In recent years, curial incidents and accidents have been reported due to un-intended control caused by misjudgment of statistical machine learning (SML), which include deep learning. The international functional safety standards for Electric/Electronic/Programmable (E/E/P) systems have been widely spread to improve safety. However, most of them do not recom-mended to use SML in safety critical systems so far. In practical the new concepts and methods are urgently required to enable SML to be safely used in safety critical systems. In this paper, we organize five kinds of technical safety concepts (TSCs) for SML components toward accordance with functional safety standards. We discuss not only quantitative evaluation criteria, but also development process based on XAI (eXplainable Artificial Intelligence) and Automotive SPICE to improve explainability and reliability in development phase. Fi-nally, we briefly compare the TSCs in cost and difficulty, and expect to en-courage further discussion in many communities and domain.

I hope you have all been enjoying our recent online meetups, brought to in collaboration with the NY and Hungarian NLP groups. I'm pleased to announce a further online event, hosted by Seth Grimes of NY NLP. Moshe Wasserblat, NLP and Deep Learning Research Manager at Intel, will present on Simple and Efficient Deep Learning for Natural Language Processing. Our program starts at 1 pm US-Eastern (11 am US-Pacific, 6 pm BST, 7 pm CEST, 8 pm Israel). Description: Large transformer-based neural networks such as BERT, GPT and XLNET have recently achieved state-of-the-art results in many NLP tasks.

Hi there, how are you doing, i hope it's great! In my last article we used Multi-variate LSTM that is multiple inputs for LSTM to forecast a Time Series data. This time we will use take one step further with step wise forecasting. For this example we will forecast 3 months. The article was originally found in'machine learning mastery' by Jason.

Python is one of the most popular and widely known programming languages that has replaced many programming languages in the industry. It is one of the most loved programming languages that data science professionals use more because it is an ocean of libraries. Python is known as the beginner's level programming language because of its simplicity and easiness, its programming syntax is simple to learn and is of high level compared to C, Java, and C . Pytorch is an open source library, it basically a replacement of Numpy. PyTorch comes with higher-level functionality useful for building a deep neural network.

When OpenAI first published a paper on their new language generation AI, GPT-3, the hype was slow to build. The paper indicated GPT-3, the biggest natural language AI model yet, was advanced, but it only had a few written examples of its output. Then OpenAI gave select access to a beta version of GPT-3 to see what developers would do with it, and minds were blown. Developers playing with GPT-3 have taken to Twitter with examples of its capabilities: short stories, press releases, articles about itself, a search engine. Perhaps most surprising was the discovery GPT-3 can write simple computer code. When web developer, Sharif Shameem, modified it to spit out HTML instead of natural language, the program generated code for webpage layouts from prompts like "a button that looks like a watermelon."

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Deep Learning framework is now getting further and more profound. With these bigger networks, we can accomplish better prediction exactness. However, this was not the case a few years ago. Deep Learning was having overfitting issue. The concept revolutionized Deep Learning.

Two Thursdays ago, I was sitting alone in my SoMa loft, screaming at my computer. I was reading something that can only be described as magic. It was a post about how to run an effective board meeting. Part of what makes it so hard to build a strong board is a lack of focus and direction. When you go out and recruit board members, you have to be very intentional about your recruiting efforts and have a defined process.