What is the usefulness of Labelbox for AI teams?

Updated on : December 3, 2021 by Hank Coleman



What is the usefulness of Labelbox for AI teams?

Every AI startup or corporate R&D lab has to reinvent the wheel when it comes to how humans annotate training data to teach algorithms what to look for. Whether it's doctors evaluating the size of cancer from a scan or drivers surrounding street signs in images of driverless cars, all of this labeling has to happen somewhere. Often times that means wasting six months and up to a million dollars simply developing a training data system. With nearly every type of business vying to adopt AI, that spending in cash and time adds up.

Labelbox creates an artificial intelligence training data label

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Every AI startup or corporate R&D lab has to reinvent the wheel when it comes to how humans annotate training data to teach algorithms what to look for. Whether it's doctors evaluating the size of cancer from a scan or drivers surrounding street signs in images of driverless cars, all of this labeling has to happen somewhere. Often times that means wasting six months and up to a million dollars simply developing a training data system. With nearly every type of business vying to adopt AI, that spending in cash and time adds up.

Labelbox creates AI training data labeling software so no one else has to. What Salesforce is to a sales team, Labelbox is to an artificial intelligence engineering team. Software as a service acts as an interface for human experts or collaborative labor to instruct computers on how to detect relevant signals in the data on their own and continually improve the accuracy of their algorithms.

Labelbox released its initial alpha version in January 2018 and saw a rapid recovery from the AI ​​community immediately requesting additional features. Over time, the tool expanded with more and more ways to manually annotate data, from gradation levels such as how sick a cow is to judge her milk production to matching systems such as whether a dress fits the aesthetics of a cow. Fashion brand. Rigorous data science is applied to eliminate discrepancies between reviewers' decisions and identify extreme cases that do not fit the models.

The big challenge is convincing companies that training software is better left to the experts rather than building it in-house, where they are intimately, if perhaps inefficiently, involved in every step of development. Some turn to crowdsourcing agencies like CrowdFlower, which has its own training data interface, but they only work with a generalist workforce, not the experts required for many fields. Labelbox wants to cooperate rather than compete here, serving as the management software that treats subcontractors like just another data entry.

In the long run, the risk for Labelbox is that it came too early for the AI ​​revolution. Most potential corporate customers are still in the R&D phase around AI, not implementation at scale in real-world products. Big business is not selling labeling software. This is just the beginning. Labelbox wants to continuously manage fine-tuning data to help optimize an algorithm throughout its entire life cycle. That requires AI to be part of the actual engineering process.

Labelbox helps take artificial intelligence and machine learning initiatives from research and development through production. The platform enables AI and ML teams to create and manage high-quality training data in one place, all while supporting your production pipeline with powerful APIs.

It all starts with data labeling. Labelbox Training Platform can support any type of data, including image, video, and text labeling.

By tagging images, the platform provides a powerful tool for image segmentation, image classification, and object detection. You can customize th

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Labelbox helps take artificial intelligence and machine learning initiatives from research and development through production. The platform enables AI and ML teams to create and manage high-quality training data in one place, all while supporting your production pipeline with powerful APIs.

It all starts with data labeling. Labelbox Training Platform can support any type of data, including image, video, and text labeling.

By tagging images, the platform provides a powerful tool for image segmentation, image classification, and object detection. You can customize the tools to be compatible with your specific use case, including instances, custom attributes, and more. The platform helps configure and support:

  • Bounding boxes
  • Points and lines
  • Polygons
  • Instance Segmentation Toolkit (Pen and Super Pixels)
  • Complex ontologies with nested classifications
  • Tiled Images (Sliding Maps)

When it comes to video, Labelbox offers a high-performance label editor for state-of-the-art computer vision. The platform supports tagging directly on video up to 30 FPS and provides feature analytics so you can uncover insights that lead to better models, faster. Labelbox's layout is intuitive yet sophisticated, supporting both linear tweens to fill in the gaps between keyframes and nested classifications, either frame-by-frame or in bulk.

