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) …
Chimpanzees get more selective over who they associate themselves with as they age, new research reveals. In a study spanning two decades in a Ugandan national park, US experts observed social interactions among 21 wild male chimps, ranging in age from 15 to 58 years. Both chimps and humans prefer to be around the company of old friends and spend less time among new faces, the experts conclude. Ageing male chimps have more mutual and positive friendships than younger chimps, who have more one-sided, antagonistic relationships. Chimps also showed a shift from negative interactions to more positive ones as they reached their twilight years, 'like humans looking for some peace and quiet'.
The gradient descent algorithm is an approach to find the minimum point or optimal solution for a given dataset. It follows the steepest descent approach. That is it moves in the negative gradient direction to find the local or global minima, starting out from a random point. We use gradient descent to reach the lowest point of the cost function. In Machine Learning, it is used to update the coefficients of our model.
Automation and a new division of labor between humans and machines will disrupt 85 million jobs globally by 2025, according to a report by the World Economic Forum (WEF) on Wednesday. The Future of Jobs 2020 report showed that COVID-19 has led the labor market to change faster than anticipated. "More than 80% of business executives are accelerating plans to digitize work processes and deploy new technologies," the report said. It said that contrary to the previous years, job creation is now slowing down while job losses fasten. Some 43% of businesses surveyed indicated that they are set to reduce their workforce due to technology integration, while 34% plan to expand their workforce, the report said.
Deep Learning has been one of the most recent breakthroughs in AI Research. In this Session, we will learn about a few basics of Deep Learning, their applications and learn how to apply these concepts using H2O-3 via a hands-on session. This fast-paced session starts with a simple yet complete neural network, and builds on top of it adding functionality to and diving into different nuances of Neural Networks using a simple example with the H2O-3 API for a prediction problem. Speaker: Sanyam Bhutani is a Machine Learning Engineer and AI Content Creator at H2O.ai. He is also an inc42, Economic Times recognized Machine Learning Practitioner.
Data Push: Push-based strategies are the default model. Automated the delivery on pre-determined specification, a forwarder is installed close to the source of the data, or built into the data generator/collector and pushes the events to an indexer. Data Pull: This approach provides significant flexibility by letting you create reports from multiple data sources and multiple data sets, and by letting you store and manage reports with an enterprise reporting server. Pull based cannot be reliable for real-time reports and information. Also, Pull base system most tolerate, its lack of real-time information cannot be best fit for supervisory Financial Institution as they demand real-time reporting with greater insights to financial health conditions of FIs. Supervisors can use machine learning tools to create a "risk score" for supervised entities. FINTRAC, the Financial Transactions and Reports Analysis Centre of Canada, has created one such score, evaluating the risk factors related to an institution's profile, compliance history, reporting behavior, and more.
The University of Miami in recent days rebutted claims it uses facial-recognition technology after students accused campus police of using the tool to identify them at a protest related to the coronavirus pandemic. Two students claim UM's dean of students told a handful of campus protesters at a virtual meeting on Sept. 22 that they were identified at an unsanctioned demonstration using specialized software that analyzed camera footage of the event.
We've built and are now sharing Dynabench, a first-of-its-kind platform for dynamic data collection and benchmarking in artificial intelligence. It uses both humans and models together "in the loop" to create challenging new data sets that will lead to better, more flexible AI. Dynabench radically rethinks AI benchmarking, using a novel procedure called dynamic adversarial data collection to evaluate AI models. It measures how easily AI systems are fooled by humans, which is a better indicator of a model's quality than current static benchmarks provide. Ultimately, this metric will better reflect the performance of AI models in the circumstances that matter most: when interacting with people, who behave and react in complex, changing ways that can't be reflected in a fixed set of data points.
Online surveys have grown in popularity because of the ease with which they give organizations valuable insights into everything from product design and packaging to consumer buying habits. But today's research platforms often impose a tradeoff between speed and simplicity and the richness of actionable insights. A combination of machine learning technology and crowdsourcing concepts is solving this problem. It enables researchers to shorten online survey time without having to resort to matrix tables that often make surveys uncomfortably long and can skew results. At the same time, these technologies deliver the higher accuracy, deeper insights and superior user experience of open-ended questions. Matrix Table Challenges Researchers have typically accelerated online surveys by asking questions not one by one but in a more space-efficient matrix format (questions are in the table's rows, response scale options in its columns).
Tesla's autonomous driving package, known as Full Self Driving or FSD, is about to get pricier. Early Thursday, Tesla CEO Elon Musk announced that the price for the next-level Autopilot system is going up by $2,000. It's currently listed at $8,000 on the Tesla website. FSD is strange in that Tesla charges people for self-driving features that aren't available yet. Eventually, Musk envisions Tesla vehicles operating as robotaxis with no one at the steering wheel.