications in the field are developed and proposed by private corporations, such as the screening for suicidal ideation implemented by Facebook in 2017.
Such applications outside the healthcare system raise various professional, ethical and regulatory questions".

"Primary Care:
Primary care has become one key development area for AI technologies.
AI in primary care has been used for supporting decision making, predictive modelling, and business analytics.
Despite the rapid advances in AI technologies, general practitioners' view on the role of AI in primary care is very limited–mainly focused on administrative and routine documentation tasks".

"Disease Diagnosis:
There are many diseases and there also many ways that AI has been used to efficiently and accurately diagnose them. Some of the diseases that are the most notorious such as Diabetes, and Cardiovascular Disease (CVD) which are both in the top ten for causes of death worldwide have been the basis behind a lot of the research/testing to help get an accurate diagnosis. Due to such a high mortality rate being associated with these diseases there have been efforts to integrate various methods in helping get accurate diagnosis".

"Telehealth:
The increase of telemedicine, has shown the rise of possible AI applications. The ability to monitor patients using AI may allow for the communication of information to physicians if possible disease activity may have occurred. A wearable device may allow for constant monitoring of a patient and also allow for the ability to notice changes that may be less distinguishable by humans.
Electronic health records:
Electronic health records are crucial to the digitalization and information spread of the healthcare industry. However, logging all of this data comes with its own problems like cognitive overload and burnout for users. EHR developers are now automating much of the process and even starting to use natural language processing (NLP) tools to improve this process. One study conducted by the Centerstone research institute found that predictive modeling of EHR data has achieved 70–72% accuracy in predicting individualized treatment response at baseline.
Meaning using an AI tool that scans EHR data. It can pretty accurately predict the course of disease in a person"..

"Drug Interactions:
Improvements in natural language processing led to the development of algorithms to identify drug-drug interactions in medical literature. Drug-drug interactions pose a threat to those taking multiple medications simultaneously, and the danger increases with the number of medications being taken.
To address the difficulty of tracking all known or suspected drug-drug interactions, machine learning algorithms have been created to extract information on interacting drugs and their possible effects from medical literature. Efforts were consolidated in 2013 in the DDIExtraction Challenge, in which a team of researchers at Carlos III University assembled a corpus of literature on drug-drug interactions to form a standardized test for such algorithms:
Competitors were tested on their ability to accurately determine, from the text, which drugs were shown to interact and what the characteristics of their interactions were. Researchers continue to use this corpus to standardize the measurement of the effectiveness of their algorithms".

"Artificial Intelligence in Education:
Artificial Intelligence technology brings a lot of benefits to various fields, including education. Many researchers claim that Artificial Intelligence and Machine Learning can increase the level of education.
The latest innovations allow developers to teach a computer to do complicated tasks. It leads to the opportunity to improve the learning processes. However, it’s impossible to replace the tutor or professor. AI provides many benefits for students and teachers.
In this guide, we’ll discuss the advantages and disadvantages of Artificial Intelligence in education, existing solutions on the market, and how to create your AI-enabled platform.
Benefits of AI for Education:
Educational apps can be utilized by two types of users — students and teachers. Of course, such solutions bring different benefits to them".

"Advantages of AI in Education for Students:
Education at any time. Young people spend a lot of time on the go. They prefer doing everyday tasks using their smartphones or tablets. AI-based applications provide an opportunity to study in free time, spending ten or fifteen minutes. Additionally, the students can get feedback from tutors in a real-time mode.
Various options due to the students’ needs. AI-based solutions can adapt due to the students’ level of knowledge, interesting topics, and so on. The system tends to help students with their weak sides. It offers learning materials based on their weaknesses. For example, the student does the test before starting to use the app; the app analyzes it and provides suitable tasks and courses.
Virtual mentors. AI-based platforms offer virtual mentors to track the students’ progress. Of course, only human teachers can understand the scholars’ needs better, but it’s good to get instant feedback from the virtual tutor.
Advantages of AI in Education for Teachers and Schools:
Opportunity to see weaknesses. Different training courses allow seeing the gaps in students’ knowledge. For example, the Coursera platform can notify the teacher if many students chose incorrect answers to a particular question. As a result, the tutor has an opportunity to pay attention to the demanded topic".