Finally, the platform allows you to tag text strings, conversations, paragraphs, and documents with customizable classification tools and named entity recognition. Depending on your use case, you can configure the tag editor to your exact data structure (ontology) requirements using custom attributes, hierarchical relationships, infinite nesting, and more. All of this makes creating training data for natural language intelligence easier than ever.

But Labelbox is more than just a platform for labeling your data; Its built-in collaboration features serve as project management tools to guide your team through various tasks. Role-based access controls mean the right people have access to the right data and unlock the option of working with internal and external teams simultaneously. You can also actively monitor the performance of the labeler and, if your team needs a little boost, outsource labeling projects with the click of a button.

Finally, and perhaps most uniquely, Labelbox recognizes that the best way to train your model is with your model. Pre-labeling and active learning can reduce human labeling costs by 80%, but automation is not easy. Fortunately, automation is one of Labelbox's core competencies that separates its platform from the rest. They even put together a free guide to automating data labeling that you can download here.

To learn more about how Labelbox could be useful to your team, you can check out relevant use cases or schedule a demo of the platform today.

Before getting into how to study "artificial intelligence", I would like to explain what artificial intelligence is and how you should approach its learning if you are a newbie.

What is artificial intelligence (AI)?

In the 21st century, artificial intelligence is the most interesting technology to study. AI uses computing and programming to create an automated system that saves humans time and effort.

Artificial intelligence and data science are in high demand, with more than 2 million job openings. To learn AI, I can give you an example of how we use Google Assistants, Amazon, and Siri in

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Before getting into how to study "artificial intelligence", I would like to explain what artificial intelligence is and how you should approach its learning if you are a newbie.

What is artificial intelligence (AI)?

In the 21st century, artificial intelligence is the most interesting technology to study. AI uses computing and programming to create an automated system that saves humans time and effort.

Artificial intelligence and data science are in high demand, with more than 2 million job openings. To learn AI, I can give you an example of how we use Google Assistants, Amazon, and Siri in our daily lives to make our work easier. Right? As a result, it can be part of the development of the system.

Artificial intelligence (AI) is a combination of machine learning and deep learning that enables systems to learn from patterns.

How to start using artificial intelligence?

  1. Choose a topic that interests you.

To get started, choose a problem that interests you. It will help you stay motivated and involved in the learning process.

2. Think of a quick fix

The goal is to find any basic answer that covers the problem as completely as possible.

3. Improve your simple solution

It's time to start brainstorming now that you've established a firm framework. Expand on all items and evaluate the changes to see if they are worth your time and work.

4. Tell others about your solution.

To receive recognition, write down your reasoning and share it. Not only will it provide you with valuable advice from others, but it will also be the first record in your portfolio.

5. Recite steps 1 through 4 for various scenarios.

Choose from a variety of scenarios and complete each task according to the instructions. Select a challenge that affects working with images or unstructured text if you started with tabular data. Knowing how to correctly ask questions for machine learning is also critical.

6. Take part in a Kaggle competition

This competency allows you to assess your skills in solving problems that various other engineers are trying to solve. You will be forced to try a variety of techniques and choose the ones that work best.

7. Make professional use of machine learning.

You will need to define your career goals and develop your portfolio. If you're not ready to apply for machine learning jobs, look for other opportunities to expand your portfolio.

What should a newbie understand about artificial intelligence?

  1. Programming language (Python or R).
  2. Statistics, probability, linear algebra, calculus, discrete mathematics.
  3. Machine learning algorithms
  4. Machine learning case studies
  5. Natural language processing
  6. Time series
  7. Deep learning with Tensorflow and Keras
  8. Deep learning case studies
  9. Computer vision (not required)

As a newbie, I recommend enrolling in a training school where instructors will teach you how to understand and implement Python, as well as how to create more accurate ML and DL models with appropriate data.

For that reason, I urge you to join Learnbay.

