Month: February 2021

Categories
Data Labeling

Unethical Practices among few Labeling Companies

DL - social media - unethical practices

Enterprises across multiple verticals like agriculture, retail, entertainment, and robotics, all rush to apply AI to their business operations. Lately, they have been continuously overcoming their ongoing obstacles over data labeling at scale. Business enterprises today are flooded with the need for the production of usable data. They do not lack raw data; on the contrary, brands possess a lot of data in their organizations. A massive amount of data from cameras, sensors, and other types of equipment are gathered by these organizations at any given time. The prime challenge is how to process and label the data to make it effective and usable. 

Relevant labeled data ensures that machine learning systems establish reliable models for pattern recognition, which forms the foundation of every artificial intelligence project. But, applying complex attributes and various annotations, leads organizations to deploy deep learning and machine learning models, which takes up to 80% of the AI project time. At the same time, 19% of the businesses led to the lack of data and data quality issues and the adoption of Artificial Intelligence.

What misleading Data Labeling can get you through? 

Data Labeling can be misleading and intentional at times if the creator promotes the agenda on purpose. This might result in data errors or the misunderstanding of data or the data labeling process. But, whatever might be the reason, misleading data labeling do not have any place in eLearning as they confuse and misinform the learners. 

The primary ways through which labeling could mislead learners are… 

  1. Presenting large data
  2. Hiding the relevant data
  3. Misinforming the presentation of data
  4. Inaccurate data annotations

Now let’s get in depth about each of these:

  1. Presenting Large Data

Sometimes, looking at the bigger picture could make it tough to identify the salient data. The entire data set is visualized and studied separately. This phenomenon is known as Simpson’s Paradox. Examination of the data revealed that the data period covered an era with huge growth in numbers and a range of data. 

The learners will require a bigger picture and a thorough visualization of data. Hence, the designers must consider a series of data visualizations. New media mostly does this with large data stories showing a national map, for instance, with broad representations of data via state or region, narrowly focuses visualizations that focus on important trends or other information. 

  • Hiding the Relevant Data

Highlighting a particular benefit or hiding a significant data point could lead the learners to focus on a small fraction of the data story at the expense of an accurate understanding of the bigger picture. Any individual statistics or parameter could reveal useful information. So, data visualization presents more complete data, leading the learners to adopt a different approach.

  • Misinforming the Presentation of Data

Emphasizing these selected data could lead to errors which results in selecting the wrong format for the data visualization or not completely realizing the data. These errors could be unintentional, still few presentations of the data distort in ways which appear to be agenda-driven or intentional. 

This type of distortion could be found in marketing, consumer advertising, public relations materials, and more. 

  • Inaccurate Data Annotations

A specific unethical way that leads to the utilization of data visualizations is, mislabeling of data inaccurately. Data annotators generate metadata in the form of code snippets which categorize data. A brand makes use of data annotations to identify patterns and make data searchable. However, organizations are concentrating their resources on data annotations for preparing data stacks for structured or unstructured machine learning.  

Artificial Intelligence and machine learning is the latest technology to fulfill the new vision of the future. The intersection of data science and computer science is the first step towards the computational representation of everything, where algorithms and big data are the two keys. Algorithms and big data go hand in hand to generate models to process machine learning. 

About Data Labeler

From offering the highest quality training datasets using an advanced workforce to allowing the companies to focus on their core AI/ML business, Data Labeler powers your algorithms. 

Boxes for Object Detection, Polygons for Semantic and Instance Segmentation, Points for facial recognition and body pose detection, and more. 

Contact us for effective Data Labeling Services – Sales@DataLabeler.com

Categories
Artificial Intelligence

The Digital Divide that is being caused by Artificial Intelligence

DL - blog - rise in AI

“Diffusion of Innovation,” a book by Everett Rogers, explains the conditions needed for new ideas and technologies which are easy to outspread throughout society. There he raised the issue of unintended consequences. The tremendous benefits of innovation could result in unintentional negative effects, which would create a condition of disequilibrium. An innovation that advances faster than the society, research, and policies, reduces the ability to identify or assess the adverse effects. 

Over time, artificial intelligence (AI) has created a state of disequilibrium in the society as well as in education. Presently, AI could be found driving the search engines, enabling text-to-speech, translations, smart tutoring technologies, and many more. And these technologies have prospered education faster than research. As a result, despite all the promises, there could be real and significant consequences, specifically when it comes to digital equity. 

Educators & policymakers have already warned about the effects of the digital divide in the 1990s. In the beginning, this deficit referred to the lack of access to computers as well as the internet. By 2016, the National Education Technology Plan which is warned of another issue that is emerging as digital use divide, as few students realized the use of technology for the active creation of knowledge and understanding while others remained passive customers of digital content. Therefore, with the growth, another chasm may rise as a result of varying experiences and exposure to this kind of innovation.

How Artificial Intelligence Used Divide? 