"Better engagement. Modern technologies like VR and gamification help involve students in the education process, making it more interactive.
Personalization. Various AI-enabled algorithms can analyze the users’ knowledge and interests and provide more personalized recommendations and training programs.
Curriculum automatic creating. Teachers get a great benefit from AI development. These days, they don’t need to create a curriculum from scratch. As a result, tutors spend less time searching for necessary educational materials.

Opportunity to find a good teacher. Educational platforms have a lot of teachers, so the student has an opportunity to communicate with specialists from other countries. The AI-enabled educational platform offers appropriate tutors, depending on the teaching experience and soft skills.
How to Use Artificial Intelligence in Education:
One of the forms of Artificial Intelligence is Machine Learning. ML tends to analyze information, get conclusions, and make decisions or suggestions. It means that the ML-based platform can be taught with a lot of data. After that, it can fulfill various tasks".

"There are several use cases of Artificial Intelligence for the education field. Let’s discuss them more precisely.
Individualized Learning:
Artificial Intelligence allows focusing on the individual needs of the student. Many large education platforms like Carnegie Learning invest in AI to provide more personalized courses. These days, it’s possible to create individual instructions, testing, and feedback. As a result, learners work with the material they are ready for and fill the gaps in their knowledge.
As Artificial Intelligence becomes smarter, it might be possible to scan and analyze students’ facial expressions. If the material is too complicated, the platform can change the lesson depending on their needs.
Voice Assistants:
Such voice assistants as Amazon Alexa, Apple Siri, Google Home allow interacting with various learning materials without communication with a teacher. As a result, it’s possible to use an education platform anywhere and anytime.
For example, Arizona State University uses Alexa for routine campus needs. The assistant can answer common questions or follow the student’s schedule.
Additionally, using such assistants, it quite interesting and exciting for learners, so they are mare engaged in the education process".

"Smart Content:
Smart content stands for various learning materials, from digitized textbooks to customized interfaces.
Let’s consider two examples.
Content Technologies, Inc. is a development company that works with Artificial Intelligence. Its primary goals are to automate business processes and improve users’ experience. The company has already created solutions for the education field. For example, Cram101 can break the textbook’s content into parts. They can include a chapter summary, tests, and so on.
Netex Learning is one more company that focuses on creating smart content platforms. The solution is full of AI-based features — for example, real-time feedback and digital curriculum. Netex platform also offers personalized cloud platforms with virtual training, conferences, and more.
Global Learning
AI brings a lot of opportunities to share knowledge all over the world. Using Artificial Intelligence solutions, students can study various courses and training programs. There are a lot of platforms with interactive learning materials from the best tutors.
AI also provides opportunities for students who speak different languages or have visual or hearing problems. For example, Presentation Translator is an AI-based solution that creates subtitles in real-time mode. Using AI Speech Recognition, students can hear or read in their native language".

"Existing AI-Based Solutions in Education
There are a lot of tech-driven solutions in the education industry. Among AI examples are DreamBox, Khan Academy, Achieve3000, and many others.
These platforms can analyze the level of knowledge, offer backward communication, provide a plan for improvements, and so on.
Third Space Learning. This system was created with the help of scholars from London University College and actively use Artificial Intelligence opportunities. The system can recommend ways to improve teaching techniques. For example, if the teacher speaks too fast or slow, the systems send a notification.
Little Dragon. It’s a startup company that creates smart apps using Artificial Intelligence. Such applications can analyze the users’ emotions and adapt the user interface depending on it. The company also makes educational games for kids".

"CTI. This company also uses AI to develop tech-driven solutions for education. The primary goal is creating smart content. For example, Cram101 can analyze the textbook or other learning materials and choose the critical information to create texts.
Brainly. It is a social network for students’ cooperation. For instance, learners can discuss issues connected to their homework or get new knowledge from other students. The company utilizes Machine Learning to provide a better user experience. ML assists in selecting spam and inappropriate content. Additionally, AI is used to offer more personalized materials.
Carnegie Learning. This system tends to provide more customized education materials, making the learning process more comfortable. This solution offers real-time education for school students. Carnegie Learning analyses the users’ keystroke and allows the tutor to see the students’ progress.
ThinkerMath. This AI-enabled solution helps small kids learn Math. There are various games and rewards to achieve better engagement results. The app also offers a personalized learning plan depending on the child’s knowledge.
There are a lot of AI-based solutions that improve the education field. This industry is quite promising due to incredible opportunities for development.
How to Develop AI-Enabled Platform for Education".

