No longer is Robotics only a research field within artificial intelligence as it has become a field of application where you can involve all aspects of AI to achieve better results.
Its major example comes from the U.S where hospitals use healthcare robots in the form of a delivery bot to ferry medicine and supplies. An interesting feature is you just need to call the name of a bot, and it comes to you in seconds and does your work.
The growth of AI has resulted in increased adoption of automation, robotics, and artificial intelligence. As per results of this year’s survey, forty-one percent of respondents consider AI in robotics the topic of 2018. In addition to it, forty-seven percent of respondents say they are deeply focusing on AI to kickstart their automation projects.
The advancement in robotics is worth paying attention for. For example, AI-based robots walk, talk, and go from one place to another just like humans. Core programming has taken robotics to the next level where they have become more human and help people in their daily affairs. All thanks to experts, investments, and courses, for example, the Artificial Intelligence Course that can help you make most out of robotics, though AI, in every sector, ranging from electronics to healthcare.
Just like humans have their sensory system, robots also work on the basis of their sensors. Jumping onto AI in robotics won’t make sense until you know what the building blocks of robots are.
This post thoroughly covers the advancement of AI and robotics; however, prior to starting with it, I would like to acknowledge to you the pacemakers of robots called sensors that are no different from human sensors.
Some of its types are:
Sonar sensors are one of the oldest forms of sensors that emit sensor rays in the perpendicular direction to the sensor. They are not in use now as they are dependent on the reflection of a reflective surface. The total distance between the sensor and the perceived object is the time between the emission of sound and its rebound from the perceived obstacle.
Infrared sensors emit infrared rays in the opposite direction to the sensor and receive rebound from a reservoir. The time between transmission and reception is the total distance between the sensor and the obstacle in the environment. They work within a range of 80 cm.
Laser sensors purely depend on the type of laser you use. Here the total time between emission and the reception beam does not calculate the distance to an obstacle in the environment. They can perform a sweep in the 180-degree plane.
The use of the lasers mentioned above in robots marks down their efficiency.
Going through various shades of AI in robotics, I have come up with two solid points that AI techies really need to know to get better at AI
Automation is in the air
A real example of automation comes from the retail industry. One of the leading grocery retailers, Albertsons, has a plan for AI-powered robots to experience customers hassle free order picking. Manual order picking consumers a lot of time; therefore, the company looks forward to robots to fasten the whole process of delivery as robots will bring the goods ordered by customers online to workers. This will eliminate the time taken to pick goods, per orders and then get them delivered.
Another example is of the Chinese online retailer JD.com. Its partnership with a Tokyo-based startup Mujin has created buzz for the first automated warehouse. Here robots will be responsible for picking and packing orders. This can greatly help countries like Japan, which strives for an experienced workforce.
Secure future actions
Computers are problem solvers but in limited fields. When it comes to AI for problem-solving, the idea is quite simple; however, it is difficult to implement. Computers do not have the ability to learn. They collect facts about a situation through sensors and compare that information to the data already stored and present results. The computer walks through various possible actions and predicts the right action.
All actions of the computer depend on its programming as it lacks the generalized analytical ability that modern computers usually encompass.
AI-based robots come up with limited capacity due to their deep neural network. Apart from automation, learning robots are capable of evaluating actions and provide you with business related insights.
The best part is robots can store information and take a successful action when the same scenario occurs. This is where robots outpace computers.
Today, Japan owns a robot that can dance by anticipating the moves of the body.
Another example is of Kismet, a robot at M.I.T’s Artificial Intelligence Lab. The robot is capable of analyzing and evaluating human body language and voice inflection.
The idea is to lay the foundation of a human-like learning system based on the visual cue and the tone of speech.
Recently Arsene Wenger has made a prediction that robots will soon perform the job of managers standing on the touchline.
Adding to the statement, the former Arsenal boss said that social media polls will be used to decide regarding “robo managers”. Some consider this step could invite controversies whereas others call it a great step to uplift transparency.
No matter if we have not achieved AI to the fullest, we are aware of its potential for transforming lives. We are in a position where we can make insightful decisions based on the results of AI. What you just need to do is to understand big data analytics and how to leverage it.
In 2019 we are likely to confront new innovations in AI, and our plans, strategies, and requirements will decide that how we will make AI part of our culture.