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Science fiction writers, futurists, and filmmakers have been predicting for decades that the spectacular (and sometimes catastrophic) changes that may arise with the advent of widespread artificial intelligence.

The educational world is becoming more comfortable and personalized thanks to the many applications of AI for education. It has transformed the process people learn, as educational materials are available to everyone through smart devices and computers.

Now, students do not have to visit physical classes to study as long as they have computers and an internet connection. Artificial Intelligence also enables the self-regulation of administrative tasks, enabling organizations to reduce the time needed to complete difficult tasks so that educators can spend more time with students. It is time to discuss the transformations that AI has brought in education

Although we have not yet created self-aware robots such as Pepper Popular Movies 2001: A Space Odyssey and Star Wars, we have made smart and frequent use of AI technology in a wide range of applications that, while not mind-blowing as androids, still change our daily lives. One spot where artificial intelligence is ready to perform big changes (and in some cases already) is in education.

While we may not see humanoid robots acting as teachers in the next decade, there are already many projects that use computer intelligence to help students and teachers gain more educational experience. Those tools are just a few of the paths here, and those that follow will shape and define the future educational experience.

1. Artificial intelligence automates basic activities in education such as grading.

In college, grading homework and testing for large lecture courses can be tedious, even when TAs are split between them. Even in the lower grades, teachers often find that grading takes considerable time and effort to communicate with students, prepare for class, or strive for professional development.

AI may never really replace human grading, but it’s pretty close. It is now possible for teachers to automate grading for almost all types of multiple-choice, and the automatic testing of blank testing and student writing may not be far behind. Today, Essay-grading software is still in its infancy and is not quite the same, although it will improve (and will) in the coming years, enabling teachers to focus more on class activities and student interactions than grading.

2. Educational software is tailored to the needs of students.

From kindergarten to graduate school, one of the key ways to influence artificial intelligence education is to use higher levels of personalized learning. Some of this is already happening in the growing number of custom learning programs, games and software. These systems respond to the needs of the student, put too much emphasis on certain topics, repeat things that students are not familiar with, and generally help students work at their own pace.

To Read More: AI In Education: Top 12 AI Applications in Education Industry

This sort of custom-tailored learning can be a machine-assisted solution to help students of different levels work together in a classroom, while teachers facilitate learning and provide help and assistance when needed. Adaptive training has previously made a huge impact on education across the country (especially through programs like the Khan Academy), and as AI develops over the coming decades, such adaptive programs will improve and expand.

3. It can point out areas where courses need to be improved.

Teachers may not always be aware of the gaps in their lectures and educational materials, which may confuse students about certain topics. Artificial intelligence provides a way to solve that problem. Coursera, a large open online course provider, is already putting it into practice. When a large number of learners submit a wrong answer to a homework assignment, the system alerts the teacher and gives prospective students a customized message that hints at the correct answer.

This type of system helps fill in the gaps in interpretation that occur in courses and helps ensure that students are building the same conceptual foundation. Instead of waiting to hear back from the professor, students get immediate feedback, which helps them understand a concept and remember exactly how to do it next time.

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4. Students may receive additional support from AI tutors.

While it is clear that human tutors can provide those machines, at least not yet, most students in the future will be taught by tutors who have zeros and only them. There are already some training programs based on Artificial Intelligence and help students with basic math, writing and other subjects.

Is Artificial Intelligence A Game Changer In Education Industry, To Read More……

These programs can teach students the fundamentals, but so far, they are not ideal for helping students learn high-order thinking and creativity, and are still needed to facilitate real-world teachers. It should not rule out the possibility that AI tutors will do these things in the future. With the rapid pace of technological advances that have been recognized over the past few decades, advanced training systems may not be the pipe dream.

5. AI-driven programs can provide helpful feedback to students and faculty.

AI not only helps teachers and students to create courses that are customized to their needs but also provide feedback on both the success of the entire course. Some institutions, especially online offerings, use AI systems to monitor student progress and alert professors when there is a problem with student performance.

These types of AI in education systems allow students to get the help they need and find areas where professors can improve instruction for students who are struggling with the subject matter. However, the AI programs in these schools do not offer advice on individual courses. Some students are working to develop systems that help them choose majors based on areas of success and difficulty. Students do not need to take advice, it represents a brave new world of college major choice for future students.