"Considering the information we’ve discussed, you may wonder how to build an e-learning website and integrate Artificial Intelligence. There are six main steps that you need to follow.
Step 1. Study the competitors’ solution.
Step 2. Consider interactive and interesting content.
Step 3. Set your project requirements and discuss them with the developers.
Step 4. Test your app properly to avoid bugs.
Step 5. Release the app and get users’ feedback.
Step 6. Update your solution regularly.

Step #1. Study the Competitors.
Before creating your solution, you need to analyze the competitors carefully. These days, users are quite spoilt, so you need to offer them some new features".

"Additionally, knowing the existing platforms, you can generate more interesting concepts for your project. You can study the tech stack or design ideas.
Step #2. Consider Interactive and Useful Content.
Creating a solution for education, you need to offer users’ useful content.
For example, you can choose several fields or topics like Math, Literature, and others. After that, you can cooperate with tutors from universities or colleges.
Additionally, you can gather learning materials from various sources like training programs, courses, and more.
Step #3. Set Your Project Requirements
Before starting the development, you need to clarify your project requirements and business goals.
You should consider the number of required features. For example, you can create an MVP version of your platform with some basic features. It allows you to get some feedback from users.
After that, you can update the system regularly, adding advanced features. By the way, it’s good to study what kind of features the users want to have".

"To start the development, you need to cooperate with experienced software developers. They need to have experience in Artificial Intelligence.
There are two possible variants to work with software builders — create an in-house team or cooperate with an outsourcing company. Each option has pros and cons. For example, vendors tend to offer more affordable hourly rates. However, it’s easier to communicate with an in-house team of developers.
Step #4. Test the Platform Properly to Avoid Bugs
To gain more loyal users, you need to provide an excellent user experience. Of course, users don’t want to interact with a platform full of bugs.
To find and fix bugs before launching the project, you need to cooperate with qualified Quality Assurance engineers.
Step #5. Release Your Solution and Get Users’ Feedback.

"We’ve already mentioned that it’s better to create an MVP version of your solution to get feedback.
Users’ opinions can help you understand what parts of the system you need to improve. Additionally, students and teachers can tell about their expectations and requirements for your application.
Step #6. Update the System Regularly
It’s vital to improve your platform regularly, offering users new opportunities and exciting features.
To be competitive in the market, you should stay up-to-date. It’s better to cooperate with various tutors and offer new training programs.
As you can see, the education field offers a lot of opportunities to integrate and use Artificial Intelligence. The market already has several great solutions for adults, children, tutors, and even schools. AI-based applications can analyze an enormous amount of information, offering users more personalized learning materials".

"The Role of Artificial intelligence in Agriculture Sector
Artificial intelligence technology is supporting different sectors to boost productivity and efficiency. AI solutions are assisting to overcome the traditional challenges in every field. Likewise. AI in agriculture is helping farmers to improve their efficiency and reduce environmental hostile impacts. The agriculture industry strongly and openly embraced AI into their practice to change the overall outcome. AI is shifting the way our food is produced where the agricultural sector’s emissions have decreased by 20%. Adapting AI technology is helping to control and manage any uninvited natural condition.
Today, the majority of startups in agriculture are adapting AI-enabled approach to increase the efficiency of agricultural production. The Market study report stated that the global Artificial Intelligence (AI) in Agriculture market size is expected to reach 1550 million US$ by the end of 2025. Implementing AI-empowered approaches could detect diseases or climate changes sooner and respond smartly. The businesses in agriculture with the help of AI are processing the agricultural data to reduce the adverse outcomes".

