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Attention not needed!

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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
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58th Annual Meeting of ACL (Association for Computational Linguistics) 2020 July 5 - July 8

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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
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MLOPS: Emerging best practices for deploying ML Models in production

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
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Natural Language Processing (NLP): A Key to Human Advancement

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
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Are you tired of waiting ? - AI can help!

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
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