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Attention, Attention For as long as we have been using Transformer models, there has been a question about how to put it in production because it needs a lot of computing power. Google took about a year from when BERT (Bidirectional Encoder Representations from Transformers) was introduced to being incorporated into their search algorithm. It is not just the advancement in understanding searches using BERT; they also needed new hardware. “But it’s not just advancements in software that can make this possible: we needed new hardware too. Some of the models we can build with BERT are so complex that they push the limits of what we can do using traditional hardware, so for the first time, we’re using the latest  Cloud TPUs  to serve search results and get you more relevant information quickly. “( https://blog.google/products/search/search-language-understanding-bert/ ) One of the complexities in BERT is the ‘self-attention’ modules, these are neural network layers that compute a compatibi

Intelligent systems are augmenting our ability to make decisions. This ability to make decisions is based on machine learning models or deep learning models that use large amounts of data that are non-parametric that fit into non-linear functions.  These models are hard to comprehend and explain. We are plugging a lot of these models into business processes in every industry.  Soon it will be hard to monitor and justify the root of a decision made because of these models. Take, for example, a step in a process where a human decides.  We can ask the human about the reason for the decision. There is also discretion on the decision based on the ethics of the one who decides. The decision might not be as consistent or efficient to maximize any benefit after that decision, but at least somebody can explain the details about the decision and understand the reason behind it. Now let's take the data behind past human choices and create a model using  AI in an intelligent system. We opt

For the longest time, our ancestors have transferred a lot of knowledge through social learning. Social learning in a family or community setting builds the foundation for adaptability as an adult. An extended childhood with a lot of opportunities for social learning is one of our adaptation. ( https://www.americanscientist.org/article/the-benefits-of-a-long-childhood ). All humans learn socially and individually. We use whatever we learn to guide our behavior or decision to what we think would be beneficial to us. The context of social learning is an ever-changing landscape. The setting of our social learning and the influences that we have from people are now shifting online. With just a few clicks of the button, you can learn from professors from ivy league universities, consult with a doctor, or even cook dinner with a world-class chef. Gone are the days that you learn your trade from your father or immediate family. You no longer are bound by the tradition of your ancestry o

We are at a time where leveraging Artificial Intelligence becomes commonplace. Every business organization is now looking into its data and building intelligent agents that predict, recommend, and even decide on organizational transactions.  Automated intelligent systems are under scrutiny because of the recent biases uncovered from systems rolled out by some of the giants in tech right now. It is being proposed that such intelligent agents are regulated, developed, and implemented ethically. Equality, non-discrimination, accountability, and safety are being suggested as guiding principles when building such intelligent agents. Regulating automated intelligent systems is going to be huge in impact in every industry. Need To Be Accountable Most intelligent systems are an ensemble of Machine Learning Algorithms or Deep Learning Neural Networks. Which fundamentally is fitting a function over past data. Some are from a math formula developed through simulation or past data. Ulti