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Jury: Evaluating performance of NLG models

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Jury is an evaluation package for NLG systems. It allows using many metrics in one go. Also, it implements concurrency among evaluation metrics and supports evaluating with multiple predictions. Jury uses datasets package for metrics, and thus supports any metrics that datasets package has. Default evaluation metrics are, BLEU, METEOR and ROUGE-L. As of today 28 metrics are available in the "datasets" package, to see all supported metrics, see datasets/metrics.


Semantic Similarity Using Transformers

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Semantic Similarity, or Semantic Textual Similarity, is a task in the area of Natural Language Processing (NLP) that scores the relationship between texts or documents using a defined metric. Semantic Similarity has various applications, such as information retrieval, text summarization, sentiment analysis, etc. There have been a lot of approaches for Semantic Similarity. The most straightforward and effective method now is to use a powerful model (e.g. The similarity score indicates whether two texts have similar or more different meanings.


As AI Becomes More Ever Capable, Will It End Up Helping, Or Hindering, The Hackers?

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Hacking events have increasingly been in the news this year, as a range of serious ransomware and supply chain hacks have wrecked chaos on businesses and infrastructure. The latest (as of July 2021) is a supply-chain-ransomware attack against Miami-based software firm Kaseya, affecting 1500 of its customers - with the hackers (threat-actors) demanding $70 million in cryptocurrency to release the data. According to the World Economic Forum, cyber-attacks now stand side by side with climate change and natural disasters as one of the most pressing threats to humanity. No doubt ways will eventually be found to detect and pre-empt these latest styles of attack. The cybersecurity industry is defined by continual, if largely gradual, innovation - as new threats emerge, technology that protects, detects and responds to the attacks also emerges. This cat and mouse dynamic has been a fundamental trait of the industry to date: a permanently iterating relationship that supercharges the development of new technologies on both sides, where even a small edge over adversaries can pay dividends (or ransoms).


AADS: Augmented autonomous driving simulation using data-driven algorithms

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Simulation systems have become essential to the development and validation of autonomous driving (AD) technologies. The prevailing state-of-the-art approach for simulation uses game engines or high-fidelity computer graphics (CG) models to create driving scenarios. However, creating CG models and vehicle movements (the assets for simulation) remain manual tasks that can be costly and time consuming. In addition, CG images still lack the richness and authenticity of real-world images, and using CG images for training leads to degraded performance. Here, we present our augmented autonomous driving simulation (AADS). Our formulation augmented real-world pictures with a simulated traffic flow to create photorealistic simulation images and renderings. More specifically, we used LiDAR and cameras to scan street scenes. From the acquired trajectory data, we generated plausible traffic flows for cars and pedestrians and composed them into the background. The composite images could be resynthesized with different viewpoints and sensor models (camera or LiDAR). The resulting images are photorealistic, fully annotated, and ready for training and testing of AD systems from perception to planning. We explain our system design and validate our algorithms with a number of AD tasks from detection to segmentation and predictions. Compared with traditional approaches, our method offers scalability and realism. Scalability is particularly important for AD simulations, and we believe that real-world complexity and diversity cannot be realistically captured in a virtual environment. Our augmented approach combines the flexibility of a virtual environment (e.g., vehicle movements) with the richness of the real world to allow effective simulation.


S5 E8 How AI Avatars Can Transform Workplace Training -- The Art & Science of Learning

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One of the great challenges in learning new skills is being able to put them to practice in real scenarios. Although practicing in real scenarios offers critical learning, it also comes with serious risks. Can AI be the answer in creating on the job learning without the risks? My guest is an internationally recognized expert in AI in education and the technology he created helps people learn faster and more effectively. Dr Lewis Johnson co-founded Alelo in 2005 as a spinout of the University of Southern California, under his leadership Alelo has developed into a major producer of AI-driven learning products focusing on communication skills.


Council Post: Legal AI: An Automated Versus Autonomous Future

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Globally recognized business builder, thought leader, author, former consulting partner and high-tech executive. Corporate legal departments have historically been resistant to automation and technology adoption, but the effects of the pandemic forced many to shift gears and pursue, or at least actively consider, more automation for legal activities. Artificial intelligence (AI) has been the cornerstone of this strategy, and mapping key investments to business outcomes remains a challenge. Similar to how email and the internet changed how legal departments functioned, AI is growing its impact. This cusp of a revolution will transform the practice of law.


MLOps in 2021: The pillar for seamless Machine Learning Lifecycle

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MLOps is the new terminology defining the operational work needed to push machine learning projects from research mode to production. While Software Engineering involves DevOps for operationalizing Software Applications, MLOps encompass the processes and tools to manage end-to-end Machine Learning lifecycle. Machine Learning defines the models' hypothesis learning relationships among independent(input) variables and predicting target(output) variables. Machine Learning projects involve different roles and responsibilities starting from the Data Engineering team collecting, processing, and transforming data, Data Scientists experimenting with algorithms and datasets, and the MLOps team focusing on moving the trained models to production. Machine Learning Lifecycle represents the complete end-to-end lifecycle of machine learning projects from research mode to production.


Machine learning applications need less data than has been assumed

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A combined team of researchers from the University of British Columbia and the University of Alberta has found that at least some machine learning applications can learn from far fewer examples than has been assumed. In their paper published in the journal Nature Machine Intelligence, the group describes testing they carried out with machine learning applications created to predict certain types of molecular structures. Machine learning can be used in a wide variety of applications--one of the most well-known is learning to spot people or objects in photographs. Such applications typically require huge amounts of data for training. In this new effort, the researchers have found that in some instances, machine learning applications do not need such huge amounts of data to be useful.


StreetLight Data Partnership Aims to Help Expand EV Chargers

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With electric vehicles slowly gaining momentum toward becoming the dominant form of transportation in the U.S., two startups have struck up a partnership to help cities and utilities figure out where to put more car chargers. StreetLight Data, which sells transportation data to local governments, will offer Volta Charging's PredictEV tool to its customers. The tool uses AI to generate suggestions about where electric charging infrastructure would be most useful -- an urban planning consideration that is becoming more important as more electric vehicles hit the streets. Today, electric vehicles make up only around 2 percent of new vehicles sold in the U.S., but that number is rising rapidly. In 2020, Pew Research found that the number of EVs sold in the country had more than tripled since 2016.


The Rise of the Transformers: Explaining the Tech Underlying GPT-3

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The capabilities of GPT -3 has led to a debate between some as to whether or not GPT-3 and its underlying architecture will enable Artificial General Intelligence (AGI) in the future against those (many being from the school of logic and symbolic AI) who believe that without some form of logic there can be no AGI. The truth of the matter is that we don't know as we don't really fully understand the human brain. With science and engineering we work upon the basis of observation and testing. This section also addresses points raised by Esaú Flores. Gary Grossman in an article entitled Are we entering the AI Twilight Zone between AI and AGI? observed that in February 2020, Geoffrey Hinton, the University of Toronto professor who is a pioneer of Deep Learning, noted: "There are one trillion synapses in a cubic centimeter of the brain. If there is such a thing as general AI, [the system] would probably require one trillion synapses." The human brain has a huge number of synapses. Each of the 1011 (one hundred billion) neurons has on average 7,000 synaptic connections (synapses) to other neurons. It has been estimated that the brain of a three-year-old child has about 1015 synapses (1 quadrillion).