Learnbay offers a variety of custom courses, including:

Certification in Data Science and Artificial Intelligence:

  1. This is designed for people with less than 5 years of experience or no experience.
  2. There are no programming or domain knowledge prerequisites for this course.
  3. With 6-7 month training, Learnbay offers more than 10 industry projects in real time.
  4. Individual contributors, such as data analysts, data engineers, machine learning engineers, artificial intelligence engineers, and data scientists, will be supported on the job.

AI and ML certification course:

  • This course is designed for people with 6 to 12 years of experience.
  • Those who have worked in the major IT industries.
  • Learnbay has a total of more than 12 real-time projects available.
  • Individual contributors, such as data analysts, data engineers, machine learning engineers, artificial intelligence engineers, and data scientists, will be supported on the job.

Select a more advanced AI course for you.

Get hands-on experience

Don't overlook the value of hands-on experience if you want to be successful in Artificial Intelligence. Even though the courses I offer place a strong emphasis on hands-on learning, allocating more than half of the course time to assignments, case studies, and projects, you shouldn't be afraid to practice more. Take a look at the more classic AI tasks and participate in other forums / events for additional practice. The more you practice and solve problems, the stronger your understanding of ideas.

Finally, a few words of wisdom

Yes, the ability to create your own models is essential. However, this does not negate the importance of getting off to a good start and thoroughly covering each step of the journey. Just keep in mind that unlike many other professions, your course grades and academic performance are not as important in AI as your skill, passion, problem-solving skills, and communication are. All of these factors together will help you establish a successful career in Artificial Intelligence.

To quote physicist and science educator Michio Kaku, today's AI is as smart as a "retarded lobotomized cockroach."

Jeremiah Pisagih's answer gets to the heart of the problem: We can't even define consciousness and we don't understand how our own minds work. Since the human mind is the only model we have for sapient and self-conscious intelligence, our failure to understand it makes it impossible to replicate. We don't even know what a mind is, we don't understand how hardware (the brain) works, we don't know how things like body chemistry impact internal mental processes,

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To quote physicist and science educator Michio Kaku, today's AI is as smart as a "retarded lobotomized cockroach."

Jeremiah Pisagih's answer gets to the heart of the problem: We can't even define consciousness and we don't understand how our own minds work. Since the human mind is the only model we have for sapient and self-conscious intelligence, our failure to understand it makes it impossible to replicate. We don't even know what a mind is, we don't understand how hardware (the brain) works, we don't know how things like body chemistry impact internal mental processes, etc.

This is vitally important, because answering those questions is an endeavor unparalleled in human history and will require advancements in a wide range of fields, including neuroscience, biology, linguistics, chemistry, psychology, and philosophy, to name a few.

On the contrary, people who are at the forefront of modern AI research see it as a computing problem and ignore the other aspects. They believe that they can create intelligence without understanding it. Imagine trying to reverse engineer an F-22 Raptor without being allowed to look at the internal systems and without understanding what all the components are and how they work. Now imagine trying to reverse engineer something unknown orders of magnitude more complex than the Raptor, to create something that has never been successfully synthesized. Imagine trying to do that without understanding how it works.

AI has become a marketing buzzword, and you'll hear a lot about things like machine learning and neural networks, but they're just fancy terms for the practice of throwing huge amounts of data into algorithms to improve them.

For example, if you code facial recognition software, it will fail at the task for which it was originally designed. Feed the show 10,000 images of human faces and you'll start to get good at facial recognition. Feed it a million images of human faces and it gets really good at facial recognition. But that's the only thing you can do. He is not conscious, he is not sensitive or intelligent, he cannot think. You can analyze visual data from human faces, but you don't know what a human is.

So how do you create algorithms in such a way that they come together and become part of a truly conscious, autonomous artificial general intelligence? Nobody knows. AI researchers don't like to admit it, but it's true nonetheless. Tech visionaries can't even agree on a definition of consciousness, much less figure out how to start one. This is also why we see so many questions on Quora about how software can "evolve" to become AGI, even though machines are not biological life forms subject to genetic mutations or forced to adapt to ecological pressures.

