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) …
Have you ever thought about what would happen if you combined the power of machine learning and artificial intelligence with financial engineering? Today, you can stop imagining, and start doing. This course will teach you the core fundamentals of financial engineering, with a machine learning twist. We will learn about the greatest flub made in the past decade by marketers posing as "machine learning experts" who promise to teach unsuspecting students how to "predict stock prices with LSTMs". You will learn exactly why their methodology is fundamentally flawed and why their results are complete nonsense.
'The Ingraham Angle' host examines the president's approach to diplomacy and foreign policy A drone reportedly dropped explosives at a U.S.-led base near the Erbil airport in Iraq on Wednesday night. There were no reports of injuries, Reuters reported, citing Kurdish officials. It was the first known drone attack believed to be targeting U.S. service members but rocket attacks have hit U.S. bases in the country. A Turkish soldier was reportedly killed in a separate rocket attack Wednesday, Turkish officials said, according to Reuters. A group thought to be aligned with Iran praised the drone attack but no one has explicitly claimed responsibility for it. The U.S. has blamed the attacks on Iran-backed militias, which have called for the withdrawal of all foreign troops, according to Reuters.
When looking to hone your natural language processing (NLP) skills, finding accessible and relevant datasets can be one of the biggest bottlenecks of the experience. Lots of time can be spent trying to locate existing datasets for the learning task at hand, or attempting to curate your own data instead. It would be great to have a centralized listing of available NLP datasets... wouldn't it? That's where The Big Bad NLP Database (BBNLPDB), managed by Quantum Stat, comes in. If you are seeking datasets to work on your NLP skills, you should definitely check out.
Retailers are now applying AI, ML, and robotics in significant parts of the value chain. Above all, AI technologies could eliminate many manual activities in assortments, promotions, and supply chains. The three most remarkable opportunities in the short to medium term are promotions, arrangement, and replenishment. Significant retailers are trying different things with AI around these areas. "Digital native" e-commerce organizations are driving the way, using AI to anticipate trends, optimize advanced warehousing and logistics, set costs, and customize advancements and promotions.
Organizations hoping to deploy artificial intelligence have to know what problems they're solving -- no vague questions allowed. Artificial intelligence (AI) and machine learning have come a long way, both in terms of adoption across the broader technology landscape and in the insurance industry specifically. That said, there is still much more territory to cover, helping integral employees like claims adjusters do their jobs better, faster and easier. Data science is currently being used to uncover insights that claims representatives wouldn't have found otherwise, which can be extremely valuable. Data science steps in to identify patterns within massive amounts of data that are too large for humans to comprehend on their own; machines can alert users to relevant, actionable insights that improve claim outcomes and facilitate operational efficiency.
With the current pandemic accelerating the revolution of AI in healthcare, where is the industry heading in the next 5-10 years? What are the key challenges and most exciting opportunities? To answer those questions, DeepLearning.AI and Stanford Institute for Human-Centered Artificial Intelligence (HAI) are proud to present our virtual event, Healthcare's AI Future: A Conversation with Fei-Fei Li & Andrew Ng, at 10am PT on April 29. What's special about this event is that you get to decide what our speakers talk about. If you'd like to submit and upvote questions for our speakers, please sign up for the Q&A General access ticket.
We encounter artificial intelligence (AI) every day. AI describes computer systems that are able to perform tasks that normally require human intelligence. When you search something on the internet, the top results you see are decided by AI. Any recommendations you get from your favorite shopping or streaming websites will also be based on an AI algorithm. These algorithms use your browser history to find things you might be interested in.
Parsons Corporation (NYSE: PSN) is developing and deploying artificial intelligence (AI) across a wide array of federal solutions and critical infrastructure projects to solve our customer's most challenging problems, produce actionable intelligence, and improve the user experience. Data analytics, AI, and edge computing are ingrained in company offerings across all business units. For example, the company developed an AI-enabled weapon-target pairing algorithm, with initial tests showing outstanding accuracy and speed results; produced an electronic warfare (EW) planning optimization tool-set named TEMPO (Tactical Electronic Warfare Machine Learning Planning Optimization); and recently won a classified research and development contract to develop constellation task scheduling algorithms based on organically developed AI technology. "Parsons' artificial intelligence capabilities align with our customer's vision by improving situational awareness, decision-making, the safety of operating equipment, streamlining business processes, and protecting critical infrastructure," said Ricardo Lorenzo, chief technology officer for Parsons. "By combining our AI technical expertise with our operational understanding of the all-domain environment and critical infrastructure markets, we're working closely with our customers to develop leap-ahead technology that empowers operators at the tactical edge and beyond. We're also developing differentiated capabilities that ensure the efficiency and security of existing energy and water networks."
With the proliferation of female robots such as Sophia and the popularity of female virtual assistants such as Siri (Apple), Alexa (Amazon), and Cortana (Microsoft), artificial intelligence seems to have a gender issue. This gender imbalance in AI is a pervasive trend that has drawn sharp criticism in the media (even Unesco warned against the dangers of this practice) because it could reinforce stereotypes about women being objects. But why is femininity injected in artificial intelligent objects? If we want to curb the massive use of female gendering in AI, we need to better understand the deep roots of this phenomenon. In an article published in the journal Psychology & Marketing, we argue that research on what makes people human can provide a new perspective into why feminization is systematically used in AI.
In 2017, the Economist stated that the world's most valuable resource is no longer oil, but data. Four years later, this concept is only increasing in truth. Thanks to the revolutionary promises of 5G, artificial intelligence (AI) and machine learning (ML) possibilities are transforming the value of the data collected on consumers and our habits every single day. With 5G usage predicted to explode in coming years with over 1 billion 5G connections by 2023, the possibilities of AI and ML solutions are seemingly becoming limitless. Gone are the days when your mobile phone or laptop are the only devices collecting your data.