According to a report by the State of Creativity in Schools, there’s no difference between the learning experience of students across multiple grade levels, geography, and students who attended schools at distinct underserved communities who reported fewer opportunities for creating learning experiences with transformative uses of technologies. 

As artificial intelligence (AI) continues to penetrate the education space, a similar dichotomy might soon arise. And students might leverage AI in support of complex problem solving, critical thinking, or to create new forms of Artificial Intelligence. 

Moreover, if students have equal access to artificial intelligence, that exposure might aid in expanding the digital use divide.  

Opportunity Gap

The great promise of Artificial Intelligence is wider personalization as a platform that intends to stimulate the experience of learning alongside a personal teacher. Since the 1920s, educators are trying to create and train machines to provide immediate learning experiences on a large scale. AI platforms guide you in suggesting resources, problem-solving, or analyzing writing or speech. So, a computer decides what, when, and how students learn, or questions emerge about whether an experience could be described as personalized or leaving very little to student’s interest. 

Similarly, the volumes of evidence demonstrate that students learn the best when inspired with curiosity and opportunities for developing new thinking through social interactions and authentic experiences, which could test different ideas within a supportive environment. So, consider the potential implementation of AI platforms for personalization where the meaningful face-to-face learning experience is absent. Few of the backlashes to AI and personalized learning could be attributed to the perception that learners spent their days in technology-rich spaces.

Digital Literacy & Skills

Research reveals that the digital divide is more than only an access issue that cannot be alleviated by offering the necessary equipment. There are a minimum of three factors, information utilization, information accessibility, and information receptiveness. An individual needs to know how to utilize information and communication tools when they exist in a community. Information professionals can bridge the gaps and provide reference and information services to aid the people in learning and utilizing the technologies that have access without taking into account the economic status of the people seeking help.  

Artificial Intelligence could bring amazing benefits to education, and it could also fan flames of existing inequities and further widen the digital divide. 

About Data Labeler

Data Labeler caters to a crucial service that empowers the companies to concentrate on their Machine Learning or Artificial Intelligence business. We create the data sets that you require to power the algorithms. 

Contact us for specialized & quality datasets.

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Artificial Intelligence continues to penetrate the education space as it intensifies the existing inequities and further widen the digital divide. 

Categories
Artificial Intelligence

The Rise of Robotics in the fields of AI & ML

DL - robotics_ai_ml

Artificial intelligence is all set to disrupt every imaginable industry, including industrial robotics. Presently the robust combination of Artificial Intelligence or Machine Learning has already opened multiple new automation possibilities. Yet, it enhances the capabilities of the industrial robotic systems. Though industries are yet to make use of the full potential of machine learning as well as robotics and the applications are seem to be promising.

Robots are one of the first automated machines which are developed for performing the various actions which aid humans in completing day to day tasks in a short period. Also, by utilizing machine learning robotics, developers build AI robots that could better understand the various scenarios and work more effectively. 

The thriving field of Robotics 

From agriculture to the manufacturing sector, artificial robots play an influential role in making the production process faster and cost-effective due to mass production and achieving economies of scale. Hence, it helped those industries by producing the goods more economically than others. 

Moreover, data labeling companies provide advanced data annotation services integrated with Artificial Intelligence applications in robotics and autonomous utility vehicles, including multiple use cases. Some of these use cases are drones, intelligence gathering, machine or human interactions, security monitoring, warehouse logistics, and many more.

Nowadays, brands possess deep expertise in Training Data annotation and collection for Artificial Intelligence and Machine Learning applications. Their data annotation platforms cater to highly accurate data labeling in the clouds and any brands’ secure computing environment. Best data labeling companies aid a few of the world’s largest brands for training utilizing AI/ML models, data, services, and software.

Data labeling brands also help in manufacturing industrial goods and services, building industrial robots, and improving the accuracy of the computer vision models through cost-efficient and high-quality labeled datasets.

Artificial Intelligence & Machine Learning Applications offers excellent potential. There are experts who expertise in robotics, engineering, and related fields of science.

Here are some examples of why an artificial intelligence robot could master the recent technologies.

How industrial robots could be integrated with AI for making people more aware of their surroundings

The Industrial sector has beautifully leveraged robotics for doing multiple things without errors. And indeed, safety is the key when robots are deployed in the workplace. So the offerings of AI robotics have played a pivotal role in the current environment.

For instance, Veo Robotics has an advanced, industrial robotics system which combines AI, sensors, and computer visions. This will allow them to setup their machines for working full-fledged by themselves without any human interference.

Also, autonomous mobile robots (AMR) are fully equipped with robotics technology and offer a dynamic performance.

Machine learning empowers robots to adapt and learn from their mistakes

More people are getting smarter day by day with both technology and experience. Although technologies like robotics applications or machine learning might hold the same abilities. Therefore, when that happens, they might or might not continue with the intensive training they receive from the humans. Rather learning might happen through vivid use.

