Salford Business School
Mid-Term Assignment | Student# @00582731
Amit Joshi, Goutam Challagalla | Dec 2019
The objectives of this webinar are:
At the beginning of the webinar, Artificial Intelligence in defined, and the uses and capabilities of AI discussed. Some example provided to clarify the picture.
“AI is a superhuman pattern recognition machine,” and “Artificial Intelligence (AI) is going to change the future. No matter what industry you are in and what role you have in your organization, you will face some form of AI challenge”. I think the AI is not always about pattern recognition. There will be some questions; what about the learning? How can education be a pattern? Are there any reliable data on this? If so, how can we grab the model and increase learning efficiency, besides machine learning?
Also, “Artificial Intelligence” is defined as the “Machine Learning” while Machine Learning would not be possible without pattern recognition. Handwriting recognition is discussed as an example of machine learning and how the AI can recognize different patterns for each letter or word. As another example, a phone call is presented and how the machines will change/transmit the sound to 0s and 1s, back and forth. Also, the speed of transmission and calculations is discussed. Better examples could be addressed, such as color recognition, speaking, image processing, etc.
General AI is another term utilizing commonly as machine learning by population. “We are nowhere near AI that can replace humans in general tasks.”
Every day we see a piece of the puzzle being advertised and introduced to the world, indicating that AI can solve a specific problem. But I think in general the real AI should provide the solutions. As soon as the machine can start learning, they can grow, change, develop, and replace us. Pattern recognition, as a basic of machine learning, is almost achieved by the current technology available.
In the 1940s and 1950s, the basics of expert systems and AI [Role based AI] introduced were following a specific role/criteria is the main goal and learning was not involved. “We do not want self-driving cars to take the shortest ways or following other cars; what if the car is trying to break a specific role or what if the car sees another car breaking the role and following it!”
As another topic, Replicating the Brain was discussed, in which the machines are experiencing a particular situation several times. “How Google’s image processing services are working” is one of the best examples, which unfortunately was not mentioned. Neural Network introduced and discussed very shortly. As another concern, neural networks and related technologies have shown promising outcomes and open many doors in this field. Besides mastering the language, AI needs language processing. As an example: Olive oil is made of olives, but Baby oil is not made of babies!. Other examples could be presented here: different image processing barriers [a real car vs. the picture of the car on the wall]. Additionally, Google and other giant mobile companies as the masters in the field have been provided with the technology of face recognition currently available in almost all smart devices. Additionally, in the prediction field, recruitment is discussed as an example and how the AI can predict/recognize the best candidate within a shortlist. The objectives of the utilization of AI are partially discussed accordingly since it was announced in the headline of the webinar.
In the next section, the topic of the expense of AI and its obligation and obstacles for integrating into different businesses is discussed.
Improve the Business Model Recreating Business Model via AI
The topic of ethical issues of the utilization of machine learning is discussed.