"Advantage of implementing AI in Agriculture
The use of Artificial intelligence in agriculture helps the farmers to understand the data insights such as temperature, precipitation, wind speed, and solar radiation. The data analysis of historic values, offers a better comparison of the desired outcomes. The best part of implementing AI in agriculture that it won’t eliminate the jobs of human farmers rather it will improve their processes.
 AI provides more efficient ways to produce, harvest and sell essential crops.
 AI implementation emphasis on checking defective crops and improving the potential for healthy crop production.
 The growth in Artificial Intelligence technology has strengthened agro-based businesses to run more efficiently.
AI is being used in applications such as automated machine adjustments for weather forecasting and disease or pest identification.
Artificial intelligence can improve crop management practices thus, helping many tech businesses invest in algorithms that are becoming useful in agriculture.
AI solutions have the potential to solve the challenges farmers face such as climate variation, an infestation of pests and weeds that reduces yields.
Impact of Artificial Intelligence in Agriculture".

"AI technology is rapidly rectifying the problems while recommending specific action that is required to overcome the problem. AI is efficient in monitoring the information to find solutions quickly. Let’s see how AI is being used in agriculture to improve results with a minimal environmental cost. By implementing AI can identify a disease with 98% accuracy. Thus, AI helps farmers monitor the fruit and vegetable by adjusting the light to accelerate production.
Forecasted Weather data
AI in an advanced way is helping the farmer to remain updated with the data related to weather forecasting. The forecasted/ predicted data help farmers increase yields and profits without risking the crop. The analysis of the data generated helps the farmer to take the precaution by understanding and learning with AI. By implementing such practice helps to make a smart decision on time.
Monitoring Crop and Soil Health".

"Utilizing AI is an efficient way to conduct or monitor identifies possible defects and nutrient deficiencies in the soil. With the image recognition approach, AI identifies possible defects through images captured by the camera. With the help of Al deep learning application are developed to analysis flora patterns in agriculture. Such AI-enabled applications are supportive in understanding soil defects, plant pests, and diseases.
Decrease pesticide usage.
Farmers can use AI to manage weeds by implementing computer vision, robotics, and machine learning. With the help of the AI, data are gathered to keep a check on the weed which helps the farmers to spray chemicals only where the weeds are. This directly reduced the usage of the chemical spraying an entire field. As a result, AI reduces herbicide usage in the field comparatively the volume of chemicals normally sprayed".

"AI Agriculture Bots
AI-enabled agriculture bots help farmers to find more efficient ways to protect their crops from weeds. This is also helping to overcome the labor challenge. AI bots in the agriculture field can harvest crops at a higher volume and faster pace than human laborers. By leveraging computer vision helps to monitor the weed and spray them. Thus, Artificial Intelligence is helping farmers find more efficient ways to protect their crops from weeds".

"Final thoughts
Today AI-powered technologies are used for solving several industries’ purposes. AI is being utilized in sectors such as finance, transport, healthcare, and now in agriculture. AI is helping the farmers to monitor their crops without the need to invigilate personally into the farm. Many startups and enterprises are looking forward to AI development in agriculture. AI is redefining the traditional pattern of agriculture. The future of AI in agriculture is way ahead in offering radical transformation with advanced approaches.
ARTIFICIAL INTELLIGENCE AND NATURAL LANGUAGE PROCESSING TRANSFORM CHATBOTS".

"Since the time chatbots have entered the advanced world, every organization and marketer are interested to utilize them as a significant tool to interact with their clients on a daily basis. Some of them were sufficiently quick to try things out, while others are still at the thinking stage. Brands can connect with their customers and interact with them in a personal manner by means of chatbots.
With the capability of chatbots to give client support more than ever, brands can now increase their sales. Subsequently, chatbots can provide opportunities to improve brand engagement, assist organizations with accomplishing business growth, and make monetary profits. Businesses as well as customers are cherishing this innovation.
The problem of waiting for long periods of time to connect with customer care executives gets wiped out. In addition, chatbots can provide solutions to clients in any event, even during non-operational hours".

