Once in a while, a development takes place that alters the trajectory of humankind and has a far-reaching impact on many aspects of our lives.
The automobile, or motor car, is possibly one such invention which, combined with the assembly line production pioneered by Ford, made cars affordable and commonplace.
It got rid of people’s reliance on horse-drawn transportation for traveling any kind of long distance. Distances that could be travelled became longer as the means of transport did not need to be rested or fed. Traveling for work or vacation rose, with a consequent rise in motels and quick-food outlets to house and feed travelers on the move. Suburban living came into being, since it became possible to commute some distance every day for work.
More than anything, it fed the yearning for independence, even rebellion, and acquired a prominent place in popular culture with movies, books and even music paying homage to the idea of the automobile, like a James Dean driving a Mercury coupe in Rebel Without A Cause or Don McLean singing about driving his ‘Chevy to the levee’ in American Pie are unforgettable images and sounds emblazoned on our minds.
Efforts have been ongoing to enhance designs, technology, comfort, speed and many other aspects of cars, but they have all been incremental, small changes, without altering much the position of the automobile in common perception.
We now seem to be on the threshold of a development that could change the trajectory of humankind once again. The development is the introduction of
autonomous or self-driving cars. For over a hundred years cars have relied on human intelligence to be driven. The fine senses and perceptions of that magnificent organ, the human brain, has found no match in the technology world.
While the superiority of the human brain remains unchallenged, a form of technology called Artificial Intelligence (AI) has emerged in the last few years that has shown potential to understand and interpret unstructured information that has, so far, not been possible for computers to do. They have only been able to understand and interpret and act upon structure information that is fed to them in the form of software code. Trials are being conducted by several leading technology companies, and introduction into the real world could happen soon. Content categorization for self-driving cars is one of the many key processes required to make this possible.
Whether it fires popular imagination in the same way as the manually driven automobile, with the attitude and romance associated with it, remains to be seen. But, from the commercial significance perspective, it is bound to be a significant event.
The leadership team of BPO provider oWorkers comes with hands-on experience of over 20 years in the industry. They actively track developments in the field of industry and technology in order to stay ahead of the curve in supporting clients. It is no surprise that oWorkers has figured in the top three global
BPO providers list for data services on multiple occasions.
Content categorization for self-driving cars. How does it work?
AI models rely upon Machine Learning (ML) through which training is provided to the software that will eventually become an AI engine.
While it has been possible to understand structured textual information for a long time, what we typically refer to as software code, unstructured information has been beyond their ken. Only human beings have been able to understand and interpret unstructured information and act on it.
What is unstructured information?
All information is unstructured information for a machine, except which it has been trained or created to understand. For example:
- Text – Textual content, when written in the format of software code, can be understood by a computer. Any other arrangement of characters cannot be understood.
- Audio – A computer currently cannot understand audio the way the human ear can. It needs to be converted to a text string with the help of Natural Language Processing (NLP) technologies and then read. Most of the time, however, it will yield unstructured information.
- Image – An image is a random collection of pixels, possibly in different colors and shades, for a computer, unlike the human brain for which it could be a piece of art.
- Video – Being a combination of image sequences overlayed with audio, video is bound to be met with the same blank look, if it had one, to convey its inability to understand it.
ML seeks to train the engine by familiarizing it with the type of content it will encounter in real life, creating connections with interpretation or understanding, and take actions based on that understanding. Content categorization for self-driving cars is the process through which different objects are slotted into categories based on which actions can be taken.
The ability to understand unstructured information gives it the equivalence of the human brain, by taking information in from the various sensors that capture information around the vehicle, and put meaning to it. This is then interpreted and actioned for the purpose of navigating and driving the vehicle. The closer it gets to identifying and interpreting every little object, stationary or moving, if moving at what speed and in which direction, information that a human brain can intuitively understand and process, the closer it comes to the human brain.
As the effort continues to enable machines to acquire human-like brains, oWorkers supports client projects with the help of the human brains of its processing team. Being a preferred employer in each of its territories, it has access to the smartest human brains available to the industry. This also rubs off positively on its costs as it does not need to spend much to attract talent. They walk in on their own. This is due, in no small measure, to oWorkers actively participating in the community.
A related advantage, and a huge cost advantage to clients, is their ability to support short-term volume ramps. oWorkers can hire almost a hundred additional resources within 48 hours of a request. This obviates the need for clients to carry additional headcount during the rest of the period, in order to support that peak period which usually last only a few days.