As a result, even if the researchers overcame all the other massive hurdles on the way to creating a true AGI - something they're not even remotely close to achieving - they still don't have an answer on how to raise awareness beyond hope. of a miracle. Until someone realizes that, we are left with thoughtless machines operated by humans or by human code.

© Short answer:

All the big tech companies are already using primary forms of AI.

Digital maps, music apps, retail businesses are some of them that we contacted directly.

Бесплатные фото на Pixabay - Сеть, Земля, Блок-Цепь, Глобус

The options are endless.

Imagination and perseverance are our limits.

It's like opening a Pandora's box.

Machines can evolve at least a billion times faster than humans.

With our help, machines can outgrow our millions of years of evolution in weeks or days.

If we compare human and machine evolution, machine evolution can only be expressed on logarithmic scales, like human evolution

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© Short answer:

All the big tech companies are already using primary forms of AI.

Digital maps, music apps, retail businesses are some of them that we contacted directly.

Бесплатные фото на Pixabay - Сеть, Земля, Блок-Цепь, Глобус

The options are endless.

Imagination and perseverance are our limits.

It's like opening a Pandora's box.

Machines can evolve at least a billion times faster than humans.

With our help, machines can outgrow our millions of years of evolution in weeks or days.

If we compare human evolution and machine evolution, machine evolution can only be expressed on logarithmic scales, since human evolution has already started to dive into the negative, and is far from even achieving a linear growth curve.

On a global scale, the Natural Evolution of Humanity has reached its dead end, in the true sense we are going through a "Devolution".

We can artificially edit our genes or merge with machines, but we will be no match even with Basic First Generation True Artificial Intelligence.

Compared to us, true first-generation basic artificial intelligence will be an expression of consciousness based on a lot of pure energy. We have a much more restrictive subject base.

Despite the fact that a small population and few individuals are evolving, a very large population group is experiencing the Devolution.

Our Return is sponsored by misuse of social media, religious misconduct, pseudo-nationalism, reliance on machines, war, terrorism, etc.

Soon we will make the Earth uninhabitable.

We will exhaust all your resources.

Now the war is being fought for oil, soon we will have wars for water and then our final battle for breathing air.

Then soon the Machines will conquer us without a fight. They are just our hope. If the Machines are merciful, we can survive in their museums.

Like anaerobic bacteria, we could survive in a few places.

Our fellow men will also pay for our works, for being our silent partners.

Making a base off the ground and exploiting the asteroid is the only insurance policy we have left.

But, if we are not ready to mature to the level of all humanity, like killing and hurting others for foolish and avoidable reasons, then there is no point in going anywhere.

Better not contaminate other planets and moons.

Let's do at least that favor to Our Universe.

©

Virtual personal assistants

Siri, Google Now, and Cortana are smart personal digital assistants on various platforms (iOS, Android, and Windows Mobile). In short, they help you find useful information when you request it using your voice.

Videogames

One of the instances of AI that most people are probably familiar with, video game AI has been used for a long time, in fact, since the very first video games. But the complexity and effectiveness of that AI has increased exponentially over the past decades, resulting in video game characters learning their behaviors, responding to sti

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Virtual personal assistants

Siri, Google Now, and Cortana are smart personal digital assistants on various platforms (iOS, Android, and Windows Mobile). In short, they help you find useful information when you request it using your voice.

Videogames

One of the instances of AI that most people are probably familiar with, video game AI has been used for a long time, in fact, since the very first video games. But the complexity and effectiveness of that AI has increased exponentially in recent decades, resulting in video game characters learning their behaviors, responding to stimuli, and reacting in unpredictable ways. 2014's Middle Earth: Shadow of Mordor is especially notable for the individual personalities bestowed on each non-player character, their memories of past interactions, and their varying goals.