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5 Roles That Artificial Intelligence A Game Changer In Education Industry was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Check out this freely available book, All of Statistics: A Concise Course in Statistical Inference, and learn the probability and statistics needed for success in data science.

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The new framework uses contrastive learning to improve image analysis in unlabeled datasets.

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AI is already playing a significant role in various fields, similarly, agriculture is the field where it can be implemented through various applications, system or machines that can perform various actions independently or analyze the useful data for better farming and agriculture.

In agriculture you can use automated tractors, drones, robots and other computer vision based machines to visualize the various scenario and help agriculture sector boos the productivity. Robots or AI drones are trained through machine learning algorithms, and to train the algorithms, you need certain amount of data sets containing the annotated images of crops and harvestings.

Training Data Set for Machine Learning & AI in Agriculture

To train the robots, you need the data set that contains the object of interest which could be either crops, fruits and vegetables are annotated with various techniques like bounding box to detect the object precisely. Anolytics provides the bounding box annotation for crop detection, fruit detection and unwanted crops in agriculture creating the high-quality data set for robots and autonomous machines.

Training Data for Robots in Agriculture and Farming

You can create the set of data for AI robots in agriculture with annotated fruits, vegetables showing the actual condition of such plants, and take action like plucking, spraying pesticides or detecting the weeds or unwanted crops to remove them from the fields that are eating the nutrition of soil. Using the training data sets for harvesting and crop controlling is possible when you create a right data.

Training Data for Aerial View Mapping of Agricultural Field

Another type of data you need for AI in agriculture is for drones that can provides the useful details of agricultural fields to check the soil condition through geo sensing and monitor the health of the crops. In farming sector, AI drones can monitor the live stocks like cow, buffalos, sheep and other creatures used in animal husbandry. Only such high-quality training data can help drones to learn from such data sets.

Training Data for Live Stock Management in Farming

For livestock management drones are used to monitor the animals grazing the grass in the open field. Hence, to make these objects and animals, recognizable to machines (drones) you need to create the data set containing the annotated fields and different types of animals. Semantic segmentation and polygon annotation are the image annotation technique used to create the training data for AI.

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Similarly, drones and robots can be trained to perform various tasks like spraying the pesticides, aerial view monitoring of crops to prevent from harmful insects and other animals. Similarly, AI based various applications can also provide the details to grow the crop in better way and improve the yield.

Making the all types of objects including crops, fruits, vegetables and other things in the agricultural filed need to annotated and feed into the machine learning algorithms for agriculture to make the model visualize various situations and take the action accordingly. Semantic segmentation is also one of the most crucial image annotation technique for deep learning in agriculture.

Anolytics provide the high-quality training data for machine learning and AI in agriculture. It can provide you the annotated images of all types of objects in the agricultural field with best level of accuracy, It is specialized in image annotation services to provide the machine learning training data sets to develop the different types of AI models with high rate of success when used in real-life.

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How to Create Training Data Set for Machine Learning & AI in Agriculture? was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Via https://becominghuman.ai/how-to-create-training-data-set-for-machine-learning-ai-in-agriculture-8febc02fda9a?source=rss—-5e5bef33608a—4

What COVID-19 teaches us about the existential threat of artificial intelligence.

COVID-19, carbon-induced climate change, and nuclear winter carry existential risk, not due to their intelligence, but because they destroy large systems upon which we depend.

Our first existential digital threat is widely expected to be an artificial general intelligence, a grand AI capable of achieving any goal. Surprisingly, the real AI threat is not big and scary, like a monster, but small, systemic, and decentralized, like the coronavirus.

Replication

This threat is right under our noses. Internet worms, a type of computer virus that seems fairly innocuous, carry a creepy potential. These pieces of code autonomously spread from machine to machine. And they are already unstoppable.

Consider MyDoom, a worm unleashed in 2003. Samples of MyDoom were found in ~1% of all emails sent in 2018. Almost a trillion copies of this worm are scattered every year, waiting to be picked up by an unprotected host.

MyDoom exhibits considerable variation. As the worm spreads, it changes its code to avoid detection. This not only mimics the variation seen in biological evolution, but also provides a great foundation for evolutionary algorithms. Trillions of samples mean a juicy dataset, so many mutations can be tested and models can be trained.

