If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
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, 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.
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).
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.
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.
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 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 algorithms are used to find patterns in data that humans wouldn't otherwise notice, and are being deployed to help inform decisions big and small – from COVID-19 vaccination development to Netflix recommendations. New award-winning research from the Cornell Ann S. Bowers College of Computing and Information Science explores how to help nonexperts effectively, efficiently and ethically use machine-learning algorithms to better enable industries beyond the computing field to harness the power of AI. "We don't know much about how nonexperts in machine learning come to learn algorithmic tools," said Swati Mishra, a Ph.D. student in the field of information science. "The reason is that there's a hype that's developed that suggests machine learning is for the ordained." Mishra is lead author of "Designing Interactive Transfer Learning Tools for ML Non-Experts," which received a Best Paper Award at the annual ACM CHI Virtual Conference on Human Factors in Computing Systems, held in May. As machine learning has entered fields and industries traditionally outside of computing, the need for research and effective, accessible tools to enable new users in leveraging artificial intelligence is unprecedented, Mishra said.
Deep Genomics, an artificial intelligence startup founded by the University of Toronto's Brendan Frey, has secured US$180 million from investors, including Japanese multinational Softbank and Canada Pension Plan Investments, the Globe and Mail reported. Launched in 2015, the startup uses machine learning to develop treatments for genetic diseases. According to the Globe and Mail, Deep Genomics currently has 10 drugs in pre-clinical development, four of which are set to enter human trials by mid-2023. It is also working with San Francisco Bay-area biopharmaceutical company BioMarin Pharmaceutical Inc. to identify drug candidates for rare diseases. "These are all new chemical entities that would not exist" without Deep Genomics' technology," Frey, who is CEO of Deep Genomics and a professor in U of T's Faculty of Applied Science & Engineering, told the Globe.
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.