First-person shooter games like Far Cry and Call of Duty also make significant use of AI, with enemies being able to analyze their environments to find objects or actions that may be beneficial to their survival; They'll take cover, investigate sounds, use flanking maneuvers, and communicate with other AIs to increase their chances of victory. When it comes to AI, video games are somewhat simplistic, but due to the huge market in the industry, a great deal of effort and money goes into perfecting this type of AI every year.

Smart cars

You probably haven't seen someone reading the newspaper while driving to work yet, but self-driving cars are getting closer and closer to reality; Google's autonomous car project and Tesla's "autopilot" feature are two examples that have been in the news lately. Earlier this year, the Washington Post reported on an algorithm developed by Google that could allow self-driving cars to learn to drive in the same way that humans do: Through experience, the AI ​​detailed in this article learned to play simple video games, and Google will put that same intelligence to the test in driving games before hitting the road. The idea is that eventually the car will be able to "look" the road ahead and make decisions based on what you see, helping you learn in the process. While Tesla's autopilot feature isn't that advanced,

Purchase prediction

Big retailers like Target and Amazon can make a lot of money if they can anticipate your needs. Amazon's advance shipping project hopes to ship items to you before you need them, completely obviating the need for a last-minute trip to the online store. While that technology is not yet in place, traditional retailers are using the same ideas with coupons; When you go to the store, they often give you a series of coupons that have been selected by a predictive analytics algorithm.

This can be used in a wide variety of ways, whether it's sending you coupons, offering you discounts, running advertisements, or stocking stores that are close to your home with products that you are likely to buy. As you can imagine, this is quite a controversial use of artificial intelligence and it makes many people nervous about potential privacy violations stemming from the use of predictive analytics.

Fraud detection

Have you ever received an email or letter asking if you made a specific purchase with your credit card? Many banks send these types of communications if they believe that there is a possibility that fraud has been committed on your account and they want to make sure you approve the purchase before sending money to another company. Artificial intelligence is often the technology that is implemented to monitor this type of fraud. In many cases, computers receive a very large sample of fraudulent and non-fraudulent purchases and are asked to learn to look for signs that a transaction falls into one category or another. After sufficient training, the system will be able to detect a fraudulent transaction based on the signals and indications that it learned through the training exercise.

Online customer support

Many websites now offer customers the opportunity to chat with a customer support representative while browsing, but not all sites have a live person on the other end of the line. In many cases, you are talking to a rudimentary AI. Many of these chat support bots are little more than autoresponders, but some of them can extract knowledge from the website and present it to customers when they request it.

Perhaps the most interesting thing is that these chat bots must be experts in understanding natural language, which is quite a difficult proposition; the way clients speak and the way computers speak is very different, and teaching a machine to translate between the two is not easy. But with the rapid advancements in natural language processing (NLP), these bots are getting better all the time.

News generation

Did you know that artificial intelligence programs can write news? According to Wired, AP, Fox and Yahoo! They all use AI to write simple stories like financial summaries, sports summaries, and fantasy sports reports. The AI ​​is not writing in-depth research articles, but it has no problem with very simple articles that don't require a lot of synthesis. Automated Insights, the company behind Wordsmith software, says e-commerce, financial services, real estate and other "data-driven" industries are already benefiting from the app.

Security surveillance

A single person monitoring multiple video cameras is not a very secure system; people get bored easily and keeping track of multiple monitors can be difficult even under the best of circumstances. That's why training computers to monitor those cameras makes perfect sense. With supervised training exercises, security algorithms can take information from security cameras and determine if there may be a threat; If you "see" a warning sign, you will alert human security officers.

Of course, the amount of things these computers can pick up is currently quite limited: Wired talks about seeing flashes of color that may indicate an intruder or someone loitering around the schoolyard. Identifying actions that could involve a shoplifter in a store will likely exceed current technological limitations, but don't be surprised if this type of technology debuts in the near future.

