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Computer Vision Advances and Challenges

Computer vision refers to the field of training computers to visualize data as humans do. This technology has the potential to reach a stage wherein computers can understand images and videos better than humans. Also, the use cases are practically limitless, despite the technology still existing in its nascent stage of exploration. 

Computer Vision

Computer vision as a concept has been around since the 1950s. In its infancy, computers were trained to distinguish between shapes such as squares and triangles. Later on, training shifted towards distinguishing between typed and handwritten text.

Reasons for popularity

The main reason for computer vision’s popularity is its potential to revolutionize many every-day aspects of our lives. Computer vision drives autonomous vehicles and allows them to distinguish between traffic signal lights, medians, pedestrians, etc. It can also be used in healthcare, for detecting tumors in advance and identifying skin issues. 

There is a huge opportunity for employing computer vision in agriculture as well. It can be used to monitor the quality of crops, locate weeds and pests, based on which farmers can take action. 

Applications of Computer Vision

How about facial recognition? Yes, computer vision is already being used in new-generation smartphones to detect the user’s face. Even QR code scanning is an example of the adoption of computer vision. This technology can also be used in supermarkets to identify which users are making which purchases. 

Amazon is testing a convenience store called Amazon Go, which doesn’t have a billing counter. Instead, the store uses computer vision to identify customers and the items they add to their cart. A bill is sent to them online through the Amazon Go App once they leave the store with these items.

Advantages of computer vision

While computer vision has a lot more to achieve, it has already achieved ground-breaking innovations. That makes sense because this technology brings a lot of advantages to daily and professional life. 

Reliability

The human eye grows tired of scanning its environment. Factors such as fatigue and health come into the picture. With computer vision, this is eliminated because cameras and computers never get tired. Since the human factor is removed, it is easier to rely on the result. 

Numerous use cases 

From healthcare and agriculture to banking and automobiles, if explored smartly, computer vision can be employed in almost every aspect of our lives. These machines learn by viewing thousands of labeled images, thus understanding the traits of what’s being visualized. The same primary computer vision technology that evaluates the quality of packages in a factory can also be used to identify trends in the stock market.

Cost reduction

Computer vision can be used to increase productivity in operations and eliminate faulty products from hitting the shelves. This technology will also allow companies to manage their teams efficiently by identifying staff that could be used for other activities that require attention. For example, in Amazon fulfillment centers, productivity among workers is measured to improve efficiency and resource allocation.

Challenges faced by Computer Vision

Every emerging technology starts with a few significant drawbacks. From this technology’s development to its impact on society, there is a lot to look forward to, but a lot to be concerned about as well.

The challenge of making systems human-like

As much as computer vision is making huge leaps in its progress, it is difficult to simulate something as complex as the human visual system. The human brain-eye coordination is a marvel to behold, and its ability to understand its environment and make decisions is unparalleled by computer vision systems, at least at the moment.

Tasks such as object detection are complicated since objects of interest in images and videos may appear in a variety of sizes and aspect ratios. Also, a computer vision system will have to distinguish one object from multiple others within its view. This is a skill that computers are taking time to get better at.

Computer vision also hasn’t reached the stage wherein it can identify the difference between handwritten and typed text. This is due to the variety of handwriting styles, curves, and shapes employed while writing.

Privacy

This is arguably the biggest social threat that computer vision poses. The qualities that make computer vision effective are also the concerns of humans that value their privacy. With computers learning from thousands and thousands of images and videos, computers are getting better at recognizing individuals by their facial features, and everyone’s information is stored on a cloud.

Computer vision can track people’s whereabouts and monitor their habits. With such information, governments and businesses could be lured into penalizing and rewarding workers based on their actions. China, a nation with strong AI capabilities, is already looking to use computer vision to monitor its citizens and provide information that funds its controversial social credit system. On the other hand, San Fransisco has banned the use of facial recognition technology by the police and other related agencies.

It is psychologically unhealthy for humans to know that they are constantly being observed and monitored during every aspect of their lives. It would be interesting to see how governments intend to tackle this issue.

Final Thoughts

Computer vision’s progress can make people truly feel like they’re living through a sci-fi film. The future of this technology is filled with a range of use cases to be catered to. Numerous businesses within this realm are collecting millions of images and videos that can be used to train their computer vision systems. Also, existing businesses are exploring ways to employ computer vision into their operations. 

Challenges of Computer Vision

Computer vision has its present challenges, but the humans working on this technology are steadily improving it. Every emerging technology brings its fair share of advantages and disadvantages. While it is important to celebrate its progress, it is equally important to gauge its potential negative effect on society. This is the only way to ensure that computer vision makes our lives more comfortable and less constrained.

