The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Google today announced TensorFlow Similarity, a Python package designed to train similarity models with the company's TensorFlow machine learning framework. Similarity models search for related items, for example finding similar-looking clothes and identifying currently playing songs. As Google explains, many similarity models are trained using a technique called contrastive learning. Contrastive learning, in turn, relies on clustering algorithms, which automatically identify patterns in data by operating on the theory that data points in groups should have similar features.
Artificial intelligence is being deployed in many different areas. Within higher education, it is used for college admissions and financial aid decisions. Health researchers employ it to scan the scientific literature for chemical compounds that may generate new medical treatments. E-commerce sites deploy algorithms to make product recommendations for consumers based on their areas of interest.1 But one of the most important growth areas lies in finance and operations. Both public and private sector organizations have large budgets to manage and it is important to operate efficiently and effectively. Accusations of budget inefficiencies or wasteful spending decrease public confidence and make it important to figure out how to manage resources in fair ways. To help with budgetary oversight, AI is being used for financial management and fraud detection. Advanced algorithms can spot abnormalities and outliers that can be referred to human investigators to determine if fraud actually has taken place. It is a way to use technology to improve budget audits, personnel performance, and organizational activities. Yet is it crucial to overcome several problems that plague public sector innovation: procurement obstacles, insufficiently trained workers, data limitations, a lack of technical standards, cultural barriers to organizational change, and making sure anti-fraud applications adhere to responsible AI principles.
A technology based on artificial intelligence is helping to spot biomarkers and document the progression of amyotrophic lateral sclerosis (ALS) in a large speech study being conducted by EverythingALS. The technology, developed by Modality.ai, is a web-based computer program that uses audio (speech) and video (facial) recordings to assess neurological states automatically through AI and machine learning algorithms. Its greatest advantage is that data can be collected remotely at home on any computer device with the help of a virtual assistant called "Tina." This is important for people with ALS, who often have limited mobility due to muscle weakness, which may affect their ability to participate in clinical studies. "Our mission is to discover and deploy initiatives that focus on new ways to diagnose and treat neurological disorders at the intersection of computing and brain science with a focus on ALS," Indu Navar, CEO and co-founder of EverythingALS, a U.S. nonprofit that is part of the Peter Cohen Foundation, said in a press release.
Earth system models are the most important tools for quantitatively describing the physical state of Earth, and--for example, in the context of climate models--predicting how it might change in the future under the influence of human activities. How the increasingly used methods of artificial intelligence (AI) can help to improve these forecasts and where the limits of the two approaches lie has now been investigated by an international team led by Christopher Irrgang from the German Research Centre for Geosciences Potsdam (GFZ) in a Perspectives article for the journal Nature Machine Intelligence. One key proposal: To merge both approaches into a self-learning "neural Earth system modeling." The development of Earth is a complex interplay of many factors, including the land surface with flora and fauna, the oceans with their ecosystem, the polar regions, the atmosphere, the carbon cycle and other biogeochemical cycles, and radiation processes. Researchers therefore speak of the Earth system.
Machine learning models trained on the visual representation of website code can help improve the accuracy and speed of detecting phishing websites. This is according to a paper (PDF) by security researchers at the University of Plymouth and the University of Portsmouth, UK. The researchers aim to address the shortcomings of existing detection methods, which are either too slow or not accurate enough. The technique developed by the researchers uses "binary visualization" libraries to transform the markup and code of web pages into images. Using this method, they created a dataset of legitimate and phishing images of websites.
The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. AI-powered coding tools, which generate code using machine learning algorithms, have attracted increasing attention over the last decade. In theory, systems like OpenAI's Codex could reduce the time people spend writing software as well as computational and operational costs. But existing systems have major limitations, leading to undesirable results like errors. In search of a better approach, researchers at Salesforce open-sourced a machine learning system called CodeT5, which can understand and generate code in real time. The team claims that CodeT5 achieves state-of-the-art performance on coding tasks including code defect detection, which predicts whether code is vulnerable to exploits, and clone detection, which predicts whether two code snippets have the same functionality.
Until now, chip design has been the domain of electrical engineers, but a recent Google study could change that. It showed that the AI-created chip layout was "superior or comparable to those produced by humans in all key metrics, including power consumption, performance, and chip area." Thanks to a machine-learning technique known as reinforcement learning, artificial intelligence completed the task in only six hours, compared with weeks by humans. Although Alphabet's Google GOOG, -0.44% and Nvidia NVDA, -1.43% have been performing tests and discussing the use of AI-powered techniques to boost chip-production capabilities, Samsung Electronics was among the first to actually create chips using the method. Relying on software made by Synopsys SNPS, 0.08%, a chip design software company, Samsung designed Exynos, a processor used in company's wearables, smartphones, car infotainment systems, and other gadgets.
A study in which machine-learning models were trained to assess over 1 million companies has shown that artificial intelligence (AI) can accurately determine whether a startup firm will fail or become successful. The outcome is a tool, Venhound, that has the potential to help investors identify the next unicorn. It is well known that around 90% of startups are unsuccessful: Between 10% and 22% fail within their first year, and this presents a significant risk to venture capitalists and other investors in early-stage companies. In a bid to identify which companies are more likely to succeed, researchers have developed machine-learning models trained on the historical performance of over 1 million companies. Their results, published in KeAi's The Journal of Finance and Data Science, show that these models can predict the outcome of a company with up to 90% accuracy.
It's a technique used by many scientific fields, as it provides a structured guideline to answering a question logically and rationally, using empirical evidence -- an approach that ushered humanity out of the dark ages and into today's era where breakthrough discoveries in physics, astronomy and modern medicine are possible. But are there situations in scientific investigation where the scientific method is not needed? A team of researchers at Princeton University's Plasma Physics Laboratory (PPPL) are now proposing that this is indeed possible -- by using a machine learning algorithm that can predict the physical orbits of planets, without the need for it to be based on the laws of physics. The paper on the work, which was recently published in Scientific Reports, outlines how the team trained a machine-learning algorithm on data about the known orbits of Mercury, Venus, Earth, Mars, Jupiter, and the dwarf planet Ceres. This machine-learning algorithm, paired along with what the team calls a "serving algorithm", was then used to predict the orbits of other planets -- including the parabolic and hyperbolic escaping orbits, of the solar system -- without needing to input Newtonian laws of motion and gravitation. Instead, the approach forms what the team calls a discrete field theory, which models the universe as a kind of "black box."
The Workshop Program of the Association for the Advancement of Artificial Intelligence's Thirty-Fifth Conference on Artificial Intelligence was held virtually from February 8-9, 2021. There were twenty-six workshops in the program: Affective Content Analysis, AI for Behavior Change, AI for Urban Mobility, Artificial Intelligence Safety, Combating Online Hostile Posts in Regional Languages during Emergency Situations, Commonsense Knowledge Graphs, Content Authoring and Design, Deep Learning on Graphs: Methods and Applications, Designing AI for Telehealth, 9th Dialog System Technology Challenge, Explainable Agency in Artificial Intelligence, Graphs and More Complex Structures for Learning and Reasoning, 5th International Workshop on Health Intelligence, Hybrid Artificial Intelligence, Imagining Post-COVID Education with AI, Knowledge Discovery from Unstructured Data in Financial Services, Learning Network Architecture During Training, Meta-Learning and Co-Hosted Competition, ...