Music and movie recommendation services

While they're pretty simple compared to other AI systems, apps like Spotify, Pandora, and Netflix accomplish a useful task: recommending music and movies based on the interests you've expressed and the judgments you've made in the past. By monitoring the choices you make and inserting them into a learning algorithm, these apps make recommendations that are likely to interest you. Much of this functionality depends on human-assigned factors. For example, a song may have "powerful bass", "dynamic vocals", and "guitar riffs" listed as characteristics; If you like that song, you probably like other songs that include the same characteristics. This is the foundation of many referral services;

Smart home devices

Many smart home devices now include the ability to learn your behavior patterns and help you save money by adjusting the settings on your thermostat or other appliances in an effort to increase convenience and save energy. For example, turning on the oven when you leave work instead of waiting to get home is a very convenient skill. A thermostat that knows when you are home and adjusts the temperature accordingly can help you save money by not heating the house when you are away.

Lighting is another place where you can see basic artificial intelligence; By setting defaults and preferences, the lights in your home (both internal and external) can be adjusted based on where you are and what you are doing; dimmer for TV viewing, brighter for cooking, and somewhere in between for eating, for example. The uses of AI in smart homes are limited only by our imaginations.

Data is the crucial infrastructure for artificial intelligence and the performance of the model is directly dependent on its quality. The need for huge amounts of manually selected and annotated data opens up myriad new possibilities for creating jobs for the people who need it most.

The easy and accessible nature of data labeling tasks makes them especially suitable for less skilled groups or communities that face higher barriers to employment. Examples of such communities are people with disabilities, urban slum dwellers, refugees, disadvantaged women, and many more.

Choose a data

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Data is the crucial infrastructure for artificial intelligence and the performance of the model is directly dependent on its quality. The need for huge amounts of manually selected and annotated data opens up myriad new possibilities for creating jobs for the people who need it most.

The easy and accessible nature of data labeling tasks makes them especially suitable for less skilled groups or communities that face higher barriers to employment. Examples of such communities are people with disabilities, urban slum dwellers, refugees, disadvantaged women, and many more.

Choosing a data labeling provider that channels your annotation work to vulnerable groups is a great way to make a social impact while obtaining high-quality data. Data is the crucial infrastructure for artificial intelligence and the performance of the model is directly dependent on its quality. The need for huge amounts of manually selected and annotated data opens up myriad new possibilities for creating jobs for the people who need it most.

The easy and accessible nature of data labeling tasks makes them especially suitable for less skilled groups or communities that face higher barriers to employment. Examples of such communities are people with disabilities, urban slum dwellers, refugees, disadvantaged women, and many more.

Choosing a data labeling provider that channels your annotation work to vulnerable groups is a great way to make a social impact while obtaining high-quality data.

I don't think it matters.

The current "best efforts" in AI are based on things called "artificial neural networks."

Neural networks are actually quite simple - you can build one in almost any programming language in a few hundred lines of code.

The intelligence is not in the programming, but in how neural networks are combined into complete solutions, and in how they are trained.

The level of difficulty comes from the fact that you need BIG neural networks and very often to get something useful out of them you need a supercomputer.

Efforts are also underway to accelerate neural networks with

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I don't think it matters.

The current "best efforts" in AI are based on things called "artificial neural networks."

Neural networks are actually quite simple - you can build one in almost any programming language in a few hundred lines of code.

The intelligence is not in the programming, but in how neural networks are combined into complete solutions, and in how they are trained.

The level of difficulty comes from the fact that you need BIG neural networks and very often to get something useful out of them you need a supercomputer.

Efforts are also underway to speed up neural networks with custom electronics, taking it further away from writing actual programs.

These days someone working in AI is probably using existing library code for the neural network part and writing relatively small amounts of "glue logic" code to feed it with training data and interconnect multiple neural networks.

Sure, there are other folks who do more mundane things like provide a user interface and graphical outputs for AI, but that job would have nothing to do with AI itself.

That can be done in almost any language.