Top 7 ai trends in 2019

Artificial Intelligence is a method for making a system, a computer-controlled robot. AI uses information science and algorithms to mechanize, advance and discover worth escaped from the human eye. Most of us are pondering about “what’s next for AI in 2019 paving the way to 2020?” How about we explore the latest trends in AI in 2019.

AI-Enabled Chips

Companies over the globe are accommodating Artificial Intelligence in their frameworks however the procedure of cognification is a noteworthy concern they are confronting. Hypothetically, everything is getting more astute and cannier, yet the current PC chips are not good enough and are hindering the procedure.

In contrast to other programming technologies, AI vigorously depends on specific processors that supplement the CPU. Indeed, even the quickest and most progressive CPU may not be capable to improve the speed of training an AI model. The model would require additional equipment to perform scientific estimations for complex undertakings like identifying objects or items and facial recognition.

In 2019, Leading chip makers like Intel, NVidia, AMD, ARM, Qualcomm will make chips that will improve the execution speed of AI-based applications. Cutting edge applications from the social insurance and vehicle ventures will depend on these chips for conveying knowledge to end-users.

Augmented Reality

Augmented reality AI trend in 2019

Augmented reality (AR) is one of the greatest innovation patterns at this moment, and it’s just going to become greater as AR cell phones and different gadgets become increasingly available around the globe. The best examples could be Pokémon Go and Snapchat.

Objects generated from computers coexist together and communicate with this present reality in a solitary, vivid scene. This is made conceivable by melding information from numerous sensors such as cameras, gyroscopes, accelerometers, GPS, and so forth to shape a computerized portrayal of the world that can be overlaid over the physical one.

AR and AI are distinct advancements in the field of technology; however, they can be utilized together to make one of a kind encounters in 2019. Augmented reality (AR) and Artificial Intelligence (AI) advances are progressively relevant to organizations that desire to pick up a focused edge later on the work environment. In AR, a 3D portrayal of the world must be developed to enable computerized objects to exist close by physical ones. With companies such as Apple, Google, Facebook and so on offering devices and tools to make the advancement of AR-based applications simpler, 2019 will see an upsurge in the quantity of AR applications being discharged.

Neural Networks

A neural network is an arrangement of equipment as well as programming designed after the activity of neurons in the human cerebrum. Neural networks – most commonly called artificial neural networks are an assortment of profound learning innovation, which likewise falls under the umbrella of AI.

Neural networks can adjust to evolving input; so, the system produces the most ideal outcome without expecting to overhaul the yield criteria. The idea of neural networks, which has its foundations in AI, is quickly picking up prominence in the improvement of exchanging frameworks. ANN emulate the human brain. The current neural network advances will be enhanced in 2019. This would empower AI to turn out to be progressively modern as better preparing strategies and system models are created. Areas of artificial intelligence where the neural network was successfully applied include Image Recognition, Natural Language Processing, Chatbots, Sentiment Analysis, and Real-time Transcription.

The convergence of AI and IoT

IoT & AI trends in 2019

The most significant job AI will play in the business world is expanding client commitment, as indicated by an ongoing report issued by Microsoft. The Internet of Things is reshaping life as we probably are aware of it from the home to the workplace and past. IoT items award us expanded control over machines, lights, and door locks.

Organizational IoT applications would get higher exactness and expanded functionalities by the use of AI. In actuality, self-driving cars is certifiably not a commonsense plausibility without IoT working intimately with AI. The sensors utilized by a car to gather continuous information is empowered by the IoT.

Artificial intelligence and IoT will progressively combine at edge computing. Most Cloud-based models will be put at the edge layer. 2019 would see more instances of the intermingling of AI with IoT and AI with Blockchain. IoT is good to go to turn into the greatest driver of AI in the undertaking. Edge devices will be furnished with the unique AI chips dependent on FPGAs and ASICs.

Computer Vision

Computer Vision is the procedure of systems and robots reacting to visual data sources — most normally pictures and recordings. To place it in a basic way, computer vision progresses the info (yield) steps by reading (revealing) data at a similar visual level as an individual and along these lines evacuating the requirement for interpretation into machine language (the other way around). Normally, computer vision methods have the potential for a more elevated amount of comprehension and application in the human world.

While computer vision systems have been around since the 1960s, it wasn’t until recently that they grabbed the pace to turn out to be useful assets. Advancements in Machine Learning, just as the progressively skilled capacity and computational devices have empowered the ascent in the stock of Computer Vision techniques. What follows is also an explanation of how Artificial Intelligence is born. Computer vision, as a region of AI examines, has entered a far cry in a previous couple of years.

Facial Recognition

Facial recognition AI trends in 2019

Facial recognition is a type of AI application that aides in recognizing an individual utilizing their digital picture or patterns of their facial highlights. A framework utilized to perform facial recognition utilizes biometrics to outline highlights from the photograph or video. It contrasts this data and a huge database of recorded countenances to find the right match. 2019 would see an expansion in the use of this innovation with higher exactness and dependability.

