How many times have you taken yet another online course on machine learning or read yet another paper on a new emerging topic, to be up-to-date in this crazy fast-paced AI/ML world -- only to keep feeling like an ML engineer impostor? These three personal tips can help you overcome the classic (and common) impostor syndrome behind every emerging ML engineer who wants to be better at what you do. When I first applied to Toptal, I wanted to become both a freelancer and a "real ML engineer" at the same time. Before that, I worked as a Machine Learning engineer at Nordeus, a top mobile gaming company famous for having Mourinho's face on its flagship game: TopEleven. My Machine Learning adventure at Nordeus consisted of designing and implementing an intelligent system to help the customer support team resolve player issues faster.

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So, what is artificial intelligence? Artificial intelligence is the overarching name for a branch of computer science that deals with smart machines or robots that can think and perform tasks like a human. In artificial intelligence you can teach a computer to learn and make its own decisions. AI is the broad concept of machines being able to carry out tasks in a way that we would consider'smart', and we encounter these technologies on a daily basis: from voice assistants in our home, to Netflix recommendations or autonomous cars. AI relies on data and on training algorithms.

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The growing popularity of 3D printing for manufacturing all sorts of items, from customized medical devices to affordable homes, has created more demand for new 3D printing materials designed for very specific uses. To cut down on the time it takes to discover these new materials, researchers at MIT have developed a data-driven process that uses machine learning to optimize new 3D printing materials with multiple characteristics, like toughness and compression strength. By streamlining materials development, the system lowers costs and lessens the environmental impact by reducing the amount of chemical waste. The machine learning algorithm could also spur innovation by suggesting unique chemical formulations that human intuition might miss. "Materials development is still very much a manual process. A chemist goes into a lab, mixes ingredients by hand, makes samples, tests them, and comes to a final formulation. But rather than having a chemist who can only do a couple of iterations over a span of days, our system can do hundreds of iterations over the same time span," says Mike Foshey, a mechanical engineer and project manager in the Computational Design and Fabrication Group (CDFG) of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and co-lead author of the paper.

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At a recent conference on the challenges of artificial intelligence, Christof Koch made clear in his remarks that the stakes were high: "By mid-century, humanity will be surrounded by ubiquitous, flexible, highly intelligent autonomous agents, and this will profoundly affect our future--including whether we have any." Dr. Koch--who is the chief scientist of the Mindscope Program at the Allen Institute for brain science in Seattle--was speaking to a group of roughly a hundred academics, diplomats and journalists. The conference was hosted by the Vatican at the Cancelleria, a 15th-century Renaissance palace in Rome, and centered around the theme of "the challenge of artificial intelligence for human society and the idea of the human person." This was the second event at the Vatican to focus on artificial intelligence, commonly abbreviated as A.I. Just before Italy entered into a nationwide lockdown last year, the Pontifical Academy for Life held a workshop on A.I. in February 2020. This workshop ultimately produced a "Call for AI Ethics," which was signed by Microsoft, IBM, the Food and Agricultural Organization of the United Nations and the Italian government, in addition to the Academy.

I recently started an AI-focused educational newsletter, that already has over 100,000 subscribers. TheSequence is a no-BS (meaning no hype, no news etc) ML-oriented newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Quantifying trust and fairness is one of the most important challenges to ensure the mainstream adoption of deep learning systems. But what does trust truly means in the context of deep learning systems?

Today we see our working routine very different from what it was at least 10 years ago. Due to technology advancement and access to the Internet we are able to telecommute. And for many of us it has become the only alternative because of the pandemic of Covid-19. Nevertheless, even remote workers need to be supervised for sake of productivity. Here are some practical advices how to make it happen.

On October 14, 2021, the U.S. Food and Drug Administration ("FDA" or the "Agency") held a virtual workshop entitled, Transparency of Artificial Intelligence ("AI")/Machine Learning ("ML")-enabled Medical Devices. The workshop builds upon previous Agency efforts in the AI/ML space. Back in 2019, FDA issued a discussion paper and request for feedback called, Proposed Regulatory Framework for Modifications to AI/ML-Based Software as a Medical Device ("SaMD"). To support continued framework development and to increase collaboration and innovation between key stakeholders and specialists, FDA created the Digital Health Center of Excellence in 2020. And, in January 2021, FDA published an AI/ML Action Plan, based, in part, on stakeholder feedback to the 2019 discussion paper.