As the entire world has entered a paradigm shift in 2020 due to the virus, trends across every industry may have changed to meet these changing times. In data science and AI, many practitioners and researchers have had to shift their focus to meet the demands of their company, academic institutions, or personal research endeavors. Now that the year is more than halfway over, what has stood out in 2020 so far, and what are leading data scientists seeing in their work? Model bias remains an issue Dr. Jon Krohn, Chief Data Scientist untapt The big item for me is that the ML community is beginning to wake up to the widespread, unwanted bias that is present in data-driven models. Whether it's related to machine vision, natural language processing, or other applications, the researchers and developers devising the models underlying these applications are not a representative sample of the broader population demographics. The data sets that they tend to work with likewise are not a representative sample of broader population demographics.

Language is an intricate thing. It is colourful, diverse and powerful to the point that it dictates our thinking and at times actions. In case you haven't heard yet, there is a new technology called GPT-3, an OpenAI developed tool that practically has access to all the knowledge on the web and can deliver this knowledge to you in a simplified way, at a click of a button. So how about we try to get a better understanding of how artificial intelligence algorithm works, how can it change our lives and most importantly, what does it teach us about language and how we, the humans, think and operate. I will be straight with you.

Effective October 1st, 2020, we're reducing the prices for ml.p3 and ml.p2 instances in Amazon SageMaker by up to 18% so you can maximize your machine learning (ML) budgets and innovate with deep learning using these accelerated compute instances. The new price reductions apply to ml.p3 and ml.p2 instances of all sizes for Amazon SageMaker Studio notebooks, on-demand notebooks, processing, training, real-time inference, and batch transform. Customers including Intuit, Thomson Reuters, Cerner, and Zalando are already reducing their total cost of ownership (TCO) by at least 50% using Amazon SageMaker. Amazon SageMaker removes the heavy lifting from each step of the ML process and makes it easy to apply advanced deep learning techniques at scale. Amazon SageMaker provides lower TCO because it's a fully managed service, so you don't need to build, manage, or maintain any infrastructure and tooling for your ML workloads.

At the present scenario, Artificial Intelligence and machine learning have been portraying a critical role in the advancement of the tech sector. Social media platforms have been performing a significant role when it comes to keeping updated with the latest and trending information. One such platform is Twitter. Twitter not only helps you keep track of the latest social and economic news, but also it allows you to both share and acquire knowledge about emerging technologies. Below here, we jotted a list down the top ten AI accounts, based on alphabetical order, one must follow on Twitter.

There's been a great deal of hype and excitement in the artificial intelligence (AI) world around a newly developed technology known as GPT-3. Put simply; it's an AI that is better at creating content that has a language structure – human or machine language – than anything that has come before it. GPT-3 has been created by OpenAI, a research business co-founded by Elon Musk and has been described as the most important and useful advance in AI for years. But there's some confusion over exactly what it does (and indeed doesn't do), so here I will try and break it down into simple terms for any non-techy readers interested in understanding the fundamental principles behind it. I'll also cover some of the problems it raises, as well as why some people think its significance has been overinflated somewhat by hype.

At the end of this program, you will be able to lead and manage data science and deep learning projects and acquire a master level of all data science libraries highly demanded in the industrial sector.

Here are the most tweeted papers that were uploaded onto arXiv during September 2020. Results are powered by Arxiv Sanity Preserver. Abstract: Hardware, systems and algorithms research communities have historically had different incentive structures and fluctuating motivation to engage with each other explicitly. This historical treatment is odd given that hardware and software have frequently determined which research ideas succeed (and fail). This essay introduces the term hardware lottery to describe when a research idea wins because it is suited to the available software and hardware and not because the idea is superior to alternative research directions.

Rapid developments in AI have triggered digital advancements in almost every industry. The technology is capable of construing data contextually to provide requested information, supply analysis, and push events based on findings. Simultaneously, businesses need to meet social, investor and regulatory requirements regarding how they use advanced technologies like AI. Significantly, it is also crucial that organizations must commit to using the technology with a purpose, which leads to the way of sustainable development. In its recent study, the Allen Institute for AI argued the prioritization of "Green AI" efforts that focus on the energy efficiency of AI systems. The study was based on many high-profile advances in AI that have wavered carbon footprints.

There's been a great deal of hype and excitement in the artificial intelligence (AI) world around a newly developed technology known as GPT-3. Put simply; it's an AI that is better at creating content that has a language structure – human or machine language – than anything that has come before it. GPT-3 has been created by OpenAI, a research business co-founded by Elon Musk and has been described as the most important and useful advance in AI for years. But there's some confusion over exactly what it does (and indeed doesn't do), so here I will try and break it down into simple terms for any non-techy readers interested in understanding the fundamental principles behind it. I'll also cover some of the problems it raises, as well as why some people think its significance has been overinflated somewhat by hype.

Focus on research in Artificial Intelligence (AI) is nowadays growing more and more every year, particularly in fields such as Deep Learning, Reinforcement Learning and Natural Language Processing (Figure 1). State of the art research in AI is usually carried out in top universities research groups and research-focused companies such as Deep Mind or Open AI, but what if you would like to give your own contribution in your spare time? In this article, we are going to explore different possible approaches you can take in order to be always up to date with the latest in research and how to provide your own contribution. One of the main problems which have affected the AI research field is the possible inability to efficiently reproduce models and results claimed in some publications (Reproducibility Challenge). In fact, many research articles published every year contains just an explanation of the derided topic and model developed but no source code to reproduce their results.