In spite of having a lot of negative press lately, facial recognition is viewed as the Artificial Intelligence applications future because of its gigantic prominence. It guarantees a gigantic development in 2019. The year 2019 will observe development in the utilization of facial recognition with greater unwavering quality and upgraded precision.

Open-Source AI

Open Source AI would be the following stage in the growth of AI. Most of the Cloud-based advancements that we use today have their beginning in open source ventures. Artificial intelligence is relied upon to pursue a similar direction as an ever-increasing number of organizations are taking a gander at a joint effort and information sharing.

Open Source AI would be the following stage in the advancement of AI. Numerous organizations would begin publicly releasing their AI stacks for structuring a more extensive encouraging group of people of AI communities. This would prompt the improvement of a definitive AI open source stack.

Conclusion

Numerous innovation specialists propose that the eventual fate of AI and ML is sure. It is the place where the world is headed. In 2019 and beyond these advancements are going to support as more organizations come to understand the advantages. However, the worries encompassing the dependability and cybersecurity will keep on being fervently discussed. The ML and AI trends for 2019 and beyond hold guarantees to enhance business development while definitely contracting the dangers.

Drone Revolution | Blog | Bridged.co

It’s a bird, it’s a plane… Oh, wait it’s a Drone!

Also known as Unmanned Aerial Vehicles (UAVs), drones have no human pilot onboard and are controlled by either a person with a remote control/smartphone on the ground or autonomously via a computer program.

These devices are already popular in various industries like Defense, Film making and Photography and are gaining popularity in fields like Farming, Atmospheric research, and Disaster relief. But even after so much innovation and experimentation, we have not explored the full capacity of data gained from drones.

We at Bridged AI are aware of this fact and are contributing to this revolution by helping the drone companies in perfecting their models by providing them with curated training data.

Impact of Drones

Drones inspecting power lines

Drones are being used by companies like GE to inspect their infrastructure, including power lines and pipelines. They can be used by companies and service organizations to provide instant surveillance in multiple locations instantly.

Surveillance by drones

They can be used for tasks like patrolling borders, tracking storms, and monitoring security. Drones are already being used by some defense services.

Border patrolling
Drones surveying farms

In agriculture, drones are used by farmers to analyze their farms for keeping a check on yield, unwanted plants or any other significant changes the crops go through.

Drones at their best

Drones can only unlock their full potential when they are at a high degree of automation. Some sectors in which drones are being used in combination with artificial intelligence are:

Image Recognition

Drones can only unlock their full potential when they are at a high degree of automation. Some sectors in which drones are being used in combination with artificial intelligence are:

Image Recognition

Drones use sensors such as electro-optical, stereo-optical, and LiDAR to perceive and absorb the environment or objects in some way.

Computer Vision

Computer Vision is concerned with the automatic extraction, analysis, and understanding of useful information from one or more drone images.

Deep Learning

Deep learning is a specialized method of information processing and a subset of machine learning that uses neural networks and huge amounts of data for decision-making.

DJI’s Drone

Drones with Artificial Intelligence

The term Artificial intelligence is now routinely used in the Drone industry.

The goal of drones and artificial intelligence is to make efficient use of large data sets as automated and seamless as possible.

A large amount of data nowadays is collected by drones in different forms.

This amount of data is very difficult to handle, and proper tools and techniques are required to turn the data to a usable form.

Combination of drones with AI has turned out to be very astounding and indispensable.

AI describes the capability of machines that can perform sophisticated tasks which have characteristics of human intelligence and includes things like reasoning, problem-solving, planning and learning.

Future with Drones and AI

In just a few years, drones have influenced and redefined a variety of industries.

When on the one hand the business tycoons believe that automated drones are the future, on the other hand, many people are threatened by the possibility of this technology becoming wayward. This belief is inspired by many sci-fi movies like The Terminator, Blade Runner and recently Avengers: The Age of Ultron.

What happens when a robot develops a brain of its own? What happens if they realize their ascendancy? What happens if they start thinking of humans as an inferior race? What if they take up arms?!

“We do not have long to act,” Elon Musk, Stephen Hawking, and 114 other specialists wrote. “Once this Pandora’s box is opened, it will be hard to close.”

Having said that, it is the inherent nature of humans to explore and invent. The possibilities that AI-powered drones bring along are too charming and exciting to let go.

At Bridged AI we are not only working on the goal of utilising AI-powered drone data but also helping other AI companies by creating curated data sets to train machine learning algorithms for various purposes — Self-driving Cars, Facial Recognition, Agri-tech, Chatbots, Customer Service bots, Virtual Assistants, NLP and more.