"Because of Chabot’s brief answers and 24*7 accessibility, 69 percent of customers today favor communicating with chatbots as opposed to people.
Hence, chatbots have become an absolute necessity for organizations to survive. Initially, when chatbots were new, they failed to lead discussions.
Today, chatbots have developed to become refined and sophisticated models. In any case, chatbots sometimes despite everything come up short on understanding the client’s expectations and language. They should, in this manner, be trained well to comprehend the specific situation, user intent, and sarcasm in human language.
Artificial intelligence plays an important role in increasing the efficiency of chatbots. Artificial intelligence gives a human touch to each discussion chatbot strikes. The bot comprehends the customer’s inquiry and triggers a precise response in the same manner in which humans can understand each other’s concerns and give a reaction accordingly".

"Chatbot with AI capabilities makes your bot capable and clever to answer complex inquiries. The communication is engaging, conversational, and lively. Chatbot learns from each discussion it has with the clients. It analyses the past interaction to improve the current response. This movement assists with improving the proficiency of bot reaction.
In addition, it helps to understand your client’s choices and preferences. Smart conversations spare customer’s time by helping them to locate the correct data and address their queries. Machine learning is an algorithm that causes the chatbot to learn from questions and the information given by organizations during bot training.
At the point when a query is triggered, machine learning encourages the bot to initially screen the previous discussion it had with the client and give a response accordingly. Along with AI and machine learning, NLP also plays a major role in revolutionizing chatbots. NLP assists organizations with offering a pleasant experience to customers".

"With regards to chatbots, NLP can be utilized to recognize what the client is really attempting to tell or inquire about. Along these lines, brands can interact with their clients in a personal, more sympathetic way, which can at last make them stand unique among their competitors. NLP systems widely use machine learning to parse client input in order to take out the important elements and comprehend client intent.
Chatbots with natural language processing can parse numerous client messages to limit failures. When it comes to natural language processing, designers can train the bot on numerous interactions and discussions it will experience as well as giving various instances of content it will interact with as that will in a general sense give it a much more extensive scope with which it can additionally evaluate and decipher inquiries more adequately".

""Businesses should train chatbots to switch their tones, from formal to casual, to keep customers engaged and intrigued. Be that as it may, to lead such human-like discussions, chatbots must have a profound context-awareness ability. Furthermore, for that ability, designing chatbots with NLP is fundamental.
With NLP, chatbots can without much of a problem comprehend the mind-boggling human language. Each individual possesses a different style while communicating. With NLP, chatbots can rapidly understand an individual’s personality and react in the same manner. Moreover, chatbots can understand sarcasm, humor, and other conversational tones better with NLP. Truly, NLP gives chatbot their very own personality".

"With computational algorithms, context extraction, content summary, and sentiment examination, NLP can help chatbots decipher the raw content, process it, and convey enhanced data to clients. Natural language processing assists with understanding and deciphering customer requests, difficulties and issues, and then utilizing an advanced level of AI to help convey the suitable actions to fulfill the client’s needs.
Advanced NLP can even analyze the meaning of a client’s messages. For instance, if you are making an inquiry or saying something. While this may appear to be unimportant, it can deeply affect a chatbot’s capacity to carry on an effective discussion with a user.
While natural language processing positively can’t produce miracles and guarantee that a chatbot appropriately reacts to each message, it is incredible enough to become the deciding factor in a chatbot’s prosperity. It’s important to not think little of this significant and often ignored part of chatbots.
AI in Transportation – Current and Future Business-Use Applications".

"The transportation domain is beginning to apply Artificial Intelligence (AI) in mission-critical tasks (for example, self-driving vehicles carrying passengers) where the reliability and safety of an AI system will be under question from the general public. Major challenges in the transportation industry like capacity problems, safety, reliability, environmental pollution, and wasted energy are providing ample opportunity (and potential for high ROI) for AI innovation.
For the sake of this article, ‘transportation’ will include all technologies that move people and cargo. Over the course of this article we aim to answer the following questions:
What AI technologies are currently applied in transportation applications? What is involved in the process of AI integration for these applications?
What are the current use-cases artificial intelligence transportation?".

"What can we expect in terms of the technology roadmap going forward?
Based on the applications revealed in our research, we segment AI in the transportation industry as follows:
Current applications,
Public passenger, transportation
Autonomous trucks,
AI applications in railway cargo transportation
Just around the corner,
Transportation planning.
Future applications:
We explore each of the applications and the future of their technology roadmap in more detail below.
Current Applications:
The compatibility of AI to transportation applications is a somewhat natural fit. Yet, as is the case with AI in many other industries, the adoption of these applications currently varies across industries and geographies".