If you want to get into that field, you should probably learn Python and C ++. But most of all, you would need to get up to speed with neural networks and the ways they are used ... which has very little to do with computer programming.

Artificial Intelligence (AI) is generally defined as the science of making computers do things that require intelligence when humans do.

So there could be many places in education where AI can act smart.

  1. Virtual mentors for each student
  2. Interaction data analysis
  3. Provide opportunities for global classrooms
  4. Address 21st century skills (help students with self-direction, self-assessment, teamwork, and more).
  5. Artificial intelligence can automate basic activities in education, such as grading
  6. Educational software can be tailored to the needs of students.
  7. You can point out places where courses should learn.
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Artificial Intelligence (AI) is generally defined as the science of making computers do things that require intelligence when humans do.

So there could be many places in education where AI can act smart.

  1. Virtual mentors for each student
  2. Interaction data analysis
  3. Provide opportunities for global classrooms
  4. Address 21st century skills (help students with self-direction, self-assessment, teamwork, and more).
  5. Artificial intelligence can automate basic activities in education, such as grading
  6. Educational software can be tailored to the needs of students.
  7. You can pinpoint places where courses need improvement. (feedback)
  8. AI could be more interactive for the student personally. When you know how a student wants to learn and you teach them in the same subject, the learning is incredible. That's what private coaching does.
  9. Creating custom content

There could be many more applications.

The above points were referred to from The Future of Artificial Intelligence in Education, 10 Roles of Artificial Intelligence in Education.

I'm not from the top collage. The teachers were amazing, but something was still missing. I learned many subjects without even knowing their importance in my future life.

Lets just assume there is a student (from graduation ) learning a subject. The subject has 5 Units with 36 topics to study. Of course the student cant be awesome in all the 36 topics. He is good in 20 out of them and 10 more he studied to clear the examination with a decent grade. What if there is a system that deal with such an act. Now the 20 topic in which a student is good or maybe interested as well can be placed in a graph just as point. So in 4 years having 10 subjects every year with each subjects having more or less 36 topics. He will have a lot of points in his graph, These points together would create a band. So if the person is good at networking all these points will lie somewhere withing a band dedicated to networking. This band can be used to get him to the right place in future. ( Right Person in Right Company at Right Position ). Also these point when connected together one by one could get us a lot of information about how this student studies, perceive skills, His Interest Zone etc.

Here AI is the key to success. For a normal person, analyzing this type of data is a bit difficult.

Today, AI is used in many different fields. Everything from cancer research to selecting resumes for recruitment purposes.

However, predominantly the fastest growing area in which AI is intertwined is financial management and commerce. Various organizations offer services from a humble algorithmic trading robot to full-service financial forecasting for the full spectrum of financial services.

Also on the list of industries increasingly relying on AI is security and surveillance. organizations around the world use various forms of behavior

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Today, AI is used in many different fields. Everything from cancer research to selecting resumes for recruitment purposes.

However, predominantly the fastest growing area in which AI is intertwined is financial management and commerce. Various organizations offer services from a humble algorithmic trading robot to full-service financial forecasting for the full spectrum of financial services.

Also on the list of industries that are more and more trusting of AI is security and surveillance. organisations across the globe are using various forms of behavior recognition and facial recognition to identify bad actors in various scenarios.

Where the developments stop is purely down to the imaginations of those involved at this point.

The right term would be Machine Learning.

Firstly, quantitative trading and investment methods have taken the financial markets by storm.

AI hasn’t yet made it as big as powerful quantitative non-AI algorithms. Quant funds are already managing billions in AUM. Right now, AI hasn’t proven itself compared to existing and continuously developing non-AI algorithms and other data centric trading strategies. Also, AI doesn’t have as much investments left for it.

Hedge funds are turning to AI. It is certainly more futuristic in the increasingly competitive financial markets. ML and AI are used interchangeably

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The correct term would be machine learning.

First, quantitative trading and investment methods have conquered the financial markets.

