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) …
On a less-trafficked floor of the Whitney Museum, curators have scoured the museum's permanent collection to display art that uses "instructions, sets of rules, and code" to investigate a world "increasingly driven by automated systems." In the nineties, the game designer Frank Lantz produced such work. "I would make some marks on a page, and then I would just connect the endpoints of all the lines to the nearest unconnected endpoint, and then I would add another rule," he said. His method had a whiff of misanthropy. He wanted to render himself obsolete and let something else take over.
Expert System is making enhancements to Cogito, its Artificial Intelligence platform that understands textual information and automatically processes natural language, delivering key updates in the areas of knowledge graphs, machine learning, and RPA. Cogito 14.4 enables users to more easily customize its Knowledge Graph of approximately 350,000 concepts connected by 2.8 Million relationships and lets them import targeted knowledge from any sources (such as company repositories Wikipedia or Geonames) in only a few clicks, enabling the platform to resolve references to real-world entities (such as people, companies, locations) and to link them to knowledge repositories by using standardized identifiers. Cogito 14.4 also extends its Natural Language Processing (NLP) extraction pipeline with a new active learning workflow that accelerates machine-learning-based analytics projects. Through an intuitive web application, Cogito 14.4's active learning workflow enables end-users to visualize the quality of extraction and provide feedback to the engine, which iteratively retrains the engine to reach the user's quality goals, thus reducing the amount of manual annotation needed Cogito 14.4 includes a Robotic Process Automation (RPA) connector that extends the use of RPA bots into process automation leveraging knowledge (and not only structured data) as well as requiring human-like judgement. The Cogito RPA Connector leverages deep contextual understanding to extract precise data from unstructured business documents.
As Artificial Intelligence (AI) becomes an integral part of our life, the development of explainable AI, embodied in the decision-making process of an AI or robotic agent, becomes imperative. For a robotic teammate, the ability to generate explanations to explain its behavior is one of the key requirements of an explainable agency. Prior work on explanation generation focuses on supporting the reasoning behind the robot's behavior. These approaches, however, fail to consider the cognitive effort needed to understand the received explanation. In particular, the human teammate is expected to understand any explanation provided before the task execution, no matter how much information is presented in the explanation. In this work, we argue that an explanation, especially complex ones, should be made in an online fashion during the execution, which helps to spread out the information to be explained and thus reducing the cognitive load of humans. However, a challenge here is that the different parts of an explanation are dependent on each other, which must be taken into account when generating online explanations. To this end, a general formulation of online explanation generation is presented. We base our explanation generation method in a model reconciliation setting introduced in our prior work. Our approach is evaluated both with human subjects in a standard planning competition (IPC) domain, using NASA Task Load Index (TLX), as well as in simulation with four different problems.
His demand for a ban triggered a legal and moral quagmire, as the Pentagon faced the prospect of throwing out service members who had willingly come forward as transgender after being promised they would be protected and allowed to serve. And as legal battles blocked the ban from taking effect, the Obama-era policy continued and transgender individuals were allowed to begin enlisting in the military a little more than a year ago.
WASHINGTON – The Defense Department has approved a new policy that will largely bar most transgender troops and military recruits from transitioning to another sex, and require most individuals to serve in their birth gender. The new policy comes after a lengthy and complicated legal battle, and it falls short of the all-out transgender ban that was initially ordered by President Donald Trump. But it will likely force the military to eventually discharge transgender individuals who need hormone treatments or surgery and can't or won't serve in their birth gender. The order says the military services must implement the new policy in 30 days, giving some individuals a short window of time to qualify for gender transition if needed. And it allows service secretaries to waive the policy on a case-by-case basis.
In this lecture, I will offer you a definition of artificial intelligence, or AI, and give you a brief overview of its history from its inception in the 1950s. Let's start by saying what AI isn't. AI is not machines that think, or even computers that work the way the brain works. AI is what machines do, not how they do it. The authors of a leading textbook on AI have offered eight possible definitions of the term.
In recent years, the real-world impact of machine learning (ML) has grown in leaps and bounds. In large part, this is due to the advent of deep learning models, which allow practitioners to get state-of-the-art scores on benchmark datasets without any hand-engineered features. Given the availability of multiple open-source ML frameworks like TensorFlow and PyTorch, and an abundance of available state-of-the-art models, it can be argued that high-quality ML models are almost a commoditized resource now. There is a hidden catch, however: the reliance of these models on massive sets of hand-labeled training data. These hand-labeled training sets are expensive and time-consuming to create -- often requiring person-months or years to assemble, clean, and debug -- especially when domain expertise is required.
The term "artificial intelligence" was coined by John McCarthy in 1956 at the Dartmouth Conference, which is now widely considered the birthplace of modern AI research. Since then, AI has gone through at least two "winters," or periods where funding has dried up and research has slowed. The first AI winter occurred between 1974 and 1980 when there was neither not enough money and not enough RAM, or processing power, to make effective strides. A resurgence occurred in the '80s with the creation of "expert systems" or simple AI programs that could automate many computer functions that were previously manual (a precursor to modern robotic process automation). Typically, expert systems were used by enterprises to perform analysis, design or monitoring tasks.
Machine learning is an often-used term that has been promised to do everything from making workers more productive to taking over individuals' jobs entirely. Frankly, it will likely be many years before anyone should be concerned about being replaced by artificial intelligence (AI) at their job. However, doctors might find AI impinging upon their jobs sooner rather than later. The medical field has some characteristics that make it an attractive target for machine learning. The high stakes nature of correct disease diagnosis, coupled with over-worked and fatigued doctors, can lead to cases where patients with easily treatable diseases go undiagnosed and suffer greatly from this.
Ensuring fairness and safety in artificial intelligence(AI) applications is considered by many the biggest challenge in the space. As AI systems match or surpass human intelligence in many areas, it is essential that we establish a guideline to align this new form of intelligence with human values. The challenge is that, as humans, we understand very little about how our values are represented in the brain or we can't even formulate specific rules to describe a specific value. While AI operates in a data universe, human values are a byproduct of our evolution as social beings. We don't describe human values like fairness or justice using neuroscientific terms but using arguments from social sciences like psychology, ethics or sociology Recently, researchers from OpenAI published a paper describing the importance of social sciences to improve the safety and fairness or AI algorithms in processes that require human intervention.