For example, how one could be able to train a robot by making use of machine learning. 

Conclusion

The transformation of multiple technologies begins when brands onboard several technological platforms, which are often integrated with AI, ML, or robotics. These technological innovations have been proven beneficial to several organizations.

For instance, data collection is nothing but a rigorous process of gathering, storing, and transforming data for making it ready for predictive modeling. Since the arrival of these innovations relevant data could be collected based on our business requirements very easily.

Image annotations, image labeling, data labeling, and data annotations have also made the task of the data experts easier.

About Data Labeler

Data Labeler increases your competitive advantage, provides you unlimited support, and guarantees exponential growth.

Data labeler empowers business organizations by offering convenient, accurate, expedited, and quality labeled data sets for robust Artificial Intelligence and Machine Learning initiatives.

Categories
Artificial Intelligence

Artificial Intelligence in Space

DL AI-in-space-exploration

As super computers have become a reality, science fiction caters to several examples of artificial intelligence, machine learning, or smart robots in outer space. From star wars to hitchhikers’ guide to the galaxy, it seems that AI and space go hand in hand. Since these are fiction, we are trying to find examples in the real world which make use of artificial intelligence to help in commercializing space.

Astronauts were trained both physically and psychologically for dealing with extreme space situations. Living in a confined space without gravity could be stressful and might hamper the decision making processes. This is where artificial intelligence which came into the picture.

Multiple years after the first moon landing, experts are looking for emerging technologies to realize space exploration in a better way. And recent breakthroughs and discoveries have led to show the immense potential in space exploration like global navigation, earth observation, and other communication from both sides. Machine (Learning) algorithms have been used in monitoring the spacecraft, controlling systems, autonomous navigation of the spacecraft, controlling systems, and smartly detecting objects in the road path. Hence in this way, to help astronauts meet their goals to in reaching mars and beyond, AI-based assistants are created. These assistants are primarily created to understand and predict the crew’s needs and apprehend the astronaut’s emotion and mental health.

What emerges as a Game Changer in Space Exploration? 

Artificial Intelligence has positioned itself as a game-changer in the present society and also  the space industry. Government and agencies have been leveraging artificial intelligence technologies for a long time to gather imaging data related to space explorations. Robotics has been utilized by government agencies to conduct modern surveillance, identify and mitigate risks for by analyzing a substantial amount of data collection.

The European Space Agency (ESA) report reveals that satellites could produce more than 150 terabytes of data every day. Now, with the use of artificial AI technologies, one could reduce the mission’s costs, extend battery (any other form of resource) life, and analyze  massive amount of imaging data which is produced by the satellites.

Another significant utilization of artificial intelligence in the space industry is the process of increasing the spectrum efficiency and connectivity through real-time adjustments. So, in this case of space exploration, as the satellites could learn to transmit data using the appropriate frequencies, deep learning technology could simplify the communications furthermore. Reports reveal that AI technology is utilized for RLAN to reduce the chance of interference and increase spectral efficiency.

These technologies would aid in telemetry and controlling the geostationary orbit as well as non-geostationary orbit’s frequency and physical coordination. Deep learning technologies are not only meant to reduce the interference burden for satellite networks but also avoiding co-channel interference at various stages of the satellite orbit.

Artificial Intelligence on the basis of Exploration

For navigating space travel profoundly, NASA is continuously making strides towards artificial intelligence applications. Moreover, NASA has also developed an AI update, which aids in automating the laser-firing capabilities of the rover. Hence, with an increased pace in data collection, a trained AI system is a perfect match for monitoring the spacecraft and lowering the downtime and possible risks.

Also, with the help of Google’s trained model, NASA manages to discover other planets like Kepler-90i & Kepler-80g.

European aerospace company Airbus has introduced artificial intelligence in space exploration. AI-powered 3D printed spherical robots (Crew Interactive Mobile Companion CIMON) were also being developed for helping astronauts and their accuracy in everyday tasks at the International Space Station with uniform empathy level of human. A second version of this technology was developed with various extended functionalities and capabilities known as CIMON 2.

Artificial intelligence led by Space Race

The modern space race, with the help of artificial intelligence, is different from the 20th-century completion between two Cold War rivals, namely the Soviet Union and the United States, who gave their first spaceflight capabilities.

According to reports, the space industry is set to reach a worth of 1 trillion USD by 2040 and become one of the most prominent businesses in the world. And big names like Jeff Bezos, Elon Musk, and Richard Branson.

Conclusion

The space technology would be secure with the influence of artificial intelligence. It will also show clear potential for exploring the interstellar space with multiple innovative machines and projects. For each and every innovation, technology is coming a step closer to provide newer insights and proving a major advantage.

About Data Labeler

Data Labeler caters to world-class annotation services. From offering bounding boxes, polygons, points, texts to select or multi-select services, Data Labeler provides advanced quality training data sets for using a knowledgeable workforce. We also cater excellent data services for building data sets that you need to power your algorithms.