Worm-style replication is so cheap and quick, it’s already a grand success. The classic sci-fi alternative — some kind of Terminator factory, staffed by Terminators that build yet more Terminators — seems silly once this simple, proven technique comes into view. Of course, that vision has been refined. Even in 1953, computing pioneer John von Neumann imagined the problem more abstractly: a pile of parts, and machines that used these parts to create clones of themselves. Today we see researchers pursuing 3d printing and self-assembly to realize contemporary, refined versions of such visions.

However, we are naive if we expect the first existential AI threat to use a complex and costly physical process to reproduce. Genius AI would not take the most expensive path. Internet worms provide a cheap and well-proven method for reproduction that happens to provide ample resource for mischief. Once a worm is inside a machine, it can do most anything that machine can do.

Destruction

In terms of social and financial destruction, internet worms have nothing on the coronavirus. A particular strain of ransomware called GandCrab stands out for causing a mere $2 billion in damages. This cost is nothing compared with the trillion-dollar macroeconomic consequences of COVID-19.

Nevertheless, GandCrab stands high above the financial damages caused by leading-edge AI. Excellence in pattern recognition and decision-making does not translate to convincing embezzlement. Any smart AI would likely use one of the proven, dumb techniques to extort, like locking you out of your laptop and demanding a bitcoin ransom.

So far, in terms of physical destruction, internet worms haven’t held a candle to old-fashioned warfare. The Stuxnet worm gained international fame for a rather paltry act of physical destruction, the annihilation of a few centrifuges. But this may change in the future.

As computer-controlled vehicles and weapon systems develop, the physical risks of hostile worm infestations grow more dire. We don’t need to imagine some generalized AI driving around a killer drone. The navigation and targeting solutions — old-school, specific AI — could be triggered by a dumb worm.

Traditionally, viruses have not had much capacity to perform work. Due to network and machine limitations, viruses could not handle advanced computation. But things have changed. Payloads are growing. The Flame worm was noteworthy almost a decade ago for having a large, component-based software architecture that executed a variety of attacks. These days, the more common setup is a distributed botnet. Worms run massive cybermining operations, one of the most power-hungry (and profitable) forms of work a computer can do all by itself.

We don’t have to wait for a genius, general AI to realize that infinite sustenance for replication exists on a botnet. A very dumb AI (like the even dumber coronavirus) could instead merely stumble into such an ample host.

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Havoc

All of this potential for destruction, of course, doesn’t mean that armageddon will indeed be unleashed. There is copious debate in the AI community for what forces (if any) might cause a complex artificial general intelligence to do evil. We could spend weeks weighing the merits of various speculations.

However, with internet worms, such projections need not be performed. A history of evil is their wake. Every day, human actors fight to make code to overpower our computational ecosystem. With so much human bad intention that could accidentally get a bit out of control, the independent evolution of machine evil is not needed. We already live in a world where our militaries join independent hackers in the hunt for disproportionate power.

The very forces that drive black hats to write malware could lead them to unleash an unstoppable distributed evil. Advanced intelligence is not necessary. While toiling away at the worm smart enough to create new attack vectors, some smarter hacker will instead write the bot that can scan and deploy threats as they are reported (unpatched vulnerabilities) without even understanding them.

The exploits themselves, as the incremental results of AI research are applied, are getting stronger and stronger. Learning systems have been used to automate spear phishing. Chatbots have been developed to invade social networking sites.

That there are humans working toward this very adaptable form of malware is not a question, but a reality. The powder keg is surrounded by sparks.

The Future Existential Threat

We’re fighting a microscopic, distributed population of pseudo-organisms. All of the arguments about when a machine will become “intelligent” become moot when something far more stupid can cause life as we know it to come crashing to a halt. As the coronavirus has shown, not very much intelligence is needed to tear society apart.

Don’t confuse “malicious” with “intelligent”. Existential threats to humanity are by definition malicious but, like the coronavirus, need not be intelligent.

The lofty threat of an out-of-control artificial general intelligence may indeed exist. But let’s be real, before that threat ever materializes, we will face threats from dumber artificial brains, like the threats we already face.

Our primate brains are built to worry about monsters that we can see, fight or run away from. Scary monsters trigger our primal fears, developed over millennia living exposed in nature.

Our natural intuition does not serve us well in the late Information Age. Counterintuitively, our existential threats are not smart robots but rather dumb and distributed snippets of code.

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The Impending AI Pandemic was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Via https://becominghuman.ai/the-impending-ai-pandemic-58d29cab83ab?source=rss—-5e5bef33608a—4