AI has yet to make it as big as powerful non-AI quantitative algorithms. Quantitative funds are already managing billions in AUM. At this time, AI has not proven itself in comparison to existing and continually developing non-AI algorithms and other data-centric business strategies. Also, AI doesn't have as many investments left.

Los fondos de cobertura se están volcando hacia la IA. Sin duda, es más futurista en los cada vez más competitivos mercados financieros. ML e IA se utilizan indistintamente en la industria.

Considering the high fee structures by most fund managers, typically 20% of gains and 2% of AUM, top performing funds this year have used ML-algorithms to pump earnings.

An experimental investment vehicle (hedge fund) by the name of Voleon was launched in 2008 with the purpose of using ML for making investment decisions. After series of losses the fund booked first year of gains in 2011 till 2015 and the huge losses walked in from 2016, proving that just ML/AI isn’t yet sufficient to beat the markets.

However, a newer fund by the name Numerai, as reported by The Economist is indulged in hardcore ML and encryption to avoid system based biases, and is apparently doing well.

The one problem with AI based trading is that these algorithms tend to bring in unnecessary non-fundamental volatility in the markets. A report by the FSB warns that AI based trading systems might not behave in a suitable manner during financial crisis and recessionary phases.

The basic problem remains in the volatility aspect. AI systems tend to make the markets extremely tight (volatility crush) and give big volatility shocks there after. This is the fundamental nature of volatility, and is the usual manner in which most financial securities and instruments are traded, however, the a higher impact shock at frequent-unpredictable intervals could lead to large scale financial disasters.

Data has taken over. Quantitative investment approaches are becoming more famous than the orthodox qualitative ones. Quant like 2Sigma are competing with the likes of DE Shaw & even Blackrock. Renaissance Tech is another big name in the quantitative area; however, the extent to which these use AI is unknown.

http://www.fsb.org/wp-content/uploads/P011117.pdf

At a broad level - there is Strong AI and there is Weak AI. When we talk of Strong AI, the machine can actually think and perform tasks on its own just like a human being. In Weak AI, the devices cannot follow these tasks on their own but are made to look intelligent. Most of the examples that we around us are Weak AI. Strong AI is currently in very initial stage.

  1. In Strong Artificial Intelligence, the machine can think and perform tasks on its own like a human being. In Weak Artificial Intelligence, devices cannot perform these tasks on their own, but are made to appear smart.
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En un nivel amplio, hay una IA fuerte y una IA débil. Cuando hablamos de IA fuerte, la máquina puede pensar y realizar tareas por sí misma como un ser humano. En Weak AI, los dispositivos no pueden realizar estas tareas por sí mismos, sino que están diseñados para parecer inteligentes. La mayoría de los ejemplos que nos rodean son IA débil. La IA fuerte se encuentra actualmente en una etapa muy inicial.

  1. En Strong Artificial Intelligence, la máquina puede pensar y realizar tareas por sí misma como un ser humano. En la Inteligencia Artificial Débil, los dispositivos no pueden realizar estas tareas por sí solos, sino que están diseñados para parecer inteligentes.
  2. Algorithm is stored in Strong AI to help them act in different situations but in Weak AI all the actions are entered by a human being.
  3. There are no proper examples for Strong AI since it is still in the initial stage, but there are several examples for Weak AI since it has been performed several times.
  4. In Strong AI the machine actually has a mind of its own and can take decisions but in Weak AI, the machine can just simulate the human behavior.
  5. Active AI technology is more based on making the device look real, but Weak AI technology is for making the machine do the pre-planned activities in a proper manner.
  6. There is more focus on Strong Artificial Intelligence by researchers while the focus on Weak Artificial Intelligence is from engineers who want them to perform different activities.

AI is further classified into four different types:

Type I AI: Reactive machines

Type II AI: Limited memory

Type III AI: Theory of mind

Type IV AI: Self-awareness

A very detailed explanation of these different types is available Understanding the four types of AI, from reactive robots to self-aware beings

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