"This effectively translates to the fact that AI application in transport can paradoxically be both
complicated and straightforward, implausible and probable, distant and just-around-the-corner, based on environment and geographical factors. We explore a few examples for current applications of AI in the transportation industry below:
Public Passenger Transportation:
Autonomous Buses
Small scale autonomous bus trials have been initiated all over the world in recent times most prominently in Finland, Singapore and China. The global non-uniformity in built-up structures, city infrastructures, road surfaces, weather patterns, traffic patterns etc. make AI applications in autonomous trucks for on-time delivery of people and packages, highly environment specific".

"Olli by Local Motors:
Olli is a self-driving, ‘cognitive’ electric shuttle from American company Local Motors. The
company is focused on low volume manufacturing of open-source vehicle designs, using multiple microfactories.
Powered by IBM’s Watson Internet of Things (IoT) for Automotive, Olli can perform functions like transportation of passengers to requested locations, providing suggestions on local sights and answering questions about how Olli’s self-driving service functions. According to IBM, five developer APIs from the Watson IoT for Automotive platform were integrated with Olli including Speech to Text, Natural Language Classifier, Conversation, Entity Extraction and Text to Speech.
The 4-minute video below explains some of Olli’s features, and demonstrates some of its trial runs in Washington D.C".

"Traffic Management Operations:
AI solutions have been frequently applied in resolving control and optimization problems. Business leaders would find it interesting to note that AI is already being used in applications like prediction and detection of traffic accidents and conditions (by converting traffic sensors into ‘intelligent’ agents using cameras). We discuss the case of Rapid Flow Technologies, a Carnegie Mellon University spin-off.
Surtrac by Rapid Flow Technologies:
Headquartered in Pittsburg, Rapid Flow technologies’ Surtrac system was originally developed in the Intelligent Coordination and Logistics Laboratory in the Robotics Institute at Carnegie Mellon University as part of the Traffic21 research initiative. Rapid Flow is also a part of the NSF I-Corps Site program at Carnegie Mellon".

"In June 2012, Rapid Flow installed the Surtrac system for a pilot in the East Liberty neighborhood of Pittsburgh. The solution was a network of nine traffic signals in three major roads (Penn Circle, Penn Avenue, and Highland Avenue). Rapid Flow claims that Surtrac helped reduce travel times by more than 25% on average, and wait times declined an average of 40% during the course of the trial. Following the pilot project, Rapid flow has collaborated with local Pittsburg administration to expand the solution to other parts of the city (around 50 traffic signals). Readers may also see our previous post on artificial intelligence application in smart cities for more on Surtrac".

"Autonomous Trucks:
The transportation industry is facing environmental challenges and stricter emission
regulations from government agencies. A report by the International Transport Forum (ITF) claims that autonomous trucks will save costs, lower emissions, and improve road safety as compared to traditional trucking with human drivers. We discuss the current major use-cases for autonomous trucks here:
Uber Advanced Technologies Group
In October 2016, San Francisco startup Otto (now called Uber Advanced Technologies Group after being bought by Uber for $680 million in 2017) successfully completed the world’s first autonomous truck delivery carrying around 50,000 cans of Budweiser beer over a distance of 120 miles from Fort Collins to Colorado Springs, CO".

"TuSimple:
TuSimple a Chinese startup, founded in 2015, successfully completed a 200-mile level 4 (see the standard levels of autonomous driving) test drive for a driverless truck passed from Yuma, Arizona, to San Diego, California. TuSimple claims that it’s driving system was trained using deep learning to simulate tens of millions of miles of road driving.
TuSimple uses Nvidia graphic processing units (GPUs) including the NVIDIA DRIVE PX 2 computer, TensorRT deep learning inference optimizer and runtime engine, Jetson TX2 AI supercomputer on a module, CUDA parallel computing platform and programming model, and cuDNN CUDA deep neural network library. At the time of writing there was no information available on the specifics of the integration.
AI Applications: Railway Cargo Transportation.
GE, Transportation".

No comments:

Post a Comment