How Deep Learning Mimics the Brain

Artificial Intelligence (AI) is slowly developing at the moment because, as John Light infers (2017), it is both a technology AND a philosophy. I mean, will we really ever replicate human intelligence and sentience in machines?

Although we have gone from hard coding rules into a machine to the machine sensing, comprehending, acting and learning on its own, we have only simulated the logical operations of the mind so we still have our work cut out for us when it comes to machine consciousness because obviously, consciousness is not in our power to create.

Machine Learning (ML) is a branch of AI that enables machines to make predictions from any given inputs, acting without being explicitly programmed and Deep Learning (DL) is a method of ML which uses artificial neural networks for classification tasks and is inspired by biological neural networks which Nagify Richard (2018) explains.

Just as the neuron receives signals from the dendrites and sends them down the axon as inputs to other neurons, repeating the process, Nagify Richard (2018) relates how artificial neurons can do the same to classify data which results in Computer Vision which allows a computer to see and visualize like a human would (Playment, 2018) and also speech recognition etc.

Machines will only ever mimic math logic because it is quantifiable according to the laws of this physical dimension whereas consciousness and emotions are reserved for sentient life forms and cannot be measured or weighed in this physical dimension.

References:

1. Morikawa, R (2018). What's Slowing Down the Pace of AI Innovation?. [online] Gengo. Available at: https://gengo.ai/articles/whats-slowing-down-the-pace-of-ai-innovation/ [Accessed 12 March 2019].
2. Light, J (2017). Why is AI Development so Slow at the Moment. [online] Quora. Available at: https://www.quora.com/Why-is-AI-development-so-slow-at-the-moment [Accessed 12 March 2019].
3. Richard, N (2018). The Difference Between Artificial and Biological Neural Networks. [online] Towards Data Science. Available at: https://towardsdatascience.com/the-differences-between-artificial-and-biological-neural-networks-a8b46db828b7 [Accessed 12 March 2019].
4. Playment (2018). What is Deep Learning and Why you Need it. [online] Becoming Human. Available at: https://becominghuman.ai/what-is-deep-learning-and-why-you-need-it-9e2fc0f0e61b [Accessed 12 March 2019].

How Ingenius is Design Thinking?

I was recently introduced to Design Thinking at work as part of the training and I took some time to grasp the basics. From the team exercises we performed, I perceived how a project team can come together to ideate and collaborate with the client. Ideas are in sympathy to the consumer's feelings and needs making this design approach human-centric to provide innovative solutions that people love.

Design Thinking is also a way for businesses to design for change (especially in IT's rapidly changing environment and disruptive market) by getting a good understanding of the problem and its context and also using many techniques for iterative ideation - which is why it also fits seamlessly into the agile process.

To drive innovative solutions that people love Design Thinking's collaborative efforts include a diverse group of people in the process of generating ideas in a playful atmosphere for a serious task. Design Thinking does not feel like work but yields innovative solutions for complex problems, to deliver products and services.

What I enjoy the most is that when generating ideas no idea is too silly. There is no judgement when ideating as you come up with as may ideas as possible. From there the best ideas are selected then framed for the problem statement. You would be surprised at how ingenius the solutions can be.