In LinkedIn, I stumbled upon a request for old essays for an AI-project. The researcher - Dr. Stephens - is working on creating an AI-powered teaching assistant. I am thinking of providing some of my old essays. The AI project by Dr. Stephens reminds me of another project I contributed to. I was an evaluator of a master's thesis prototype (system) to automatically extract UML class models from natural language requirements text.

Etienne Bernard, is the Co-Founder & CEO of NuMind a software company founded in June 2022 specializing in developing machine learning tools. Etienne is an expert in AI & machine learning. After a PhD (ENS) & postdoc (MIT) in statistical physics, Etienne joined Wolfram Research where he became the head of machine learning for 7 years. During this time, Etienne led the development of automatic learning tools, a user-friendly deep learning framework, and various machine learning applications. What initially attracted you to machine learning?

In the first part of the Autodidacts path to a MSc level in AI/Machine Learning, using UCL's MSc as a lighthouse to guide us through the rough waters of building a Machine Learning MSc curriculum, we had a look at some of the most established and helpful resources for a beginning ML engineer. Moving on to the second part of our attempt to build a curriculum for the autodidact enthusiast of Machine Learning, we will dive into one of the hot topics during the past decade. This is no other than Deep Learning. Although technically a sub-category of Machine Learning, Deep Learning has evolved into its own paradigm and has earned the title of'one of the pillars of ML' and for good reasons. The past decade has seen a huge number of successful applications and technological advancements that utilise Deep Learning.

Data scientist Oisín Boydell is working on a project that seeks to democratise access to an ever-increasing volume of Earth observation data. Dr Oisín Boydell is principal data scientist and head of the applied research group at CeADAR, the SFI-funded centre for applied AI at University College Dublin (UCD). His primary research interests include trustworthy AI, deep learning, natural language processing and applications of AI to Earth observation data. After working as a software developer in the UK, Boydell returned to UCD to undertake a PhD in computer science, researching novel approaches for personalised information retrieval. Prior to joining CeADAR he worked with SMEs and multinationals on big data analytics and machine learning solutions for the telecommunications industry.

The year 2021 was filled with numerous challenges and ups and downs but despite these, I emerged a better and stronger person. When I started my career in tech, I was inclined to the hardware part. However, on getting into the software space, I developed a keen interest in AI and wanted to get to learn the skill but I was confused and lost. I had no idea where and how to start. But, I took the leap of faith towards my path of interest.

After helping to launch the Partnership on AI with Google, Facebook and others; and doubling down on AI research, today Microsoft unveiled a new initiative that points to how it plans to target specific verticals in what can potentially be a very nebulous field -- while also raising the public image of AI as some grow concerned about the implications of its encroaching influence. The event was led by Harry Shum, Microsoft's EVP of its AI and Research Group, along with Emma Williams, GM of Bing Studio; Chris Bishop, Distinguished Scientist and Laboratory Director at Microsoft Research Cambridge; and Eric Horvitz, technical fellow and director of Microsoft Research Labs. Microsoft's R&D labs in Cambridge, UK (the first Microsoft set up outside of the US) are 20 years old this year, and Microsoft is unveiling some other new programs in the area -- including a new Microsoft Research AI group; a new "Aether Advisory Panel" (an acronym for "AI and Ethics in Engineering and Research") that will report directly to senior management; a new partnership with the Amsterdam Machine Learning Lab; and a few new experimental products that are using AI, such as a new PowerPoint Presentation Translator. This is an interesting and important twist on the AI challenge: many worry about how AI will replace humans, and/or will quietly help evade ethical and privacy oversights -- "societal angst" as Microsoft's Emma Williams, the GM of Bing Studio and its "EQ Expert", put it (EQ: emotional quotient).

After helping to launch the Partnership on AI with Google, Facebook and others; and doubling down on AI research, today Microsoft unveiled a new initiative that points to how it plans to target specific verticals in what can potentially be a very nebulous field -- while also raising the public image of AI as some grow concerned about the implications of its encroaching influence. Today, the company announced AI for Earth, a new program that will be dedicated to AI-based projects in the areas of agriculture, water, biodiversity and climate change, where Microsoft proposes to donate up to $2 million in Microsoft tools, services and training per project to help them get a leg up. It will be led by Microsoft Chief Environmental Scientist Lucas Joppa. AI for Earth will not include money grants as such -- although as recent investments from Microsoft Ventures show (and also its acquisitions), the company is also willing to put money where its mouth is, and to take equity in the most interesting projects, too. This program could help create a funnel to identify and follow some of the more interesting projects (and get them using Microsoft services to boot).

I describe here the projects that I worked on, as well as career progress, starting 25 years ago as a PhD student in statistics, until today, and the transformation from statistician to data scientist that occurred slowly and started more than 20 years ago. This also illustrates many applications of data science, most are still active. My interest in mathematics started when I was 7 or 8, I remember being fascinated by the powers of 2 in primary school, and later purchasing cheap russian math books (Mir publisher) translated in French, for my entertainement. In high school, I participated in the mathematical olympiads, and did my own math research during math classes, rather than listening to the very boring lessons. When I attended college, I stopped showing up in the classroom altogether - afterall, you could just read the syllabus, memorize the material before the exam and regurgitate it at the exam.

A new book by Lentin Joseph, ROS Robotics Programming, outlines more than 14 robotics projects using ROS that can be engaged with without requiring a lot of hardware. The book starts with an introduction to ROS and its installation procedure. After discussing the basics, you'll be taken through great projects such as building a self-driving car, an autonomous mobile robot, and image recognition using deep learning and ROS. You can find ROS robotic applications for beginner, intermediate, and expert levels inside. This book is unique in that it focuses on ROS via the lens of robotics projects only.

I describe here the projects that I worked on, as well as career progress, starting 25 years ago as a PhD student in statistics, until today, and the transformation from statistician to data scientist that occurred slowly and started more than 20 years ago. This also illustrates many applications of data science, most are still active. My interest in mathematics started when I was 7 or 8, I remember being fascinated by the powers of 2 in primary school, and later purchasing cheap russian math books (Mir publisher) translated in French, for my entertainement. In high school, I participated in the mathematical olympiads, and did my own math research during math classes, rather than listening to the very boring lessons. When I attended college, I stopped showing up in the classroom altogether - afterall, you could just read the syllabus, memorize the material before the exam and regurgitate it at the exam.