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Unlike the NAACL (North American Chapter of the Association for Computational Linguistics which is the regional arm of ACL ) conference last year, held in Minneapolis, ACL 2020 becoming virtual was very convenient. There were no issues with parking spots, which allows everybody to watch more sessions than usual. One essential item to consider with virtual conferences is time zones. Presenters are around the globe, so make sure the presentation time is in local time. Here is my account of ACL 2020 and a quick framing of what I think the effect and impact of sessions in ACL 2020 would be. ACL 2020 - Effect and Impact BERT  (Bidirectional Encoder Representations from Transformers) is not going away anytime soon. Bidirectionality in deep neural networks gives better representations of words because it takes into consideration the left and the right context. The built-in bidirectionality in BERT shows that context helps in most NLP (Natural Language Processing) tasks. That is why BERT i

It comes as no surprise that machine learning models are in production systems right now. How a machine learning algorithm got there is a little bit complicated story. So in the recently concluded MLOps (Machine Learning and IT Operations), that was held virtually from June 15th to 18th, one thing is clear, the proliferation of machine learning algorithms in production systems is a challenge to deploy. The theme of most of the talks in the conference is centered around taking software development practices and DevOps (software development and IT operations) and applying them to deploying machine learning algorithms. The workshop gave meaningful insights into how an organization can adapt some of the platforms presented at the conference. If we want to leverage machine learning algorithms in software products, we need a platform to consistently develop, test, and deploy machine learning algorithms in production. The workshop presented some of the platforms that can be used right now, li

Abstract  In this paper, we will explore why NLP (Natural Language Processing) techniques contribute to Human Advancement. We will revisit how the interpretation of a text, learning from writing, and interaction with NLP based technologies affect our lives, decision, and behaviors. We will also see how NLP affects our community and social structure. 1 Introduction  NLP technique is key to our human advancement. It is crucial now for interpreting a corpus of text. It will be critical as more sources of text are digitized. It is also the reason why we need to advocate digitization adaptation. NLP techniques are how we are going to sustain the information needs of our society. NLP is also a key technology in our learning. It is becoming a necessity for our human development as we shift our social interactions online. As we produce more text that can be analyzed, we can look into a person’s profile and information without actually revealing it. Profiling can be used to target and influence

Are you tired of waiting?  Almost every day of your life will be spent waiting. You will be waiting in traffic. You will be waiting to buy or acquire a service. You will be waiting on the internet for your movie, music, or social media update. For the longest time, the human race has been inventing a lot of things for us to save time. But why are we still waiting?  We live in only one Earth, where we are bound within finite space and resources. More human beings are getting introduced to this home of ours. There are only enough resources that we can share. So, the future will bring us longer waiting times for us to share these resources because there will be more of us. There will be longer wait times because there are a lot more of us (humans) vying for the same resources. Roadways, Food, Water, Air, Internet, and Services (e.g., Healthcare, Legal, Social) will need to be efficiently used and scheduled. We have systems in place to access these resources. Our current policies a

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