The field of machine learning and AI is evolving faster than before. New concepts and innovations are developing every day. If you are using machine learning, AI, or data science concepts for your projects or even in your career, keeping up with the latest news in the industry is like one of your everyday activities. In this article, I have shared some interesting articles related to machine learning that I read this week. This intriguing article has revealed an exciting application of AI for collecting unpaid taxes.

George Pipis provides a very high-level introduction to the world of word encoding within NLP. This article includes information on methods leveraged for encoding words in Natural Language Processing tasks. It also consists of the advantages and disadvantages of traditional approaches to word encoding such as One-hot encoding and more modern and rich techniques such as Word Embedding. Highly recommended read, as this article can serve as both a refresher for seasons NLP practitioners, or act as a taster to the world of word encoding for readers starting their learning journey into NLP related topics. George also cleverly Illustrates the shortcomings of the traditional methods of word encoding in order to directly provide a case for the introduction of more approaches that capture context and word relations within sentences.

Tommy Shrove provides machine learning relevant information on the newly introduced M1 processor by Apple. Machine learning processes and implementation are tied closely with computational hardware resources, and within this article provides information on the details of components within the M1 processor and how they impact machine learning processes. Tommy provides information on the performance and efficiency of the components, which are then translated into their benefits in a practical scenario involving machine learning processes. Tommy also mentions how the M1 processor is expected to perform with a suite of conventional machine learning tools and applications commonly utilised by ML practitioners, for example, VS Code, Jupyter etc.

Lisa Palmer has written an article that explores the utilisation, benefits and impact of ConversationalAI within B2B(business to business) organisations. According to an explanation within the article, ConversationalAI can come in two primary forms, either an AI-powered human voice or an AI chatbot. Although the article explores the business impact and organisational shift brought on by ConverstaionalAI, there's also an inclusion of a description of ConverstaionalAI and how it is applied in organisation websites and applications to augment their customer/business relations. I would recommend this article to machine learning practitioners in managerial roles or business decision making roles, as this article creates a case for the integration of a technology that is no doubt going to be prevalent within the next few years. You can already witness the impact of Natural Language Processing within applied AI, especially with several websites having an AI chatbot to address common queries asked by customers and visitors.

Awais briefly explores the AI applications and technologies that government bodies and agencies in China recruited to curb the spread of Covid19, whilst maintaining a form of functioning society for its citizens. China use of its mass surveillance system has always been a subject of controversy when it comes to the handling of personal privacy. Awais explores the benefits that this once frowned upon method of citizen governing and monitoring is the reason why China's handling of the Covid-19 crisis better than certain nations around the globe, even though initially reported cases of Covid-19 originated in China. AI technology, big data and robots were more than instrumental in the effective tackling of the spread of Covid-19 within China. Awais mentions the utilisation of various forms of AI-based technology such as facial recognition, mask detection and heat reading, to monitor, alert and inform citizens on all Covid-19 related cases in their local vicinity.

This particular article needs more visibility on Medium, especially to Machiner Learning practitioners. Wael transcribes an interview with Michael Kanaan, an individual that held an AI leadership role in the US Airforce, and is currently working with MIT's primer AI Lab. Early on in the interview, Michael quickly discards the stereotypical portrayal of AI within Hollywood movies and provides the reader with a more accurate description of AI. Michael accurately points out that what we call AI are simply machine learning algorithms that can derive patterns from data, which in turn creates a predictive model of subject of interest, such as a person's behaviour, stock prices etc. The interview goes on to include discussions around the type of individuals that are suitable for roles in AI, a conversation in which Michael debunks the myth that AI-based positions are reserved for individuals with a STEM(Science, technology, engineering, and mathematics) background.

Alex Fly's article presents some key statistics related to the development of AI and the utilisation of AI-based technologies within enterprises. The included statistics and figures are sourced from reputable surveyors such as Deloitte, Gartner, McKinsey & Company etc. The statistics and figures presented in the article paint a picture of how AI is transforming the entirety of a typical enterprise day to day functions. AI impact is felt from hiring initiatives to profitable revenue sources. One major takeaway from Alex's article is that AI is here to stay and enterprises are adapting at a pace that will see entire companies, industries and nation transform within the next five years.

An explanation of an integral component within a widely used convolutional neural network architecture. Andre Ye writes about Google's Inception architecture and the reason for its success in image classification and detection. Andre explains in this article that the Inception architecture success is primarily due to the efficient utilisation of the neural network architecture computing resources through the incorporation of 1x1 convolutions. This article explores in impressive detail as to why 1x1 convolutions are an integral component o the Inception CNN architecture. With the inclusion of detailed descriptions and creative illustrations, Andre is able to point out how convolutions operate on different formats of images (grayscale and RGB).

Get an overview of the ever-changing face of Data privacy laws, and the prevalent loopholes that exist. Aren Carpenter casts a light on the importance of adaptability within data privacy regulation as he showcases notorious misses of the wide net governing bodies throw over the handling of personal and private data. Aren's article delves into the evolution of data privacy laws from HIPAA (1996) Omnibus Final Rule (2013) to the more current GDPR. Aren makes statements that allude to the ambiguity caused by the blurred lines and vague privacy laws that make it difficult for an organisation to navigate patient privacy boundaries. Nonetheless, the first section of this article illustrates an effort by government and regulating bodies to update data privacy laws in synchronicity with the advancement of technology and information gathering.

Ganes Kesari presents methods employers can leverage to create a safe environment for employees returning to the workplace. The methods and techniques presented by Ganes in his article utilize AI to create a data-centric and machine learning based approach to implementing procedures and arrangements to ensure creating a Covid free workplace environment is possible. Ganes's article starts with the inclusion of a software blueprint to how an AI workforce scheduling system could look like. The description of the system described by Ganes sounds simple enough, but obviously, the logistical and technical efforts could perhaps turn this into a 3–6 months project. Nonetheless, the workforce environment has changed, so it could be a project worth pursuing.