ML from scratch is a student-led tutorial / seminar series initiated by Johannes Bill and others from Jan Drugowitsch Lab at Harvard Medical School. The objective is to teach neuroscience students to learn cutting edge machine learning models by implementing them. I started participating from 2022, and I prepared the tutorial and led a few seminars in it!

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Open AI has trained a neural network to play Minecraft by Video PreTraining (VPT) on a massive unlabeled video dataset of human Minecraft play, while using just a small amount of labeled contractor data. With a bit of fine-tuning, the AI research and deployment company is confident that its model can learn to craft diamond tools, a task that usually takes proficient humans over 20 minutes (24,000 actions). Its model uses the native human interface of keypresses and mouse movements, making it quite general, and represents a step towards general computer-using agents. A spokesperson for the Microsoft-backed firm said: "The internet contains an enormous amount of publicly available videos that we can learn from. You can watch a person make a gorgeous presentation, a digital artist draw a beautiful sunset, and a Minecraft player build an intricate house. However, these videos only provide a record of what happened but not precisely how it was achieved, i.e. you will not know the exact sequence of mouse movements and keys pressed. "If we would like to build large-scale foundation models in these domains as we've done in language with GPT, this lack of action labels poses a new challenge not present in the language domain, where "action labels" are simply the next words in a sentence." In order to utilise the wealth of unlabeled video data available on the internet, Open AI introduces a novel, yet simple, semi-supervised imitation learning method: Video PreTraining (VPT). The team begin by gathering a small dataset from contractors where it records not only their video, but also the actions they took, which in its case are keypresses and mouse movements. With this data the company can train an inverse dynamics model (IDM), which predicts the action being taken at each step in the video. Importantly, the IDM can use past and future information to guess the action at each step. The spokesperson added: "This task is much easier and thus requires far less data than the behavioral cloning task of predicting actions given past video frames only, which requires inferring what the person wants to do and how to accomplish it.

Indonesia is a country with enormous economic potential, which has not gone unnoticed by the international community. Indonesia, the largest economy in Southeast Asia, possesses a variety of qualities that position the country well for advanced economic development. Furthermore, the central government has recently shown significant support for reducing Indonesia's historic reliance on (raw) commodity exports while increasing the importance of the manufacturing industry in the economy. Looking ahead, Indonesia's information and communication technology (ICT) sector have a promising future, since the country is only getting started with IT solutions adoption, leaving plenty of space for expansion. Much of Indonesia's potential has yet to be realised, making the country one of the most promising ICT markets in the future years.

This collaboration will increase diversity in the AI tech space where women have been historically excluded. The Finnish Center for Artificial Intelligence (FCAI) and Women in AI Ethics (WAIE) have announced their partnership to increase diversity and ethics in AI. Both organizations focus on empowering people to solve real-life problems through AI that's designed and deployed in an ethical and trustworthy manner. This alliance will increase representation of diverse voices from the Nordic region in the WAIE directory, an online resource to help recruiters and event organizers find diverse talent, and open up opportunities for these talented women to be recognized through WAIE's highly regarded annual "100 Brilliant Women in AI Ethics" list. This international collaboration will include co-hosting of educational workshops by diverse leaders in the AI ethics space, offering a critical lens for real-world solutions. It will also provide a virtuous cycle of mentorship opportunities for FCAI's faculty as well as students and WAIE's rich network of women as well as non-binary folks who are in all stages of their AI ethics careers to inspire others from marginalized communities to join this important space.

This course offers a deep and wide range of skills set from Programming to statistics and machine learning algorithms. The skills you will attain from this course could make you an expert Data Analyst, Quality Analyst and Business Analyst and Statistical Analyst roles. Machine learning algorithms such as Regression, Clustering, Classification and prominent libraries such as Pandas, Matplotlib, SciKit -learn is covered from this course. The main goal of the course is to provide a deeper understanding and hands-on learning experience on the Data Science domain with the help of Python programming language along with real-time Data Science projects to provide an overall knowledge on Data Science domain. This course covers all the topics from Mathematics to Programming to Visualization techniques that are needed for a Data Scientist role.

Having the skills and the knowledge to attack every aspect of point cloud processing opens up many ideas and development doors. It is like a toolbox for 3D research creativity and development agility. And at the core, there is this incredible Artificial Intelligence space that targets 3D scene understanding. It is particularly relevant due to its importance for many applications, such as self-driving cars, autonomous robots, 3D mapping, virtual reality, and the Metaverse. And if you are an automation geek like me, it is hard to resist the temptation to have new paths to answer these challenges! This tutorial aims to give you what I consider the essential footing to do just that: the knowledge and code skills for developing 3D Point Cloud Semantic Segmentation systems. But actually, how can we apply semantic segmentation? And how challenging is 3D Machine Learning? Let me present a clear, in-depth 201 hands-on course focused on 3D Machine Learning.

British-born Andrew Ng has had a rich career in the technology industry as Co-Founder and Head of Google Brain, former Chief Scientist at Baidu and Co-Founder of Coursera. At Baidu, Ng built the company's artificial intelligence (AI) sector into a team of several people. In an interview with Lex Fridman, Ng shared where his passion for the industry started: " Growing up in Hong Kong and Singapore, I started learning to code when I was five or six years old. At that time I was learning the BASIC programming language and they would take these folks and they'll tell you type this program into your computer." "So I typed out programs on my computer and as the result of all the typing, I would get to play these very simple, shoot them up games that I had implemented on my little computer. So I thought it was fascinating as a young kid that I could write this code. I was really just copying code from a book into my computer to then play these cool little video games. Another moment for me was when I was a teenager and my father was a doctor was reading about expert systems and about neural networks. So he got me to read some of these books and I thought it was really cool that you could write a computer that started to exhibit intelligence." he continued.

There has been a large number of courses that teach the fundamentals of programming and data science. They do a good job in reinforcing various concepts in machine learning and show various steps that are usually followed when building a project with ML capabilities. While these courses mostly focus on the theoretical aspects of machine learning, it can be handy if one learns to put more emphasis on the good practices when building applications related to data science and machine learning. With the rise in data and an exponential increase in the compute power, there has been a rapid increase in the demand for people who would make use of the data and generate predictions along with useful insights depending on the use case of the project. Furthermore, there are numerous data related positions such as data engineer, data architect, data scientists, deep learning engineer and machine learning engineer.