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) …
I have over 7000 forum threads in an excel file. What I want to accomplish is sort these forum threads into different categories. And I can do it only by reading text from the subject and body. Does anyone have a recommendation for a tool that can do this. Primarily, the tool should have Control operators (If, elseif, etc.) and Boolean Operators(AND, OR, NOT).
The release of two machine learning (ML) model builders have made it easier for software engineers to create and run ML models, even without specialized training. Microsoft and Amazon Web Services' (AWS) Gluon is an open source project that eliminates some of the difficult work required to develop artificial intelligence (AI) systems. It provides training algorithms and neural network models, two important components of a deep learning system, that developers can use to develop their own ML systems. Google's ML engine is part of its cloud platform and is offered as a managed service for developers to build ML models that work on any type of data, of any size. Similar to Gluon, Google's service provides pre-trained models for developers to generate their own tailored ML models.
In the 2013 movie Her, a virtual personal assistant named Samantha quickly evolves from merely competent to awesomely so. Rather than merely scheduling meetings, Samantha becomes a life coach, cajoling the main character, Theodore, a writer, to publish his letters and to move on with his divorce. That movie is science fiction, but those who are attuned to the rapid improvements in artificial intelligence say a version of Samantha (minus the later romantic attachment and singularity) is on the near horizon. For businesses, the promise of AI is that Samanthas will be embedded across all aspects of the organization. Such agents will analyze data, discover patterns over time and then make decisions based on predictive analysis.
Deep learning has revolutionized how we process the vast firehoses of data that define modern life. Yet, the daily drumbeat of AI headlines tends to center on the commercial applications of AI and how it is reshaping how companies do business. In a refreshing twist, a new open AI challenge by the World Bank, in collaboration with WeRobotics and OpenAerialMap, illustrates the incredible potential of deep learning for humanitarian applications, especially in the critical hours and days after a major natural disaster. One of the most exciting application areas of modern deep learning tools has been the use of neural networks to examine imagery at accuracy and detail levels impossible just a few years ago. Today state-of-the-art neural systems can examine hundreds of millions of images, cataloging them into tens of thousands of categories, estimating the location they were taken, their emotion, look in the background for pollution and natural disaster damage and even estimate the level of "violence" they portray, while creating new models is increasingly becoming point-and-click.
Jeff Heepke knows where to plant corn on his 4,500-acre farm in Illinois because of artificial intelligence (AI). He uses a smartphone app called Climate Basic, which divides Heepke's farmland (and, in fact, the entire continental U.S.) into plots that are 10 meters square. The app draws on local temperature and erosion records, expected precipitation, soil quality, and other agricultural data to determine how to maximize yields for each plot. If a rainy cold front is expected to pass by, Heepke knows which areas to avoid watering or irrigating that afternoon. As the U.S. Department of Agriculture noted, this use of artificial intelligence across the industry has produced the largest crops in the country's history.
In August 2015, a number of carefully selected Facebook users in the Bay Area discovered a new feature on Facebook Messenger. Known as M, the service was designed to rival Google Now and Apple's Siri. A personal assistant that would answer questions in a natural way, make restaurant reservations and help with Uber bookings, M was meant to be a step forward in natural language understanding, the virtual assistant that – unlike Siri – wasn't a dismal experience. Fast forward a couple of years, and the general purpose personal assistant has been demoted within Facebook's product offering. Poor M. The hope was that it would tell users jokes and act as a guide, life coach and optimisation tool.
KeyBanc Capital Markets' Brent Bracelin and his team write that they met with 18 tech firms from Adobe (ADBE) to Microsoft (MSFT) on a recent AI and cloud-based trip to the West Coast, and one of their main takeaways is that cost savings have become the "killer app" for AI. Bracelin writes that although consumer applications for AI tend to grab headlines, he sees 2018 as the year that it really takes off on the enterprise side. And cost savings are a major way this will happen, as using basic machine algorithms to automate mundane tasks can be a huge boon, freeing up employees for more complex tasks or reducing the headcount needed in a department. That may sound scary from a jobs perspective, but many argue that AI isn't the job killer many fear, and that a human touch will be more valuable with the rise of machine learning. Ultimately, he argues that as corporates adopt AI tools, often embedded in cloud-native applications, they should lead to material cost savings.
Lee Kai-Fu has always been very bullish about the future of artificial intelligence (AI) in China. He started off his keynote speech at an AI conference at the Massachusetts Institute of Technology in November by predicting that self-driving cars will become a mass phenomenon in the U.S. in 15 to 20 years. But in China, he said, it will take "more like 10 years." "Although there are concerns about whether there is an emerging AI bubble in China, I'd say there isn't one," he told Caixin. Lee is a real insider when it comes to assessing the state of AI development in both North America and China. He completed his doctorate in computer-aided speech recognition at Carnegie-Mellon University (CMU) in 1988 and went on to work at Apple Inc., Silicon Graphics Inc. and Microsoft Corp., and head Google Inc.'s China business.
WATERxRIVAL is the confluence my current investigations (collaboration with a deep learning AI trained on my artwork and image archive, named RIVAL) and a previous investigation (speculative web-page about water evaporation as a 4th-dimensional phenomenon). This convergence becomes an exploration more ecological in scope and perspective. RIVAL's algorithmic flows/processes are let out into the open waters of internet image & keyword searches, returning with specimens and samples for me to compose and cultivate meaning(fulness) with. The initial set of images (from the speculative exploration of water and space) are put into relationship with the ocean of media flooding all around us. The WATERxRIVAL webpage provides an opportunity for the viewer to glimpse the deluge (this process produces); additionally, it will expand and swell weekly throughout the exhibition's duration.
Chris Manson, 50, is chief executive of Newable, a provider of advice and loans controlled by the 33 London boroughs and the City of London Corporation. Newable, which bought the London Business Angels network last year, invests in funding rounds of up to £2m, focusing on space, digital healthcare and artificial intelligence. It has backed medical device business Oxtex and Advizzo, a developer of machine learning software. Professional angels Angel investing has been a bit of a cottage industry. It sprang up from clubs and networks of like-minded people.