How is understanding gained?
In order to gain an understanding, let us look at it in simple terms.
An autonomous car is likely to encounter many different objects when it drives around, like traffic signals, like pedestrians, like other vehicles, like trees and buildings and other stationary objects, and many others. Each of these objects will have their individual dimensions and attributes. Two trees will not be equally tall or wide. Two vehicles could be traveling at different speeds. But that is perhaps the next level of detail. Let us go back to the initial level, of the existence of different objects on the road.
As we have seen, before any action can be initiated, one needs to obtain an understanding of the object in respect to which action has to be taken. The car may need to slow down if it ‘sees’ pedestrians crossing the road. It may need to continue driving if it ‘sees’ a green traffic signal. It may need to manoeuvre its way around a tree if it ‘sees’ one standing in the way.
As part of the learning process, ML will enable the software to recognise an object when it sees one, a tree for instance. In other words, it enables categorization of objects based on which actions can be taken. This can be viewed as content categorization for self-driving cars.
As the ‘eye’ of the autonomous car will see everything around it as an image or as a sequence of images, ML will feed the software with images that the car is likely to encounter and connect them with the objects based on which it will be required to take actions. For instance, a certain collection of pixels arranged in a certain manner might be a tree whereas another collection of pixels in another manner could be a traffic signal. Then again, there could be many different pixel combinations that could represent a tree and the same is the case with a traffic signal. By feeding more and more information to the software, ML keeps making the knowledge base of the engine richer and richer till it is in a position to identify objects based on the arrangement of pixels that its ‘eye’ encounters.
The enduring partnerships that oWorkers has been able to forge with leading technology providers around the world ensures that they can access the latest tools for their (client) requirements. Once again, clients are a direct beneficiary as the technology is deployed for the delivery of their projects. Technology that in the ordinary course they would not have access to.
Being GDPR compliant and ISO (27001:2013 & 9001:2015) certified is the starting point for oWorkers. Their facilities are secure and they were one of the first
BPOs to create infrastructure enabling staff to work from home in a secure environment, given the constraints placed by the Covid-19 epidemic.
Why content categorization for self-driving cars is important
Incorrect classification of objects is a challenge frequently faced by AI models. And it could just be on account of a few pixels being classified incorrectly.
A small inaccuracy can lead to a big consequence since we are dealing with the real world here. Mistaking a pedestrian who can move for a tree that is stationary, or mistaking a moving vehicle with a business name printed on it for a stationary shop front, can have disastrous consequences. If a large proportion of objects identified as cars are black, the software could associate the color black with being a car.
ML algorithms can self-learn. In other words, they have the capacity to evolve based on the inputs they receive. It becomes possible to feed a significant amount of varied inputs to the system in order that the AI engine can develop a holistic ‘view.’ Being a machine, it also has the ability to overcome human limitations, such as identifying pixel-level differences, or interpreting rules faithfully or doing the same or similar tasks over and over again in a predictable manner.
Accurate content categorization for self-driving cars becomes possible with greater and varied training inputs. It is a slow process nevertheless. Time is also needed after the training phase to validate that the right output is being produced and applied. Of course, it is far from perfect, and far from the innate intelligence of the human brain. That sixth sense of a child running out suddenly from behind the stationary vehicle, of the eye contact and tacit agreement as to who will go first, cannot be replicated. Yet. However, with the latest developments, real-life usage might become possible soon.
Do more with oWorkers
If more affirmation is required, oWorkers has a transparent pricing policy. They generally offer a choice between output-based and input-based cost to its clients, many of whom report savings of over 80% after outsourcing work to oWorkers. Their ability to run a tight ship reflects on the competitive pricing they offer. They work with employees, not freelancers or contractors, as some competitors seem to choose. While this brings greater responsibility for staff development, it results in greater engagement of workers as well as provides flexibility to the company. They pay social taxes for their staff and are generally rated in excess of 4.65 on a 5-point scale by employees on Glassdoor.
With centers in three distinct geographical locations and a policy of multicultural, multi-ethnic employment, oWorkers offers many of its services in 22 languages, and is open to expanding the list, with suitable prior information. Its centers are equipped to operate on a 24×7 basis to meet client requirements and, together, create a redundancy pool through which clients can avail business continuity benefits.
Several unicorn marketplaces and technology companies rely on oWorkers to keep their business running. We hope that for content categorization for self-driving cars, you will, too.
